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>

.gitea/workflows/ci.yml

@@ -0,0 +1,48 @@
+name: CI
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    timeout-minutes: 45
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        run: |
+          curl -LsSf https://astral.sh/uv/install.sh | sh
+          echo "$HOME/.local/bin" >> "$GITHUB_PATH"
+
+      - name: Install dependencies
+        run: uv sync
+
+      - name: Lint
+        run: uv run ruff check .
+
+      - name: Test
+        run: uv run pytest -q
+
+      - name: Notify ntfy
+        if: always()
+        env:
+          NTFY_USER: ${{ secrets.NTFY_USER }}
+          NTFY_PASS: ${{ secrets.NTFY_PASS }}
+        run: |
+          if [ "${{ job.status }}" = "success" ]; then
+            title="CI passed: ${{ gitea.repository }}"
+            tags="white_check_mark"
+          else
+            title="CI failed: ${{ gitea.repository }}"
+            tags="x"
+          fi
+          curl -s -u "$NTFY_USER:$NTFY_PASS" \
+            -H "Title: $title" \
+            -H "Tags: $tags" \
+            -H "Click: ${{ gitea.server_url }}/${{ gitea.repository }}/actions/runs/${{ gitea.run_id }}" \
+            -d "Branch ${{ gitea.ref_name }} @ ${{ gitea.sha }} (${{ job.status }})" \
+            "https://notify.daubney.dev/aurora_task"

.gitignore

@@ -0,0 +1,9 @@
+__pycache__/
+*.pyc
+.venv/
+.ruff_cache/
+.pytest_cache/
+*.egg-info/
+.idea/
+.air/
+.junie/

pyproject.toml

@@ -0,0 +1,38 @@
+[project]
+name = "battery-dispatch"
+version = "0.1.0"
+description = "Profit-maximising dispatch model for a 2 MW / 4 MWh battery across two wholesale electricity markets"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "pandas>=2.0",
+    "openpyxl>=3.1",
+    "pulp>=2.8",
+    "matplotlib>=3.8",
+]
+
+[project.optional-dependencies]
+dev = ["pytest>=8.0", "ruff>=0.6"]
+
+[project.scripts]
+battery-dispatch = "battery_dispatch.cli:main"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/battery_dispatch"]
+
+[dependency-groups]
+dev = ["pytest>=8.0", "ruff>=0.6"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+
+[tool.ruff]
+line-length = 100
+target-version = "py311"
+
+[tool.ruff.lint]
+select = ["E", "F"]

src/battery_dispatch/__init__.py

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+"""Profit-maximising dispatch model for a grid-scale battery in two wholesale markets."""
+
+from battery_dispatch.config import BatterySpec, RunConfig
+from battery_dispatch.markets import Market
+
+__all__ = ["BatterySpec", "RunConfig", "Market"]
+__version__ = "0.1.0"

src/battery_dispatch/battery.py

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+"""Battery state: charge, cycle counting and capacity fade.
+
+Degradation is deliberately applied *between* rolling-horizon windows rather
+than inside the optimisation. Making usable capacity a function of throughput
+within the same problem would couple the capacity bound to the decision
+variables and make the programme non-linear; over a single 48-hour window the
+capacity change is of order 1e-5, far below any decision-relevant threshold, so
+updating it after each committed block costs nothing in accuracy.
+
+An *equivalent full cycle* follows Attachment 1's definition: energy discharged
+from storage, divided by the nominal capacity. Partial charges and discharges
+therefore accumulate into whole cycles, exactly as the specification describes.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from battery_dispatch.config import BatterySpec
+
+
+@dataclass
+class BatteryState:
+    """Mutable state carried across rolling-horizon windows."""
+
+    spec: BatterySpec
+    soc_mwh: float = 0.0
+    equivalent_full_cycles: float = 0.0
+    total_charged_grid_mwh: float = 0.0
+    total_discharged_grid_mwh: float = 0.0
+    total_discharged_storage_mwh: float = 0.0
+
+    @property
+    def capacity_mwh(self) -> float:
+        """Usable capacity after fade, floored at zero."""
+        fade = self.spec.degradation_fraction_per_cycle * self.equivalent_full_cycles
+        return max(0.0, self.spec.capacity_mwh * (1.0 - fade))
+
+    @property
+    def capacity_retained(self) -> float:
+        """Usable capacity as a fraction of nominal."""
+        return self.capacity_mwh / self.spec.capacity_mwh
+
+    def apply_cycles(self, discharged_storage_mwh: float) -> None:
+        """Add throughput to the cycle count, which in turn fades capacity."""
+        if discharged_storage_mwh < 0:
+            raise ValueError("discharged_storage_mwh must be non-negative")
+        self.equivalent_full_cycles += discharged_storage_mwh / self.spec.capacity_mwh
+
+    def commit(
+        self,
+        *,
+        soc_mwh: float,
+        charged_grid_mwh: float,
+        discharged_grid_mwh: float,
+    ) -> None:
+        """Record a committed block of dispatch and advance the state."""
+        discharged_storage = discharged_grid_mwh / self.spec.discharge_efficiency
+
+        self.soc_mwh = soc_mwh
+        self.total_charged_grid_mwh += charged_grid_mwh
+        self.total_discharged_grid_mwh += discharged_grid_mwh
+        self.total_discharged_storage_mwh += discharged_storage
+        self.apply_cycles(discharged_storage)
+
+
+def implied_life_years(
+    spec: BatterySpec,
+    cycles_per_year: float,
+) -> float:
+    """Expected life, whichever of the calendar or cycle limit binds first."""
+    if cycles_per_year <= 0:
+        return spec.lifetime_years
+    return min(spec.lifetime_years, spec.lifetime_cycles / cycles_per_year)

src/battery_dispatch/cli.py

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+"""Command-line entry point: ``battery-dispatch run``."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import logging
+import sys
+from pathlib import Path
+
+import pandas as pd
+
+from battery_dispatch import metrics as metrics_module
+from battery_dispatch import plots
+from battery_dispatch.config import BatterySpec, RunConfig
+from battery_dispatch.data import load_prices, slice_whole_days
+from battery_dispatch.optimiser import run_rolling_horizon
+from battery_dispatch.validation import validate_schedule
+
+logger = logging.getLogger("battery_dispatch")
+
+DEFAULT_SPEC_FILE = Path("data/Attachment 1.xlsx")
+DEFAULT_PRICE_FILE = Path("data/Attachment 2.xlsx")
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        prog="battery-dispatch",
+        description="Optimise battery dispatch across two wholesale electricity markets.",
+    )
+    subparsers = parser.add_subparsers(dest="command", required=True)
+
+    run = subparsers.add_parser("run", help="optimise a period and write results")
+    run.add_argument("--start", default=None, help="first day, e.g. 2018-01-01")
+    run.add_argument("--end", default=None, help="last day (inclusive), e.g. 2020-12-31")
+    run.add_argument("--spec-file", type=Path, default=DEFAULT_SPEC_FILE)
+    run.add_argument("--price-file", type=Path, default=DEFAULT_PRICE_FILE)
+    run.add_argument("--out", type=Path, default=Path("outputs"))
+    run.add_argument("--window-hours", type=int, default=48, help="lookahead horizon")
+    run.add_argument("--commit-hours", type=int, default=24, help="committed prefix")
+    run.add_argument(
+        "--max-seconds-per-window", type=float, default=None,
+        help="solver time cap per window (omit for no cap)",
+    )
+    mode = run.add_mutually_exclusive_group()
+    mode.add_argument(
+        "--lp-only", action="store_true",
+        help="skip the MILP fallback (faster; may allow simultaneous charge/discharge)",
+    )
+    mode.add_argument(
+        "--milp-always", action="store_true",
+        help="always solve with binaries (slower; same answer)",
+    )
+    run.add_argument(
+        "--degradation-cost", type=float, default=0.0, metavar="GBP_PER_MWH",
+        help="marginal cost charged against energy drawn from storage; 0 (default) "
+             "maximises gross trading profit",
+    )
+    run.add_argument("--no-plots", action="store_true", help="skip figure rendering")
+    run.add_argument("--quiet", action="store_true", help="only warnings and errors")
+    return parser
+
+
+def _format_summary(summary: dict[str, float | int | str], annual: pd.DataFrame) -> str:
+    lines = [
+        "",
+        f"Period                 {summary['start']} .. {summary['end']}"
+        f"  ({summary['days']:.0f} days)",
+        f"Gross trading profit   £{summary['gross_trading_profit_gbp']:,.2f}",
+        f"  Market 1             £{summary['profit_market_1_gbp']:,.2f}"
+        f"  ({summary['profit_share_market_1']:.1%})",
+        f"  Market 2             £{summary['profit_market_2_gbp']:,.2f}"
+        f"  ({summary['profit_share_market_2']:.1%})",
+        f"Per year               £{summary['gross_profit_per_year_gbp']:,.2f}",
+        f"Less fixed opex        £{summary['net_of_opex_gbp']:,.2f}",
+        f"Energy discharged      {summary['energy_discharged_to_grid_mwh']:,.1f} MWh"
+        f"  (charged {summary['energy_charged_from_grid_mwh']:,.1f} MWh)",
+        f"Captured spread        "
+        f"£{summary['average_captured_spread_gbp_per_mwh']:,.2f}/MWh discharged",
+        f"Equivalent cycles      {summary['equivalent_full_cycles']:,.1f}"
+        f"  ({summary['equivalent_full_cycles_per_year']:,.1f}/yr,"
+        f" {summary['cycle_budget_used_fraction']:.1%} of budget)",
+        f"Capacity remaining     {summary['capacity_end_mwh']:.4f} MWh"
+        f"  ({summary['capacity_retained_fraction']:.2%} of nominal)",
+        f"Implied life           {summary['implied_life_years']:.1f} years",
+        "",
+        "Per year:",
+    ]
+    table = annual[
+        ["gross_profit_gbp", "profit_market_1_gbp", "profit_market_2_gbp",
+         "discharged_mwh", "equivalent_full_cycles"]
+    ].round(1)
+    lines.append(table.to_string())
+    return "\n".join(lines)
+
+
+def run_command(args: argparse.Namespace) -> int:
+    logging.basicConfig(
+        level=logging.WARNING if args.quiet else logging.INFO,
+        format="%(levelname)s  %(message)s",
+        stream=sys.stderr,
+    )
+
+    solver_mode = "auto"
+    if args.lp_only:
+        solver_mode = "lp-only"
+    elif args.milp_always:
+        solver_mode = "milp-always"
+
+    config = RunConfig(
+        start=args.start,
+        end=args.end,
+        window_hours=args.window_hours,
+        commit_hours=args.commit_hours,
+        solver_mode=solver_mode,
+        max_seconds_per_window=args.max_seconds_per_window,
+        out_dir=args.out,
+        degradation_cost_gbp_per_mwh=args.degradation_cost,
+    )
+
+    spec = BatterySpec.from_excel(args.spec_file)
+    logger.info(
+        "Battery: %.1f MW / %.1f MWh, round-trip efficiency %.1f%%",
+        spec.max_charge_mw, spec.capacity_mwh, 100 * spec.round_trip_efficiency,
+    )
+
+    prices = load_prices(args.price_file)
+    prices = slice_whole_days(prices, config.start, config.end)
+    logger.info(
+        "Optimising %d half-hours (%s .. %s), %dh window / %dh commit",
+        len(prices), prices.index[0], prices.index[-1],
+        config.window_hours, config.commit_hours,
+    )
+
+    result, state, stats = run_rolling_horizon(prices, spec, config)
+
+    report = validate_schedule(result, spec)
+    annual = metrics_module.annual_summary(result, spec)
+    monthly = metrics_module.monthly_summary(result, spec)
+    summary = metrics_module.headline_metrics(result, spec, state)
+
+    out_dir: Path = config.out_dir
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    result.round(10).to_csv(out_dir / "dispatch.csv")
+    annual.round(6).to_csv(out_dir / "annual_summary.csv")
+    monthly.round(6).to_csv(out_dir / "monthly_summary.csv")
+
+    # Deliberately excludes wall-clock time so repeated runs are byte-identical.
+    payload = {
+        "summary": {
+            key: (round(value, 6) if isinstance(value, float) else value)
+            for key, value in summary.items()
+        },
+        "validation": {
+            "ok": report.ok,
+            "checks": report.checks,
+            "failures": report.failures,
+            "details": {k: round(v, 12) for k, v in report.details.items()},
+        },
+        "solver": {
+            "mode": config.solver_mode,
+            "windows": stats.windows,
+            "milp_fallbacks": stats.milp_fallbacks,
+            "window_hours": config.window_hours,
+            "commit_hours": config.commit_hours,
+            "degradation_cost_gbp_per_mwh": config.degradation_cost_gbp_per_mwh,
+        },
+    }
+    (out_dir / "summary.json").write_text(
+        json.dumps(payload, indent=2, sort_keys=True) + "\n"
+    )
+
+    if not args.no_plots:
+        spread = metrics_module.captured_spread_per_cycle(result)
+        written = plots.make_all(result, monthly, spread, spec, out_dir)
+        logger.info("Wrote %d figures", len(written))
+
+    print(_format_summary(summary, annual))
+    print(
+        f"\nSolver  {stats.windows} windows, {stats.milp_fallbacks} MILP fallbacks, "
+        f"{stats.seconds:.1f}s"
+    )
+    print(f"Outputs {out_dir}/")
+    print(f"Validation  {report.summary()}")
+
+    if not report.ok:
+        return 1
+    return 0
+
+
+def main(argv: list[str] | None = None) -> int:
+    args = build_parser().parse_args(argv)
+    if args.command == "run":
+        return run_command(args)
+    raise AssertionError(f"unhandled command {args.command!r}")
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

