from dataclasses import dataclass from typing import Dict, Optional, Any, Tuple, List import numpy as np import pandas as pd import matplotlib.pyplot as plt import matplotlib.dates as mdates @dataclass() class DaySolution: """ Represents the solution of the optimization for a single day. date : The date of the optimization day. input : The input data used for the optimization schedule : The resulting schedule from the optimization status : The status of the optimization solver : The solver name used for the optimization. """ date: pd.Timestamp input: pd.DataFrame schedule: pd.DataFrame status: str solver: str def plot_results(self, config: Optional[Dict[str, str]] = None): """ Create a multi-panel plot showing: Reserve bids (FCR, aFRR UP/DOWN) and SoC Energy prices over time Reserve capacity prices over time config : Configuration dictionary containing column names for prices. """ inp = self.input.copy() sch = self.schedule.copy() sch = sch.reindex(inp.index) c_price_e = config["columns"]["energy"] c_price_fcr = config["columns"]["fcr"] c_price_up = config["columns"]["afrr_up"] c_price_down = config["columns"]["afrr_down"] c_r_fcr = "r_fcr_mw" c_r_up = "r_afrr_up_mw" c_r_down = "r_afrr_down_mw" c_soc = "soc_mwh" x = inp.index if len(x) >= 2: dt_seconds = float(np.median(np.diff(x.view("int64")) / 1e9)) else: dt_seconds = 900.0 width_days = (dt_seconds / 86400.0) * 0.85 r_fcr = sch[c_r_fcr].to_numpy(dtype=float) r_up = sch[c_r_up].to_numpy(dtype=float) r_down = sch[c_r_down].to_numpy(dtype=float) soc = sch[c_soc].to_numpy(dtype=float) def nan_to_zero(a: np.ndarray) -> np.ndarray: return np.nan_to_num(a, nan=0.0) r_fcr = nan_to_zero(r_fcr) r_up = nan_to_zero(r_up) r_down = nan_to_zero(r_down) fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(11, 9), sharex=True) ax1.set_title(f"Reserve Bids and SoC — {self.date.date()} ({self.status}, {self.solver})") x_num = mdates.date2num(x.to_pydatetime()) w = width_days ax1.bar(x_num - w/3, r_fcr, width=w/3, label="FCR bid (MW)") ax1.bar(x_num, r_down, width=w/3, label="aFRR DOWN bid (MW)") ax1.bar(x_num + w/3, r_up, width=w/3, label="aFRR UP bid (MW)") ax1.set_ylabel("Bid size (MW)") ax1.grid(True, alpha=0.3) ax1b = ax1.twinx() ax1b.plot(x, soc, linewidth=2.0, label="SoC") ax1b.set_ylabel("SoC (MWh)") h1, l1 = ax1.get_legend_handles_labels() h2, l2 = ax1b.get_legend_handles_labels() ax1.legend(h1 + h2, l1 + l2, loc="upper right", frameon=True) ax2.plot(x, inp[c_price_e].to_numpy(dtype=float), label="Spot / DA Price") ax2.set_ylabel("Price (€/MWh)") ax2.grid(True, alpha=0.3) ax2.legend(loc="upper left", frameon=True) ax2.set_title("Energy Prices Over Time") ax3.plot(x, inp[c_price_fcr].to_numpy(dtype=float), label="FCR Price") ax3.plot(x, inp[c_price_up].to_numpy(dtype=float), label="aFRR UP Price") ax3.plot(x, inp[c_price_down].to_numpy(dtype=float), label="aFRR DOWN Price") ax3.set_ylabel("Reserve price (€/MW/interval)") ax3.grid(True, alpha=0.3) ax3.legend(loc="upper right", frameon=True) ax3.set_title("Reserve Capacity Prices Over Time") ax3.xaxis.set_major_locator(mdates.HourLocator(interval=2)) ax3.xaxis.set_major_formatter(mdates.DateFormatter("%H:%M")) fig.autofmt_xdate(rotation=0) fig.tight_layout() fig.show() def plot_global_results(solutions: List[DaySolution], config: Dict[str, Any]): """ Concatenate all daily solutions and plot the full horizon results. solutions : List of daily solutions to concatenate and plot. config : Configuration dictionary containing column names for prices. """ full_input = pd.concat([s.input for s in solutions]) full_schedule = pd.concat([s.schedule for s in solutions]) full_input.sort_index(inplace=True) full_schedule.sort_index(inplace=True) c_price_e = config["columns"]["energy"] c_price_fcr = config["columns"]["fcr"] c_price_up = config["columns"]["afrr_up"] c_price_down = config["columns"]["afrr_down"] x = full_input.index dt_seconds = (x[1] - x[0]).total_seconds() width_days = (dt_seconds / 86400.0) * 0.85 r_fcr = full_schedule["r_fcr_mw"].reindex(x).fillna(0.0).to_numpy() r_up = full_schedule["r_afrr_up_mw"].reindex(x).fillna(0.0).to_numpy() r_down = full_schedule["r_afrr_down_mw"].reindex(x).fillna(0.0).to_numpy() soc = full_schedule["soc_mwh"].reindex(x).to_numpy() x_num = mdates.date2num(x.to_pydatetime()) w = width_days fig, (ax_fcr, ax_afrr, ax_energy, ax_prices) = plt.subplots( 4, 1, figsize=(12, 12), sharex=True ) start_str = x[0].date() end_str = x[-1].date() fig.suptitle(f"Global Optimization Results: {start_str} to {end_str}", y=0.995) ax_fcr.bar(x_num, r_fcr, width=w, label="FCR bid (MW)") ax_fcr.set_ylabel("FCR (MW)") ax_fcr.grid(True, alpha=0.3) ax_fcr.legend(loc="upper left") ax_fcr_soc = ax_fcr.twinx() ax_fcr_soc.plot(x, soc, "k", linewidth=1.2, label="SoC") ax_fcr_soc.set_ylabel("SoC (MWh)") ax_fcr_soc.legend(loc="upper right") ax_afrr.bar(x_num, r_down, color='tab:green', width=w, label="aFRR DOWN bid (MW)") ax_afrr.bar(x_num, r_up, color='tab:orange', width=w, bottom=r_down, label="aFRR UP bid (MW)") ax_afrr.set_ylabel("aFRR (MW)") ax_afrr.grid(True, alpha=0.3) ax_afrr.legend(loc="upper left") ax_afrr_soc = ax_afrr.twinx() ax_afrr_soc.plot(x, soc, "k", linewidth=1.2, label="SoC") ax_afrr_soc.set_ylabel("SoC (MWh)") ax_energy.plot(x, full_input[c_price_e].to_numpy(dtype=float), label="Spot / DA Price") ax_energy.set_ylabel("€/MWh") ax_energy.grid(True, alpha=0.3) ax_energy.legend(loc="upper left") ax_prices.plot(x, full_input[c_price_fcr].to_numpy(dtype=float), label="FCR Price") ax_prices.plot(x, full_input[c_price_up].to_numpy(dtype=float), label="aFRR UP Price") ax_prices.plot(x, full_input[c_price_down].to_numpy(dtype=float), label="aFRR DOWN Price") ax_prices.set_ylabel("€/MW/interval") ax_prices.grid(True, alpha=0.3) ax_prices.legend(loc="upper left") locator = mdates.AutoDateLocator() formatter = mdates.ConciseDateFormatter(locator) ax_prices.xaxis.set_major_locator(locator) ax_prices.xaxis.set_major_formatter(formatter) fig.tight_layout(rect=[0, 0, 1, 0.985]) plt.show()