Calliope: a multi-scale energy systems modelling framework¶

v0.6.6 (Release history)

This is the documentation for version 0.6.6. See the main project website for contact details and other useful information.

Calliope focuses on flexibility, high spatial and temporal resolution, the ability to execute many runs based on the same base model, and a clear separation of framework (code) and model (data). Its primary focus is on planning energy systems at scales ranging from urban districts to entire continents. In an optional operational mode it can also test a pre-defined system under different operational conditions. Calliope’s built-in tools allow interactive exploration of results:

A model based on Calliope consists of a collection of text files (in YAML and CSV formats) that define the technologies, locations and resource potentials. Calliope takes these files, constructs an optimisation problem, solves it, and reports results in the form of xarray Datasets which in turn can easily be converted into Pandas data structures, for easy analysis with Calliope’s built-in tools or the standard Python data analysis stack.

Calliope is developed in the open on GitHub and contributions are very welcome (see the Development guide).

Key features of Calliope include:

Model specification in an easy-to-read and machine-processable YAML format
Generic technology definition allows modelling any mix of production, storage and consumption
Resolved in space: define locations with individual resource potentials
Resolved in time: read time series with arbitrary resolution
Able to run on high-performance computing (HPC) clusters
Uses a state-of-the-art Python toolchain based on Pyomo, xarray, and Pandas
Freely available under the Apache 2.0 license

User guide¶

Introduction¶

The basic process of modelling with Calliope is based on three steps:

Create a model from scratch or by adjusting an existing model (Building a model)
Run your model (Running a model)
Analyse and visualise model results (Analysing a model)

Energy system models¶

Energy system models allow analysts to form internally coherent scenarios of how energy is extracted, converted, transported, and used, and how these processes might change in the future. These models have been gaining renewed importance as methods to help navigate the climate policy-driven transformation of the energy system.

Calliope is an attempt to design an energy system model from the ground of up with specific design goals in mind (see below). Therefore, the model approach and data format layout may be different from approaches used in other models. The design of the nodes approach used in Calliope was influenced by the power nodes modelling framework by [Heussen2010], but Calliope is different from traditional power system modelling tools, and does not provide features such as power flow analysis.

Calliope was designed to address questions around the transition to renewable energy, so there are tools that are likely to be more suitable for other types of questions. In particular, the following related energy modelling systems are available under open source or free software licenses:

SWITCH: A power system model focused on renewables integration, using multi-stage stochastic linear optimisation, as well as hourly resource potential and demand data. Written in the commercial AMPL language and GPL-licensed [Fripp2012].
Temoa: An energy system model with multi-stage stochastic optimisation functionality which can be deployed to computing clusters, to address parametric uncertainty. Written in Python/Pyomo and AGPL-licensed [Hunter2013].
OSeMOSYS: A simplified energy system model similar to the MARKAL/TIMES model families, which can be used as a stand-alone tool or integrated in the LEAP energy model. Written in GLPK, a free subset of the commercial AMPL language, and Apache 2.0-licensed [Howells2011].

Additional energy models that are partially or fully open can be found on the Open Energy Modelling Initiative’s wiki.

Rationale¶

Calliope was designed with the following goals in mind:

Designed from the ground up to analyze energy systems with high shares of renewable energy or other variable generation
Formulated to allow arbitrary spatial and temporal resolution, and equipped with the necessary tools to deal with time series input data
Allow easy separation of model code and data, and modular extensibility of model code
Make models easily modifiable, archiveable and auditable (e.g. in a Git repository), by using well-defined and human-readable text formats
Simplify the definition and deployment of large numbers of model runs to high-performance computing clusters
Able to run stand-alone from the command-line, but also provide an API for programmatic access and embedding in larger analyses
Be a first-class citizen of the Python world (installable with conda and pip, with properly documented and tested code that mostly conforms to PEP8)
Have a free and open-source code base under a permissive license

Acknowledgments¶

Development has been partially funded by several grants throughout throughout the years. We would particularly like to acknowledge the following:

The Grantham Institute at Imperial College London.
the European Institute of Innovation & Technology’s Climate-KIC program.
Engineering and Physical Sciences Research Council, reference number: EP/L016095/1.
The Swiss Competence Center for Energy Research − Supply of Electricity (SCCER SoE), contract number 1155002546.
Swiss Federal Office for Energy (SFOE), grant number SI/501768-01.
European Research Council TRIPOD grant, grant agreement number 715132.
The SENTINEL project of the European Union’s Horizon 2020 research and innovation programme under grant agreement No 837089.

License¶

Calliope is released under the Apache 2.0 license, which is a permissive open-source license much like the MIT or BSD licenses. This means that Calliope can be incorporated in both commercial and non-commercial projects.

Copyright since 2013 Calliope contributors listed in AUTHORS

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

References¶

Fripp2012: Fripp, M., 2012. Switch: A Planning Tool for Power Systems with Large Shares of Intermittent Renewable Energy. Environ. Sci. Technol., 46(11), p.6371–6378. DOI: 10.1021/es204645c
Heussen2010: Heussen, K. et al., 2010. Energy storage in power system operation: The power nodes modeling framework. In Innovative Smart Grid Technologies Conference Europe (ISGT Europe), 2010 IEEE PES. pp. 1–8. DOI: 10.1109/ISGTEUROPE.2010.5638865
Howells2011: Howells, M. et al., 2011. OSeMOSYS: The Open Source Energy Modeling System: An introduction to its ethos, structure and development. Energy Policy, 39(10), p.5850–5870. DOI: 10.1016/j.enpol.2011.06.033
Hunter2013: Hunter, K., Sreepathi, S. & DeCarolis, J.F., 2013. Modeling for insight using Tools for Energy Model Optimization and Analysis (Temoa). Energy Economics, 40, p.339–349. DOI: 10.1016/j.eneco.2013.07.014

Citing Calliope in academic literature¶

Calliope is published in the Journal of Open Source Software. We encourage you to use this academic reference.

Download and installation¶

Requirements¶

Calliope has been tested on Linux, macOS, and Windows.

Running Calliope requires four things:

The Python programming language, version 3.7 or higher.
A number of Python add-on modules (see below for the complete list).
A solver: Calliope has been tested with CBC, GLPK, Gurobi, and CPLEX. Any other solver that is compatible with Pyomo should also work.
The Calliope software itself.

Recommended installation method¶

The easiest way to get a working Calliope installation is to use the free conda package manager, which can install all of the four things described above in a single step.

To get conda, download and install the “Miniconda” distribution for your operating system (using the version for Python 3).

With Miniconda installed, you can create a new environment called "calliope" with all the necessary modules, including the free and open source GLPK solver, by running the following command in a terminal or command-line window

$ conda create -c conda-forge -n calliope calliope

To use Calliope, you need to activate the calliope environment each time

$ conda activate calliope

You are now ready to use Calliope together with the free and open source GLPK solver. However, we recommend to not use this solver where possible, since it performs relatively poorly (both in solution time and stability of result). Indeed, our example models use the free and open source CBC solver instead, but installing it on Windows requires an extra step. Read the next section for more information on installing alternative solvers.

Updating an existing installation¶

If following the recommended installation method above, the following command, assuming the conda environment is active, will update Calliope to the newest version

$ conda update -c conda-forge calliope

Solvers¶

You need at least one of the solvers supported by Pyomo installed. CBC (open-source) or Gurobi (commercial) are recommended for large problems, and have been confirmed to work with Calliope. Refer to the documentation of your solver on how to install it.

CBC¶

CBC is our recommended option if you want a free and open-source solver. CBC can be installed via conda on Linux and macOS by running `conda install -c conda-forge coincbc`. Windows binary packages are somewhat more difficult to install, due to limited information on the CBC website. We recommend you download the relevant binary for CBC 2.9.9 and add cbc.exe to a directory known to PATH (e.g. an Anaconda environment ‘bin’ directory).

GLPK¶

GLPK is free and open-source, but can take too much time and/or too much memory on larger problems. If using the recommended installation approach above, GLPK is already installed in the calliope environment. To install GLPK manually, refer to the GLPK website.

Gurobi¶

Gurobi is commercial but significantly faster than CBC and GLPK, which is relevant for larger problems. It needs a license to work, which can be obtained for free for academic use by creating an account on gurobi.com.

While Gurobi can be installed via conda (conda install -c gurobi gurobi) we recommend downloading and installing the installer from the Gurobi website, as the conda package has repeatedly shown various issues.

After installing, log on to the Gurobi website and obtain a (free academic or paid commercial) license, then activate it on your system via the instructions given online (using the grbgetkey command).

CPLEX¶

Another commercial alternative is CPLEX. IBM offer academic licenses for CPLEX. Refer to the IBM website for details.

Python module requirements¶

Refer to requirements/base.yml in the Calliope repository for a full and up-to-date listing of required third-party packages.

Some of the key packages Calliope relies on are:

Pyomo
Pandas
Xarray
Plotly
Jupyter (optional, but highly recommended, and used for the example notebooks in the tutorials)

Building a model¶

In short, a Calliope model works like this: supply technologies can take a resource from outside of the modeled system and turn it into a specific energy carrier in the system. The model specifies one or more locations along with the technologies allowed at those locations. Transmission technologies can move energy of the same carrier from one location to another, while conversion technologies can convert one carrier into another at the same location. Demand technologies remove energy from the system, while storage technologies can store energy at a specific location. Putting all of these possibilities together allows a modeller to specify as simple or as complex a model as necessary to answer a given research question.

In more technical terms, Calliope allows a modeller to define technologies with arbitrary characteristics by “inheriting” basic traits from a number of included base tech groups – supply, supply_plus, demand, conversion, conversion_plus, and transmission. These groups are described in more detail in Abstract base technology groups.

Terminology¶

The terminology defined here is used throughout the documentation and the model code and configuration files:

Technology: a technology that produces, consumes, converts or transports energy
Location: a site which can contain multiple technologies and which may contain other locations for energy balancing purposes
Resource: a source or sink of energy that can (or must) be used by a technology to introduce into or remove energy from the system
Carrier: an energy carrier that groups technologies together into the same network, for example electricity or heat.

As more generally in constrained optimisation, the following terms are also used:

Parameter: a fixed coefficient that enters into model equations
Variable: a variable coefficient (decision variable) that enters into model equations
Set: an index in the algebraic formulation of the equations
Constraint: an equality or inequality expression that constrains one or several variables

Files that define a model¶

Calliope models are defined through YAML files, which are both human-readable and computer-readable, and CSV files (a simple tabular format) for time series data.

It makes sense to collect all files belonging to a model inside a single model directory. The layout of that directory typically looks roughly like this (+ denotes directories, - files):

+ example_model
    + model_config
        - locations.yaml
        - techs.yaml
    + timeseries_data
        - solar_resource.csv
        - electricity_demand.csv
    - model.yaml
    - scenarios.yaml

In the above example, the files model.yaml, locations.yaml and techs.yaml together are the model definition. This definition could be in one file, but it is more readable when split into multiple. We use the above layout in the example models and in our research!

Inside the timeseries_data directory, timeseries are stored as CSV files. The location of this directory can be specified in the model configuration, e.g. in model.yaml.

Note

The easiest way to create a new model is to use the calliope new command, which makes a copy of one of the built-in examples models:

$ calliope new my_new_model

This creates a new directory, my_new_model, in the current working directory.

By default, calliope new uses the national-scale example model as a template. To use a different template, you can specify the example model to use, e.g.: --template=urban_scale.

Model configuration (`model`)¶

The model configuration specifies all aspects of the model to run. It is structured into several top-level headings (keys in the YAML file): model, techs, locations, links, and run. We will discuss each of these in turn, starting with model:

model:
    name: 'My energy model'
    timeseries_data_path: 'timeseries_data'
    reserve_margin:
        power: 0
    subset_time: ['2005-01-01', '2005-01-05']

Besides the model’s name (name) and the path for CSV time series data (timeseries_data_path), group constraints can be set, like reserve_margin.

To speed up model runs, the above example specifies a time subset to run the model over only five days of time series data (subset_time: [‘2005-01-01’, ‘2005-01-05’])– this is entirely optional. Usually, a full model will contain at least one year of data, but subsetting time can be useful to speed up a model for testing purposes.

Technologies (`techs`)¶

The techs section in the model configuration specifies all of the model’s technologies. In our current example, this is in a separate file, model_config/techs.yaml, which is imported into the main model.yaml file alongside the file for locations described further below:

import:
    - 'model_config/techs.yaml'
    - 'model_config/locations.yaml'

Note

The import statement can specify a list of paths to additional files to import (the imported files, in turn, may include further files, so arbitrary degrees of nested configurations are possible). The import statement can either give an absolute path or a path relative to the importing file.

The following example shows the definition of a ccgt technology, i.e. a combined cycle gas turbine that delivers electricity:

ccgt:
    essentials:
        name: 'Combined cycle gas turbine'
        color: '#FDC97D'
        parent: supply
        carrier_out: power
    constraints:
        resource: inf
        energy_eff: 0.5
        energy_cap_max: 40000  # kW
        energy_cap_max_systemwide: 100000  # kW
        energy_ramping: 0.8
        lifetime: 25
    costs:
        monetary:
            interest_rate: 0.10
            energy_cap: 750  # USD per kW
            om_con: 0.02  # USD per kWh

Each technology must specify some essentials, most importantly a name, the abstract base technology it is inheriting from (parent), and its energy carrier (carrier_out in the case of a supply technology). Specifying a color is optional but useful for using the built-in visualisation tools (see Analysing a model).

The constraints section gives all constraints for the technology, such as allowed capacities, conversion efficiencies, the life time (used in levelised cost calculations), and the resource it consumes (in the above example, the resource is set to infinite via inf).

The costs section gives costs for the technology. Calliope uses the concept of “cost classes” to allow accounting for more than just monetary costs. The above example specifies only the monetary cost class, but any number of other classes could be used, for example co2 to account for emissions. Additional cost classes can be created simply by adding them to the definition of costs for a technology.

By default, only the monetary cost class is used in the objective function, i.e., the default objective is to minimize total costs.

Multiple cost classes can be considered in the objective by setting the cost_class key. It must be a dictionary of cost classes and their weights in the objective, e.g. objective_options: {‘cost_class’: {‘monetary’: 1, ‘emissions’: 0.1}}. In this example, monetary costs are summed as usual and emissions are added to this, scaled by 0.1 (emulating a carbon price).

To use a different sense (minimize/maximize) you can set sense: objective_options: {‘cost_class’: …, ‘sense’: ‘minimize’}.

To use a single alternative cost class, disabling the consideration of the default monetary, set the weight of the monetary cost class to zero to stop considering it and the weight of another cost class to a non-zero value, e.g. objective_options: {‘cost_class’: {‘monetary’: 0, ‘emissions’: 1}}.

Allowing for unmet demand¶

For a model to find a feasible solution, supply must always be able to meet demand. To avoid the solver failing to find a solution, you can ensure feasibility:

run:
    ensure_feasibility: true

This will create an unmet_demand decision variable in the optimisation, which can pick up any mismatch between supply and demand, across all energy carriers. It has a very high cost associated with its use, so it will only appear when absolutely necessary.

Note

When ensuring feasibility, you can also set a big M value (run.bigM). This is the “cost” of unmet demand. It is possible to make model convergence very slow if bigM is set too high. default bigM is 1x10 ⁹, but should be close to the maximum total system cost that you can imagine. This is perhaps closer to 1x10 ⁶ for urban scale models.

Time series data¶

For parameters that vary in time, time series data can be added to a model in two ways:

by reading in CSV files
by passing pandas dataframes as arguments in calliope.Model called from a python session.

Reading in CSV files is possible from both the command-line tool as well running interactively with python (see Running a model for details). However, passing dataframes as arguments in calliope.Model is possible only when running from a python session.

Reading in CSV files¶

To read in CSV files, specify resource: file=filename.csv to pick the desired CSV file from within the configured timeseries data path (model.timeseries_data_path).

By default, Calliope looks for a column in the CSV file with the same name as the location. It is also possible to specify a column to use when setting resource per location, by giving the column name with a colon following the filename: resource: file=filename.csv:column

For example, a simple photovoltaic (PV) tech using a time series of hour-by-hour electricity generation data might look like this:

pv:
    essentials:
        name: 'Rooftop PV'
        color: '#B59C2B'
        parent: supply
        carrier_out: power
    constraints:
        resource: file=pv_resource.csv
        energy_cap_max: 10000  # kW

By default, Calliope expects time series data in a model to be indexed by ISO 8601 compatible time stamps in the format YYYY-MM-DD hh:mm:ss, e.g. 2005-01-01 00:00:00. This can be changed by setting model.timeseries_dateformat based on strftime` directives <http://strftime.org/>`_, which defaults to ``'%Y-%m-%d %H:%M:%S'.

For example, the first few lines of a CSV file, called pv_resource.csv giving a resource potential for two locations might look like this, with the first column in the file always being read as the date-time index:

,location1,location2
2005-01-01 00:00:00,0,0
2005-01-01 01:00:00,0,11
2005-01-01 02:00:00,0,18
2005-01-01 03:00:00,0,49
2005-01-01 04:00:00,11,110
2005-01-01 05:00:00,45,300
2005-01-01 06:00:00,90,458

Reading in timeseries from `pandas` dataframes¶

When running models from python scripts or shells, it is also possible to pass timeseries directly as pandas dataframes. This is done by specifying resource: df=tskey where tskey is the key in a dictionary containing the relevant dataframes. For example, if the same timeseries as above is to be passed, a dataframe called pv_resource may be in the python namespace:

pv_resource

t                     location1  location2
2005-01-01 00:00:00           0          0
2005-01-01 01:00:00           0         11
2005-01-01 02:00:00           0         18
2005-01-01 03:00:00           0         49
2005-01-01 04:00:00          11        110
2005-01-01 05:00:00          45        300
2005-01-01 06:00:00          90        458

To pass this timeseries into the model, create a dictionary, called timeseries_dataframes here, containing all relevant timeseries identified by their tskey. In this case, this has only one key, called pv_resource:

timeseries_dataframes = {'pv_resource': pv_resource}

The keys in this dictionary must match the tskey specified in the YAML files. In this example, specifying resource: df=pv_resource will identify the pv_resource key in timeseries_dataframes. All relevant timeseries must be put in this dictionary. For example, if a model contains three timeseries referred to in the configuration YAML files, called demand_1, demand_2 and pv_resource, the timeseries_dataframes dictionary may look like

timeseries_dataframes = {'demand_1': demand_1,
                         'demand_2': demand_2,
                         'pv_resource': pv_resource}

where demand_1, demand_2 and pv_resource are dataframes of the relevant timeseries. The timeseries_dataframes can then be called in calliope.Model:

model = calliope.Model('model.yaml', timeseries_dataframes=timeseries_dataframes)

Just like when using CSV files (see above), Calliope looks for a column in the dataframe with the same name as the location. It is also possible to specify a column to use when setting resource per location, by giving the column name with a colon following the filename: resource: df=tskey:column.

The time series index must be ISO 8601 compatible time stamps and can be a standard pandas DateTimeIndex (see discussion above).

Note

If a parameter is not explicit in time and space, it can be specified as a single value in the model definition (or, using location-specific definitions, be made spatially explicit). This applies both to parameters that never vary through time (for example, cost of installed capacity) and for those that may be time-varying (for example, a technology’s available resource). However, each model must contain at least one time series.
Only the subset of parameters listed in file_allowed in the model configuration can be loaded from file or dataframe in this way. It is advised not to update this default list unless you are developing the core code, since the model will likely behave unexpectedly.
You _cannot_ have a space around the = symbol when pointing to a timeseries file or dataframe key, i.e. resource: file = filename.csv is not valid.
If running from a command line interface (see Running a model), timeseries must be read from CSV and cannot be passed from dataframes via df=....
It’s possible to mix reading in from CSVs and dataframes, by setting some config values as file=... and some as df=....
The default value of timeseries_dataframes is None, so if you want to read all timeseries in from CSVs, you can omit this argument. When running from command line, this is done automatically.

Locations and links (`locations`, `links`)¶

A model can specify any number of locations. These locations are linked together by transmission technologies. By consuming an energy carrier in one location and outputting it in another, linked location, transmission technologies allow resources to be drawn from the system at a different location from where they are brought into it.

The locations section specifies each location:

locations:
    region1:
        coordinates: {lat: 40, lon: -2}
        techs:
            unmet_demand_power:
            demand_power:
            ccgt:
                constraints:
                    energy_cap_max: 30000

Locations can optionally specify coordinates (used in visualisation or to compute distance between them) and must specify techs allowed at that location. As seen in the example above, each allowed tech must be listed, and can optionally specify additional location-specific parameters (constraints or costs). If given, location-specific parameters supersede any group constraints a technology defines in the techs section for that location.

The links section specifies possible transmission links between locations in the form location1,location2:

links:
    region1,region2:
        techs:
            ac_transmission:
                constraints:
                    energy_cap_max: 10000
                costs.monetary:
                    energy_cap: 100

In the above example, an high-voltage AC transmission line is specified to connect region1 with region2. For this to work, a transmission technology called ac_transmission must have previously been defined in the model’s techs section. There, it can be given group constraints or costs. As in the case of locations, the links section can specify per-link parameters (constraints or costs) that supersede any model-wide parameters.

The modeller can also specify a distance for each link, and use per-distance constraints and costs for transmission technologies.

See also

Per-tech constraints, Per-tech costs.

Run configuration (`run`)¶

The only required setting in the run configuration is the solver to use:

run:
    solver: cbc
    mode: plan

the most important parts of the run section are solver and mode. A model can run in planning mode (plan), operational mode (operate), or SPORES mode (spores). In planning mode, capacities are determined by the model, whereas in operational mode, capacities are fixed and the system is operated with a receding horizon control algorithm. In SPORES mode, the model is first run in planning mode, then run N number of times to find alternative system configurations with similar monetary cost, but maximally different choice of technology capacity and location.

Possible options for solver include glpk, gurobi, cplex, and cbc. The interface to these solvers is done through the Pyomo library. Any solver compatible with Pyomo should work with Calliope.

For solvers with which Pyomo provides more than one way to interface, the additional solver_io option can be used. In the case of Gurobi, for example, it is usually fastest to use the direct Python interface:

run:
    solver: gurobi
    solver_io: python

Note

The opposite is currently true for CPLEX, which runs faster with the default solver_io.

Further optional settings, including debug settings, can be specified in the run configuration.

Scenarios and overrides¶

To make it easier to run a given model multiple times with slightly changed settings or constraints, for example, varying the cost of a key technology, it is possible to define and apply scenarios and overrides. “Overrides” are blocks of YAML that specify configurations that expand or override parts of the base model. “Scenarios” are combinations of any number of such overrides. Both are specified at the top level of the model configuration, as in this example model.yaml file:

scenarios:
    high_cost_2005: ["high_cost", "year2005"]
    high_cost_2006: ["high_cost", "year2006"]

overrides:
    high_cost:
        techs.onshore_wind.costs.monetary.energy_cap: 2000
    year2005:
        model.subset_time: ['2005-01-01', '2005-12-31']
    year2006:
        model.subset_time: ['2006-01-01', '2006-12-31']

model:
    ...

run:
    ...

Each override is given by a name (e.g. high_cost) and any number of model settings – anything in the model configuration can be overridden by an override. In the above example, one override defines higher costs for an onshore_wind tech while the two other overrides specify different time subsets, so would run an otherwise identical model over two different periods of time series data.

One or several overrides can be applied when running a model, as described in Running a model. Overrides can also be combined into scenarios to make applying them at run-time easier. Scenarios consist of a name and a list of override names which together form that scenario.

Scenarios and overrides can be used to generate scripts that run a single Calliope model many times, either sequentially, or in parallel on a high-performance cluster (see Generating scripts to run a model many times).

Note

Overrides can also import other files. This can be useful if many overrides are defined which share large parts of model configuration, such as different levels of interconnection between model zones. See Importing other YAML files in overrides for details.

Running a model¶

There are essentially three ways to run a Calliope model:

With the calliope run command-line tool.
By programmatically creating and running a model from within other Python code, or in an interactive Python session.
By generating and then executing scripts with the calliope generate_runs command-line tool, which is primarily designed for running many scenarios on a high-performance cluster.

Running with the command-line tool¶

We can easily run a model after creating it (see Building a model), saving results to a single NetCDF file for further processing

$ calliope run testmodel/model.yaml --save_netcdf=results.nc

The calliope run command takes the following options:

--save_netcdf={filename.nc}: Save complete model, including results, to the given NetCDF file. This is the recommended way to save model input and output data into a single file, as it preserves all data fully, and allows later reconstruction of the Calliope model for further analysis.
--save_csv={directory name}: Save results as a set of CSV files to the given directory. This can be handy if the modeler needs results in a simple text-based format for further processing with a tool like Microsoft Excel.
--save_plots={filename.html}: Save interactive plots to the given HTML file (see Analysing a model for further details on the plotting functionality).
--debug: Run in debug mode, which prints more internal information, and is useful when troubleshooting failing models.
--scenario={scenario} and --override_dict={yaml_string}: Specify a scenario, or one or several overrides, to apply to the model, or apply specific overrides from a YAML string (see below for more information)
--help: Show all available options.

Multiple options can be specified, for example, saving NetCDF, CSV, and HTML plots simultaneously

$ calliope run testmodel/model.yaml --save_netcdf=results.nc --save_csv=outputs --save_plots=plots.html

Warning

Unlike in versions prior to 0.6.0, the command-line tool in Calliope 0.6.0 and upward does not save results by default – the modeller must specify one of the -save options.

Applying a scenario or override¶

The --scenario can be used in three different ways:

It can be given the name of a scenario defined in the model configuration, as in --scenario=my_scenario
It can be given the name of a single override defined in the model configuration, as in --scenario=my_override
It can be given a comma-separated string of several overrides defined in the model configuration, as in --scenario=my_override_1,my_override_2

In the latter two cases, the given override(s) is used to implicitly create a “scenario” on-the-fly when running the model. This allows quick experimentation with different overrides without explicitly defining a scenario combining them.

Assuming we have specified an override called milp in our model configuration, we can apply it to our model with

$ calliope run testmodel/model.yaml --scenario=milp --save_netcdf=results.nc

Note that if both a scenario and an override with the same name, such as milp in the above example, exist, Calliope will raise an error, as it will not be clear which one the user wishes to apply.

It is also possible to use the –override_dict option to pass a YAML string that will be applied after anything applied through --scenario

$ calliope run testmodel/model.yaml --override_dict="{'model.subset_time': ['2005-01-01', '2005-01-31']}" --save_netcdf=results.nc

Running interactively with Python¶

The most basic way to run a model programmatically from within a Python interpreter is to create a Model instance with a given model.yaml configuration file, and then call its run() method:

import calliope
model = calliope.Model('path/to/model.yaml')
model.run()

Note

If config is not specified (i.e. model = Model()), an error is raised. See Built-in example models for information on instantiating a simple example model without specifying a custom model configuration.

Other ways to load a model interactively are:

Passing an AttrDict or standard Python dictionary to the Model constructor, with the same nested format as the YAML model configuration (top-level keys: model, run, locations, techs).
Loading a previously saved model from a NetCDF file with model = calliope.read_netcdf(‘path/to/saved_model.nc’). This can either be a pre-processed model saved before its run method was called, which will include input data only, or a completely solved model, which will include input and result data.

After instantiating the Model object, and before calling the run() method, it is possible to manually inspect and adjust the configuration of the model. The pre-processed inputs are all held in the xarray Dataset model.inputs.

After the model has been solved, an xarray Dataset containing results (model.results) can be accessed. At this point, the model can be saved with either to_csv() or to_netcdf(), which saves all inputs and results, and is equivalent to the corresponding --save options of the command-line tool.

See also

An example of interactive running in a Python session, which also demonstrates some of the analysis possibilities after running a model, is given in the tutorials. You can download and run the embedded notebooks on your own machine (if both Calliope and the Jupyter Notebook are installed).

Scenarios and overrides¶

There are two ways to override a base model when running interactively, analogously to the use of the command-line tool (see Applying a scenario or override above):

By setting the scenario argument, e.g.:

model = calliope.Model('model.yaml', scenario='milp')

By passing the override_dict argument, which is a Python dictionary, an AttrDict, or a YAML string of overrides:
model = calliope.Model( 'model.yaml', override_dict={'run.solver': 'gurobi'} )

Note

Both scenario and override_dict can be defined at once. They will be applied in order, such that scenarios are applied first, followed by dictionary overrides. As such, the override_dict can be used to override scenarios.

Tracking progress¶

When running Calliope in the command line, logging of model pre-processing and solving occurs automatically. Interactively, for example in a Jupyter notebook, you can enable verbose logging by setting the log level using calliope.set_log_verbosity(level) immediately after importing the Calliope package. By default, calliope.set_log_verbosity() also sets the log level for the backend model to DEBUG, which turns on output of solver output. This can be disabled by calliope.set_log_verbosity(level, include_solver_output=False). Possible log levels are (from least to most verbose):

CRITICAL: only show critical errors.
ERROR: only show errors.
WARNING: show errors and warnings (default level).
INFO: show errors, warnings, and informative messages. Calliope uses the INFO level to show a message at each stage of pre-processing, sending the model to the solver, and post-processing, including timestamps.
DEBUG: SOLVER logging, with heavily verbose logging of a number of function outputs. Only for use when troubleshooting failing runs or developing new functionality in Calliope.

Generating scripts for many model runs¶

Scripts to simplify the creation and execution of a large number of Calliope model runs are generated with the calliope generate_runs command-line tool. More detail on this is available in Generating scripts to run a model many times.

Improving solution times¶

Large models will take time to solve. The easiest is often to just let a model run on a remote device (another computer, or a high performance computing cluster) and forget about it until it is done. However, if you need results now, there are ways to improve solution time.

Details on strategies to improve solution times are given in Troubleshooting.

Debugging failing runs¶

What will typically go wrong, in order of decreasing likelihood:

The model is improperly defined or missing data. Calliope will attempt to diagnose some common errors and raise an appropriate error message.

The model is consistent and properly defined but infeasible. Calliope will be able to construct the model and pass it on to the solver, but the solver (after a potentially long time) will abort with a message stating that the model is infeasible.

There is a bug in Calliope causing the model to crash either before being passed to the solver, or after the solver has completed and when results are passed back to Calliope.

Calliope provides help in diagnosing all of these model issues. For details, see Troubleshooting.

Analysing a model¶

Calliope inputs and results are designed for easy handling. Whatever software you prefer to use for data processing, either the NetCDF or CSV output options should provide a path to importing your Calliope results. If you prefer to not worry about writing your own scripts, then we have that covered too! The built-in plotting functions in plot are built on Plotly’s interactive visualisation tools to bring your data to life.

Accessing model data and results¶

A model which solved successfully has two primary Datasets with data of interest:

model.inputs: contains all input data, such as renewable resource capacity factors
model.results: contains all results data, such as dispatch decisions and installed capacities

In both of these, variables are indexed over concatenated sets of locations and technologies, over a dimension we call loc_techs. For example, if a technology called boiler only exists in location X1 and not in locations X2 or X3, then it will have a single entry in the loc_techs dimension called X1::boiler. For parameters which also consider different energy carriers, we use a loc_tech_carrier dimension, such that we would have, in the case of the prior boiler example, X1::boiler::heat.

This concatenated set formulation is memory-efficient but cumbersome to deal with, so the model.get_formatted_array(name_of_variable) function can be used to retrieve a DataArray indexed over separate dimensions (any of techs, locs, carriers, costs, timesteps, depending on the desired variable).

Note

On saving to CSV (see the command-line interface documentation), all variables are saved to a single file each, which are always indexed over all dimensions rather than just the concatenated dimensions.

Visualising results¶

In an interactive Python session, there are four primary visualisation functions: capacity, timeseries, transmission, and summary. To gain access to result visualisation without the need to interact with Python, the summary plot can also be accessed from the command line interface (see below).

Refer to the API documentation for the analysis module for an overview of available analysis functionality.

Refer to the tutorials for some basic analysis techniques.

