Add battery dispatch implementation and Gitea CI pipeline

Implements the package from PLAN.md: config/data loading, the LP-first
MILP-fallback rolling-horizon optimiser, battery degradation tracking,
independent schedule validation, metrics, plots and the CLI, plus the
matching test suite. Adds a Gitea Actions workflow (lint + tests) that
posts a pass/fail notification to ntfy on every run, and a .gitignore
for build/cache artefacts that had been tracked by mistake.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

tests/conftest.py

@@ -0,0 +1,75 @@
+"""Shared fixtures: a spec matching Attachment 1 and helpers for tiny markets."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from battery_dispatch.config import BatterySpec, RunConfig
+from battery_dispatch.markets import MARKET_1, MARKET_2, Market
+
+# Equal to Attachment 1, but stated literally so the tests do not depend on the
+# workbook being present or unchanged.
+SPEC = BatterySpec(
+    max_charge_mw=2.0,
+    max_discharge_mw=2.0,
+    capacity_mwh=4.0,
+    charge_efficiency=0.95,
+    discharge_efficiency=0.95,
+    lifetime_years=10.0,
+    lifetime_cycles=5000.0,
+    degradation_pct_per_cycle=0.001,
+    capex_gbp=500_000.0,
+    fixed_opex_gbp_per_year=5_000.0,
+)
+
+
+@pytest.fixture
+def spec() -> BatterySpec:
+    return SPEC
+
+
+@pytest.fixture
+def config() -> RunConfig:
+    return RunConfig()
+
+
+def markets_from(
+    market_1_prices: list[float],
+    market_2_prices: list[float] | None = None,
+) -> list[Market]:
+    """Build both markets from a half-hourly Market 1 price list.
+
+    ``market_2_prices`` is given per *hour*; when omitted Market 2 is priced so
+    low that it never trades, isolating Market 1 behaviour.
+    """
+    n = len(market_1_prices)
+    if market_2_prices is None:
+        broadcast = np.zeros(n)
+    else:
+        assert n == 2 * len(market_2_prices), "one Market 2 price per hour"
+        broadcast = np.repeat(np.asarray(market_2_prices, dtype=float), 2)
+
+    return [
+        Market(MARKET_1, 1, np.asarray(market_1_prices, dtype=float)),
+        Market(MARKET_2, 2, broadcast),
+    ]
+
+
+def prices_frame(
+    market_1_prices: list[float],
+    market_2_prices: list[float],
+    start: str = "2018-01-01",
+) -> pd.DataFrame:
+    """A price frame shaped like the one ``load_prices`` returns."""
+    n = len(market_1_prices)
+    index = pd.date_range(start, periods=n, freq="30min")
+    return pd.DataFrame(
+        {
+            "market_1_price": np.asarray(market_1_prices, dtype=float),
+            "market_2_price": np.repeat(np.asarray(market_2_prices, dtype=float), 2),
+            "hour_index": np.arange(n) // 2,
+        },
+        index=index,
+    )