Calliope: a multi-scale energy systems (MUSES) modelling framework¶
v0.6.2 (Release history)
This is the documentation for version 0.6.2. 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).
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’s built-in tools allow interactive exploration of results, as shown in the following example of a model that includes three energy carriers (electricity, heat, and gas):
Calliope is developed in the open on GitHub and contributions are very welcome (see the Development guide). See the list of open issues and planned milestones for an overview of where development is heading, and join us on Gitter to ask questions or discuss code.
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].
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
andpip
, with properly documented and tested code that mostly conforms to PEP8) - Have a free and open-source code base under a permissive license
Acknowledgments¶
Initial development was partially funded by the Grantham Institute at Imperial College London and the European Institute of Innovation & Technology’s Climate-KIC program.
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 2013-2018 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 |
Download and installation¶
Requirements¶
Calliope has been tested on Linux, macOS, and Windows.
Running Calliope requires four things:
- The Python programming language, version 3.6 or higher.
- A number of Python add-on modules (see below for the complete list).
- A solver: Calliope has been tested with GLPK, CBC, 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 Python 3.6 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 python=3.6 calliope
To use Calliope, you need to activate the calliope
environment each time. On Linux and macOS:
$ source activate calliope
On Windows:
$ activate calliope
You are now ready to use Calliope together with the free and open source GLPK solver. Read the next section for more information on 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. CPLEX or Gurobi 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.
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.
CBC¶
CBC is another free and open-source option. CBC can be installed via conda on Linux and macOS by running conda install -c conda-forge coincbc
. Windows binary packages and further documentation are available at the CBC website.
Gurobi¶
Gurobi is commercial but significantly faster than GLPK and CBC, 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).
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:
New in v0.6.0¶
Version 0.6 is backwards incompatible with version 0.5. If you are familiar with how Calliope functions then this page will act as a reference for moving to version 0.6.
Converting a 0.5.x model to 0.6.0¶
Version 0.6 provides a conversion script to models created with Calliope 0.5 into 0.6-compatible models:
calliope convert run.yaml model.yaml output_dir
run.yaml
and model.yaml
are the main run and model configuration files of the 0.5 model to be converted, while output_dir
is a directory into which the converted model will be saved.
The file structure of the input model is preserved (all YAML and CSV input files and their folder structure), but all information from the run configuration file is merged into the main model configuration file.
Time series are handled by the script: the set_t.csv
required in Calliope 0.5 models is removed, with the time step information inserted directly into each of the remaining time series CSV files.
The conversion script has some important limitations:
- Comments in YAML files are lost
- Parallel run configurations are not converted to the new override group configuration format
- The
group_fraction
constraint is not converted, as its formulation has changed substantially in 0.6.0 carrier_ratios
are not converted- Operational mode configuration is not converted
Functionality which is not converted, has been removed in version 0.6.0, or keys not known to the script, are moved into a __disabled
key in the output YAML files, so they remain visible for the user to remove or to manually convert if needed.
The script also adds interest_rate
and lifetime
for each technology, using the implicit default values from Calliope 0.5 (25 years lifetime, a 0.10 interest rate for the monetary cost class and 0 for other cost classes).
See also
0.6.0 model configuration changes¶
This page lists the full contents of calliope/config/conversion_0.6.0.yaml
, which documents the changes in model configuration from 0.5.x to 0.6.0:
# Structure of this file:
# Each major section of model / run configuration has its own top-level key.
# Within each top-level section, changes are given as ``old name: new name``,
# and if the specific setting has been removed, the new name is ``null``,
# possibly with a comment indicating
run_config:
import: null # use import statements in model configuration file instead
subset_y: null
subset_x: null
subset_t: model.subset_time
solver: run.solver
solver_options: run.solver_options
name: null
model: null # since there is no separate run configuration file any more, there is no need to specify a model configuration file
mode: run.mode
output.format: null
output.path: null
debug.keep_temp_files: run.save_logs
debug.symbolic_solver_labels: null
override: null # now achieved with override groups, see override.yaml in examples
parallel: null
random_seed: model.random_seed
debug: null # no longer available
model_config:
opmode: null
startup_time: null
name: model.name
data_path: model.timeseries_data_path
objective: null # custom objective not implemented yet
constraints: null # custom constraints not implemented yet
system_margin: model.reserve_margin
group_fraction: null # major change to structure of this constraint, not automatically converted
metadata: null ### SPECIAL LOGIC: move coordinates over to location configuration
tech_config:
parent: essentials.parent
group: null # Use tech_groups to specify groups
name: essentials.name
stack_weight: null # stack_weight is no longer supported
color: essentials.color
x_map: null # now achieved by directly specifying file=filename.csv:column
carrier: essentials.carrier
primary_carrier: essentials.primary_carrier_out
carrier_in: essentials.carrier_in # If conversion_plus, now a list of carrier names. Ratios between carriers found in constraints.carrier_ratios
carrier_in_2: essentials.carrier_in_2 # If conversion_plus, now a list of carrier names. Ratios between carriers found in constraints.carrier_ratios
carrier_in_3: essentials.carrier_in_3 # If conversion_plus, now a list of carrier names. Ratios between carriers found in constraints.carrier_ratios
carrier_out: essentials.carrier_out # If conversion_plus, now a list of carrier names. Ratios between carriers found in constraints.carrier_ratios
carrier_out_2: essentials.carrier_out_2 # If conversion_plus, now a list of carrier names. Ratios between carriers found in constraints.carrier_ratios
carrier_out_3: essentials.carrier_out_3 # If conversion_plus, now a list of carrier names. Ratios between carriers found in constraints.carrier_ratios
export: constraints.export_carrier
constraints.r: constraints.resource
constraints.force_r: constraints.force_resource
constraints.r_unit: constraints.resource_unit
constraints.r_eff: constraints.resource_eff
constraints.r_area.min: constraints.resource_area_min
constraints.r_area.max: constraints.resource_area_max
constraints.r_area.equals: constraints.resource_area_equals
constraints.r_area_per_e_cap: constraints.resource_area_per_energy_cap
constraints.r_cap.min: constraints.resource_cap_min
constraints.r_cap.max: constraints.resource_cap_max
constraints.r_cap.equals: constraints.resource_cap_equals
constraints.r_cap_equals_e_cap: constraints.resource_cap_equals_energy_cap
constraints.r_scale: constraints.resource_scale
constraints.r_scale_to_peak: constraints.resource_scale_to_peak
constraints.s_init: constraints.storage_initial
constraints.s_cap.min: constraints.storage_cap_min
constraints.s_cap.max: constraints.storage_cap_max
constraints.s_cap.equals: constraints.storage_cap_equals
constraints.s_cap_per_unit: constraints.storage_cap_per_unit
constraints.c_rate: constraints.charge_rate
constraints.s_time.max: null
constraints.use_s_time: null
constraints.s_loss: constraints.storage_loss
constraints.e_prod: constraints.energy_prod
constraints.e_con: constraints.energy_con
constraints.p_eff: constraints.parasitic_eff
constraints.e_eff: constraints.energy_eff
constraints.e_eff_per_distance: constraints.energy_eff_per_distance
constraints.e_cap.min: constraints.energy_cap_min
constraints.e_cap.max: constraints.energy_cap_max
constraints.e_cap.equals: constraints.energy_cap_equals
constraints.e_cap_total.max: constraints.energy_cap_max_systemwide
constraints.e_cap_total.equals: constraints.energy_cap_equals_systemwide
constraints.e_cap_scale: constraints.energy_cap_scale
constraints.e_cap.min_use: constraints.energy_cap_min_use
constraints.e_cap_min_use: constraints.energy_cap_min_use
constraints.e_cap_per_unit: constraints.energy_cap_per_unit
constraints.e_ramping: constraints.energy_ramping
constraints.export_cap: constraints.export_cap
constraints.export_carrier: constraints.export_carrier
constraints.units.min: constraints.units_min
constraints.units.max: constraints.units_max
constraints.units.equals: constraints.units_equals
constraints.r_scale_to_peak: null
constraints.allow_r2: null
constraints.r2_startup_only: null
constraints.r2_eff: null
constraints.r2_cap.min: null
constraints.r2_cap.max: null
constraints.r2_cap.equals: null
constraints.r2_cap_follow: null
constraints.r2_cap_follow_mode: null
constraints.s_time.max: null
weight: null
per_distance: null
tech_constraints_per_distance_config:
e_loss: constraints.energy_eff_per_distance
tech_costs_config:
s_cap: storage_cap
r_area: resource_area
r_cap: resource_cap
r2_cap: null
e_cap: energy_cap
om_frac: om_annual_investment_fraction
om_fixed: om_annual
om_var: om_prod
om_fuel: om_con
export: export
purchase: purchase
tech_costs_per_distance_config:
e_cap: energy_cap_per_distance
location_config:
override: techs
techs: null # List as keys of the subdict 'techs'
within: null
###
depreciation_config: # manually processed in convert.py, listed here for completeness
plant_life: constraints.lifetime
interest: costs.{cost_class}.interest_rate
Removed functionality¶
If you require any of the removed functionality, we recommend you open an issue on GitHub for it to be built into a later revision of 0.6.