Plotting time series¶

The following example shows a timeseries plot of the built-in urban scale example model:

In Python, we get this function by calling model.plot.timeseries(). It includes all relevant timeseries information, from both inputs and results. We can force it to only have particular results in the dropdown menu:

# Only inputs or only results
model.plot.timeseries(array='inputs')
model.plot.timeseries(array='results')

# Only consumed resource
model.plot.timeseries(array='resource_con')

# Only consumed resource and 'power' carrier flow
model.plot.timeseries(array=['power', 'resource_con'])

The data used to build the plots can also be subset and ordered by using the subset argument. This uses xarray’s ‘loc’ indexing functionality to access subsets of data:

# Only show region1 data (rather than the default, which is a sum of all locations)
model.plot.timeseries(subset={'locs': ['region1']})

# Only show a subset of technologies
model.plot.timeseries(subset={'techs': ['ccgt', 'csp']})

# Assuming our model has three techs, 'ccgt', 'csp', and 'battery',
# specifying `subset` lets us order them in the stacked barchart
model.plot.timeseries(subset={'techs': ['ccgt', 'battery', 'csp']})

When aggregating model timesteps with clustering methods, the timeseries plots are adjusted accordingly (example from the built-in time_clustering example model):

Plotting capacities¶

The following example shows a capacity plot of the built-in urban scale example model:

Functionality is similar to timeseries, this time called by model.plot.capacity(). Here we show capacity limits set at input and chosen capacities at output. Choosing dropdowns and subsetting works in the same way as for timeseries plots

Plotting transmission¶

The following example shows a transmission plot of the built-in urban scale example model:

By calling model.plot.transmission() you will see installed links, their capacities (on hover), and the locations of the nodes. This functionality only works if you have physically pinpointed your locations using the coordinates key for your location.

The above plot uses Mapbox to overlay our transmission plot on Openstreetmap. By creating an account at Mapbox and acquiring a Mapbox access token, you can also create similar visualisations by giving the token to the plotting function: model.plot.transmission(mapbox_access_token=’your token here’).

Without the token, the plot will fall back on simple country-level outlines. In this urban scale example, the background is thus just grey (zoom out to see the UK!):

Note

If the coordinates were in x and y, not lat and lon, the transmission trace would be given on a cartesian plot.

Plotting flows¶

The following example shows an energy flow plot of the built-in urban scale example model:

By calling model.plot.flows() you will see a plot similar to transmission. However, you can see carrier production at each node and along links, at every timestep (controlled by moving a slider). This functionality only works if you have physically pinpointed your locations using the coordinates key for your location. It is possible to look at only a subset of the timesteps in the model using the timestep_index_subset argument, or to show only every X timestep (where X is an integer) using the timestep_cycle argument.

Note

If the timestep dimension is particularly large in your model, you will find this visualisation to be slow. Time subsetting is recommended for such a case.

If you cannot see the carrier production for a technology on hovering, it is likely masked by another technology at the same location or on the same link. Hide the masking technology to get the hover info for the technology below.

Summary plots¶

If you want all the data in one place, you can run model.plot.summary(to_file=’path/to/file.html’), which will build a HTML file of all the interactive plots (maintaining the interactivity) and save it to ‘path/to/file.html’. This HTML file can be opened in a web browser to show all the plots. This funcionality is made available in the command line interface by using the command --save_plots=filename.html when running the model.

See an example of such a HTML plot here.

See also

Running with the command-line tool

Saving publication-quality SVG figures¶

On calling any of the three primary plotting functions, you can also set to_file=path/to/file.svg for a high quality vector graphic to be saved. This file can be prepared for publication in programs like Inkscape.

Note

For similar results in the command line interface, you’ll currently need to save your model to netcdf (--save_netcdf={filename.nc}) then load it into a Calliope Model object in Python. Once there, you can use the above functions to get your SVGs.

Reading solutions¶

Calliope provides functionality to read a previously-saved model from a single NetCDF file:

solved_model = calliope.read_netcdf('my_saved_model.nc')

In the above example, the model’s input data will be available under solved_model.inputs, while the results (if the model had previously been solved) are available under solved_model.results.

Both of these are xarray.Datasets and can be further processed with Python.

See also

The xarray documentation should be consulted for further information on dealing with Datasets. Calliope’s NetCDF files follow the CF conventions and can easily be processed with any other tool that can deal with NetCDF.

Tutorials¶

The tutorials are based on the built-in example models, they explain the key steps necessary to set up and run simple models. Refer to the other parts of the documentation for more detailed information on configuring and running more complex models. The built-in examples are simple on purpose, to show the key components of a Calliope model with which models of arbitrary complexity can be built.

The first tutorial builds a model for part of a national grid, exhibiting the following Calliope functionality:

Use of supply, supply_plus, demand, storage and transmission technologies
Nested locations
Multiple cost types

The second tutorial builds a model for part of a district network, exhibiting the following Calliope functionality:

Use of supply, demand, conversion, conversion_plus, and transmission technologies
Use of multiple energy carriers
Revenue generation, by carrier export

The third tutorial extends the second tutorial, exhibiting binary and integer decision variable functionality (extended an LP model to a MILP model)

Tutorial 1: national scale¶

This example consists of two possible power supply technologies, a power demand at two locations, the possibility for battery storage at one of the locations, and a transmission technology linking the two. The diagram below gives an overview:

Overview of the built-in urban-scale example model

Overview of the built-in national-scale example model¶

Supply-side technologies¶

The example model defines two power supply technologies.

The first is ccgt (combined-cycle gas turbine), which serves as an example of a simple technology with an infinite resource. Its only constraints are the cost of built capacity (energy_cap) and a constraint on its maximum built capacity.

The layout of a supply node, in this case ccgt, which has an infinite resource, a carrier conversion efficiency (\(energy_{eff}\)), and a constraint on its maximum built \(energy_{cap}\) (which puts an upper limit on \(energy_{prod}\)).¶

The definition of this technology in the example model’s configuration looks as follows:

ccgt:
    essentials:
        name: 'Combined cycle gas turbine'
        color: '#E37A72'
        parent: supply
        carrier_out: power
    constraints:
        resource: inf
        energy_eff: 0.5
        energy_cap_max: 40000  # kW
        energy_cap_max_systemwide: 100000  # kW
        energy_ramping: 0.8
        lifetime: 25
    costs:
        monetary:
            interest_rate: 0.10
            energy_cap: 750  # USD per kW
            om_con: 0.02  # USD per kWh

There are a few things to note. First, ccgt defines essential information: a name, a color (given as an HTML color code, for later visualisation), its parent, supply, and its carrier_out, power. It has set itself up as a power supply technology. This is followed by the definition of constraints and costs (the only cost class used is monetary, but this is where other “costs”, such as emissions, could be defined).

Note

There are technically no restrictions on the units used in model definitions. Usually, the units will be kW and kWh, alongside a currency like USD for costs. It is the responsibility of the modeler to ensure that units are correct and consistent. Some of the analysis functionality in the postprocess module assumes that kW and kWh are used when drawing figure and axis labels, but apart from that, there is nothing preventing the use of other units.

The second technology is csp (concentrating solar power), and serves as an example of a complex supply_plus technology making use of:

a finite resource based on time series data
built-in storage
plant-internal losses (parasitic_eff)

More complex node, with resource storage

The layout of a more complex node, in this case csp, which makes use of most node-level functionality available.¶

This definition in the example model’s configuration is more verbose:

csp:
    essentials:
        name: 'Concentrating solar power'
        color: '#F9CF22'
        parent: supply_plus
        carrier_out: power
    constraints:
        storage_cap_max: 614033
        energy_cap_per_storage_cap_max: 1
        storage_loss: 0.002
        resource: file=csp_resource.csv
        resource_unit: energy_per_area
        energy_eff: 0.4
        parasitic_eff: 0.9
        resource_area_max: inf
        energy_cap_max: 10000
        lifetime: 25
    costs:
        monetary:
            interest_rate: 0.10
            storage_cap: 50
            resource_area: 200
            resource_cap: 200
            energy_cap: 1000
            om_prod: 0.002

Again, csp has the definitions for name, color, parent, and carrier_out. Its constraints are more numerous: it defines a maximum storage capacity (storage_cap_max), an hourly storage loss rate (storage_loss), then specifies that its resource should be read from a file (more on that below). It also defines a carrier conversion efficiency of 0.4 and a parasitic efficiency of 0.9 (i.e., an internal loss of 0.1). Finally, the resource collector area and the installed carrier conversion capacity are constrained to a maximum.

The costs are more numerous as well, and include monetary costs for all relevant components along the conversion from resource to carrier (power): storage capacity, resource collector area, resource conversion capacity, energy conversion capacity, and variable operational and maintenance costs. Finally, it also overrides the default value for the monetary interest rate.

Storage technologies¶

The second location allows a limited amount of battery storage to be deployed to better balance the system. This technology is defined as follows:

A storage node with an \(energy_{eff}\) and \(storage_{loss}\).¶

battery:
    essentials:
        name: 'Battery storage'
        color: '#3B61E3'
        parent: storage
        carrier: power
    constraints:
        energy_cap_max: 1000  # kW
        storage_cap_max: inf
        energy_cap_per_storage_cap_max: 4
        energy_eff: 0.95  # 0.95 * 0.95 = 0.9025 round trip efficiency
        storage_loss: 0  # No loss over time assumed
        lifetime: 25
    costs:
        monetary:
            interest_rate: 0.10
            storage_cap: 200  # USD per kWh storage capacity

The contraints give a maximum installed generation capacity for battery storage together with a maximum ratio of energy capacity to storage capacity (energy_cap_per_storage_cap_max) of 4, which in turn limits the storage capacity. The ratio is the charge/discharge rate / storage capacity (a.k.a the battery reservoir). In the case of a storage technology, energy_eff applies twice: on charging and discharging. In addition, storage technologies can lose stored energy over time – in this case, we set this loss to zero.

Other technologies¶

Three more technologies are needed for a simple model. First, a definition of power demand:

A simple demand node.¶

demand_power:
    essentials:
        name: 'Power demand'
        color: '#072486'
        parent: demand
        carrier: power

Power demand is a technology like any other. We will associate an actual demand time series with the demand technology later.

What remains to set up is a simple transmission technology. Transmission technologies (like conversion technologies) look different than other nodes, as they link the carrier at one location to the carrier at another (or, in the case of conversion, one carrier to another at the same location):

A simple transmission node with an \(energy_{eff}\).¶

ac_transmission:
    essentials:
        name: 'AC power transmission'
        color: '#8465A9'
        parent: transmission
        carrier: power
    constraints:
        energy_eff: 0.85
        lifetime: 25
    costs:
        monetary:
            interest_rate: 0.10
            energy_cap: 200
            om_prod: 0.002

free_transmission:
    essentials:
        name: 'Local power transmission'
        color: '#6783E3'
        parent: transmission
        carrier: power
    constraints:
        energy_cap_max: inf
        energy_eff: 1.0
    costs:
        monetary:
            om_prod: 0

ac_transmission has an efficiency of 0.85, so a loss during transmission of 0.15, as well as some cost definitions.

free_transmission allows local power transmission from any of the csp facilities to the nearest location. As the name suggests, it applies no cost or efficiency losses to this transmission.

Locations¶

In order to translate the model requirements shown in this section’s introduction into a model definition, five locations are used: region-1, region-2, region1-1, region1-2, and region1-3.

The technologies are set up in these locations as follows:

Locations and their technologies in the example model¶

Let’s now look at the first location definition:

region1:
    coordinates: {lat: 40, lon: -2}
    techs:
        demand_power:
            constraints:
                resource: file=demand-1.csv:demand
        ccgt:
            constraints:
                energy_cap_max: 30000 # increased to ensure no unmet_demand in first timestep

There are several things to note here:

The location specifies a dictionary of technologies that it allows (techs), with each key of the dictionary referring to the name of technologies defined in our techs.yaml file. Note that technologies listed here must have been defined elsewhere in the model configuration.
It also overrides some options for both demand_power and ccgt. For the latter, it simply sets a location-specific maximum capacity constraint. For demand_power, the options set here are related to reading the demand time series from a CSV file. CSV is a simple text-based format that stores tables by comma-separated rows. Note that we did not define any resource option in the definition of the demand_power technology. Instead, this is done directly via a location-specific override. For this location, the file demand-1.csv is loaded and the column demand is taken (the text after the colon). If no column is specified, Calliope will assume that the column name matches the location name region1-1. Note that in Calliope, a supply is positive and a demand is negative, so the stored CSV data will be negative.
Coordinates are defined by latitude (lat) and longitude (lon), which will be used to calculate distance of transmission lines (unless we specify otherwise later on) and for location-based visualisation.

The remaining location definitions look like this:

region2:
    coordinates: {lat: 40, lon: -8}
    techs:
        demand_power:
            constraints:
                resource: file=demand-2.csv:demand
        battery:

region1-1.coordinates: {lat: 41, lon: -2}
region1-2.coordinates: {lat: 39, lon: -1}
region1-3.coordinates: {lat: 39, lon: -2}

region1-1, region1-2, region1-3:
    techs:
        csp:

region2 is very similar to region1, except that it does not allow the ccgt technology. The three region1- locations are defined together, except for their location coordinates, i.e. they each get the exact same configuration. They allow only the csp technology, this allows us to model three possible sites for CSP plants.

For transmission technologies, the model also needs to know which locations can be linked, and this is set up in the model configuration as follows:

region1,region2:
    techs:
        ac_transmission:
            constraints:
                energy_cap_max: 10000
region1,region1-1:
    techs:
        free_transmission:
region1,region1-2:
    techs:
        free_transmission:
region1,region1-3:
    techs:
        free_transmission:

We are able to override constraints for transmission technologies at this point, such as the maximum capacity of the specific region1 to region2 link shown here.

Running the model¶

We now take you through running the model in a Jupyter notebook, which you can view here. After clicking on that link, you can also download and run the notebook yourself (you will need to have Calliope installed).

Tutorial 2: urban scale¶

This example consists of two possible sources of electricity, one possible source of heat, and one possible source of simultaneous heat and electricity. There are three locations, each describing a building, with transmission links between them. The diagram below gives an overview:

Overview of the built-in urban-scale example model¶

Supply technologies¶

This example model defines three supply technologies.

The first two are supply_gas and supply_grid_power, referring to the supply of gas (natural gas) and electricity, respectively, from the national distribution system. These ‘inifinitely’ available national commodities can become energy carriers in the system, with the cost of their purchase being considered at supply, not conversion.

The layout of a simple supply technology, in this case supply_gas, which has a resource input and a carrier output. A carrier conversion efficiency (\(energy_{eff}\)) can also be applied (although isn’t considered for our supply technologies in this problem).¶

The definition of these technologies in the example model’s configuration looks as follows:

supply_grid_power:
    essentials:
        name: 'National grid import'
        color: '#C5ABE3'
        parent: supply
        carrier: electricity
    constraints:
        resource: inf
        energy_cap_max: 2000
        lifetime: 25
    costs:
        monetary:
            interest_rate: 0.10
            energy_cap: 15
            om_con: 0.1 # 10p/kWh electricity price #ppt

supply_gas:
    essentials:
        name: 'Natural gas import'
        color: '#C98AAD'
        parent: supply
        carrier: gas
    constraints:
        resource: inf
        energy_cap_max: 2000
        lifetime: 25
    costs:
        monetary:
            interest_rate: 0.10
            energy_cap: 1
            om_con: 0.025 # 2.5p/kWh gas price #ppt

The final supply technology is pv (solar photovoltaic power), which serves as an inflexible supply technology. It has a time-dependant resource availablity, loaded from file, a maximum area over which it can capture its resource (resource_area_max) and a requirement that all available resource must be used (force_resource: True). This emulates the reality of solar technologies: once installed, their production matches the availability of solar energy.

The efficiency of the DC to AC inverter (which occurs after conversion from resource to energy carrier) is considered in parasitic_eff and the resource_area_per_energy_cap gives a link between the installed area of solar panels to the installed capacity of those panels (i.e. kWp).

In most cases, domestic PV panels are able to export excess energy to the national grid. We allow this here by specifying an export_carrier. Revenue for export will be considered on a per-location basis.

The definition of this technology in the example model’s configuration looks as follows:

pv:
    essentials:
        name: 'Solar photovoltaic power'
        color: '#F9D956'
        parent: supply_power_plus
    constraints:
        export_carrier: electricity
        resource: file=pv_resource.csv:per_area  # Already accounts for panel efficiency - kWh/m2. Source: Renewables.ninja Solar PV Power - Version: 1.1 - License: https://creativecommons.org/licenses/by-nc/4.0/ - Reference: https://doi.org/10.1016/j.energy.2016.08.060
        resource_unit: energy_per_area
        parasitic_eff: 0.85 # inverter losses
        energy_cap_max: 250
        resource_area_max: 1500
        force_resource: true
        resource_area_per_energy_cap: 7 # 7m2 of panels needed to fit 1kWp of panels
        lifetime: 25
    costs:
        monetary:
            interest_rate: 0.10
            energy_cap: 1350

Finally, the parent of the PV technology is not supply_plus, but rather supply_power_plus. We use this to show the possibility of an intermediate technology group, which provides the information on the energy carrier (electricity) and the ultimate abstract base technology (supply_plus):

tech_groups:
    supply_power_plus:
        essentials:
            parent: supply_plus
            carrier: electricity

Intermediate technology groups allow us to avoid repetition of technology information, be it in essentials, constraints, or costs, by linking multiple technologies to the same intermediate group.

Conversion technologies¶

The example model defines two conversion technologies.

The first is boiler (natural gas boiler), which serves as an example of a simple conversion technology with one input carrier and one output carrier. Its only constraints are the cost of built capacity (costs.monetary.energy_cap), a constraint on its maximum built capacity (constraints.energy_cap.max), and an energy conversion efficiency (energy_eff).

The layout of a simple node, in this case boiler, which has one carrier input, one carrier output, a carrier conversion efficiency (\(energy_{eff}\)), and a constraint on its maximum built \(energy_{cap}\) (which puts an upper limit on \(carrier_{prod}\)).¶

The definition of this technology in the example model’s configuration looks as follows:

boiler:
    essentials:
        name: 'Natural gas boiler'
        color: '#8E2999'
        parent: conversion
        carrier_out: heat
        carrier_in: gas
    constraints:
        energy_cap_max: 600
        energy_eff: 0.85
        lifetime: 25
    costs:
        monetary:
            interest_rate: 0.10
            om_con: 0.004  # .4p/kWh

There are a few things to note. First, boiler defines a name, a color (given as an HTML color code), and a stack_weight. These are used by the built-in analysis tools when analyzing model results. Second, it specifies its parent, conversion, its carrier_in gas, and its carrier_out heat, thus setting itself up as a gas to heat conversion technology. This is followed by the definition of constraints and costs (the only cost class used is monetary, but this is where other “costs”, such as emissions, could be defined).

The second technology is chp (combined heat and power), and serves as an example of a possible conversion_plus technology making use of two output carriers.

More complex conversion technology, with multiple output carriers

The layout of a more complex node, in this case chp, which makes use of multiple output carriers.¶

This definition in the example model’s configuration is more verbose:

chp:
    essentials:
        name: 'Combined heat and power'
        color: '#E4AB97'
        parent: conversion_plus
        primary_carrier_out: electricity
        carrier_in: gas
        carrier_out: electricity
        carrier_out_2: heat
    constraints:
        export_carrier: electricity
        energy_cap_max: 1500
        energy_eff: 0.405
        carrier_ratios.carrier_out_2.heat: 0.8
        lifetime: 25
    costs:
        monetary:
            interest_rate: 0.10
            energy_cap: 750
            om_prod: 0.004  # .4p/kWh for 4500 operating hours/year
            export: file=export_power.csv

See also

The conversion_plus tech

Again, chp has the definitions for name, color, parent, and carrier_in/out. It now has an additional carrier (carrier_out_2) defined in its essential information, allowing a second carrier to be produced at the same time as the first carrier (carrier_out). The carrier ratio constraint tells us the ratio of carrier_out_2 to carrier_out that we can achieve, in this case 0.8 units of heat are produced every time a unit of electricity is produced. to produce these units of energy, gas is consumed at a rate of carrier_prod(carrier_out) / energy_eff, so gas consumption is only a function of power output.

As with the pv, the chp an export eletricity. The revenue gained from this export is given in the file export_power.csv, in which negative values are given per time step.

Demand technologies¶

Electricity and heat demand are defined here:

demand_electricity:
    essentials:
        name: 'Electrical demand'
        color: '#072486'
        parent: demand
        carrier: electricity

demand_heat:
    essentials:
        name: 'Heat demand'
        color: '#660507'
        parent: demand
        carrier: heat

Electricity and heat demand are technologies like any other. We will associate an actual demand time series with each demand technology later.

Transmission technologies¶

In this district, electricity and heat can be distributed between locations. Gas is made available in each location without consideration of transmission.

A simple transmission node with an \(energy_{eff}\).¶

power_lines:
    essentials:
        name: 'Electrical power distribution'
        color: '#6783E3'
        parent: transmission
        carrier: electricity
    constraints:
        energy_cap_max: 2000
        energy_eff: 0.98
        lifetime: 25
    costs:
        monetary:
            interest_rate: 0.10
            energy_cap_per_distance: 0.01

heat_pipes:
    essentials:
        name: 'District heat distribution'
        color: '#823739'
        parent: transmission
        carrier: heat
    constraints:
        energy_cap_max: 2000
        energy_eff_per_distance: 0.975
        lifetime: 25
    costs:
        monetary:
            interest_rate: 0.10
            energy_cap_per_distance: 0.3

power_lines has an efficiency of 0.95, so a loss during transmission of 0.05. heat_pipes has a loss rate per unit distance of 2.5%/unit distance (or energy_eff_per_distance of 97.5%). Over the distance between the two locations of 0.5km (0.5 units of distance), this translates to \(2.5^{0.5}\) = 1.58% loss rate.

Locations¶

In order to translate the model requirements shown in this section’s introduction into a model definition, four locations are used: X1, X2, X3, and N1.

The technologies are set up in these locations as follows:

Locations and their technologies in the urban-scale example model¶

Let’s now look at the first location definition:

X1:
    techs:
        chp:
        pv:
        supply_grid_power:
            costs.monetary.energy_cap: 100 # cost of transformers
        supply_gas:
        demand_electricity:
            constraints.resource: file=demand_power.csv
        demand_heat:
            constraints.resource: file=demand_heat.csv
    available_area: 500
    coordinates: {x: 2, y: 7}

There are several things to note here:

The location specifies a dictionary of technologies that it allows (techs), with each key of the dictionary referring to the name of technologies defined in our techs.yaml file. Note that technologies listed here must have been defined elsewhere in the model configuration.
It also overrides some options for both demand_electricity, demand_heat, and supply_grid_power. For the latter, it simply sets a location-specific cost. For demands, the options set here are related to reading the demand time series from a CSV file. CSV is a simple text-based format that stores tables by comma-separated rows. Note that we did not define any resource option in the definition of these demands. Instead, this is done directly via a location-specific override. For this location, the files demand_heat.csv and demand_power.csv are loaded. As no column is specified (see national scale example model) Calliope will assume that the column name matches the location name X1. Note that in Calliope, a supply is positive and a demand is negative, so the stored CSV data will be negative.
Coordinates are defined by cartesian coordinates x and y, which will be used to calculate distance of transmission lines (unless we specify otherwise later on) and for location-based visualisation. These coordinates are abstract, unlike latitude and longitude, and can be used when we don’t know (or care) about the geographical location of our problem.
An available_area is defined, which will limit the maximum area of all resource_area technologies to the e.g. roof space available at our location. In this case, we just have pv, but the case where solar thermal panels compete with photovoltaic panels for space, this would the sum of the two to the available area.

The remaining location definitions look like this:

X2:
    techs:
        boiler:
            costs.monetary.energy_cap: 43.1 # different boiler costs
        pv:
            costs.monetary:
                    om_prod: -0.0203 # revenue for just producing electricity
                    export: -0.0491 # FIT return for PV export
        supply_gas:
        demand_electricity:
            constraints.resource: file=demand_power.csv
        demand_heat:
            constraints.resource: file=demand_heat.csv
    available_area: 1300
    coordinates: {x: 8, y: 7}

X3:
    techs:
        boiler:
            costs.monetary.energy_cap: 78 # different boiler costs
        pv:
            constraints:
                energy_cap_max: 50 # changing tariff structure below 50kW
            costs.monetary:
                    om_annual: -80.5 # reimbursement per kWp from FIT
        supply_gas:
        demand_electricity:
            constraints.resource: file=demand_power.csv
        demand_heat:
            constraints.resource: file=demand_heat.csv
    available_area: 900
    coordinates: {x: 5, y: 3}

X2 and X3 are very similar to X1, except that they do not connect to the national electricity grid, nor do they contain the chp technology. Specific pv cost structures are also given, emulating e.g. commercial vs. domestic feed-in tariffs.

N1 differs to the others by virtue of containing no technologies. It acts as a branching station for the heat network, allowing connections to one or both of X2 and X3 without double counting the pipeline from X1 to N1. Its definition look like this:

N1: # location for branching heat transmission network
    coordinates: {x: 5, y: 7}

For transmission technologies, the model also needs to know which locations can be linked, and this is set up in the model configuration as follows:

X1,X2:
    techs:
        power_lines:
            distance: 10
X1,X3:
    techs:
        power_lines:
X1,N1:
    techs:
        heat_pipes:
N1,X2:
    techs:
        heat_pipes:
N1,X3:
    techs:
        heat_pipes:

The distance measure for the power line is larger than the straight line distance given by the coordinates of X1 and X2, so we can provide more information on non-direct routes for our distribution system. These distances will override any automatic straight-line distances calculated by coordinates.

Revenue by export¶

Defined for both PV and CHP, there is the option to accrue revenue in the system by exporting electricity. This export is considered as a removal of the energy carrier electricity from the system, in exchange for negative cost (i.e. revenue). To allow this, carrier_export: electricity has been given under both technology definitions and an export value given under costs.

The revenue from PV export varies depending on location, emulating the different feed-in tariff structures in the UK for commercial and domestic properties. In domestic properties, the revenue is generated by simply having the installation (per kW installed capacity), as export is not metered. Export is metered in commercial properties, thus revenue is generated directly from export (per kWh exported). The revenue generated by CHP depends on the electricity grid wholesale price per kWh, being 80% of that. These revenue possibilities are reflected in the technologies’ and locations’ definitions.

Running the model¶

We now take you through running the model in a Jupyter notebook, which you can view here. After clicking on that link, you can also download and run the notebook yourself (you will need to have Calliope installed).

Tutorial 3: Mixed Integer Linear Programming¶

This example is based on the urban scale example model, but with an override. In the model’s scenarios.yaml file overrides are defined which trigger binary and integer decision variables, creating a MILP model, rather than a conventional LP model.

Units¶

The capacity of a technology is usually a continuous decision variable, which can be within the range of 0 and energy_cap_max (the maximum capacity of a technology). In this model, we introduce a unit limit on the CHP instead:

    chp:
        constraints:
            units_max: 4
            energy_cap_per_unit: 300
            energy_cap_min_use: 0.2
        costs:
            monetary:
                energy_cap: 700
                purchase: 40000

A unit maximum allows a discrete, integer number of CHP to be purchased, each having a capacity of energy_cap_per_unit. Any of energy_cap_max, energy_cap_min, or energy_cap_equals are now ignored, in favour of units_max, units_min, or units_equals. A useful feature unlocked by introducing this is the ability to set a minimum operating capacity which is only enforced when the technology is operating. In the LP model, energy_cap_min_use would force the technology to operate at least at that proportion of its maximum capacity at each time step. In this model, the newly introduced energy_cap_min_use of 0.2 will ensure that the output of the CHP is 20% of its maximum capacity in any time step in which it has a non-zero output.

Purchase cost¶

The boiler does not have a unit limit, it still utilises the continuous variable for its capacity. However, we have introduced a purchase cost:

    boiler:
        costs:
            monetary:
                energy_cap: 35
                purchase: 2000

By introducing this, the boiler now has a binary decision variable associated with it, which is 1 if the boiler has a non-zero energy_cap (i.e. the optimisation results in investment in a boiler) and 0 if the capacity is 0. The purchase cost is applied to the binary result, providing a fixed cost on purchase of the technology, irrespective of the technology size. In physical terms, this may be associated with the cost of pipework, land purchase, etc. The purchase cost is also imposed on the CHP, which is applied to the number of integer CHP units in which the solver chooses to invest.

MILP functionality can be easily applied, but convergence is slower as a result of integer/binary variables. It is recommended to use a commercial solver (e.g. Gurobi, CPLEX) if you wish to utilise these variables outside this example model.

Asynchronous energy production/consumption¶

The heat pipes which distribute thermal energy in the network may be prone to dissipating heat in an unphysical way. I.e. given that they have distribution losses associated with them, in any given timestep, a link could produce and consume energy in the same timestep, losing energy to the atmosphere in both instances, but having a net energy transmission of zero. This allows e.g. a CHP facility to overproduce heat to produce more cheap electricity, and have some way of dumping that heat. The asynchronous_prod_con binary constraint ensures this phenomenon is avoided:

    heat_pipes:
        constraints:
            force_asynchronous_prod_con: true

Now, only one of carrier_prod and carrier_con can be non-zero in a given timestep. This constraint can also be applied to storage technologies, to similarly control charge/discharge.

Running the model¶

We now take you through running the model in a Jupyter notebook, which you can view here. After clicking on that link, you can also download and run the notebook yourself (you will need to have Calliope installed).

Advanced constraints¶

This section, as the title suggests, contains more info and more details, and in particular, information on some of Calliope’s more advanced functionality.

We suggest you read the Building a model, Running a model and Analysing a model sections first.

The `supply_plus` tech¶

The plus tech groups offer complex functionality, for technologies which cannot be described easily. Supply_plus allows a supply technology with internal storage of resource before conversion to the carrier happens. This could be emulated with dummy carriers and a combination of supply, storage, and conversion techs, but the supply_plus tech allows for concise and mathematically more efficient formulation.

Representation of the supply_plus technology¶

An example use of supply_plus is to define a concentrating solar power (CSP) technology which consumes a solar resource, has built-in thermal storage, and produces electricity. See the national-scale built-in example model for an application of this.

See the listing of supply_plus configuration in the abstract base tech group definitions for the additional constraints that are possible.

Warning

When analysing results from supply_plus, care must be taken to correctly account for the losses along the transformation from resource to carrier. For example, charging of storage from the resource may have a resource_eff-associated loss with it, while discharging storage to produce the carrier may have a different loss resulting from a combination of energy_eff and parasitic_eff. Such intermediate conversion losses need to be kept in mind when comparing discharge from storage with carrier_prod in the same time step.

The `conversion_plus` tech¶

The plus tech groups offer complex functionality, for technologies which cannot be described easily. Conversion_plus allows several carriers to be converted to several other carriers. Describing such a technology requires that the user understands the carrier_ratios, i.e. the interactions and relative efficiencies of carrier inputs and outputs.

Representation of the most complex conversion_plus technology available¶

The conversion_plus technologies allows for up to three carrier groups as inputs (carrier_in, carrier_in_2 and carrier_in_3) and up to three carrier groups as outputs (carrier_out, carrier_out_2 and carrier_out_3). A carrier group can contain any number of carriers.

The efficiency of a conversion_plus tech dictates how many units of carrier_out are produced per unit of consumed carrier_in. A unit of carrier_out_2 and of carrier_out_3 is produced each time a unit of carrier_out is produced. Similarly, a unit of Carrier_in_2 and of carrier_in_3 is consumed each time a unit of carrier_in is consumed. Within a given carrier group (e.g. carrier_out_2) any number of carriers can meet this one unit. The carrier_ratio of any carrier compares it either to the production of one unit of carrier_out or to the consumption of one unit of carrier_in.

