.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "gat_plot_examples/10_uc_vs_ed_comparison.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_gat_plot_examples_10_uc_vs_ed_comparison.py: Model vs Model: UC, ED, and Emulator from One Store --------------------------------------------------- The Sienna fixture's simulation store contains three models solved in sequence — unit commitment (UC), economic dispatch (ED, with UC commitments fed forward), and an Emulator stage. ``from_simulation_models`` splits the store into a scenario per model, and ``MultiScenario`` puts them behind one API with ``Scenario`` as the outer column level — the same machinery GAT uses to compare scenarios across tools. Shown here, top-down: system stacks and the redispatch delta, then per-technology timeseries (solid vs dashed), then a single generator, then a single line (ED vs Emulator). .. GENERATED FROM PYTHON SOURCE LINES 16-47 .. code-block:: Python import warnings warnings.filterwarnings("ignore", category=DeprecationWarning) # The fixture has no storage; MultiScenario's pass-through getters use # defaults, so silence the resulting missing-dataset UserWarnings. warnings.filterwarnings("ignore", category=UserWarning) import matplotlib.pyplot as plt from gat.scenariohandlers import MultiScenario, SiennaScenario import gat.quickplots as qp from gat.quickplots.utils import get_colormap # GAT's standard technology colors — used for every comparison below so # a technology keeps its identity across panels and against the stacks. tech_colors = get_colormap() # These examples use the in-repo Sienna RTS-GMLC fixture. Regenerate it via # `make sienna-fixture-v4`. For project-based workflows, use `gat.load(...)` # instead — see docs/source/python_api_load.md. sienna_v4 = "../../example_data/sienna/v4" scenarios = SiennaScenario.from_simulation_models( simulation_files=f"{sienna_v4}/simulation_store.h5", system_file=f"{sienna_v4}/sys.json", ) print("models in store:", list(scenarios)) # The two optimization stages, behind one MultiScenario API. ms = MultiScenario({name: scenarios[name] for name in ("UC", "ED")}) .. rst-class:: sphx-glr-script-out .. code-block:: none models in store: ['ED', 'UC', 'Emulator'] .. GENERATED FROM PYTHON SOURCE LINES 48-50 Generation stacks, one per model -------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 50-63 .. code-block:: Python dispatch = {} fig, axs = plt.subplots(1, 2, figsize=(12, 5), sharey=True) for ax, name in zip(axs, ("UC", "ED")): dispatch[name] = scenarios[name].get_area_dispatch( include_charging=False, include_use=False ) qp.plot_annual_system_dispatch_stack(dispatch[name], ax=ax) ax.set_title(f"{name} — Generation by Technology") plt.tight_layout() plt.show() .. image-sg:: /gat_plot_examples/images/sphx_glr_10_uc_vs_ed_comparison_001.png :alt: UC — Generation by Technology, ED — Generation by Technology :srcset: /gat_plot_examples/images/sphx_glr_10_uc_vs_ed_comparison_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 64-71 Redispatch delta (ED minus UC) ------------------------------ Positive bars are technologies ED leans on harder than UC planned; negative bars are technologies ED backs down. The deltas are small relative to total energy — ED redispatches within UC's commitments — which is exactly what the feedforward is supposed to enforce. .. GENERATED FROM PYTHON SOURCE LINES 71-87 .. code-block:: Python totals = { name: df.T.groupby(level="Technology").sum().T.sum() for name, df in dispatch.items() } delta = (totals["ED"] - totals["UC"]).sort_values() fig, ax = plt.subplots(figsize=(10, 4)) delta.plot.barh(ax=ax, color=[tech_colors.get(t, "gray") for t in delta.index]) ax.set_xlabel("ED − UC energy (MWh)") ax.set_title("Redispatch: where ED deviates from the UC plan") ax.axvline(0, color="black", linewidth=0.8) plt.tight_layout() plt.show() .. image-sg:: /gat_plot_examples/images/sphx_glr_10_uc_vs_ed_comparison_002.png :alt: Redispatch: where ED deviates from the UC plan :srcset: /gat_plot_examples/images/sphx_glr_10_uc_vs_ed_comparison_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 88-95 Per-technology timeseries — system level (solid UC, dashed ED) -------------------------------------------------------------- ``MultiScenario.get_area_dispatch()`` returns one frame with a ``Scenario`` column level, so slicing out comparable timeseries is a groupby away. One color per technology; line style distinguishes the model. .. GENERATED FROM PYTHON SOURCE LINES 95-118 .. code-block:: Python ms_dispatch = ms.get_area_dispatch() by_tech = ms_dispatch.T.groupby(level=["Scenario", "Technology"]).sum().T top_techs = by_tech["UC"].sum().sort_values(ascending=False).head(4).index fig, ax = plt.subplots(figsize=(12, 5)) styles = {"UC": "-", "ED": "--"} for tech in top_techs: for name, style in