Technology constraints¶
- s_time (providing a minimum/maximum/exact time of stored energy available for discharge) no longer exists. This constraint was relatively unpredictable in its effects when providing any combination of s_cap, e_cap, c_rate and time clustering.
- The variable r2 (providing a secondary resource that could be used by a supply/supply_plus technology), along with all its constraints, have been removed. To utilise multiple resource inputs, conversion_plus can be used instead.
- r_scale_to_peak (allowing a user to provide a value for the peak resource to which the entire time series would be scaled accordingly) has been removed. resource_scale (previously r_scale) can still be used for scaling resource values by the given scale factor.
- weight (giving a technology a disproportionate weight in the objective function calculation) has been removed.
Custom objectives¶
The ability to load additional constraints or objectives has been removed. It is still possible to define a custom objective, but to load it, a modeller needs to use a development installation of Calliope and load the function manually.
See also
Updated functionality¶
Verbosity¶
Almost all sets, constraints, costs, and variables have been updated to be more verbose, making models more readable. The primary updates are:
Sets¶
- y -> techs
- x -> locs
- c -> carriers
- k -> costs
Constraints/Costs¶
- e -> energy, e.g. e_cap -> energy_cap
- r -> resource, e.g. r_cap -> resource_cap
- s -> storage, e.g. s_cap -> storage_cap
- c_rate -> charge_rate
- p_eff -> parasitic_eff
Variables¶
- r -> resource_con: an output from the model giving how much of a resource was consumed
- r -> resource: the available resource as an input parameter to the model
- c_prod/c_con -> carrier_prod/carrier_con: The produced/consumed carrier energy in each time storage_cap
Model and run configuration¶
run.yaml no longer exists. Instead, all information needed to run a model is now stored in model.yaml under the headings model and run.
run only contains information about the solver: which one to use and any specific solver options to apply.
model contains all other information: time subsetting, model mode, output format, parallel runs, and time clustering.
To solve a model, point to the model.yaml file, e.g.: calliope run path/to/model.yaml
.
Overrides¶
Overrides are no longer applied within run.yaml (or even model.yaml). Instead, overrides are grouped and placed into a separate YAML file, called for example overrides.yaml.
Each group defines any number of overrides to the technology, location, link, model, or run definitions. One or several such groups can then be applied when solving a model, e.g.:
overrides.yaml:
higher_costs:
techs.ccgt.costs.monetary.energy_cap: 10
locations.region2.techs.csp.costs.monetary.energy_cap: 100
winter:
model.subset_time: ['2005-01-01', '2005-02-28']
Running in the command line:
calliope run model.yaml --override_file=overrides.yaml:higher_costs
calliope run model.yaml --override_file=overrides.yaml:higher_costs,winter
Running interactively:
# only apply the 'higher_costs' override group
model = calliope.Model(
'model.yaml',
override_file='overrides.yaml:higher_costs'
)
# apply both the 'higher_costs' and 'winter' override groups
model2 = calliope.Model(
'model.yaml',
override_file='overrides.yaml:higher_costs,winter'
)
As in version 0.5, overrides can be applied when creating a Model object, via the argument override_dict. A dictionary can then be given:
higher_costs = {
'techs.ccgt.costs.monetary.energy_cap': 10,
'locations.region2.techs.csp.costs.monetary.energy_cap': 100
}
model = calliope.Model('model.yaml', override_dict=higher_costs)
Parallel runs¶
Building on the simplified way to define overrides (see above) and on lessons learnt during the development of Calliope so far, the functionality to generate multiple runs to run either on a single machine or in parallel on a high-performance cluster has been greatly simplified and improved.
Location and technology subsets¶
In model configuration, subset_x and subset_y (subsetting the used locations and technologies, respectively) no longer exist. subset_t, now called subset_time, does still exist.
To remove specific technologies or locations from a model, the new and much more powerful exists
option can be used.
See also
Technology definition¶
A technology is now defined in three parts: essentials, constraints, and costs. All top-level definitions (parent, carrier_out, etc.) are now given under essentials and cannot be defined per-location – they are defined only once for a given technology and apply model-wide. Both constraints and costs remain the same as in 0.5, but with more verbose naming:
Old:
supply_grid_power:
name: 'National grid import'
parent: supply
carrier: power
constraints:
r: inf
e_cap.max: 2000
costs:
monetary:
e_cap: 15
om_fuel: 0.1
New:
supply_grid_power:
essentials:
name: 'National grid import'
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
Carrier ratios and export carriers have also been moved from essentials into constraints:
Old:
chp:
name: 'Combined heat and power'
stack_weight: 100
parent: conversion_plus
export: true
primary_carrier_out: power
carrier_in: gas
carrier_out: power
carrier_out_2:
heat: 0.8
constraints:
e_cap.max: 1500
e_eff: 0.405
costs:
monetary:
e_cap: 750
om_var: 0.004
export: file=export_power.csv
New:
chp:
essentials:
name: 'Combined heat and power'
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
export: file=export_power.csv
Per distance constraints and costs have now been incorporated under the constraints and costs keys, with a ‘_per_distance’ suffix:
Old:
heat_pipes:
name: 'District heat distribution'
parent: transmission
carrier: heat
constraints:
e_cap.max: 2000
constraints_per_distance:
e_loss: 0.025
costs_per_distance:
monetary:
e_cap: 0.3
New:
heat_pipes:
essentials:
name: 'District heat distribution'
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
Interest rates and life times¶
As seen in the above examples, technology lifetime and interest rate must now be defined for each technology, under costs. In version 0.5, technologies not defining these would silently use implicit default values of 0.10 for interest rate and 25 years for life time. Setting these explicitly for any technology which has investment costs (i.e. those which are not om_… or export) is now mandatory; no default values exist any more.
Location definition¶
In version 0.5, location definitions included a list of technologies to permit at that location(s). An additional overrides key permitted per-location changes to model-wide technology definitions.
In 0.6, “overriding” refers only to model-wide overrides applied as described above. At each location, techs simply lists all allowed technologies and any possible changes to model-wide configuration values to apply at this location only, as shown below:
Old:
locations:
region1:
techs: [ccgt, csp]
overrides:
ccgt:
constraints:
energy_cap: 100
New:
locations:
region1:
techs:
ccgt:
constraints:
energy_cap: 100
# Note that csp must be listed to be permitted here,
# even though it has no location-specific configuration.
csp:
Loading time series data from CSV files¶
x_map (mapping a technology name to a column in a CSV file) has been removed. Instead, a user can define the time series file column when defining the file name, separated from the file name by a :. If no column name is provided, Calliope will look for a column with the location name.
Old:
# will look for the column `demand` in the file `demand_heat_r.csv`
locations:
region1:
techs: [demand_power]
overrides:
demand_power:
x_map: demand
constraints:
r: file
New:
# will look for the column `demand` in the file `demand_heat_r.csv`
locations:
region1:
techs:
demand_power:
constraints:
resource: file=demand_heat.csv:demand
Link definition¶
Links have remained much the same as before. However, there is a slightly different structure in defining technologies, bringing the definition of link technologies more in line with the rest of the model configuration format.
