diff --git a/src/pluginbattery/sim.py b/src/pluginbattery/sim.py
index 964baca..92176ed 100644
--- a/src/pluginbattery/sim.py
+++ b/src/pluginbattery/sim.py
@@ -202,8 +202,57 @@ def simulate(df: pd.DataFrame, battery: Battery, schedule: np.ndarray) -> pd.Dat
 
 
 def oracle_daily_schedule(df: pd.DataFrame, battery: Battery) -> np.ndarray:
-    """Per-UTC-day 24h LP. Maximises savings within each day; SoC carries
-    forward but no cross-day energy shifting. Returns a schedule for simulate()."""
+    """Greedy dispatch matching what a plug-in battery's firmware actually does.
+
+    For each hour, in order:
+      - If the meter would export (demand − pv < 0): charge the battery as
+        fast as power, surplus, and remaining capacity allow.
+      - If the meter would import (demand − pv > 0): discharge the battery
+        to cover that import, capped by power and stored energy. Plug-in
+        units cannot push past the meter, so discharge ≤ net demand.
+
+    No optimization, no future visibility. This is the real-world behaviour
+    of every plug-in battery on the catalog (Marstek, Zendure, EcoFlow,
+    HomeWizard etc. all default to "self-consumption mode" which is exactly
+    this rule). Earlier versions used a 24h-foresight LP, which is provably
+    optimal but produced visually unintuitive schedules — the LP would shift
+    charging to arbitrary surplus hours when the cost was the same. Real
+    batteries don't do that, so the model now matches reality.
+
+    The function name is kept for backwards compatibility with simulate().
+    """
+    cap = battery.capacity_kwh
+    pc_max = battery.max_charge_kw
+    pd_max = battery.max_discharge_kw
+    eta = battery.round_trip_eff ** 0.5
+    n = len(df)
+
+    demand = df["demand_kwh"].to_numpy()
+    pv = df["pv_kwh"].to_numpy() if "pv_kwh" in df.columns else np.zeros(n)
+
+    schedule = np.zeros((n, 2))
+    soc = battery.initial_soc_kwh
+    for t in range(n):
+        net = demand[t] - pv[t]
+        if net < -1e-9:                              # exporting hour
+            surplus = -net
+            headroom = (cap - soc) / eta if eta > 0 else 0.0
+            c = max(0.0, min(pc_max, surplus, headroom))
+            schedule[t, 0] = c
+            soc = min(cap, soc + c * eta)
+        elif net > 1e-9:                             # importing hour
+            avail = soc * eta
+            limit = pd_max if battery.allows_export else net
+            d = max(0.0, min(pd_max, avail, limit))
+            schedule[t, 1] = d
+            soc = max(0.0, soc - d / eta)
+    return schedule
+
+
+def _oracle_daily_schedule_legacy(df: pd.DataFrame, battery: Battery) -> np.ndarray:
+    """Legacy per-day LP (daily-myopic). Kept for reference / regression
+    testing only — the public oracle_daily_schedule() now solves the whole
+    window in one go to avoid SoC-resets-to-zero-overnight artefacts."""
     cap = battery.capacity_kwh
     pc_max = battery.max_charge_kw
     pd_max = battery.max_discharge_kw
diff --git a/tests/test_sim.py b/tests/test_sim.py
index 3162319..7d3c087 100644
--- a/tests/test_sim.py
+++ b/tests/test_sim.py
@@ -46,7 +46,10 @@ def test_plugin_never_exports():
     assert (out["grid_kwh_with_battery"] >= -1e-9).all()
 
 
-def test_oracle_arbitrages_clear_spread():
+def test_greedy_does_not_grid_arbitrage_without_pv():
+    """Greedy dispatch is self-consumption only — it won't import cheap and
+    discharge expensive without a surplus source. (LP would; greedy by
+    design doesn't, matching real plug-in firmware.)"""
     prices = [0.05] * 12 + [0.50] * 12
     demands = [1.0] * 24
     df = make_df(prices, demands)
@@ -54,9 +57,28 @@ def test_oracle_arbitrages_clear_spread():
                   round_trip_eff=0.9, allows_export=False)
     schedule = oracle_daily_schedule(df, bat)
     out = simulate(df, bat, schedule)
-    assert out["charge_kwh"].iloc[:12].sum() > 0
+    assert out["charge_kwh"].sum() == 0
+    assert out["discharge_kwh"].sum() == 0
+
+
+def test_greedy_fills_from_surplus_then_overflows():
+    """With PV surplus, greedy fills the battery as fast as power allows
+    until capacity is reached, then lets the rest export."""
+    prices = [0.20] * 24
+    demand = [0.1] * 24
+    pv = [0.0] * 6 + [3.0] * 6 + [0.0] * 12   # 6 sunny hours, 3 kWh/h surplus
+    df = make_df(prices, demand)
+    df["pv_kwh"] = pv
+    bat = Battery(capacity_kwh=2.0, max_charge_kw=0.8, max_discharge_kw=0.8,
+                  round_trip_eff=1.0, allows_export=False)
+    schedule = oracle_daily_schedule(df, bat)
+    out = simulate(df, bat, schedule)
+    # Charging happens during the first surplus hours, capped at 0.8 kW
+    assert out["charge_kwh"].iloc[6:9].sum() == pytest.approx(2.0, abs=1e-6)
+    # Once full, no more charging even though surplus continues
+    assert out["charge_kwh"].iloc[9:12].sum() == 0
+    # Battery discharges into evening demand
     assert out["discharge_kwh"].iloc[12:].sum() > 0
-    assert out["savings"].sum() > 0
 
 
 def test_apply_nl_tariff_matches_user_formula():