Initial import: home-battery ROI simulator + cracked thuisbatterijgids calc

- Hourly data exporter (InfluxDB → CSV) for prices, P1, irradiance.
- LP-based 24h-foresight oracle dispatch with SoC-consistent state engine.
- Reverse-engineered thuisbatterijgids.nl formula (matches their quotes
  to within €0.50 across three battery configs).
- Catalog scraper for the 52 batteries on thuisbatterijgids.net via their
  /wp-json REST endpoint.
- Web app (Flask) that ranks every catalog battery by honest payback and
  contrasts with the store's quote, deployable via the included Procfile.

scripts/battery_leaderboard.py

@@ -0,0 +1,147 @@
+#!/usr/bin/env python3
+"""Run every battery in the catalog through both calculators and rank by payback.
+
+Default scenario matches the dynamic-mode store-test:
+  4000 kWh/yr demand, avg retail €0.25, no PV, no saldering, 3% inflation.
+
+Override with CLI flags to test a different scenario (e.g., dad's situation
+with --pv-kwp 3.0). Output: data/processed/battery_leaderboard.csv.
+"""
+from __future__ import annotations
+
+import argparse
+import csv
+from pathlib import Path
+
+from pluginbattery.sim import (
+    Battery,
+    apply_nl_tariff,
+    load_hourly,
+    oracle_daily_schedule,
+    simulate,
+    synthesize_pv,
+)
+from pluginbattery.store_calc import (
+    Scenario,
+    StoreParams,
+    payback_years,
+    quote as store_quote,
+)
+
+
+def main() -> None:
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--catalog", default="data/raw/thuisbatterijgids_catalog.csv")
+    p.add_argument("--out", default="data/processed/battery_leaderboard.csv")
+    p.add_argument("--retail", type=float, default=0.25)
+    p.add_argument("--demand", type=float, default=4000.0)
+    p.add_argument("--eta", type=float, default=0.88)
+    p.add_argument("--pv-kwp", type=float, default=0.0)
+    p.add_argument("--pv-yield", type=float, default=875.0)
+    p.add_argument("--saldering", action="store_true")
+    p.add_argument("--inflation", type=float, default=0.03)
+    p.add_argument("--top", type=int, default=20, help="How many rows to print")
+    args = p.parse_args()
+
+    # ─── Load and prep the simulation data once ────────────────────────
+    base = load_hourly("data/raw"); base = apply_nl_tariff(base)
+    df = base.copy()
+    annual_demand = base["demand_kwh"].sum() * (8766.0 / len(base))
+    df["demand_kwh"] = base["demand_kwh"] * (args.demand / annual_demand)
+    scale = args.retail / base["eur_per_kwh"].mean()
+    df["eur_per_kwh"] = base["eur_per_kwh"] * scale
+    df["epex_eur_per_kwh"] = base["epex_eur_per_kwh"] * scale
+    if args.pv_kwp > 0:
+        df = synthesize_pv(df, kwp=args.pv_kwp,
+                           target_kwh_per_kwp_per_year=args.pv_yield)
+    df["export_eur_per_kwh"] = df["eur_per_kwh"] if args.saldering else 0.0
+
+    avg_epex = float(df["epex_eur_per_kwh"].mean())
+    store_params = StoreParams(avg_epex_eur_per_kwh=avg_epex)
+
+    # ─── Load catalog ─────────────────────────────────────────────────
+    rows_in = []
+    with open(args.catalog) as f:
+        for r in csv.DictReader(f):
+            try:
+                cap = float(r["capacity_kwh"])
+                pw = float(r["power_w"]) / 1000.0  # → kW
+                price = float(r["price_eur"])
+            except (TypeError, ValueError):
+                continue
+            if cap <= 0 or pw <= 0 or price <= 0:
+                continue