Old:
links:
region1,region2:
ac_transmission:
constraints:
e_cap: 1000
New:
links:
region1,region2:
techs:
ac_transmission:
constraints:
energy_cap: 1000
Location metadata¶
Location coordinates, previously given under the metadata key, are now given directly per location:
Old:
metadata:
# metadata given in cartesian coordinates, not lat, lon.
map_boundary:
lower_left:
x: 0
y: 0
upper_right:
x: 1
y: 1
location_coordinates:
region1: {x: 2, y: 7}
region2: {x: 8, y: 7}
New:
locations:
region1:
techs:
ccgt:
csp:
coordinates: {x: 2, y: 7}
region2:
techs:
demand_power:
coordinates: {x: 8, y: 7}
charge_rate
¶
When first introduced, charge rate was used to hard-link energy_cap and storage_cap for a storage/supply_plus technology. This meant that on defining energy_cap_max
and charge_rate
, a user was implicitly defining storage_cap_max
. This hard-link has now been removed, replaced with only one constraint concerning charge rate: \(storage_{cap}(loc::tech) \geq energy_{cap}(loc:tech) \times charge\_rate(loc:tech)\).
See also
Pre-processed data¶
Version 0.5 kept pre-processed data in either a dictionary (static data), pandas dataframe (location data) or an xarray Dataset (timeseries data). To view a value that would be used in optimisation, the user would call model.get_option(). Similarly, to edit a value before running the model, a user could use model.set_option().
Now, all pre-processed data is held in a single unified xarray Dataset: model.inputs.
To view and edit this data before it is sent to the solver, a user need only use standard xarray functionality (see their documentation for more information).
Plotting data¶
Note
Advanced plotting is still under construction. In case our current functionality is insufficient, input and output data can be plotted by the user using their preferred Python plotting tools, or any other language that can access either NetCDF or CSV data.
Plotting functions can now be called directly on the model and now use Plotly instead of 0.5’s matplotlib.
Changes are:
calliope.analysis.plot_capacity(model.solution)
tomodel.plot.capacity()
calliope.analysis.plot_transmission(model.solution, carrier='power', tech='ac_transmission')
tomodel.plot.transmission()
calliope.analysis.plot_carrier_production(model.solution, carrier='power')
tomodel.plot.timeseries()
All available data is plotted, with dropdown menus available for a user to move between plots. A summary of all plotting can also be produced using model.plot.summary()
, a function that is also available via the command line interface.
See also
Operational mode¶
In 0.6, running in operational mode changes capacities from decision variables to parameters, preventing various issues that plagued operational mode in prior versions. Additional sense checks were added to ensure that functionality incompatible with operational mode, such as time clustering, is not accidentally used together with it.
See also
New functionality¶
Debugging & checks¶
A user can now output a data structure of all model input data (the model_run dictionary) after Calliope’s internal pre-processing, into a YAML file, for debugging. This debug file includes comments as to where constraint/cost values have originated (e.g. having been set by a location-specific configuration, or from a model-wide override group).
Similarly, sense checks are undertaken at several points during pre-processing to ensure the model being built is robust. This includes checks for missing data, possibly misspelled constraints, incompatible inputs, and much more.
This functionality will not find all possible user input errors, as this is an impossible task. However, it flags common mistakes, and the format of implementation allows for further checks to be applied in the future.
Pre-processed model¶
Having the pre-processed model available in one xarray Dataset allows a model to be saved to file before being run. Although pre-processing is quick, this allows a user to avoid pre-processing the same file multiple times. Instead, they can read in a previously saved NetCDF file which fully describes the model.
Multiple backends¶
Our primary solver backend is Pyomo. However, we have now extracted all pre-processing stages from the backend, with all data for a model run being stored in a single xarray Dataset. This permits the implementation of additional backends.
One such backend currently in an experimental state is based on JuMP in the Julia programming language. Linking Calliope to Julia is a long-term project, for which we welcome any contributions.
Pyomo warmstart¶
Warmstart functionality can be used in solvers other than GLPK. They allow a previously constructed model to be changed slightly without having to be fully rebuilt. This can speed up re-running a model when you have just a few input parameters you would like to change (the cost of a technology, for instance).
Although the use of warmstart existed in operational mode in version 0.5, now it extends to all possible parameters in all models. This functionality is currently undocumented in Calliope, but the Pyomo documentation provides some information and the Pyomo model built by Calliope can be accessed by model._backend_model.
Backend interface¶
Once the backend model has been built, it can be accessed by a user, via Calliope. Parameters can be checked and changed, constraints can be activated/deactivated and a model can be re run, all without having to build the backend again. User who are familiar with building large models with Pyomo will be aware of the time penalty associated with processing the model in Pyomo. This additional functionality helps mitigate this, as changing a few parameters need not require complete model rebuild.
See also
Logging¶
In an interactive Python session (e.g. using Jupyter notebook), output from Calliope can be triggered at different levels of verbosity. By default on building the model (calliope.Model()
) and running it (model.run()
), there is no logging displayed unless it is at least a WARNING. For helpful information on where the model is in its pre-processing and running in the solver, verbosity can be increased using calliope.set_log_level()
.
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 List of 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
orheat
.
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
- overrides.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
), model-wide 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.
By default the monetary
cost class is used in the objective function, which seeks to minimize total costs. Additional cost classes can be created simply by adding them to the definition of costs for a technology. To use an alternative cost class and/or sense (minimize/maximize) in the objective function, the objective_options
parameter can be set in the run configuration, e.g. objective_options: {'cost_class': 'emissions', 'sense': 'minimize'}
.
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.
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 constraints. If given, location-specific constraints supersede any model-wide 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
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 model-wide constraints such as costs. As in the case of locations, the links
section can specify per-link constraints that supersede any model-wide constraints.
The modeller can also specify a distance for each link, and use per-distance constraints and costs for transmission technologies.
Run configuration (run
)¶
The only required setting in the run configuration is the solver to use:
run:
solver: glpk
model: plan
the most important parts of the run
section are solver
and mode
. A model can run either in planning mode (plan
) or operational mode (operate
). 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.
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.
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 “override groups” in a separate file (in the above example, overrides.yaml
):
run1:
model.subset_time: ['2005-01-01', '2005-01-31']
run2:
model.subset_time: ['2005-02-01', '2005-02-31']
Each group is given by a name (above, run1
and run2
) and any number of model settings – anything in the model configuration can be overridden by an override group. In the above example, the two runs specify different time subsets, so would run an otherwise identical model over two different periods of time series data.
One or several override groups can be applied when running a model, as described in Running a model. They can also be used to generate scripts that run a Calliope model with slightly changed settings many times, either sequentially, or in parallel on a high-performance cluster.
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.--override_file={filename.yaml}:{override_groups}
: Specify override groups to apply to the model (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.
Overrides¶
Assuming we have specified an override group called milp
in a file called overrides.yaml
, we can apply it to our model with:
$ calliope run testmodel/model.yaml --override_file=overrides.yaml:milp --save_netcdf=results.nc
Multiple overrides from the YAML file can be applied at once. For example, we may want to change some of the costs through an additional override group called high_cost_scenario
. We could then use --override_file=overrides.yaml:milp,high_cost_scenario
to apply both overrides simultaneously.
See also
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.
Note
Calliope logs useful progress information to the INFO log level, but does not change the default log level from WARNING. To see progress information when running interactively, call calliope.set_log_level('INFO')
immediately after importing Calliope.
Other ways to load a model interactively are:
- Passing an
AttrDict
or standard Python dictionary to theModel
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 itsrun
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).
Overrides¶
There are two ways to apply override groups interactively:
By setting the override_file argument analogously to use in the command-line tool, e.g.:
model = calliope.Model( 'model.yaml', override_file='overrides.yaml:milp' )
By passing the override_dict argument, which is a Python dictionary or
AttrDict
of overrides:model = calliope.Model( 'model.yaml', override_dict={'run.solver': 'gurobi'} )
Tracking progress¶
When running Calliope in command line, logging of model pre-processing and solving occurs automatically. Interactively, for example in a Jupyter notebook, you can enable verbose logging by running the following code before instantiating and running a Calliope model:
import logging
logging.basicConfig(
level=logging.INFO,
format='%(levelname)s: %(message)s',
)
logger = logging.getLogger()
This will include model processing output, as well as the output of the chosen solver.
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 most basic advice is to just let it 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, invariably at the expense of model ‘accuracy’.
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
Solver choice¶
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.
See also
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
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 model issues. See the section on debugging failing runs.
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 factorsmodel.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.