src/battery_dispatch/config.py

@@ -0,0 +1,122 @@
+"""Battery specification and run configuration.
+
+The efficiency convention used throughout the package is stated once, here, and
+pinned by a unit test:
+
+* ``charge_efficiency`` -- a fraction ``eta_c`` of energy imported from the grid
+  survives to be stored. Importing ``I`` MWh from the grid stores ``eta_c * I``.
+* ``discharge_efficiency`` -- a fraction ``eta_d`` of energy drawn from storage
+  survives to reach the grid. Exporting ``E`` MWh to the grid draws ``E / eta_d``
+  from storage.
+
+Attachment 1 quotes *losses* (0.05), so the efficiencies are ``1 - loss = 0.95``.
+Money is always settled on the grid-side quantity, because that is what the
+market meters and pays for.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+
+import pandas as pd
+
+# Row labels as they appear in the first column of Attachment 1.
+_SPEC_ROWS = {
+    "max_charge_mw": "Max charging rate",
+    "max_discharge_mw": "Max discharging rate",
+    "capacity_mwh": "Max storage volume",
+    "charge_loss": "Battery charging efficiency",
+    "discharge_loss": "Battery discharging efficiency",
+    "lifetime_years": "Lifetime (1)",
+    "lifetime_cycles": "Lifetime (2)",
+    "degradation_pct_per_cycle": "Storage volume degradation rate",
+    "capex_gbp": "Capex",
+    "fixed_opex_gbp_per_year": "Fixed Operational Costs",
+}
+
+HALF_HOUR = 0.5  # hours per settlement period; the model's time step
+
+
+@dataclass(frozen=True)
+class BatterySpec:
+    """Physical and commercial parameters of the battery."""
+
+    max_charge_mw: float
+    max_discharge_mw: float
+    capacity_mwh: float
+    charge_efficiency: float
+    discharge_efficiency: float
+    lifetime_years: float
+    lifetime_cycles: float
+    degradation_pct_per_cycle: float
+    capex_gbp: float
+    fixed_opex_gbp_per_year: float
+
+    @property
+    def degradation_fraction_per_cycle(self) -> float:
+        """Capacity lost per equivalent full cycle, as a fraction (not a percent)."""
+        return self.degradation_pct_per_cycle / 100.0
+
+    @property
+    def round_trip_efficiency(self) -> float:
+        return self.charge_efficiency * self.discharge_efficiency
+
+    @classmethod
+    def from_excel(cls, path: str | Path) -> "BatterySpec":
+        """Read the specification from ``Attachment 1.xlsx`` by row label."""
+        frame = pd.read_excel(path, sheet_name=0, header=0, index_col=0)
+        values = frame.iloc[:, 0]
+
+        missing = [label for label in _SPEC_ROWS.values() if label not in values.index]
+        if missing:
+            raise ValueError(f"{path}: missing expected spec rows: {missing}")
+
+        raw = {key: float(values[label]) for key, label in _SPEC_ROWS.items()}
+
+        # Attachment 1 states losses; the model works in efficiencies.
+        charge_loss = raw.pop("charge_loss")
+        discharge_loss = raw.pop("discharge_loss")
+        for name, loss in (("charging", charge_loss), ("discharging", discharge_loss)):
+            if not 0.0 <= loss < 1.0:
+                raise ValueError(f"{name} loss {loss} outside [0, 1)")
+
+        return cls(
+            charge_efficiency=1.0 - charge_loss,
+            discharge_efficiency=1.0 - discharge_loss,
+            **raw,
+        )
+
+
+@dataclass(frozen=True)
+class RunConfig:
+    """Everything that varies between runs of the model."""
+
+    start: pd.Timestamp | None = None
+    end: pd.Timestamp | None = None
+    window_hours: int = 48
+    commit_hours: int = 24
+    solver_mode: str = "auto"  # "auto" | "lp-only" | "milp-always"
+    max_seconds_per_window: float | None = None
+    out_dir: Path = Path("outputs")
+
+    # Optional marginal cost charged against energy drawn from storage, in
+    # £/MWh. Zero (the default) maximises gross trading profit, which is what
+    # the exercise asks for. A positive value prices the cycle-life the trade
+    # consumes, suppressing marginal cycling; capex / lifetime_cycles /
+    # capacity is the natural reference level. See README.
+    degradation_cost_gbp_per_mwh: float = 0.0
+
+    def __post_init__(self) -> None:
+        if self.commit_hours <= 0 or self.window_hours <= 0:
+            raise ValueError("window_hours and commit_hours must be positive")
+        if self.commit_hours > self.window_hours:
+            raise ValueError("commit_hours cannot exceed window_hours")
+        if self.solver_mode not in {"auto", "lp-only", "milp-always"}:
+            raise ValueError(f"unknown solver_mode {self.solver_mode!r}")
+        if self.degradation_cost_gbp_per_mwh < 0:
+            raise ValueError("degradation_cost_gbp_per_mwh must be non-negative")
+
+    def cycle_cost_reference(self, spec: "BatterySpec") -> float:
+        """Capex amortised over the cycle budget, in £/MWh drawn from storage."""
+        return spec.capex_gbp / spec.lifetime_cycles / spec.capacity_mwh

src/battery_dispatch/data.py

@@ -0,0 +1,167 @@
+"""Loading and aligning the two price series.
+
+Attachment 2 holds Market 1 at half-hourly resolution and Market 2 at hourly
+resolution, on two sheets. Three things about the raw data need explicit
+handling, and each is done loudly rather than silently:
+
+1. **Trailing blank rows.** The hourly sheet is as long as the half-hourly one
+   but only its first 26,304 rows carry prices. The rest are dropped and the
+   surviving count is asserted.
+
+2. **Spring clock-change labelling.** Market 1 timestamps duplicate ``02:00``
+   and ``02:30`` and skip ``01:00`` and ``01:30`` on 2018-03-25, 2019-03-31 and
+   2020-03-29. Every day nevertheless carries a clean 48 rows. Rather than
+   guess at the intended local-time semantics, the loader rebuilds a regular
+   half-hourly index from the series start and logs the discarded labels. This
+   is an assumption, documented in the README: it treats the series as 52,608
+   consecutive half-hours, which is what the dispatch model actually needs.
+
+3. **Sub-second timestamp noise.** Hourly timestamps are off by up to 6 ms from
+   the exact hour (an Excel float artefact), so a timestamp join would be
+   fragile. Market 2 is aligned to Market 1 by integer position instead --
+   two half-hours per hour -- after asserting both row counts.
+"""
+
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+from battery_dispatch.markets import MARKET_1, MARKET_2, Market
+
+logger = logging.getLogger(__name__)
+
+HALF_HOURS_PER_DAY = 48
+HOURS_PER_DAY = 24
+EXPECTED_DAYS = 1096  # 2018-01-01 .. 2020-12-31 inclusive, including 2020 leap day
+
+HALF_HOURLY_SHEET = "Half-hourly data"
+HOURLY_SHEET = "Hourly data"
+
+SERIES_START = pd.Timestamp("2018-01-01 00:00")
+
+
+def _read_sheet(path: str | Path, sheet: str) -> pd.DataFrame:
+    """Read one price sheet into ``timestamp`` / ``price`` columns, blanks dropped."""
+    frame = pd.read_excel(path, sheet_name=sheet, header=0)
+    if frame.shape[1] < 2:
+        raise ValueError(f"{path}:{sheet} has {frame.shape[1]} columns, expected 2")
+
+    frame = frame.iloc[:, :2]
+    frame.columns = ["timestamp", "price"]
+
+    blank = frame["price"].isna().sum()
+    if blank:
+        logger.info("%s: dropping %d blank price rows", sheet, blank)
+        frame = frame.dropna(subset=["price"])
+
+    frame["price"] = frame["price"].astype(float)
+    return frame.reset_index(drop=True)
+
+
+def _log_clock_change_anomalies(timestamps: pd.Series) -> None:
+    """Report timestamps that do not match a regular half-hourly index."""
+    regular = pd.Series(
+        pd.date_range(SERIES_START, periods=len(timestamps), freq="30min")
+    )
+    mismatched = timestamps[timestamps.values != regular.values]
+    if mismatched.empty:
+        return
+
+    affected = sorted({ts.date() for ts in mismatched})
+    logger.info(
+        "Market 1: %d timestamps do not match a regular half-hourly index "
+        "(clock-change labelling on %s); rebuilding the index by position",
+        len(mismatched),
+        ", ".join(str(day) for day in affected),
+    )
+
+
+def load_prices(
+    path: str | Path,
+    *,
+    expected_days: int = EXPECTED_DAYS,
+) -> pd.DataFrame:
+    """Load both markets onto one regular half-hourly index.
+
+    Returns a frame indexed by ``timestamp`` with columns ``market_1_price``,
+    ``market_2_price`` and ``hour_index`` (the 0-based hour each half-hour
+    belongs to, used to group Market 2 commitments).
+    """
+    half_hourly = _read_sheet(path, HALF_HOURLY_SHEET)
+    hourly = _read_sheet(path, HOURLY_SHEET)
+
+    expected_half_hours = expected_days * HALF_HOURS_PER_DAY
+    expected_hours = expected_days * HOURS_PER_DAY
+
+    if len(half_hourly) != expected_half_hours:
+        raise ValueError(
+            f"{HALF_HOURLY_SHEET}: expected {expected_half_hours} rows "
+            f"({expected_days} days x {HALF_HOURS_PER_DAY}), got {len(half_hourly)}"
+        )
+    if len(hourly) != expected_hours:
+        raise ValueError(
+            f"{HOURLY_SHEET}: expected {expected_hours} rows "
+            f"({expected_days} days x {HOURS_PER_DAY}), got {len(hourly)}"
+        )
+
+    _log_clock_change_anomalies(half_hourly["timestamp"])
+
+    index = pd.date_range(SERIES_START, periods=expected_half_hours, freq="30min")
+
+    # Two half-hours per hour, aligned by position rather than by timestamp.
+    market_2 = np.repeat(hourly["price"].to_numpy(), 2)
+
+    prices = pd.DataFrame(
+        {
+            "market_1_price": half_hourly["price"].to_numpy(),
+            "market_2_price": market_2,
+            "hour_index": np.arange(expected_half_hours) // 2,
+        },
+        index=index,
+    )
+    prices.index.name = "timestamp"
+    return prices
+
+
+def slice_whole_days(
+    prices: pd.DataFrame,
+    start: pd.Timestamp | str | None = None,
+    end: pd.Timestamp | str | None = None,
+) -> pd.DataFrame:
+    """Clip ``prices`` to whole days between ``start`` and ``end`` inclusive.
+
+    ``end`` names a day, and that whole day is included. Keeping the slice on
+    day boundaries keeps every rolling-horizon window aligned to hour blocks.
+    """
+    first = prices.index[0] if start is None else pd.Timestamp(start).normalize()
+    last_day = prices.index[-1].normalize() if end is None else pd.Timestamp(end).normalize()
+    last = last_day + pd.Timedelta(days=1) - pd.Timedelta(minutes=30)
+
+    if first > last:
+        raise ValueError(f"start {first} is after end {last}")
+
+    window = prices.loc[first:last]
+    if window.empty:
+        raise ValueError(f"no price data between {first} and {last}")
+    if len(window) % HALF_HOURS_PER_DAY:
+        raise ValueError(
+            f"slice {first}..{last} covers {len(window)} half-hours, "
+            "which is not a whole number of days"
+        )
+
+    # hour_index is re-based so the slice can be optimised standalone.
+    window = window.copy()
+    window["hour_index"] = np.arange(len(window)) // 2
+    return window
+
+
+def build_markets(prices: pd.DataFrame) -> list[Market]:
+    """Wrap a price frame as the list of markets the optimiser trades in."""
+    return [
+        Market(MARKET_1, 1, prices["market_1_price"].to_numpy()),
+        Market(MARKET_2, 2, prices["market_2_price"].to_numpy()),
+    ]

