# backend/services/planning_engine.py # # EMS Platform – plánovací engine # Obsahuje: hlavní denní plán + rolling 15min replan # # Spouštění (APScheduler v main.py): # scheduler.add_job(run_daily_plan, 'cron', hour=15, minute=0) # scheduler.add_job(run_rolling_replan, 'cron', minute='*/15') import time import logging from dataclasses import dataclass, replace from datetime import datetime, timezone, timedelta from types import SimpleNamespace from typing import Optional import pulp from pulp import HiGHS_CMD logger = logging.getLogger(__name__) # ============================================================ # Konstanty # ============================================================ HORIZON_HOURS = 36 # horizont denního plánu INTERVAL_H = 0.25 # 15 minut v hodinách CURTAILMENT_PENALTY = 0.001 # Kč/Wh – malá penalizace za omezení FVE pole A SOLVER_TIME_LIMIT = 10 # sekund CORRECTION_WINDOW_H = 1 # hodina zpět pro výpočet korekčního faktoru CORRECTION_MIN_CLAMP = 0.5 # spodní limit korekčního faktoru CORRECTION_MAX_CLAMP = 1.5 # horní limit korekčního faktoru # Útlum korekce: čím dál od aktuálního času, tím méně korigujeme forecast CORRECTION_DECAY_SLOTS = 16 # po 16 slotech (4h) klesne korekce na 0 # ============================================================ # Datové třídy (lze nahradit pydantic modely) # ============================================================ @dataclass class PlanningSlot: interval_start: datetime buy_price: float # Kč/kWh sell_price: float # Kč/kWh pv_a_forecast_w: int # W – pole A (řiditelné) pv_b_forecast_w: int # W – pole B (zelený bonus, pevné) load_baseline_w: int # W – predikce bazální spotřeby ev1_connected: bool ev2_connected: bool @dataclass class DispatchResult: interval_start: datetime battery_setpoint_w: int # kladné = nabíjení, záporné = vybíjení battery_soc_target: float # % SoC na konci intervalu grid_setpoint_w: int # kladné = import, záporné = export ev1_setpoint_w: Optional[int] ev2_setpoint_w: Optional[int] ev1_via_bat_w: int ev2_via_bat_w: int heat_pump_enabled: bool heat_pump_setpoint_w: int pv_a_curtailed_w: int expected_cost_czk: float effective_buy_price: float effective_sell_price: float # ============================================================ # Korekce forecastu na základě skutečné výroby # ============================================================ async def compute_correction_factor( site_id: int, now: datetime, db, window_h: float = CORRECTION_WINDOW_H, ) -> tuple[float, dict]: """ Spočítá korekční faktor FVE forecastu z posledních window_h hodin. Vrátí (factor, log_data) kde factor je v rozsahu [CORRECTION_MIN_CLAMP, CORRECTION_MAX_CLAMP]. factor = 1.0 pokud není dostatek dat nebo je rozdíl zanedbatelný. """ window_start = now - timedelta(hours=window_h) # Skutečná výroba za okno (z telemetrie) actual = await db.fetchval(""" SELECT COALESCE(SUM(pv_power_w) * 0.25 / 1000.0, 0) -- kWh FROM ems.telemetry_inverter WHERE site_id = $1 AND measured_at >= $2 AND measured_at < $3 """, site_id, window_start, now) # Předpovídaná výroba za stejné okno (z nejnovějšího forecastu který platil tehdy) forecast = await db.fetchval(""" SELECT COALESCE(SUM(fpi.power_w) * 0.25 / 1000.0, 0) FROM ems.forecast_pv_interval fpi JOIN ems.forecast_pv_run fpr ON fpr.id = fpi.run_id WHERE fpr.site_id = $1 AND fpi.interval_start >= $2 AND fpi.interval_start < $3 AND fpr.status = 'ok' AND fpr.created_at = ( SELECT MAX(fpr2.created_at) FROM ems.forecast_pv_run fpr2 WHERE fpr2.site_id = $1 AND fpr2.status = 'ok' AND fpr2.created_at <= $2 ) """, site_id, window_start, now) log_data = { "window_start": window_start, "window_end": now, "actual_pv_wh": actual * 1000, "forecast_pv_wh": forecast * 1000, } # Pokud forecast nebo actual jsou příliš malé (noc, <0.1 kWh) → žádná korekce if forecast < 0.1 or actual < 0.05: log_data["correction_factor"] = 1.0 log_data["reason"] = "insufficient_data" return 1.0, log_data raw_factor = actual / forecast factor = max(CORRECTION_MIN_CLAMP, min(CORRECTION_MAX_CLAMP, raw_factor)) log_data["correction_factor"] = factor log_data["raw_factor"] = raw_factor return factor, log_data def apply_forecast_correction( slots: list[PlanningSlot], now: datetime, factor: float, decay_slots: int = CORRECTION_DECAY_SLOTS, ) -> list[PlanningSlot]: """ Aplikuje korekční faktor na FVE forecast zbývajících slotů. Korekce se lineárně utlumuje: na 1. slotu plná korekce, na decay_slots-tém slotu žádná korekce. Příklad: factor=0.85, slot 0 → pv_a *= 0.85, slot 8 → pv_a *= 0.925, slot 16+ → žádná korekce """ corrected = [] for i, slot in enumerate(slots): if factor == 1.0 or i >= decay_slots: corrected.append(slot) continue # Lineární útlum: weight klesá od 1.0 (slot 0) do 0.0 (slot decay_slots) weight = 1.0 - (i / decay_slots) effective_factor = 1.0 + (factor - 1.0) * weight corrected.append( replace( slot, pv_a_forecast_w=max(0, int(slot.pv_a_forecast_w * effective_factor)), pv_b_forecast_w=max(0, int(slot.pv_b_forecast_w * effective_factor)), ) ) return corrected # ============================================================ # LP Solver # ============================================================ def solve_dispatch( slots: list[PlanningSlot], battery, heat_pump, grid, ev_sessions: list, # aktivní EV sessions [ev1_session, ev2_session] vehicles: list, # [vehicle1, vehicle2] current_soc_wh: float, current_tuv_temp_c: float, ) -> tuple[list[DispatchResult], int]: """ LP solver pro dispatch optimalizaci. Vrátí (výsledky, solver_duration_ms). """ T = len(slots) EV = len(vehicles) # počet EV (typicky 2) EV_ROUNDTRIP_FACTOR = 1.0 / (battery.charge_efficiency * battery.discharge_efficiency) prob = pulp.LpProblem("ems_dispatch", pulp.LpMinimize) # --- Proměnné --- gi = [pulp.LpVariable(f"gi_{t}", 0, grid.max_import_power_w) for t in range(T)] ge = [pulp.LpVariable(f"ge_{t}", 0, grid.max_export_power_w) for t in range(T)] bc = [pulp.LpVariable(f"bc_{t}", 0, battery.max_charge_power_w) for t in range(T)] bd = [pulp.LpVariable(f"bd_{t}", 0, battery.max_discharge_power_w) for t in range(T)] soc = [pulp.LpVariable(f"soc_{t}", battery.reserve_soc_wh, battery.soc_max_wh) for t in range(T)] ca = [pulp.LpVariable(f"ca_{t}", 0, slots[t].pv_a_forecast_w) for t in range(T)] hp = [pulp.LpVariable(f"hp_{t}", 0, heat_pump.rated_heating_power_w) for t in range(T)] # EV proměnné per vozidlo ev_direct = [[pulp.LpVariable(f"evd_{e}_{t}", 0, min(vehicles[e].max_charge_power_w, grid.max_import_power_w)) for t in range(T)] for e in range(EV)] ev_via_bat = [[pulp.LpVariable(f"evb_{e}_{t}", 0, vehicles[e].max_charge_power_w) for t in range(T)] for e in range(EV)] # --- Účelová funkce --- prob += pulp.lpSum( gi[t] * slots[t].buy_price * INTERVAL_H / 1000 - ge[t] * slots[t].sell_price * INTERVAL_H / 1000 + (bc[t] + bd[t]) * battery.degradation_cost_czk_kwh * INTERVAL_H / 1000 + pulp.lpSum( ev_direct[e][t] * slots[t].buy_price * INTERVAL_H / 1000 + ev_via_bat[e][t] * slots[t].buy_price * EV_ROUNDTRIP_FACTOR * INTERVAL_H / 1000 for e in range(EV) ) + ca[t] * CURTAILMENT_PENALTY for t in range(T) ) # --- Omezení --- for t in range(T): s = slots[t] pv_a_net = s.pv_a_forecast_w - ca[t] ev_total_t = pulp.lpSum(ev_direct[e][t] + ev_via_bat[e][t] for e in range(EV)) # Energetická bilance prob += ( pv_a_net + s.pv_b_forecast_w + gi[t] + bd[t] == s.load_baseline_w + ev_total_t + hp[t] + bc[t] + ge[t] ) # SoC kontinuita soc_prev = current_soc_wh if t == 0 else soc[t - 1] prob += soc[t] == ( soc_prev + bc[t] * battery.charge_efficiency * INTERVAL_H - bd[t] / battery.discharge_efficiency * INTERVAL_H ) # ev_via_bat kryto z discharge prob += pulp.lpSum(ev_via_bat[e][t] for e in range(EV)) <= bd[t] # Záporná prodejní cena → zakázat export if s.sell_price < 0: prob += ge[t] == 0 # Záporná nákupní cena → cap import na reálnou spotřebu if s.buy_price < 0: prob += gi[t] <= ( battery.max_charge_power_w + sum(v.max_charge_power_w for v in vehicles) + heat_pump.rated_heating_power_w ) # EV – limity a připojení for e in range(EV): connected = ( (e == 0 and s.ev1_connected) or (e == 1 and s.ev2_connected) ) if not connected: prob += ev_direct[e][t] == 0 prob += ev_via_bat[e][t] == 0 else: prob += ev_direct[e][t] + ev_via_bat[e][t] <= vehicles[e].max_charge_power_w # Deadline constraints pro EV for e, session in enumerate(ev_sessions): if session and session.target_deadline and session.energy_needed_wh > 0: t_dl = next( (t for t, s in enumerate(slots) if s.interval_start >= session.target_deadline), T - 1 ) prob += pulp.lpSum( (ev_direct[e][t] + ev_via_bat[e][t]) * INTERVAL_H for t in range(t_dl + 1) if (e == 0 and slots[t].ev1_connected) or (e == 1 and slots[t].ev2_connected) ) >= session.energy_needed_wh # Nouzový ohřev TUV if current_tuv_temp_c < heat_pump.tuv_min_temp_c: prob += hp[0] >= heat_pump.rated_heating_power_w * 0.8 # --- Řešení --- t_start = time.monotonic() solver = HiGHS_CMD(msg=False, timeLimit=SOLVER_TIME_LIMIT) status = prob.solve(solver) duration_ms = int((time.monotonic() - t_start) * 1000) if pulp.LpStatus[status] != 'Optimal': raise RuntimeError(f"Solver: {pulp.LpStatus[status]}") # --- Post-processing --- results = [] for t in range(T): hp_raw = pulp.value(hp[t]) hp_on = hp_raw > heat_pump.rated_heating_power_w * 0.3 batt_w = round(pulp.value(bc[t]) - pulp.value(bd[t])) grid_w = round(pulp.value(gi[t]) - pulp.value(ge[t])) soc_pct = round(pulp.value(soc[t]) / battery.usable_capacity_wh * 100, 1) cost = ( pulp.value(gi[t]) * slots[t].buy_price * INTERVAL_H / 1000 - pulp.value(ge[t]) * slots[t].sell_price * INTERVAL_H / 1000 ) results.append(DispatchResult( interval_start = slots[t].interval_start, battery_setpoint_w = batt_w, battery_soc_target = soc_pct, grid_setpoint_w = grid_w, ev1_setpoint_w = round(pulp.value(ev_direct[0][t]) + pulp.value(ev_via_bat[0][t])) if slots[t].ev1_connected else None, ev2_setpoint_w = round(pulp.value(ev_direct[1][t]) + pulp.value(ev_via_bat[1][t])) if slots[t].ev2_connected else None, ev1_via_bat_w = round(pulp.value(ev_via_bat[0][t])), ev2_via_bat_w = round(pulp.value(ev_via_bat[1][t])), heat_pump_enabled = hp_on, heat_pump_setpoint_w = heat_pump.rated_heating_power_w if hp_on else 0, pv_a_curtailed_w = round(pulp.value(ca[t])), expected_cost_czk = round(cost, 4), effective_buy_price = slots[t].buy_price, effective_sell_price = slots[t].sell_price, )) return results, duration_ms # ============================================================ # Denní plán (15:00) # ============================================================ async def run_daily_plan(site_id: int, db, triggered_by: str = "scheduler:daily") -> tuple[int, int]: """ Hlavní denní plánování. Spouštět v 15:00 po importu cen (14:00) a aktualizaci forecastu (14:30). Horizont: od začátku aktuálního 15min slotu do +36h. """ now = datetime.now(timezone.utc) horizon_from = _current_slot_start(now) horizon_to = horizon_from + timedelta(hours=HORIZON_HOURS) logger.info(f"[site={site_id}] Daily plan: {horizon_from} → {horizon_to}") slots = await _load_slots(site_id, horizon_from, horizon_to, db) battery, hp, grid, vehicles, ev_sessions, soc_wh, tuv_temp = await _load_site_context( site_id, db ) results, duration_ms = solve_dispatch( slots, battery, hp, grid, ev_sessions, vehicles, soc_wh, tuv_temp ) run_id = await _save_planning_run( site_id, results, horizon_from, horizon_to, run_type="daily", triggered_by=triggered_by, replan_from=None, soc_wh=soc_wh, duration_ms=duration_ms, correction=1.0, db=db, ) logger.info(f"[site={site_id}] Daily plan done in {duration_ms} ms") return run_id, duration_ms # ============================================================ # Rolling replan (každých 15min) # ============================================================ async def run_rolling_replan( site_id: int, db, *, triggered_by: str = "scheduler:rolling", allow_skip: bool = True, ) -> tuple[Optional[int], Optional[int]]: """ Rolling replan každých 15 minut. 1. Zjistí aktuální SoC baterie z telemetrie 2. Spočítá korekční faktor FVE forecastu z poslední hodiny 3. Aplikuje korekci na forecast zbytku dne (s útlumem) 4. Spustí solver pro zbývající horizont aktivního plánu 5. Uloží jako nový planning_run (aktivní plán se stane superseded) Pokud allow_skip=True (scheduler) a horizont je vyčerpaný → vrátí (None, None). Pokud allow_skip=False (API) → spustí denní plán jako náhradu. """ now = datetime.now(timezone.utc) replan_from = _current_slot_start(now) active_run = await db.fetchrow(""" SELECT id, horizon_end FROM ems.planning_run WHERE site_id = $1 AND status = 'active' ORDER BY created_at DESC LIMIT 1 """, site_id) if not active_run: logger.warning(f"[site={site_id}] Rolling replan: no active plan, triggering daily plan") return await run_daily_plan(site_id, db, triggered_by=triggered_by) horizon_to = active_run["horizon_end"] if (horizon_to - replan_from).total_seconds() < 1800: if allow_skip: logger.info(f"[site={site_id}] Rolling replan: horizon almost exhausted, skipping") return None, None logger.info(f"[site={site_id}] Rolling replan: horizon exhausted, running daily plan") return await run_daily_plan(site_id, db, triggered_by=triggered_by) logger.info(f"[site={site_id}] Rolling replan from {replan_from} → {horizon_to}") battery, hp, grid, vehicles, ev_sessions, soc_wh, tuv_temp = await _load_site_context( site_id, db ) correction_factor, correction_log = await compute_correction_factor(site_id, now, db) slots = await _load_slots(site_id, replan_from, horizon_to, db) slots = apply_forecast_correction(slots, now, correction_factor) results, duration_ms = solve_dispatch( slots, battery, hp, grid, ev_sessions, vehicles, soc_wh, tuv_temp ) run_id = await _save_planning_run( site_id, results, replan_from, horizon_to, run_type="rolling", triggered_by=triggered_by, replan_from=replan_from, soc_wh=soc_wh, duration_ms=duration_ms, correction=correction_factor, db=db, ) await db.execute( """ INSERT INTO ems.forecast_correction_log (site_id, window_start, window_end, actual_pv_wh, forecast_pv_wh, correction_factor, applied_to_run_id) VALUES ($1,$2,$3,$4,$5,$6,$7) """, site_id, correction_log["window_start"], correction_log["window_end"], correction_log.get("actual_pv_wh"), correction_log.get("forecast_pv_wh"), correction_factor, run_id, ) logger.info( f"[site={site_id}] Rolling replan done in {duration_ms} ms " f"(correction={correction_factor:.3f})" ) return run_id, duration_ms async def run_plan_api( site_id: int, plan_type: str, db, *, triggered_by: str = "api", ) -> tuple[int, int]: """Ruční / UI spuštění plánu. Vždy vrátí (run_id, solver_duration_ms).""" pt = plan_type.lower().strip() if pt == "daily": return await run_daily_plan(site_id, db, triggered_by=triggered_by) if pt == "rolling": rid, ms = await run_rolling_replan( site_id, db, triggered_by=triggered_by, allow_skip=False ) if rid is None or ms is None: raise RuntimeError("Rolling replan did not return a run") return rid, ms raise ValueError(f"Unknown plan_type: {plan_type!r} (use daily or rolling)") # ============================================================ # Pomocné funkce # ============================================================ def _current_slot_start(dt: datetime) -> datetime: """Zaokrouhlí čas dolů na začátek aktuálního 15min slotu.""" minute = (dt.minute // 15) * 15 return dt.replace(minute=minute, second=0, microsecond=0) def _ev_session_ctx(row) -> Optional[SimpleNamespace]: """Kontext deadline constraintu pro jedno EV (nebo None).""" if row is None or row["target_deadline"] is None: return None cap_kwh = row["veh_cap_kwh"] if cap_kwh is None: return None cap_wh = float(cap_kwh) * 1000.0 tgt = row["target_soc_pct"] if tgt is None: tgt = row["default_target_soc_pct"] if tgt is None: return None tgt_f = float(tgt) soc0 = row["soc_at_connect_pct"] if soc0 is None: return None needed_wh = (tgt_f - float(soc0)) / 100.0 * cap_wh delivered = float(row["energy_delivered_wh"] or 0) remaining = max(0.0, needed_wh - delivered) if remaining <= 0: return None return SimpleNamespace( target_deadline=row["target_deadline"], energy_needed_wh=remaining, ) async def _load_site_context(site_id: int, db): """ Načte baterii, TČ, síť, 2× vozidlo, otevřené EV session, SoC a TUV pro solver. """ brow = await db.fetchrow( """ SELECT bat.usable_capacity_wh, bat.reserve_soc_percent, bat.max_soc_percent, bat.charge_efficiency, bat.discharge_efficiency, bat.degradation_cost_czk_kwh, inv.max_charge_power_w, inv.max_discharge_power_w FROM ems.asset_battery bat JOIN ems.asset_inverter inv ON inv.id = bat.inverter_id AND inv.site_id = bat.site_id WHERE bat.site_id = $1 ORDER BY bat.id LIMIT 1 """, site_id, ) if brow is None: raise RuntimeError(f"No asset_battery for site_id={site_id}") uc = float(brow["usable_capacity_wh"]) reserve_wh = float(brow["reserve_soc_percent"]) / 100.0 * uc soc_max_wh = float(brow["max_soc_percent"]) / 100.0 * uc battery = SimpleNamespace( usable_capacity_wh=uc, reserve_soc_wh=reserve_wh, soc_max_wh=soc_max_wh, charge_efficiency=float(brow["charge_efficiency"]), discharge_efficiency=float(brow["discharge_efficiency"]), degradation_cost_czk_kwh=float(brow["degradation_cost_czk_kwh"]), max_charge_power_w=int(brow["max_charge_power_w"]), max_discharge_power_w=int(brow["max_discharge_power_w"]), ) hrow = await db.fetchrow( """ SELECT COALESCE(rated_heating_power_w, 8000) AS rated_heating_power_w, COALESCE(tuv_min_temp_c, 45) AS tuv_min_temp_c FROM ems.asset_heat_pump WHERE site_id = $1 ORDER BY id