Running models in different modes¶

Models can be built and solved in different modes:

base mode. In base mode, the user defines upper and lower boundaries for technology capacities and the model decides on an optimal system configuration. In this configuration, the total cost of investing in technologies and then using them to meet demand in every timestep (e.g., every hour) is as low as possible.
operate mode. In operate mode, all capacity constraints are fixed and the system is operated with a receding horizon control algorithm. This is sometimes known as a dispatch model - we're only concerned with the dispatch of technologies whose capacities are already fixed. Optimisation is limited to a time horizon which
spores mode. SPORES refers to Spatially-explicit Practically Optimal REsultS. This run mode allows a user to generate any number of alternative results which are within a certain range of the optimal cost.

In this notebook we will run the Calliope national scale example model in these three modes.

More detail on these modes is given in the advanced section of the Calliope documentation.

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import plotly.express as px
import plotly.graph_objects as go
import xarray as xr

import calliope

# We update logging to show a bit more information but to hide the solver output, which can be long.
calliope.set_log_verbosity("INFO", include_solver_output=False)

import plotly.express as px
import plotly.graph_objects as go
import xarray as xr

import calliope

# We update logging to show a bit more information but to hide the solver output, which can be long.
calliope.set_log_verbosity("INFO", include_solver_output=False)

Running in 'base' mode.¶

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# We subset to the same time range as operate mode
model_base = calliope.examples.national_scale(
    subset={"timesteps": ["2005-01-01", "2005-01-10"]}
)
model_base.build()
model_base.solve()
# We subset to the same time range as operate mode
model_base = calliope.examples.national_scale(
    subset={"timesteps": ["2005-01-01", "2005-01-10"]}
)
model_base.build()
model_base.solve()

[2026-04-22 17:26:53] INFO     Math init | loading pre-defined math.

[2026-04-22 17:26:53] INFO     Math init | loading math files {'milp', 'operate', 'storage_inter_cluster', 'spores', 'base'}.

[2026-04-22 17:26:53] INFO     Model: preprocessing data

[2026-04-22 17:26:53] INFO     Math build | building applied math with ['base'].

[2026-04-22 17:26:53] INFO     input data `color` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:26:53] INFO     input data `name` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:26:53] INFO     input data `color` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:26:53] INFO     input data `name` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:26:53] INFO     Model: initialisation complete

[2026-04-22 17:26:53] INFO     Model: backend build starting

[2026-04-22 17:26:53] INFO     Optimisation Model | parameters/lookups | Generated.

[2026-04-22 17:26:54] INFO     Optimisation Model | variables | Generated.

[2026-04-22 17:26:54] INFO     Optimisation Model | global_expressions | Generated.

[2026-04-22 17:26:56] INFO     Optimisation Model | constraints | Generated.

[2026-04-22 17:26:56] INFO     Optimisation Model | piecewise_constraints | Generated.

[2026-04-22 17:26:56] INFO     Optimisation Model | objectives | Generated.

[2026-04-22 17:26:56] INFO     Model: backend build complete

[2026-04-22 17:26:56] INFO     Optimisation model | starting model in base mode.

[2026-04-22 17:26:57] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:26:57] INFO     Backend: solver finished running. Time since start of solving optimisation problem: 0:00:01.145374

[2026-04-22 17:26:57] INFO     Postprocessing: applied zero threshold 1e-10 to model results.

[2026-04-22 17:26:57] INFO     Postprocessing: ended. Time since start of solving optimisation problem: 0:00:01.180416

[2026-04-22 17:26:57] INFO     Backend: model solve completed. Time since start of solving optimisation problem: 0:00:01.180703

Running in 'operate' mode.¶

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model_operate = calliope.examples.national_scale(scenario="operate")
model_operate.build()
model_operate.solve()
model_operate = calliope.examples.national_scale(scenario="operate")
model_operate.build()
model_operate.solve()

[2026-04-22 17:26:57] INFO     (scenarios, operate ) | Applying the following overrides: ['operate'].

[2026-04-22 17:26:57] INFO     Math init | loading pre-defined math.

[2026-04-22 17:26:57] INFO     Math init | loading math files {'milp', 'operate', 'storage_inter_cluster', 'spores', 'base'}.

[2026-04-22 17:26:57] INFO     Model: preprocessing data

[2026-04-22 17:26:57] INFO     Math build | building applied math with ['base', 'operate'].