src/battery_dispatch/markets.py

@@ -0,0 +1,55 @@
+"""Market definitions.
+
+Both markets are expressed on the common half-hourly grid. A market whose
+trading block is longer than a half-hour (Market 2, hourly) is described by
+``block_half_hours``; the optimiser creates one decision variable per *block*
+and references it from every half-hour inside that block, so the commitment
+rule cannot be violated by construction.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import numpy as np
+
+MARKET_1 = "market_1"
+MARKET_2 = "market_2"
+
+
+@dataclass(frozen=True)
+class Market:
+    """A market the battery can trade in.
+
+    Attributes:
+        name: Identifier used in output column names.
+        block_half_hours: Number of half-hours a single commitment must span.
+        prices: One price per half-hour, broadcast from the block price.
+    """
+
+    name: str
+    block_half_hours: int
+    prices: np.ndarray
+
+    def __post_init__(self) -> None:
+        if self.block_half_hours < 1:
+            raise ValueError("block_half_hours must be >= 1")
+        if self.prices.ndim != 1:
+            raise ValueError("prices must be one-dimensional")
+
+    def block_of(self, half_hour: int) -> int:
+        """Index of the trading block containing ``half_hour``."""
+        return half_hour // self.block_half_hours
+
+    def slice(self, start: int, stop: int) -> "Market":
+        """A view of this market over half-hours ``[start, stop)``.
+
+        ``start`` must fall on a block boundary, otherwise the window would cut
+        a trading block in half and the commitment rule would be meaningless.
+        """
+        if start % self.block_half_hours:
+            raise ValueError(
+                f"{self.name}: window start {start} is not aligned to a "
+                f"{self.block_half_hours}-half-hour block boundary"
+            )
+        return Market(self.name, self.block_half_hours, self.prices[start:stop])

src/battery_dispatch/metrics.py

@@ -0,0 +1,123 @@
+"""Headline and periodic performance metrics derived from a dispatch schedule."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from battery_dispatch.battery import BatteryState, implied_life_years
+from battery_dispatch.config import HALF_HOUR, BatterySpec
+from battery_dispatch.markets import MARKET_1, MARKET_2
+
+MARKETS = (MARKET_1, MARKET_2)
+
+
+def _energy_columns(result: pd.DataFrame) -> pd.DataFrame:
+    """Per-half-hour energies and revenues, in MWh and £."""
+    frame = pd.DataFrame(index=result.index)
+    for name in MARKETS:
+        frame[f"charge_{name}_mwh"] = result[f"charge_{name}_mw"] * HALF_HOUR
+        frame[f"discharge_{name}_mwh"] = result[f"discharge_{name}_mw"] * HALF_HOUR
+        frame[f"revenue_{name}_gbp"] = result[f"revenue_{name}_gbp"]
+
+    frame["charge_mwh"] = sum(frame[f"charge_{n}_mwh"] for n in MARKETS)
+    frame["discharge_mwh"] = sum(frame[f"discharge_{n}_mwh"] for n in MARKETS)
+    frame["revenue_gbp"] = sum(frame[f"revenue_{n}_gbp"] for n in MARKETS)
+    return frame
+
+
+def _aggregate(frame: pd.DataFrame, spec: BatterySpec, freq: str) -> pd.DataFrame:
+    grouped = frame.resample(freq).sum()
+    grouped = grouped[grouped[["charge_mwh", "discharge_mwh"]].abs().sum(axis=1) > 0]
+
+    summary = pd.DataFrame(index=grouped.index)
+    summary["gross_profit_gbp"] = grouped["revenue_gbp"]
+    for name in MARKETS:
+        summary[f"profit_{name}_gbp"] = grouped[f"revenue_{name}_gbp"]
+    summary["charged_mwh"] = grouped["charge_mwh"]
+    summary["discharged_mwh"] = grouped["discharge_mwh"]
+    summary["equivalent_full_cycles"] = (
+        grouped["discharge_mwh"] / spec.discharge_efficiency / spec.capacity_mwh
+    )
+    summary["captured_spread_gbp_per_mwh"] = np.where(
+        grouped["discharge_mwh"] > 0,
+        grouped["revenue_gbp"] / grouped["discharge_mwh"].replace(0, np.nan),
+        0.0,
+    )
+    return summary
+
+
+def annual_summary(result: pd.DataFrame, spec: BatterySpec) -> pd.DataFrame:
+    """Per-calendar-year breakdown."""
+    summary = _aggregate(_energy_columns(result), spec, "YE")
+    summary.index = summary.index.year
+    summary.index.name = "year"
+    return summary
+
+
+def monthly_summary(result: pd.DataFrame, spec: BatterySpec) -> pd.DataFrame:
+    """Per-calendar-month breakdown."""
+    summary = _aggregate(_energy_columns(result), spec, "ME")
+    summary.index = summary.index.to_period("M").astype(str)
+    summary.index.name = "month"
+    return summary
+
+
+def headline_metrics(
+    result: pd.DataFrame,
+    spec: BatterySpec,
+    state: BatteryState,
+) -> dict[str, float | int | str]:
+    """Whole-run totals, suitable for serialising to ``summary.json``."""
+    frame = _energy_columns(result)
+
+    total_profit = float(frame["revenue_gbp"].sum())
+    discharged = float(frame["discharge_mwh"].sum())
+    charged = float(frame["charge_mwh"].sum())
+
+    days = len(result) / 48.0
+    years = days / 365.25
+    cycles_per_year = state.equivalent_full_cycles / years if years else 0.0
+
+    metrics: dict[str, float | int | str] = {
+        "start": str(result.index[0]),
+        "end": str(result.index[-1]),
+        "half_hours": int(len(result)),
+        "days": round(days, 2),
+        "gross_trading_profit_gbp": total_profit,
+        "gross_profit_per_year_gbp": total_profit / years if years else 0.0,
+        "energy_charged_from_grid_mwh": charged,
+        "energy_discharged_to_grid_mwh": discharged,
+        "round_trip_efficiency": spec.round_trip_efficiency,
+        "equivalent_full_cycles": state.equivalent_full_cycles,
+        "equivalent_full_cycles_per_year": cycles_per_year,
+        "cycle_budget": spec.lifetime_cycles,
+        "cycle_budget_used_fraction": state.equivalent_full_cycles
+        / spec.lifetime_cycles,
+        "capacity_start_mwh": spec.capacity_mwh,
+        "capacity_end_mwh": state.capacity_mwh,
+        "capacity_retained_fraction": state.capacity_retained,
+        "implied_life_years": implied_life_years(spec, cycles_per_year),
+        "average_captured_spread_gbp_per_mwh": (
+            total_profit / discharged if discharged else 0.0
+        ),
+        "fixed_opex_gbp": spec.fixed_opex_gbp_per_year * years,
+        "net_of_opex_gbp": total_profit - spec.fixed_opex_gbp_per_year * years,
+    }
+
+    for name in MARKETS:
+        profit = float(frame[f"revenue_{name}_gbp"].sum())
+        metrics[f"profit_{name}_gbp"] = profit
+        metrics[f"profit_share_{name}"] = profit / total_profit if total_profit else 0.0
+        metrics[f"discharged_{name}_mwh"] = float(frame[f"discharge_{name}_mwh"].sum())
+        metrics[f"charged_{name}_mwh"] = float(frame[f"charge_{name}_mwh"].sum())
+
+    return metrics
+
+
+def captured_spread_per_cycle(result: pd.DataFrame) -> pd.Series:
+    """Daily average revenue per MWh discharged -- the spread actually captured."""
+    frame = _energy_columns(result)
+    daily = frame.resample("D").sum()
+    discharged = daily["discharge_mwh"].replace(0, np.nan)
+    return (daily["revenue_gbp"] / discharged).dropna()