Summary plots¶
If you want all the data in one place, you can run model.plot.summary(out_file='path/to/file.html')
, which will build a HTML file of all the interactive plots (maintaining the interactivity) and save it to out_file
. This HTML file can be opened in a web browser to show all the plots. This funcionality is made avaiable 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
Saving publication-quality SVG figures¶
On calling any of the three primary plotting functions, you can also set save_svg=True
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 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 analysis
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
)
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
charge_rate: 1
storage_loss: 0.002
resource: file=csp_resource.csv
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
charge_rate: 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 charge rate (charge_rate
) of 4, which in turn limits the storage capacity. The charge rate is the charge/discharge rate / storage capacity (a.k.a the battery resevoir). 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 technologies. 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 ourtechs.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
andccgt
. For the latter, it simply sets a location-specific maximum capacity constraint. Fordemand_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 anyresource
option in the definition of thedemand_power
technology. Instead, this is done directly via a location-specific override. For this location, the filedemand-1.csv
is loaded and the columndemand
is taken (the text after the colon). If no column is specified, Calliope will assume that the column name matches the location nameregion1-1
. 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 is included fully below. To download and run the notebook yourself, you can find it here. You will need to have Calliope installed.
Calliope National Scale Example Model¶
import calliope
# cufflinks allows for easy plotly plots to be
# produced from a pandas DataFrame
import cufflinks
# We increase logging verbosity
calliope.set_log_level('INFO')
model = calliope.examples.national_scale()
# Model inputs can be viewed at `model.inputs`.
# Variables are indexed over any combination of `techs`, `locs`, `carriers`, `costs` and `timesteps`,
# although `techs`, `locs`, and `carriers` are often concatenated.
# e.g. `ccgt`, `region1`, `power` -> `region1::ccgt::power`
model.inputs
# Individual data variables can be accessed easily, `to_pandas()` reformats the data to look nicer
model.inputs.resource.to_pandas()
# To reformat the array, deconcatenating loc_techs / loc_tech_carriers, you can use model.get_formatted_array()
# You can then apply loc/tech/carrier only operations, like summing information over locations:
model.get_formatted_array('resource').sum('locs').to_pandas()
# Solve the model. Results are loaded into `model.results`.
# By including logging (see package importing), we can see the timing of parts of the run, as well as the solver's log
model.run()
# Model results are held in the same structure as model inputs.
# The results consist of the optimal values for all decision variables, including capacities and carrier flow
# There are also results, like system capacity factor and levelised costs, which are calculated in postprocessing
# before being added to the results Dataset
model.results
# We can sum power output over all locations and turn the result into a pandas DataFrame
df_power = model.get_formatted_array('carrier_prod').loc[{'carriers':'power'}].sum('locs').to_pandas().T
#The information about the dataframe tells us about the amount of data it holds in the index and in each column
df_power.info()
# Using .head() to see the first few rows of power generation and demand
# Note: power output in ac_transmission:region1 is power received by the high voltage line at any location connected to `r1`
df_power.head()
# We can plot this by using the timeseries plotting functionality.
# The top-left dropdown gives us the chance to scroll through other timeseries data too.
model.plot.timeseries()
# plot.capacities gives a graphical view of the non-timeseries variables, both input and output
model.plot.capacity()
# We can also examine total technology costs
# notice all the NaN values which appear when seperating loc::techs to locs and techs.
# Any NaN value means we never considered that combination of `loc` and `tech` for the variable
costs = model.get_formatted_array('cost').loc[{'costs': 'monetary'}].to_pandas()
costs
# We might like to plot this, which is outside the scope of internal calliope analysis functions
# But by using cufflinks, we can plot directly from our pandas DataFrame
# Note that the colours are not correct for our technologies here
costs.iplot(kind='bar')
# We can examine levelized costs for each location and technology
lcoes = model.results.systemwide_levelised_cost.loc[{'carriers': 'power', 'costs':'monetary'}].to_pandas()
lcoes
# We could plot this using lcoes.iplot(kind='bar', title='Systemwide levelised costs')
# Or we can just plot it directly using calliope analysis functionality
model.plot.capacity(array='systemwide_levelised_cost')
# Plotting a map of locations and transmission lines is easily possible
# In our example, the system is purely hypothetical and the resulting map
# gives us little useful information...
model.plot.transmission()
See the Calliope documentation for more details on setting up and running a Calliope model.
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 reosurce (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 # 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
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.
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
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 ourtechs.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
, andsupply_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 anyresource
option in the definition of these demands. Instead, this is done directly via a location-specific override. For this location, the filesdemand_heat.csv
anddemand_power.csv
are loaded. As no column is specified (see national scale example model) Calliope will assume that the column name matches the location nameX1
. 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
andy
, 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 allresource_area
technologies to the e.g. roof space available at our location. In this case, we just havepv
, 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 is included fully below. To download and run the notebook yourself, you can find it here. You will need to have Calliope installed.
Calliope Urban Scale Example Model¶
import calliope
# cufflinks allows for easy plotly plots to be
# produced from a pandas DataFrame
import cufflinks
# We increase logging verbosity
calliope.set_log_level('INFO')
model = calliope.examples.urban_scale()
# Model inputs can be viewed at `model.inputs`.
# Variables are indexed over any combination of `techs`, `locs`, `carriers`, `costs` and `timesteps`,
# although `techs`, `locs`, and `carriers` are often concatenated.
# e.g. `chp`, `X1`, `heat` -> `X1::chp::heat`
model.inputs
# Individual data variables can be accessed easily, `to_pandas()` reformats the data to look nicer
model.inputs.resource.to_pandas()
# To reformat the array, deconcatenating loc_techs / loc_tech_carriers, you can use model.get_formatted_array()
# You can then apply loc/tech/carrier only operations, like summing information over locations:
model.get_formatted_array('resource').sum('locs').to_pandas()
# Solve the model. Results are loaded into `model.results`.
# By including logging (see package importing), we can see the timing of parts of the run, as well as the solver's log
model.run()
# Model results are held in the same structure as model inputs.
# The results consist of the optimal values for all decision variables, including capacities and carrier flow
# There are also results, like system capacity factor and levelised costs, which are calculated in postprocessing
# before being added to the results Dataset
model.results
# We can sum heat output over all locations and turn the result into a pandas DataFrame
df_heat = model.get_formatted_array('carrier_prod').loc[{'carriers':'heat'}].sum('locs').to_pandas().T
#The information about the dataframe tells us about the amount of data it holds in the index and in each column
df_heat.info()
# Using .head() to see the first few rows of heat generation and demand
# Note: carrier production in heat_pipes:N1 is heat received by the heat network at any location connected to `N1`
df_heat.head()
# We can plot this by using the timeseries plotting functionality.
# The top-left dropdown gives us the chance to scroll through other timeseries data too.
model.plot.timeseries()
# plot.capacities gives a graphical view of the non-timeseries variables, both input and output
model.plot.capacity()
# We can also examine total technology costs
# notice all the NaN values which appear when seperating loc::techs to locs and techs.
# Any NaN value means we never considered that combination of `loc` and `tech` for the variable
costs = model.get_formatted_array('cost').loc[{'costs': 'monetary'}].to_pandas()
costs
# We might like to plot this, which is outside the scope of internal calliope analysis functions
# But by using cufflinks, we can plot directly from our pandas DataFrame
# Note that the colours are not correct for our technologies here
costs.iplot(kind='bar')
# We can examine levelized costs for each location and technology
lcoes = model.results.systemwide_levelised_cost.loc[{'carriers': 'electricity', 'costs':'monetary'}].to_pandas()
lcoes
# We could plot this using lcoes.iplot(kind='bar', title='Systemwide levelised costs')
# Or we can just plot it directly using calliope analysis functionality
model.plot.capacity(array='systemwide_levelised_cost')
# Plotting a map of locations and transmission lines is easily possible
# In our example, the system is purely hypothetical and the resulting map
# gives us little useful information...
model.plot.transmission()
See the Calliope documentation for more details on setting up and running a Calliope model.
Tutorial 3: Mixed Integer Linear Programming¶
This example is based on the urban scale example model, but with an override. An override file exists in which binary and integer decision variables are triggered, creating a MILP model, rather than the conventional Calliope LP model.
Warning
Integer and Binary variables are still experimental and may not cover all edge cases as intended. Please raise an issue on GitHub if you see unexpected behaviour.
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.
Running the model¶
We now take you through running the model in a Jupyter notebook, which is included fully below. To download and run the notebook yourself, you can find it here. You will need to have Calliope installed.