In this section, we give examples of a few conversion_plus technologies alongside the YAML formulation required to construct them:

Combined heat and power¶

A combined heat and power plant produces electricity, in this case from natural gas. Waste heat that is produced can be used to meet nearby heat demand (e.g. via district heating network). For every unit of electricity produced, 0.8 units of heat are always produced. This is analogous to the heat to power ratio (HTP). Here, the HTP is 0.8.

chp:
    essentials:
        name: Combined heat and power
        carrier_in: gas
        carrier_out: electricity
        carrier_out_2: heat
        primary_carrier_out: electricity
    constraints:
        energy_eff: 0.45
        energy_cap_max: 100
        carrier_ratios.carrier_out_2.heat: 0.8

Air source heat pump¶

The output energy from the heat pump can be either heat or cooling, simulating a heat pump that can be useful in both summer and winter. For each unit of electricity input, one unit of output is produced. Within this one unit of carrier_out, there can be a combination of heat and cooling. Heat is produced with a COP of 5, cooling with a COP of 3. If only heat were produced in a timestep, 5 units of it would be available in carrier_out; similarly 3 units for cooling. In another timestep, both heat and cooling might be produced with e.g. 2.5 units heat + 1.5 units cooling = 1 unit of carrier_out.

ahp:
    essentials:
        name: Air source heat pump
        carrier_in: electricity
        carrier_out: [heat, cooling]
        primary_carrier_out: heat

    constraints:
        energy_eff: 1
        energy_cap_max: 100
        carrier_ratios:
            carrier_out:
                heat: 5
                cooling: 3

Combined cooling, heat and power (CCHP)¶

A CCHP plant can use generated heat to produce cooling via an absorption chiller. As with the CHP plant, electricity is produced at 45% efficiency. For every unit of electricity produced, 1 unit of carrier_out_2 must be produced, which can be a combination of 0.8 units of heat and 0.5 units of cooling. Some example ways in which the model could decide to operate this unit in a given time step are:

1 unit of gas (carrier_in) is converted to 0.45 units of electricity (carrier_out) and (0.8 * 0.45) units of heat (carrier_out_2)
1 unit of gas is converted to 0.45 units electricity and (0.5 * 0.45) units of cooling
1 unit of gas is converted to 0.45 units electricity, (0.3 * 0.8 * 0.45) units of heat, and (0.7 * 0.5 * 0.45) units of cooling

cchp:
    essentials:
        name: Combined cooling, heat and power
        carrier_in: gas
        carrier_out: electricity
        carrier_out_2: [heat, cooling]
        primary_carrier_out: electricity

    constraints:
        energy_eff: 0.45
        energy_cap_max: 100
        carrier_ratios.carrier_out_2: {heat: 0.8, cooling: 0.5}

Advanced gas turbine¶

This technology can choose to burn methane (CH:sub:4) or send hydrogen (H:sub:2) through a fuel cell to produce electricity. One unit of carrier_in can be met by any combination of methane and hydrogen. If all methane, 0.5 units of carrier_out would be produced for 1 unit of carrier_in (energy_eff). If all hydrogen, 0.25 units of carrier_out would be produced for the same amount of carrier_in (energy_eff * hydrogen carrier ratio).

gt:
    essentials:
        name: Advanced gas turbine
        carrier_in: [methane, hydrogen]
        carrier_out: electricity

    constraints:
        energy_eff: 0.5
        energy_cap_max: 100
        carrier_ratios:
            carrier_in: {methane: 1, hydrogen: 0.5}

Complex fictional technology¶

There are few instances where using the full capacity of a conversion_plus tech is physically possible. Here, we have a fictional technology that combines fossil fuels with biomass/waste to produce heat, cooling, and electricity. Different ‘grades’ of heat can be produced, the higher grades having an alternative. High grade heat (high_T_heat) is produced and can be used directly, or used to produce electricity (via e.g. organic rankine cycle). carrier_out is thus a combination of these two. carrier_out_2 can be 0.3 units mid grade heat for every unit carrier_out or 0.2 units cooling. Finally, 0.1 units carrier_out_3, low grade heat, is produced for every unit of carrier_out.

complex:
    essentials:
        name: Complex fictional technology
        carrier_in: [coal, gas, oil]
        carrier_in_2: [biomass, waste]
        carrier_out: [high_T_heat, electricity]
        carrier_out_2: [mid_T_heat, cooling]
        carrier_out_3: low_T_heat
        primary_carrier_out: electricity

    constraints:
        energy_eff: 1
        energy_cap_max: 100
        carrier_ratios:
            carrier_in: {coal: 1.2, gas: 1, oil: 1.6}
            carrier_in_2: {biomass: 1, waste: 1.25}
            carrier_out: {high_T_heat: 0.8, electricity: 0.6}
            carrier_out_2: {mid_T_heat: 0.3, cooling: 0.2}
            carrier_out_3.low_T_heat: 0.15

A primary_carrier_out must be defined when there are multiple carrier_out values defined, similarly primary_carrier_in can be defined for carrier_in. primary_carriers can be defined as any carrier in a technology’s input/output carriers (including secondary and tertiary carriers). The chosen output carrier will be the one to which production costs are applied (reciprocally, input carrier for consumption costs).

Note

Conversion_plus technologies can also export any one of their output carriers, by specifying that carrier as carrier_export.

Resource area constraints¶

Several optional constraints can be used to specify area-related restrictions on technology use.

To make use of these constraints, one should set resource_unit: energy_per_area for the given technologies. This scales the available resource at a given location for a given technology with its resource_area decision variable.

The following related settings are available:

resource_area_equals, resource_area_max, resource_area_min: Set uppper or lower bounds on resource_area or force it to a specific value
resource_area_per_energy_cap: False by default, but if set to true, it forces resource_area to follow energy_cap with the given numerical ratio (e.g. setting to 1.5 means that resource_area == 1.5 * energy_cap)

By default, resource_area_max is infinite and resource_area_min is 0 (zero).

Group constraints¶

Group constraints are applied to named sets of locations and techs, called “constraint groups”, specified through a top-level group_constraints key (sitting alongside other top-level keys like model and run).

The below example shows two such named groups. The first does not specify a subset of techs or locations and is thus applied across the entire model. In the example, we use cost_max with the co2 cost class to specify a model-wide emissions limit (assuming the technologies in the model have co2 costs associated with them). We also use the demand_share_min constraint to force wind and PV to supply at least 40% of electricity demand in Germany, which is modelled as two locations (North and South):

run:
    ...

model:
    ...

group_constraints:
    # A constraint group to apply a systemwide CO2 cap
    systemwide_co2_cap:
        cost_max:
            co2: 100000
    # A constraint group to enforce renewable generation in Germany
    renewable_minimum_share_in_germany:
        techs: ['wind', 'pv']
        locs: ['germany_north', 'germany_south']
        demand_share_min:
            electricity: 0.4

When specifying group constraints, a named group must give at least one constraint, but can list an arbitrary amount of constraints, and optionally give a subset of techs and locations:

group_constraints:
    group_name:
        techs: []  # Optional, can be left out if empty
        locs: []  # Optional, can be left out if empty
        # Any number of constraints can be specified for the given group
        constraint_1: ...
        constraint_2: ...
        ...

The below table lists all available group constraints.

Note that when computing the share for demand_share constraints, only demand technologies are counted, and that when computing the share for supply_share constraints, supply and supply_plus technologies are counted.

Group constraints¶
Constraint	Dimensions	Description
`demand_share_min`	carriers	Minimum share of carrier demand met from a set of technologies across a set of locations, on average over the entire model period.
`demand_share_max`	carriers	Maximum share of carrier demand met from a set of technologies across a set of locations, on average over the entire model period.
`demand_share_equals`	carriers	Share of carrier demand met from a set of technologies across a set of locations, on average over the entire model period.
`demand_share_per_timestep_min`	carriers	Minimum share of carrier demand met from a set of technologies across a set of locations, in each individual timestep.
`demand_share_per_timestep_max`	carriers	Maximum share of carrier demand met from a set of technologies across a set of locations, in each individual timestep.
`demand_share_per_timestep_equals`	carriers	Share of carrier demand met from a set of technologies across a set of locations, in each individual timestep.
`demand_share_per_timestep_decision`	carriers	Turns the per-timestep share of carrier demand met from a set of technologies across a set of locations into a model decision variable.
`carrier_prod_share_min`	carriers	Minimum share of carrier production met from a set of technologies across a set of locations, on average over the entire model period.
`carrier_prod_share_max`	carriers	Maximum share of carrier production met from a set of technologies across a set of locations, on average over the entire model period.
`carrier_prod_share_equals`	carriers	Share of carrier production met from a set of technologies across a set of locations, on average over the entire model period.
`carrier_prod_share_per_timestep_min`	carriers	Minimum share of carrier production met from a set of technologies across a set of locations, in each individual timestep.
`carrier_prod_share_per_timestep_max`	carriers	Maximum share of carrier production met from a set of technologies across a set of locations, in each individual timestep.
`carrier_prod_share_per_timestep_equals`	carriers	Share of carrier production met from a set of technologies across a set of locations, in each individual timestep.
`net_import_share_min`	carriers	Minimum share of demand met from transmission technologies into a set of locations, on average over the entire model period. All transmission technologies of the chosen carrier are added automatically and technologies must thus not be defined explicitly.
`net_import_share_max`	carriers	Maximum share of demand met from transmission technologies into a set of locations, on average over the entire model period. All transmission technologies of the chosen carrier are added automatically and technologies must thus not be defined explicitly.
`net_import_share_equals`	carriers	Share of demand met from transmission technologies into a set of locations, on average over the entire model. All transmission technologies of the chosen carrier are added automatically and technologies must thus not be defined explicitly. period.
`carrier_prod_min`	carriers	Minimum absolute sum of supplied energy (carrier_prod) over all timesteps for a set of technologies across a set of locations.
`carrier_prod_max`	carriers	Maximum absolute sum of supplied energy (carrier_prod) over all timesteps for a set of technologies across a set of locations.
`carrier_prod_equals`	carriers	Exact absolute sum of supplied energy (carrier_prod) over all timesteps for a set of technologies across a set of locations.
`carrier_con_min`	carriers	Minimum sum of consumed energy (carrier_con) over all timesteps for a set of conversion/demand technologies across a set of locations. Values are negative and are relative to zero, i.e. a minimum value of -1 means that consumed energy must be < -1
`carrier_con_max`	carriers	Maximum sum of consumed energy (carrier_con) over all timesteps for a set of conversion/demand technologies across a set of locations. Values are negative and are relative to zero, i.e. a maximum value of -1 means that consumed energy must be > -1
`carrier_con_equals`	carriers	Exact sum of consumed energy (carrier_con) over all timesteps for a set of conversion/demand technologies across a set of locations. Values are negative.
`cost_max`	costs	Maximum total cost from a set of technologies across a set of locations.
`cost_min`	costs	Minimum total cost from a set of technologies across a set of locations.
`cost_equals`	costs	Total cost from a set of technologies across a set of locations must equal given value.
`cost_var_max`	costs	Maximum variable cost from a set of technologies across a set of locations.
`cost_var_min`	costs	Minimum variable cost from a set of technologies across a set of locations.
`cost_var_equals`	costs	Variable cost from a set of technologies across a set of locations must equal given value.
`cost_investment_max`	costs	Maximum investment cost from a set of technologies across a set of locations.
`cost_investment_min`	costs	Minimum investment cost from a set of technologies across a set of locations.
`cost_investment_equals`	costs	Investment cost from a set of technologies across a set of locations must equal given value.
`energy_cap_share_min`	–	Minimum share of installed capacity from a set of technologies across a set of locations.
`energy_cap_share_max`	–	Maximum share of installed capacity from a set of technologies across a set of locations.
`energy_cap_share_equals`	–	Exact share of installed capacity from a set of technologies across a set of locations.
`energy_cap_min`	–	Minimum installed capacity from a set of technologies across a set of locations.
`energy_cap_max`	–	Maximum installed capacity from a set of technologies across a set of locations.
`energy_cap_equals`	–	Exact installed capacity from a set of technologies across a set of locations.
`resource_area_min`	–	Minimum resource area used by a set of technologies across a set of locations.
`resource_area_max`	–	Maximum resource area used by a set of technologies across a set of locations.
`resource_area_equals`	–	Exact resource area used by a set of technologies across a set of locations.

For specifics of the mathematical formulation of the available group constraints, see Group constraints in the mathematical formulation section.

See also

The built-in national-scale example’s scenarios.yaml shows two example uses of group constraints: limiting shared capacity with energy_cap_max and enforcing a minimum shared power generation with carrier_prod_share_min.

Per-distance constraints and costs¶

Transmission technologies can additionally specify per-distance efficiency (loss) with energy_eff_per_distance and per-distance costs with energy_cap_per_distance:

techs:
    my_transmission_tech:
        essentials:
            ...
        constraints:
            # "efficiency" (1-loss) per unit of distance
            energy_eff_per_distance: 0.99
        costs:
            monetary:
                # cost per unit of distance
                energy_cap_per_distance: 10

The distance is specified in transmission links:

links:
    location1,location2:
        my_transmission_tech:
            distance: 500
            constraints:
                energy_cap.max: 10000

If no distance is given, but the locations have been given lat and lon coordinates, Calliope will compute distances automatically (based on the length of a straight line connecting the locations).

One-way transmission links¶

Transmission links are bidirectional by default. To force unidirectionality for a given technology along a given link, you have to set the one_way constraint in the constraint definition of that technology, for that link:

links:
    location1,location2:
        transmission-tech:
            constraints:
                one_way: true

This will only allow transmission from location1 to location2. To swap the direction, the link name must be inverted, i.e. location2,location1.

Cyclic storage¶

With storage and supply_plus techs, it is possible to link the storage at either end of the timeseries, using cyclic storage. This allows the user to better represent multiple years by just modelling one year. Cyclic storage is activated by default (to deactivate: run.cyclic_storage: false). As a result, a technology’s initial stored energy at a given location will be equal to its stored energy at the end of the model’s last timestep.

For example, for a model running over a full year at hourly resolution, the initial storage at Jan 1st 00:00:00 will be forced equal to the storage at the end of the timestep Dec 31st 23:00:00. By setting storage_initial for a technology, it is also possible to fix the value in the last timestep. For instance, with run.cyclic_storage: true and a storage_initial of zero, the stored energy must be zero by the end of the time horizon.

Without cyclic storage in place (as was the case prior to v0.6.2), the storage tech can have any amount of stored energy by the end of the timeseries. This may prove useful in some cases, but has less physical meaning than assuming cyclic storage.

Note

Cyclic storage also functions when time clustering, if allowing storage to be tracked between clusters (see Time resolution adjustment). However, it cannot be used in operate run mode.

Revenue and export¶

It is possible to specify revenues for technologies simply by setting a negative cost value. For example, to consider a feed-in tariff for PV generation, it could be given a negative operational cost equal to the real operational cost minus the level of feed-in tariff received.

Export is an extension of this, allowing an energy carrier to be removed from the system without meeting demand. This is analogous to e.g. domestic PV technologies being able to export excess electricity to the national grid. A cost (or negative cost: revenue) can then be applied to export.

Note

Negative costs can be applied to capacity costs, but the user must an ensure a capacity limit has been set. Otherwise, optimisation will be unbounded.

Binary and mixed-integer constraints¶

Calliope models are purely linear by default. However, several constraints can turn a model into a binary or mixed-integer model. Because solving problems with binary or integer variables takes considerably longer than solving purely linear models, it usually makes sense to carefully consider whether the research question really necessitates going beyond a purely linear model.

By applying a purchase cost to a technology, that technology will have a binary variable associated with it, describing whether or not it has been “purchased”.

By applying units.max, units.min, or units.equals to a technology, that technology will have a integer variable associated with it, describing how many of that technology have been “purchased”. If a purchase cost has been applied to this same technology, the purchasing cost will be applied per unit.

Warning

Integer and binary variables are a recent addition to Calliope and may not cover all edge cases as intended. Please raise an issue on GitHub if you see unexpected behavior.

Asynchronous energy production/consumption¶

The asynchronous_prod_con binary constraint ensures that only one of carrier_prod and carrier_con can be non-zero in a given timestep.

This constraint can be applied to storage or transmission technologies. This example shows use with a heat transmission technology:

    heat_pipes:
        constraints:
            force_asynchronous_prod_con: true

In the above example, heat pipes which distribute thermal energy in the network may be prone to dissipating heat in an unphysical way. I.e. given that they have distribution losses associated with them, in any given timestep, a link could produce and consume energy in the same timestep, losing energy to the atmosphere in both instances, but having a net energy transmission of zero. This might allow e.g. a CHP facility to overproduce heat to produce more cheap electricity, and have some way of dumping that heat. Enabling the asynchronous_prod_con constraint ensures that this does not happen.

User-defined custom constraints¶

It is possible to pass custom constraints to the Pyomo backend, using the backend interface. This requires an understanding of the structure of Pyomo constraints. As an example, the following code reproduces the constraint which limits the maximum carrier consumption to less than or equal to the technology capacity:

model = calliope.Model(...)
model.run()  # or `model.run(build_only=True)` if you don't want the model to be optimised before adding the new constraint

constraint_name = 'max_capacity_90_constraint'
constraint_sets = ['loc_techs_supply']

def max_capacity_90_constraint_rule(backend_model, loc_tech):

    return backend_model.energy_cap[loc_tech] <= (
        backend_model.energy_cap_max[loc_tech] * 0.9
    )

# Add the constraint
model.backend.add_constraint(constraint_name, constraint_sets, max_capacity_90_constraint_rule)

# Rerun the model with new constraint.
new_model = model.backend.rerun()  # `new_model` is a calliope model *without* a backend, it is only useful for saving the results to file

Note

We like the convention that constraint names end with ‘constraint’ and constraint rules have the same text, with an appended ‘_rule’, but you are not required to follow this convention to have a working constraint.
model.run(force_rerun=True) will not implement the new constraint, model.backend.rerun() is required. If you run model.run(force_rerun=True), the backend model will be rebuilt, killing any changes you’ve made.

Advanced features¶

Once you’re comfortable with building, running, and analysing one of the built-in example models, you may want to explore Calliope’s advanced functionality. With these features, you will be able to build and run complex models in no time.

Time resolution adjustment¶

Models have a default timestep length (defined implicitly by the timesteps of the model’s time series data). This default resolution can be adjusted over parts of the dataset by specifying time resolution adjustment in the model configuration, for example:

model:
    time:
        function: resample
        function_options: {'resolution': '6H'}

In the above example, this would resample all time series data to 6-hourly timesteps.

Calliope’s time resolution adjustment functionality allows running a function that can perform arbitrary adjustments to the time series data in the model.

The available options include:

Uniform time resolution reduction through the resample function, which takes a pandas-compatible rule describing the target resolution (see above example).
Deriving representative days from the input time series, by applying the clustering method implemented in calliope.time.clustering, for example:

model:
    time:
        function: apply_clustering
        function_options:
            clustering_func: kmeans
            how: mean
            k: 20

When using representative days, a number of additional constraints are added, based on the study undertaken by Kotzur et al. These constraints require a new decision variable storage_inter_cluster, which tracks storage between all the dates of the original timeseries. This particular functionality can be disabled by including storage_inter_cluster: false in the function_options given above.

Note

It is also possible to load user-defined representative days, by pointing to a file in clustering_func in the same format as pointing to timeseries files in constraints, e.g. clustering_func: file=clusters.csv:column_name. Clusters are unique per datestep, so the clustering file is most readable if the index is at datestep resolution. But, the clustering file index can be in timesteps (e.g. if sharing the same file as a constraint timeseries), with the cluster number repeated per timestep in a day. Cluster values should be integer, starting at zero.

Heuristic selection of time steps, that is, the application of one or more of the masks defined in calliope.time.masks, which will mark areas of the time series to retain at maximum resolution (unmasked) and areas where resolution can be lowered (masked). Options can be passed to the masking functions by specifying options. A time.function can still be specified and will be applied to the masked areas (i.e. those areas of the time series not selected to remain at the maximum resolution), as in this example, which looks for the week of minimum and maximum potential wind generation (assuming a wind technology was specified), then reduces the rest of the input time series to 6-hourly resolution:

model:
    time:
        masks:
            - {function: extreme, options: {padding: 'calendar_week', tech: 'wind', how: 'max'}}
            - {function: extreme, options: {padding: 'calendar_week', tech: 'wind', how: 'min'}}
        function: resample
        function_options: {'resolution': '6H'}

Warning

When using time clustering or time masking, the resulting timesteps will be assigned different weights depending on how long a period of time they represent. Weights are used for example to give appropriate weight to the operational costs of aggregated typical days in comparison to individual extreme days, if both exist in the same processed time series. The weighting is accessible in the model data, e.g. through model.inputs.timestep_weights. The interpretation of results when weights are not 1 for all timesteps requires caution. Production values are not scaled according to weights, but costs are multiplied by weight, in order to weight different timesteps appropriately in the objective function. This means that costs and production values are not consistent without manually post-processing them by either multipyling production by weight (production would then be inconsistent with capacity) or dividing costs by weight. The computation of levelised costs and of capacity factors takes weighting into account, so these values are consisten and can be used as usual.

See also

See the implementation of constraints in calliope.backend.pyomo.constraints for more detail on timestep weights and how they affect model constraints.

Setting a random seed¶

By specifying model.random_seed in the model configuration, any alphanumeric string can be used to initialise the random number generator at the very start of model processing.

This is useful for full reproducibility of model results where time series clustering is used, as clustering methods such as k-means depend on randomly generated initial conditions.

Note that this affects only the random number generator used in Calliope’s model preprocessing and not in any way the solver used to solve the model (any solver-specific options need to be set specifically for that solver; see Specifying custom solver options).

Using `tech_groups` to group configuration¶

In a large model, several very similar technologies may exist, for example, different kinds of PV technologies with slightly different cost data or with different potentials at different model locations.

To make it easier to specify closely related technologies, tech_groups can be used to specify configuration shared between multiple technologies. The technologies then give the tech_group as their parent, rather than one of the abstract base technologies.

You can as well extend abstract base technologies, by adding an attribute that will be in effect for all technologies derived from the base technology. To do so, use the name of the abstract base technology for your group, but omit the parent.

For example:

tech_groups:
    supply:
        constraints:
            monetary:
                interest_rate: 0.1
    pv:
        essentials:
            parent: supply
            carrier: power
        constraints:
            resource: file=pv_resource.csv
            lifetime: 30
        costs:
            monetary:
                om_annual_investment_fraction: 0.05
                depreciation_rate: 0.15

techs:
    pv_large_scale:
        essentials:
            parent: pv
            name: 'Large-scale PV'
        constraints:
            energy_cap_max: 2000
        costs:
            monetary:
                energy_cap: 750
    pv_rooftop:
        essentials:
            parent: pv
            name: 'Rooftop PV'
        constraints:
            energy_cap_max: 10000
        costs:
            monetary:
                energy_cap: 1000

None of the tech_groups appear in model results, they are only used to group model configuration values.

Removing techs, locations and links¶

By specifying exists: false in the model configuration, which can be done for example through overrides, model components can be removed for debugging or scenario analysis.

This works for:

Techs: techs.tech_name.exists: false
Locations: locations.location_name.exists: false
Links: links.location1,location2.exists: false
Techs at a specific location: locations.location_name.techs.tech_name.exists: false
Transmission techs at a specific location: links.location1,location2.techs.transmission_tech.exists: false
Group constraints: group_constraints.my_constraint.exists: false

Operational mode¶

In planning mode, constraints are given as upper and lower boundaries and the model decides on an optimal system configuration. In operational mode, all capacity constraints are fixed and the system is operated with a receding horizon control algorithm.

To specify a runnable operational model, capacities for all technologies at all locations must have be defined. This can be done by specifying energy_cap_equals. In the absence of energy_cap_equals, constraints given as energy_cap_max are assumed to be fixed in operational mode.

Operational mode runs a model with a receding horizon control algorithm. This requires two additional settings:

run:
    operation:
        horizon: 48  # hours
        window: 24  # hours

horizon specifies how far into the future the control algorithm optimises in each iteration. window specifies how many of the hours within horizon are actually used. In the above example, decisions on how to operate for each 24-hour window are made by optimising over 48-hour horizons (i.e., the second half of each optimisation run is discarded). For this reason, horizon must always be larger than window.

SPORES mode¶

SPORES refers to Spatially-explicit Practically Optimal REsultS. This run mode allows a user to generate any number of alternative results which are within a certain range of the optimal cost. It follows on from previous work in the field of modelling to generate alternatives (MGA), with a particular emphasis on alternatives that vary maximally in the spatial dimension. This run mode was developed for and implemented in a study on the future Italian energy system. As an example, if you wanted to generate 10 SPORES, all of which are within 10% of the optimal system cost, you would define the following in your run configuration:

run.mode: spores
run.spores_options:
    spores_number: 10  # The number of SPORES to generate
    slack: 0.1  # The fraction above the cost-optimal cost to set the maximum cost during SPORES
    score_cost_class: spores_score  # The cost class to optimise against when generating SPORES
    slack_cost_group: systemwide_cost_max  # The group constraint name in which the `cost_max` constraint is assigned, for use alongside the slack and cost-optimal cost

You will also need to manually set up some other parts of your model to deal with SPORES:

Set up a group constraint that can limit the total cost of your system to the SPORES cost (i.e. optimal + 10%). The initial value being infinite ensures it does not impinge on the initial cost-optimal run; the constraint will be adapted internally to set a new value which corresponds to the optimal cost plus the slack.

group_constraints:
    systemwide_cost_max.cost_max.monetary: .inf

Assign a spores_score cost to all technologies and locations that you want to limit within the scope of finding alternatives. The spores_score is the cost class against which the model optimises in the generation of SPORES: technologies at locations with higher scores will be penalised in the objective function, so are less likely to be chosen. In the National Scale example model, this looks like:

techs.ccgt.costs.spores_score.energy_cap: 0
techs.ccgt.costs.spores_score.interest_rate: 1
techs.csp.costs.spores_score.energy_cap: 0
techs.csp.costs.spores_score.interest_rate: 1
techs.battery.costs.spores_score.energy_cap: 0
techs.battery.costs.spores_score.interest_rate: 1
techs.ac_transmission.costs.spores_score.energy_cap: 0
techs.ac_transmission.costs.spores_score.interest_rate: 1

Note

We use and recommend using ‘spores_score’ and ‘systemwide_cost_max’ to define the cost class and group constraint, respectively. However, these are user-defined, allowing you to choose terminology that best fits your use-case.

Generating scripts to run a model many times¶

Scenarios and overrides can be used to run a given model multiple times with slightly changed settings or constraints.

This functionality can be used together with the calliope generate_runs and calliope generate_scenarios command-line tools to generate scripts that run a model many times over in a fully automated way, for example, to explore the effect of different technology costs on model results.

calliope generate_runs, at a minimum, must be given the following arguments:

the model configuration file to use
the name of the script to create
--kind: Currently, three options are available. windows creates a Windows batch (.bat) script that runs all models sequentially, bash creates an equivalent script to run on Linux or macOS, bsub creates a submission script for a LSF-based high-performance cluster, and sbatch creates a submission script for a SLURM-based high-performance cluster.
--scenarios: A semicolon-separated list of scenarios (or overrides/combinations of overrides) to generate scripts for, for example, scenario1;scenario2 or override1,override2a;override1,override2b. Note that when not using manually defined scenario names, a comma is used to group overrides together into a single model – in the above example, override1,override2a would be applied to the first run and override1,override2b be applied to the second run

A fully-formed command generating a Windows batch script to run a model four times with each of the scenarios “run1”, “run2”, “run3”, and “run4”:

calliope generate_runs model.yaml run_model.bat --kind=windows --scenarios "run1;run2;run3;run4"

Optional arguments are:

--cluster_threads: specifies the number of threads to request on a HPC cluster
--cluster_mem: specifies the memory to request on a HPC cluster
--cluster_time: specifies the run time to request on a HPC cluster
--additional_args: A text string of any additional arguments to pass directly through to calliope run in the generated scripts, for example, --additional_args=”–debug”.
--debug: Print additional debug information when running the run generation script.

An example generating a script to run on a bsub-type high-performance cluster, with additional arguments to specify the resources to request from the cluster:

calliope generate_runs model.yaml submit_runs.sh --kind=bsub --cluster_mem=1G --cluster_time=100 --cluster_threads=5  --scenarios "run1;run2;run3;run4"

Running this will create two files:

submit_runs.sh: The cluster submission script to pass to bsub on the cluster.
submit_runs.array.sh: The accompanying script defining the runs for the cluster to execute.

In all cases, results are saved into the same directory as the script, with filenames of the form out_{run_number}_{scenario_name}.nc (model results) and plots_{run_number}_{scenario_name}.html (HTML plots), where {run_number} is the run number and {scenario_name} is the name of the scenario (or the string defining the overrides applied). On a cluster, log files are saved to files with names starting with log_ in the same directory.

Finally, the calliope generate_scenarios tool can be used to quickly generate a file with scenarios definition for inclusion in a model, if a large enough number of overrides exist to make it tedious to manually combine them into scenarios. Assuming that in model.yaml a range of overrides exist that specify a subset of time for the years 2000 through 2010, called “y2000” through “y2010”, and a set of cost-related overrides called “cost_low”, “cost_medium” and “cost_high”, the following command would generate scenarios with combinations of all years and cost overrides, calling them “run_1”, “run_2”, and so on, and saving them to scenarios.yaml:

calliope generate_scenarios model.yaml scenarios.yaml y2000;y2001;y2002;2003;y2004;y2005;y2006;2007;2008;y2009;2010 cost_low;cost_medium;cost_high --scenario_name_prefix="run_"

Importing other YAML files in overrides¶

When using overrides (see Scenarios and overrides), it is possible to have import statements within overrides for more flexibility. The following example illustrates this:

overrides:
    some_override:
        techs:
            some_tech.constraints.energy_cap_max: 10
        import: [additional_definitions.yaml]

additional_definitions.yaml:

techs:
    some_other_tech.constraints.energy_eff: 0.1

This is equivalent to the following override:

overrides:
    some_override:
        techs:
            some_tech.constraints.energy_cap_max: 10
            some_other_tech.constraints.energy_eff: 0.1

Interfacing with the solver backend¶

On loading a model, there is no solver backend, only the input dataset. The backend is generated when a user calls run() on their model. Currently this will call back to Pyomo to build the model and send it off to the solver, given by the user in the run configuration run.solver. Once built, solved, and returned, the user has access to the results dataset model.results and interface functions with the backend model.backend.

You can use this interface to:

Get the raw data on the inputs used in the optimisation.
By running model.backend.get_input_params() a user get an xarray Dataset which will look very similar to model.inputs, except that assumed default values will be included. You may also spot a bug, where a value in model.inputs is different to the value returned by this function.
Update a parameter value.
If you are interested in updating a few values in the model, you can run model.backend.update_param(). For example, to update the energy efficiency of your ccgt technology in location region1 from 0.5 to 0.1, you can run model.backend.update_param(‘energy_eff’, {‘region1::ccgt: 0.1})`. This will not affect results at this stage, you’ll need to rerun the backend (point 4) to optimise with these new values.

Note

If you are interested in updating the objective function cost class weights, you will need to set ‘objective_cost_class’ as the parameter, e.g. model.backend.update_param(‘objective_cost_class’, {‘monetary’: 0.5}).

Activate / Deactivate a constraint or objective.
Constraints can be activated and deactivate such that they will or will not have an impact on the optimisation. All constraints are active by default, but you might like to remove, for example, a capacity constraint if you don’t want there to be a capacity limit for any technologies. Similarly, if you had multiple objectives, you could deactivate one and activate another. The result would be to have a different objective when rerunning the backend.

Note

Currently Calliope does not allow you to build multiple objectives, you will need to understand Pyomo and add an additional objective yourself to make use of this functionality. The Pyomo ConcreteModel() object can be accessed at model._backend_model.

Rerunning the backend.
If you have edited parameters or constraint activation, you will need to rerun the optimisation to propagate the effects. By calling model.backend.rerun(), the optimisation will run again, with the updated backend. This will not affect your model, but instead will return a new calliope Model object associated with that specific rerun. You can analyse the results and inputs in this new model, but there is no backend interface available. You’ll need to return to the original model to access the backend again, or run the returned model using new_model.run(force_rerun=True). In the original model, model.results will not change, and can only be overwritten by model.run(force_rerun=True).

Note

By calling model.run(force_rerun=True) any updates you have made to the backend will be overwritten.

See also

Pyomo backend interface

Specifying custom solver options¶

Gurobi¶

Refer to the Gurobi manual, which contains a list of parameters. Simply use the names given in the documentation (e.g. “NumericFocus” to set the numerical focus value). For example:

run:
    solver: gurobi
    solver_options:
        Threads: 3
        NumericFocus: 2

CPLEX¶

Refer to the CPLEX parameter list. Use the “Interactive” parameter names, replacing any spaces with underscores (for example, the memory reduction switch is called “emphasis memory”, and thus becomes “emphasis_memory”). For example:

run:
    solver: cplex
    solver_options:
        mipgap: 0.01
        mip_polishafter_absmipgap: 0.1
        emphasis_mip: 1
        mip_cuts: 2
        mip_cuts_cliques: 3

Configuration and defaults¶

This section lists the available configuration options and constraints along with their default values. Defaults are automatically applied in constraints whenever there is no user input for a particular value.