[2026-04-22 17:26:58] INFO     input data `color` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:26:58] INFO     input data `name` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:26:58] INFO     input data `color` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:26:58] INFO     input data `name` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:26:58] INFO     Model: initialisation complete

[2026-04-22 17:26:58] INFO     Model: backend build starting

[2026-04-22 17:26:58] INFO     Optimisation Model | parameters/lookups | Generated.

[2026-04-22 17:26:58] INFO     Optimisation Model | variables | Generated.

[2026-04-22 17:26:58] INFO     Optimisation Model | global_expressions | Generated.

[2026-04-22 17:27:00] INFO     Optimisation Model | constraints | Generated.

[2026-04-22 17:27:00] INFO     Optimisation Model | piecewise_constraints | Generated.

[2026-04-22 17:27:00] INFO     Optimisation Model | objectives | Generated.

[2026-04-22 17:27:00] INFO     Model: backend build complete

[2026-04-22 17:27:00] INFO     Optimisation model | starting model in operate mode.

[2026-04-22 17:27:00] INFO     Optimisation model | Running first time window.

[2026-04-22 17:27:00] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:00] INFO     Optimisation model | Running time window starting at 2005-01-01 12:00:00.

[2026-04-22 17:27:00] INFO     Optimisation model | parameters:storage_initial | The optimisation problem components ['balance_storage', 'balance_supply_with_storage', 'set_storage_initial'] will be re-built.

[2026-04-22 17:27:01] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:01] INFO     Optimisation model | Running time window starting at 2005-01-02 00:00:00.

[2026-04-22 17:27:02] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:02] INFO     Optimisation model | Running time window starting at 2005-01-02 12:00:00.

[2026-04-22 17:27:03] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:03] INFO     Optimisation model | Running time window starting at 2005-01-03 00:00:00.

[2026-04-22 17:27:03] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:03] INFO     Optimisation model | Running time window starting at 2005-01-03 12:00:00.

[2026-04-22 17:27:04] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:04] INFO     Optimisation model | Running time window starting at 2005-01-04 00:00:00.

[2026-04-22 17:27:04] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:04] INFO     Optimisation model | Running time window starting at 2005-01-04 12:00:00.

[2026-04-22 17:27:05] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:05] INFO     Optimisation model | Running time window starting at 2005-01-05 00:00:00.

[2026-04-22 17:27:06] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:06] INFO     Optimisation model | Running time window starting at 2005-01-05 12:00:00.

[2026-04-22 17:27:06] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:06] INFO     Optimisation model | Running time window starting at 2005-01-06 00:00:00.

[2026-04-22 17:27:07] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:07] INFO     Optimisation model | Running time window starting at 2005-01-06 12:00:00.

[2026-04-22 17:27:08] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:08] INFO     Optimisation model | Running time window starting at 2005-01-07 00:00:00.

[2026-04-22 17:27:08] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:08] INFO     Optimisation model | Running time window starting at 2005-01-07 12:00:00.

[2026-04-22 17:27:09] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:09] INFO     Optimisation model | Running time window starting at 2005-01-08 00:00:00.

[2026-04-22 17:27:10] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:10] INFO     Optimisation model | Running time window starting at 2005-01-08 12:00:00.

[2026-04-22 17:27:10] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:10] INFO     Optimisation model | Running time window starting at 2005-01-09 00:00:00.

[2026-04-22 17:27:11] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:11] INFO     Optimisation model | Running time window starting at 2005-01-09 12:00:00.

[2026-04-22 17:27:12] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:12] INFO     Optimisation model | Running time window starting at 2005-01-10 00:00:00.

[2026-04-22 17:27:12] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:12] INFO     Optimisation model | Running time window starting at 2005-01-10 12:00:00.

[2026-04-22 17:27:12] INFO     Optimisation model | Reaching the end of the timeseries. Re-building model with shorter time horizon.

[2026-04-22 17:27:12] INFO     Model: backend build starting

[2026-04-22 17:27:12] INFO     Optimisation Model | parameters/lookups | Generated.

[2026-04-22 17:27:12] INFO     Optimisation Model | variables | Generated.

[2026-04-22 17:27:13] INFO     Optimisation Model | global_expressions | Generated.

[2026-04-22 17:27:14] INFO     Optimisation Model | constraints | Generated.

[2026-04-22 17:27:14] INFO     Optimisation Model | piecewise_constraints | Generated.

[2026-04-22 17:27:14] INFO     Optimisation Model | objectives | Generated.

[2026-04-22 17:27:14] INFO     Model: backend build complete

[2026-04-22 17:27:14] INFO     Optimisation model | parameters:storage_initial | The optimisation problem components ['balance_storage', 'balance_supply_with_storage', 'set_storage_initial'] will be re-built.

