Merge pull request #903 from PyPSA/cluster-network-replace-pyomo
Cluster network replace pyomo
This commit is contained in:
commit
0bb70b42ef
10
.github/workflows/ci.yaml
vendored
10
.github/workflows/ci.yaml
vendored
@ -53,16 +53,6 @@ jobs:
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run: |
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run: |
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echo -ne "url: ${CDSAPI_URL}\nkey: ${CDSAPI_TOKEN}\n" > ~/.cdsapirc
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echo -ne "url: ${CDSAPI_URL}\nkey: ${CDSAPI_TOKEN}\n" > ~/.cdsapirc
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- name: Add solver to environment
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run: |
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echo -e "- glpk\n- ipopt<3.13.3" >> envs/environment.yaml
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if: ${{ matrix.os }} == 'windows-latest'
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- name: Add solver to environment
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run: |
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echo -e "- glpk\n- ipopt" >> envs/environment.yaml
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if: ${{ matrix.os }} != 'windows-latest'
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- name: Setup micromamba
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- name: Setup micromamba
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uses: mamba-org/setup-micromamba@v1
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uses: mamba-org/setup-micromamba@v1
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with:
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with:
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@ -60,6 +60,11 @@ Upcoming Release
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* The rule ``plot_network`` has been split into separate rules for plotting
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* The rule ``plot_network`` has been split into separate rules for plotting
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electricity, hydrogen and gas networks.
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electricity, hydrogen and gas networks.
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* To determine the optimal topology to meet the number of clusters, the workflow used pyomo in combination with ``ipopt`` or ``gurobi``. This dependency has been replaced by using ``linopy`` in combination with ``scipopt`` or ``gurobi``. The environment file has been updated accordingly.
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* The ``highs`` solver was added to the default environment file.
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PyPSA-Eur 0.9.0 (5th January 2024)
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PyPSA-Eur 0.9.0 (5th January 2024)
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==================================
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==================================
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@ -35,8 +35,9 @@ dependencies:
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- netcdf4
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- netcdf4
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- networkx
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- networkx
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- scipy
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- scipy
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- glpk
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- shapely>=2.0
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- shapely>=2.0
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- pyomo
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- pyscipopt
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- matplotlib
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- matplotlib
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- proj
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- proj
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- fiona
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- fiona
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@ -47,7 +48,6 @@ dependencies:
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- tabula-py
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- tabula-py
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- pyxlsb
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- pyxlsb
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- graphviz
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- graphviz
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- ipopt
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# Keep in conda environment when calling ipython
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# Keep in conda environment when calling ipython
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- ipython
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- ipython
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@ -60,3 +60,4 @@ dependencies:
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- pip:
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- pip:
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- tsam>=2.3.1
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- tsam>=2.3.1
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- highspy
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@ -122,14 +122,15 @@ Exemplary unsolved network clustered to 37 nodes:
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"""
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"""
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import logging
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import logging
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import os
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import warnings
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import warnings
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from functools import reduce
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from functools import reduce
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import geopandas as gpd
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import geopandas as gpd
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import linopy
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import matplotlib.pyplot as plt
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import matplotlib.pyplot as plt
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import numpy as np
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import numpy as np
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import pandas as pd
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import pandas as pd
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import pyomo.environ as po
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import pypsa
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import pypsa
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import seaborn as sns
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import seaborn as sns
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from _helpers import configure_logging, update_p_nom_max
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from _helpers import configure_logging, update_p_nom_max
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@ -214,7 +215,7 @@ def get_feature_for_hac(n, buses_i=None, feature=None):
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return feature_data
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return feature_data
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def distribute_clusters(n, n_clusters, focus_weights=None, solver_name="cbc"):
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def distribute_clusters(n, n_clusters, focus_weights=None, solver_name="scip"):
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"""
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"""
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Determine the number of clusters per country.
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Determine the number of clusters per country.
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"""
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"""
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@ -254,31 +255,22 @@ def distribute_clusters(n, n_clusters, focus_weights=None, solver_name="cbc"):
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L.sum(), 1.0, rtol=1e-3
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L.sum(), 1.0, rtol=1e-3
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), f"Country weights L must sum up to 1.0 when distributing clusters. Is {L.sum()}."
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), f"Country weights L must sum up to 1.0 when distributing clusters. Is {L.sum()}."
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m = po.ConcreteModel()
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m = linopy.Model()
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clusters = m.add_variables(
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def n_bounds(model, *n_id):
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lower=1, upper=N, coords=[L.index], name="n", integer=True
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return (1, N[n_id])
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m.n = po.Var(list(L.index), bounds=n_bounds, domain=po.Integers)
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m.tot = po.Constraint(expr=(po.summation(m.n) == n_clusters))
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m.objective = po.Objective(
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expr=sum((m.n[i] - L.loc[i] * n_clusters) ** 2 for i in L.index),
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sense=po.minimize,
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)
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)
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m.add_constraints(clusters.sum() == n_clusters, name="tot")
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opt = po.SolverFactory(solver_name)
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# leave out constant in objective (L * n_clusters) ** 2
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if solver_name == "appsi_highs" or not opt.has_capability("quadratic_objective"):
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m.objective = (clusters * clusters - 2 * clusters * L * n_clusters).sum()
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logger.warning(
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if solver_name == "gurobi":
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f"The configured solver `{solver_name}` does not support quadratic objectives. Falling back to `ipopt`."
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logging.getLogger("gurobipy").propagate = False
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elif solver_name != "scip":
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logger.info(
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f"The configured solver `{solver_name}` does not support quadratic objectives. Falling back to `scip`."
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)
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)
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opt = po.SolverFactory("ipopt")
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solver_name = "scip"
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m.solve(solver_name=solver_name)
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results = opt.solve(m)
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return m.solution["n"].to_series().astype(int)
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assert (
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results["Solver"][0]["Status"] == "ok"
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), f"Solver returned non-optimally: {results}"
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return pd.Series(m.n.get_values(), index=L.index).round().astype(int)
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def busmap_for_n_clusters(
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def busmap_for_n_clusters(
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@ -372,7 +364,7 @@ def busmap_for_n_clusters(
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return (
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return (
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n.buses.groupby(["country", "sub_network"], group_keys=False)
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n.buses.groupby(["country", "sub_network"], group_keys=False)
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.apply(busmap_for_country)
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.apply(busmap_for_country, include_groups=False)
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.squeeze()
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.squeeze()
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.rename("busmap")
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.rename("busmap")
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)
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)
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@ -385,7 +377,7 @@ def clustering_for_n_clusters(
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aggregate_carriers=None,
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aggregate_carriers=None,
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line_length_factor=1.25,
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line_length_factor=1.25,
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aggregation_strategies=dict(),
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aggregation_strategies=dict(),
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solver_name="cbc",
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solver_name="scip",
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algorithm="hac",
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algorithm="hac",
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feature=None,
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feature=None,
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extended_link_costs=0,
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extended_link_costs=0,
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@ -462,7 +454,6 @@ if __name__ == "__main__":
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params = snakemake.params
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params = snakemake.params
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solver_name = snakemake.config["solving"]["solver"]["name"]
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solver_name = snakemake.config["solving"]["solver"]["name"]
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solver_name = "appsi_highs" if solver_name == "highs" else solver_name
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n = pypsa.Network(snakemake.input.network)
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n = pypsa.Network(snakemake.input.network)
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