Calliope Urban Scale MILP Example Model¶
For more details on analysing input/output data, see the full urban scale example model
import calliope
# cufflinks allows for easy plotly plots to be
# produced from a pandas DataFrame
import cufflinks
# We increase logging verbosity
calliope.set_log_level('INFO')
model = calliope.examples.milp()
# Note, we see the overrides that we have applied printed here, thanks to inreasing logging verbosity
# Model inputs can be viewed at `model.inputs`.
# Variables are indexed over any combination of `techs`, `locs`, `carriers`, `costs` and `timesteps`,
# although `techs`, `locs`, and `carriers` are often concatenated.
# e.g. `chp`, `X1`, `heat` -> `X1::chp::heat`
model.inputs
# Individual data variables can be accessed easily, `to_pandas()` reformats the data to look nicer
# Here we look at one of the MILP overrides that we have added, the fixed `purchase` cost
model.inputs.cost_purchase.to_pandas().dropna(axis=1)
# Solve the model. Results are loaded into `model.results`.
# By including logging (see package importing), we can see the timing of parts of the run, as well as the solver's log
model.run()
# Model results are held in the same structure as model inputs.
# The results consist of the optimal values for all decision variables, including capacities and carrier flow
# There are also results, like system capacity factor and levelised costs, which are calculated in postprocessing
# before being added to the results Dataset
model.results
# We can sum operating units of CHP over all locations and turn the result into a pandas DataFrame
df_units = model.get_formatted_array('operating_units').sum('locs').to_pandas().T
#The information about the dataframe tells us about the amount of data it holds in the index and in each column
df_units.info()
# Using .head() to see the first few rows of operating units
df_units.head()
# We can plot this by using the timeseries plotting functionality.
# The top-left dropdown gives us the chance to scroll through other timeseries data too.
model.plot.timeseries()
# plot.capacities gives a graphical view of the non-timeseries variables, both input and output
# Note, because we fix unit size, CHP now has a maximum capacity of 300kW,
# compared to 260kW in the non-MILP case
model.plot.capacity()
See the Calliope documentation for more details on setting up and running a Calliope model.
More info¶
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.
Advanced functionality¶
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:
e_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
.
Time series data¶
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).
For parameters that vary in time, time series data can be read from CSV files, by specifying 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 too use when setting resource
per location, by giving the column name with a colon following the filename: resource: file=filename.csv:column
All time series data in a model must be indexed by ISO 8601 compatible time stamps (usually in the format YYYY-MM-DD hh:mm:ss
, e.g. 2005-01-01 00:00:00
), i.e., the first column in the CSV file must be time stamps.
For example, the first few lines of a CSV file giving a resource potential for two locations might look like this:
,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
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 specifyingoptions
. Atime.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 awind
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.
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.
Cyclic storage¶
With storage
and supply_plus
techs, it is possible to link the storage at either end of the timeseries. This allows the user to better represent multiple years by just modelling one year. By activating cyclig storage (run.cyclic_storage: true
), the initial stored energy for each storage technology at each location passed to the model’s first timestep will be equal to the 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.
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
.
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.
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.
For example:
tech_groups:
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:
e_cap: 750
pv_rooftop:
essentials:
parent: pv
name: 'Rooftop PV'
constraints:
energy_cap_max: 10000
costs:
monetary:
e_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 through override groups, 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
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
.
Generating scripts to run a model many times¶
Override groups 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
command-line tool 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, andbsub
creates a submission script for a bsub-based high-performance cluster.--override_file
: The file that specifies override groups.--groups
: A semicolon-separated list of override groups to generate scripts for, for example,run1;run2
. A comma is used to group override groups together into a single model – for example,run1,high_costs;run1,low_costs
would run the model twice, once applying therun1
andhigh_costs
override groups, and once applyingrun1
andlow_costs
.
A fully-formed command generating a Windows batch script to run a model four times with each of the override groups “run1”, “run2”, “run3”, and “run4”:
calliope generate_runs model.yaml run_model.bat --kind=windows --override_file=overrides.yaml --groups "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 tocalliope 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 --override_file=overrides.yaml --groups "run1,run2,run3,run4"
Running this will create two files:
submit_runs.sh
: The cluster submission script to pass tobsub
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}_{groups}.nc
(model results) and plots_{run_number}_{groups}.html
(HTML plots), where {run_number}
is the run number and {groups}
is the specified set of groups. On a cluster, log files are saved to files with names starting with log_
in the same directory.
Binary and mixed-integer models¶
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.
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 tomodel.inputs
, except that assumed default values will be included. You may also spot a bug, where a value inmodel.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, ou can run
model.backend.update_param()
. For example, to update your the energy efficiency of your ccgt technology in location region1 from 0.5 to 0.1, you can runmodel.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.
- 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 dataset of the inputs/results associated with that specific rerun. It is up to you to store this dataset as you see fit.model.results
will remain to be the initial run, and can only be overwritten bymodel.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
Debugging failing runs¶
A Calliope model provides a method to save a fully built and commented model to a single YAML file with Model.save_commented_model_yaml(path)
. Comments in the resulting YAML file indicate where 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.
Two configuration settings can further aid in debugging failing models:
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.
run.save_logs
Off by default, if given, 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).
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.
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
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
- overrides.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
# 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: 0.6.2
# 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: glpk
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
overrides.yaml
:
##
# Overrides for different example model configuratiions
##
profiling:
model.name: 'National-scale example model (profiling run)'
model.subset_time: ['2005-01-01', '2005-01-15']
run.solver: glpk
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}}
operate_mode:
run.mode: operate
run.operation:
window: 12
horizon: 24
techs.csp.constraints.charge_rate: 0.0162857697 # energy_cap_max / storage_cap_max
techs.csp.constraints.energy_cap_max: null
techs.battery.constraints.storage_cap_max: null
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 the group_share constraints
##
cold_fusion: # Defines a hypothetical cold fusion tech to use in group_share
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
group_share_cold_fusion_prod:
model:
group_share:
# At least 85% of power supply must come from CSP and cold fusion together
csp,cold_fusion:
carrier_prod_min:
power: 0.85
group_share_cold_fusion_cap:
model:
group_share:
# At most 20% of total energy_cap can come from CSP and cold fusion together
csp,cold_fusion:
energy_cap_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
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
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
charge_rate: 1
storage_loss: 0.002
resource: file=csp_resource.csv
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
charge_rate: 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'
model:
name: Urban-scale example model
# What version of Calliope this model is intended for
calliope_version: 0.6.2
# 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: glpk
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
overrides.yaml
:
##
# Overrides for different example model configuratiions
##
milp:
model.name: 'Urban-scale example model with MILP'
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
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.istance: 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 # 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
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
Listing of configuration options¶
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 modelmodel
: model settingsrun
: run settingstechs
: 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.
List of model settings¶
Setting | Default | Comments |
---|---|---|
name | Model name | |
calliope_version | Calliope framework version this model is intended for | |
timeseries_data_path | Path to time series data | |
timeseries_dateformat | %Y-%m-%d %H:%M:%S | Timestamp format of all time series data when read from file |
timeseries_data | Dict of dataframes with time series data (when passing in dicts rather than YAML files to Model constructor) | |
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’ | |
reserve_margin | {} | Per-carrier system-wide reserve margins |
random_seed | Seed for random number generator used during clustering | |
time | {} | Optional settings to adjust time resolution, see Time resolution adjustment for the available options |
group_share | {} | Optional settings for the group_share constraint |
List of run settings¶
Setting | Default | Comments |
---|---|---|
backend | pyomo | Backend to use to build and solve the model. As of v0.6.0, only pyomo is available |
solver | glpk | Which solver to use |
solver_options | A list of options, which are passed on to the chosen solver, and are therefore solver-dependent | |
solver_io | What method the Pyomo backend should use to communicate with the solver | |
save_logs | Directory into which to save logs and temporary files. Also turns on symbolic solver labels in the Pyomo backend | |
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 |
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 |
operation | {} | Settings for operational mode |
objective | minmax_cost_optimization | Name of internal objective function to use, currently only min/max cost-based optimisation is available |
objective_options | {} | Arguments to pass to objective function. If cost-based objective function in use, should include ‘cost_class’ and ‘sense’ (maximize/minimize) |
mode | plan | Which mode to run the model in: ‘plan’ or ‘operation’ |
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 |
cyclic_storage | False | If true, storage in the last timestep of the timeseries is considered to be the ‘previous timestep’ in the first timestep of the timeseries |
List of possible 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.e_cap.max
.