[2026-04-22 17:27:15] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:15] INFO     Backend: solver finished running. Time since start of solving optimisation problem: 0:00:15.418405

[2026-04-22 17:27:15] INFO     Postprocessing: applied zero threshold 1e-10 to model results.

[2026-04-22 17:27:15] INFO     Postprocessing: ended. Time since start of solving optimisation problem: 0:00:15.444002

[2026-04-22 17:27:15] INFO     Backend: model solve completed. Time since start of solving optimisation problem: 0:00:15.444282

Note how we have capacity variables as parameters in the inputs and only dispatch variables in the results

Running in 'spores' mode.¶

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# We subset to the same time range as operate/base mode
model_spores = calliope.examples.national_scale(
    scenario="spores", subset={"timesteps": ["2005-01-01", "2005-01-10"]}
)
model_spores.build()
model_spores.solve()
# We subset to the same time range as operate/base mode
model_spores = calliope.examples.national_scale(
    scenario="spores", subset={"timesteps": ["2005-01-01", "2005-01-10"]}
)
model_spores.build()
model_spores.solve()

[2026-04-22 17:27:15] INFO     (scenarios, spores ) | Applying the following overrides: ['spores'].

[2026-04-22 17:27:15] INFO     Math init | loading pre-defined math.

[2026-04-22 17:27:15] INFO     Math init | loading math files {'milp', 'operate', 'storage_inter_cluster', 'spores', 'base'}.

[2026-04-22 17:27:15] INFO     Model: preprocessing data

[2026-04-22 17:27:15] INFO     Math build | building applied math with ['base', 'spores'].

[2026-04-22 17:27:15] INFO     input data `spores_tracker` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:27:15] INFO     input data `color` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:27:15] INFO     input data `name` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:27:16] INFO     input data `spores_tracker` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:27:16] INFO     input data `color` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:27:16] INFO     input data `name` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:27:16] INFO     Model: initialisation complete

[2026-04-22 17:27:16] INFO     Model: backend build starting

[2026-04-22 17:27:16] INFO     Optimisation Model | parameters/lookups | Generated.

[2026-04-22 17:27:16] INFO     Optimisation Model | variables | Generated.

[2026-04-22 17:27:17] INFO     Optimisation Model | global_expressions | Generated.

[2026-04-22 17:27:19] INFO     Optimisation Model | constraints | Generated.

[2026-04-22 17:27:19] INFO     Optimisation Model | piecewise_constraints | Generated.

[2026-04-22 17:27:19] INFO     Optimisation Model | objectives | Generated.

[2026-04-22 17:27:19] INFO     Model: backend build complete

[2026-04-22 17:27:19] INFO     Optimisation model | starting model in spores mode.

[2026-04-22 17:27:19] INFO     Optimisation model | Resetting SPORES parameters.

[2026-04-22 17:27:19] INFO     Optimisation model | parameters:spores_score | The optimisation problem components ['min_spores'] will be re-built.

[2026-04-22 17:27:19] INFO     Optimisation model | Running baseline model.

[2026-04-22 17:27:19] INFO     Optimisation model | objectives:min_cost_optimisation | Objective deactivated.

[2026-04-22 17:27:19] INFO     Optimisation model | objectives:min_cost_optimisation | Objective activated.

[2026-04-22 17:27:20] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:20] INFO     Optimisation model | parameters:spores_baseline_cost | The optimisation problem components ['total_system_cost_max'] will be re-built.

[2026-04-22 17:27:20] INFO     Optimisation model | objectives:min_cost_optimisation | Objective deactivated.

[2026-04-22 17:27:20] INFO     Optimisation model | objectives:min_spores | Objective activated.

[2026-04-22 17:27:20] INFO     Optimisation model | Running SPORES with `integer` scoring algorithm.

[2026-04-22 17:27:20] INFO     Optimisation model | Running SPORE 1.

[2026-04-22 17:27:20] INFO     Optimisation model | parameters:spores_score | The optimisation problem components ['min_spores'] will be re-built.

[2026-04-22 17:27:22] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:22] INFO     Optimisation model | Running SPORE 2.

[2026-04-22 17:27:23] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:23] INFO     Optimisation model | Running SPORE 3.

[2026-04-22 17:27:25] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:25] INFO     Backend: solver finished running. Time since start of solving optimisation problem: 0:00:05.857957

[2026-04-22 17:27:25] INFO     Postprocessing: applied zero threshold 1e-10 to model results.