Model configuration¶

Setting	Default	Comments
calliope_version		Calliope framework version this model is intended for
group_share	{}	Optional settings for the group_share constraint - deprecated and will be removed in v0.7.0
name		Model name
random_seed		Seed for random number generator used during clustering
reserve_margin	{}	Per-carrier system-wide reserve margins
subset_time		Subset of timesteps as a two-element list giving the range, e.g. [‘2005-01-01’, ‘2005-01-05’], or a single string, e.g. ‘2005-01’
time	{}	Optional settings to adjust time resolution, see Time resolution adjustment for the available options
timeseries_data_path		Path to time series data
timeseries_data		Dict of dataframes with time series data (when passing in dicts rather than YAML files to Model constructor)
timeseries_dateformat	%Y-%m-%d %H:%M:%S	Timestamp format of all time series data when read from file
file_allowed	[‘clustering_func’, ‘energy_con’, ‘energy_eff’, ‘energy_prod’, ‘energy_ramping’, ‘export’, ‘force_resource’, ‘om_con’, ‘om_prod’, ‘parasitic_eff’, ‘resource’, ‘resource_eff’, ‘storage_loss’, ‘carrier_ratios’]	List of configuration options allowed to specify “file=” to load timeseries data. This can be updated if you’re adding a new custom constraint that requires a newly defined parameter to be a timeseries. If updating existing parameters, you can expect existing constraints to not change behaviour or to break on being constructed. ## # Base technology groups ##

Run configuration¶

Setting	Default	Comments
backend	pyomo	Backend to use to build and solve the model. As of v0.6.0, only pyomo is available
bigM	1000000000.0	Used for unmet demand, but should be of a similar order of magnitude as the largest cost that the model could achieve. Too high and the model will not converge
cyclic_storage	True	If true, storage in the last timestep of the timeseries is considered to be the ‘previous timestep’ in the first timestep of the timeseries
ensure_feasibility	False	If true, unmet_demand will be a decision variable, to account for an ability to meet demand with the available supply. If False and a mismatch occurs, the optimisation will fail due to infeasibility
mode	plan	Which mode to run the model in: ‘plan’, ‘operation’ or ‘spores’
objective_options	{}	Arguments to pass to objective function. If cost-based objective function in use, should include ‘cost_class’ and ‘sense’ (maximize/minimize)
objective	minmax_cost_optimization	Name of internal objective function to use, currently only min/max cost-based optimisation is available
operation	{}	Settings for operational mode
spores_options	{}	settings for SPORES (spatially-explicit, practically optimal results) mode
save_logs		Directory into which to save logs and temporary files. Also turns on symbolic solver labels in the Pyomo backend
solver_io		What method the Pyomo backend should use to communicate with the solver
solver_options		A list of options, which are passed on to the chosen solver, and are therefore solver-dependent
solver	cbc	Which solver to use
zero_threshold	1e-10	Any value coming out of the backend that is smaller than this threshold (due to floating point errors, probably) will be set to zero

Per-tech constraints¶

The following table lists all available technology constraint settings and their default values. All of these can be set by tech_identifier.constraints.constraint_name, e.g. nuclear.constraints.energy_cap.max.

Setting	Default	Name	Unit	Comments
carrier_ratios	False	Carrier ratios	fraction	Ratio of summed output of carriers in [‘out_2’, ‘out_3’] / [‘in_2’, ‘in_3’] to the summed output of carriers in ‘out’ / ‘in’. given in a nested dictionary.
charge_rate	False	Charge rate	hour ^-1	(do not use, replaced by energy_cap_per_storage_cap_max) ratio of maximum charge/discharge (kW) for a given maximum storage capacity (kWh).
energy_cap_per_storage_cap_min	False	Minimum energy capacity per storage capacity	hour ^-1	ratio of minimum charge/discharge (kW) for a given storage capacity (kWh).
energy_cap_per_storage_cap_max	False	Maximum energy capacity per storage capacity	hour ^-1	ratio of maximum charge/discharge (kW) for a given storage capacity (kWh).
energy_cap_per_storage_cap_equals	False	Tie energy capacity to storage capacity	hour ^-1
energy_cap_equals	False	Specific installed energy capacity	kW	fixes maximum/minimum if decision variables `carrier_prod`/`carrier_con` and overrides `_max` and `_min` constraints.
energy_cap_equals_systemwide	False	System-wide specific installed energy capacity	kW	fixes the sum to a maximum/minimum, for a particular technology, of the decision variables `carrier_prod`/`carrier_con` over all locations.
energy_cap_max	inf	Maximum installed energy capacity	kW	Limits decision variables `carrier_prod`/`carrier_con` to a maximum/minimum.
energy_cap_max_systemwide	inf	System-wide maximum installed energy capacity	kW	Limits the sum to a maximum/minimum, for a particular technology, of the decision variables `carrier_prod`/`carrier_con` over all locations.
energy_cap_min	0	Minimum installed energy capacity	kW	Limits decision variables `carrier_prod`/`carrier_con` to a minimum/maximum.
energy_cap_min_use	False	Minimum carrier production	fraction	Set to a value between 0 and 1 to force minimum carrier production as a fraction of the technology maximum energy capacity. If non-zero and technology is not defined by `units`, this will force the technology to operate above its minimum value at every timestep.
energy_cap_per_unit	False	Energy capacity per purchased unit	kW/unit	Set the capacity of each integer unit of a technology purchased
energy_cap_scale	1.0	Energy capacity scale	float	Scale all `energy_cap` min/max/equals/total_max/total_equals constraints by this value
energy_con	False	Energy consumption	boolean	Allow this technology to consume energy from the carrier (static boolean, or from file as timeseries).
energy_eff	1.0	Energy efficiency	fraction	conversion efficiency (static, or from file as timeseries), from `resource`/`storage`/`carrier_in` (tech dependent) to `carrier_out`.
energy_eff_per_distance	1.0	Energy efficiency per distance	fraction/distance	Set as value between 1 (no loss) and 0 (all energy lost).
energy_prod	False	Energy production	boolean	Allow this technology to supply energy to the carrier (static boolean, or from file as timeseries).
energy_ramping	False	Ramping rate	fraction / hour	Set to `false` to disable ramping constraints, otherwise limit maximum carrier production to a fraction of maximum capacity, which increases by that fraction at each timestep.
export_cap	inf	Export capacity	kW	Maximum allowed export of produced energy carrier for a technology.
export_carrier		Export carrier	N/A	Name of carrier to be exported. Must be an output carrier of the technology
force_asynchronous_prod_con	False	Force asynchronous production consumption	boolean	If True, carrier_prod and carrier_con cannot both occur in the same timestep
force_resource	False	Force resource	boolean	Forces this technology to use all available `resource`, rather than making it a maximum upper boundary (for production) or minimum lower boundary (for consumption). Static boolean, or from file as timeseries
lifetime		Technology lifetime	years	Must be defined if fixed capital costs are defined. A reasonable value for many technologies is around 20-25 years.
one_way	False	One way	boolean	Forces a transmission technology to only move energy in one direction on the link, in this case from default_location_from to default_location_to
parasitic_eff	1.0	Plant parasitic efficiency	fraction	Additional losses as energy gets transferred from the plant to the carrier (static, or from file as timeseries), e.g. due to plant parasitic consumption
resource	0	Available resource	kWh \| kWh/m² \| kWh/kW	Maximum available resource (static, or from file as timeseries). Unit dictated by `resource_unit`
resource_area_equals	False	Specific installed resource area	m²
resource_area_max	inf	Maximum usable resource area	m²	If set to a finite value, restricts the usable area of the technology to this value.
resource_area_min	0	Minimum usable resource area	m²
resource_area_per_energy_cap	False	Resource area per energy capacity	m:sup: 2/kW	If set, forces `resource_area` to follow `energy_cap` with the given numerical ratio (e.g. setting to 1.5 means that `resource_area == 1.5 * energy_cap`)
resource_cap_equals	False	Specific installed resource consumption capacity	kW	overrides `_max` and `_min` constraints.
resource_cap_equals_energy_cap	False	Resource capacity equals energy cpacity	boolean	If true, `resource_cap` is forced to equal `energy_cap`
resource_cap_max	inf	Maximum installed resource consumption capacity	kW
resource_cap_min	0	Minimum installed resource consumption capacity	kW
resource_eff	1.0	Resource efficiency	fraction	Efficiency (static, or from file as timeseries) in capturing resource before it reaches storage (if storage is present) or conversion to carrier.
resource_min_use	False	Minimum resource consumption	fraction	Set to a value between 0 and 1 to force minimum resource consumption for production technologies
resource_scale	1.0	Resource scale	fraction	Scale resource (either static value or all values in timeseries) by this value
resource_unit	energy	Resource unit	N/A	Sets the unit of `resource` to either `energy` (i.e. kWh), `energy_per_area` (i.e. kWh/m2), or `energy_per_cap` (i.e. kWh/kW). `energy_per_area` uses the `resource_area` decision variable to scale the available resource while `energy_per_cap` uses the `energy_cap` decision variable.
storage_cap_equals	False	Specific storage capacity	kWh	If not defined, `energy_cap_equals` * `energy_cap_per_storage_cap_max` will be used as the capacity and overrides `_max` and `_min` constraints.
storage_cap_max	inf	Maximum storage capacity	kWh	If not defined, `energy_cap_max` * `energy_cap_per_storage_cap_max` will be used as the capacity.
storage_cap_min	0	Minimum storage capacity	kWh
storage_cap_per_unit	False	Storage capacity per purchased unit	kWh/unit	Set the storage capacity of each integer unit of a technology purchased.
storage_discharge_depth	0	Storage depth of discharge	fraction	Defines the minimum level of storage state of charge, as a fraction of total storage capacity
storage_initial	0	Initial storage level	fraction	Set stored energy in device at the first timestep, as a fraction of total storage capacity
storage_loss	0	Storage loss rate	fraction/hour	rate of storage loss per hour (static, or from file as timeseries), used to calculate lost stored energy as `(1 - storage_loss)^hours_per_timestep`
units_equals	False	Specific number of purchased units	integer	Turns the model from LP to MILP.
units_equals_systemwide	False	System-wide specific installed energy capacity	kW	fixes the sum to a specific value, for a particular technology, of the decision variables `carrier_prod`/`carrier_con` over all locations.
units_max	False	Maximum number of purchased units	integer	Turns the model from LP to MILP.
units_max_systemwide	inf	System-wide maximum installed energy capacity	kW	Limits the sum to a maximum/minimum, for a particular technology, of the decision variables `carrier_prod`/`carrier_con` over all locations.
units_min	False	Minimum number of purchased units	integer	Turns the model from LP to MILP.

Per-tech costs¶

These are all the available costs, which are set to \(0\) by default for every defined cost class. Costs are set by tech_identifier.costs.cost_class.cost_name, e.g. nuclear.costs.monetary.energy_cap.

Setting	Name	Unit	Comments
energy_cap	Cost of energy capacity	kW _gross ^-1
energy_cap_per_distance	Cost of energy capacity, per unit distance	kW _gross ^-1 / distance	Applied to transmission links only
export	Carrier export cost	kWh ^-1	Usually used in the negative sense, as a subsidy.
interest_rate	Interest rate	fraction	Used when computing levelized costs
om_annual	Yearly O&M costs	kW _{energy_cap} ^-1
om_annual_investment_fraction	Fractional yearly O&M costs	fraction / total investment
om_con	Carrier consumption cost	kWh ^-1	Applied to carrier consumption of a technology
om_prod	Carrier production cost	kWh ^-1	Applied to carrier production of a technology
purchase	Purchase cost	unit ^-1	Triggers a binary variable for that technology to say that it has been purchased or is applied to integer variable `units`
resource_area	Cost of resource area	m^-2
resource_cap	Cost of resource consumption capacity	kW ^-1
storage_cap	Cost of storage capacity	kWh ^-1

Technology depreciation settings apply when calculating levelized costs. The interest rate and life times must be set for each technology with investment costs.

Group constraints¶

See Group constraints for a full listing of available group constraints.

Abstract base technology groups¶

Technologies must always define a parent, and this can either be one of the pre-defined abstract base technology groups or a user-defined group (see Using tech_groups to group configuration). The pre-defined groups are:

supply: Supplies energy to a carrier, has a positive resource.
supply_plus: Supplies energy to a carrier, has a positive resource. Additional possible constraints, including efficiencies and storage, distinguish this from supply.
demand: Demands energy from a carrier, has a negative resource.
storage: Stores energy.
transmission: Transmits energy from one location to another.
conversion: Converts energy from one carrier to another.
conversion_plus: Converts energy from one or more carrier(s) to one or more different carrier(s).

A technology inherits the configuration that its parent group specifies (which, in turn, may inherit from its own parent).

Note

The identifiers of the abstract base tech groups are reserved and cannot be used for user-defined technologies. However, you can amend an abstract base technology group for example by a lifetime attribute that will be in effect for all technologies derived from that group (see Using tech_groups to group configuration).

The following lists the pre-defined base tech groups and the defaults they provide.

supply¶

Default constraints provided by the parent tech group:

essentials:
  parent:
constraints:
  energy_prod: true
  resource: inf
  resource_unit: energy
costs: {}

Required constraints, allowed constraints, and allowed costs:

required_constraints: []
allowed_constraints:
-   energy_cap_equals
-   energy_cap_equals_systemwide
-   energy_cap_max
-   energy_cap_max_systemwide
-   energy_cap_min
-   energy_cap_min_use
-   energy_cap_per_unit
-   energy_cap_scale
-   energy_eff
-   energy_prod
-   energy_ramping
-   export_cap
-   export_carrier
-   force_resource
-   lifetime
-   resource
-   resource_area_equals
-   resource_area_max
-   resource_area_min
-   resource_area_per_energy_cap
-   resource_min_use
-   resource_scale
-   resource_unit
-   units_equals
-   units_equals_systemwide
-   units_max
-   units_max_systemwide
-   units_min
allowed_costs:
-   depreciation_rate
-   energy_cap
-   export
-   interest_rate
-   om_annual
-   om_annual_investment_fraction
-   om_con
-   om_prod
-   purchase
-   resource_area

supply_plus¶

Default constraints provided by the parent tech group:

essentials:
  parent:
constraints:
  energy_prod: true
  resource: inf
  resource_eff: 1.0
  resource_unit: energy
costs: {}

Required constraints, allowed constraints, and allowed costs:

required_constraints: []
allowed_constraints:
-   charge_rate
-   energy_cap_per_storage_cap_min
-   energy_cap_per_storage_cap_max
-   energy_cap_per_storage_cap_equals
-   energy_cap_equals
-   energy_cap_equals_systemwide
-   energy_cap_max
-   energy_cap_max_systemwide
-   energy_cap_min
-   energy_cap_min_use
-   energy_cap_per_unit
-   energy_cap_scale
-   energy_eff
-   energy_prod
-   energy_ramping
-   export_cap
-   export_carrier
-   force_resource
-   lifetime
-   parasitic_eff
-   resource
-   resource_area_equals
-   resource_area_max
-   resource_area_min
-   resource_area_per_energy_cap
-   resource_cap_equals
-   resource_cap_equals_energy_cap
-   resource_cap_max
-   resource_cap_min
-   resource_eff
-   resource_min_use
-   resource_scale
-   resource_unit
-   storage_cap_equals
-   storage_cap_max
-   storage_cap_min
-   storage_cap_per_unit
-   storage_initial
-   storage_loss
-   units_equals
-   units_equals_systemwide
-   units_max
-   units_max_systemwide
-   units_min
allowed_costs:
-   depreciation_rate
-   energy_cap
-   export
-   interest_rate
-   om_annual
-   om_annual_investment_fraction
-   om_con
-   om_prod
-   purchase
-   resource_area
-   resource_cap
-   storage_cap

demand¶

Default constraints provided by the parent tech group:

essentials:
  parent:
constraints:
  energy_con: true
  force_resource: true
  resource_unit: energy
costs: {}

Required constraints, allowed constraints, and allowed costs:

required_constraints:
-   resource
allowed_constraints:
-   energy_con
-   force_resource
-   resource
-   resource_area_equals
-   resource_scale
-   resource_unit
allowed_costs:
-   om_con

storage¶

Default constraints provided by the parent tech group:

essentials:
  parent:
constraints:
  energy_con: true
  energy_prod: true
  storage_cap_max: inf
costs: {}

Required constraints, allowed constraints, and allowed costs:

required_constraints: []
allowed_constraints:
-   charge_rate
-   energy_cap_per_storage_cap_min
-   energy_cap_per_storage_cap_max
-   energy_cap_per_storage_cap_equals
-   energy_cap_equals
-   energy_cap_equals_systemwide
-   energy_cap_max
-   energy_cap_max_systemwide
-   energy_cap_min
-   energy_cap_min_use
-   energy_cap_per_unit
-   energy_cap_scale
-   energy_con
-   energy_eff
-   energy_prod
-   energy_ramping
-   export_cap
-   export_carrier
-   force_asynchronous_prod_con
-   lifetime
-   storage_cap_equals
-   storage_cap_max
-   storage_cap_min
-   storage_cap_per_unit
-   storage_initial
-   storage_loss
-   storage_time_max
-   storage_discharge_depth
-   units_equals
-   units_equals_systemwide
-   units_max
-   units_max_systemwide
-   units_min
allowed_costs:
-   depreciation_rate
-   energy_cap
-   export
-   interest_rate
-   om_annual
-   om_annual_investment_fraction
-   om_prod
-   purchase
-   storage_cap

transmission¶

Default constraints provided by the parent tech group:

essentials:
  parent:
constraints:
  energy_con: true
  energy_prod: true

##
# Default technology
##
costs: {}

Required constraints, allowed constraints, and allowed costs:

required_constraints: []
allowed_constraints:
-   energy_cap_equals
-   energy_cap_min
-   energy_cap_max
-   energy_cap_per_unit
-   energy_cap_scale
-   energy_con
-   energy_eff
-   energy_eff_per_distance
-   energy_prod
-   force_asynchronous_prod_con
-   lifetime
-   one_way
allowed_costs:
-   depreciation_rate
-   energy_cap
-   energy_cap_per_distance
-   interest_rate
-   om_annual
-   om_annual_investment_fraction
-   om_prod
-   purchase
-   purchase_per_distance

conversion¶

Default constraints provided by the parent tech group:

essentials:
  parent:
constraints:
  energy_con: true
  energy_prod: true
costs: {}

Required constraints, allowed constraints, and allowed costs:

required_constraints: []
allowed_constraints:
-   energy_cap_equals
-   energy_cap_equals_systemwide
-   energy_cap_max
-   energy_cap_max_systemwide
-   energy_cap_min
-   energy_cap_min_use
-   energy_cap_per_unit
-   energy_cap_scale
-   energy_con
-   energy_eff
-   energy_prod
-   energy_ramping
-   export_cap
-   export_carrier
-   lifetime
-   units_equals
-   units_equals_systemwide
-   units_max
-   units_max_systemwide
-   units_min
allowed_costs:
-   depreciation_rate
-   energy_cap
-   export
-   interest_rate
-   om_annual
-   om_annual_investment_fraction
-   om_con
-   om_prod
-   purchase

conversion_plus¶

Default constraints provided by the parent tech group:

essentials:
  parent:
constraints:
  energy_con: true
  energy_prod: true
costs: {}

Required constraints, allowed constraints, and allowed costs:

required_constraints: []
allowed_constraints:
-   carrier_ratios
-   energy_cap_equals
-   energy_cap_equals_systemwide
-   energy_cap_max
-   energy_cap_max_systemwide
-   energy_cap_min
-   energy_cap_min_use
-   energy_cap_per_unit
-   energy_cap_scale
-   energy_con
-   energy_eff
-   energy_prod
-   energy_ramping
-   export_cap
-   export_carrier
-   lifetime
-   units_equals
-   units_equals_systemwide
-   units_max
-   units_max_systemwide
-   units_min
allowed_costs:
-   depreciation_rate
-   energy_cap
-   export
-   interest_rate
-   om_annual
-   om_annual_investment_fraction
-   om_con
-   om_prod
-   purchase

Troubleshooting¶

General strategies¶

Building a smaller model: model.subset_time allows specifying a subset of timesteps to be used. This can be useful for debugging purposes as it can dramatically speed up model solution times. The timestep subset can be specified as [startdate, enddate], e.g. [‘2005-01-01’, ‘2005-01-31’], or as a single time period, such as 2005-01 to select January only. The subsets are processed before building the model and applying time resolution adjustments, so time resolution reduction functions will only see the reduced set of data.
Retaining logs and temporary files: The setting run.save_logs, disabled by default, sets the directory into which to save logs and temporary files from the backend, to inspect solver logs and solver-generated model files. This also turns on symbolic solver labels in the Pyomo backend, so that all model components in the backend model are named according to the corresponding Calliope model components (by default, Pyomo uses short random names for all generated model components).
Analysing the optimisation problem without running the model: If you are comfortable with navigating Pyomo objects, then you can build the Pyomo model backend without running the optimisation problem, using model.run(build_only=True). Pyomo objects are then accessible within model._backend_model. For instance, the constraint limiting energy capacity can be viewed by calling model._backend_model.energy_capacity_constraint.pprint(‘hi’). Alternatively, if you are working from the command line or have little experience with Pyomo, you can generate an LP file. The LP file contains the mathematical model formulation of a fully built Calliope model. It is a standard format that can be passed to various solvers. Examining the LP file manually or using additional tools (see below) can help find issues when a model is infeasible or unbounded. To build a model and save it to LP without actually solving it, use:
```
calliope run my_model.yaml --save_lp=my_saved_model.lp
```
or, interactively:
```
model.to_lp('my_saved_model.lp')
```

Improving solution times¶

One way to improve solution time is to reduce the size of a problem (another way is to address potential numerical issues, which is dealt with further below in Understanding infeasibility and numerical instability).

Number of variables¶

The sets locs, techs, timesteps, carriers, and costs all contribute to model complexity. A reduction of any of these sets will reduce the number of resulting decision variables in the optimisation, which in turn will improve solution times.

Note

By reducing the number of locations (e.g. merging nearby locations) you also remove the technologies linking those locations to the rest of the system, which is additionally beneficial.

Currently, we only provide automatic set reduction for timesteps. Timesteps can be resampled (e.g. 1hr -> 2hr intervals), masked (e.g. 1hr -> 12hr intervals except one week of particular interest), or clustered (e.g. 365 days to 5 days, each representing 73 days of the year, with 1hr resolution). In so doing, significant solution time improvements can be acheived.

Complex technologies¶

Calliope is primarily an LP framework, but application of certain constraints will trigger binary or integer decision variables. When triggered, a MILP model will be created.

In both cases, there will be a time penalty, as linear programming solvers are less able to converge on solutions of problems which include binary or integer decision variables. But, the additional functionality can be useful. A purchasing cost allows for a cost curve of the form \(y = Mx + C\) to be applied to a technology, instead of the LP costs which are all of the form \(y = Mx\). Integer units also trigger per-timestep decision variables, which allow technologies to be “on” or “off” at each timestep.

Additionally, in LP models, interactions between timesteps (in storage technologies) can lead to longer solution time. The exact extent of this is as-yet untested.

Model mode¶

Solution time increases more than linearly with the number of decision variables. As it splits the model into ~daily chunks, operational mode can help to alleviate solution time of big problems. This is clearly at the expense of fixing technology capacities. However, one solution is to use a heavily time clustered plan mode to get indicative model capacities. Then run operate mode with these capacities to get a higher resolution operation strategy. If necessary, this process could be iterated.

See also

Operational mode

Influence of solver choice on speed¶

The open-source solvers (GLPK and CBC) are slower than the commercial solvers. If you are an academic researcher, it is recommended to acquire a free licence for Gurobi or CPLEX to very quickly improve solution times. GLPK in particular is slow when solving MILP models. CBC is an improvement, but can still be several orders of magnitude slower at reaching a solution than Gurobi or CPLEX.

We tested solution time for various solver choices on our example models, extended to run over a full year (8760 hours). These runs took place on the University of Cambridge high performance computing cluster, with a maximum run time of 5 hours. As can be seen, CBC is far superior to GLPK. If introducing binary constraints, although CBC is an improvement on GLPK, access to a commercial solver is preferable.

National scale example model size

Variables : 420526 [Nneg: 219026, Free: 105140, Other: 96360]
Linear constraints : 586972 [Less: 315373, Greater: 10, Equal: 271589]

MILP urban scale example model

Variables: 586996 [Nneg: 332913, Free: 78880, Binary: 2, General Integer: 8761, Other: 166440]
Linear constraints: 788502 [Less: 394226, Greater: 21, Equal: 394255]

Solution time

Solver	Solution time
Solver	National	Urban
GLPK	4:35:40	>5hrs
CBC	0:04:45	0:52:13
Gurobi (1 thread)	0:02:08	0:03:21
CPLEX (1 thread)	0:04:55	0:05:56
Gurobi (4 thread)	0:02:27	0:03:08
CPLEX (4 thread)	0:02:16	0:03:26

See also

Specifying custom solver options

Understanding infeasibility and numerical instability¶

Note

A good first step when faced with an infeasible model is often to remove constraints, in particular more complex constraints. For example, different combinations of group constraints can easily introduce mutually exclusive requirements on capacities or output from specific technologies. Once a minimal model works, more complex constraints can be turned on again one after the other.

Using the Gurobi solver¶

To understand infeasible models:

Set run.solver_options.DualReductions: 0 to see whether a model is infeasible or unbounded.
To analyse infeasible models, save an LP file with the --save_lp command-line option, then use Gurobi to generate an Irreducible Inconsistent Subsystem that shows which constraints are infeasible:
```
gurobi_cl ResultFile=result.ilp my_saved_model.lp
```
More detail on this is in the official Gurobi documentation.

To deal with numerically unstable models, try setting run.solver_options.Presolve: 0, as large numeric ranges can cause the pre-solver to generate an infeasible or numerically unstable model. The Gurobi Guidelines for Numerical Issues give detailed guidance for strategies to address numerically difficult optimisation problems.

Using the CPLEX solver¶

There are two ways to understand infeasibility when using the CPLEX solver, the first is quick and the second is more involved:

Save solver logs for your model (run.save_logs: path/to/log_directory). In the directory, open the file ending in ‘.cplex.log’ to see the CPLEX solver report. If the model is infeasible or unbounded, the offending constraint will be identified (e.g. “SOLVER: Infeasible variable = slack c_u_carrier_production_max_constraint(region1_2__csp__power_2005_01_01_07_00_00)_”). This may be enough to understand why the model is failing, if not…
Open the LP file in CPLEX interactive (run cplex in the command line to invoke a CPLEX interactive session). The LP file for the problem ends with ‘.lp’ in the log folder (read path/to/file.lp). Once loaded, you can try relaxing variables / constraints to see if the problem can be solved with relaxation (FeasOpt). You can also identify conflicting constraints (tools conflict) and print those constraints directly (display conflict all). There are many more commands available to analyse individual constraints and variables in the Official CPLEX documentation.

Similar to Gurobi, numerically unstable models may lead to unexpected infeasibility, so you can try run.solver_options.preprocessing_presolve: 0 or you can request CPLEX to more aggressively scale the problem itself using the solver option read_scale: 1 . The CPLEX documentation page on numeric difficulties goes into more detail on numeric instability.

Rerunning a model¶

After running, if there is an infeasibility you want to address, or simply a few values you dont think were quite right, you can change them and rerun your model. If you change them in model.inputs, just rerun the model as model.run(force_rerun=True).

Note

model.run(force_rerun=True) will replace you current model.results and rebuild he entire model backend. You may want to save your model before doing this.

Particularly if your problem is large, you may not want to rebuild the backend to change a few small values. Instead you can interface directly with the backend using the model.backend functions, to update individual parameter values and switch constraints on/off. By rerunning the backend specifically, you can optimise your problem with these backend changes, without rebuilding the backend entirely.

Note

model.inputs and model.results will not be changed when updating and rerunning the backend. Instead, a new xarray Dataset is returned.

See also

Interfacing with the solver backend

Debugging model errors¶

Calliope provides a method to save its fully built and commented internal representation of a model to a single YAML file with Model.save_commented_model_yaml(path). Comments in the resulting YAML file indicate where original values were overridden.

Because this is Calliope’s internal representation of a model directly before the model_data xarray.Dataset is built, it can be useful for debugging possible issues in the model formulation, for example, undesired constraints that exist at specific locations because they were specified model-wide without having been superseded by location-specific settings.

Further processing of the data does occur before solving the model. The final values of parameters used by the backend solver to generate constraints can be analysed when running an interactive Python session by running model.backend.get_input_params(). This provides a user with an xarray Dataset which will look very similar to model.inputs, except that assumed default values will be included. An attempt at running the model has to be made in order to be able to run this command.

See also

If using Calliope interactively in a Python session, we recommend reading up on the Python debugger and (if using Jupyter notebooks) making use of the %debug magic.

More info (reference)¶

This section contains additional information useful as reference: a list of all example models and their configuration, a listing of different possible configuration values, and the detailed mathematical formulation.

We suggest you read the Building a model, Running a model and Analysing a model sections first before referring to this section.

Built-in example models¶

This section gives a listing of all the YAML configuration files included in the built-in example models. Refer to the tutorials section for a brief overview of how these parts together provide a working model.

The example models are accessible in the calliope.examples module. To create an instance of an example model, call its constructor function, e.g.

urban_model = calliope.examples.urban_scale()

The available example models and their constructor functions are:

calliope.examples.national_scale(*args, **kwargs)[source]¶: Returns the built-in national-scale example model.

calliope.examples.time_clustering(*args, **kwargs)[source]¶: Returns the built-in national-scale example model with time clustering.

calliope.examples.time_resampling(*args, **kwargs)[source]¶: Returns the built-in national-scale example model with time resampling.

calliope.examples.urban_scale(*args, **kwargs)[source]¶: Returns the built-in urban-scale example model.

calliope.examples.milp(*args, **kwargs)[source]¶: Returns the built-in urban-scale example model with MILP constraints enabled.

calliope.examples.operate(*args, **kwargs)[source]¶: Returns the built-in urban-scale example model in operate mode.

calliope.examples.time_masking(*args, **kwargs)[source]¶: Returns the built-in urban-scale example model with time masking.

National-scale example¶

Available as calliope.examples.national_scale.