Setting | Default | Name | Unit | Comments |
---|---|---|---|---|
lifetime | Technology lifetime | years | Must be defined if fixed capital costs are defined. A reasonable value for many technologies is around 20-25 years. | |
carrier_ratios | {} | 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. |
resource | 0 | Available resource | kWh/m2 | kW/m2 | Maximum available resource (static, or from file as timeseries). Unit dictated by reosurce_unit |
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 |
resource_unit | power | Resource unit | N/A | Sets the unit of resource to either power (i.e. kW) or energy (i.e. kWh), which affects how resource time series are processed when performing time resolution adjustments |
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_area_min | 0 | Minimum installed collector area | m2 | |
resource_area_max | False | Maximum installed collector area | m2 | Set to false by default in order to disable this constraint |
resource_area_equals | False | Specific installed collector area | m2 | |
resource_area_per_energy_cap | False | Energy capacity per unit collector ares | boolean | 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_min | 0 | Minimum installed resource consumption capacity | kW | |
resource_cap_max | inf | Maximum installed resource consumption capacity | kW | |
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_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 valuesin timeseries) by this value |
storage_initial | 0 | Initial storage level | kWh | Set stored energy in device at the first timestep |
storage_cap_min | 0 | Minimum storage capacity | kWh | |
storage_cap_max | inf | Maximum storage capacity | kWh | If not defined, energy_cap_max * charge_rate will be used as the capacity. |
storage_cap_equals | False | Specific storage capacity | kWh | If not defined, energy_cap_equals * charge_rate will be used as the capacity and overrides _max and _min constraints. |
storage_cap_per_unit | False | Storage capacity per purchased unit | kWh/unit | Set the storage capacity of each integer unit of a technology perchased. |
charge_rate | False | Charge rate | hour -1 | ratio of maximum charge/discharge (kW) for a given maximum storage capacity (kWh) |
storage_loss | 0 | Storage loss rate | hour -1 | 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 |
energy_prod | False | Energy production | boolean | Allow this technology to supply energy to the carrier (static boolean, or from file as timeseries). |
energy_con | False | Energy consumption | boolean | Allow this technology to consume energy from the carrier (static boolean, or from file as timeseries). |
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 |
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 | distance -1 | Set as value between 1 (no loss) and 0 (all energy lost) |
energy_cap_min | 0 | Minimum installed energy capacity | kW | Limits decision variables carrier_prod /carrier_con to a minimum/maximum. |
energy_cap_max | inf | Maximum installed energy capacity | kW | Limits decision variables carrier_prod /carrier_con to a maximum/minimum. |
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_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_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_scale | 1.0 | Energy capacity scale | float | Scale all energy_cap min/max/equals/total_max/total_equals constraints by this value |
energy_cap_min_use | False | Minimum carrier production | fraction | Set to a value between 0 and 1 to force minimum carrer 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_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 | |
units_min | False | Minimum number of purchased units | integer | Turns the model from LP to MILP. |
units_max | False | Maximum number of purchased units | integer | Turns the model from LP to MILP. |
units_equals | False | Specific 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_equals_systemwide | False | System-wide specific installed energy capacity | kW | fixes the sum to a |
List of possible 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.e_cap
.
Setting | Default | Name | Unit | Comments |
---|---|---|---|---|
interest_rate | 0 | Interest rate | fraction | Used when computing levelized costs |
storage_cap | 0 | Cost of storage capacity | kWh -1 | |
resource_area | 0 | Cost of resource collector area | m-2 | |
resource_cap | 0 | Cost of resource consumption capacity | kW -1 | |
energy_cap | 0 | Cost of energy capacity | kW gross -1 | |
energy_cap_per_distance | 0 | Cost of energy capacity, per unit distance | kW gross -1 / distance | Applied to transmission links only |
om_annual_investment_fraction | 0 | Fractional yearly O&M costs | fraction / total investment | |
om_annual | 0 | Yearly O&M costs | kW energy_cap -1 | |
om_prod | 0 | Carrier production cost | kWh -1 | Applied to carrier production of a technology |
om_con | 0 | Carrier consumption cost | kWh -1 | Applied to carrier consumption of a technology |
export | 0 | Carrier export cost | kWh -1 | Usually used in the negative sense, as a subsidy. |
purchase | 0 | 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 |
Technology depreciation settings apply when calculating levelized costs. The interest rate and life times must be set for each technology with investment costs.
List of 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 fromsupply
.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 a user-defined technology or tech group.
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:
resource: inf
resource_unit: power
energy_prod: true
costs: {}
Required constraints, allowed constraints, and allowed costs:
required_constraints:
- [energy_cap_max, energy_cap_equals, energy_cap_per_unit]
allowed_costs:
- interest_rate
- resource_area
- energy_cap
- om_annual_investment_fraction
- om_annual
- om_prod
- om_con
- export
- purchase
- depreciation_rate
allowed_constraints:
- energy_prod
- lifetime
- resource
- force_resource
- resource_min_use
- resource_unit
- resource_area_min
- resource_area_max
- resource_area_equals
- resource_area_per_energy_cap
- resource_scale
- energy_eff
- energy_cap_min
- energy_cap_max
- energy_cap_equals
- energy_cap_max_systemwide
- energy_cap_equals_systemwide
- energy_cap_scale
- energy_cap_min_use
- energy_cap_per_unit
- energy_ramping
- energy_eff_per_distance
- export_cap
- export_carrier
- units_min
- units_max
- units_equals
- units_max_systemwide
- units_equals_systemwide
supply_plus¶
Default constraints provided by the parent tech group:
essentials:
parent:
constraints:
resource: inf
resource_unit: power
resource_eff: 1.0
energy_prod: true
costs: {}
Required constraints, allowed constraints, and allowed costs:
required_constraints:
- [energy_cap_max, energy_cap_equals, energy_cap_per_unit]
allowed_costs:
- interest_rate
- storage_cap
- resource_area
- resource_cap
- energy_cap
- om_annual_investment_fraction
- om_annual
- om_prod
- om_con
- export
- purchase
- depreciation_rate
allowed_constraints:
- energy_prod
- lifetime
- resource
- force_resource
- resource_min_use
- resource_unit
- resource_eff
- resource_area_min
- resource_area_max
- resource_area_equals
- resource_area_per_energy_cap
- resource_cap_min
- resource_cap_max
- resource_cap_equals
- resource_cap_equals_energy_cap
- resource_scale
- parasitic_eff
- energy_eff
- energy_cap_min
- energy_cap_max
- energy_cap_equals
- energy_cap_max_systemwide
- energy_cap_equals_systemwide
- energy_cap_scale
- energy_cap_min_use
- energy_cap_per_unit
- energy_ramping
- energy_eff_per_distance
- export_cap
- export_carrier
- units_min
- units_max
- units_equals
- units_max_systemwide
- units_equals_systemwide
- storage_initial
- storage_cap_min
- storage_cap_max
- storage_cap_equals
- storage_cap_per_unit
- charge_rate
- storage_time_max
- storage_loss
demand¶
Default constraints provided by the parent tech group:
essentials:
parent:
constraints:
resource_unit: power
force_resource: true
energy_con: true
costs: {}
Required constraints, allowed constraints, and allowed costs:
required_constraints:
- resource
allowed_costs: []
allowed_constraints:
- energy_con
- resource
- force_resource
- resource_unit
- resource_scale
- resource_area_equals
storage¶
Default constraints provided by the parent tech group:
essentials:
parent:
constraints:
energy_prod: true
energy_con: true
storage_cap_max: inf
costs: {}
Required constraints, allowed constraints, and allowed costs:
required_constraints:
- [energy_cap_max, energy_cap_equals, energy_cap_per_unit]
- [storage_cap_max, storage_cap_equals]
allowed_costs:
