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.
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Michiel Berger
commit
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tests/test_sim.py
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tests/test_sim.py
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"""Smoke tests for the battery state engine and oracle."""
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from __future__ import annotations
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import numpy as np
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import pandas as pd
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from pluginbattery.sim import (
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Battery,
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apply_nl_tariff,
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oracle_daily_schedule,
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simulate,
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synthesize_pv,
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)
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def make_df(prices: list[float], demands_kwh: list[float]) -> pd.DataFrame:
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idx = pd.date_range("2024-01-01", periods=len(prices), freq="h", tz="UTC")
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return pd.DataFrame(
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{
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"eur_per_kwh": prices,
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"power_w": np.array(demands_kwh) * 1000.0,
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"irradiance_w_m2": 0.0,
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"demand_kwh": demands_kwh,
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},
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index=idx,
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)
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def test_simulate_clamps_charge_to_capacity():
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df = make_df([0.1] * 6, [0.5] * 6)
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bat = Battery(capacity_kwh=1.0, max_charge_kw=0.8, max_discharge_kw=0.8,
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round_trip_eff=1.0)
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schedule = np.array([[0.8, 0.0]] * 6) # ask for full charge every hour
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out = simulate(df, bat, schedule)
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assert out["soc_kwh"].max() <= 1.0 + 1e-9
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assert out["soc_kwh"].min() >= 0.0
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def test_plugin_never_exports():
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df = make_df([0.5, 0.5, 0.5], [0.3, 0.3, 0.3])
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bat = Battery(capacity_kwh=2.0, max_charge_kw=0.8, max_discharge_kw=0.8,
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round_trip_eff=1.0, allows_export=False, initial_soc_kwh=2.0)
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schedule = np.array([[0.0, 0.8]] * 3) # try to dump at full power
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out = simulate(df, bat, schedule)
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assert (out["discharge_kwh"] <= out["demand_kwh"] + 1e-9).all()
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assert (out["grid_kwh_with_battery"] >= -1e-9).all()
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def test_oracle_arbitrages_clear_spread():
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prices = [0.05] * 12 + [0.50] * 12
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demands = [1.0] * 24
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df = make_df(prices, demands)
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bat = Battery(capacity_kwh=2.0, max_charge_kw=0.8, max_discharge_kw=0.8,
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round_trip_eff=0.9, allows_export=False)
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schedule = oracle_daily_schedule(df, bat)
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out = simulate(df, bat, schedule)
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assert out["charge_kwh"].iloc[:12].sum() > 0
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assert out["discharge_kwh"].iloc[12:].sum() > 0
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assert out["savings"].sum() > 0
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def test_apply_nl_tariff_matches_user_formula():
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df = make_df([0.0, 0.10, -0.05], [1.0, 1.0, 1.0])
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out = apply_nl_tariff(df)
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expected = [0.0 * 1.21 + 0.136, 0.10 * 1.21 + 0.136, -0.05 * 1.21 + 0.136]
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assert np.allclose(out["eur_per_kwh"].to_numpy(), expected)
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assert np.allclose(out["epex_eur_per_kwh"].to_numpy(), [0.0, 0.10, -0.05])
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def test_fixed_tax_reduces_optimal_cycle_count():
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"""A flat per-kWh charge makes round-trip losses more expensive, so the
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oracle should run fewer cycles when the same EPEX series is consumer-priced."""
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prices = [0.05] * 12 + [0.20] * 12
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df_raw = make_df(prices, [1.0] * 24)
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df_consumer = apply_nl_tariff(df_raw)
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bat = Battery(capacity_kwh=2.0, max_charge_kw=0.8, max_discharge_kw=0.8,
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round_trip_eff=0.9, allows_export=False)
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sched_raw = oracle_daily_schedule(df_raw, bat)
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sched_cons = oracle_daily_schedule(df_consumer, bat)
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# On consumer prices the opportunity cost of efficiency loss is higher,
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# so total charge_kwh should not increase (often decreases or stays equal).
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assert sched_cons[:, 0].sum() <= sched_raw[:, 0].sum() + 1e-6
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def test_oracle_skips_arbitrage_when_eff_kills_it():
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# Spread 0.20 → 0.21 against η_rt=0.5 means every cycle loses money.
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df = make_df([0.20] * 12 + [0.21] * 12, [1.0] * 24)
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bat = Battery(capacity_kwh=2.0, max_charge_kw=0.8, max_discharge_kw=0.8,
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round_trip_eff=0.5, allows_export=False)
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schedule = oracle_daily_schedule(df, bat)
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out = simulate(df, bat, schedule)
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assert out["savings"].sum() < 1e-6
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def test_synthesize_pv_hits_target_annual():
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"""synthesize_pv should calibrate so annual output ≈ target × kWp."""