LIMIT 1 """, site_id, ) if hrow is None: heat_pump = SimpleNamespace(rated_heating_power_w=0, tuv_min_temp_c=0.0) else: hp_w = int(hrow["rated_heating_power_w"]) heat_pump = SimpleNamespace( rated_heating_power_w=max(hp_w, 0), tuv_min_temp_c=float(hrow["tuv_min_temp_c"]), ) grow = await db.fetchrow( """ SELECT max_import_power_w, max_export_power_w FROM ems.site_grid_connection WHERE site_id = $1 ORDER BY id LIMIT 1 """, site_id, ) if grow is None: raise RuntimeError(f"No site_grid_connection for site_id={site_id}") grid = SimpleNamespace( max_import_power_w=int(grow["max_import_power_w"]), max_export_power_w=int(grow["max_export_power_w"]), ) vrows = await db.fetch( """ SELECT v.battery_capacity_kwh, v.max_charge_power_w, v.default_target_soc_pct, ch.code AS charger_code FROM ems.asset_vehicle v JOIN ems.asset_ev_charger ch ON ch.id = v.default_charger_id WHERE v.site_id = $1 AND ch.code IN ('ev-charger-1', 'ev-charger-2') ORDER BY ch.code """, site_id, ) vehicles: list[SimpleNamespace] = [ SimpleNamespace( max_charge_power_w=int(r["max_charge_power_w"]), battery_capacity_kwh=float(r["battery_capacity_kwh"]), default_target_soc_pct=float(r["default_target_soc_pct"]), ) for r in vrows ] while len(vehicles) < 2: vehicles.append( SimpleNamespace( max_charge_power_w=0, battery_capacity_kwh=1.0, default_target_soc_pct=80.0, ) ) srows = await db.fetch( """ SELECT es.target_deadline, es.target_soc_pct, es.soc_at_connect_pct, es.energy_delivered_wh, ch.code AS charger_code, v.battery_capacity_kwh AS veh_cap_kwh, v.default_target_soc_pct FROM ems.ev_session es JOIN ems.asset_ev_charger ch ON ch.id = es.charger_id LEFT JOIN ems.asset_vehicle v ON v.id = es.vehicle_id WHERE es.site_id = $1 AND es.session_end IS NULL """, site_id, ) by_charger = {r["charger_code"]: r for r in srows} ev_sessions = [ _ev_session_ctx(by_charger.get("ev-charger-1")), _ev_session_ctx(by_charger.get("ev-charger-2")), ] soc_pct = await db.fetchval( """ SELECT battery_soc_percent FROM ems.telemetry_inverter WHERE site_id = $1 ORDER BY measured_at DESC LIMIT 1 """, site_id, ) if soc_pct is None: soc_wh = uc * 0.5 else: soc_wh = float(soc_pct) / 100.0 * uc soc_wh = max(reserve_wh, min(soc_wh, soc_max_wh)) tuv = await db.fetchval( """ SELECT tuv_tank_temp_c FROM ems.telemetry_heat_pump WHERE site_id = $1 ORDER BY measured_at DESC LIMIT 1 """, site_id, ) tuv_temp = float(tuv) if tuv is not None else 50.0 return battery, heat_pump, grid, vehicles, ev_sessions, soc_wh, tuv_temp async def _load_slots(site_id, from_dt, to_dt, db) -> list[PlanningSlot]: """Načte 15min sloty s cenami, forecasty a stavem EV z DB.""" rows = await db.fetch(""" SELECT ep.interval_start, ep.effective_buy_price_czk_kwh AS buy_price, ep.effective_sell_price_czk_kwh AS sell_price, COALESCE(fpi_a.power_w, 0) AS pv_a_forecast_w, COALESCE(fpi_b.power_w, 0) AS pv_b_forecast_w, COALESCE(cbi.power_w, 500) AS load_baseline_w, -- EV připojení z poslední telemetrie nabíječek (bez řádku = nepřipojeno) (COALESCE(ev1.status, 'available') NOT IN ('available', 'unavailable')) AS ev1_connected, (COALESCE(ev2.status, 'available') NOT IN ('available', 'unavailable')) AS ev2_connected FROM ems.vw_site_effective_price ep -- FVE pole A forecast LEFT JOIN LATERAL ( SELECT fpi.power_w FROM ems.forecast_pv_interval fpi JOIN ems.forecast_pv_run fpr ON fpr.id = fpi.run_id JOIN ems.asset_pv_array apa ON apa.id = fpi.pv_array_id AND apa.site_id = fpr.site_id WHERE fpr.site_id = $1 AND apa.code = 'pv-a' AND