src/battery_dispatch/optimiser.py

@@ -0,0 +1,369 @@
+"""Dispatch optimisation: window construction, solving, and the rolling horizon.
+
+The dispatch problem is a linear programme on the half-hourly grid. Two
+structural choices do most of the work:
+
+* **One variable per trading block, shared by its half-hours.** Market 2 trades
+  hourly, so a single pair of variables covers both half-hours of the hour.
+  The commitment rule is then impossible to violate, rather than being imposed
+  as a constraint that could be mis-specified or later relaxed.
+
+* **A single shared state-of-charge balance.** Every market's charge and
+  discharge flows through the same stored-energy account, so "the same unit of
+  energy cannot be sold into both markets" needs no explicit constraint -- it
+  is a consequence of energy conservation.
+
+The LP relaxation can prefer to charge and discharge at the same instant and be
+paid at both ends -- either across negative prices, or by importing from the
+cheaper market while exporting to the dearer one. Neither is physically
+possible for a single battery, so the solver runs **LP first with a MILP
+fallback**: solve the relaxation, check for simultaneous charge and discharge,
+and re-solve the offending windows with binaries.
+
+On this dataset the fallback fires in essentially every window, because the
+persistent Market 1 / Market 2 price gap makes the cheat attractive almost
+daily. The LP pass is therefore mostly wasted work here and ``--milp-always``
+is the faster choice; the auto mode is kept as the safe default, and
+``--lp-only`` exists to make the size of the cheat visible for comparison.
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+from dataclasses import dataclass, field
+
+import numpy as np
+import pandas as pd
+import pulp
+
+from battery_dispatch.battery import BatteryState
+from battery_dispatch.config import HALF_HOUR, BatterySpec, RunConfig
+from battery_dispatch.data import build_markets
+from battery_dispatch.markets import Market
+
+logger = logging.getLogger(__name__)
+
+# Powers below this are solver noise, not dispatch.
+POWER_TOLERANCE = 1e-6
+
+
+@dataclass
+class WindowProblem:
+    """A built (but unsolved) single-window optimisation problem."""
+
+    problem: pulp.LpProblem
+    charge: dict[str, list[pulp.LpVariable]]
+    discharge: dict[str, list[pulp.LpVariable]]
+    soc: list[pulp.LpVariable]
+    markets: list[Market]
+    relaxed: bool
+
+
+@dataclass
+class WindowSolution:
+    """Dispatch recovered from a solved window, in MW and MWh."""
+
+    charge_mw: dict[str, np.ndarray]
+    discharge_mw: dict[str, np.ndarray]
+    soc_mwh: np.ndarray
+    objective_gbp: float
+    relaxed: bool
+
+
+@dataclass
+class SolveStats:
+    """Bookkeeping for how the rolling horizon went."""
+
+    windows: int = 0
+    milp_fallbacks: int = 0
+    seconds: float = 0.0
+    fallback_starts: list[pd.Timestamp] = field(default_factory=list)
+
+
+def _block_count(n_half_hours: int, block: int) -> int:
+    return (n_half_hours + block - 1) // block
+
+
+def build_window(
+    markets: list[Market],
+    spec: BatterySpec,
+    soc_start: float,
+    capacity_mwh: float,
+    *,
+    relaxed: bool,
+    degradation_cost_gbp_per_mwh: float = 0.0,
+) -> WindowProblem:
+    """Construct the LP (``relaxed``) or MILP for one horizon window."""
+    n = len(markets[0].prices)
+    if any(len(m.prices) != n for m in markets):
+        raise ValueError("all markets must cover the same half-hours")
+    if soc_start < -POWER_TOLERANCE:
+        raise ValueError(f"soc_start {soc_start} is negative")
+    soc_start = max(0.0, soc_start)
+
+    problem = pulp.LpProblem("battery_dispatch_window", pulp.LpMaximize)
+
+    charge: dict[str, list[pulp.LpVariable]] = {}
+    discharge: dict[str, list[pulp.LpVariable]] = {}
+    for market in markets:
+        blocks = _block_count(n, market.block_half_hours)
+        charge[market.name] = [
+            pulp.LpVariable(f"c_{market.name}_{b}", 0, spec.max_charge_mw)
+            for b in range(blocks)
+        ]
+        discharge[market.name] = [
+            pulp.LpVariable(f"d_{market.name}_{b}", 0, spec.max_discharge_mw)
+            for b in range(blocks)
+        ]
+
+    # Capacity fades between windows, so energy carried in can sit marginally
+    # above the newly-reduced bound. Raising the bound to admit it keeps energy
+    # conserved across the seam; clipping instead would silently destroy a few
+    # 1e-5 MWh per day and break an independent state-of-charge recomputation.
+    soc_upper = max(capacity_mwh, soc_start)
+    soc = [pulp.LpVariable(f"e_{t}", 0, soc_upper) for t in range(n)]
+    indicator = (
+        None
+        if relaxed
+        else [pulp.LpVariable(f"z_{t}", cat=pulp.LpBinary) for t in range(n)]
+    )
+
+    for t in range(n):
+        total_charge = pulp.lpSum(
+            charge[m.name][m.block_of(t)] for m in markets
+        )
+        total_discharge = pulp.lpSum(
+            discharge[m.name][m.block_of(t)] for m in markets
+        )
+
+        if indicator is None:
+            problem += total_charge <= spec.max_charge_mw, f"charge_cap_{t}"
+            problem += total_discharge <= spec.max_discharge_mw, f"discharge_cap_{t}"
+        else:
+            # Binaries make the two modes mutually exclusive as well as capped.
+            problem += (
+                total_charge <= spec.max_charge_mw * indicator[t],
+                f"charge_cap_{t}",
+            )
+            problem += (
+                total_discharge <= spec.max_discharge_mw * (1 - indicator[t]),
+                f"discharge_cap_{t}",
+            )
+
+        previous = soc_start if t == 0 else soc[t - 1]
+        problem += (
+            soc[t]
+            == previous
+            + HALF_HOUR
+            * (
+                spec.charge_efficiency * total_charge
+                - total_discharge / spec.discharge_efficiency
+            ),
+            f"soc_balance_{t}",
+        )
+
+    # Settled on grid-side energy: price x power x duration, summed per market.
+    trading_profit = pulp.lpSum(
+        HALF_HOUR
+        * float(market.prices[t])
+        * (
+            discharge[market.name][market.block_of(t)]
+            - charge[market.name][market.block_of(t)]
+        )
+        for market in markets
+        for t in range(n)
+    )
+
+    if degradation_cost_gbp_per_mwh:
+        # Charged against energy drawn from storage, which is what consumes
+        # cycle life. Linear in the decision variables, so the programme keeps
+        # its structure.
+        cycling_cost = pulp.lpSum(
+            degradation_cost_gbp_per_mwh
+            * HALF_HOUR
+            / spec.discharge_efficiency
+            * discharge[market.name][market.block_of(t)]
+            for market in markets
+            for t in range(n)
+        )
+        problem += trading_profit - cycling_cost
+    else:
+        problem += trading_profit
+
+    return WindowProblem(problem, charge, discharge, soc, markets, relaxed)
+
+
+def _solver(config: RunConfig) -> pulp.LpSolver:
+    """A fixed, silent CBC configuration -- no randomness, reproducible runs."""
+    return pulp.PULP_CBC_CMD(msg=False, timeLimit=config.max_seconds_per_window)
+
+
+def _extract(window: WindowProblem) -> WindowSolution:
+    n = len(window.soc)
+    charge_mw = {
+        m.name: np.array(
+            [window.charge[m.name][m.block_of(t)].value() or 0.0 for t in range(n)]
+        )
+        for m in window.markets
+    }
+    discharge_mw = {
+        m.name: np.array(
+            [window.discharge[m.name][m.block_of(t)].value() or 0.0 for t in range(n)]
+        )
+        for m in window.markets
+    }
+    soc = np.array([var.value() or 0.0 for var in window.soc])
+    return WindowSolution(
+        charge_mw=charge_mw,
+        discharge_mw=discharge_mw,
+        soc_mwh=soc,
+        objective_gbp=float(pulp.value(window.problem.objective) or 0.0),
+        relaxed=window.relaxed,
+    )
+
+
+def _has_simultaneous_flow(solution: WindowSolution) -> bool:
+    total_charge = sum(solution.charge_mw.values())
+    total_discharge = sum(solution.discharge_mw.values())
+    return bool(
+        np.any(
+            (total_charge > POWER_TOLERANCE) & (total_discharge > POWER_TOLERANCE)
+        )
+    )
+
+
+def solve_window(
+    markets: list[Market],
+    spec: BatterySpec,
+    soc_start: float,
+    capacity_mwh: float,
+    config: RunConfig,
+) -> tuple[WindowSolution, bool]:
+    """Solve one window. Returns the solution and whether MILP was needed."""
+    solver = _solver(config)
+
+    if config.solver_mode != "milp-always":
+        window = build_window(
+            markets, spec, soc_start, capacity_mwh, relaxed=True,
+            degradation_cost_gbp_per_mwh=config.degradation_cost_gbp_per_mwh,
+        )
+        status = window.problem.solve(solver)
+        if pulp.LpStatus[status] != "Optimal":
+            raise RuntimeError(
+                f"LP window did not solve to optimality: {pulp.LpStatus[status]}"
+            )
+        solution = _extract(window)
+
+        if config.solver_mode == "lp-only" or not _has_simultaneous_flow(solution):
+            return solution, False
+
+    window = build_window(
+        markets, spec, soc_start, capacity_mwh, relaxed=False,
+        degradation_cost_gbp_per_mwh=config.degradation_cost_gbp_per_mwh,
+    )
+    status = window.problem.solve(solver)
+    if pulp.LpStatus[status] != "Optimal":
+        raise RuntimeError(
+            f"MILP window did not solve to optimality: {pulp.LpStatus[status]}"
+        )
+    return _extract(window), config.solver_mode != "milp-always"
+
+
+def run_rolling_horizon(
+    prices: pd.DataFrame,
+    spec: BatterySpec,
+    config: RunConfig,
+) -> tuple[pd.DataFrame, BatteryState, SolveStats]:
+    """Dispatch the whole period as overlapping windows with a committed prefix.
+
+    Each window looks ``window_hours`` ahead but only the first ``commit_hours``
+    is kept; state of charge and capacity fade carry into the next window. This
+    avoids the myopia of independent daily solves while keeping every individual
+    solve small.
+    """
+    markets = build_markets(prices)
+    n = len(prices)
+    window_hh = config.window_hours * 2
+    commit_hh = config.commit_hours * 2
+
+    state = BatteryState(spec=spec)
+    stats = SolveStats()
+    started = time.perf_counter()
+
+    frames: list[pd.DataFrame] = []
+    position = 0
+    while position < n:
+        stop = min(position + window_hh, n)
+        commit_stop = min(position + commit_hh, n)
+
+        capacity = state.capacity_mwh
+        soc_start = state.soc_mwh
+        window_markets = [m.slice(position, stop) for m in markets]
+
+        solution, used_milp = solve_window(
+            window_markets, spec, soc_start, capacity, config
+        )
+        stats.windows += 1
+        if used_milp:
+            stats.milp_fallbacks += 1
+            stats.fallback_starts.append(prices.index[position])
+
+        kept = commit_stop - position
+        frame = _committed_frame(
+            prices.iloc[position:commit_stop], solution, kept, capacity
+        )
+        frames.append(frame)
+
+        charged = float(
+            sum(v[:kept].sum() for v in solution.charge_mw.values()) * HALF_HOUR
+        )
+        discharged = float(
+            sum(v[:kept].sum() for v in solution.discharge_mw.values()) * HALF_HOUR
+        )
+        state.commit(
+            soc_mwh=float(solution.soc_mwh[kept - 1]),
+            charged_grid_mwh=charged,
+            discharged_grid_mwh=discharged,
+        )
+
+        position = commit_stop
+
+    stats.seconds = time.perf_counter() - started
+    result = pd.concat(frames)
+    logger.info(
+        "Solved %d windows in %.1fs (%d MILP fallbacks)",
+        stats.windows,
+        stats.seconds,
+        stats.milp_fallbacks,
+    )
+    return result, state, stats
+
+
+def _committed_frame(
+    prices_slice: pd.DataFrame,
+    solution: WindowSolution,
+    kept: int,
+    capacity_mwh: float,
+) -> pd.DataFrame:
+    """Assemble the committed half-hours of a window into output columns."""
+    data: dict[str, np.ndarray] = {
+        "market_1_price": prices_slice["market_1_price"].to_numpy(),
+        "market_2_price": prices_slice["market_2_price"].to_numpy(),
+    }
+    for name, values in solution.charge_mw.items():
+        data[f"charge_{name}_mw"] = values[:kept]
+    for name, values in solution.discharge_mw.items():
+        data[f"discharge_{name}_mw"] = values[:kept]
+
+    data["soc_mwh"] = solution.soc_mwh[:kept]
+    data["capacity_mwh"] = np.full(kept, capacity_mwh)
+
+    for name in solution.charge_mw:
+        price = data[f"{name}_price"]
+        net_export = data[f"discharge_{name}_mw"] - data[f"charge_{name}_mw"]
+        data[f"revenue_{name}_gbp"] = HALF_HOUR * price * net_export
+
+    frame = pd.DataFrame(data, index=prices_slice.index)
+    frame.index.name = "timestamp"
+    return frame