Model settings¶

The layout of the model directory is as follows (+ denotes directories, - files):

- model.yaml
- scenarios.yaml
+ timeseries_data
    - csp_resource.csv
    - demand-1.csv
    - demand-2.csv
+ model_config
    - locations.yaml
    - techs.yaml

model.yaml:

import:  # Import other files from paths relative to this file, or absolute paths
    - 'model_config/techs.yaml'  # This file specifies the model's technologies
    - 'model_config/locations.yaml'  # This file specifies the model's locations
    - 'scenarios.yaml'  # Scenario and override group definitions

# Model configuration: all settings that affect the built model
model:
    name: National-scale example model

    # What version of Calliope this model is intended for
    calliope_version: 0.6.6

    # Time series data path - can either be a path relative to this file, or an absolute path
    timeseries_data_path: 'timeseries_data'

    subset_time: ['2005-01-01', '2005-01-05']  # Subset of timesteps

# Run configuration: all settings that affect how the built model is run
run:
    solver: cbc

    ensure_feasibility: true  # Switches on the "unmet demand" constraint

    bigM: 1e6  # Sets the scale of unmet demand, which cannot be too high, otherwise the optimisation will not converge

    zero_threshold: 1e-10  # Any value coming out of the backend that is smaller than this (due to floating point errors, probably) will be set to zero

    mode: plan  # Choices: plan, operate

    objective_options.cost_class: {monetary: 1}

scenarios.yaml:

##
# Scenarios are optional, named combinations of overrides
##
scenarios:
    cold_fusion_with_production_share: ['cold_fusion', 'cold_fusion_prod_share']
    cold_fusion_with_capacity_share: ['cold_fusion', 'cold_fusion_cap_share']


##
# Overrides are the building blocks from which scenarios can be defined
##
overrides:
    profiling:
        model.name: 'National-scale example model (profiling run)'
        model.subset_time: ['2005-01-01', '2005-01-15']
        run.solver: cbc

    time_resampling:
        model.name: 'National-scale example model with time resampling'
        model.subset_time: '2005-01'
        # Resample time resolution to 6-hourly
        model.time: {function: resample, function_options: {'resolution': '6H'}}

    time_clustering:
        model.random_seed: 23
        model.name: 'National-scale example model with time clustering'
        model.subset_time: null  # No time subsetting
        # Cluster timesteps using k-means
        model.time: {function: apply_clustering, function_options: {clustering_func: 'kmeans', how: 'closest', k: 10}}

    spores:
        run.mode: spores
        run.spores_options:
            score_cost_class: 'spores_score'
            slack_cost_group: 'systemwide_cost_max'
            slack: 0.1
            spores_number: 3
        run.objective_options.cost_class: {'monetary': 1, 'spores_score': 0}
        group_constraints:
            systemwide_cost_max.cost_max.monetary: .inf

        techs.ccgt.costs.spores_score.energy_cap: 0
        techs.ccgt.costs.spores_score.interest_rate: 1
        techs.csp.costs.spores_score.energy_cap: 0
        techs.csp.costs.spores_score.interest_rate: 1
        techs.battery.costs.spores_score.energy_cap: 0
        techs.battery.costs.spores_score.interest_rate: 1
        techs.ac_transmission.costs.spores_score.energy_cap: 0
        techs.ac_transmission.costs.spores_score.interest_rate: 1

    operate:
        run.mode: operate
        run.operation:
            window: 12
            horizon: 24
        model.subset_time: ['2005-01-01', '2005-01-10']
        locations:
            region1.techs.ccgt.constraints.energy_cap_equals: 30000

            region2.techs.battery.constraints.energy_cap_equals: 1000
            region2.techs.battery.constraints.storage_cap_equals: 5240

            region1-1.techs.csp.constraints.energy_cap_equals: 10000
            region1-1.techs.csp.constraints.storage_cap_equals: 244301
            region1-1.techs.csp.constraints.resource_area_equals: 130385

            region1-2.techs.csp.constraints.energy_cap_equals: 0
            region1-2.techs.csp.constraints.storage_cap_equals: 0
            region1-2.techs.csp.constraints.resource_area_equals: 0

            region1-3.techs.csp.constraints.energy_cap_equals: 2534
            region1-3.techs.csp.constraints.storage_cap_equals: 25301
            region1-3.techs.csp.constraints.resource_area_equals: 8487

        links:
            region1,region2.techs.ac_transmission.constraints.energy_cap_equals: 3231
            region1,region1-1.techs.free_transmission.constraints.energy_cap_equals: 9000
            region1,region1-2.techs.free_transmission.constraints.energy_cap_equals: 0
            region1,region1-3.techs.free_transmission.constraints.energy_cap_equals: 2281

    check_feasibility:
        run:
            ensure_feasibility: False
            objective: 'check_feasibility'
        model:
            subset_time: '2005-01-04'

    reserve_margin:
        model:
            # Model-wide settings for the system-wide reserve margin
            # Even setting a reserve margin of zero activates the constraint,
            # forcing enough installed capacity to cover demand in
            # the maximum demand timestep
            reserve_margin:
                power: 0.10  # 10% reserve margin for power

    ##
    # Overrides to demonstrate the run generator ("calliope generate_runs")
    ##

    run1:
        model.subset_time: ['2005-01-01', '2005-01-31']
    run2:
        model.subset_time: ['2005-02-01', '2005-02-31']
    run3:
        model.subset_time: ['2005-01-01', '2005-01-31']
        locations.region1.techs.ccgt.constraints.energy_cap_max: 0  # Disallow CCGT
    run4:
        subset_time: ['2005-02-01', '2005-02-31']
        locations.region1.techs.ccgt.constraints.energy_cap_max: 0  # Disallow CCGT

    ##
    # Overrides to demonstrate group constraints
    ##

    cold_fusion:  # Defines a hypothetical cold fusion tech to use in group constraints
        techs:
            cold_fusion:
                essentials:
                    name: 'Cold fusion'
                    color: '#233B39'
                    parent: supply
                    carrier_out: power
                constraints:
                    energy_cap_max: 10000
                    lifetime: 50
                costs:
                    monetary:
                        interest_rate: 0.20
                        energy_cap: 100
        locations.region1.techs.cold_fusion: null
        locations.region2.techs.cold_fusion: null

    cold_fusion_prod_share:
        group_constraints:
            min_carrier_prod_share_group:
                techs: ['csp', 'cold_fusion']
                carrier_prod_share_min:
                    # At least 85% of power supply must come from CSP and cold fusion together
                    power: 0.85

    cold_fusion_cap_share:
        group_constraints:
            max_cap_share_group:
                techs: ['csp', 'cold_fusion']
                # At most 20% of total energy_cap can come from CSP and cold fusion together
                energy_cap_share_max: 0.20
        locations:
            region1:
                techs:
                    ccgt:
                        constraints:
                            energy_cap_max: 100000  # Increased to keep model feasible

    minimize_emissions_costs:
        run:
            objective_options:
                cost_class: {'emissions': 1, 'monetary': 0}
        techs:
            ccgt:
                costs:
                    emissions:
                        om_prod: 100 # kgCO2/kWh
            csp:
                costs:
                    emissions:
                        om_prod: 10 # kgCO2/kWh

    maximize_utility_costs:
        run:
            objective_options:
                cost_class: {'utility': 1 , 'monetary': 0}
                sense: maximize
        techs:
            ccgt:
                costs:
                    utility:
                        om_prod: 10 # arbitrary utility value
            csp:
                costs:
                    utility:
                        om_prod: 100 # arbitrary utility value

techs.yaml:

##
# TECHNOLOGY DEFINITIONS
##

# Note: '-start' and '-end' is used in tutorial documentation only

techs:

    ##
    # Supply
    ##

    # ccgt-start
    ccgt:
        essentials:
            name: 'Combined cycle gas turbine'
            color: '#E37A72'
            parent: supply
            carrier_out: power
        constraints:
            resource: inf
            energy_eff: 0.5
            energy_cap_max: 40000  # kW
            energy_cap_max_systemwide: 100000  # kW
            energy_ramping: 0.8
            lifetime: 25
        costs:
            monetary:
                interest_rate: 0.10
                energy_cap: 750  # USD per kW
                om_con: 0.02  # USD per kWh
    # ccgt-end

    # csp-start
    csp:
        essentials:
            name: 'Concentrating solar power'
            color: '#F9CF22'
            parent: supply_plus
            carrier_out: power
        constraints:
            storage_cap_max: 614033
            energy_cap_per_storage_cap_max: 1
            storage_loss: 0.002
            resource: file=csp_resource.csv
            resource_unit: energy_per_area
            energy_eff: 0.4
            parasitic_eff: 0.9
            resource_area_max: inf
            energy_cap_max: 10000
            lifetime: 25
        costs:
            monetary:
                interest_rate: 0.10
                storage_cap: 50
                resource_area: 200
                resource_cap: 200
                energy_cap: 1000
                om_prod: 0.002
    # csp-end

    ##
    # Storage
    ##
    # battery-start
    battery:
        essentials:
            name: 'Battery storage'
            color: '#3B61E3'
            parent: storage
            carrier: power
        constraints:
            energy_cap_max: 1000  # kW
            storage_cap_max: inf
            energy_cap_per_storage_cap_max: 4
            energy_eff: 0.95  # 0.95 * 0.95 = 0.9025 round trip efficiency
            storage_loss: 0  # No loss over time assumed
            lifetime: 25
        costs:
            monetary:
                interest_rate: 0.10
                storage_cap: 200  # USD per kWh storage capacity
    # battery-end

    ##
    # Demand
    ##
    # demand-start
    demand_power:
        essentials:
            name: 'Power demand'
            color: '#072486'
            parent: demand
            carrier: power
    # demand-end

    ##
    # Transmission
    ##

    # transmission-start
    ac_transmission:
        essentials:
            name: 'AC power transmission'
            color: '#8465A9'
            parent: transmission
            carrier: power
        constraints:
            energy_eff: 0.85
            lifetime: 25
        costs:
            monetary:
                interest_rate: 0.10
                energy_cap: 200
                om_prod: 0.002

    free_transmission:
        essentials:
            name: 'Local power transmission'
            color: '#6783E3'
            parent: transmission
            carrier: power
        constraints:
            energy_cap_max: inf
            energy_eff: 1.0
        costs:
            monetary:
                om_prod: 0
    # transmission-end

locations.yaml:

##
# LOCATIONS
##

locations:
    # region-1-start
    region1:
        coordinates: {lat: 40, lon: -2}
        techs:
            demand_power:
                constraints:
                    resource: file=demand-1.csv:demand
            ccgt:
                constraints:
                    energy_cap_max: 30000 # increased to ensure no unmet_demand in first timestep
    # region-1-end
    # other-locs-start
    region2:
        coordinates: {lat: 40, lon: -8}
        techs:
            demand_power:
                constraints:
                    resource: file=demand-2.csv:demand
            battery:

    region1-1.coordinates: {lat: 41, lon: -2}
    region1-2.coordinates: {lat: 39, lon: -1}
    region1-3.coordinates: {lat: 39, lon: -2}

    region1-1, region1-2, region1-3:
        techs:
            csp:
    # other-locs-end

##
# TRANSMISSION CAPACITIES
##

links:
    # links-start
    region1,region2:
        techs:
            ac_transmission:
                constraints:
                    energy_cap_max: 10000
    region1,region1-1:
        techs:
            free_transmission:
    region1,region1-2:
        techs:
            free_transmission:
    region1,region1-3:
        techs:
            free_transmission:
    # links-end

Urban-scale example¶

Available as calliope.examples.urban_scale.

Model settings¶

model.yaml:

import:  # Import other files from paths relative to this file, or absolute paths
    - 'model_config/techs.yaml'
    - 'model_config/locations.yaml'
    - 'scenarios.yaml'

model:
    name: Urban-scale example model

    # What version of Calliope this model is intended for
    calliope_version: 0.6.6

    # Time series data path - can either be a path relative to this file, or an absolute path
    timeseries_data_path: 'timeseries_data'

    subset_time: ['2005-07-01', '2005-07-02']  # Subset of timesteps

run:
    mode: plan  # Choices: plan, operate

    solver: cbc

    ensure_feasibility: true # Switching on unmet demand

    bigM: 1e6 # setting the scale of unmet demand, which cannot be too high, otherwise the optimisation will not converge

    objective_options.cost_class: {monetary: 1}

scenarios.yaml:

##
# Overrides for different example model configuratiions
##

overrides:
    milp:
        model.name: 'Urban-scale example model with MILP'
        run.solver_options.mipgap: 0.05
        techs:
            # chp-start
            chp:
                constraints:
                    units_max: 4
                    energy_cap_per_unit: 300
                    energy_cap_min_use: 0.2
                costs:
                    monetary:
                        energy_cap: 700
                        purchase: 40000
            # chp-end
            # boiler-start
            boiler:
                costs:
                    monetary:
                        energy_cap: 35
                        purchase: 2000
            # boiler-end
            # heat_pipes-start
            heat_pipes:
                constraints:
                    force_asynchronous_prod_con: true
            # heat_pipes-end

    mapbox_ready:
        locations:
            X1.coordinates: {lat: 51.4596158, lon: -0.1613446}
            X2.coordinates: {lat: 51.4652373, lon: -0.1141548}
            X3.coordinates: {lat: 51.4287016, lon: -0.1310635}
            N1.coordinates: {lat: 51.4450766, lon: -0.1247183}
        links:
            X1,X2.techs.power_lines.distance: 10
            X1,X3.techs.power_lines.distance: 5
            X1,N1.techs.heat_pipes.distance: 3
            N1,X2.techs.heat_pipes.distance: 3
            N1,X3.techs.heat_pipes.distance: 4

    operate:
        run.mode: operate
        run.operation:
            window: 24
            horizon: 48
        model.subset_time: ['2005-07-01', '2005-07-10']
        locations:
            X1:
                techs:
                    chp.constraints.energy_cap_max: 300
                    pv.constraints.energy_cap_max: 0
                    supply_grid_power.constraints.energy_cap_max: 40
                    supply_gas.constraints.energy_cap_max: 700

            X2:
                techs:
                    boiler.constraints.energy_cap_max: 200
                    pv.constraints.energy_cap_max: 70
                    supply_gas.constraints.energy_cap_max: 250

            X3:
                techs:
                    boiler.constraints.energy_cap_max: 0
                    pv.constraints.energy_cap_max: 50
                    supply_gas.constraints.energy_cap_max: 0

        links:
            X1,X2.techs.power_lines.constraints.energy_cap_max: 300
            X1,X3.techs.power_lines.constraints.energy_cap_max: 60
            X1,N1.techs.heat_pipes.constraints.energy_cap_max: 300
            N1,X2.techs.heat_pipes.constraints.energy_cap_max: 250
            N1,X3.techs.heat_pipes.constraints.energy_cap_max: 320

    time_masking:
        model.name: 'Urban-scale example model with time masking'
        model.subset_time: '2005-01'
        # Resample time resolution to 6-hourly
        model.time:
            masks:
                - {function: extreme_diff, options: {tech0: demand_heat, tech1: demand_electricity, how: max, n: 2}}
            function: resample
            function_options: {resolution: 6H}

techs.yaml:

##
# TECHNOLOGY DEFINITIONS
##

# Note: '-start' and '-end' is used in tutorial documentation only

# supply_power_plus-start
tech_groups:
    supply_power_plus:
        essentials:
            parent: supply_plus
            carrier: electricity
# supply_power_plus-end

techs:

##-GRID SUPPLY-##
    # supply-start
    supply_grid_power:
        essentials:
            name: 'National grid import'
            color: '#C5ABE3'
            parent: supply
            carrier: electricity
        constraints:
            resource: inf
            energy_cap_max: 2000
            lifetime: 25
        costs:
            monetary:
                interest_rate: 0.10
                energy_cap: 15
                om_con: 0.1 # 10p/kWh electricity price #ppt

    supply_gas:
        essentials:
            name: 'Natural gas import'
            color: '#C98AAD'
            parent: supply
            carrier: gas
        constraints:
            resource: inf
            energy_cap_max: 2000
            lifetime: 25
        costs:
            monetary:
                interest_rate: 0.10
                energy_cap: 1
                om_con: 0.025 # 2.5p/kWh gas price #ppt
    # supply-end

##-Renewables-##
    # pv-start
    pv:
        essentials:
            name: 'Solar photovoltaic power'
            color: '#F9D956'
            parent: supply_power_plus
        constraints:
            export_carrier: electricity
            resource: file=pv_resource.csv:per_area  # Already accounts for panel efficiency - kWh/m2. Source: Renewables.ninja Solar PV Power - Version: 1.1 - License: https://creativecommons.org/licenses/by-nc/4.0/ - Reference: https://doi.org/10.1016/j.energy.2016.08.060
            resource_unit: energy_per_area
            parasitic_eff: 0.85 # inverter losses
            energy_cap_max: 250
            resource_area_max: 1500
            force_resource: true
            resource_area_per_energy_cap: 7 # 7m2 of panels needed to fit 1kWp of panels
            lifetime: 25
        costs:
            monetary:
                interest_rate: 0.10
                energy_cap: 1350
    # pv-end

# Conversion
    # boiler-start
    boiler:
        essentials:
            name: 'Natural gas boiler'
            color: '#8E2999'
            parent: conversion
            carrier_out: heat
            carrier_in: gas
        constraints:
            energy_cap_max: 600
            energy_eff: 0.85
            lifetime: 25
        costs:
            monetary:
                interest_rate: 0.10
                om_con: 0.004  # .4p/kWh
    # boiler-end

# Conversion_plus
    # chp-start
    chp:
        essentials:
            name: 'Combined heat and power'
            color: '#E4AB97'
            parent: conversion_plus
            primary_carrier_out: electricity
            carrier_in: gas
            carrier_out: electricity
            carrier_out_2: heat
        constraints:
            export_carrier: electricity
            energy_cap_max: 1500
            energy_eff: 0.405
            carrier_ratios.carrier_out_2.heat: 0.8
            lifetime: 25
        costs:
            monetary:
                interest_rate: 0.10
                energy_cap: 750
                om_prod: 0.004  # .4p/kWh for 4500 operating hours/year
                export: file=export_power.csv
    # chp-end

##-DEMAND-##
    # demand-start
    demand_electricity:
        essentials:
            name: 'Electrical demand'
            color: '#072486'
            parent: demand
            carrier: electricity

    demand_heat:
        essentials:
            name: 'Heat demand'
            color: '#660507'
            parent: demand
            carrier: heat
    # demand-end

##-DISTRIBUTION-##
    # transmission-start
    power_lines:
        essentials:
            name: 'Electrical power distribution'
            color: '#6783E3'
            parent: transmission
            carrier: electricity
        constraints:
            energy_cap_max: 2000
            energy_eff: 0.98
            lifetime: 25
        costs:
            monetary:
                interest_rate: 0.10
                energy_cap_per_distance: 0.01

    heat_pipes:
        essentials:
            name: 'District heat distribution'
            color: '#823739'
            parent: transmission
            carrier: heat
        constraints:
            energy_cap_max: 2000
            energy_eff_per_distance: 0.975
            lifetime: 25
        costs:
            monetary:
                interest_rate: 0.10
                energy_cap_per_distance: 0.3
    # transmission-end

locations.yaml:

locations:
    # X1-start
    X1:
        techs:
            chp:
            pv:
            supply_grid_power:
                costs.monetary.energy_cap: 100 # cost of transformers
            supply_gas:
            demand_electricity:
                constraints.resource: file=demand_power.csv
            demand_heat:
                constraints.resource: file=demand_heat.csv
        available_area: 500
        coordinates: {x: 2, y: 7}
    # X1-end
    # other-locs-start
    X2:
        techs:
            boiler:
                costs.monetary.energy_cap: 43.1 # different boiler costs
            pv:
                costs.monetary:
                        om_prod: -0.0203 # revenue for just producing electricity
                        export: -0.0491 # FIT return for PV export
            supply_gas:
            demand_electricity:
                constraints.resource: file=demand_power.csv
            demand_heat:
                constraints.resource: file=demand_heat.csv
        available_area: 1300
        coordinates: {x: 8, y: 7}

    X3:
        techs:
            boiler:
                costs.monetary.energy_cap: 78 # different boiler costs
            pv:
                constraints:
                    energy_cap_max: 50 # changing tariff structure below 50kW
                costs.monetary:
                        om_annual: -80.5 # reimbursement per kWp from FIT
            supply_gas:
            demand_electricity:
                constraints.resource: file=demand_power.csv
            demand_heat:
                constraints.resource: file=demand_heat.csv
        available_area: 900
        coordinates: {x: 5, y: 3}
    # other-locs-end
    # N1-start
    N1: # location for branching heat transmission network
        coordinates: {x: 5, y: 7}
    # N1-end

links:
    # links-start
    X1,X2:
        techs:
            power_lines:
                distance: 10
    X1,X3:
        techs:
            power_lines:
    X1,N1:
        techs:
            heat_pipes:
    N1,X2:
        techs:
            heat_pipes:
    N1,X3:
        techs:
            heat_pipes:
    # links-end

Configuration reference¶

Configuration layout¶

There must always be at least one model configuration YAML file, probably called model.yaml or similar. This file can import any number of additional files.

This file or this set of files must specify the following top-level configuration keys:

name: the name of the model
model: model settings
run: run settings
techs: technology definitions
(optionally) tech_groups: tech group definitions
locations: location definitions
(optionally) links: transmission link definitions

Note

Model settings (model) affect how the model and its data are built by Calliope, while run settings (run) only take effect once a built model is run (e.g. interactively via model.run()). This means that run settings, unlike model settings, can be updated after a model is built and before it is run, by modifying attributes in the built model dataset.

YAML configuration file format¶

All configuration files (with the exception of time series data files) are in the YAML format, “a human friendly data serialisation standard for all programming languages”.

Configuration for Calliope is usually specified as option: value entries, where value might be a number, a text string, or a list (e.g. a list of further settings).

Calliope allows an abbreviated form for long, nested settings:

one:
   two:
      three: x

can be written as:

one.two.three: x

Calliope also allows a special import: directive in any YAML file. This can specify one or several YAML files to import. If both the imported file and the current file define the same option, the definition in the current file takes precedence.

Using quotation marks (' or ") to enclose strings is optional, but can help with readability. The three ways of setting option to text below are equivalent:

option: "text"
option: 'text'
option: text

Sometimes, a setting can be either enabled or disabled, in this case, the boolean values true or false are used.

Comments can be inserted anywhere in YAML files with the # symbol. The remainder of a line after # is interpreted as a comment.

See the YAML website for more general information about YAML.

Calliope internally represents the configuration as AttrDicts, which are a subclass of the built-in Python dictionary data type (dict) with added functionality such as YAML reading/writing and attribute access to keys.

Mathematical formulation¶

This section details the mathematical formulation of the different components. For each component, a link to the actual implementing function in the Calliope code is given.

Note

Make sure to also refer to the detailed listing of constraints and costs along with their units and default values.

Decision variables¶

calliope.backend.pyomo.variables.initialize_decision_variables(backend_model)[source]¶

Defines decision variables.

Variable	Dimensions
energy_cap	loc_techs
carrier_prod	loc_tech_carriers_prod, timesteps
carrier_con	loc_tech_carriers_con, timesteps
cost	costs, loc_techs_cost
resource_area	loc_techs_area,
storage_cap	loc_techs_store
storage	loc_techs_store, timesteps
resource_con	loc_techs_supply_plus, timesteps
resource_cap	loc_techs_supply_plus
carrier_export	loc_tech_carriers_export, timesteps
cost_var	costs, loc_techs_om_cost, timesteps
cost_investment	costs, loc_techs_investment_cost
purchased	loc_techs_purchase
units	loc_techs_milp
operating_units	loc_techs_milp, timesteps
unmet_demand	loc_carriers, timesteps
unused_supply	loc_carriers, timesteps

Objective functions¶

calliope.backend.pyomo.objective.minmax_cost_optimization(backend_model)[source]¶

Minimize or maximise total system cost for specified cost class or a set of cost classes. cost_class is a string or dictionary. If a string, it is automatically converted to a dictionary with a single key:value pair where value == 1. The dictionary provides a weight for each cost class of interest: {cost_1: weight_1, cost_2: weight_2, etc.}.

If unmet_demand is in use, then the calculated cost of unmet_demand is added or subtracted from the total cost in the opposite sense to the objective.

\[ \begin{align}\begin{aligned}min: z = \sum_{loc::tech_{cost},k} (cost(loc::tech, cost=cost_{k}) \times weight_{k}) + \sum_{loc::carrier,timestep} (unmet\_demand(loc::carrier, timestep) \times bigM)\\max: z = \sum_{loc::tech_{cost},k} (cost(loc::tech, cost=cost_{k}) \times weight_{k}) - \sum_{loc::carrier,timestep} (unmet\_demand(loc::carrier, timestep) \times bigM)\end{aligned}\end{align} \]

calliope.backend.pyomo.objective.check_feasibility(backend_model)[source]¶: Dummy objective, to check that there are no conflicting constraints.

\[min: z = 1\]

Constraints¶

Energy Balance¶

calliope.backend.pyomo.constraints.energy_balance.system_balance_constraint_rule(backend_model, loc_carrier, timestep)[source]¶: System balance ensures that, within each location, the production and consumption of each carrier is balanced.

\[\sum_{loc::tech::carrier_{prod} \in loc::carrier} \boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep) + \sum_{loc::tech::carrier_{con} \in loc::carrier} \boldsymbol{carrier_{con}}(loc::tech::carrier, timestep) + \sum_{loc::tech::carrier_{export} \in loc::carrier} \boldsymbol{carrier_{export}}(loc::tech::carrier, timestep) \quad \forall loc::carrier \in loc::carriers, \forall timestep \in timesteps\]

calliope.backend.pyomo.constraints.energy_balance.balance_supply_constraint_rule(backend_model, loc_tech, timestep)[source]¶

Limit production from supply techs to their available resource

\[min\_use(loc::tech) \times available\_resource(loc::tech, timestep) \leq \frac{\boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep)}{\eta_{energy}(loc::tech, timestep)} \geq available\_resource(loc::tech, timestep) \quad \forall loc::tech \in loc::techs_{supply}, \forall timestep \in timesteps\]

If \(force\_resource(loc::tech)\) is set:

\[\frac{\boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep)}{\eta_{energy}(loc::tech, timestep)} = available\_resource(loc::tech, timestep) \quad \forall loc::tech \in loc::techs_{supply}, \forall timestep \in timesteps\]

Where:

\[available\_resource(loc::tech, timestep) = resource(loc::tech, timestep) \times resource\_scale(loc::tech)\]

if \(loc::tech\) is in \(loc::techs_{area}\):

\[available\_resource(loc::tech, timestep) = resource(loc::tech, timestep) \times resource\_scale(loc::tech) \times \boldsymbol{resource_{area}}(loc::tech)\]

calliope.backend.pyomo.constraints.energy_balance.balance_demand_constraint_rule(backend_model, loc_tech, timestep)[source]¶

Limit consumption from demand techs to their required resource.

\[\boldsymbol{carrier_{con}}(loc::tech::carrier, timestep) \times \eta_{energy}(loc::tech, timestep) \geq required\_resource(loc::tech, timestep) \quad \forall loc::tech \in loc::techs_{demand}, \forall timestep \in timesteps\]

If \(force\_resource(loc::tech)\) is set:

\[\boldsymbol{carrier_{con}}(loc::tech::carrier, timestep) \times \eta_{energy}(loc::tech, timestep) = required\_resource(loc::tech, timestep) \quad \forall loc::tech \in loc::techs_{demand}, \forall timestep \in timesteps\]

Where:

\[required\_resource(loc::tech, timestep) = resource(loc::tech, timestep) \times resource\_scale(loc::tech)\]

if \(loc::tech\) is in \(loc::techs_{area}\):

\[required\_resource(loc::tech, timestep) = resource(loc::tech, timestep) \times resource\_scale(loc::tech) \times \boldsymbol{resource_{area}}(loc::tech)\]

calliope.backend.pyomo.constraints.energy_balance.resource_availability_supply_plus_constraint_rule(backend_model, loc_tech, timestep)[source]¶

Limit production from supply_plus techs to their available resource.

\[\boldsymbol{resource_{con}}(loc::tech, timestep) \leq available\_resource(loc::tech, timestep) \quad \forall loc::tech \in loc::techs_{supply^{+}}, \forall timestep \in timesteps\]

If \(force\_resource(loc::tech)\) is set:

\[\boldsymbol{resource_{con}}(loc::tech, timestep) = available\_resource(loc::tech, timestep) \quad \forall loc::tech \in loc::techs_{supply^{+}}, \forall timestep \in timesteps\]

Where:

\[available\_resource(loc::tech, timestep) = resource(loc::tech, timestep) \times resource_{scale}(loc::tech)\]

if \(loc::tech\) is in \(loc::techs_{area}\):

\[available\_resource(loc::tech, timestep) = resource(loc::tech, timestep) \times resource_{scale}(loc::tech) \times resource_{area}(loc::tech)\]

calliope.backend.pyomo.constraints.energy_balance.balance_transmission_constraint_rule(backend_model, loc_tech, timestep)[source]¶

Balance carrier production and consumption of transmission technologies

\[-1 * \boldsymbol{carrier_{con}}(loc_{from}::tech:loc_{to}::carrier, timestep) \times \eta_{energy}(loc::tech, timestep) = \boldsymbol{carrier_{prod}}(loc_{to}::tech:loc_{from}::carrier, timestep) \quad \forall loc::tech:loc \in locs::techs:locs_{transmission}, \forall timestep \in timesteps\]

Where a link is the connection between \(loc_{from}::tech:loc_{to}\) and \(loc_{to}::tech:loc_{from}\) for locations to and from.

calliope.backend.pyomo.constraints.energy_balance.balance_supply_plus_constraint_rule(backend_model, loc_tech, timestep)[source]¶

Balance carrier production and resource consumption of supply_plus technologies alongside any use of resource storage.

\[\boldsymbol{storage}(loc::tech, timestep) = \boldsymbol{storage}(loc::tech, timestep_{previous}) \times (1 - storage\_loss(loc::tech, timestep))^{timestep\_resolution(timestep)} + \boldsymbol{resource_{con}}(loc::tech, timestep) \times \eta_{resource}(loc::tech, timestep) - \frac{\boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep)}{\eta_{energy}(loc::tech, timestep) \times \eta_{parasitic}(loc::tech, timestep)} \quad \forall loc::tech \in loc::techs_{supply^{+}}, \forall timestep \in timesteps\]

If no storage is defined for the technology, this reduces to:

\[\boldsymbol{resource_{con}}(loc::tech, timestep) \times \eta_{resource}(loc::tech, timestep) = \frac{\boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep)}{\eta_{energy}(loc::tech, timestep) \times \eta_{parasitic}(loc::tech, timestep)} \quad \forall loc::tech \in loc::techs_{supply^{+}}, \forall timestep \in timesteps\]

calliope.backend.pyomo.constraints.energy_balance.balance_storage_constraint_rule(backend_model, loc_tech, timestep)[source]¶: Balance carrier production and consumption of storage technologies, alongside any use of the stored volume.

\[\boldsymbol{storage}(loc::tech, timestep) = \boldsymbol{storage}(loc::tech, timestep_{previous}) \times (1 - storage\_loss(loc::tech, timestep))^{resolution(timestep)} - \boldsymbol{carrier_{con}}(loc::tech::carrier, timestep) \times \eta_{energy}(loc::tech, timestep) - \frac{\boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep)}{\eta_{energy}(loc::tech, timestep)} \quad \forall loc::tech \in loc::techs_{storage}, \forall timestep \in timesteps\]

calliope.backend.pyomo.constraints.energy_balance.balance_storage_inter_cluster_rule(backend_model, loc_tech, datestep)[source]¶

When clustering days, to reduce the timeseries length, balance the daily stored energy across all days of the original timeseries.

Ref: DOI 10.1016/j.apenergy.2018.01.023

\[\boldsymbol{storage_{inter\_cluster}}(loc::tech, datestep) = \boldsymbol{storage_{inter\_cluster}}(loc::tech, datestep_{previous}) \times (1 - storage\_loss(loc::tech, timestep))^{24} + \boldsymbol{storage}(loc::tech, timestep_{final, cluster(datestep))}) \quad \forall loc::tech \in loc::techs_{store}, \forall datestep \in datesteps\]

Where \(timestep_{final, cluster(datestep_{previous}))}\) is the final timestep of the cluster in the clustered timeseries corresponding to the previous day

calliope.backend.pyomo.constraints.energy_balance.storage_initial_rule(backend_model, loc_tech)[source]¶

If storage is cyclic, allow an initial storage to still be set. This is applied to the storage of the final timestep/datestep of the series as that, in cyclic storage, is the ‘storage_previous_step’ for the first timestep/datestep.

If clustering and storage_inter_cluster exists:

\[\boldsymbol{storage_{inter\_cluster}}(loc::tech, datestep_{final}) \times ((1 - storage_loss) ** 24) = storage_{initial}(loc::tech) \times storage_{cap}(loc::tech) \quad \forall loc::tech \in loc::techs_{store}, \forall datestep \in datesteps\]

Where \(datestep_{final}\) is the last datestep of the timeseries

Else: .. container:: scrolling-wrapper

\[\boldsymbol{storage}(loc::tech, timestep_{final}) \times ((1 - storage_loss) ** 24) = storage_{initial}(loc::tech) \times storage_{cap}(loc::tech) \quad \forall loc::tech \in loc::techs_{store}, \forall timestep \in timesteps\]

Where \(timestep_{final}\) is the last timestep of the timeseries

Capacity¶

calliope.backend.pyomo.constraints.capacity.storage_capacity_constraint_rule(backend_model, loc_tech)[source]¶

Set maximum storage capacity. Supply_plus & storage techs only

The first valid case is applied:

\[\begin{split}\boldsymbol{storage_{cap}}(loc::tech) \begin{cases} = storage_{cap, equals}(loc::tech),& \text{if } storage_{cap, equals}(loc::tech)\\ \leq storage_{cap, max}(loc::tech),& \text{if } storage_{cap, max}(loc::tech)\\ \text{unconstrained},& \text{otherwise} \end{cases} \forall loc::tech \in loc::techs_{store}\end{split}\]

and (if equals not enforced):

\[\boldsymbol{storage_{cap}}(loc::tech) \geq storage_{cap, min}(loc::tech) \quad \forall loc::tech \in loc::techs_{store}\]

calliope.backend.pyomo.constraints.capacity.energy_capacity_storage_constraint_rule_old(backend_model, loc_tech)[source]¶

Set an additional energy capacity constraint on storage technologies, based on their use of charge_rate.

This is deprecated and will be removed in Calliope 0.7.0. Instead of charge_rate, please use energy_cap_per_storage_cap_max.

\[\boldsymbol{energy_{cap}}(loc::tech) \leq \boldsymbol{storage_{cap}}(loc::tech) \times charge\_rate(loc::tech) \quad \forall loc::tech \in loc::techs_{store}\]

calliope.backend.pyomo.constraints.capacity.energy_capacity_storage_min_constraint_rule(backend_model, loc_tech)[source]¶: Limit energy capacities of storage technologies based on their storage capacities.

\[\begin{split}\boldsymbol{energy_{cap}}(loc::tech) \geq \boldsymbol{storage_{cap}}(loc::tech) \times energy\_cap\_per\_storage\_cap\_min(loc::tech)\\ \forall loc::tech \in loc::techs_{store}\end{split}\]

calliope.backend.pyomo.constraints.capacity.energy_capacity_storage_max_constraint_rule(backend_model, loc_tech)[source]¶: Limit energy capacities of storage technologies based on their storage capacities.

\[\begin{split}\boldsymbol{energy_{cap}}(loc::tech) \leq \boldsymbol{storage_{cap}}(loc::tech) \times energy\_cap\_per\_storage\_cap\_max(loc::tech)\\ \forall loc::tech \in loc::techs_{store}\end{split}\]

calliope.backend.pyomo.constraints.capacity.energy_capacity_storage_equals_constraint_rule(backend_model, loc_tech)[source]¶: Limit energy capacities of storage technologies based on their storage capacities.

\[\boldsymbol{energy_{cap}}(loc::tech) = \boldsymbol{storage_{cap}}(loc::tech) \times energy\_cap\_per\_storage\_cap\_equals(loc::tech) \forall loc::tech \in loc::techs_{store}\]

calliope.backend.pyomo.constraints.capacity.resource_capacity_constraint_rule(backend_model, loc_tech)[source]¶

Add upper and lower bounds for resource_cap.

