chjo2

backend/services/planning_engine.py

@@ -55,6 +55,11 @@ PEAK_EXPORT_SHORTFALL_PENALTY_CZK_KWH = 40.0
 PV_CHARGE_SHORTFALL_PENALTY_CZK_KWH = 120.0
 # Curtailment při sell<0 + allow_charge: nesmí být téměř zdarma oproti nabíjení (BA81).
 NEG_SELL_CURTAIL_PENALTY_CZK_KWH = 1.0
+# Odměna v objective za FVE→baterie při sell<0 (doplňuje shortfall; BA81 fixed tarif).
+NEG_SELL_PV_CHARGE_REWARD_CZK_KWH = 0.8
+# Cíl SoC v okně záporného výkupu (podíl soc_max) — safety_soc_target z SQL jde jen k ~50 %.
+NEG_SELL_CHARGE_SOC_FRAC_OF_MAX = 0.92
+PLANNER_BUILD_TAG = "2026-05-24-neg-sell-v2"
 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
@@ -1021,19 +1026,19 @@ def solve_dispatch(
         charge_slots |= {
             t for t, s in enumerate(slots) if float(s.buy_price) < 0.0
         }
-        if bool(getattr(grid, "block_export_on_negative_sell", False)):
-            charge_slots |= {
-                t
-                for t, s in enumerate(slots)
-                if float(s.sell_price) < 0.0
-                and max(
-                    0,
-                    int(s.pv_a_forecast_w)
-                    + int(s.pv_b_forecast_w)
-                    - int(s.load_baseline_w),
-                )
-                > 0
-            }
+        # Stejně jako R__063 (sell<0 + PV přebytek): shortfall/curtail penalizace i bez block_export.
+        charge_slots |= {
+            t
+            for t, s in enumerate(slots)
+            if float(s.sell_price) < 0.0
+            and max(
+                0,
+                int(s.pv_a_forecast_w)
+                + int(s.pv_b_forecast_w)
+                - int(s.load_baseline_w),
+            )
+            > 500
+        }
         discharge_export_slots = {
             t for t, s in enumerate(slots) if s.allow_discharge_export
         }
@@ -1248,6 +1253,18 @@ def solve_dispatch(
                 if om == "AUTO" and t in discharge_export_slots
                 else 0
             )
+            - (
+                bc_pv[t]
+                * NEG_SELL_PV_CHARGE_REWARD_CZK_KWH
+                * INTERVAL_H
+                / 1000
+                if (
+                    om == "AUTO"
+                    and float(slots[t].sell_price) < 0.0
+                    and t in charge_slots
+                )
+                else 0
+            )
             + 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
@@ -1399,7 +1416,17 @@ def solve_dispatch(
         sv = safety_vars[t]
         tgt_s = slots[t].safety_soc_target_wh if daytime_en else None
         if sv is not None and tgt_s is not None:
-            prob += sv >= float(tgt_s) - soc[t]
+            eff_tgt_s = float(tgt_s)
+            if (
+                om == "AUTO"
+                and float(s.sell_price) < 0.0
+                and t in charge_slots
+            ):
+                eff_tgt_s = max(
+                    eff_tgt_s,
+                    float(battery.soc_max_wh) * NEG_SELL_CHARGE_SOC_FRAC_OF_MAX,
+                )
+            prob += sv >= eff_tgt_s - soc[t]
 
         # ev_via_bat kryto z discharge
         prob += pulp.lpSum(ev_via_bat[e][t] for e in range(EV)) <= bd[t]
@@ -1646,7 +1673,14 @@ def solve_dispatch(
                 float(s.pv_b_forecast_w) > 0
                 and not getattr(grid, "block_export_on_negative_sell", False)
                 and sell_t < 0
+                and not fixed_tariff_like_pre
             )
+            if (
+                fixed_tariff_like_pre
+                and sell_t < 0
+                and t in charge_slots
+            ):
+                prob += ge_pv[t] <= max(0.0, float(s.pv_b_forecast_w))
             if (
                 not allow_pre_neg_pv_export
                 and not skip_pv_store_block
@@ -1904,6 +1938,7 @@ def solve_dispatch(
     night0 = slots[0]
     solver_snapshot: dict[str, Any] = {
         "version": 1,
+        "planner_build_tag": PLANNER_BUILD_TAG,
         "inputs": {
             "current_soc_wh": float(current_soc_wh),
             "operating_mode": operating_mode,