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/compare_with_store.py

@@ -0,0 +1,136 @@
+#!/usr/bin/env python3
+"""Run a scenario through both our LP and the cracked store calculator.
+
+This lets us see, for any battery / PV / saldering / rate combination, what
+thuisbatterijgids.nl claims you'll save vs what the actual physics permit.
+
+Examples:
+    # Reproduce the dynamic-mode tests:
+    python scripts/compare_with_store.py --capacity 1.92 --power 0.8 --cost 700 --retail 0.25
+    python scripts/compare_with_store.py --capacity 5.12 --power 0.8 --cost 1339 --retail 0.25
+    python scripts/compare_with_store.py --capacity 5.12 --power 2.5 --cost 1339 --retail 0.25
+
+    # PV scenario, fixed retail, no saldering:
+    python scripts/compare_with_store.py --pv-kwp 3.0 --fixed --retail 0.28
+"""
+from __future__ import annotations
+
+import argparse
+
+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__)
+    # Battery
+    p.add_argument("--capacity", type=float, default=1.92, help="Battery capacity in kWh")
+    p.add_argument("--power", type=float, default=0.8, help="Battery in/out power in kW")
+    p.add_argument("--cost", type=float, default=700, help="Battery price (incl. VAT) in EUR")
+    p.add_argument("--eta", type=float, default=0.88, help="LP round-trip efficiency")
+    # Tariff
+    p.add_argument("--retail", type=float, default=0.25, help="Average retail EUR/kWh")
+    p.add_argument("--demand", type=float, default=4000.0, help="Annual demand kWh/yr")
+    p.add_argument("--fixed", action="store_true",
+                   help="Fixed-rate mode (no time-of-day variation; kills arbitrage)")
+    p.add_argument("--saldering", action="store_true",
+                   help="Full saldering on (export = retail). Default: no saldering.")
+    # Solar
+    p.add_argument("--pv-kwp", type=float, default=0.0, help="PV size in kWp; 0 = no PV")
+    p.add_argument("--pv-yield", type=float, default=875.0,
+                   help="Annual PV yield in kWh/kWp (store uses 875 in their footer)")
+    # Inflation
+    p.add_argument("--inflation", type=float, default=0.03)
+    args = p.parse_args()
+
+    # ─── Honest LP ─────────────────────────────────────────────────────
+    base = load_hourly("data/raw"); base = apply_nl_tariff(base)
+    df = base.copy()
+
+    # Demand scaling.
+    annual_demand = base["demand_kwh"].sum() * (8766.0 / len(base))
+    df["demand_kwh"] = base["demand_kwh"] * (args.demand / annual_demand)
+
+    # Retail price.
+    if args.fixed:
+        df["eur_per_kwh"] = args.retail
+        epex_in_use = base["epex_eur_per_kwh"]  # raw EPEX kept
+    else:
+        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
+        epex_in_use = df["epex_eur_per_kwh"]
+
+    # PV.
+    if args.pv_kwp > 0:
+        df = synthesize_pv(df, kwp=args.pv_kwp, target_kwh_per_kwp_per_year=args.pv_yield)
+
+    # Saldering.
+    if args.saldering:
+        df["export_eur_per_kwh"] = df["eur_per_kwh"]
+    else:
+        df["export_eur_per_kwh"] = 0.0  # post-saldering default in this script
+
+    bat = Battery(
+        capacity_kwh=args.capacity,
+        max_charge_kw=args.power,
+        max_discharge_kw=args.power,
+        round_trip_eff=args.eta,
+        allows_export=False,
+    )
+    out = simulate(df, bat, oracle_daily_schedule(df, bat))
+    lp_year1 = float(out["savings"].sum())
+    lp_payback = payback_years(lp_year1, args.cost, args.inflation)
+
+    # ─── Store calculator (cracked) ────────────────────────────────────
+    avg_epex_used = float(epex_in_use.mean())
+    store_params = StoreParams(avg_epex_eur_per_kwh=avg_epex_used)
+    scenario = Scenario(
+        capacity_kwh=args.capacity,
+        max_charge_kw=args.power,
+        battery_cost_eur=args.cost,
+        avg_retail_eur_per_kwh=args.retail,
+        has_pv=args.pv_kwp > 0,
+        has_saldering=args.saldering,
+        dynamic_rate=not args.fixed,
+    )
+    sq = store_quote(scenario, store_params, inflation=args.inflation)
+
+    # ─── Report ─────────────────────────────────────────────────────────
+    print(f"Scenario:")
+    print(f"  Battery        : {args.capacity} kWh / {args.power} kW / €{args.cost:.0f}")
+    print(f"  Tariff         : avg retail €{args.retail}, "
+          f"{'FIXED' if args.fixed else 'DYNAMIC'} rate, "
+          f"{'WITH' if args.saldering else 'NO'} saldering")
+    print(f"  PV             : {args.pv_kwp} kWp"
+          f"{' @ ' + str(args.pv_yield) + ' kWh/kWp/yr' if args.pv_kwp else ''}")
+    print(f"  Demand         : {args.demand:.0f} kWh/yr")
+    print(f"  Inflation      : {args.inflation*100:.1f}%/yr")
+    print()
+    print(f"{'':22s}  {'year-1 €':>10s}  {'payback':>9s}")
+    print(f"  {'Honest LP':20s}  €{lp_year1:>8.2f}    {lp_payback:>5.2f} yr")
+    print(f"  {'Store calculator':20s}  €{sq['year1_total']:>8.2f}    {sq['payback_years']:>5.2f} yr")
+    if sq["dynamic_arbitrage"] > 0 or sq["pv_self_consumption"] > 0:
+        print(f"     ↳ arbitrage     €{sq['dynamic_arbitrage']:>8.2f}")
+        print(f"     ↳ self-consume  €{sq['pv_self_consumption']:>8.2f}")
+    if sq["year1_total"] > 0:
+        ratio = sq["year1_total"] / max(lp_year1, 1e-9)
+        print()
+        print(f"  Store overstates savings by {ratio:.2f}× "
+              f"(€{sq['year1_total'] - lp_year1:+.2f}/yr)")
+
+
+if __name__ == "__main__":
+    main()