fpi.interval_start = ep.interval_start AND fpr.status = 'ok' ORDER BY fpr.created_at DESC LIMIT 1 ) fpi_a ON true -- FVE pole B forecast LEFT JOIN LATERAL ( SELECT fpi.power_w FROM ems.forecast_pv_interval fpi JOIN ems.forecast_pv_run fpr ON fpr.id = fpi.run_id JOIN ems.asset_pv_array apa ON apa.id = fpi.pv_array_id AND apa.site_id = fpr.site_id WHERE fpr.site_id = $1 AND apa.code = 'pv-b' AND fpi.interval_start = ep.interval_start AND fpr.status = 'ok' ORDER BY fpr.created_at DESC LIMIT 1 ) fpi_b ON true -- Bazální spotřeba LEFT JOIN ems.consumption_baseline_interval cbi ON cbi.site_id = $1 AND cbi.interval_start = ep.interval_start AND cbi.data_type = 'forecast' -- Stav EV nabíječek (aktuální, pro celý horizont stejný) LEFT JOIN LATERAL ( SELECT t.status FROM ems.telemetry_ev_charger t JOIN ems.asset_ev_charger ch ON ch.id = t.charger_id WHERE t.site_id = $1 AND ch.code = 'ev-charger-1' ORDER BY t.measured_at DESC LIMIT 1 ) ev1 ON true LEFT JOIN LATERAL ( SELECT t.status FROM ems.telemetry_ev_charger t JOIN ems.asset_ev_charger ch ON ch.id = t.charger_id WHERE t.site_id = $1 AND ch.code = 'ev-charger-2' ORDER BY t.measured_at DESC LIMIT 1 ) ev2 ON true WHERE ep.site_id = $1 AND ep.interval_start >= $2 AND ep.interval_start < $3 ORDER BY ep.interval_start """, site_id, from_dt, to_dt) out: list[PlanningSlot] = [] for r in rows: d = dict(r) out.append( PlanningSlot( interval_start=d["interval_start"], buy_price=float(d["buy_price"]), sell_price=float(d["sell_price"]), pv_a_forecast_w=int(d["pv_a_forecast_w"] or 0), pv_b_forecast_w=int(d["pv_b_forecast_w"] or 0), load_baseline_w=int(d["load_baseline_w"] or 0), ev1_connected=bool(d["ev1_connected"]), ev2_connected=bool(d["ev2_connected"]), ) ) if not out: raise RuntimeError( "No planning slots available – check market prices and horizon settings" ) return out async def _save_planning_run( site_id, results, horizon_from, horizon_to, run_type, triggered_by, replan_from, soc_wh, duration_ms, correction, db ) -> int: """Uloží výsledky solveru jako nový planning_run, deaktivuje předchozí.""" run_id = await db.fetchval(""" INSERT INTO ems.planning_run (site_id, horizon_start, horizon_end, status, run_type, triggered_by, replan_from, soc_at_replan_wh, solver_duration_ms, forecast_correction_factor) VALUES ($1,$2,$3,'draft',$4,$5,$6,$7,$8,$9) RETURNING id """, site_id, horizon_from, horizon_to, run_type, triggered_by, replan_from, soc_wh, duration_ms, correction) # Bulk insert výsledků await db.executemany(""" INSERT INTO ems.planning_interval (run_id, interval_start, battery_setpoint_w, battery_soc_target_pct, grid_setpoint_w, ev1_setpoint_w, ev2_setpoint_w, ev1_via_bat_w, ev2_via_bat_w, heat_pump_enabled, heat_pump_setpoint_w, pv_a_curtailed_w, expected_cost_czk, effective_buy_price, effective_sell_price) VALUES ($1,$2,$3,$4,$5,$6,$7,$8,$9,$10,$11,$12,$13,$14,$15) """, [ (run_id, r.interval_start, r.battery_setpoint_w, r.battery_soc_target, r.grid_setpoint_w, r.ev1_setpoint_w, r.ev2_setpoint_w, r.ev1_via_bat_w, r.ev2_via_bat_w, r.heat_pump_enabled, r.heat_pump_setpoint_w, r.pv_a_curtailed_w, r.expected_cost_czk, r.effective_buy_price, r.effective_sell_price) for r in results ]) # Aktivovat nový plán, supersede předchozí await db.execute(""" UPDATE ems.planning_run SET status = 'superseded' WHERE site_id = $1 AND status = 'active' AND id <> $2 """, site_id, run_id) await db.execute( "UPDATE ems.planning_run SET status = 'active' WHERE id = $1", run_id ) return run_id