styles.items(): ax.plot( by_tech[(name, tech)], style, color=tech_colors.get(tech, "gray"), label=f"{tech} ({name})" if name == "UC" else None, ) ax.legend(ncols=2, title="solid = UC, dashed = ED") ax.set_ylabel("MW") ax.set_title("System generation by technology — UC vs ED") plt.tight_layout() plt.show() .. image-sg:: /gat_plot_examples/images/sphx_glr_10_uc_vs_ed_comparison_003.png :alt: System generation by technology — UC vs ED :srcset: /gat_plot_examples/images/sphx_glr_10_uc_vs_ed_comparison_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 119-124 Per-technology timeseries — one region -------------------------------------- The same comparison scoped to a single area: keep the ``Area`` level before grouping. .. GENERATED FROM PYTHON SOURCE LINES 124-150 .. code-block:: Python area = ms_dispatch.columns.get_level_values("Area").unique()[0] area_by_tech = ( ms_dispatch.xs(area, axis=1, level="Area") .T.groupby(level=["Scenario", "Technology"]) .sum() .T ) area_top = area_by_tech["UC"].sum().sort_values(ascending=False).head(3).index fig, ax = plt.subplots(figsize=(12, 4)) for tech in area_top: for name, style in styles.items(): ax.plot( area_by_tech[(name, tech)], style, color=tech_colors.get(tech, "gray"), label=f"{tech} ({name})" if name == "UC" else None, ) ax.legend(ncols=2, title="solid = UC, dashed = ED") ax.set_ylabel("MW") ax.set_title(f"Area {area} generation by technology — UC vs ED") plt.tight_layout() plt.show() .. image-sg:: /gat_plot_examples/images/sphx_glr_10_uc_vs_ed_comparison_004.png :alt: Area 1 generation by technology — UC vs ED :srcset: /gat_plot_examples/images/sphx_glr_10_uc_vs_ed_comparison_004.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 151-158 A single generator ------------------ Entity-level drill-down: ``MultiScenario.get_generation()`` carries every generator under each scenario. Pick the unit whose behavior changes most between the models — commitment differences show up as a unit running in one model and sitting idle in the other. .. GENERATED FROM PYTHON SOURCE LINES 158-181 .. code-block:: Python gen = ms.get_generation() common = gen["UC"].columns.intersection(gen["ED"].columns) diff = (gen["ED"][common] - gen["UC"][common]).abs().sum().sort_values() unit = diff.index[-1] # Color the unit by its technology so it matches the panels above # (get_generators_tech returns (Technology, Component) columns). gen_tech = scenarios["UC"].get_generators_tech() matches = [t for t, u in gen_tech.columns if u == unit] unit_tech = matches[0] if matches else None unit_color = tech_colors.get(unit_tech, "#333333") fig, ax = plt.subplots(figsize=(12, 4)) ax.plot(gen[("UC", unit)], "-", color=unit_color, label="UC") ax.plot(gen[("ED", unit)], "--", color=unit_color, label="ED") ax.legend() ax.set_ylabel("MW") ax.set_title(f"Generator {unit} — largest UC/ED divergence") plt.tight_layout() plt.show() .. image-sg:: /gat_plot_examples/images/sphx_glr_10_uc_vs_ed_comparison_005.png :alt: Generator 122_WIND_1 — largest UC/ED divergence :srcset: /gat_plot_examples/images/sphx_glr_10_uc_vs_ed_comparison_005.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 182-188 A single line (ED vs Emulator) ------------------------------ For line flows the interesting comparison is the dispatch model against the Emulator stage — how the planned flow differs from the emulated one. Same MultiScenario pattern, different pair. .. GENERATED FROM PYTHON SOURCE LINES 188-209 .. code-block:: Python ms_flow = MultiScenario({name: scenarios[name] for name in ("ED", "Emulator")}) flow = ms_flow.get_line_flow() common_lines = flow["ED"].columns.intersection(flow["Emulator"].columns) line_diff = ( (flow["Emulator"][common_lines] - flow["ED"][common_lines]) .abs() .sum() .sort_values() ) line_name = line_diff.index[-1] fig, ax = plt.subplots(figsize=(12, 4)) ax.plot(flow[("ED", line_name)], "-", color="#333333", label="ED") ax.plot(flow[("Emulator", line_name)], "--", color="#0079C2", label="Emulator") ax.legend() ax.set_ylabel("Flow (MW)") ax.set_title(f"Line {line_name} — planned (ED) vs emulated flow") plt.tight_layout() plt.show() .. image-sg:: /gat_plot_examples/images/sphx_glr_10_uc_vs_ed_comparison_006.png :alt: Line CA-1 — planned (ED) vs emulated flow :srcset: /gat_plot_examples/images/sphx_glr_10_uc_vs_ed_comparison_006.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.483 seconds) .. _sphx_glr_download_gat_plot_examples_10_uc_vs_ed_comparison.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: 10_uc_vs_ed_comparison.ipynb <10_uc_vs_ed_comparison.ipynb>` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: 10_uc_vs_ed_comparison.py <10_uc_vs_ed_comparison.py>` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: 10_uc_vs_ed_comparison.zip <10_uc_vs_ed_comparison.zip>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_