src/battery_dispatch/plots.py

@@ -0,0 +1,284 @@
+"""Figures summarising a dispatch run.
+
+Charting conventions applied throughout:
+
+* Market 1 is blue and Market 2 is orange, in that fixed order, everywhere.
+  The pair is validated for colour-vision deficiency (worst-case protanopia
+  Delta E 24.7, well clear of the 8 threshold) against the chart surface.
+* Measures on different scales never share an axis. Prices, power and state of
+  charge are three stacked panels sharing a time axis, not one panel with
+  several y-scales.
+* Every figure with two series carries a legend, so identity is never conveyed
+  by colour alone. Grid lines and spines are recessive.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")  # headless, deterministic
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from matplotlib.ticker import FuncFormatter
+
+from battery_dispatch.config import HALF_HOUR, BatterySpec
+from battery_dispatch.markets import MARKET_1, MARKET_2
+
+SURFACE = "#fcfcfb"
+INK = "#0b0b0b"
+INK_SECONDARY = "#52514e"
+GRID = "#e3e2de"
+
+COLOUR = {MARKET_1: "#2a78d6", MARKET_2: "#eb6834"}
+LABEL = {MARKET_1: "Market 1 (half-hourly)", MARKET_2: "Market 2 (hourly)"}
+SOC_COLOUR = "#4a3aa7"
+
+FIGURE_DPI = 140
+
+
+def _style_axes(ax: plt.Axes, ylabel: str = "") -> None:
+    ax.set_facecolor(SURFACE)
+    ax.grid(True, color=GRID, linewidth=0.8, alpha=0.9)
+    ax.set_axisbelow(True)
+    for side in ("top", "right"):
+        ax.spines[side].set_visible(False)
+    for side in ("left", "bottom"):
+        ax.spines[side].set_color(GRID)
+    ax.tick_params(colors=INK_SECONDARY, labelsize=9)
+    if ylabel:
+        ax.set_ylabel(ylabel, color=INK_SECONDARY, fontsize=9)
+
+
+def _new_figure(nrows: int, figsize: tuple[float, float], **kwargs):
+    fig, axes = plt.subplots(nrows, 1, figsize=figsize, facecolor=SURFACE, **kwargs)
+    return fig, axes
+
+
+def _save(fig: plt.Figure, path: Path) -> Path:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    fig.savefig(path, dpi=FIGURE_DPI, bbox_inches="tight", facecolor=SURFACE)
+    plt.close(fig)
+    return path
+
+
+def _money(value: float, _pos: int = 0) -> str:
+    if abs(value) >= 1000:
+        return f"£{value / 1000:,.0f}k"
+    return f"£{value:,.0f}"
+
+
+def representative_week(result: pd.DataFrame) -> pd.Timestamp:
+    """Start of the week whose revenue is the median of all complete weeks.
+
+    Choosing the median rather than the best week keeps the illustration honest.
+    """
+    revenue = result[f"revenue_{MARKET_1}_gbp"] + result[f"revenue_{MARKET_2}_gbp"]
+    weekly = revenue.resample("W-MON", label="left", closed="left").sum()
+    counts = revenue.resample("W-MON", label="left", closed="left").count()
+    complete = weekly[counts == 7 * 48]
+    if complete.empty:
+        return result.index[0]
+    ordered = complete.sort_values()
+    return ordered.index[len(ordered) // 2]
+
+
+def plot_week(
+    result: pd.DataFrame,
+    out_dir: Path,
+    week_start: pd.Timestamp | None = None,
+) -> Path:
+    """Prices, dispatch by market, and state of charge over one week."""
+    start = representative_week(result) if week_start is None else week_start
+    stop = start + pd.Timedelta(days=7) - pd.Timedelta(minutes=30)
+    week = result.loc[start:stop]
+
+    fig, (ax_price, ax_power, ax_soc) = _new_figure(
+        3, (12, 8.5), sharex=True, gridspec_kw={"height_ratios": [1.1, 1.1, 0.8]}
+    )
+
+    for name in (MARKET_1, MARKET_2):
+        ax_price.step(
+            week.index,
+            week[f"{name}_price"],
+            where="post",
+            color=COLOUR[name],
+            linewidth=2,
+            label=LABEL[name],
+        )
+    ax_price.axhline(0, color=INK_SECONDARY, linewidth=1, alpha=0.5)
+    _style_axes(ax_price, "Price (£/MWh)")
+    ax_price.legend(frameon=False, fontsize=9, labelcolor=INK, ncol=2, loc="upper left")
+    ax_price.set_title(
+        f"Representative week from {start:%-d %B %Y}  ·  median-revenue week of the run",
+        color=INK,
+        fontsize=13,
+        loc="left",
+        pad=12,
+    )
+
+    # Charging is drawn below the axis, discharging above: the sign is the
+    # direction of energy flow, so the two can never be confused.
+    width = 1 / 48
+    bottom_pos = np.zeros(len(week))
+    bottom_neg = np.zeros(len(week))
+    for name in (MARKET_1, MARKET_2):
+        discharge = week[f"discharge_{name}_mw"].to_numpy()
+        charge = -week[f"charge_{name}_mw"].to_numpy()
+        ax_power.bar(
+            week.index, discharge, width=width, bottom=bottom_pos,
+            color=COLOUR[name], linewidth=0, label=LABEL[name],
+        )
+        ax_power.bar(
+            week.index, charge, width=width, bottom=bottom_neg,
+            color=COLOUR[name], linewidth=0,
+        )
+        bottom_pos += discharge
+        bottom_neg += charge
+    ax_power.axhline(0, color=INK_SECONDARY, linewidth=1)
+    _style_axes(ax_power, "Power (MW)")
+    ax_power.legend(frameon=False, fontsize=9, labelcolor=INK, ncol=2, loc="upper left")
+    ax_power.text(
+        0.995, 0.04, "discharge above zero · charge below",
+        transform=ax_power.transAxes, ha="right", fontsize=8, color=INK_SECONDARY,
+    )
+
+    ax_soc.fill_between(
+        week.index, week["soc_mwh"], color=SOC_COLOUR, alpha=0.18, linewidth=0
+    )
+    ax_soc.plot(week.index, week["soc_mwh"], color=SOC_COLOUR, linewidth=2)
+    ax_soc.plot(
+        week.index, week["capacity_mwh"], color=INK_SECONDARY,
+        linewidth=1, linestyle="--",
+    )
+    ax_soc.text(
+        week.index[2], week["capacity_mwh"].iloc[0] * 0.90, "usable capacity",
+        fontsize=8, color=INK_SECONDARY,
+    )
+    _style_axes(ax_soc, "Stored energy (MWh)")
+    ax_soc.set_ylim(0, week["capacity_mwh"].max() * 1.15)
+    ax_soc.set_xlabel("")
+
+    fig.align_ylabels()
+    return _save(fig, out_dir / "week_dispatch.png")
+
+
+def plot_monthly_revenue(monthly: pd.DataFrame, out_dir: Path) -> Path:
+    """Monthly gross profit, stacked by market."""
+    fig, ax = _new_figure(1, (12, 5))
+
+    index = np.arange(len(monthly))
+    bottom = np.zeros(len(monthly))
+    for name in (MARKET_1, MARKET_2):
+        values = monthly[f"profit_{name}_gbp"].to_numpy()
+        ax.bar(
+            index, values, bottom=bottom, width=0.78, color=COLOUR[name],
+            label=LABEL[name],
+            # A 2px surface-coloured edge separates the stacked segments.
+            edgecolor=SURFACE, linewidth=2,
+        )
+        bottom += values
+
+    _style_axes(ax, "Gross profit")
+    ax.yaxis.set_major_formatter(FuncFormatter(_money))
+    ax.set_xticks(index[::3])
+    ax.set_xticklabels(monthly.index[::3], rotation=45, ha="right")
+    ax.legend(frameon=False, fontsize=9, labelcolor=INK, ncol=2, loc="upper left")
+    ax.set_title(
+        "Monthly gross trading profit by market", color=INK, fontsize=13,
+        loc="left", pad=12,
+    )
+    return _save(fig, out_dir / "monthly_revenue.png")
+
+
+def plot_degradation(result: pd.DataFrame, spec: BatterySpec, out_dir: Path) -> Path:
+    """Cumulative equivalent full cycles and the capacity fade they cause.
+
+    Cycles and capacity are different measures, so they get one panel each
+    rather than being forced onto a shared axis.
+    """
+    discharged_storage = (
+        (result[f"discharge_{MARKET_1}_mw"] + result[f"discharge_{MARKET_2}_mw"])
+        * HALF_HOUR
+        / spec.discharge_efficiency
+    )
+    cycles = (discharged_storage / spec.capacity_mwh).cumsum()
+
+    fig, (ax_cycles, ax_capacity) = _new_figure(2, (12, 6.5), sharex=True)
+
+    ax_cycles.fill_between(
+        cycles.index, cycles, color=COLOUR[MARKET_1], alpha=0.16, linewidth=0
+    )
+    ax_cycles.plot(cycles.index, cycles, color=COLOUR[MARKET_1], linewidth=2)
+    _style_axes(ax_cycles, "Equivalent full cycles")
+    ax_cycles.annotate(
+        f"{cycles.iloc[-1]:,.0f} cycles used of a {spec.lifetime_cycles:,.0f} budget"
+        f"  ({cycles.iloc[-1] / spec.lifetime_cycles:.0%})",
+        xy=(cycles.index[-1], cycles.iloc[-1]),
+        xytext=(-10, -14), textcoords="offset points",
+        ha="right", fontsize=9, color=INK,
+    )
+    ax_cycles.set_title(
+        "Cycling and capacity fade over the modelled period",
+        color=INK, fontsize=13, loc="left", pad=12,
+    )
+
+    retained = 100 * result["capacity_mwh"] / spec.capacity_mwh
+    ax_capacity.plot(retained.index, retained, color=SOC_COLOUR, linewidth=2)
+    _style_axes(ax_capacity, "Capacity retained (%)")
+    ax_capacity.set_ylim(min(99.0, retained.min() - 0.3), 100.1)
+    ax_capacity.annotate(
+        f"{retained.iloc[-1]:.2f}% of nominal remaining "
+        f"({result['capacity_mwh'].iloc[-1]:.3f} MWh)",
+        xy=(retained.index[-1], retained.iloc[-1]),
+        xytext=(-10, 10), textcoords="offset points",
+        ha="right", fontsize=9, color=INK,
+    )
+
+    fig.align_ylabels()
+    return _save(fig, out_dir / "degradation.png")
+
+
+def plot_spread_distribution(spread: pd.Series, out_dir: Path) -> Path:
+    """Distribution of the daily average spread the battery actually captured."""
+    fig, ax = _new_figure(1, (10, 5))
+
+    ax.hist(
+        spread, bins=60, color=COLOUR[MARKET_1], edgecolor=SURFACE, linewidth=1.2
+    )
+    median = float(spread.median())
+    ax.axvline(median, color=COLOUR[MARKET_2], linewidth=2)
+    ax.annotate(
+        f"median £{median:,.1f}/MWh",
+        xy=(median, ax.get_ylim()[1] * 0.92),
+        xytext=(8, 0), textcoords="offset points",
+        fontsize=9, color=INK,
+    )
+
+    _style_axes(ax, "Days")
+    ax.set_xlabel("Revenue per MWh discharged (£/MWh)", color=INK_SECONDARY, fontsize=9)
+    ax.set_title(
+        "Daily captured spread", color=INK, fontsize=13, loc="left", pad=12
+    )
+    return _save(fig, out_dir / "captured_spread.png")
+
+
+def make_all(
+    result: pd.DataFrame,
+    monthly: pd.DataFrame,
+    spread: pd.Series,
+    spec: BatterySpec,
+    out_dir: Path,
+) -> list[Path]:
+    """Render every figure, returning the paths written."""
+    paths = [
+        plot_week(result, out_dir),
+        plot_monthly_revenue(monthly, out_dir),
+        plot_degradation(result, spec, out_dir),
+    ]
+    if len(spread) > 1:
+        paths.append(plot_spread_distribution(spread, out_dir))
+    return paths

