import pandas as pd import numpy as np from matplotlib import pyplot as plt import matplotlib.dates as mdates import argparse import yaml from mix_simul.scenarios import Scenario parser = argparse.ArgumentParser() parser.add_argument("--begin", help="begin date (YYYY-MM-DD), between 1985-01-01 and 2015-01-01", required=True) parser.add_argument("--end", help="end date (YYYY-MM-DD), between 1985-01-01 and 2015-01-01", required=True) parser.add_argument("--flexibility", help="enable load flexibility modeling", action="store_true") parser.add_argument("--scenarios", help="path to scenarios parameters yml file", default="scenarios/rte_2050.yml") args = parser.parse_args() with open(args.scenarios, 'r') as f: scenarios = yaml.load(f, Loader=yaml.FullLoader) potential = pd.read_parquet("data/potential.parquet") potential = potential.loc['1985-01-01 00:00:00':'2015-01-01 00:00:00', :] potential.fillna(0, inplace=True) begin = args.begin end = args.end flexibility = args.flexibility potential = potential.loc[( slice(f'{begin} 00:00:00', f'{end} 00:00:00'), 'FR'), :] # intermittent sources potential p = potential[["onshore", "offshore", "solar"]].to_xarray().to_array() p = np.insert(p, 3, 0.7, axis=0) # nuclear power-like times = potential.index.get_level_values(0) n_scenarios = len(scenarios) fig, axes = plt.subplots(nrows=n_scenarios, ncols=2, sharex="col", sharey=True) w, h = fig.get_size_inches() fig.set_figwidth(w*1.5) fig.set_figheight(h*1.5) fig_storage, axes_storage = plt.subplots(nrows=n_scenarios, ncols=2, sharex="col", sharey=True) fig_storage.set_figwidth(w*1.5) fig_storage.set_figheight(h*1.5) fig_dispatch, axes_dispatch = plt.subplots(nrows=n_scenarios, ncols=2, sharex="col", sharey=True) fig_dispatch.set_figwidth(w*1.5) fig_dispatch.set_figheight(h*1.5) fig_gap_distribution, axes_gap_distribution = plt.subplots(nrows=int(np.ceil(n_scenarios/2)), ncols=2, sharex="col", sharey=True) fig_gap_distribution.set_figwidth(w*1.5) fig_gap_distribution.set_figheight(h*1.5) months = [ "Février", "Juin" ] labels = [ "load (GW)", "production (GW)", "available power (production-storage) (GW)", "power deficit" ] labels_storage = [ "Batteries (TWh)", "STEP (TWh)", "P2G (TWh)" ] labels_dispatch = [ "Hydro (GW)", "Biomass (GW)", "Thermal (GW)" ] date_fmt = mdates.DateFormatter('%d/%m') # for step in np.linspace(start, stop, 2050-2022, True)[::-1]: row = 0 for scenario in scenarios: if not flexibility: scenarios[scenario]["flexibility_power"] = 0 scenario_model = Scenario(**scenarios[scenario]) S, load, production, gap, storage, dp = scenario_model.run(times, p) print(f"{scenario}:", S, gap.max(), np.quantile(gap, 0.95)) print(f"exports: {np.minimum(np.maximum(-gap, 0), 39).sum()/1000} TWh; imports: {np.minimum(np.maximum(gap, 0), 39).sum()/1000} TWh") print(f"dispatchable: " + ", ".join([f"{dp[i].sum()/1000:.2f} TWh" for i in range(dp.shape[0])])) potential['load'] = load potential['production'] = production potential['available'] = production-np.diff(storage.sum(axis=0), append=0) for i in range(3): potential[f"storage_{i}"] = np.diff(storage[i,:], append=0)#storage[i,:]/1000 potential[f"storage_{i}"] = storage[i,:]/1000 for i in range(dp.shape[0]): potential[f"dispatch_{i}"] = dp[i,:] potential["dispatch"] = dp.sum(axis=0) data = [ potential.loc[(slice('2013-02-01 00:00:00', '2013-03-01 00:00:00'), 'FR'), :], potential.loc[(slice('2013-06-01 00:00:00', '2013-07-01 00:00:00'), 'FR'), :] ] for col in range(2): ax = axes[row, col] if axes.ndim > 1 else axes[col] ax.plot(data[col].index.get_level_values(0), data[col] ["load"], label="adjusted load (GW)", lw=1) ax.plot(data[col].index.get_level_values(0), data[col] ["production"], label="production (GW)", ls="dotted", lw=1) ax.plot(data[col].index.get_level_values(0), data[col]["available"], label="available power (production-d(storage)/dt) (GW)", lw=1) ax.fill_between( data[col].index.get_level_values(0), data[col]["available"], data[col]["load"], where=data[col]["load"] > data[col]["available"], color='red', alpha=0.15 ) ax.xaxis.set_major_formatter(date_fmt) ax.text( 0.5, 0.87, f"Scénario {scenario} ({months[col]})", ha='center', transform=ax.transAxes) ax.set_ylim(10, 210) ax = axes_storage[row, col] if axes.ndim > 1 else axes_storage[col] for i in np.arange(3): if i == 2: base = 0 else: base = np.sum([data[col][f"storage_{j}"] for j in np.arange(i+1,3)], axis=0) ax.fill_between(data[col].index.get_level_values(0), base, base+data[col] [f"storage_{i}"], label=f"storage {i}", alpha=0.5) ax.plot(data[col].index.get_level_values(0), base+data[col] [f"storage_{i}"], label=f"storage {i}", lw=0.25) ax.xaxis.set_major_formatter(date_fmt) ax.text( 0.5, 0.87, f"Scénario {scenario} ({months[col]})", ha='center', transform=ax.transAxes) ax = axes_dispatch[row, col] if axes.ndim > 1 else axes_dispatch[col] for i in range(dp.shape[0]): if i == 0: base = 0 else: base = np.sum([data[col][f"dispatch_{j}"] for j in np.arange(i)], axis=0) ax.fill_between(data[col].index.get_level_values(0), base, base+data[col] [f"dispatch_{i}"], label=f"dispatch {i}", alpha=0.5) ax.plot(data[col].index.get_level_values(0), base+data[col] [f"dispatch_{i}"], label=f"dispatch {i}", lw=0.25) ax.xaxis.set_major_formatter(date_fmt) ax.text( 0.5, 0.87, f"Scénario {scenario} ({months[col]})", ha='center', transform=ax.transAxes) div = int(np.ceil(n_scenarios/2)) ax = axes_gap_distribution[row%div, row//div] if axes_gap_distribution.ndim > 1 else axes_gap_distribution[row%div] hist, bin_edges = np.histogram(-gap, bins=1000) hist = np.cumsum(hist) hist = 100*(hist - hist.min()) / hist.ptp() keep = np.abs(bin_edges[:-1]) < 50 ax.plot(bin_edges[:-1][keep], hist[keep], label="power gap") ax.text( 0.5, 0.87, f"Scénario {scenario}", ha='center', transform=ax.transAxes) row += 1 for axs in [axes, axes_dispatch, axes_storage]: if axes.ndim > 1: for label in axs[-1, 0].get_xmajorticklabels() + axs[-1, 1].get_xmajorticklabels(): label.set_rotation(30) label.set_horizontalalignment("right") else: for label in axs[0].get_xmajorticklabels() + axs[-1].get_xmajorticklabels(): label.set_rotation(30) label.set_horizontalalignment("right") flex = "With" if flexibility else "Without" plt.subplots_adjust(wspace=0, hspace=0) fig.suptitle(f"Simulations based on {begin}--{end} weather data.\n{flex} consumption flexibility; no nuclear seasonality (unrealistic)") fig.text(1, 0, 'Lucas Gautheron', ha="right") fig.legend(labels, loc='lower right', bbox_to_anchor=(1, -0.1), ncol=len(labels), bbox_transform=fig.transFigure) fig.savefig("output/load_supply.png", bbox_inches="tight", dpi=200) fig_storage.suptitle(f"Simulations based on {begin}--{end} weather data.\n{flex} consumption flexibility; no nuclear seasonality (unrealistic)") fig_storage.text(1, 0, 'Lucas Gautheron', ha="right") fig_storage.legend(labels_storage, loc='lower right', bbox_to_anchor=(1, -0.1), ncol=len(labels_storage), bbox_transform=fig_storage.transFigure) fig_storage.savefig("output/storage.png", bbox_inches="tight", dpi=200) fig_dispatch.suptitle(f"Simulations based on {begin}--{end} weather data.\n{flex} consumption flexibility; no nuclear seasonality (unrealistic)") fig_dispatch.text(1, 0, 'Lucas Gautheron', ha="right") fig_dispatch.legend(labels_dispatch, loc='lower right', bbox_to_anchor=(1, -0.1), ncol=len(labels_dispatch), bbox_transform=fig_dispatch.transFigure) fig_dispatch.savefig("output/dispatch.png", bbox_inches="tight", dpi=200) fig_gap_distribution.suptitle(f"Power gap cumulative distribution (%)\nSimulations based on {begin}--{end} weather data.\n{flex} consumption flexibility; no nuclear seasonality (unrealistic)") fig_gap_distribution.legend(["Power gap (available-load) (GW)"], loc='lower right', bbox_to_anchor=(1, -0.1), ncol=1, bbox_transform=fig_dispatch.transFigure) fig_gap_distribution.text(1, 0, 'Lucas Gautheron', ha="right") fig_gap_distribution.savefig("output/gap_distribution.png", bbox_inches="tight", dpi=200) plt.show()