import numpy as np import pandas as pd from scipy.optimize import Bounds, LinearConstraint, milp from scipy.sparse import lil_matrix DT_H = 5.0 / 60.0 def train_artifact(kind): return {"trained": True, "type": "battery_48h_cost_milp_v3", "source": kind} def _cfg_float(config, key, default): try: value = config.get(key, default) return float(default if value is None or value == "" else value) except Exception: return float(default) def _cfg_bool(config, key, default=False): value = config.get(key, default) if value is None or value == "": return bool(default) if isinstance(value, bool): return value return str(value).strip().lower() in {"1", "true", "yes", "ja", "on"} def _use_dynamic(config, key): value = str(config.get(key, "") or "").strip().lower() return any(token in value for token in ("dynam", "marktpreis", "referenzmarktpreis", "market")) def _price(data_obj, timestamp, column, fallback, dynamic_enabled): if not dynamic_enabled: return fallback frame = data_obj["df_fut"] if column in frame.columns and timestamp in frame.index: try: value = float(frame.at[timestamp, column]) if np.isfinite(value): return value except Exception: pass return fallback def _battery_meta(config, data_obj): cap_kwh = _cfg_float(config, "batt_capacity_kwh", 0.0) max_power_w = _cfg_float(config, "batt_power_kw", 0.0) * 1000.0 min_soc = _cfg_float(config, "batt_min_soc", _cfg_float(config, "batt_min_soc_percent", 0.0)) max_soc = _cfg_float(config, "batt_max_soc", _cfg_float(config, "batt_max_soc_percent", 100.0)) start_soc_value = data_obj.get("current_soc_perc") if start_soc_value is None: start_soc_value = min_soc try: start_soc = float(start_soc_value) except (TypeError, ValueError): start_soc = min_soc min_soc = max(0.0, min(100.0, min_soc)) max_soc = max(min_soc, min(100.0, max_soc)) start_soc = max(min_soc, min(max_soc, start_soc)) charge_eff = max(0.01, min(1.0, _cfg_float(config, "batt_charge_efficiency", 0.95))) discharge_eff = max(0.01, min(1.0, _cfg_float(config, "batt_discharge_efficiency", 0.95))) return cap_kwh, max_power_w, min_soc, max_soc, start_soc, charge_eff, discharge_eff def _quarter_groups(index): groups = {} for position, timestamp in enumerate(index): quarter = timestamp.floor("15min") if hasattr(timestamp, "floor") else position // 3 groups.setdefault(quarter, []).append(position) return list(groups.values()) def _fallback_plan(index, residual_w): grid = {timestamp: float(value) for timestamp, value in zip(index, residual_w)} battery = {timestamp: 0.0 for timestamp in index} return {"grid": grid, "battery": battery, "solver": "fallback"} def optimize_battery_plan(data_obj, pv_dict, load_dict): config = data_obj["config"] cap_kwh, max_power_w, min_soc, max_soc, start_soc, charge_eff, discharge_eff = _battery_meta(config, data_obj) index = list(data_obj["df_fut"].index) if not index: return {"grid": {}, "battery": {}, "solver": "empty"} load_w = np.array([max(0.0, float(load_dict.get(t, 0.0))) for t in index]) pv_w = np.array([max(0.0, float(pv_dict.get(t, 0.0))) for t in index]) residual_w = load_w - pv_w if cap_kwh <= 0.0 or max_power_w <= 0.0: return _fallback_plan(index, residual_w) import_fixed = _cfg_float(config, "tarif_bezug_fest", 0.30) export_fixed = _cfg_float(config, "tarif_einspeisung_fest", 0.10) import_dynamic = _use_dynamic(config, "tarif_bezug") export_dynamic = _use_dynamic(config, "tarif_einspeisung") import_price = np.array([ _price(data_obj, t, "import_price", import_fixed, import_dynamic) for t in index ]) export_price = np.array([ _price(data_obj, t, "export_price", export_fixed, export_dynamic) for t in index ]) n = len(index) imp, exp, charge, discharge, curtail, soc = 0, n, 2 * n, 3 * n, 4 * n, 5 * n peak = 6 * n + 1 battery_mode = peak + 1 grid_mode = battery_mode + n variable_count = grid_mode + n max_import_w = max(float(load_w.max(initial=0.0)) + max_power_w, max_power_w, 1.0) configured_import_limit = _cfg_float(config, "grid_import_limit_w", 0.0) if configured_import_limit > 0.0: max_import_w = min(max_import_w, configured_import_limit) max_export_w = max(float(pv_w.max(initial=0.0)) + max_power_w, max_power_w, 1.0) configured_export_limit = _cfg_float(config, "grid_export_limit_w", 0.0) if configured_export_limit > 0.0: max_export_w = min(max_export_w, configured_export_limit) lower = np.zeros(variable_count) upper = np.full(variable_count, np.inf) upper[imp:imp + n] = max_import_w