[2026-04-22 17:27:25] INFO     Postprocessing: ended. Time since start of solving optimisation problem: 0:00:05.899258

[2026-04-22 17:27:25] INFO     Backend: model solve completed. Time since start of solving optimisation problem: 0:00:05.899578

Note how we have a new spores dimension in our results.

We can track the SPORES scores used between iterations using the spores_score_cumulative result. This scoring mechanism is based on increasing the score of any technology-node combination where the

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# We do some prettification of the outputs
model_spores.results.spores_score_cumulative.to_series().where(
    lambda x: x > 0
).dropna().unstack("spores")
# We do some prettification of the outputs
model_spores.results.spores_score_cumulative.to_series().where(
    lambda x: x > 0
).dropna().unstack("spores")

Out[8]:

		spores	1	2	3
nodes	techs	carriers
region1	ccgt	power	1000.0	2000.0	3000.0
region1_1	csp	power	1000.0	2000.0	2000.0
region1_3	csp	power	1000.0	1000.0	2000.0
region1_2	csp	power	NaN	1000.0	2000.0

Visualising results¶

We can use plotly to quickly examine our results. These are just some examples of how to visualise Calliope data.

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# We set the color mapping to use in all our plots by extracting the colors defined in the technology definitions of our model.
# We also create some reusable plotting functions.
colors = model_base.inputs.color.to_series().to_dict()


def plot_flows(results: xr.Dataset) -> go.Figure:
    df_electricity = (
        (results.flow_out.fillna(0) - results.flow_in.fillna(0))
        .sel(carriers="power")
        .sum("nodes")
        .to_series()
        .where(lambda x: x != 0)
        .dropna()
        .to_frame("Flow in/out (kWh)")
        .reset_index()
    )
    df_electricity_demand = df_electricity[df_electricity.techs == "demand_power"]
    df_electricity_other = df_electricity[df_electricity.techs != "demand_power"]

    fig = px.bar(
        df_electricity_other,
        x="timesteps",
        y="Flow in/out (kWh)",
        color="techs",
        color_discrete_map=colors,
    )
    fig.add_scatter(
        x=df_electricity_demand.timesteps,
        y=-1 * df_electricity_demand["Flow in/out (kWh)"],
        marker_color="black",
        name="demand",
    )
    return fig


def plot_capacity(results: xr.Dataset, **plotly_kwargs) -> go.Figure:
    df_capacity = (
        results.flow_cap.where(results.techs != "demand_power")
        .sel(carriers="power")
        .to_series()
        .where(lambda x: x != 0)
        .dropna()
        .to_frame("Flow capacity (kW)")
        .reset_index()
    )

    fig = px.bar(
        df_capacity,
        x="nodes",
        y="Flow capacity (kW)",
        color="techs",
        color_discrete_map=colors,
        **plotly_kwargs,
    )
    return fig
# We set the color mapping to use in all our plots by extracting the colors defined in the technology definitions of our model.
# We also create some reusable plotting functions.
colors = model_base.inputs.color.to_series().to_dict()


def plot_flows(results: xr.Dataset) -> go.Figure:
    df_electricity = (
        (results.flow_out.fillna(0) - results.flow_in.fillna(0))
        .sel(carriers="power")
        .sum("nodes")
        .to_series()
        .where(lambda x: x != 0)
        .dropna()
        .to_frame("Flow in/out (kWh)")
        .reset_index()
    )
    df_electricity_demand = df_electricity[df_electricity.techs == "demand_power"]
    df_electricity_other = df_electricity[df_electricity.techs != "demand_power"]

    fig = px.bar(
        df_electricity_other,
        x="timesteps",
        y="Flow in/out (kWh)",
        color="techs",
        color_discrete_map=colors,
    )
    fig.add_scatter(
        x=df_electricity_demand.timesteps,
        y=-1 * df_electricity_demand["Flow in/out (kWh)"],
        marker_color="black",
        name="demand",
    )
    return fig


def plot_capacity(results: xr.Dataset, **plotly_kwargs) -> go.Figure:
    df_capacity = (
        results.flow_cap.where(results.techs != "demand_power")
        .sel(carriers="power")
        .to_series()
        .where(lambda x: x != 0)
        .dropna()
        .to_frame("Flow capacity (kW)")
        .reset_index()
    )

    fig = px.bar(
        df_capacity,
        x="nodes",
        y="Flow capacity (kW)",
        color="techs",
        color_discrete_map=colors,
        **plotly_kwargs,
    )
    return fig

Using different `spores` scoring algorithms.¶

We make a number of scoring algorithms accessible out-of-the-box, based on those we present in Lombardi et al. (2023). You can call them on solving the model. Here, we'll compare the result on flow_cap from running each.