- interest_rate
- storage_cap
- energy_cap
- om_annual_investment_fraction
- om_annual
- om_prod
- export
- purchase
- depreciation_rate
allowed_constraints:
- energy_prod
- energy_con
- lifetime
- energy_eff
- energy_cap_min
- energy_cap_max
- energy_cap_equals
- energy_cap_max_systemwide
- energy_cap_equals_systemwide
- energy_cap_scale
- energy_cap_min_use
- energy_cap_per_unit
- energy_ramping
- storage_initial
- storage_cap_min
- storage_cap_max
- storage_cap_equals
- storage_cap_per_unit
- charge_rate
- storage_time_max
- storage_loss
- export_cap
- export_carrier
- units_min
- units_max
- units_equals
- units_max_systemwide
- units_equals_systemwide
transmission¶
Default constraints provided by the parent tech group:
essentials:
parent:
constraints:
energy_prod: true
energy_con: true
costs: {}
Required constraints, allowed constraints, and allowed costs:
required_constraints:
- [energy_cap_max, energy_cap_equals, energy_cap_per_unit]
allowed_costs:
- interest_rate
- energy_cap
- energy_cap_per_distance
- om_annual_investment_fraction
- om_annual
- om_prod
- purchase
- purchase_per_distance
- depreciation_rate
allowed_constraints:
- energy_prod
- energy_con
- lifetime
- energy_con
- energy_prod
- energy_eff_per_distance
- energy_eff
- one_way
- energy_cap_scale
conversion¶
Default constraints provided by the parent tech group:
essentials:
parent:
constraints:
energy_prod: true
energy_con: true
costs: {}
Required constraints, allowed constraints, and allowed costs:
required_constraints:
- [energy_cap_max, energy_cap_equals, energy_cap_per_unit]
allowed_costs:
- interest_rate
- energy_cap
- om_annual_investment_fraction
- om_annual
- om_prod
- om_con
- export
- purchase
- depreciation_rate
allowed_constraints:
- energy_prod
- energy_con
- lifetime
- energy_eff
- energy_cap_min
- energy_cap_max
- energy_cap_equals
- energy_cap_max_systemwide
- energy_cap_equals_systemwide
- energy_cap_scale
- energy_cap_min_use
- energy_cap_per_unit
- energy_ramping
- energy_eff_per_distance
- export_cap
- export_carrier
- units_min
- units_max
- units_equals
- units_max_systemwide
- units_equals_systemwide
conversion_plus¶
Default constraints provided by the parent tech group:
essentials:
parent:
constraints:
energy_prod: true
energy_con: true
costs: {}
Required constraints, allowed constraints, and allowed costs:
required_constraints:
- [energy_cap_max, energy_cap_equals, energy_cap_per_unit]
allowed_costs:
- interest_rate
- energy_cap
- om_annual_investment_fraction
- om_annual
- om_prod
- om_con
- export
- purchase
- depreciation_rate
allowed_constraints:
- energy_prod
- energy_con
- lifetime
- carrier_ratios
- energy_eff
- energy_cap_min
- energy_cap_max
- energy_cap_equals
- energy_cap_max_systemwide
- energy_cap_equals_systemwide
- energy_cap_scale
- energy_cap_min_use
- energy_cap_per_unit
- energy_ramping
- energy_eff_per_distance
- export_cap
- export_carrier
- units_min
- units_max
- units_equals
- units_max_systemwide
- units_equals_systemwide
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 AttrDict
s, 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.
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
Objective functions¶
-
calliope.backend.pyomo.objective.
minmax_cost_optimization
(backend_model, cost_class, sense)[source]¶ Minimize or maximise total system cost for specified cost class.
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.
\[min: z = \sum_{loc::tech_{cost}} cost(loc::tech, cost=cost_{k})) + \sum_{loc::carrier,timestep} unmet\_demand(loc::carrier, timestep) \times bigM max: z = \sum_{loc::tech_{cost}} cost(loc::tech, cost=cost_{k})) - \sum_{loc::carrier,timestep} unmet\_demand(loc::carrier, timestep) \times bigM\]
-
calliope.backend.pyomo.objective.
check_feasibility
(backend_model, **kwargs)[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:\[\boldsymbol{storage_{inter\_cluster}}(loc::tech, datestep_{final}) \times ((1 - storage_loss) ** 24) = storage_{initial}(loc::tech) \quad \forall loc::tech \in loc::techs_{store}, \forall datestep \in datesteps\]Where \(datestep_{final}\) is the last datestep of the timeseries
Else: .. container:: scrolling-wrapper
\[\boldsymbol{storage}(loc::tech, timestep_{final}) \times ((1 - storage_loss) ** 24) = storage_{initial}(loc::tech) \quad \forall loc::tech \in loc::techs_{store}, \forall timestep \in timesteps\]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
(backend_model, loc_tech)[source]¶ Set an additional energy capacity constraint on storage technologies, based on their use of charge_rate.
\[\boldsymbol{energy_{cap}}(loc::tech) \leq \boldsymbol{storage_{cap}}(loc::tech) \times charge\_rate(loc::tech) \quad \forall loc::tech \in loc::techs_{store}\]
-
calliope.backend.pyomo.constraints.capacity.
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.
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.
\[ \begin{align}\begin{aligned}\boldsymbol{cost_{investment}}(cost, loc::tech) = cost_{fractional\_om}(cost, loc::tech) + cost_{fixed\_om}(cost, loc::tech) + cost_{con}(cost, loc::tech)\\cost_{con}(cost, loc::tech) = depreciation\_rate * ts\_weight * (cost_{energy\_cap}(cost, loc::tech) \times \boldsymbol{energy_{cap}}(loc::tech) + cost_{storage\_cap}(cost, loc::tech) \times \boldsymbol{storage_{cap}}(loc::tech) + cost_{resource\_cap}(cost, loc::tech) \times \boldsymbol{resource_{cap}}(loc::tech) + cost_{resource\_area}(cost, loc::tech)) \times \boldsymbol{resource_{area}}(loc::tech)\\\begin{split}depreciation\_rate = \begin{cases} = 1 / plant\_life,& \text{if } interest\_rate = 0\\ = \frac{interest\_rate \times (1 + interest\_rate)^{plant\_life}}{(1 + interest\_rate)^{plant\_life} - 1},& \text{if } interest\_rate \gt 0\\ \end{cases}\end{split}\\ts\_weight = \sum_{timestep \in timesteps} (time\_res(timestep) \times weight(timestep)) \times \frac{1}{8760}\end{aligned}\end{align} \]
-
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_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_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_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_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_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_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_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_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_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_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_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:
\[\begin{split}\sum_{loc}\boldsymbol{units}(loc::tech) + \boldsymbol{purchased}(loc::tech) \begin{cases} = units_{equals, systemwide}(tech),& \text{if } units_{equals, systemwide}(tech)\\ \leq units_{max, systemwide}(tech),& \text{if } units_{max, systemwide}(tech)\\ \text{unconstrained},& \text{otherwise} \end{cases} \forall tech \in techs\end{split}\]
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’]:
\[\sum_{loc::tech::carrier \in loc::tech::carriers_{in}} \frac{\boldsymbol{carrier_{con}}(loc::tech::carrier, timestep)}{ carrier\_ratio(loc::tech::carrier, `in')} = \sum_{loc::tech::carrier \in loc::tech::carriers_{tier}} \frac{\boldsymbol{carrier_{prod}}(loc::tech::carrier, timestep)}{ carrier\_ratio(loc::tech::carrier, tier)} \quad \forall \text{ tier } \in [`in_2', `in_3'], \forall loc::tech \in loc::techs_{conversion^{+}}, \forall timestep \in timesteps\]
Network¶
Policy¶
Enforce shares in energy_cap for groups of technologies. Applied to
supply
andsupply_plus
technologies only.\[\sum_{loc::tech \in given\_group} energy_{cap}(loc::tech) = fraction \times \sum_{loc::tech \in loc\_techs\_supply \or loc\_techs\_supply\_plus} energy_{cap}(loc::tech)\]
Enforce shares in carrier_prod for groups of technologies. Applied to
loc_tech_carriers_supply_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.
\[\sum_{loc::tech::carrier \in loc\_tech\_carriers\_supply\_all} energy_{cap}(loc::tech::carrier, timestep_{max\_demand}) \geq \sum_{loc::tech::carrier \in loc\_tech\_carriers\_demand} carrier_{con}(loc::tech::carrier, timestep_{max\_demand}) \times -1 \times \frac{1}{time\_resolution_{max\_demand}} \times (1 + reserve\_margin)\]
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.
If you only want to track the latest commit, without having a local Calliope repository, then just download the base.yml and latest.yml requirements files and run (assuming both are saved into a directory called requirements):
$ conda env create -n calliope_latest --file=requirements/base.yml --file=requirements/latest.yml
This will create a conda environment called calliope_latest
.