The first valid case is applied:

\[\begin{split}\boldsymbol{resource_{cap}}(loc::tech) \begin{cases} = resource_{cap, equals}(loc::tech),& \text{if } resource_{cap, equals}(loc::tech)\\ \leq resource_{cap, max}(loc::tech),& \text{if } resource_{cap, max}(loc::tech)\\ \text{unconstrained},& \text{otherwise} \end{cases} \forall loc::tech \in loc::techs_{finite\_resource\_supply\_plus}\end{split}\]

and (if equals not enforced):

\[\boldsymbol{resource_{cap}}(loc::tech) \geq resource_{cap, min}(loc::tech) \quad \forall loc::tech \in loc::techs_{finite\_resource\_supply\_plus}\]

calliope.backend.pyomo.constraints.capacity.resource_capacity_equals_energy_capacity_constraint_rule(backend_model, loc_tech)[source]¶: Add equality constraint for resource_cap to equal energy_cap, for any technologies which have defined resource_cap_equals_energy_cap.

\[\boldsymbol{resource_{cap}}(loc::tech) = \boldsymbol{energy_{cap}}(loc::tech) \quad \forall loc::tech \in loc::techs_{finite\_resource\_supply\_plus} \text{ if } resource\_cap\_equals\_energy\_cap = \text{True}\]

calliope.backend.pyomo.constraints.capacity.resource_area_constraint_rule(backend_model, loc_tech)[source]¶

Set upper and lower bounds for resource_area.

The first valid case is applied:

\[\begin{split}\boldsymbol{resource_{area}}(loc::tech) \begin{cases} = resource_{area, equals}(loc::tech),& \text{if } resource_{area, equals}(loc::tech)\\ \leq resource_{area, max}(loc::tech),& \text{if } resource_{area, max}(loc::tech)\\ \text{unconstrained},& \text{otherwise} \end{cases} \forall loc::tech \in loc::techs_{area}\end{split}\]

and (if equals not enforced):

\[\boldsymbol{resource_{area}}(loc::tech) \geq resource_{area, min}(loc::tech) \quad \forall loc::tech \in loc::techs_{area}\]

calliope.backend.pyomo.constraints.capacity.resource_area_per_energy_capacity_constraint_rule(backend_model, loc_tech)[source]¶: Add equality constraint for resource_area to equal a percentage of energy_cap, for any technologies which have defined resource_area_per_energy_cap

\[\boldsymbol{resource_{area}}(loc::tech) = \boldsymbol{energy_{cap}}(loc::tech) \times area\_per\_energy\_cap(loc::tech) \quad \forall loc::tech \in locs::techs_{area} \text{ if } area\_per\_energy\_cap(loc::tech)\]

calliope.backend.pyomo.constraints.capacity.resource_area_capacity_per_loc_constraint_rule(backend_model, loc)[source]¶: Set upper bound on use of area for all locations which have available_area constraint set. Does not consider resource_area applied to demand technologies

\[\sum_{tech} \boldsymbol{resource_{area}}(loc::tech) \leq available\_area \quad \forall loc \in locs \text{ if } available\_area(loc)\]

calliope.backend.pyomo.constraints.capacity.energy_capacity_constraint_rule(backend_model, loc_tech)[source]¶

Set upper and lower bounds for energy_cap.

The first valid case is applied:

\[\begin{split}\frac{\boldsymbol{energy_{cap}}(loc::tech)}{energy_{cap, scale}(loc::tech)} \begin{cases} = energy_{cap, equals}(loc::tech),& \text{if } energy_{cap, equals}(loc::tech)\\ \leq energy_{cap, max}(loc::tech),& \text{if } energy_{cap, max}(loc::tech)\\ \text{unconstrained},& \text{otherwise} \end{cases} \forall loc::tech \in loc::techs\end{split}\]

and (if equals not enforced):

\[\frac{\boldsymbol{energy_{cap}}(loc::tech)}{energy_{cap, scale}(loc::tech)} \geq energy_{cap, min}(loc::tech) \quad \forall loc::tech \in loc::techs\]

calliope.backend.pyomo.constraints.capacity.energy_capacity_systemwide_constraint_rule(backend_model, tech)[source]¶

Set constraints to limit the capacity of a single technology type across all locations in the model.

The first valid case is applied:

\[\begin{split}\sum_{loc}\boldsymbol{energy_{cap}}(loc::tech) \begin{cases} = energy_{cap, equals, systemwide}(loc::tech),& \text{if } energy_{cap, equals, systemwide}(loc::tech)\\ \leq energy_{cap, max, systemwide}(loc::tech),& \text{if } energy_{cap, max, systemwide}(loc::tech)\\ \text{unconstrained},& \text{otherwise} \end{cases} \forall tech \in techs\end{split}\]

Dispatch¶

calliope.backend.pyomo.constraints.dispatch.carrier_production_max_constraint_rule(backend_model, loc_tech_carrier, timestep)[source]¶: Set maximum carrier production. All technologies.

\[\boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep) \leq energy_{cap}(loc::tech) \times timestep\_resolution(timestep) \times parasitic\_eff(loc::tec)\]

calliope.backend.pyomo.constraints.dispatch.carrier_production_min_constraint_rule(backend_model, loc_tech_carrier, timestep)[source]¶: Set minimum carrier production. All technologies except conversion_plus.

\[\boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep) \geq energy_{cap}(loc::tech) \times timestep\_resolution(timestep) \times energy_{cap,min\_use}(loc::tec)\]

calliope.backend.pyomo.constraints.dispatch.carrier_consumption_max_constraint_rule(backend_model, loc_tech_carrier, timestep)[source]¶: Set maximum carrier consumption for demand, storage, and transmission techs.

\[\boldsymbol{carrier_{con}}(loc::tech::carrier, timestep) \geq -1 \times energy_{cap}(loc::tech) \times timestep\_resolution(timestep)\]

calliope.backend.pyomo.constraints.dispatch.resource_max_constraint_rule(backend_model, loc_tech, timestep)[source]¶: Set maximum resource consumed by supply_plus techs.

\[\boldsymbol{resource_{con}}(loc::tech, timestep) \leq timestep\_resolution(timestep) \times resource_{cap}(loc::tech)\]

calliope.backend.pyomo.constraints.dispatch.storage_max_constraint_rule(backend_model, loc_tech, timestep)[source]¶: Set maximum stored energy. Supply_plus & storage techs only.

\[\boldsymbol{storage}(loc::tech, timestep) \leq storage_{cap}(loc::tech)\]

calliope.backend.pyomo.constraints.dispatch.storage_discharge_depth_constraint_rule(backend_model, loc_tech, timestep)[source]¶: Forces storage state of charge to be greater than the allowed depth of discharge.

\[\boldsymbol{storage}(loc::tech, timestep) >= \boldsymbol{storage_discharge_depth}\forall loc::tech \in loc::techs_{storage}, \forall timestep \in timesteps\]

calliope.backend.pyomo.constraints.dispatch.ramping_up_constraint_rule(backend_model, loc_tech_carrier, timestep)[source]¶: Ramping up constraint.

\[diff(loc::tech::carrier, timestep) \leq max\_ramping\_rate(loc::tech::carrier, timestep)\]

calliope.backend.pyomo.constraints.dispatch.ramping_down_constraint_rule(backend_model, loc_tech_carrier, timestep)[source]¶: Ramping down constraint.

\[-1 \times max\_ramping\_rate(loc::tech::carrier, timestep) \leq diff(loc::tech::carrier, timestep)\]

calliope.backend.pyomo.constraints.dispatch.ramping_constraint(backend_model, loc_tech_carrier, timestep, direction=0)[source]¶

Ramping rate constraints.

Direction: 0 is up, 1 is down.

\[ \begin{align}\begin{aligned}\boldsymbol{max\_ramping\_rate}(loc::tech::carrier, timestep) = energy_{ramping}(loc::tech, timestep) \times energy_{cap}(loc::tech)\\\boldsymbol{diff}(loc::tech::carrier, timestep) = (carrier_{prod}(loc::tech::carrier, timestep) + carrier_{con}(loc::tech::carrier, timestep)) / timestep\_resolution(timestep) - (carrier_{prod}(loc::tech::carrier, timestep-1) + carrier_{con}(loc::tech::carrier, timestep-1)) / timestep\_resolution(timestep-1)\end{aligned}\end{align} \]

calliope.backend.pyomo.constraints.dispatch.storage_intra_max_rule(backend_model, loc_tech, timestep)[source]¶

When clustering days, to reduce the timeseries length, set limits on intra-cluster auxiliary maximum storage decision variable. Ref: DOI 10.1016/j.apenergy.2018.01.023

\[\boldsymbol{storage}(loc::tech, timestep) \leq \boldsymbol{storage_{intra\_cluster, max}}(loc::tech, cluster(timestep)) \quad \forall loc::tech \in loc::techs_{store}, \forall timestep \in timesteps\]

Where \(cluster(timestep)\) is the cluster number in which the timestep is located.

calliope.backend.pyomo.constraints.dispatch.storage_intra_min_rule(backend_model, loc_tech, timestep)[source]¶

When clustering days, to reduce the timeseries length, set limits on intra-cluster auxiliary minimum storage decision variable. Ref: DOI 10.1016/j.apenergy.2018.01.023

\[\boldsymbol{storage}(loc::tech, timestep) \geq \boldsymbol{storage_{intra\_cluster, min}}(loc::tech, cluster(timestep)) \quad \forall loc::tech \in loc::techs_{store}, \forall timestep \in timesteps\]

Where \(cluster(timestep)\) is the cluster number in which the timestep is located.

calliope.backend.pyomo.constraints.dispatch.storage_inter_max_rule(backend_model, loc_tech, datestep)[source]¶

When clustering days, to reduce the timeseries length, set maximum limit on the intra-cluster and inter-date stored energy. intra-cluster = all timesteps in a single cluster datesteps = all dates in the unclustered timeseries (each has a corresponding cluster) Ref: DOI 10.1016/j.apenergy.2018.01.023

\[\boldsymbol{storage_{inter\_cluster}}(loc::tech, datestep) + \boldsymbol{storage_{intra\_cluster, max}}(loc::tech, cluster(datestep)) \leq \boldsymbol{storage_{cap}}(loc::tech) \quad \forall loc::tech \in loc::techs_{store}, \forall datestep \in datesteps\]

Where \(cluster(datestep)\) is the cluster number in which the datestep is located.

calliope.backend.pyomo.constraints.dispatch.storage_inter_min_rule(backend_model, loc_tech, datestep)[source]¶

When clustering days, to reduce the timeseries length, set minimum limit on the intra-cluster and inter-date stored energy. intra-cluster = all timesteps in a single cluster datesteps = all dates in the unclustered timeseries (each has a corresponding cluster) Ref: DOI 10.1016/j.apenergy.2018.01.023

\[\boldsymbol{storage_{inter\_cluster}}(loc::tech, datestep) \times (1 - storage\_loss(loc::tech, timestep))^{24} + \boldsymbol{storage_{intra\_cluster, min}}(loc::tech, cluster(datestep)) \geq 0 \quad \forall loc::tech \in loc::techs_{store}, \forall datestep \in datesteps\]

Where \(cluster(datestep)\) is the cluster number in which the datestep is located.

Costs¶

calliope.backend.pyomo.constraints.costs.cost_constraint_rule(backend_model, cost, loc_tech)[source]¶: Combine investment and time varying costs into one cost per technology.

\[\boldsymbol{cost}(cost, loc::tech) = \boldsymbol{cost_{investment}}(cost, loc::tech) + \sum_{timestep \in timesteps} \boldsymbol{cost_{var}}(cost, loc::tech, timestep)\]

calliope.backend.pyomo.constraints.costs.cost_investment_constraint_rule(backend_model, cost, loc_tech)[source]¶

Calculate costs from capacity decision variables.

Transmission technologies “exist” at two locations, so their cost is divided by 2.

\[ \begin{align}\begin{aligned}\boldsymbol{cost_{investment}}(cost, loc::tech) = cost_{fractional\_om}(cost, loc::tech) + cost_{fixed\_om}(cost, loc::tech) + cost_{cap}(cost, loc::tech)\\cost_{cap}(cost, loc::tech) = depreciation\_rate * ts\_weight * (cost_{energy\_cap}(cost, loc::tech) \times \boldsymbol{energy_{cap}}(loc::tech) + cost_{storage\_cap}(cost, loc::tech) \times \boldsymbol{storage_{cap}}(loc::tech) + cost_{resource\_cap}(cost, loc::tech) \times \boldsymbol{resource_{cap}}(loc::tech) + cost_{resource\_area}(cost, loc::tech)) \times \boldsymbol{resource_{area}}(loc::tech)\\\begin{split}depreciation\_rate = \begin{cases} = 1 / plant\_life,& \text{if } interest\_rate = 0\\ = \frac{interest\_rate \times (1 + interest\_rate)^{plant\_life}}{(1 + interest\_rate)^{plant\_life} - 1},& \text{if } interest\_rate \gt 0\\ \end{cases}\end{split}\\ts\_weight = \sum_{timestep \in timesteps} (time\_res(timestep) \times weight(timestep)) \times \frac{1}{8760}\end{aligned}\end{align} \]

calliope.backend.pyomo.constraints.costs.cost_var_constraint_rule(backend_model, cost, loc_tech, timestep)[source]¶: Calculate costs from time-varying decision variables

\[ \begin{align}\begin{aligned}\boldsymbol{cost_{var}}(cost, loc::tech, timestep) = cost_{prod}(cost, loc::tech, timestep) + cost_{con}(cost, loc::tech, timestep)\\cost_{prod}(cost, loc::tech, timestep) = cost_{om\_prod}(cost, loc::tech, timestep) \times weight(timestep) \times \boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep)\\\begin{split}prod\_con\_eff = \begin{cases} = \boldsymbol{resource_{con}}(loc::tech, timestep),& \text{if } loc::tech \in loc\_techs\_supply\_plus \\ = \frac{\boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep)}{energy_eff(loc::tech, timestep)},& \text{if } loc::tech \in loc\_techs\_supply \\ \end{cases}\end{split}\\cost_{con}(cost, loc::tech, timestep) = cost_{om\_con}(cost, loc::tech, timestep) \times weight(timestep) \times prod\_con\_eff\end{aligned}\end{align} \]

Export¶

calliope.backend.pyomo.constraints.export.update_system_balance_constraint(backend_model, loc_carrier, timestep)[source]¶

Update system balance constraint (from energy_balance.py) to include export

Math given in system_balance_constraint_rule()

calliope.backend.pyomo.constraints.export.export_balance_constraint_rule(backend_model, loc_tech_carrier, timestep)[source]¶: Ensure no technology can ‘pass’ its export capability to another technology with the same carrier_out, by limiting its export to the capacity of its production

\[\boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep) \geq \boldsymbol{carrier_{export}}(loc::tech::carrier, timestep) \quad \forall loc::tech::carrier \in locs::tech::carriers_{export}, \forall timestep \in timesteps\]

calliope.backend.pyomo.constraints.export.update_costs_var_constraint(backend_model, cost, loc_tech, timestep)[source]¶: Update time varying cost constraint (from costs.py) to include export

\[\boldsymbol{cost_{var}}(cost, loc::tech, timestep) += cost_{export}(cost, loc::tech, timestep) \times \boldsymbol{carrier_{export}}(loc::tech::carrier, timestep) * timestep_{weight} \quad \forall cost \in costs, \forall loc::tech \in loc::techs_{cost_{var}, export}, \forall timestep \in timesteps\]

calliope.backend.pyomo.constraints.export.export_max_constraint_rule(backend_model, loc_tech_carrier, timestep)[source]¶

Set maximum export. All exporting technologies.

\[\boldsymbol{carrier_{export}}(loc::tech::carrier, timestep) \leq export_{cap}(loc::tech) \quad \forall loc::tech::carrier \in locs::tech::carriers_{export}, \forall timestep \in timesteps\]

If the technology is defined by integer units, not a continuous capacity, this constraint becomes:

\[\boldsymbol{carrier_{export}}(loc::tech::carrier, timestep) \leq export_{cap}(loc::tech) \times \boldsymbol{operating_{units}}(loc::tech, timestep)\]

MILP¶

calliope.backend.pyomo.constraints.milp.unit_commitment_milp_constraint_rule(backend_model, loc_tech, timestep)[source]¶: Constraining the number of integer units \(operating_units(loc_tech, timestep)\) of a technology which can operate in a given timestep, based on maximum purchased units \(units(loc_tech)\)

\[\boldsymbol{operating\_units}(loc::tech, timestep) \leq \boldsymbol{units}(loc::tech) \quad \forall loc::tech \in loc::techs_{milp}, \forall timestep \in timesteps\]

calliope.backend.pyomo.constraints.milp.unit_capacity_milp_constraint_rule(backend_model, loc_tech)[source]¶

Add upper and lower bounds for purchased units of a technology

\[\begin{split}\boldsymbol{units}(loc::tech) \begin{cases} = units_{equals}(loc::tech),& \text{if } units_{equals}(loc::tech)\\ \leq units_{max}(loc::tech),& \text{if } units_{max}(loc::tech)\\ \text{unconstrained},& \text{otherwise} \end{cases} \quad \forall loc::tech \in loc::techs_{milp}\end{split}\]

and (if equals not enforced):

\[\boldsymbol{units}(loc::tech) \geq units_{min}(loc::tech) \quad \forall loc::tech \in loc::techs_{milp}\]

calliope.backend.pyomo.constraints.milp.carrier_production_max_milp_constraint_rule(backend_model, loc_tech_carrier, timestep)[source]¶

Set maximum carrier production of MILP techs that aren’t conversion plus

\[\boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep) \leq energy_{cap, per unit}(loc::tech) \times timestep\_resolution(timestep) \times \boldsymbol{operating\_units}(loc::tech, timestep) \times \eta_{parasitic}(loc::tech, timestep) \quad \forall loc::tech \in loc::techs_{milp}, \forall timestep \in timesteps\]

\(\eta_{parasitic}\) is only activated for supply_plus technologies

calliope.backend.pyomo.constraints.milp.carrier_production_max_conversion_plus_milp_constraint_rule(backend_model, loc_tech, timestep)[source]¶: Set maximum carrier production of conversion_plus MILP techs

\[\sum_{loc::tech::carrier \in loc::tech::carriers_{out}} \boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep) \leq energy_{cap, per unit}(loc::tech) \times timestep\_resolution(timestep) \times \boldsymbol{operating\_units}(loc::tech, timestep) \times \eta_{parasitic}(loc::tech, timestep) \quad \forall loc::tech \in loc::techs_{milp, conversion^{+}}, \forall timestep \in timesteps\]

calliope.backend.pyomo.constraints.milp.carrier_production_min_milp_constraint_rule(backend_model, loc_tech_carrier, timestep)[source]¶: Set minimum carrier production of MILP techs that aren’t conversion plus

\[\boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep) \geq energy_{cap, per unit}(loc::tech) \times timestep\_resolution(timestep) \times \boldsymbol{operating\_units}(loc::tech, timestep) \times energy_{cap, min use}(loc::tech) \quad \forall loc::tech \in loc::techs_{milp}, \forall timestep \in timesteps\]

calliope.backend.pyomo.constraints.milp.carrier_production_min_conversion_plus_milp_constraint_rule(backend_model, loc_tech, timestep)[source]¶: Set minimum carrier production of conversion_plus MILP techs

\[\sum_{loc::tech::carrier \in loc::tech::carriers_{out}} \boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep) \geq energy_{cap, per unit}(loc::tech) \times timestep\_resolution(timestep) \times \boldsymbol{operating\_units}(loc::tech, timestep) \times energy_{cap, min use}(loc::tech) \quad \forall loc::tech \in loc::techs_{milp, conversion^{+}}, \forall timestep \in timesteps\]

calliope.backend.pyomo.constraints.milp.carrier_consumption_max_milp_constraint_rule(backend_model, loc_tech_carrier, timestep)[source]¶: Set maximum carrier consumption of demand, storage, and transmission MILP techs

\[\boldsymbol{carrier_{con}}(loc::tech::carrier, timestep) \geq -1 * energy_{cap, per unit}(loc::tech) \times timestep\_resolution(timestep) \times \boldsymbol{operating\_units}(loc::tech, timestep) \times \eta_{parasitic}(loc::tech, timestep) \quad \forall loc::tech \in loc::techs_{milp, con}, \forall timestep \in timesteps\]

calliope.backend.pyomo.constraints.milp.energy_capacity_units_milp_constraint_rule(backend_model, loc_tech)[source]¶: Set energy capacity decision variable as a function of purchased units

\[\boldsymbol{energy_{cap}}(loc::tech) = \boldsymbol{units}(loc::tech) \times energy_{cap, per unit}(loc::tech) \quad \forall loc::tech \in loc::techs_{milp}\]

calliope.backend.pyomo.constraints.milp.storage_capacity_units_milp_constraint_rule(backend_model, loc_tech)[source]¶: Set storage capacity decision variable as a function of purchased units

\[\boldsymbol{storage_{cap}}(loc::tech) = \boldsymbol{units}(loc::tech) \times storage_{cap, per unit}(loc::tech) \quad \forall loc::tech \in loc::techs_{milp, store}\]

calliope.backend.pyomo.constraints.milp.energy_capacity_max_purchase_milp_constraint_rule(backend_model, loc_tech)[source]¶

Set maximum energy capacity decision variable upper bound as a function of binary purchase variable

The first valid case is applied:

\[\begin{split}\frac{\boldsymbol{energy_{cap}}(loc::tech)}{energy_{cap, scale}(loc::tech)} \begin{cases} = energy_{cap, equals}(loc::tech) \times \boldsymbol{purchased}(loc::tech),& \text{if } energy_{cap, equals}(loc::tech)\\ \leq energy_{cap, max}(loc::tech) \times \boldsymbol{purchased}(loc::tech),& \text{if } energy_{cap, max}(loc::tech)\\ \text{unconstrained},& \text{otherwise} \end{cases} \forall loc::tech \in loc::techs_{purchase}\end{split}\]

calliope.backend.pyomo.constraints.milp.energy_capacity_min_purchase_milp_constraint_rule(backend_model, loc_tech)[source]¶

Set minimum energy capacity decision variable upper bound as a function of binary purchase variable

and (if equals not enforced):

\[\frac{\boldsymbol{energy_{cap}}(loc::tech)}{energy_{cap, scale}(loc::tech)} \geq energy_{cap, min}(loc::tech) \times \boldsymbol{purchased}(loc::tech) \quad \forall loc::tech \in loc::techs\]

calliope.backend.pyomo.constraints.milp.storage_capacity_max_purchase_milp_constraint_rule(backend_model, loc_tech)[source]¶

Set maximum storage capacity.

The first valid case is applied:

\[\begin{split}\boldsymbol{storage_{cap}}(loc::tech) \begin{cases} = storage_{cap, equals}(loc::tech) \times \boldsymbol{purchased},& \text{if } storage_{cap, equals} \\ \leq storage_{cap, max}(loc::tech) \times \boldsymbol{purchased},& \text{if } storage_{cap, max}(loc::tech)\\ \text{unconstrained},& \text{otherwise} \end{cases} \forall loc::tech \in loc::techs_{purchase, store}\end{split}\]

calliope.backend.pyomo.constraints.milp.storage_capacity_min_purchase_milp_constraint_rule(backend_model, loc_tech)[source]¶

Set minimum storage capacity decision variable as a function of binary purchase variable

if equals not enforced for storage_cap:

\[\boldsymbol{storage_{cap}}(loc::tech) \geq storage_{cap, min}(loc::tech) \times \boldsymbol{purchased}(loc::tech) \quad \forall loc::tech \in loc::techs_{purchase, store}\]

calliope.backend.pyomo.constraints.milp.update_costs_investment_units_milp_constraint(backend_model, cost, loc_tech)[source]¶: Add MILP investment costs (cost * number of units purchased)

\[\boldsymbol{cost_{investment}}(cost, loc::tech) += \boldsymbol{units}(loc::tech) \times cost_{purchase}(cost, loc::tech) * timestep_{weight} * depreciation \quad \forall cost \in costs, \forall loc::tech \in loc::techs_{cost_{investment}, milp}\]

calliope.backend.pyomo.constraints.milp.update_costs_investment_purchase_milp_constraint(backend_model, cost, loc_tech)[source]¶: Add binary investment costs (cost * binary_purchased_unit)

\[\boldsymbol{cost_{investment}}(cost, loc::tech) += \boldsymbol{purchased}(loc::tech) \times cost_{purchase}(cost, loc::tech) * timestep_{weight} * depreciation \quad \forall cost \in costs, \forall loc::tech \in loc::techs_{cost_{investment}, purchase}\]

calliope.backend.pyomo.constraints.milp.unit_capacity_systemwide_milp_constraint_rule(backend_model, tech)[source]¶

Set constraints to limit the number of purchased units of a single technology type across all locations in the model.

The first valid case is applied:

\[\begin{split}\sum_{loc}\boldsymbol{units}(loc::tech) + \boldsymbol{purchased}(loc::tech) \begin{cases} = units_{equals, systemwide}(tech),& \text{if } units_{equals, systemwide}(tech)\\ \leq units_{max, systemwide}(tech),& \text{if } units_{max, systemwide}(tech)\\ \text{unconstrained},& \text{otherwise} \end{cases} \forall tech \in techs\end{split}\]

calliope.backend.pyomo.constraints.milp.asynchronous_con_milp_constraint_rule(backend_model, loc_tech, timestep)[source]¶: BigM limit set on carrier_con, forcing it to either be zero or non-zero, depending on whether con is zero or one, respectively.

\[- \boldsymbol{carrier_con}[loc::tech::carrier, timestep] \leq \text{bigM} \times (1 - \boldsymbol{prod_con_switch}[loc::tech, timestep]) \forall loc::tech \in loc::techs_{asynchronous_prod_con}, \forall timestep \in timesteps\]

calliope.backend.pyomo.constraints.milp.asynchronous_prod_milp_constraint_rule(backend_model, loc_tech, timestep)[source]¶: BigM limit set on carrier_prod, forcing it to either be zero or non-zero, depending on whether prod is zero or one, respectively.

\[\boldsymbol{carrier_prod}[loc::tech::carrier, timestep] \leq \text{bigM} \times \boldsymbol{prod_con_switch}[loc::tech, timestep] \forall loc::tech \in loc::techs_{asynchronous_prod_con}, \forall timestep \in timesteps\]

Conversion¶

calliope.backend.pyomo.constraints.conversion.balance_conversion_constraint_rule(backend_model, loc_tech, timestep)[source]¶: Balance energy carrier consumption and production

\[-1 * \boldsymbol{carrier_{con}}(loc::tech::carrier, timestep) \times \eta_{energy}(loc::tech, timestep) = \boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep) \times \eta_{energy}(loc::tech, timestep) \quad \forall loc::tech \in locs::techs_{conversion}, \forall timestep \in timesteps\]

calliope.backend.pyomo.constraints.conversion.cost_var_conversion_constraint_rule(backend_model, cost, loc_tech, timestep)[source]¶: Add time-varying conversion technology costs

\[\boldsymbol{cost_{var}}(loc::tech, cost, timestep) = \boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep) \times timestep_{weight}(timestep) \times cost_{om, prod}(loc::tech, cost, timestep) + \boldsymbol{carrier_{con}}(loc::tech::carrier, timestep) \times timestep_{weight}(timestep) \times cost_{om, con}(loc::tech, cost, timestep) \quad \forall loc::tech \in loc::techs_{cost_{var}, conversion}\]

Conversion_plus¶

calliope.backend.pyomo.constraints.conversion_plus.balance_conversion_plus_primary_constraint_rule(backend_model, loc_tech, timestep)[source]¶: Balance energy carrier consumption and production for carrier_in and carrier_out

\[\sum_{loc::tech::carrier \in loc::tech::carriers_{out}} \frac{\boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep)}{ carrier\_ratio(loc::tech::carrier, `out')} = -1 * \sum_{loc::tech::carrier \in loc::tech::carriers_{in}} ( \boldsymbol{carrier_{con}}(loc::tech::carrier, timestep) * carrier\_ratio(loc::tech::carrier, `in') * \eta_{energy}(loc::tech, timestep)) \quad \forall loc::tech \in loc::techs_{conversion^{+}}, \forall timestep \in timesteps\]

calliope.backend.pyomo.constraints.conversion_plus.carrier_production_max_conversion_plus_constraint_rule(backend_model, loc_tech, timestep)[source]¶: Set maximum conversion_plus carrier production.

\[\sum_{loc::tech::carrier \in loc::tech::carriers_{out}} \boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep) \leq \boldsymbol{energy_{cap}}(loc::tech) \times timestep\_resolution(timestep) \quad \forall loc::tech \in loc::techs_{conversion^{+}}, \forall timestep \in timesteps\]

calliope.backend.pyomo.constraints.conversion_plus.carrier_production_min_conversion_plus_constraint_rule(backend_model, loc_tech, timestep)[source]¶: Set minimum conversion_plus carrier production.

\[\sum_{loc::tech::carrier \in loc::tech::carriers_{out}} \boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep) \leq \boldsymbol{energy_{cap}}(loc::tech) \times timestep\_resolution(timestep) \times energy_{cap, min use}(loc::tech) \quad \forall loc::tech \in loc::techs_{conversion^{+}}, \forall timestep \in timesteps\]

calliope.backend.pyomo.constraints.conversion_plus.cost_var_conversion_plus_constraint_rule(backend_model, cost, loc_tech, timestep)[source]¶: Add time-varying conversion_plus technology costs

\[\boldsymbol{cost_{var}}(loc::tech, cost, timestep) = \boldsymbol{carrier_{prod}}(loc::tech::carrier_{primary}, timestep) \times timestep_{weight}(timestep) \times cost_{om, prod}(loc::tech, cost, timestep) + \boldsymbol{carrier_{con}}(loc::tech::carrier_{primary}, timestep) \times timestep_{weight}(timestep) \times cost_{om, con}(loc::tech, cost, timestep) \quad \forall loc::tech \in loc::techs_{cost_{var}, conversion^{+}}\]

calliope.backend.pyomo.constraints.conversion_plus.balance_conversion_plus_tiers_constraint_rule(backend_model, tier, loc_tech, timestep)[source]¶

Force all carrier_in_2/carrier_in_3 and carrier_out_2/carrier_out_3 to follow carrier_in and carrier_out (respectively).