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# We subset to the same time range as operate/base mode
model_spores = calliope.examples.national_scale(
    scenario="spores", subset={"timesteps": ["2005-01-01", "2005-01-10"]}
)
model_spores.build()

spores_results = []
for algorithm in ["integer", "evolving_average", "random", "relative_deployment"]:
    model_spores.solve(**{"spores.scoring_algorithm": algorithm}, force=True)
    spores_results.append(model_spores.results.expand_dims(algorithm=[algorithm]))

spores_results_da = xr.concat(spores_results, dim="algorithm")

spores_results_da.flow_cap.to_series().dropna().unstack("spores")
# We subset to the same time range as operate/base mode
model_spores = calliope.examples.national_scale(
    scenario="spores", subset={"timesteps": ["2005-01-01", "2005-01-10"]}
)
model_spores.build()

spores_results = []
for algorithm in ["integer", "evolving_average", "random", "relative_deployment"]:
    model_spores.solve(**{"spores.scoring_algorithm": algorithm}, force=True)
    spores_results.append(model_spores.results.expand_dims(algorithm=[algorithm]))

spores_results_da = xr.concat(spores_results, dim="algorithm")

spores_results_da.flow_cap.to_series().dropna().unstack("spores")

[2026-04-22 17:27:25] INFO     (scenarios, spores ) | Applying the following overrides: ['spores'].

[2026-04-22 17:27:25] INFO     Math init | loading pre-defined math.

[2026-04-22 17:27:25] INFO     Math init | loading math files {'milp', 'operate', 'storage_inter_cluster', 'spores', 'base'}.

[2026-04-22 17:27:25] INFO     Model: preprocessing data

[2026-04-22 17:27:25] INFO     Math build | building applied math with ['base', 'spores'].

[2026-04-22 17:27:25] INFO     input data `spores_tracker` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:27:25] INFO     input data `color` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:27:25] INFO     input data `name` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:27:25] INFO     input data `spores_tracker` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:27:25] INFO     input data `color` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:27:25] INFO     input data `name` not defined in model math; it will not be available in the optimisation problem.

[2026-04-22 17:27:25] INFO     Model: initialisation complete

[2026-04-22 17:27:25] INFO     Model: backend build starting

[2026-04-22 17:27:26] INFO     Optimisation Model | parameters/lookups | Generated.

[2026-04-22 17:27:26] INFO     Optimisation Model | variables | Generated.

[2026-04-22 17:27:27] INFO     Optimisation Model | global_expressions | Generated.

[2026-04-22 17:27:28] INFO     Optimisation Model | constraints | Generated.

[2026-04-22 17:27:28] INFO     Optimisation Model | piecewise_constraints | Generated.

[2026-04-22 17:27:29] INFO     Optimisation Model | objectives | Generated.

[2026-04-22 17:27:29] INFO     Model: backend build complete

[2026-04-22 17:27:29] INFO     Optimisation model | starting model in spores mode.

[2026-04-22 17:27:29] INFO     Optimisation model | Resetting SPORES parameters.

[2026-04-22 17:27:29] INFO     Optimisation model | parameters:spores_score | The optimisation problem components ['min_spores'] will be re-built.

[2026-04-22 17:27:29] INFO     Optimisation model | Running baseline model.

[2026-04-22 17:27:29] INFO     Optimisation model | objectives:min_cost_optimisation | Objective deactivated.

[2026-04-22 17:27:29] INFO     Optimisation model | objectives:min_cost_optimisation | Objective activated.

[2026-04-22 17:27:30] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:30] INFO     Optimisation model | parameters:spores_baseline_cost | The optimisation problem components ['total_system_cost_max'] will be re-built.

[2026-04-22 17:27:30] INFO     Optimisation model | objectives:min_cost_optimisation | Objective deactivated.

[2026-04-22 17:27:30] INFO     Optimisation model | objectives:min_spores | Objective activated.

[2026-04-22 17:27:30] INFO     Optimisation model | Running SPORES with `integer` scoring algorithm.

[2026-04-22 17:27:30] INFO     Optimisation model | Running SPORE 1.

[2026-04-22 17:27:30] INFO     Optimisation model | parameters:spores_score | The optimisation problem components ['min_spores'] will be re-built.

[2026-04-22 17:27:31] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:31] INFO     Optimisation model | Running SPORE 2.

[2026-04-22 17:27:33] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:33] INFO     Optimisation model | Running SPORE 3.