To actively contribute to Calliope development, you’ll instead want to clone the repository, giving you an editable copy. 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
Using Anaconda/conda, install all requirements, including the free and open source GLPK solver, into a new environment, e.g. calliope_dev
:
$ conda env create -f ./calliope/requirements/base.yml -n calliope_dev
$ source activate calliope_dev
On Windows:
$ conda env create -f ./calliope/requirements/base.yml -n calliope_dev
$ activate calliope_dev
Then install Calliope itself with pip:
$ pip install -e ./calliope
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: {...}
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
orrequirements/base.yml
- Version number:
- Fork conda-forge/calliope-feedstock, and update
- 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 (so0.6.0-dev
is0.6.0
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)[source]¶ Return an xr.DataArray with locs, techs, and carriers as separate dimensions.
Parameters: - var : str
Decision variable for which to return a DataArray.
-
Time series¶
-
calliope.core.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: - data : xarray.Dataset
Should be normalized
- func : str
‘kmeans’ or ‘hierarchical’ for KMeans or Agglomerative clustering, respectively
- timesteps_per_day : int
Total number of timesteps in a day
- tech : list, optional
list of strings referring to technologies by which clustering is undertaken. If none (default), all technologies within timeseries variables will be used.
- timesteps : list or str, optional
Subset of the time domain within which to apply clustering.
- k : int, optional
Number of clusters to create. If none (default), will use Hartigan’s rule to infer a reasonable number of clusters.
- variables : list, 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.
- kwargs : dict
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
- ——-
- clusters : dataframe
Indexed by timesteps and with locations as columns, giving cluster membership for first timestep of each day.
- clustered_data : sklearn.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.core.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
wherevar
for the technologytech
across the given list oflocations
is either minimal or maximal.Parameters: - data : xarray.Dataset
- tech : str
Technology whose var to find extreme for.
- var : str, optional
default ‘resource’
- how : str, optional
‘max’ (default) or ‘min’.
- length : str, optional
Defaults to ‘1D’.
- n : int, optional
Number of periods of length to look for, default is 1.
- groupby_length : str, optional
Group time series and return n periods of length for each group.
- padding : str, 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.
- normalize : bool, optional
If True (default), data is normalized using
normalized_copy()
.- kwargs : dict, optional
Dimensions of the selected var over which to index. Any remaining dimensions will be flattened by mean
-
calliope.core.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 forvar
between technologiestech0
andtech1
is either a minimum or a maximum.Parameters: - data : xarray.Dataset
- tech0 : str
First technology for which we find the extreme of var
- tech1 : str
Second technology for which we find the extreme of var
- var : str, optional
default ‘resource’
- how : str, optional
‘max’ (default) or ‘min’.
- length : str, optional
Defaults to ‘1D’.
- n : int, optional
Number of periods of length to look for, default is 1.
- groupby_length : str, optional
Group time series and return n periods of length for each group.
- padding : str, 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.
- normalize : bool, optional
If True (default), data is normalized using
normalized_copy()
.- kwargs : dict, optional
Dimensions of the selected var over which to index. Any remaining dimensions will be flattened by mean
-
calliope.core.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: - data : xarray.Dataset
calliope model data, containing only timeseries data variables
- timesteps : str or list; optional
If given, apply resampling to a subset of the timeseries data
- resolution : str
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.analysis.plotting.plotting.
ModelPlotMethods
(model)[source]¶ -
timeseries
(**kwargs)[source]¶ Parameters: - array : str 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_zoom : int, optional
Number of timesteps to show initially on the x-axis (if not given, the full time range is shown by default).
- rangeslider : bool, optional
If True, displays a range slider underneath the plot for navigating (helpful primarily in interactive use).
- subset : dict, optional
Dictionary by which data is subset (uses xarray loc indexing). Keys any of [‘timeseries’, ‘locs’, ‘techs’, ‘carriers’, ‘costs’].
- sum_dims : str, optional
List of dimension names to sum plot variable over.
- squeeze : bool, optional
Whether to squeeze out dimensions of length = 1.
- html_only : bool, optional; default = False
Returns a html string for embedding the plot in a webpage
- to_file : False 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_updates : dict, 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_updates : dict, 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: - array : str 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.
- orient : str, optional
‘h’ for horizontal or ‘v’ for vertical barchart
- subset : dict, optional
Dictionary by which data is selected (using xarray indexing loc[]). Keys any of [‘timeseries’, ‘locs’, ‘techs’, ‘carriers’, ‘costs’]).
- sum_dims : str, optional
List of dimension names to sum plot variable over.
- squeeze : bool, optional
Whether to squeeze out dimensions containing only single values.
- html_only : bool, optional; default = False
Returns a html string for embedding the plot in a webpage
- to_file : False 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_updates : dict, 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_updates : dict, 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_token : str, 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_only : bool, optional; default = False
Returns a html string for embedding the plot in a webpage
- to_file : False 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_updates : dict, 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_updates : dict, 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_file : str, optional
Path to output file to save HTML to.
- mapbox_access_token : str, 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: - param : str
Name of the parameter to update
- index : tuple of strings
Tuple of dimension indeces, in the order given in model.inputs for the reciprocal parameter
- value : int, float, bool, or str
Value to assign to the Pyomo Param at the given index
Returns: - Value 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: - constraint : str
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: - run_data : xarray.Dataset
Raw data from this rerun, including both inputs and results. to filter inputs/results, use run_data.filter_by_attrs(is_result=…) with 0 for inputs and 1 for results.
-
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.
-
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.If
resolve_imports
is True,import:
statements are resolved recursively, else they are treated like any other key.When resolving import statements, anything defined locally overrides definitions in the imported file.
-
classmethod
from_yaml_string
(string, resolve_imports=False)[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 givenvalue
. 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.
-
as_dict
(flat=False, filtered=True)[source]¶ Return the AttrDict as a pure dict (with nested dicts if necessary).
-
get_comments
(key)[source]¶ Get comments for the given key. Returns a dict of the form
{'above': ..., 'inline': ..., 'below': ...}
.
-
set_comment
(key, comment, kind='inline', exist_ok=False)[source]¶ Set a comment for the given key. Returns None if successful, KeyError if key does not exist, and ValueError if a comment already exists, unless exist_ok is set to False.
Parameters: - key : str
- comment : str
- kind : str, optional, default ‘inline’
Can be ‘inline’, ‘above’.
- exist_ok : bool, optional, default False
If True, existing comments are overwritten.
-
del_comments
(key)[source]¶ Remove any comments at the given key. Returns None if successful, KeyError if key has no comments.
-
to_yaml
(path=None)[source]¶ Saves the AttrDict to the
path
as a YAML file, or returns a YAML string ifpath
is None.
-
keys_nested
(subkeys_as='list', filtered=True)[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 inother
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 formthis.that: None
in other will be ignored if subdicts exist in self likethis.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.
ModelWarning
[source]¶ ModelWarnings should be raised for possible model errors, but where execution can still continue.
-
calliope.exceptions.
print_warnings_and_raise_errors
(warnings=None, errors=None)[source]¶ Print warnings and raise ModelError from errors.
Parameters: - warnings : list, optional
- errors : list, optional
-
calliope.core.util.logging.
set_log_level
(level)[source]¶ Set the minimum logging verbosity in a Python console. Higher verbosity levels will include their output and all those of following levels. Level options (in descending order of verbosity):
- ‘DEBUG’
- ‘SOLVER’ -> Calliope custom level, assigned value of 19,
- returns solver (e.g. GLPK) stream
- ‘INFO’ -> default level
- ‘WARNING’
- ‘ERROR’
- ‘CRITICAL’
Index¶
Release history¶
Release History¶
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. See 0.6.0 model configuration changes for a full list of changes.
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
. See 0.6.0 model configuration changes for a full list.
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/orunits.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
ande_cap
were constrained but noc_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 aModelError
rather than returning an instance of the built-in national-scale example model. Use the newcalliope.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 astechname.parent
ortechname.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 List of possible constraints for a complete list of all available constraints. Some additional constraints have name changes:
e_cap_max_scale
becomese_cap_scale
rb_cap_follows
becomesrb_cap_follow
, and addition ofrb_cap_follow_mode
s_time_max
becomess_time.max
changed backwards-incompatible All optional constraints are now grouped together, under constraints.optional
:
constraints.group_fraction.group_fraction
becomesconstraints.optional.group_fraction
constraints.ramping.ramping_rate
becomesconstraints.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 todebug.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 existingoverride
setting, and is applied first (sooverride
can overridemodel_override
). - new Run settings
output.save_constraints
andoutput.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 toparallel.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
License¶
Copyright 2013-2018 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
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.