If tier in [‘out_2’, ‘out_3’]:

\[\sum_{loc::tech::carrier \in loc::tech::carriers_{out}} ( \frac{\boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep)}{ carrier\_ratio(loc::tech::carrier, `out')} = \sum_{loc::tech::carrier \in loc::tech::carriers_{tier}} ( \frac{\boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep)}{ carrier\_ratio(loc::tech::carrier, tier)} \quad \forall \text { tier } \in [`out_2', `out_3'], \forall loc::tech \in loc::techs_{conversion^{+}}, \forall timestep \in timesteps\]

If tier in [‘in_2’, ‘in_3’]:

\[\sum_{loc::tech::carrier \in loc::tech::carriers_{in}} \frac{\boldsymbol{carrier_{con}}(loc::tech::carrier, timestep)}{ carrier\_ratio(loc::tech::carrier, `in')} = \sum_{loc::tech::carrier \in loc::tech::carriers_{tier}} \frac{\boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep)}{ carrier\_ratio(loc::tech::carrier, tier)} \quad \forall \text{ tier } \in [`in_2', `in_3'], \forall loc::tech \in loc::techs_{conversion^{+}}, \forall timestep \in timesteps\]

calliope.backend.pyomo.constraints.conversion_plus.conversion_plus_prod_con_to_zero_constraint_rule(backend_model, loc_tech_carrier_tier, timestep)[source]¶: Force any carrier production or consumption for a conversion plus technology to zero in timesteps where its carrier_ratio is zero

Network¶

calliope.backend.pyomo.constraints.network.symmetric_transmission_constraint_rule(backend_model, loc_tech)[source]¶: Constrain e_cap symmetrically for transmission nodes. Transmission techs only.

\[energy_{cap}(loc1::tech:loc2) = energy_{cap}(loc2::tech:loc1)\]

Policy¶

calliope.backend.pyomo.constraints.policy.group_share_energy_cap_constraint_rule(backend_model, techlist, what)[source]¶: Enforce shares in energy_cap for groups of technologies. Applied to supply and supply_plus technologies only.

\[\sum_{loc::tech \in given\_group} energy_{cap}(loc::tech) = fraction \times \sum_{loc::tech \in loc\_techs\_supply \or loc\_techs\_supply\_plus} energy_{cap}(loc::tech)\]

calliope.backend.pyomo.constraints.policy.group_share_carrier_prod_constraint_rule(backend_model, techlist_carrier, what)[source]¶: Enforce shares in carrier_prod for groups of technologies. Applied to loc_tech_carriers_supply_conversion_all, which includes supply, supply_plus, conversion, and conversion_plus.

\[\sum_{loc::tech::carrier \in given\_group, timestep \in timesteps} carrier_{prod}(loc::tech::carrier, timestep) = fraction \times \sum_{loc::tech:carrier \in loc\_tech\_carriers\_supply\_all, timestep\in timesteps} carrier_{prod}(loc::tech::carrier, timestep)\]

calliope.backend.pyomo.constraints.policy.reserve_margin_constraint_rule(backend_model, carrier)[source]¶: Enforces a system reserve margin per carrier.

\[\sum_{loc::tech::carrier \in loc\_tech\_carriers\_supply\_all} energy_{cap}(loc::tech::carrier, timestep_{max\_demand}) \geq \sum_{loc::tech::carrier \in loc\_tech\_carriers\_demand} carrier_{con}(loc::tech::carrier, timestep_{max\_demand}) \times -1 \times \frac{1}{time\_resolution_{max\_demand}} \times (1 + reserve\_margin)\]

Group constraints¶

calliope.backend.pyomo.constraints.group.demand_share_constraint_rule(backend_model, group_name, carrier, what)[source]¶: Enforces shares of demand of a carrier to be met by the given groups of technologies at the given locations, on average over the entire model period. The share is relative to demand technologies only.

\[\sum_{loc::tech::carrier \in given\_group, timestep \in timesteps} carrier_{prod}(loc::tech::carrier, timestep) \leq share \times \sum_{loc::tech:carrier \in loc\_techs\_demand \in given\_locations, timestep\in timesteps} carrier_{con}(loc::tech::carrier, timestep)\]

calliope.backend.pyomo.constraints.group.demand_share_per_timestep_constraint_rule(backend_model, group_name, carrier, timestep, what)[source]¶: Enforces shares of demand of a carrier to be met by the given groups of technologies at the given locations, in each timestep. The share is relative to demand technologies only.

\[\sum_{loc::tech::carrier \in given\_group} carrier_{prod}(loc::tech::carrier, timestep) \leq share \times \sum_{loc::tech:carrier \in loc\_techs\_demand \in given\_locations} carrier_{con}(loc::tech::carrier, timestep) for timestep \in timesteps\]

calliope.backend.pyomo.constraints.group.demand_share_per_timestep_decision_main_constraint_rule(backend_model, group_name, carrier, tech, timestep)[source]¶

Allows the model to decide on how a fraction demand for a carrier is met by the given groups, which will all have the same share in each timestep. The share is relative to the actual demand from demand technologies only.

The main constraint enforces that the shares are the same in each timestep.

\[ \begin{align}\begin{aligned}\sum_{loc::tech::carrier \in given\_group} carrier_{prod}(loc::tech::carrier, timestep)\\=\\\sum_{loc::tech::carrier \in given\_group} required\_resource(loc::tech::carrier, timestep)\\\times \sum_{loc::tech::carrier \in given\_group} demand\_share\_per\_timestep\_decision(loc::tech::carrier)\\\forall timestep \in timesteps\\\forall tech \in techs\end{aligned}\end{align} \]

calliope.backend.pyomo.constraints.group.demand_share_per_timestep_decision_sum_constraint_rule(backend_model, group_name, carrier)[source]¶: Allows the model to decide on how a fraction of demand for a carrier is met by the given groups, which will all have the same share in each timestep. The share is relative to the actual demand from demand technologies only.

The sum constraint ensures that all decision shares add up to the share of carrier demand specified in the constraint.

This constraint is only applied if the share of carrier demand has been set to a not-None value.

\[share = \sum_{loc::tech::carrier \in given\_group} demand\_share\_per\_timestep\_decision(loc::tech::carrier)\]

calliope.backend.pyomo.constraints.group.carrier_prod_share_constraint_rule(backend_model, constraint_group, carrier, what)[source]¶: Enforces shares of carrier_prod for groups of technologies and locations, on average over the entire model period. The share is relative to supply and supply_plus technologies only.

\[\sum_{loc::tech::carrier \in given\_group, timestep \in timesteps} carrier_{prod}(loc::tech::carrier, timestep) \leq share \times \sum_{loc::tech:carrier \in loc\_tech\_carriers\_supply\_all \in given\_locations, timestep\in timesteps} carrier_{prod}(loc::tech::carrier, timestep)\]

calliope.backend.pyomo.constraints.group.carrier_prod_share_per_timestep_constraint_rule(backend_model, constraint_group, carrier, timestep, what)[source]¶: Enforces shares of carrier_prod for groups of technologies and locations, in each timestep. The share is relative to supply and supply_plus technologies only.

\[\sum_{loc::tech::carrier \in given\_group} carrier_{prod}(loc::tech::carrier, timestep) \leq share \times \sum_{loc::tech:carrier \in loc\_tech\_carriers\_supply\_all \in given\_locations} carrier_{prod}(loc::tech::carrier, timestep) for timestep \in timesteps\]

calliope.backend.pyomo.constraints.group.net_import_share_constraint_rule(backend_model, constraint_group, carrier, what)[source]¶: Enforces demand shares of net imports from transmission technologies for groups of locations, on average over the entire model period. Transmission within the group are ignored. The share is relative to demand technologies only.

\[\sum_{loc::tech::carrier \in loc\_tech\_carriers\_transmission \in given\_locations, timestep \in timesteps} carrier_{prod}(loc::tech::carrier, timestep) + \sum_{loc::tech::carrier \in loc\_tech\_carriers\_transmission \in given\_locations, timestep \in timesteps} carrier_{con}(loc::tech::carrier, timestep) \leq share \times \sum_{loc::tech:carrier \in loc\_tech\_demand \in given\_locations, timestep\in timesteps} carrier_{con}(loc::tech::carrier, timestep)\]

calliope.backend.pyomo.constraints.group.carrier_prod_constraint_rule(backend_model, constraint_group, carrier, what)[source]¶: Enforces carrier_prod for groups of technologies and locations, as a sum over the entire model period.

\[\sum_{loc::tech::carrier \in given\_group, timestep \in timesteps} carrier_{prod}(loc::tech::carrier, timestep) \leq carrier_prod_max\]

calliope.backend.pyomo.constraints.group.carrier_con_constraint_rule(backend_model, constraint_group, carrier, what)[source]¶: Enforces carrier_con for groups of technologies and locations, as a sum over the entire model period. limits are always negative, so min/max is relative to zero (i.e. min = -1 means carrier_con must be -1 or less)

\[\sum_{loc::tech::carrier \in given\_group, timestep \in timesteps} carrier_{con}(loc::tech::carrier, timestep) \geq carrier_con_max\]

calliope.backend.pyomo.constraints.group.energy_cap_share_constraint_rule(backend_model, constraint_group, what)[source]¶: Enforces shares of energy_cap for groups of technologies and locations. The share is relative to supply and supply_plus technologies only.

\[\sum_{loc::tech \in given\_group} energy_{cap}(loc::tech) \leq share \times \sum_{loc::tech \in loc\_tech\_supply\_all \in given\_locations} energy_{cap}(loc::tech)\]

calliope.backend.pyomo.constraints.group.energy_cap_constraint_rule(backend_model, constraint_group, what)[source]¶: Enforce upper and lower bounds for energy_cap of energy_cap for groups of technologies and locations.

\[ \begin{align}\begin{aligned}\begin{split}\sum_{loc::tech \in given\_group} energy_{cap}(loc::tech) \leq energy\_cap\_max\\\end{split}\\\sum_{loc::tech \in given\_group} energy_{cap}(loc::tech) \geq energy\_cap\_min\end{aligned}\end{align} \]

calliope.backend.pyomo.constraints.group.cost_cap_constraint_rule(backend_model, group_name, cost, what)[source]¶: Limit cost for a specific cost class to a certain value, i.e. Ɛ-constrained costs, for groups of technologies and locations.

\[\sum{loc::tech \in loc\_techs_{group\_name}, timestep \in timesteps} \boldsymbol{cost}(cost, loc::tech, timestep) \begin{cases} \leq cost\_max(cost) \geq cost\_min(cost) = cost\_equals(cost) \end{cases}\]

calliope.backend.pyomo.constraints.group.cost_investment_cap_constraint_rule(backend_model, group_name, cost, what)[source]¶: Limit investment costs specific to a cost class to a certain value, i.e. Ɛ-constrained costs, for groups of technologies and locations.

\[\sum{loc::tech \in loc\_techs_{group\_name}, timestep \in timesteps} \boldsymbol{cost\_{investment}}(cost, loc::tech, timestep) \begin{cases} \leq cost\_investment\_max(cost) \geq cost\_investment\_min(cost) = cost\_investment\_equals(cost) \end{cases}\]

calliope.backend.pyomo.constraints.group.cost_var_cap_constraint_rule(backend_model, group_name, cost, what)[source]¶: Limit variable costs specific to a cost class to a certain value, i.e. Ɛ-constrained costs, for groups of technologies and locations.

\[\sum{loc::tech \in loc\_techs_{group\_name}, timestep \in timesteps} \boldsymbol{cost\_{var}}(cost, loc::tech, timestep) \begin{cases} \leq cost\_var\_max(cost) \geq cost\_var\_min(cost) = cost\_var\_equals(cost) \end{cases}\]

calliope.backend.pyomo.constraints.group.resource_area_constraint_rule(backend_model, constraint_group, what)[source]¶: Enforce upper and lower bounds of resource_area for groups of technologies and locations.

\[ \begin{align}\begin{aligned}\begin{split}\boldsymbol{resource_{area}}(loc::tech) \leq group\_resource\_area\_max\\\end{split}\\\boldsymbol{resource_{area}}(loc::tech) \geq group\_resource\_area\_min\end{aligned}\end{align} \]

Development guide¶

Contributions are very welcome! See our contributors guide on GitHub for information on how to contribute.

The code lives on GitHub at calliope-project/calliope. Development takes place in the master branch. Stable versions are tagged off of master with semantic versioning.

Tests are included and can be run with py.test from the project’s root directory.

Also see the list of open issues, planned milestones and projects for an overview of where development is heading, and join us on Gitter to ask questions or discuss code.

Installing a development version¶

As when installing a stable version, using conda is recommended.

To actively contribute to Calliope development, or simply track the latest development version, you’ll instead want to clone our GitHub repository. This will provide you with the master branch in a known location on your local device.

First, clone the repository:

$ git clone https://github.com/calliope-project/calliope

Then install all development requirements for Calliope into a new environment, calling it e.g. calliope_dev:

$ conda env create -f requirements.yml -n calliope_dev
$ conda activate calliope_dev

Finally install Calliope itself as an editable installation with pip:

$ pip install -e calliope

Note

Most of our tests depend on having the CBC solver also installed, as we have found it to be more stable than GPLK. If you are running on a Unix system, then you can run conda install coincbc to also install the CBC solver. To install solvers other than CBC, and for Windows systems, see our solver installation instructions.

We use the code formatter black and before you contribute any code, you should ensure that you have run it through black. If you don’t have a process for doing this already, you can install our configured pre-commit hook which will automatically run black on each commit:

$ pre-commit install

Creating modular extensions¶

As of version 0.6.0, dynamic loading of custom constraint generator extensions has been removed due it not not being used by users of Calliope. The ability to dynamically load custom functions to adjust time resolution remains (see below).

Time functions and masks¶

Custom functions that adjust time resolution can be loaded dynamically during model initialisation. By default, Calliope first checks whether the name of a function or time mask refers to a function from the calliope.core.time.masks or calliope.core.time.funcs module, and if not, attempts to load the function from an importable module:

time:
   masks:
       - {function: week, options: {day_func: 'extreme', tech: 'wind', how: 'min'}}
       - {function: my_custom_module.my_custom_mask, options: {...}}
   function: my_custom_module.my_custom_function
   function_options: {...}

Understanding Calliope internal implementation¶

Worried about delving into the Calliope code? Confused by the structure? Fear not! The package is structured as best as possible to follow a clear workflow, which takes inputs on a journey from YAML and CSV files, via Pyomo objects, to a NetCDF file of results.

Overview¶

Calliope enables data stored in YAML and CSV files to be prepared for optimisation in a linear solver, and the results of optimisation to be analysed and/or saved. The internal workflow is shown below. The python packages ruamel.yaml and pandas are used to parse the YAML and CSV files, respectively. Xarray is then used to restructure the data into multidimensional arrays, ready for saving, plotting, or sending to the backend. The pyomo package is currently used in the backend to transform the xarray dataset into a pyomo ConcreteModel. All parameters, sets, constraints, and decision variables are defined as pyomo objects at this stage. Pyomo produces an LP file, which can be read in by the modeller’s chosen solver. Results are extracted from pyomo into an xarray dataset, again ready to be analysed or saved.

Internal implementation¶

Taking a more detailed look at the workflow, a number of data objects are populated. On initialising a model, the model_run dictionary is created from the provided YAML and CSV files. Overrides (both from scenarios and location/link specific ones) are applied at this point. The model_run dictionary is then reformulated into multidimensional arrays of data and collated in the model_data xarray dataset. At this point, model initialisation has completed; model inputs can be accessed by the user, and edited if necessary.

On executing model.run(), only model_data is sent over to the backend, where the pyomo ConcreteModel is created and pyomo parameters (Param) and sets (Set) are populated using data from model_data. Decision variables (Var), constraints (Constraint), and the objective (Obj) are also initialised at this point. The model is then sent to the solver.

Upon solving the problem, the backend_model (pyomo ConcreteModel) is attached to the Model object and the results are added to model_data. Post-processing also occurs to clean up the results and to calculate certain indicators, such as the capacity factor of technologies. At this point, the model run has completed; model results can be accessed by the user, and saved or analysed as required.

Representation of Calliope internal implementation workflow. Five primary steps are shown, starting at the model definition and implemented clockwise. From inner edge to outer edge of the rainbow are: the data object produced by the step, primary and auxiliary python files in which functionality to produce the data object are found, and the folder containing the relevant python files for the step.¶

Exposing all methods and data attached to the Model object¶

The Model object begins as an empty class. Once called, it becomes an empty object which is populated with methods to access, analyse, and save the model data. The Model object is further augmented once run has been called, at which point, the backend model object can be accessed, directly or via a user-friendly interface. The notebook found here goes through each method and data object which can be accessed through the Model object. Most are hidden (using an underscore before the method name), as they aren’t useful for the average user.

Representation of the Calliope Model object, growing from an empty class to having methods to view, plot and save data, and to interface with the solver backend.¶

Contribution workflow¶

Have a bug fix or feature addition you’d like to see in the next stable release of Calliope? First, be sure to check out our list of open and closed issues to see whether this is something someone else has mentioned, or perhaps has even fixed. If it’s there, you can add to the discussion, give it a thumbs up, or look to implement the change yourself. If it isn’t there, then feel free to open your own issue, or you can head straight to implementing it. The below instructions are a more detailed description of our contribution guidelines, which you can refer to if you’re already comfortable with using pytest and GitHub flows.

Implementing a change¶

When you want to change some part of Calliope, whether it is the software or the documentation, it’s best to do it in a fork of the main Calliope project repository. You can find out more about how to fork a repository on GitHub’s help pages. Your fork will be a duplicate of the Calliope master branch and can be ‘cloned’ to provide you with the repository on your own device

$ git clone https://github.com/your_username/calliope

If you want the local version of your fork to be in the same folder as your local version of the main Calliope repository, then you just need to specify a new directory name

$ git clone https://github.com/your_username/calliope your_new_directory_name

Following the instructions for installing a development environment of Calliope, you can create an environment specific to this installation of Calliope.

In making changes to your local version, it’s a good idea to create a branch first, to not have your master branch diverge from that of the main Calliope repository

$ git branch new-fix-or-feature

Then, ‘checkout’ the branch so that the folder contents are specific to that branch

$ git checkout new-fix-or-feature

Finally, push the branch online, so it’s existence is also in your remote fork of the Calliope repository (you’ll find it in the dropdown list of branches at https://github.com/your_repository/calliope)

$ git push -u origin new-fix-or-feature

Now the files in your local directory can be edited with complete freedom. Once you have made the necessary changes, you’ll need to test that they don’t break anything. This can be done easily by changing to the directory into which you cloned your fork using the terminal / command line, and running pytest (make sure you have activated the conda environment and you have pytest installed: conda install pytest). Any change you make should also be covered by a test. Add it into the relevant test file, making sure the function starts with ‘test_’. Since the whole test suite takes ~25 minutes to run, you can run specific tests, such as those you add in

$ pytest calliope/test/test_filename.py::ClassName::function_name

If tests are failing, you can debug them by using the pytest arguments -x (stop at the first failed test) and --pdb (enter into the debug console).

Once everything has been updated as you’d like (see the contribution checklist below for more on this), you can commit those changes. This stores all edited files in the directory, ready for pushing online

$ git add .
$ git checkout -m "Short message explaining what has been done in this commit."

If you only want a subset of edited files to go into this commit, you can specify them in the call to git add; the period adds all edited files.

If you’re happy with your commit(s) then it is time to ‘push’ everything online using the command git push. If you’re working with someone else on a branch and they have made changes, you can bring them into your local repository using the command git pull.

Now it is time to request that these changes are added into the main Calliope project repository! You can do this by starting a pull request. One of the core Calliope team will review the pull request and either accept it or request some changes before it’s merged into the main Calliope repository. If any changes are requested, you can make those changes on your local branch, commit them, and push them online – your pull request will update automatically with those changes.

Once a pull request has been accepted, you can return your fork back to its master branch and sync it with the updated Calliope project master

$ git remote add upstream https://github.com/calliope-project/calliope
$ git fetch upstream master
$ git checkout master
$ git merge upstream/master

Contribution checklist¶

A contribution to the core Calliope code should meet the following requirements:

Test(s) added to cover contribution

Tests ensure that a bug you’ve fixed will be caught in future, if an update to the code causes it to occur again. They also allow you to ensure that additional functionality works as you expect, and any change elsewhere in the code that causes it to act differently in future will be caught.

Documentation updated

If you’ve added functionality, it should be mentioned in the documentation. You can find the reStructuredText (.rst) files for the documentation under ‘doc/user’.

Changelog updated

A brief description of the bug fixed or feature added should be placed in the changelog (changelog.rst). Depending on what the pull request introduces, the description should be prepended with fixed, changed, or new.

Coverage maintained or improved

Coverage will be shown once all tests are complete online. It is the percentage of lines covered by at least one test. If you’ve added a test or two, you should be fine. But if coverage does go down it means that not all of your contribution has been tested!

Example of coverage notification in a pull request.¶

If you’re not sure you’ve done everything to have a fully formed pull request, feel free to start it anyway. We can help guide you through making the necessary changes, once we have seen where you’ve got to.

Profiling¶

To profile a Calliope run with the built-in national-scale example model, then visualise the results with snakeviz:

make profile  # will dump profile output in the current directory
snakeviz calliope.profile  # launch snakeviz to visually examine profile

Use mprof plot to plot memory use.

Other options for visualising:

Interactive visualisation with KCachegrind (on macOS, use QCachegrind, installed e.g. with brew install qcachegrind)
pyprof2calltree -i calliope.profile -o calliope.calltree kcachegrind calliope.calltree

Generate a call graph from the call tree via graphviz

# brew install gprof2dot
gprof2dot -f callgrind calliope.calltree | dot -Tsvg -o callgraph.svg

Checklist for new release¶

Pre-release¶

Make sure all unit tests pass
Build up-to-date Plotly plots for the documentation with (make doc-plots)
Re-run tutorial Jupyter notebooks, found in doc/_static/notebooks
Make sure documentation builds without errors
Make sure the release notes are up-to-date, especially that new features and backward incompatible changes are clearly marked

Create release¶

Change _version.py version number
Update changelog with final version number and release date
Commit with message “Release vXXXX”, then add a “vXXXX” tag, push both to GitHub
Create a release through the GitHub web interface, using the same tag, titling it “Release vXXXX” (required for Zenodo to pull it in)
Upload new release to PyPI: make all-dist
Update the conda-forge package:
- Fork conda-forge/calliope-feedstock, and update recipe/meta.yaml with:
  
  Version number: {% set version = "XXXX" %}
  
  SHA256 of latest version from PyPI: {% set sha256 = "XXXX" %}
  
  Reset build: number: 0 if it is not already at zero
  
  If necessary, carry over any changed requirements from setup.py or requirements/base.yml
- Submit a pull request from an appropriately named branch in your fork (e.g. vXXXX) to the conda-forge/calliope-feedstock repository

Post-release¶

Update changelog, adding a new vXXXX-dev heading, and update _version.py accordingly, in preparation for the next master commit
Update the calliope_version setting in all example models to match the new version, but without the -dev string (so 0.6.0-dev is 0.6.0 for the example models)

Note

Adding ‘-dev’ to the version string, such as __version__ = '0.1.0-dev', is required for the custom code in doc/conf.py to work when building in-development versions of the documentation.

API documentation¶

Documents functions, classes and methods:

API Documentation¶

Model class¶

class calliope.Model(config, model_data=None, *args, **kwargs)[source]¶

A Calliope Model.

save_commented_model_yaml(path)[source]¶: Save a fully built and commented version of the model to a YAML file at the given path. Comments in the file indicate where values were overridden. This is Calliope’s internal representation of a model directly before the model_data xarray.Dataset is built, and can be useful for debugging possible issues in the model formulation.

run(force_rerun=False, **kwargs)[source]¶

Run the model. If force_rerun is True, any existing results will be overwritten.

Additional kwargs are passed to the backend.

get_formatted_array(var, index_format='index')[source]¶

Return an xr.DataArray with locs, techs, and carriers as separate dimensions.

Parameters

varstr: Decision variable for which to return a DataArray.
index_formatstr, default = ‘index’: ‘index’ to return the loc_tech(_carrier) dimensions as individual indexes, ‘multiindex’ to return them as a MultiIndex. The latter has the benefit of having a smaller memory footprint, but you cannot undertake dimension specific operations (e.g. formatted_array.sum(‘locs’))

to_netcdf(path)[source]¶: Save complete model data (inputs and, if available, results) to a NetCDF file at the given path.

to_csv(path, dropna=True)[source]¶

Save complete model data (inputs and, if available, results) as a set of CSV files to the given path.

Parameters

dropnabool, optional: If True (default), NaN values are dropped when saving, resulting in significantly smaller CSV files.

to_lp(path)[source]¶: Save built model to LP format at the given path. If the backend model has not been built yet, it is built prior to saving.

Time series¶

calliope.time.clustering.get_clusters(data, func, timesteps_per_day, tech=None, timesteps=None, k=None, variables=None, **kwargs)[source]¶

Run a clustering algorithm on the timeseries data supplied. All timeseries data is reshaped into one row per day before clustering into similar days.

Parameters

dataxarray.Dataset: Should be normalized
funcstr: ‘kmeans’ or ‘hierarchical’ for KMeans or Agglomerative clustering, respectively
timesteps_per_dayint: Total number of timesteps in a day
techlist, optional: list of strings referring to technologies by which clustering is undertaken. If none (default), all technologies within timeseries variables will be used.
timestepslist or str, optional: Subset of the time domain within which to apply clustering.
kint, optional: Number of clusters to create. If none (default), will use Hartigan’s rule to infer a reasonable number of clusters.
variableslist, optional: data variables (e.g. resource, energy_eff) by whose values the data will be clustered. If none (default), all timeseries variables will be used.
kwargsdict: Additional keyword arguments available depend on the func. For available KMeans kwargs see: http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html For available hierarchical kwargs see: http://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html
Returns
——-
clustersdataframe: Indexed by timesteps and with locations as columns, giving cluster membership for first timestep of each day.
clustered_datasklearn.cluster object: Result of clustering using sklearn.KMeans(k).fit(X) or sklearn.KMeans(k).AgglomerativeClustering(X). Allows user to access specific attributes, for detailed statistical analysis.

calliope.time.masks.extreme(data, tech, var='resource', how='max', length='1D', n=1, groupby_length=None, padding=None, normalize=True, **kwargs)[source]¶

Returns timesteps for period of length where var for the technology tech across the given list of locations is either minimal or maximal.

Parameters

dataxarray.Dataset
techstr: Technology whose var to find extreme for.
varstr, optional: default ‘resource’
howstr, optional: ‘max’ (default) or ‘min’.
lengthstr, optional: Defaults to ‘1D’.
nint, optional: Number of periods of length to look for, default is 1.
groupby_lengthstr, optional: Group time series and return n periods of length for each group.
paddingstr, optional: Either Pandas frequency (e.g. ‘1D’) or ‘calendar_week’. If Pandas frequency, symmetric padding is undertaken, either side of length If ‘calendar_week’, padding is fit to the calendar week in which the extreme day(s) are found.
normalizebool, optional: If True (default), data is normalized using normalized_copy().
kwargsdict, optional: Dimensions of the selected var over which to index. Any remaining dimensions will be flattened by mean

calliope.time.masks.extreme_diff(data, tech0, tech1, var='resource', how='max', length='1D', n=1, groupby_length=None, padding=None, normalize=True, **kwargs)[source]¶

Returns timesteps for period of length where the diffence in extreme value for var between technologies tech0 and tech1 is either a minimum or a maximum.

Parameters

dataxarray.Dataset
tech0str: First technology for which we find the extreme of var
tech1str: Second technology for which we find the extreme of var
varstr, optional: default ‘resource’
howstr, optional: ‘max’ (default) or ‘min’.
lengthstr, optional: Defaults to ‘1D’.
nint, optional: Number of periods of length to look for, default is 1.
groupby_lengthstr, optional: Group time series and return n periods of length for each group.
paddingstr, optional: Either Pandas frequency (e.g. ‘1D’) or ‘calendar_week’. If Pandas frequency, symmetric padding is undertaken, either side of length If ‘calendar_week’, padding is fit to the calendar week in which the extreme day(s) are found.
normalizebool, optional: If True (default), data is normalized using normalized_copy().
kwargsdict, optional: Dimensions of the selected var over which to index. Any remaining dimensions will be flattened by mean

calliope.time.funcs.resample(data, timesteps, resolution)[source]¶

Function to resample timeseries data from the input resolution (e.g. 1H), to the given resolution (e.g. 2H)

Parameters

dataxarray.Dataset: calliope model data, containing only timeseries data variables
timestepsstr or list; optional: If given, apply resampling to a subset of the timeseries data
resolutionstr: time resolution of the output data, given in Pandas time frequency format. E.g. 1H = 1 hour, 1W = 1 week, 1M = 1 month, 1T = 1 minute. Multiples allowed.

Analyzing models¶

class calliope.postprocess.plotting.plotting.ModelPlotMethods(model)[source]¶

timeseries(**kwargs)[source]¶

Parameters

arraystr or list; default = ‘all’

options: ‘all’, ‘results’, ‘inputs’, the name/list of any energy carrier(s) (e.g. ‘power’), the name/list of any input/output DataArray(s).

User can specify ‘all’ for all input/results timeseries plots, ‘inputs’ for just input timeseries, ‘results’ for just results timeseries, or the name of any data array to plot (in either inputs or results). In all but the last case, arrays can be picked from dropdown in visualisation. In the last case, output can be saved to SVG and a rangeslider can be used.

timesteps_zoomint, optional

Number of timesteps to show initially on the x-axis (if not given, the full time range is shown by default).

rangesliderbool, optional

If True, displays a range slider underneath the plot for navigating (helpful primarily in interactive use).

subsetdict, optional

Dictionary by which data is subset (uses xarray loc indexing). Keys any of [‘timeseries’, ‘locs’, ‘techs’, ‘carriers’, ‘costs’].

sum_dimsstr, optional

List of dimension names to sum plot variable over.

squeezebool, optional

Whether to squeeze out dimensions of length = 1.

html_onlybool, optional; default = False

Returns a html string for embedding the plot in a webpage

to_fileFalse or str, optional; default = False

Will save plot to file with the given name and extension. to_file=’plot.svg’ to save to SVG, to_file=’plot.png’ for a static PNG image. Allowed file extensions are: [‘png’, ‘jpeg’, ‘svg’, ‘webp’].

layout_updatesdict, optional

The given dict will be merged with the Plotly layout dict generated by the Calliope plotting function, overwriting keys that already exist.

plotly_kwarg_updatesdict, optional

The given dict will be merged with the Plotly plot function’s keyword arguments generated by the Calliope plotting function, overwriting keys that already exist.

capacity(**kwargs)[source]¶

Parameters

arraystr or list; default = ‘all’: options: ‘all’, ‘results’, ‘inputs’, the name/list of any energy capacity DataArray(s) from inputs/results. User can specify ‘all’ for all input/results capacities, ‘inputs’ for just input capacities, ‘results’ for just results capacities, or the name(s) of any data array(s) to plot (in either inputs or results). In all but the last case, arrays can be picked from dropdown in visualisation. In the last case, output can be saved to SVG.
orientstr, optional: ‘h’ for horizontal or ‘v’ for vertical barchart
subsetdict, optional: Dictionary by which data is selected (using xarray indexing loc[]). Keys any of [‘timeseries’, ‘locs’, ‘techs’, ‘carriers’, ‘costs’]).
sum_dimsstr, optional: List of dimension names to sum plot variable over.
squeezebool, optional: Whether to squeeze out dimensions containing only single values.
html_onlybool, optional; default = False: Returns a html string for embedding the plot in a webpage
to_fileFalse or str, optional; default = False: Will save plot to file with the given name and extension. to_file=’plot.svg’ to save to SVG, to_file=’plot.png’ for a static PNG image. Allowed file extensions are: [‘png’, ‘jpeg’, ‘svg’, ‘webp’].
layout_updatesdict, optional: The given dict will be merged with the Plotly layout dict generated by the Calliope plotting function, overwriting keys that already exist.
plotly_kwarg_updatesdict, optional: The given dict will be merged with the Plotly plot function’s keyword arguments generated by the Calliope plotting function, overwriting keys that already exist.

transmission(**kwargs)[source]¶

Parameters

mapbox_access_tokenstr, optional: If given and a valid Mapbox API key, a Mapbox map is drawn for lat-lon coordinates, else (by default), a more simple built-in map.
html_onlybool, optional; default = False: Returns a html string for embedding the plot in a webpage
to_fileFalse or str, optional; default = False: Will save plot to file with the given name and extension. to_file=’plot.svg’ to save to SVG, to_file=’plot.png’ for a static PNG image. Allowed file extensions are: [‘png’, ‘jpeg’, ‘svg’, ‘webp’].
layout_updatesdict, optional: The given dict will be merged with the Plotly layout dict generated by the Calliope plotting function, overwriting keys that already exist.
plotly_kwarg_updatesdict, optional: The given dict will be merged with the Plotly plot function’s keyword arguments generated by the Calliope plotting function, overwriting keys that already exist.

summary(**kwargs)[source]¶

Plot a summary containing timeseries, installed capacities, and transmission plots. Returns a HTML string by default, returns None if to_file given (and saves the HTML string to file).

Parameters

to_filestr, optional: Path to output file to save HTML to.
mapbox_access_tokenstr, optional: (passed to plot_transmission) If given and a valid Mapbox API key, a Mapbox map is drawn for lat-lon coordinates, else (by default), a more simple built-in map.

Pyomo backend interface¶

class calliope.backend.pyomo.interface.BackendInterfaceMethods(model)[source]¶

access_model_inputs()[source]¶: If the user wishes to inspect the parameter values used as inputs in the backend model, they can access a new Dataset of all the backend model inputs, including defaults applied where the user did not specify anything for a loc::tech

update_param(*args, **kwargs)[source]¶

A Pyomo Param value can be updated without the user directly accessing the backend model.

Parameters

paramstr: Name of the parameter to update
update_dictdict: keys are parameter indeces (either strings or tuples of strings, depending on whether there is one or more than one dimension). Values are the new values being assigned to the parameter at the given indeces.

Returns

Value(s) will be updated in-place, requiring the user to run the model again to
see the effect on results.

activate_constraint(*args, **kwargs)[source]¶

Takes a constraint or objective name, finds it in the backend model and sets its status to either active or deactive.

