rezani poole i kdyz je zlenenobonusove pole na stejnmstridaci
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@@ -562,6 +562,18 @@ def solve_dispatch(
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# by to jinak vedlo k nežádoucímu exportu / infeasible řešení.
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GEN_CUTOFF_PENALTY_CZK_KWH = 5.0
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# Heuristika: pokud existuje necurtailable PV B a v budoucnu v horizontu nastane buy < 0,
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# chceme mít motivaci držet baterii „prázdnější“ pro pozdější výhodný import / bonusové PV B okno.
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# V okně sell < 0 pak preferujeme curtail PV A (místo placeného exportu), a to tak,
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# že dočasně snížíme penalizaci ca[t] (curtailment) na 0.
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has_pv_b = any(float(s.pv_b_forecast_w) > 0.0 for s in slots)
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future_neg_buy_from: list[bool] = [False] * T
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seen_neg_buy = False
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for i in range(T - 1, -1, -1):
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if float(slots[i].buy_price) < 0.0:
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seen_neg_buy = True
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future_neg_buy_from[i] = seen_neg_buy
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# EV proměnné per vozidlo
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ev_direct = [[pulp.LpVariable(f"evd_{e}_{t}", 0,
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min(vehicles[e].max_charge_power_w, grid.max_import_power_w))
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@@ -611,7 +623,16 @@ def solve_dispatch(
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+ ev_via_bat[e][t] * slots[t].buy_price * EV_ROUNDTRIP_FACTOR * INTERVAL_H / 1000
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for e in range(EV)
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)
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+ ca[t] * CURTAILMENT_PENALTY
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+ ca[t]
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* (
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0.0
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if (
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has_pv_b
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and future_neg_buy_from[t]
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and float(slots[t].sell_price) < 0.0
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)
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else CURTAILMENT_PENALTY
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)
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for t in range(T)
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)
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+ soc_deficit_24h * soc_deficit_penalty_czk_kwh / 1000
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@@ -205,6 +205,55 @@ def replace_slot(
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class PlanningDispatchMilpTests(unittest.TestCase):
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def test_neg_sell_with_future_neg_buy_prefers_curtail_pv_a_over_export(self) -> None:
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"""
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Když:
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- aktuální slot má sell < 0 (export je náklad),
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- v horizontu existuje budoucí buy < 0,
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- a zároveň existuje PV B (necurtailable) někde v horizontu,
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solver preferuje curtail PV A (ca) místo placeného exportu ge.
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"""
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slots = [
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_slot(load=0, buy=3.0, sell=-0.1, pv_a=5000, pv_b=0),
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_slot(load=0, buy=-10.0, sell=1.0, pv_a=0, pv_b=5000),
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]
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battery = _battery(uc_wh=50_000.0)
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hp = SimpleNamespace(
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rated_heating_power_w=0,
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tuv_min_temp_c=45.0,
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tuv_target_temp_c=55.0,
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)
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grid = SimpleNamespace(max_import_power_w=20_000, max_export_power_w=20_000)
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vehicles = [
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SimpleNamespace(
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max_charge_power_w=0,
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battery_capacity_kwh=1.0,
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default_target_soc_pct=80.0,
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),
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SimpleNamespace(
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max_charge_power_w=0,
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battery_capacity_kwh=1.0,
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default_target_soc_pct=80.0,
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),
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]
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soc0 = 0.50 * battery.usable_capacity_wh
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results, _ms = solve_dispatch(
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slots,
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battery,
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hp,
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grid,
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[None, None],
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vehicles,
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soc0,
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50.0,
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tuv_delta_stats=None,
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operating_mode="AUTO",
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)
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self.assertEqual(len(results), 2)
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# Slot 0: PV A se má raději uříznout než vyvážet za zápornou cenu.
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self.assertEqual(int(results[0].pv_a_curtailed_w), 5000)
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self.assertGreaterEqual(int(results[0].grid_setpoint_w), 0)
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def test_two_tier_soc_solves_optimal(self) -> None:
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slots = [_slot()]
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battery = _battery()
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@@ -150,7 +150,9 @@ minimize:
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# Solver tak přirozeně preferuje přímé nabíjení nad průchodem baterií
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+ Σ_e ev_via_bat[e][t] * buy_price[t] * EV_ROUNDTRIP_FACTOR * interval_h
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# Malá penalizace curtailmentu pole A (preferujeme využití FVE)
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# Malá penalizace curtailmentu pole A (preferujeme využití FVE).
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# Výjimka: pokud existuje PV B a v budoucnu v horizontu nastane buy < 0, pak v okně sell < 0
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# solver preferuje curtail PV A před placeným exportem (penalizace curtailmentu se v těchto slotech snižuje na 0).
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+ pv_a_curtailed[t] * CURTAILMENT_PENALTY
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]
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```
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