src/battery_dispatch/validation.py

@@ -0,0 +1,212 @@
+"""Independent post-hoc validation of a dispatch schedule.
+
+Everything here is recomputed from the output frame alone -- prices, powers and
+nothing else. The validator never consults the solver, the optimiser's
+intermediate state, or the objective value it reported. That is the point: if
+the formulation were wrong, a check that reused it would agree with it. These
+checks would catch a sign error, a mis-broadcast hourly price, a violated
+commitment rule or an energy-balance slip.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+import numpy as np
+import pandas as pd
+
+from battery_dispatch.config import HALF_HOUR, BatterySpec
+from battery_dispatch.markets import MARKET_1, MARKET_2
+
+# Solver output carries floating-point noise; these are the thresholds beyond
+# which a discrepancy indicates a real modelling error rather than arithmetic.
+POWER_TOLERANCE_MW = 1e-6
+ENERGY_TOLERANCE_MWH = 1e-6
+MONEY_TOLERANCE_GBP = 1e-6
+
+# Capacity fades between windows, so energy carried across a window seam can
+# exceed the newly-reduced capacity by roughly one day's fade (order 1e-4 MWh).
+# This is conserved energy, not a violated bound -- see optimiser.build_window.
+CAPACITY_SEAM_TOLERANCE_MWH = 1e-3
+
+
+@dataclass
+class ValidationReport:
+    """Outcome of validating a schedule. Falsy checks are listed in ``failures``."""
+
+    checks: dict[str, bool] = field(default_factory=dict)
+    failures: list[str] = field(default_factory=list)
+    details: dict[str, float] = field(default_factory=dict)
+
+    @property
+    def ok(self) -> bool:
+        return not self.failures
+
+    def record(self, name: str, passed: bool, message: str = "") -> None:
+        self.checks[name] = passed
+        if not passed:
+            self.failures.append(message or name)
+
+    def summary(self) -> str:
+        if self.ok:
+            return f"all {len(self.checks)} validation checks passed"
+        lines = [f"{len(self.failures)} validation check(s) FAILED:"]
+        lines += [f"  - {failure}" for failure in self.failures]
+        return "\n".join(lines)
+
+
+def validate_schedule(result: pd.DataFrame, spec: BatterySpec) -> ValidationReport:
+    """Re-derive every constraint from the schedule and report any violation."""
+    report = ValidationReport()
+
+    charge_1 = result[f"charge_{MARKET_1}_mw"].to_numpy()
+    charge_2 = result[f"charge_{MARKET_2}_mw"].to_numpy()
+    discharge_1 = result[f"discharge_{MARKET_1}_mw"].to_numpy()
+    discharge_2 = result[f"discharge_{MARKET_2}_mw"].to_numpy()
+
+    total_charge = charge_1 + charge_2
+    total_discharge = discharge_1 + discharge_2
+
+    _check_power_limits(report, spec, total_charge, total_discharge)
+    _check_no_simultaneous_flow(report, total_charge, total_discharge)
+    _check_hourly_commitment(report, result, charge_2, discharge_2)
+    _check_energy_balance(report, spec, result, total_charge, total_discharge)
+    _check_revenue(report, result, charge_1, charge_2, discharge_1, discharge_2)
+
+    return report
+
+
+def _check_power_limits(
+    report: ValidationReport,
+    spec: BatterySpec,
+    total_charge: np.ndarray,
+    total_discharge: np.ndarray,
+) -> None:
+    worst_charge = float(total_charge.max(initial=0.0))
+    worst_discharge = float(total_discharge.max(initial=0.0))
+    report.details["max_total_charge_mw"] = worst_charge
+    report.details["max_total_discharge_mw"] = worst_discharge
+
+    report.record(
+        "charge_within_limit",
+        bool(np.all(total_charge >= -POWER_TOLERANCE_MW))
+        and worst_charge <= spec.max_charge_mw + POWER_TOLERANCE_MW,
+        f"combined charge reaches {worst_charge:.9f} MW, "
+        f"limit is {spec.max_charge_mw} MW",
+    )
+    report.record(
+        "discharge_within_limit",
+        bool(np.all(total_discharge >= -POWER_TOLERANCE_MW))
+        and worst_discharge <= spec.max_discharge_mw + POWER_TOLERANCE_MW,
+        f"combined discharge reaches {worst_discharge:.9f} MW, "
+        f"limit is {spec.max_discharge_mw} MW",
+    )
+
+
+def _check_no_simultaneous_flow(
+    report: ValidationReport,
+    total_charge: np.ndarray,
+    total_discharge: np.ndarray,
+) -> None:
+    both = (total_charge > POWER_TOLERANCE_MW) & (total_discharge > POWER_TOLERANCE_MW)
+    count = int(both.sum())
+    report.details["simultaneous_half_hours"] = float(count)
+    report.record(
+        "no_simultaneous_charge_discharge",
+        count == 0,
+        f"{count} half-hours charge and discharge at the same time",
+    )
+
+
+def _check_hourly_commitment(
+    report: ValidationReport,
+    result: pd.DataFrame,
+    charge_2: np.ndarray,
+    discharge_2: np.ndarray,
+) -> None:
+    """Market 2 power must be identical across both half-hours of every hour."""
+    n = len(result)
+    if n % 2:
+        report.record(
+            "hourly_commitment_constant",
+            False,
+            f"schedule has {n} half-hours, which is not a whole number of hours",
+        )
+        return
+
+    worst = 0.0
+    for values in (charge_2, discharge_2):
+        pairs = values.reshape(-1, 2)
+        worst = max(worst, float(np.abs(pairs[:, 0] - pairs[:, 1]).max(initial=0.0)))
+
+    report.details["max_hourly_commitment_gap_mw"] = worst
+    report.details["hours_checked"] = float(n // 2)
+    report.record(
+        "hourly_commitment_constant",
+        worst <= POWER_TOLERANCE_MW,
+        f"Market 2 power varies within an hour by up to {worst:.9f} MW",
+    )
+
+
+def _check_energy_balance(
+    report: ValidationReport,
+    spec: BatterySpec,
+    result: pd.DataFrame,
+    total_charge: np.ndarray,
+    total_discharge: np.ndarray,
+) -> None:
+    """Rebuild state of charge from powers alone and compare with the schedule."""
+    delta = HALF_HOUR * (
+        spec.charge_efficiency * total_charge
+        - total_discharge / spec.discharge_efficiency
+    )
+    recomputed = np.cumsum(delta)  # starts from an empty battery
+
+    reported = result["soc_mwh"].to_numpy()
+    capacity = result["capacity_mwh"].to_numpy()
+
+    drift = float(np.abs(recomputed - reported).max(initial=0.0))
+    report.details["max_soc_drift_mwh"] = drift
+    report.record(
+        "soc_matches_power_flows",
+        drift <= ENERGY_TOLERANCE_MWH * max(1.0, len(result) ** 0.5),
+        f"recomputed state of charge drifts from the reported value "
+        f"by up to {drift:.9f} MWh",
+    )
+
+    below = float(-recomputed.min(initial=0.0))
+    above = float((recomputed - capacity).max(initial=0.0))
+    report.details["max_soc_below_zero_mwh"] = max(0.0, below)
+    report.details["max_soc_above_capacity_mwh"] = max(0.0, above)
+    report.record(
+        "soc_within_bounds",
+        below <= ENERGY_TOLERANCE_MWH and above <= CAPACITY_SEAM_TOLERANCE_MWH,
+        f"state of charge leaves [0, capacity] by up to "
+        f"{max(below, above):.9f} MWh",
+    )
+
+
+def _check_revenue(
+    report: ValidationReport,
+    result: pd.DataFrame,
+    charge_1: np.ndarray,
+    charge_2: np.ndarray,
+    discharge_1: np.ndarray,
+    discharge_2: np.ndarray,
+) -> None:
+    """Recompute settlement from prices and powers and compare with the schedule."""
+    worst = 0.0
+    for name, charge, discharge in (
+        (MARKET_1, charge_1, discharge_1),
+        (MARKET_2, charge_2, discharge_2),
+    ):
+        expected = HALF_HOUR * result[f"{name}_price"].to_numpy() * (discharge - charge)
+        reported = result[f"revenue_{name}_gbp"].to_numpy()
+        worst = max(worst, float(np.abs(expected - reported).max(initial=0.0)))
+
+    report.details["max_revenue_error_gbp"] = worst
+    report.record(
+        "revenue_matches_prices_and_powers",
+        worst <= MONEY_TOLERANCE_GBP,
+        f"recomputed revenue differs from the reported value by up to £{worst:.9f}",
+    )

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,
+    )

tests/test_battery.py

@@ -0,0 +1,85 @@
+"""Cycle counting, capacity fade and end-of-life arithmetic."""
+
+from __future__ import annotations
+
+import pytest
+
+from battery_dispatch.battery import BatteryState, implied_life_years
+
+
+def test_a_fresh_battery_is_at_nominal_capacity(spec):
+    state = BatteryState(spec=spec)
+    assert state.capacity_mwh == pytest.approx(4.0)
+    assert state.capacity_retained == pytest.approx(1.0)
+    assert state.equivalent_full_cycles == 0.0
+
+
+def test_one_full_discharge_counts_as_one_cycle(spec):
+    state = BatteryState(spec=spec)
+    state.apply_cycles(spec.capacity_mwh)
+    assert state.equivalent_full_cycles == pytest.approx(1.0)
+
+
+def test_partial_discharges_accumulate_into_whole_cycles(spec):
+    """Attachment 1's definition: 75% then 25% is one cycle, not two."""
+    state = BatteryState(spec=spec)
+    state.apply_cycles(0.75 * spec.capacity_mwh)
+    state.apply_cycles(0.25 * spec.capacity_mwh)
+    assert state.equivalent_full_cycles == pytest.approx(1.0)
+
+
+def test_capacity_fades_by_one_thousandth_of_a_percent_per_cycle(spec):
+    state = BatteryState(spec=spec)
+    state.apply_cycles(100 * spec.capacity_mwh)  # 100 cycles
+
+    # 0.001 %/cycle over 100 cycles is a 0.1 % loss.
+    assert state.capacity_retained == pytest.approx(0.999)
+    assert state.capacity_mwh == pytest.approx(4.0 * 0.999)
+
+
+def test_the_full_cycle_budget_fades_capacity_by_five_percent(spec):
+    state = BatteryState(spec=spec)
+    state.apply_cycles(spec.lifetime_cycles * spec.capacity_mwh)
+
+    assert state.equivalent_full_cycles == pytest.approx(5000.0)
+    assert state.capacity_retained == pytest.approx(0.95)
+
+
+def test_commit_accounts_for_discharge_losses(spec):
+    """Grid-side export of 0.95 MWh draws 1.0 MWh from storage."""
+    state = BatteryState(spec=spec)
+    state.commit(soc_mwh=3.0, charged_grid_mwh=0.0, discharged_grid_mwh=0.95)
+
+    assert state.total_discharged_grid_mwh == pytest.approx(0.95)
+    assert state.total_discharged_storage_mwh == pytest.approx(1.0)
+    assert state.equivalent_full_cycles == pytest.approx(0.25)
+    assert state.soc_mwh == pytest.approx(3.0)
+
+
+def test_commit_is_cumulative(spec):
+    state = BatteryState(spec=spec)
+    for _ in range(3):
+        state.commit(soc_mwh=1.0, charged_grid_mwh=2.0, discharged_grid_mwh=0.95)
+
+    assert state.total_charged_grid_mwh == pytest.approx(6.0)
+    assert state.total_discharged_storage_mwh == pytest.approx(3.0)
+
+
+def test_negative_throughput_is_rejected(spec):
+    state = BatteryState(spec=spec)
+    with pytest.raises(ValueError):
+        state.apply_cycles(-1.0)
+
+
+def test_calendar_life_binds_when_cycling_is_light(spec):
+    # 300 cycles a year exhausts 5,000 cycles only after 16 years.
+    assert implied_life_years(spec, 300.0) == pytest.approx(10.0)
+
+
+def test_cycle_life_binds_when_cycling_is_heavy(spec):
+    # 1,000 cycles a year exhausts the budget in 5 years, inside the 10-year life.
+    assert implied_life_years(spec, 1000.0) == pytest.approx(5.0)
+
+
+def test_an_idle_battery_lives_out_its_calendar_life(spec):
+    assert implied_life_years(spec, 0.0) == pytest.approx(10.0)