Parameters

constraintstr: Name of the constraint/objective to activate/deactivate Built-in constraints include ‘_constraint’
active: bool, default=True: status to set the constraint/objective

rerun(*args, **kwargs)[source]¶

Rerun the Pyomo backend, perhaps after updating a parameter value, (de)activating a constraint/objective or updating run options in the model model_data object (e.g. run.solver).

Returns

new_modelcalliope.Model: New calliope model, including both inputs and results, but no backend interface.

Utility classes: AttrDict, Exceptions, Logging¶

class calliope.core.attrdict.AttrDict(source_dict=None)[source]¶

A subclass of dict with key access by attributes:

d = AttrDict({'a': 1, 'b': 2})
d.a == 1  # True

Includes a range of additional methods to read and write to YAML, and to deal with nested keys.

copy()[source]¶: Override copy method so that it returns an AttrDict

init_from_dict(d)[source]¶

Initialize a new AttrDict from the given dict. Handles any nested dicts by turning them into AttrDicts too:

d = AttrDict({'a': 1, 'b': {'x': 1, 'y': 2}})
d.b.x == 1  # True

classmethod from_yaml(f, resolve_imports=True)[source]¶

Returns an AttrDict initialized from the given path or file object f, which must point to a YAML file. The path can be a string or a pathlib.Path.

Parameters

fstr or pathlib.Path
resolve_importsbool or str, optional: If resolve_imports is True, top-level import: statements are resolved recursively. If resolve_imports is False, top-level ``import: statements are treated like any other key and not further processed. If resolve_imports is a string, such as foobar, import statements underneath that key are resolved, i.e. foobar.import:. When resolving import statements, anything defined locally overrides definitions in the imported file.

classmethod from_yaml_string(string, resolve_imports=True)[source]¶: Returns an AttrDict initialized from the given string, which must be valid YAML.

set_key(key, value)[source]¶

Set the given key to the given value. Handles nested keys, e.g.:

d = AttrDict()
d.set_key('foo.bar', 1)
d.foo.bar == 1  # True

get_key(key, default=<calliope.core.attrdict.__Missing object>)[source]¶

Looks up the given key. Like set_key(), deals with nested keys.

If default is anything but _MISSING, the given default is returned if the key does not exist.

del_key(key)[source]¶: Delete the given key. Properly deals with nested keys.

as_dict(flat=False)[source]¶: Return the AttrDict as a pure dict (with nested dicts if necessary).

to_yaml(path=None)[source]¶: Saves the AttrDict to the path as a YAML file, or returns a YAML string if path is None.

keys_nested(subkeys_as='list')[source]¶

Returns all keys in the AttrDict, sorted, including the keys of nested subdicts (which may be either regular dicts or AttrDicts).

If subkeys_as='list' (default), then a list of all keys is returned, in the form ['a', 'b.b1', 'b.b2'].

If subkeys_as='dict', a list containing keys and dicts of subkeys is returned, in the form ['a', {'b': ['b1', 'b2']}].

union(other, allow_override=False, allow_replacement=False, allow_subdict_override_with_none=False)[source]¶

Merges the AttrDict in-place with the passed other AttrDict. Keys in other take precedence, and nested keys are properly handled.

If allow_override is False, a KeyError is raised if other tries to redefine an already defined key.

If allow_replacement, allow “_REPLACE_” key to replace an entire sub-dict.

If allow_subdict_override_with_none is False (default), a key of the form this.that: None in other will be ignored if subdicts exist in self like this.that.foo: 1, rather than wiping them.

exception calliope.exceptions.ModelError[source]¶: ModelErrors should stop execution of the model, e.g. due to a problem with the model formulation or input data.

exception calliope.exceptions.BackendError[source]¶

exception calliope.exceptions.ModelWarning[source]¶: ModelWarnings should be raised for possible model errors, but where execution can still continue.

exception calliope.exceptions.BackendWarning[source]¶

calliope.exceptions.print_warnings_and_raise_errors(warnings=None, errors=None)[source]¶

Print warnings and raise ModelError from errors.

Parameters

warningslist, optional
errorslist, optional

calliope.core.util.logging.set_log_verbosity(verbosity='info', include_solver_output=True, capture_warnings=True)[source]¶

Set the verbosity of logging and setup the root logger to log to console (stdout) with timestamp output formatting.

Parameters

verbositystr, default ‘info’: Logging level to display across all of Calliope. Can be one of ‘debug’, ‘info’, ‘warning’, ‘error’, or ‘critical’.
include_solver_outputbool, default True: If True, the logging level for just the backend model is set to DEBUG, which turns on display of solver output.
capture_warningsbool, default True: If True, also capture all warnings and log them to the WARNING level. This results in more consistent output when running interactively.

Index¶

Release history¶

Release History¶

0.6.6 (2020-10-08)¶

new spores run mode now available, to find Spatially-explicit Practically Optimal REsultS (SPORES)

new New group constraints carrier_con_min, carrier_con_max, carrier_con_equals which restrict the total consumed energy of a subgroup of conversion and/or demand technologies.

new Add ability to pass timeseries as dataframes in calliope.Model instead of only as CSV files.

new Pyomo backend interfaces added to get names of all model objects (get_all_model_attrs) and to attach custom constraints to the backend model (add_constraint).

changed Parameters are assigned a domain in Pyomo based on their dtype in model_data

changed Internal code reorganisation.

changed Updated to Pyomo 5.7, pandas 1.1, and xarray 0.16

fixed One-way transmission technologies can have om costs

fixed Silent override of nested dicts when parsing YAML strings

0.6.5 (2020-01-14)¶

new New group constraints energy_cap_equals, resource_area_equals, and energy_cap_share_equals to add the equality constraint to existing min/max group constraints.

new New group constraints carrier_prod_min, carrier_prod_max, and carrier_prod_equals which restrict the absolute energy produced by a subgroup of technologies and locations.

new Introduced a storage_discharge_depth constraint, which allows to set a minimum stored-energy level to be preserved by a storage technology.

new New group constraints net_import_share_min, net_import_share_max, and net_import_share_equals which restrict the net imported energy of a certain carrier into subgroups of locations.

changed backwards-incompatible Group constraints with the prefix supply_share are renamed to use the prefix carrier_prod_share. This ensures consistent naming for all group constraints.

changed Allowed ‘energy_cap_min’ for transmission technologies.

changed Minor additions made to troubleshooting and development documentation.

changed backwards-incompatible The backend interface to update a parameter value (Model.backend.update_param()) has been updated to allow multiple values in a parameter to be updated at once, using a dictionary.

changed Allowed om_con cost for demand technologies. This is conceived to allow better representing generic international exports as demand sinks with a given revenue (e.g. the average electricity price on a given bidding zone), not restricted to any particular type of technology.

changed backwards-incompatible model.backend.rerun() returns a calliope Model object instead of an xarray Dataset, allowing a user to access calliope Model methods, such as get_formatted_array.

changed Carrier ratios can be loaded from file, to allow timeseries carrier ratios to be defined, e.g. carrier_ratios.carrier_out_2.heat: file=ratios.csv.

changed Objective function options turned into Pyomo parameters. This allows them to update through the Model.backend.update_param() functionality.

changed All model defaults have been moved to defaults.yaml, removing the need for model.yaml. A default location, link and group constraint have been added to defaults.yaml to validate input model keys.

changed backwards-incompatible Revised internal logging and warning structure. Less critical warnings during model checks are now logged directly to the INFO log level, which is displayed by default in the CLI, and can be enabled interactively by calling calliope.set_log_verbosity() without any options. The calliope.set_log_level function has been renamed to calliope.set_log_verbosity and includes the ability to easily turn on and off the display of solver output.

changed All group constraint values are parameters so they can be updated in the backend model

fixed Operate mode checks cleaned up to warn less frequently and to not be so aggressive at editing a users model to fit the operate mode requirements.

fixed Documentation distinctly renders inline Python, YAML, and shell code snippets.

fixed Tech groups are used to filter technologies to which group constraints can be applied. This ensures that transmission and storage technologies are included in cost and energy capacity group constraints. More comprehensive tests have been added accordingly.

fixed Models saved to NetCDF now include the fully built internal YAML model and debug data so that Model.save_commented_model_yaml() is available after loading a NetCDF model from disk

fixed Fix an issue preventing the deprecated charge_rate constraint from working in 0.6.4.

fixed Fix an issue that prevented 0.6.4 from loading NetCDF models saved with older versions of Calliope. It is still recommended to only load models with the same version of Calliope that they were saved with, as not all functionality will work when mixing versions.

fixed backwards-incompatible Updated to require pandas 0.25, xarray 0.14, and scikit-learn 0.22, and verified Python 3.8 compatibility. Because of a bugfix in scikit-learn 0.22, models using k-means clustering with a specified random seed may return different clusters from Calliope 0.6.5 on.

0.6.4 (2019-05-27)¶

new New model-wide constraint that can be applied to all, or a subset of, locations and technologies in a model, covering:

demand_share, supply_share, demand_share_per_timestep, supply_share_per_timestep, each of which can specify min, max, and equals, as well as energy_cap_share_min and energy_cap_share_max. These supersede the group_share constraints, which are now deprecated and will be removed in v0.7.0.
demand_share_per_timestep_decision, allowing the model to make decisions on the per-timestep shares of carrier demand met from different technologies.
cost_max, cost_min, cost_equals, cost_var_max, cost_var_min, cost_var_equals, cost_investment_max, cost_investment_min, cost_investment_equals, which allow a user to constrain costs, including those not used in the objective.
energy_cap_min, energy_cap_max, resource_area_min, resource_area_max which allow to constrain installed capacities of groups of technologies in specific locations.

new asynchronous_prod_con parameter added to the constraints, to allow a user to fix a storage or transmission technology to only be able to produce or consume energy in a given timestep. This ensures that unphysical dissipation of energy cannot occur in these technologies, by activating a binary variable (prod_con_switch) in the backend.

new Multi-objective optimisation problems can be defined by linear scalarisation of cost classes, using run.objective_options.cost_class (e.g. {‘monetary’: 1, ‘emissions’: 0.1}, which models an emissions price of 0.1 units of currency per unit of emissions)

new Storage capacity can be tied to energy capacity with a new energy_cap_per_storage_cap_equals constraint.

new The ratio of energy capacity and storage capacity can be constrained with a new energy_cap_per_storage_cap_min constraint.

new Easier way to save an LP file with a --save_lp command-line option and a Model.to_lp method

new Documentation has a new layout, better search, and is restructured with various content additions, such as a section on troubleshooting.

new Documentation for developers has been improved to include an overview of the internal package structure and a guide to contributing code via a pull request.

changed backwards-incompatible Scenarios in YAML files defined as list of override names, not comma-separated strings: fusion_scenario: cold_fusion,high_cost becomes fusion_scenario: [‘cold_fusion’, ‘high_cost’]. No change to the command-line interface.

changed charge_rate has been renamed to energy_cap_per_storage_cap_max. charge_rate will be removed in Calliope 0.7.0.

changed Default value of resource_area_max now is inf instead of 0, deactivating the constraint by default.

changed Constraint files are auto-loaded in the pyomo backend and applied in the order set by ‘ORDER’ variables given in each constraint file (such that those constraints which depend on pyomo expressions existing are built after the expressions are built).

changed Error on defining a technology in both directions of the same link.

changed Any inexistent locations and / or technologies defined in model-wide (group) constraints will be caught and filtered out, raising a warning of their existence in the process.

changed Error on required column not existing in CSV is more explicit.

changed backwards-incompatible Exit code for infeasible problems now is 1 (no success). This is a breaking change when relying on the exit code.

changed get_formatted_array improved in both speed and memory consumption.

changed model and run configurations are now available as attributes of the Model object, specifically as editable dictionaries which automatically update a YAML string in the model_data xarray dataset attribute list (i.e. the information is stored when sending to the solver backend and when saving to and loading from NetCDF file)

changed All tests and example models have been updated to solve with Coin-CBC, instead of GLPK. Documentation has been updated to reflect this, and aid in installing CBC (which is not simple for Windows users).

changed Additional and improved pre-processing checks and errors for common model mistakes.

fixed Total levelised cost of energy considers all costs, but energy generation only from supply, supply_plus, conversion, and conversion_plus.

fixed If a space is left between two locations in a link (i.e. A, B instead of A,B), the space is stripped, instead of leading to the expectation of a location existing with the name ` B`.

fixed Timeseries efficiencies can be included in operate mode without failing on preprocessing checks.

fixed Name of data variables is retained when accessed through model.get_formatted_array()

fixed Systemwide constraints work in models without transmission systems.

fixed Updated documentation on amendments of abstract base technology groups.

fixed Models without time series data fail gracefully.

fixed Unknown technology parameters are detected and the user is warned.

fixed Loc::techs with empty cost classes (i.e. value == None) are handled by a warning and cost class deletion, instead of messy failure.

0.6.3 (2018-10-03)¶

new Addition of flows plotting function. This shows production and how much they exchange with other locations. It also provides a slider in order to see flows’ evolution through time.

new calliope generate_runs in the command line interface can now produce scripts for remote clusters which require SLURM-based submission (sbatch...).

new backwards-incompatible Addition of scenarios, which complement and expand the existing overrides functionality. overrides becomes a top-level key in model configuration, instead of a separate file. The calliope run command has a new --scenario option which replaces –override_file, while calliope generate_runs has a new --scenarios option which replaces –override_file and takes a semicolon-separated list of scenario names or of group1,group2 combinations. To convert existing overrides to the new approach, simply group them under a top-level overrides key and import your existing overrides file from the main model configuration file with import: ['your_overrides_file.yaml'].

new Addition of calliope generate_scenarios command to allow automating the construction of scenarios which consist of many combinations of overrides.

new Added --override_dict option to calliope run and calliope generate_runs commands

new Added solver performance comparison in the docs. CPLEX & Gurobi are, as expected, the best options. If going open-source & free, CBC is much quicker than GLPK!

new Calliope is tested and confirmed to run on Python 3.7

changed resource_unit - available to supply, supply_plus, and demand technologies - can now be defined as ‘energy_per_area’, ‘energy’, or ‘energy_per_cap’. ‘power’ has been removed. If ‘energy_per_area’ then available resource is the resource (CSV or static value) * resource_area, if ‘energy_per_cap’ it is resource * energy_cap. Default is ‘energy’, i.e. resource = available_resource.

changed Updated to xarray v0.10.8, including updates to timestep aggregation and NetCDF I/O to handle updated xarray functionality.

changed Removed calliope convert command. If you need to convert a 0.5.x model, first use calliope convert in Calliope 0.6.2 and then upgrade to 0.6.3 or higher.

changed Removed comment persistence in AttrDict and the associated API in order to improve compatibility with newer versions of ruamel.yaml

fixed Operate mode is more robust, by being explicit about timestep and loc_tech indexing in storage_initial preparation and resource_cap checks, respectively, instead of assuming an order.

fixed When setting ensure_feasibility, the resulting unmet_demand variable can also be negative, accounting for possible infeasibility when there is unused supply, once all demand has been met (assuming no load shedding abilities). This is particularly pertinent when the force_resource constraint is in place.

fixed When applying systemwide constraints to transmission technologies, they are no longer silently ignored. Instead, the constraint value is doubled (to account for the constant existence of a pair of technologies to describe one link) and applied to the relevant transmission techs.

fixed Permit groups in override files to specify imports of other YAML files

fixed If only interest_rate is defined within a cost class of a technology, the entire cost class is correctly removed after deleting the interest_rate key. This ensures an empty cost key doesn’t break things later on. Fixes issue #113.

fixed If time clustering with ‘storage_inter_cluster’ = True, but no storage technologies, the model doesn’t break. Fixes issue #142.

0.6.2 (2018-06-04)¶

new units_max_systemwide and units_equals_systemwide can be applied to an integer/binary constrained technology (capacity limited by units not energy_cap, or has an associated purchase (binary) cost). Constraint works similarly to existing energy_cap_max_systemwide, limiting the number of units of a technology that can be purchased across all locations in the model.

new backwards-incompatible primary_carrier for conversion_plus techs is now split into primary_carrier_in and primary_carrier_out. Previously, it only accounted for output costs, by separating it, om_con and om_prod are correctly accounted for. These are required conversion_plus essentials if there’s more than one input and output carrier, respectively.

new Storage can be set to cyclic using run.cyclic_storage. The last timestep in the series will then be used as the ‘previous day’ conditions for the first timestep in the series. This also applies to storage_inter_cluster, if clustering. Defaults to False, with intention of defaulting to True in 0.6.3.

new On clustering timeseries into representative days, an additional set of decision variables and constraints is generated. This addition allows for tracking stored energy between clusters, by considering storage between every datestep of the original (unclustered) timeseries as well as storage variation within a cluster.

new CLI now uses the IPython debugger rather than built-in pdb, which provides highlighting, tab completion, and other UI improvements

new AttrDict now persists comments when reading from and writing to YAML files, and gains an API to view, add and remove comments on keys

fixed Fix CLI error when running a model without transmission technologies

fixed Allow plotting for inputs-only models, single location models, and models without location coordinates

fixed Fixed negative om_con costs in conversion and conversion_plus technologies

0.6.1 (2018-05-04)¶

new Addition of user-defined datestep clustering, accessed by clustering_func:file=filename.csv:column in time aggregation config

new Added layout_updates and plotly_kwarg_updates parameters to plotting functions to override the generated Plotly configuration and layout

changed Cost class and sense (maximize/minimize) for objective function may now be specified in run configuration (default remains monetary cost minimization)

changed Cleaned up and documented Model.save_commented_model_yaml() method

fixed Fixed error when calling --save_plots in CLI

fixed Minor improvements to warnings

fixed Pure dicts can be used to create a Model instance

fixed AttrDict.union failed on all-empty nested dicts

0.6.0 (2018-04-20)¶

Version 0.6.0 is an almost complete rewrite of most of Calliope’s internals. See New in v0.6.0 for a more detailed description of the many changes.

Major changes¶

changed backwards-incompatible Substantial changes to model configuration format, including more verbose names for most settings, and removal of run configuration files.

new backwards-incompatible Complete rewrite of Pyomo backend, including new various new and improved functionality to interact with a built model (see New in v0.6.0).

new Addition of a calliope convert CLI tool to convert 0.5.x models to 0.6.0.

new Experimental ability to link to non-Pyomo backends.

new New constraints: resource_min_use constraint for supply and supply_plus techs.

changed backwards-incompatible Removal of settings and constraints includes subset_x, subset_y, s_time, r2, r_scale_to_peak, weight.

changed backwards-incompatible system_margin constraint replaced with reserve_margin constraint.

changed backwards-incompatible Removed the ability to load additional custom constraints or objectives.

0.5.5 (2018-02-28)¶

fixed Allow r_area to be non-zero if either of e_cap.max or e_cap.equals is set, not just e_cap.max.
fixed Ensure static parameters in resampled timeseries are caught in constraint generation
fixed Fix time masking when set_t.csv contains sub-hourly resolutions

0.5.4 (2017-11-10)¶

Major changes¶

fixed r_area_per_e_cap and r_cap_equals_e_cap constraints have been separated from r_area and r_cap constraints to ensure that user specified r_area.max and r_cap.max constraints are observed.
changed technologies and location subsets are now communicated with the solver as a combined location:technology subset, to reduce the problem size, by ignoring technologies at locations in which they have not been allowed. This has shown drastic improvements in Pyomo preprocessing time and memory consumption for certain models.

Other changes¶

fixed Allow plotting carrier production using calliope.analysis.plot_carrier_production if that carrier does not have an associated demand technology (previously would raise an exception).
fixed Define time clustering method (sum/mean) for more constraints that can be time varying. Previously only included r and e_eff.
changed storage technologies default s_cap.max to inf, not 0 and are automatically included in the loc_tech_store subset. This ensures relevant constraints are not ignored by storage technologies.
changed Some values in the urban scale MILP example were updated to provide results that would show the functionality more clearly
changed technologies have set colours in the urban scale example model, as random colours were often hideous.
changed ruamel.yaml, not ruamel_yaml, is now used for parsing YAML files.
fixed e_cap constraints for unmet_demand technologies are ignored in operational mode. Capacities are fixed for all other technologies, which previously raised an exception, as a fixed infinite capacity is not physically allowable.
fixed stack_weights were strings rather than numeric datatypes on reading NetCDF solution files.

0.5.3 (2017-08-22)¶

Major changes¶

new (BETA) Mixed integer linear programming (MILP) capabilities, when using purchase cost and/or units.max/min/equals constraints. Integer/Binary decision variables will be applied to the relevant technology-location sets, avoiding unnecessary complexity by describing all technologies with these decision variables.

Other changes¶

changed YAML parser is now ruamel_yaml, not pyyaml. This allows scientific notation of numbers in YAML files (#57)
fixed Description of PV technology in urban scale example model now more realistic
fixed Optional ramping constraint no longer uses backward-incompatible definitions (#55)
fixed One-way transmission no longer forces unidirectionality in the wrong direction
fixed Edge case timeseries resource combinations, where infinite resource sneaks into an incompatible constraint, are now flagged with a warning and ignored in that constraint (#61)
fixed e_cap.equals: 0 sets a technology to a capacity of zero, instead of ignoring the constraint (#63)
fixed depreciation_getter now changes with location overrides, instead of just checking the technology level constraints (#64)
fixed Time clustering now functions in models with time-varying costs (#66)
changed Solution now includes time-varying costs (costs_variable)
fixed Saving to NetCDF does not affect in-memory solution (#62)

0.5.2 (2017-06-16)¶

changed Calliope now uses Python 3.6 by default. From Calliope 0.6.0 on, Python 3.6 will likely become the minimum required version.
fixed Fixed a bug in distance calculation if both lat/lon metadata and distances for links were specified.
fixed Fixed a bug in storage constraints when both s_cap and e_cap were constrained but no c_rate was given.
fixed Fixed a bug in the system margin constraint.

0.5.1 (2017-06-14)¶

new backwards-incompatible Better coordinate definitions in metadata. Location coordinates are now specified by a dictionary with either lat/lon (for geographic coordinates) or x/y (for generic Cartesian coordinates), e.g. {lat: 40, lon: -2} or {x: 0, y: 1}. For geographic coordinates, the map_boundary definition for plotting was also updated in accordance. See the built-in example models for details.

new Unidirectional transmission links are now possible. See the documentation on transmission links.

Other changes¶

fixed Missing urban-scale example model files are now included in the distribution
fixed Edge cases in conversion_plus constraints addressed
changed Documentation improvements

0.5.0 (2017-05-04)¶

Major changes¶

new Urban-scale example model, major revisions to the documentation to accommodate it, and a new calliope.examples module to hold multiple example models. In addition, the calliope new command now accepts a --template option to select a template other than the default national-scale example model, e.g.: calliope new my_urban_model --template=UrbanScale.

new Allow technologies to generate revenue (by specifying negative costs)

new Allow technologies to export their carrier directly to outside the system boundary

new Allow storage & supply_plus technologies to define a charge rate (c_rate), linking storage capacity (s_cap) with charge/discharge capacity (e_cap) by s_cap * c_rate => e_cap. As such, either s_cap.max & c_rate or e_cap.max & c_rate can be defined for a technology. The smallest of s_cap.max * c_rate and e_cap.max will be taken if all three are defined.

changed backwards-incompatible Revised technology definitions and internal definition of sets and subsets, in particular subsets of various technology types. Supply technologies are now split into two types: supply and supply_plus. Most of the more advanced functionality of the original supply technology is now contained in supply_plus, making it necessary to update model definitions accordingly. In addition to the existing conversion technology type, a new more complex conversion_plus was added.

Other changes¶

changed backwards-incompatible Creating a Model() with no arguments now raises a ModelError rather than returning an instance of the built-in national-scale example model. Use the new calliope.examples module to access example models.
changed Improvements to the national-scale example model and its tutorial notebook
changed Removed SolutionModel class
fixed Other minor fixes

0.4.1 (2017-01-12)¶

new Allow profiling with the --profile and --profile_filename command-line options
new Permit setting random seed with random_seed in the run configuration
changed Updated installation documentation using conda-forge package
fixed Other minor fixes

0.4.0 (2016-12-09)¶

Major changes¶

new Added new methods to deal with time resolution: clustering, resampling, and heuristic timestep selection

changed backwards-incompatible Major change to solution data structure. Model solution is now returned as a single xarray DataSet instead of multiple pandas DataFrames and Panels. Instead of as a generic HDF5 file, complete solutions can be saved as a NetCDF4 file via xarray’s NetCDF functionality.

While the recommended way to save and process model results is by NetCDF4, CSV saving functionality has now been upgraded for more flexibility. Each variable is saved as a separate CSV file with a single value column and as many index columns as required.

changed backwards-incompatible Model data structures simplified and based on xarray

Other changes¶

new Functionality to post-process parallel runs into aggregated NetCDF files in calliope.read
changed Pandas 0.18/0.19 compatibility
changed 1.11 is now the minimum required numpy version. This version makes datetime64 tz-naive by default, thus preventing some odd behavior when displaying time series.
changed Improved logging, status messages, and error reporting
fixed Other minor fixes

0.3.7 (2016-03-10)¶

Major changes¶

changed Per-location configuration overrides improved. All technology constraints can now be set on a per-location basis, as can costs. This applies to the following settings:

techname.x_map
techname.constraints.*
techname.constraints_per_distance.*
techname.costs.*

The following settings cannot be overridden on a per-location basis:

Any other options directly under techname, such as techname.parent or techname.carrier
techname.costs_per_distance.*
techname.depreciation.*

Other changes¶

fixed Improved installation instructions
fixed Pyomo 4.2 API compatibility
fixed Other minor fixes

0.3.6 (2015-09-23)¶

fixed Version 0.3.5 changes were not reflected in tutorial

0.3.5 (2015-09-18)¶

Major changes¶

new New constraint to constrain total (model-wide) installed capacity of a technology (e_cap.total_max), in addition to its per-node capacity (e_cap.max)

changed Removed the level option for locations. Level is now implicitly derived from the nested structure given by the within settings. Locations that define no or an empty within are implicitly at the topmost (0) level.

changed backwards-incompatible Revised configuration of capacity constraints: e_cap_max becomes e_cap.max, addition of e_cap.min and e_cap.equals (analogous for r_cap, s_cap, rb_cap, r_area). The e_cap.equals constraint supersedes e_cap_max_force (analogous for the other constraints). No backwards-compatibility is retained, models must change all constraints to the new formulation. See Per-tech constraints for a complete list of all available constraints. Some additional constraints have name changes:

e_cap_max_scale becomes e_cap_scale
rb_cap_follows becomes rb_cap_follow, and addition of rb_cap_follow_mode
s_time_max becomes s_time.max

changed backwards-incompatible All optional constraints are now grouped together, under constraints.optional:

constraints.group_fraction.group_fraction becomes constraints.optional.group_fraction
constraints.ramping.ramping_rate becomes constraints.optional.ramping_rate

Other changes¶

new analysis.map_results function to extract solution details from multiple parallel runs
new Various other additions to analysis functionality, particularly in the analysis_utils module
new analysis.get_levelized_cost to get technology and location specific costs
new Allow dynamically loading time mask functions
changed Improved summary table in the model solution: now shows only aggregate information for transmission technologies, also added missing s_cap column and technology type
fixed Bug causing some total levelized transmission costs to be infinite instead of zero
fixed Bug causing some CSV solution files to be empty

0.3.4 (2015-04-27)¶

fixed Bug in construction and fixed O&M cost calculations in operational mode

0.3.3 (2015-04-03)¶

Major changes¶

changed In preparation for future enhancements, the ordering of location levels is flipped. The top-level locations at which balancing takes place is now level 0, and may contain level 1 locations. This is a backwards-incompatible change.

changed backwards-incompatible Refactored time resolution adjustment functionality. Can now give a list of masks in the run configuration which will all be applied, via time.masks, with a base resolution via time.resolution (or instead, as before, load a resolution series from file via time.file). Renamed the time_functions submodule to time_masks.

Other changes¶

new Models and runs can have a name
changed More verbose calliope run
changed Analysis tools restructured
changed Renamed debug.keepfiles setting to debug.keep_temp_files and better documented debug configuration

0.3.2 (2015-02-13)¶

new Run setting model_override allows specifying the path to a YAML file with overrides for the model configuration, applied at model initialization (path is given relative to the run configuration file used). This is in addition to the existing override setting, and is applied first (so override can override model_override).
new Run settings output.save_constraints and output.save_constraints_options
new Run setting parallel.post_run
changed Solution column names more in line with model component names
changed Can specify more than one output format as a list, e.g. output.format: ['csv', 'hdf']
changed Run setting parallel.additional_lines renamed to parallel.pre_run
changed Better error messages and CLI error handling
fixed Bug on saving YAML files with numpy dtypes fixed
Other minor improvements and fixes

0.3.1 (2015-01-06)¶

Fixes to time_functions
Other minor improvements and fixes

0.3.0 (2014-12-12)¶

Python 3 and Pyomo 4 are now minimum requirements
Significantly improved documentation
Improved model solution management by saving to HDF5 instead of CSV
Calculate shares of technologies, including the ability to define groups for the purpose of computing shares
Improved operational mode
Simplified time_tools
Improved output plotting, including dispatch, transmission flows, and installed capacities, and added model configuration to support these plots
r can be specified as power or energy
Improved solution speed
Better error messages and basic logging
Better sanity checking and error messages for common mistakes
Basic distance-dependent constraints (only implemented for e_loss and cost of e_cap for now)
Other improvements and fixes

0.2.0 (2014-03-18)¶

Added cost classes with a new set k
Added energy carriers with a new set c
Added conversion technologies
Speed improvements and simplifications
Ability to arbitrarily nest model configuration files with import statements
Added additional constraints
Improved configuration handling
Ability to define timestep options in run configuration
Cleared up terminology (nodes vs locations)
Improved TimeSummarizer masking and added new masks
Removed technology classes
Improved operational mode with results output matching planning mode and dynamic updating of parameters in model instance
Working parallel_tools
Improved documentation
Apache 2.0 licensed
Other improvements and fixes

0.1.0 (2013-12-10)¶

Some semblance of documentation
Usable built-in example model
Improved and working TimeSummarizer
More flexible masking for TimeSummarizer
Ability to add additional constraints without editing core source code
Some basic test coverage
Working parallel run configuration system

Release history

License¶

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Calliope: a multi-scale energy systems modelling framework¶

User guide¶

Introduction¶

Energy system models¶

Rationale¶

Acknowledgments¶

License¶

References¶

Citing Calliope in academic literature¶

Download and installation¶

Requirements¶

Recommended installation method¶

Updating an existing installation¶

Solvers¶

CBC¶

GLPK¶

Gurobi¶

CPLEX¶

Python module requirements¶

Building a model¶

Terminology¶

Files that define a model¶

Model configuration (model)¶

Technologies (techs)¶

Allowing for unmet demand¶

Time series data¶

Reading in CSV files¶

Reading in timeseries from pandas dataframes¶

Locations and links (locations, links)¶

Run configuration (run)¶

Scenarios and overrides¶

Running a model¶

Running with the command-line tool¶

Applying a scenario or override¶

Running interactively with Python¶

Scenarios and overrides¶

Tracking progress¶

Generating scripts for many model runs¶

Improving solution times¶

Debugging failing runs¶

Analysing a model¶

Accessing model data and results¶

Visualising results¶

Plotting time series¶

Plotting capacities¶

Plotting transmission¶

Plotting flows¶

Summary plots¶

Saving publication-quality SVG figures¶

Reading solutions¶

Tutorials¶

Tutorial 1: national scale¶

Supply-side technologies¶

Storage technologies¶

Other technologies¶

Locations¶

Running the model¶

Tutorial 2: urban scale¶

Supply technologies¶

Conversion technologies¶

Demand technologies¶

Transmission technologies¶

Locations¶

Revenue by export¶

Running the model¶

Tutorial 3: Mixed Integer Linear Programming¶

Units¶

Purchase cost¶

Asynchronous energy production/consumption¶

Running the model¶

Advanced constraints¶

The supply_plus tech¶

The conversion_plus tech¶

Combined heat and power¶

Air source heat pump¶

Combined cooling, heat and power (CCHP)¶

Advanced gas turbine¶

Complex fictional technology¶

Resource area constraints¶

Group constraints¶

Model configuration (`model`)¶

Technologies (`techs`)¶

Reading in timeseries from `pandas` dataframes¶

Locations and links (`locations`, `links`)¶

Run configuration (`run`)¶

The `supply_plus` tech¶

The `conversion_plus` tech¶

`demand_share_per_timestep_decision`¶

The `group_share` constraint (deprecated)¶

Using `tech_groups` to group configuration¶