[2026-04-22 17:27:34] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:34] INFO     Backend: solver finished running. Time since start of solving optimisation problem: 0:00:05.956117

[2026-04-22 17:27:35] INFO     Postprocessing: applied zero threshold 1e-10 to model results.

[2026-04-22 17:27:35] INFO     Postprocessing: ended. Time since start of solving optimisation problem: 0:00:05.997327

[2026-04-22 17:27:35] INFO     Backend: model solve completed. Time since start of solving optimisation problem: 0:00:05.997637

[2026-04-22 17:27:35] INFO     Optimisation model | starting model in spores mode.

[2026-04-22 17:27:35] INFO     Optimisation model | Resetting SPORES parameters.

[2026-04-22 17:27:35] INFO     Optimisation model | Running baseline model.

[2026-04-22 17:27:35] INFO     Optimisation model | objectives:min_spores | Objective deactivated.

[2026-04-22 17:27:35] INFO     Optimisation model | objectives:min_cost_optimisation | Objective activated.

[2026-04-22 17:27:36] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:36] INFO     Optimisation model | objectives:min_cost_optimisation | Objective deactivated.

[2026-04-22 17:27:36] INFO     Optimisation model | objectives:min_spores | Objective activated.

[2026-04-22 17:27:36] INFO     Optimisation model | Running SPORES with `evolving_average` scoring algorithm.

[2026-04-22 17:27:36] INFO     Optimisation model | Running SPORE 1.

[2026-04-22 17:27:37] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:37] INFO     Optimisation model | Running SPORE 2.

[2026-04-22 17:27:39] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:39] INFO     Optimisation model | Running SPORE 3.

[2026-04-22 17:27:40] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:40] INFO     Backend: solver finished running. Time since start of solving optimisation problem: 0:00:05.972542

[2026-04-22 17:27:41] INFO     Postprocessing: applied zero threshold 1e-10 to model results.

[2026-04-22 17:27:41] INFO     Postprocessing: ended. Time since start of solving optimisation problem: 0:00:06.014828

[2026-04-22 17:27:41] INFO     Backend: model solve completed. Time since start of solving optimisation problem: 0:00:06.015151

[2026-04-22 17:27:41] INFO     Optimisation model | starting model in spores mode.

[2026-04-22 17:27:41] INFO     Optimisation model | Resetting SPORES parameters.

[2026-04-22 17:27:41] INFO     Optimisation model | Running baseline model.

[2026-04-22 17:27:41] INFO     Optimisation model | objectives:min_spores | Objective deactivated.

[2026-04-22 17:27:41] INFO     Optimisation model | objectives:min_cost_optimisation | Objective activated.

[2026-04-22 17:27:42] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:42] INFO     Optimisation model | objectives:min_cost_optimisation | Objective deactivated.

[2026-04-22 17:27:42] INFO     Optimisation model | objectives:min_spores | Objective activated.

[2026-04-22 17:27:42] INFO     Optimisation model | Running SPORES with `random` scoring algorithm.

[2026-04-22 17:27:42] INFO     Optimisation model | Running SPORE 1.

[2026-04-22 17:27:43] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:43] INFO     Optimisation model | Running SPORE 2.

[2026-04-22 17:27:44] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:44] INFO     Optimisation model | Running SPORE 3.

[2026-04-22 17:27:45] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:45] INFO     Backend: solver finished running. Time since start of solving optimisation problem: 0:00:04.898642

[2026-04-22 17:27:45] INFO     Postprocessing: applied zero threshold 1e-10 to model results.

[2026-04-22 17:27:45] INFO     Postprocessing: ended. Time since start of solving optimisation problem: 0:00:04.939147

[2026-04-22 17:27:45] INFO     Backend: model solve completed. Time since start of solving optimisation problem: 0:00:04.939420

[2026-04-22 17:27:45] INFO     Optimisation model | starting model in spores mode.

[2026-04-22 17:27:45] INFO     Optimisation model | Resetting SPORES parameters.

[2026-04-22 17:27:45] INFO     Optimisation model | Running baseline model.

[2026-04-22 17:27:45] INFO     Optimisation model | objectives:min_spores | Objective deactivated.

[2026-04-22 17:27:45] INFO     Optimisation model | objectives:min_cost_optimisation | Objective activated.

[2026-04-22 17:27:47] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:47] INFO     Optimisation model | objectives:min_cost_optimisation | Objective deactivated.

[2026-04-22 17:27:47] INFO     Optimisation model | objectives:min_spores | Objective activated.

[2026-04-22 17:27:47] INFO     Optimisation model | Running SPORES with `relative_deployment` scoring algorithm.

[2026-04-22 17:27:47] INFO     Optimisation model | Running SPORE 1.