tests/test_data.py

@@ -0,0 +1,189 @@
+"""Tests for loading, cleaning and aligning the two price series."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import openpyxl
+import pandas as pd
+import pytest
+
+from battery_dispatch.config import BatterySpec
+from battery_dispatch.data import (
+    HALF_HOURLY_SHEET,
+    HOURLY_SHEET,
+    build_markets,
+    load_prices,
+    slice_whole_days,
+)
+from battery_dispatch.markets import MARKET_1, MARKET_2
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+PRICE_FILE = REPO_ROOT / "data" / "Attachment 2.xlsx"
+SPEC_FILE = REPO_ROOT / "data" / "Attachment 1.xlsx"
+
+requires_data = pytest.mark.skipif(
+    not PRICE_FILE.exists(), reason="Attachment 2.xlsx not present"
+)
+
+
+def write_workbook(
+    path: Path,
+    half_hourly: list[tuple],
+    hourly: list[tuple],
+) -> Path:
+    """Build a miniature Attachment 2 with the same sheet names and layout."""
+    workbook = openpyxl.Workbook()
+    sheet = workbook.active
+    sheet.title = HALF_HOURLY_SHEET
+    sheet.append([None, "Market 1 Price [£/MWh]"])
+    for row in half_hourly:
+        sheet.append(list(row))
+
+    second = workbook.create_sheet(HOURLY_SHEET)
+    second.append([None, "Market 2 Price [£/MWh]"])
+    for row in hourly:
+        second.append(list(row))
+
+    workbook.save(path)
+    return path
+
+
+def synthetic_day(tmp_path: Path, trailing_blanks: int = 0) -> Path:
+    """One clean day: 48 half-hours and 24 hours, plus optional blank rows."""
+    half_hourly = [
+        (pd.Timestamp("2018-01-01") + pd.Timedelta(minutes=30 * i), float(i))
+        for i in range(48)
+    ]
+    hourly = [
+        (pd.Timestamp("2018-01-01") + pd.Timedelta(hours=h), 100.0 + h)
+        for h in range(24)
+    ]
+    hourly += [(None, None)] * trailing_blanks
+    return write_workbook(tmp_path / "prices.xlsx", half_hourly, hourly)
+
+
+def test_trailing_blank_rows_are_dropped(tmp_path):
+    path = synthetic_day(tmp_path, trailing_blanks=24)
+    prices = load_prices(path, expected_days=1)
+    assert len(prices) == 48
+
+
+def test_hourly_prices_are_broadcast_onto_half_hours(tmp_path):
+    prices = load_prices(synthetic_day(tmp_path), expected_days=1)
+
+    # Each hourly price covers exactly two consecutive half-hours.
+    assert prices["market_2_price"].iloc[0] == 100.0
+    assert prices["market_2_price"].iloc[1] == 100.0
+    assert prices["market_2_price"].iloc[2] == 101.0
+    assert prices["market_2_price"].iloc[47] == 123.0
+
+    pairs = prices["market_2_price"].to_numpy().reshape(-1, 2)
+    assert np.array_equal(pairs[:, 0], pairs[:, 1])
+
+    # Market 1 is untouched, and hour_index groups half-hours in twos.
+    assert prices["market_1_price"].tolist() == [float(i) for i in range(48)]
+    assert prices["hour_index"].tolist() == [i // 2 for i in range(48)]
+
+
+def test_row_count_mismatch_is_an_error_not_a_guess(tmp_path):
+    half_hourly = [
+        (pd.Timestamp("2018-01-01") + pd.Timedelta(minutes=30 * i), 1.0)
+        for i in range(47)  # one short
+    ]
+    hourly = [
+        (pd.Timestamp("2018-01-01") + pd.Timedelta(hours=h), 1.0) for h in range(24)
+    ]
+    path = write_workbook(tmp_path / "short.xlsx", half_hourly, hourly)
+
+    with pytest.raises(ValueError, match="expected 48 rows"):
+        load_prices(path, expected_days=1)
+
+
+def test_index_is_rebuilt_regularly_despite_clock_change_labels(tmp_path, caplog):
+    """Duplicated 02:00 labels are logged and replaced by a regular index."""
+    stamps = [
+        pd.Timestamp("2018-03-25") + pd.Timedelta(minutes=30 * i) for i in range(48)
+    ]
+    # Reproduce the spring-forward labelling: 01:00/01:30 skipped, 02:00/02:30 twice.
+    stamps[2] = pd.Timestamp("2018-03-25 02:00")
+    stamps[3] = pd.Timestamp("2018-03-25 02:30")
+
+    half_hourly = [(ts, 10.0) for ts in stamps]
+    hourly = [
+        (pd.Timestamp("2018-03-25") + pd.Timedelta(hours=h), 20.0) for h in range(24)
+    ]
+    path = write_workbook(tmp_path / "clocks.xlsx", half_hourly, hourly)
+
+    with caplog.at_level("INFO"):
+        prices = load_prices(path, expected_days=1)
+
+    assert "do not match a regular half-hourly index" in caplog.text
+    expected = pd.date_range("2018-01-01", periods=48, freq="30min")
+    assert prices.index.equals(expected)
+    assert not prices.index.duplicated().any()
+
+
+def test_slice_whole_days_includes_the_whole_end_day(tmp_path):
+    half_hourly = [
+        (pd.Timestamp("2018-01-01") + pd.Timedelta(minutes=30 * i), float(i))
+        for i in range(96)
+    ]
+    hourly = [
+        (pd.Timestamp("2018-01-01") + pd.Timedelta(hours=h), 1.0) for h in range(48)
+    ]
+    prices = load_prices(write_workbook(tmp_path / "two.xlsx", half_hourly, hourly),
+                         expected_days=2)
+
+    first_day = slice_whole_days(prices, "2018-01-01", "2018-01-01")
+    assert len(first_day) == 48
+    assert first_day.index[-1] == pd.Timestamp("2018-01-01 23:30")
+
+    # hour_index is re-based so a slice can be optimised standalone.
+    assert first_day["hour_index"].iloc[0] == 0
+    second_day = slice_whole_days(prices, "2018-01-02", "2018-01-02")
+    assert second_day["hour_index"].iloc[0] == 0
+
+
+def test_build_markets_sets_the_commitment_block_lengths(tmp_path):
+    prices = load_prices(synthetic_day(tmp_path), expected_days=1)
+    market_1, market_2 = build_markets(prices)
+
+    assert market_1.name == MARKET_1 and market_1.block_half_hours == 1
+    assert market_2.name == MARKET_2 and market_2.block_half_hours == 2
+    assert market_2.block_of(0) == market_2.block_of(1) == 0
+    assert market_2.block_of(2) == 1
+
+
+@requires_data
+def test_real_workbook_loads_with_the_expected_shape():
+    prices = load_prices(PRICE_FILE)
+
+    assert len(prices) == 1096 * 48
+    assert prices.index[0] == pd.Timestamp("2018-01-01 00:00")
+    assert prices.index[-1] == pd.Timestamp("2020-12-31 23:30")
+    assert prices.index.is_monotonic_increasing
+    assert not prices.index.duplicated().any()
+    assert prices.notna().all().all()
+
+    pairs = prices["market_2_price"].to_numpy().reshape(-1, 2)
+    assert np.array_equal(pairs[:, 0], pairs[:, 1])
+
+
+@requires_data
+def test_spec_reads_losses_as_efficiencies():
+    """Attachment 1 quotes losses; the model must store 1 - loss."""
+    spec = BatterySpec.from_excel(SPEC_FILE)
+
+    assert spec.max_charge_mw == 2.0
+    assert spec.max_discharge_mw == 2.0
+    assert spec.capacity_mwh == 4.0
+    assert spec.charge_efficiency == pytest.approx(0.95)
+    assert spec.discharge_efficiency == pytest.approx(0.95)
+    assert spec.round_trip_efficiency == pytest.approx(0.9025)
+    assert spec.lifetime_years == 10.0
+    assert spec.lifetime_cycles == 5000.0
+    assert spec.degradation_fraction_per_cycle == pytest.approx(1e-5)
+    assert spec.capex_gbp == 500_000.0
+    assert spec.fixed_opex_gbp_per_year == 5_000.0