upper[exp:exp + n] = max_export_w upper[charge:charge + n] = max_power_w if not _cfg_bool(config, "batt_grid_charging_enabled", False): upper[charge:charge + n] = np.minimum(max_power_w, np.maximum(0.0, pv_w - load_w)) upper[discharge:discharge + n] = max_power_w upper[curtail:curtail + n] = pv_w reserve_soc = max( min_soc, min(100.0, _cfg_float(config, "batt_economic_reserve_soc_percent", 10.0)), ) economic_min_soc = max(min_soc, min(start_soc, reserve_soc)) min_kwh = cap_kwh * economic_min_soc / 100.0 max_kwh = cap_kwh * max_soc / 100.0 lower[soc:soc + n + 1] = min_kwh upper[soc:soc + n + 1] = max_kwh current_peak_kw = max(0.0, float(data_obj.get("current_month_peak_kw", 0.0) or 0.0)) lower[peak] = current_peak_kw upper[peak] = max(current_peak_kw, max_import_w / 1000.0) upper[battery_mode:battery_mode + n] = 1.0 upper[grid_mode:grid_mode + n] = 1.0 objective = np.zeros(variable_count) objective[imp:imp + n] = import_price * DT_H / 1000.0 objective[exp:exp + n] = -export_price * DT_H / 1000.0 degradation = max(0.0, _cfg_float(config, "batt_degradation_chf_kwh", 0.03)) objective[charge:charge + n] = (degradation / 2.0 + 1e-7) * DT_H / 1000.0 objective[discharge:discharge + n] = (degradation / 2.0 + 1e-7) * DT_H / 1000.0 objective[curtail:curtail + n] = 1e-9 * DT_H / 1000.0 objective[peak] = max(0.0, _cfg_float(config, "tarif_peak_fest", 0.0)) terminal_value = _cfg_float(config, "batt_terminal_value_chf_kwh", np.median(import_price)) objective[soc + n] = -max(0.0, terminal_value) * discharge_eff equality_rows = 2 * n + 1 equality = lil_matrix((equality_rows, variable_count), dtype=float) equality_rhs = np.zeros(equality_rows) for i in range(n): equality[i, imp + i] = 1.0 equality[i, exp + i] = -1.0 equality[i, charge + i] = -1.0 equality[i, discharge + i] = 1.0 equality[i, curtail + i] = -1.0 equality_rhs[i] = residual_w[i] row = n + i equality[row, soc + i] = -1.0 equality[row, soc + i + 1] = 1.0 equality[row, charge + i] = -charge_eff * DT_H / 1000.0 equality[row, discharge + i] = DT_H / (1000.0 * discharge_eff) equality[2 * n, soc] = 1.0 equality_rhs[2 * n] = cap_kwh * start_soc / 100.0 quarter_groups = _quarter_groups(pd.Index(index)) inequality_rows = 4 * n + len(quarter_groups) inequality = lil_matrix((inequality_rows, variable_count), dtype=float) inequality_upper = np.zeros(inequality_rows) row = 0 for i in range(n): inequality[row, charge + i] = 1.0 inequality[row, battery_mode + i] = -max_power_w row += 1 inequality[row, discharge + i] = 1.0 inequality[row, battery_mode + i] = max_power_w inequality_upper[row] = max_power_w row += 1 inequality[row, imp + i] = 1.0 inequality[row, grid_mode + i] = -max_import_w row += 1 inequality[row, exp + i] = 1.0 inequality[row, grid_mode + i] = max_export_w inequality_upper[row] = max_export_w row += 1 for group in quarter_groups: for i in group: inequality[row, imp + i] = 1.0 / (len(group) * 1000.0) inequality[row, peak] = -1.0 row += 1 integrality = np.zeros(variable_count, dtype=int) integrality[battery_mode:battery_mode + n] = 1 integrality[grid_mode:grid_mode + n] = 1 constraints = [ LinearConstraint(equality.tocsr(), equality_rhs, equality_rhs), LinearConstraint(inequality.tocsr(), -np.inf, inequality_upper), ] result = milp( objective, integrality=integrality, bounds=Bounds(lower, upper), constraints=constraints, options={"time_limit": max(5.0, _cfg_float(config, "batt_optimizer_timeout_seconds", 30.0))}, ) if not result.success or result.x is None: return _fallback_plan(index, residual_w) grid_values = result.x[imp:imp + n] - result.x[exp:exp + n] battery_values = result.x[charge:charge + n] - result.x[discharge:discharge + n] threshold_w = max(25.0, max_power_w * 0.005) grid_values[np.abs(grid_values) < threshold_w] = 0.0 battery_values[np.abs(battery_values) < threshold_w] = 0.0 return { "grid": {t: float(v) for t, v in zip(index, grid_values)}, "battery": {t: float(v) for t, v in zip(index, battery_values)}, "solver": "scipy-milp", "objective_chf": float(result.fun), "planned_peak_kw": float(result.x[peak]), "start_soc_percent": float(start_soc), "economic_min_soc_percent": float(economic_min_soc), } def optimize_grid_setpoint(data_obj, pv_dict, load_dict, forecast_id=None): plan = optimize_battery_plan(data_obj, pv_dict, load_dict) if forecast_id is not None: data_obj.setdefault("battery_plans", {})[int(forecast_id)] = plan return plan["grid"]