+            rows_in.append({
+                "title": r["title"], "brand": r["brand"],
+                "capacity_kwh": cap, "power_kw": pw, "price_eur": price,
+                "url": r["url"],
+            })
+
+    # Cache LP results by (capacity, power) — many batteries share identical specs.
+    lp_cache: dict[tuple[float, float], float] = {}
+
+    print(f"Scenario: demand {args.demand:.0f} kWh/yr, avg retail €{args.retail}, "
+          f"PV {args.pv_kwp} kWp, saldering={args.saldering}, inflation {args.inflation*100:.1f}%/yr")
+    print(f"Running {len(rows_in)} batteries (caching by (capacity, power))...")
+    print()
+
+    rows_out = []
+    for i, r in enumerate(rows_in, 1):
+        key = (round(r["capacity_kwh"], 3), round(r["power_kw"], 3))
+        if key in lp_cache:
+            lp_year1 = lp_cache[key]
+        else:
+            bat = Battery(
+                capacity_kwh=r["capacity_kwh"], max_charge_kw=r["power_kw"],
+                max_discharge_kw=r["power_kw"], round_trip_eff=args.eta,
+                allows_export=False,
+            )
+            out = simulate(df, bat, oracle_daily_schedule(df, bat))
+            lp_year1 = float(out["savings"].sum())
+            lp_cache[key] = lp_year1
+        lp_payback = payback_years(lp_year1, r["price_eur"], args.inflation)
+
+        scn = Scenario(
+            capacity_kwh=r["capacity_kwh"], max_charge_kw=r["power_kw"],
+            battery_cost_eur=r["price_eur"], avg_retail_eur_per_kwh=args.retail,
+            has_pv=args.pv_kwp > 0, has_saldering=args.saldering, dynamic_rate=True,
+        )
+        sq = store_quote(scn, store_params, inflation=args.inflation)
+
+        rows_out.append({
+            "title": r["title"], "brand": r["brand"],
+            "capacity_kwh": r["capacity_kwh"], "power_kw": r["power_kw"],
+            "price_eur": r["price_eur"],
+            "lp_year1_eur": round(lp_year1, 2),
+            "lp_payback_yr": round(lp_payback, 2),
+            "store_year1_eur": round(sq["year1_total"], 2),
+            "store_payback_yr": round(sq["payback_years"], 2),
+            "overstatement": round(sq["year1_total"] / lp_year1, 2) if lp_year1 > 0 else None,
+            "url": r["url"],
+        })
+
+    # Sort by honest payback (ascending = best first).
+    rows_out.sort(key=lambda r: r["lp_payback_yr"])
+
+    out_path = Path(args.out)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    with out_path.open("w", newline="") as f:
+        writer = csv.DictWriter(f, fieldnames=rows_out[0].keys())
+        writer.writeheader()
+        writer.writerows(rows_out)
+
+    # Pretty-print top N.
+    print(f"{'rank':>4s}  {'battery':40s} {'cap':>5s} {'kW':>4s} "
+          f"{'price':>7s}  {'LP €/yr':>9s} {'LP yr':>7s}  {'Store €/yr':>11s} {'Store yr':>9s}  {'×over':>6s}")
+    print("-" * 120)
+    for i, r in enumerate(rows_out[:args.top], 1):
+        title = (r["title"][:38] + "…") if len(r["title"]) > 39 else r["title"]
+        over = f"{r['overstatement']:.2f}" if r["overstatement"] else "—"
+        print(f"{i:>4d}  {title:40s} {r['capacity_kwh']:>5.2f} {r['power_kw']:>4.1f} "
+              f"€{r['price_eur']:>5.0f}  €{r['lp_year1_eur']:>7.2f} {r['lp_payback_yr']:>5.2f}  "
+              f"€{r['store_year1_eur']:>9.2f} {r['store_payback_yr']:>7.2f}  {over:>5s}×")
+    print(f"\nFull leaderboard ({len(rows_out)} batteries) → {out_path}")
+
+
+if __name__ == "__main__":
+    main()