feat(application): integrate measured-load ingestion training and planner source

2026-10-02 21:00:05 +00:00
parent 44362e4bf5
commit fc024fcfba
96 changed files with 8933 additions and 0 deletions
@@ -0,0 +1,240 @@
+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"]