tests/test_optimiser.py

@@ -0,0 +1,162 @@
+"""Optimiser tests built on small, hand-checkable cases."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+from conftest import markets_from, prices_frame
+
+from battery_dispatch.config import RunConfig
+from battery_dispatch.markets import MARKET_1, MARKET_2
+from battery_dispatch.optimiser import (
+    POWER_TOLERANCE,
+    run_rolling_horizon,
+    solve_window,
+)
+
+
+def total_power(solution, kind: str) -> np.ndarray:
+    values = solution.charge_mw if kind == "charge" else solution.discharge_mw
+    return sum(values.values())
+
+
+def test_two_period_arbitrage_matches_analytic_revenue(spec, config):
+    """£0 then £100 in Market 1: revenue is pinned by the round-trip efficiency.
+
+    Charging at 2 MW for half an hour imports 1 MWh, of which 0.95 MWh is
+    stored; discharging all of it delivers 0.95 x 0.95 = 0.9025 MWh to the grid
+    at £100/MWh. Market 2 is priced at £50 for the hour, which is strictly
+    worse in both directions, so it must stay out of the optimum.
+    """
+    markets = markets_from([0.0, 100.0], [50.0])
+    solution, _ = solve_window(markets, spec, 0.0, spec.capacity_mwh, config)
+
+    assert solution.objective_gbp == pytest.approx(90.25, abs=0.01)
+
+    # The whole trade sits in Market 1.
+    assert solution.charge_mw[MARKET_1] == pytest.approx([2.0, 0.0], abs=1e-6)
+    assert solution.discharge_mw[MARKET_1] == pytest.approx([0.0, 1.805], abs=1e-6)
+    assert solution.charge_mw[MARKET_2] == pytest.approx([0.0, 0.0], abs=1e-6)
+    assert solution.discharge_mw[MARKET_2] == pytest.approx([0.0, 0.0], abs=1e-6)
+
+    # Energy delivered to the grid, cross-checked independently.
+    delivered = 0.5 * solution.discharge_mw[MARKET_1][1]
+    assert delivered == pytest.approx(1.0 * 0.95 * 0.95, abs=1e-6)
+
+
+def test_market_2_power_is_constant_within_an_hour(spec, config):
+    """Opposite Market 1 signals inside one hour cannot bend Market 2's power."""
+    # Market 1 pays to charge in the first half-hour and pays well to discharge
+    # in the second; Market 2 sits at a flat £50 across the whole hour.
+    markets = markets_from([-100.0, 200.0, 0.0, 0.0], [50.0, 10.0])
+    solution, _ = solve_window(markets, spec, 2.0, spec.capacity_mwh, config)
+
+    for series in (solution.charge_mw[MARKET_2], solution.discharge_mw[MARKET_2]):
+        assert series[0] == pytest.approx(series[1], abs=1e-9)
+        assert series[2] == pytest.approx(series[3], abs=1e-9)
+
+
+def test_milp_fallback_removes_cross_market_simultaneous_flow(spec):
+    """Paid to charge in one market and to discharge in the other at once.
+
+    The LP relaxation takes both sides of that trade, which no single battery
+    can do. The MILP fallback must detect and eliminate it.
+    """
+    markets = markets_from([-50.0, -50.0], [50.0])
+
+    lp_solution, _ = solve_window(
+        markets, spec, 0.0, spec.capacity_mwh, RunConfig(solver_mode="lp-only")
+    )
+    cheating = (total_power(lp_solution, "charge") > POWER_TOLERANCE) & (
+        total_power(lp_solution, "discharge") > POWER_TOLERANCE
+    )
+    assert cheating.any(), "expected the relaxation to charge and discharge at once"
+
+    solution, used_milp = solve_window(
+        markets, spec, 0.0, spec.capacity_mwh, RunConfig(solver_mode="auto")
+    )
+    assert used_milp, "the fallback should have been triggered"
+
+    honest = (total_power(solution, "charge") > POWER_TOLERANCE) & (
+        total_power(solution, "discharge") > POWER_TOLERANCE
+    )
+    assert not honest.any()
+    assert solution.objective_gbp <= lp_solution.objective_gbp + 1e-6
+
+
+def test_combined_power_respects_the_two_megawatt_cap(spec, config):
+    """Both markets attractive at once: the cap applies to their sum, not each."""
+    # A full battery and a high price in both markets. How the power splits
+    # between them is a tie, but the total is capped at 2 MW either way.
+    markets = markets_from([1000.0, 1000.0], [1000.0])
+    solution, _ = solve_window(markets, spec, spec.capacity_mwh, spec.capacity_mwh, config)
+
+    combined = total_power(solution, "discharge")
+    assert combined.max() <= spec.max_discharge_mw + POWER_TOLERANCE
+    assert combined[0] == pytest.approx(spec.max_discharge_mw, abs=1e-6)
+    assert total_power(solution, "charge").max() <= POWER_TOLERANCE
+
+
+def test_optimum_uses_whichever_market_pays_more_in_each_hour(spec, config):
+    """Across hours the model switches markets rather than favouring one."""
+    # Hour 0 cheap in both (charge); hour 1 Market 1 pays best; hour 2 Market 2 does.
+    markets = markets_from(
+        [0.0, 0.0, 300.0, 300.0, 10.0, 10.0],
+        [0.0, 10.0, 300.0],
+    )
+    solution, _ = solve_window(markets, spec, 0.0, spec.capacity_mwh, config)
+
+    assert solution.discharge_mw[MARKET_1][2:4].sum() > 0, "hour 1 should sell into M1"
+    assert solution.discharge_mw[MARKET_2][4:6].sum() > 0, "hour 2 should sell into M2"
+    assert total_power(solution, "discharge").max() <= spec.max_discharge_mw + 1e-6
+
+
+def test_negative_prices_make_charging_profitable(spec, config):
+    """A negative price is an inducement to import, not merely a cheap one."""
+    markets = markets_from([-80.0, -80.0], [-80.0])
+    solution, _ = solve_window(markets, spec, 0.0, spec.capacity_mwh, config)
+
+    assert total_power(solution, "charge")[0] == pytest.approx(2.0, abs=1e-6)
+    assert solution.objective_gbp > 0
+
+
+def test_degradation_cost_suppresses_marginal_cycling(spec):
+    """Pricing cycle life makes a thin spread not worth taking."""
+    # A £12/MWh gross spread: profitable at zero degradation cost, not at £40.
+    prices = [0.0, 0.0, 12.0, 12.0]
+    markets = markets_from(prices, [0.0, 12.0])
+
+    free, _ = solve_window(markets, spec, 0.0, spec.capacity_mwh, RunConfig())
+    priced, _ = solve_window(
+        markets, spec, 0.0, spec.capacity_mwh,
+        RunConfig(degradation_cost_gbp_per_mwh=40.0),
+    )
+
+    assert total_power(free, "discharge").sum() > 0
+    assert total_power(priced, "discharge").sum() == pytest.approx(0.0, abs=1e-6)
+
+
+def test_rolling_horizon_carries_state_across_windows(spec):
+    """State of charge at a commit boundary is the next window's starting point."""
+    # Two days: charge cheaply late on day 1, sell into the day-2 morning peak.
+    day = [0.0] * 40 + [5.0] * 8
+    day_2 = [200.0] * 8 + [50.0] * 40
+    prices = prices_frame(day + day_2, [40.0] * 48)
+
+    config = RunConfig(window_hours=48, commit_hours=24)
+    result, state, stats = run_rolling_horizon(prices, spec, config)
+
+    assert stats.windows == 2
+    assert len(result) == 96
+
+    # The lookahead must carry energy over midnight to reach the day-2 peak.
+    soc_at_midnight = result["soc_mwh"].iloc[47]
+    assert soc_at_midnight > 1.0
+    assert state.equivalent_full_cycles > 0
+
+
+def test_window_start_must_align_to_hour_blocks(spec):
+    """Slicing a window mid-hour would silently break Market 2's commitment."""
+    markets = markets_from([1.0, 2.0, 3.0, 4.0], [1.0, 2.0])
+    with pytest.raises(ValueError, match="block boundary"):
+        markets[1].slice(1, 3)

tests/test_validation.py

@@ -0,0 +1,129 @@
+"""The validator must catch each violation type when it is injected."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pytest
+from conftest import prices_frame
+
+from battery_dispatch.config import RunConfig
+from battery_dispatch.optimiser import run_rolling_horizon
+from battery_dispatch.validation import validate_schedule
+
+
+@pytest.fixture
+def schedule(spec) -> pd.DataFrame:
+    """A genuine, valid two-day schedule to mutate in each test."""
+    prices = prices_frame(
+        [0.0] * 20 + [90.0] * 8 + [10.0] * 20 + [5.0] * 16 + [120.0] * 8 + [40.0] * 24,
+        [30.0] * 48,
+    )
+    result, _, _ = run_rolling_horizon(prices, spec, RunConfig())
+    return result
+
+
+def test_a_valid_schedule_passes_every_check(schedule, spec):
+    report = validate_schedule(schedule, spec)
+    assert report.ok, report.summary()
+    assert len(report.checks) == 7
+    assert "all 7 validation checks passed" in report.summary()
+
+
+def test_catches_charging_above_the_power_limit(schedule, spec):
+    schedule.loc[schedule.index[0], "charge_market_1_mw"] = 5.0
+    report = validate_schedule(schedule, spec)
+
+    assert not report.ok
+    assert report.checks["charge_within_limit"] is False
+    assert any("combined charge" in failure for failure in report.failures)
+
+
+def test_catches_discharging_above_the_power_limit(schedule, spec):
+    schedule.loc[schedule.index[30], "discharge_market_2_mw"] = 3.5
+    report = validate_schedule(schedule, spec)
+
+    assert not report.ok
+    assert report.checks["discharge_within_limit"] is False
+
+
+def test_catches_simultaneous_charge_and_discharge(schedule, spec):
+    row = schedule.index[0]
+    schedule.loc[row, "charge_market_1_mw"] = 1.0
+    schedule.loc[row, "discharge_market_1_mw"] = 1.0
+    report = validate_schedule(schedule, spec)
+
+    assert not report.ok
+    assert report.checks["no_simultaneous_charge_discharge"] is False
+    assert report.details["simultaneous_half_hours"] == 1.0
+
+
+def test_catches_market_2_power_varying_within_an_hour(schedule, spec):
+    # Break only the second half-hour of hour zero.
+    schedule.loc[schedule.index[1], "discharge_market_2_mw"] = 1.0
+    schedule.loc[schedule.index[0], "discharge_market_2_mw"] = 0.0
+    report = validate_schedule(schedule, spec)
+
+    assert not report.ok
+    assert report.checks["hourly_commitment_constant"] is False
+
+
+def test_catches_a_state_of_charge_that_does_not_follow_the_power_flows(
+    schedule, spec
+):
+    schedule.loc[schedule.index[10], "soc_mwh"] += 1.5
+    report = validate_schedule(schedule, spec)
+
+    assert not report.ok
+    assert report.checks["soc_matches_power_flows"] is False
+
+
+def test_catches_a_state_of_charge_above_capacity(schedule, spec):
+    """Energy appearing from nowhere shows up as an out-of-bounds recompute."""
+    # Charge hard enough to overfill, and keep the reported SoC consistent so
+    # only the bounds check can fail.
+    schedule["charge_market_1_mw"] = 2.0
+    schedule["discharge_market_1_mw"] = 0.0
+    schedule["charge_market_2_mw"] = 0.0
+    schedule["discharge_market_2_mw"] = 0.0
+    delta = 0.5 * spec.charge_efficiency * schedule["charge_market_1_mw"]
+    schedule["soc_mwh"] = delta.cumsum()
+    for name in ("market_1", "market_2"):
+        schedule[f"revenue_{name}_gbp"] = (
+            0.5
+            * schedule[f"{name}_price"]
+            * (schedule[f"discharge_{name}_mw"] - schedule[f"charge_{name}_mw"])
+        )
+
+    report = validate_schedule(schedule, spec)
+    assert not report.ok
+    assert report.checks["soc_within_bounds"] is False
+    assert report.checks["soc_matches_power_flows"] is True
+
+
+def test_catches_revenue_that_does_not_match_prices_and_powers(schedule, spec):
+    schedule.loc[schedule.index[5], "revenue_market_1_gbp"] += 25.0
+    report = validate_schedule(schedule, spec)
+
+    assert not report.ok
+    assert report.checks["revenue_matches_prices_and_powers"] is False
+    assert report.details["max_revenue_error_gbp"] == pytest.approx(25.0)
+
+
+def test_catches_a_sign_error_in_the_revenue_convention(schedule, spec):
+    """Paying for exports instead of being paid is the classic sign slip."""
+    schedule["revenue_market_1_gbp"] *= -1
+    report = validate_schedule(schedule, spec)
+
+    assert not report.ok
+    assert report.checks["revenue_matches_prices_and_powers"] is False
+
+
+def test_reports_are_falsy_only_when_something_failed(schedule, spec):
+    good = validate_schedule(schedule, spec)
+    assert good.ok and not good.failures
+
+    schedule.loc[schedule.index[0], "charge_market_1_mw"] = np.float64(9.0)
+    bad = validate_schedule(schedule, spec)
+    assert not bad.ok and bad.failures
+    assert "FAILED" in bad.summary()

uv.lock

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+    "python_full_version < '3.12' and sys_platform == 'emscripten'",
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+    "python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'",
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