[2026-04-22 17:27:48] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:48] INFO     Optimisation model | Running SPORE 2.

[2026-04-22 17:27:50] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:50] INFO     Optimisation model | Running SPORE 3.

[2026-04-22 17:27:52] INFO     Optimisation Model | postprocess | Generated.

[2026-04-22 17:27:52] INFO     Backend: solver finished running. Time since start of solving optimisation problem: 0:00:06.180253

[2026-04-22 17:27:52] INFO     Postprocessing: applied zero threshold 1e-10 to model results.

[2026-04-22 17:27:52] INFO     Postprocessing: ended. Time since start of solving optimisation problem: 0:00:06.222602

[2026-04-22 17:27:52] INFO     Backend: model solve completed. Time since start of solving optimisation problem: 0:00:06.222924

Out[10]:

			spores	0	1	2	3
algorithm	nodes	techs	carriers
integer	region1	ccgt	power	30000.0000	3.000000e+04	2.835861e+04	22649.52900
		demand_power	power	39614.8160	3.961482e+04	3.961482e+04	39614.81600
		region1_to_region1_1	power	9000.0000	2.861682e+03	1.763313e-10	9000.00000
		region1_to_region1_2	power	0.0000	9.000000e+03	4.503073e+03	2863.26850
		region1_to_region1_3	power	2861.6819	0.000000e+00	9.000000e+03	7348.88410
		region1_to_region2	power	3230.4729	3.743642e+03	3.423336e+03	3876.56710
	region1_1	csp	power	10000.0000	3.179646e+03	0.000000e+00	10000.00000
	region1_1	region1_to_region1_1	power	9000.0000	2.861682e+03	1.763313e-10	9000.00000
	region1_2	csp	power	0.0000	1.000000e+04	5.003414e+03	3181.40940
	region1_2	region1_to_region1_2	power	0.0000	9.000000e+03	4.503073e+03	2863.26850
	region1_3	csp	power	3179.6465	0.000000e+00	1.000000e+04	8165.42680
	region1_3	region1_to_region1_3	power	2861.6819	0.000000e+00	9.000000e+03	7348.88410
	region2	battery	power	1000.0000	1.000000e+03	1.000000e+03	1000.00000
		demand_power	power	2909.8360	2.909836e+03	2.909836e+03	2909.83600
		region1_to_region2	power	3230.4729	3.743642e+03	3.423336e+03	3876.56710
evolving_average	region1	ccgt	power	30000.0000	3.000000e+04	2.289665e+04	22652.16600
		demand_power	power	39614.8160	3.961482e+04	3.961482e+04	39614.81600
		region1_to_region1_1	power	9000.0000	2.861682e+03	9.000000e+03	9000.00000
		region1_to_region1_2	power	0.0000	9.000000e+03	2.887397e+03	1209.51550
		region1_to_region1_3	power	2861.6819	0.000000e+00	8.254105e+03	9000.00000
		region1_to_region2	power	3230.4729	3.743642e+03	3.423336e+03	3876.56710
	region1_1	csp	power	10000.0000	3.179646e+03	1.000000e+04	10000.00000
	region1_1	region1_to_region1_1	power	9000.0000	2.861682e+03	9.000000e+03	9000.00000
	region1_2	csp	power	0.0000	1.000000e+04	3.208219e+03	1343.90610
	region1_2	region1_to_region1_2	power	0.0000	9.000000e+03	2.887397e+03	1209.51550
	region1_3	csp	power	3179.6465	0.000000e+00	9.171228e+03	10000.00000
	region1_3	region1_to_region1_3	power	2861.6819	0.000000e+00	8.254105e+03	9000.00000
	region2	battery	power	1000.0000	1.000000e+03	0.000000e+00	1000.00000
		demand_power	power	2909.8360	2.909836e+03	2.909836e+03	2909.83600
		region1_to_region2	power	3230.4729	3.743642e+03	3.423336e+03	3876.56710
random	region1	ccgt	power	30000.0000	3.000000e+04	3.000000e+04	30000.00000
		demand_power	power	39614.8160	3.961482e+04	3.961482e+04	39614.81600
		region1_to_region1_1	power	9000.0000	2.861682e+03	9.000000e+03	9000.00000
		region1_to_region1_2	power	0.0000	9.000000e+03	4.038153e+03	4038.15250
		region1_to_region1_3	power	2861.6819	0.000000e+00	0.000000e+00	0.00000
		region1_to_region2	power	3230.4729	3.894622e+03	3.423336e+03	3423.33650
	region1_1	csp	power	10000.0000	3.179646e+03	1.000000e+04	10000.00000
	region1_1	region1_to_region1_1	power	9000.0000	2.861682e+03	9.000000e+03	9000.00000
	region1_2	csp	power	0.0000	1.000000e+04	4.486836e+03	4486.83610
	region1_2	region1_to_region1_2	power	0.0000	9.000000e+03	4.038153e+03	4038.15250
	region1_3	csp	power	3179.6465	0.000000e+00	0.000000e+00	0.00000
	region1_3	region1_to_region1_3	power	2861.6819	0.000000e+00	0.000000e+00	0.00000
	region2	battery	power	1000.0000	1.000000e+03	0.000000e+00	0.00000
		demand_power	power	2909.8360	2.909836e+03	2.909836e+03	2909.83600
		region1_to_region2	power	3230.4729	3.894622e+03	3.423336e+03	3423.33650
relative_deployment	region1	ccgt	power	30000.0000	2.882452e+04	2.265217e+04	23389.69100
		demand_power	power	39614.8160	3.961482e+04	3.961482e+04	39614.81600
		region1_to_region1_1	power	9000.0000	5.738888e-09	9.000000e+03	9000.00000
		region1_to_region1_2	power	0.0000	9.000000e+03	1.209515e+03	9000.00000
		region1_to_region1_3	power	2861.6819	5.213637e+03	9.000000e+03	471.99107
		region1_to_region2	power	3230.4729	3.423336e+03	3.876567e+03	4021.21410
	region1_1	csp	power	10000.0000	0.000000e+00	1.000000e+04	10000.00000
	region1_1	region1_to_region1_1	power	9000.0000	5.738888e-09	9.000000e+03	9000.00000
	region1_2	csp	power	0.0000	1.000000e+04	1.343906e+03	10000.00000
	region1_2	region1_to_region1_2	power	0.0000	9.000000e+03	1.209515e+03	9000.00000
	region1_3	csp	power	3179.6465	5.792930e+03	1.000000e+04	524.43452
	region1_3	region1_to_region1_3	power	2861.6819	5.213637e+03	9.000000e+03	471.99107
	region2	battery	power	1000.0000	0.000000e+00	1.000000e+03	1000.00000
		demand_power	power	2909.8360	2.909836e+03	2.909836e+03	2909.83600
		region1_to_region2	power	3230.4729	3.423336e+03	3.876567e+03	4021.21410

'base' vs 'operate'¶

Here, we compare flows over the 10 days. Note how flows do not match as the rolling horizon makes it difficult to make the correct storage charge/discharge decisions.

In [11]:

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fig_flows_base = plot_flows(
    model_base.results.sel(timesteps=model_operate.results.timesteps)
)
fig_flows_base.update_layout(title="Base mode flows")
fig_flows_base = plot_flows(
    model_base.results.sel(timesteps=model_operate.results.timesteps)
)
fig_flows_base.update_layout(title="Base mode flows")

In [12]:

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fig_flows_operate = plot_flows(model_operate.results)
fig_flows_operate.update_layout(title="Operate mode flows")
fig_flows_operate = plot_flows(model_operate.results)
fig_flows_operate.update_layout(title="Operate mode flows")

'base' vs 'spores'¶

Here, we compare installed capacities between the baseline run (== base mode) and the SPORES. Note how the 0 SPORE is the same as base mode and then results deviate considerably.

In [13]:

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fig_flows_base = plot_capacity(model_base.results)
fig_flows_base.update_layout(title="Base mode capacities")
fig_flows_base = plot_capacity(model_base.results)
fig_flows_base.update_layout(title="Base mode capacities")

In [14]:

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fig_flows_spores = plot_capacity(model_spores.results, facet_col="spores")
fig_flows_spores.update_layout(title="SPORES mode capacities")
fig_flows_spores = plot_capacity(model_spores.results, facet_col="spores")
fig_flows_spores.update_layout(title="SPORES mode capacities")

Comparing 'spores' scoring algorithms¶

Here, we compare installed capacities between the different SPORES runs.

In [15]:

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fig_flows_spores = plot_capacity(
    spores_results_da, facet_col="spores", facet_row="algorithm"
)
fig_flows_spores.update_layout(
    title="SPORES mode capacities using different scoring algorithms",
    autosize=False,
    height=800,
)
fig_flows_spores = plot_capacity(
    spores_results_da, facet_col="spores", facet_row="algorithm"
)
fig_flows_spores.update_layout(
    title="SPORES mode capacities using different scoring algorithms",
    autosize=False,
    height=800,
)