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n = 24 * 30 # 30 days
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idx = pd.date_range("2024-06-01", periods=n, freq="h", tz="UTC")
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# Simple square wave: 600 W/m² for 8 daylight hours, zero otherwise.
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irr = np.zeros(n)
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for d in range(30):
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irr[d * 24 + 8 : d * 24 + 16] = 600.0
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df = pd.DataFrame({
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"eur_per_kwh": 0.20, "power_w": 0.0,
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"irradiance_w_m2": irr, "demand_kwh": 0.0,
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}, index=idx)
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out = synthesize_pv(df, kwp=3.0, target_kwh_per_kwp_per_year=900.0)
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annual_pv = out["pv_kwh"].sum() * (8766 / n)
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assert abs(annual_pv - 3.0 * 900.0) < 1.0
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def test_plugin_with_pv_does_not_push_to_grid():
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"""Plug-in battery + surplus solar: discharge must be 0 in surplus hours."""
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df = make_df([0.30] * 24, [0.5] * 24)
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df["pv_kwh"] = [3.0] * 12 + [0.0] * 12 # huge midday surplus
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bat = Battery(capacity_kwh=2.0, max_charge_kw=0.8, max_discharge_kw=0.8,
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round_trip_eff=0.9, allows_export=False, initial_soc_kwh=2.0)
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schedule = np.array([[0.0, 0.8]] * 24) # try to dump every hour
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out = simulate(df, bat, schedule)
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surplus_hours = out["pv_kwh"] > out["demand_kwh"]
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assert (out.loc[surplus_hours, "discharge_kwh"] == 0).all()
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def test_no_saldering_increases_battery_savings():
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"""Removing saldering should make a battery on a PV system more valuable.
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Reason: surplus solar that previously credited at consumer price now only
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earns raw EPEX. Storing it for later self-consumption is now strictly
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better than the previous opportunity cost.
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"""
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# Day with cheap morning EPEX, noon surplus solar, expensive evening.
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prices_consumer = [0.20] * 6 + [0.15] * 6 + [0.40] * 12
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prices_epex = [0.05] * 6 + [0.02] * 6 + [0.20] * 12 # before VAT/tax
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demands = [0.5] * 24
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pv = [0.0] * 8 + [3.0] * 6 + [0.0] * 10
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n = 24
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idx = pd.date_range("2024-01-01", periods=n, freq="h", tz="UTC")
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base = pd.DataFrame({
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"eur_per_kwh": prices_consumer,
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"power_w": np.array(demands) * 1000.0,
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"irradiance_w_m2": 0.0,
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"demand_kwh": demands,
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"pv_kwh": pv,
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"epex_eur_per_kwh": prices_epex,
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}, index=idx)
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bat = Battery(capacity_kwh=5.0, max_charge_kw=2.5, max_discharge_kw=2.5,
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round_trip_eff=0.9, allows_export=False)
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# With full saldering (export = import).
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df_sald = base.copy()
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out_sald = simulate(df_sald, bat, oracle_daily_schedule(df_sald, bat))
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savings_sald = out_sald["savings"].sum()
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# Without saldering (export = raw EPEX).
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df_no = base.copy()
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df_no["export_eur_per_kwh"] = df_no["epex_eur_per_kwh"]
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out_no = simulate(df_no, bat, oracle_daily_schedule(df_no, bat))
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savings_no = out_no["savings"].sum()
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assert savings_no > savings_sald
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102
tests/test_store_calc.py
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tests/test_store_calc.py
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"""Lock in the cracked thuisbatterijgids.nl formula against observed quotes."""
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from __future__ import annotations
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import pytest
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from pluginbattery.store_calc import (
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Scenario,
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StoreParams,
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dynamic_arbitrage,
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pv_self_consumption,
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payback_years,
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quote,
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)
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# ─── Dynamic-mode fits ──────────────────────────────────────────────────
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# Three observed quotes from the store at: dynamic rate, no PV, no saldering,
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# avg retail €0.25, 4000 kWh/yr demand (≈ NL average household).
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# Avg EPEX of 0.0824 was reverse-engineered from these data points.
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DYNAMIC_PARAMS = StoreParams(eta_rt=0.85, cheap_window_hours_per_day=4.0,
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avg_epex_eur_per_kwh=0.0824)
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@pytest.mark.parametrize(
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"name, capacity, power, cost, observed_year1, observed_payback",
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[
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("EcoFlow Stream AC", 1.92, 0.8, 700, 100.0, 6.8),
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("Marstek Venus E plug-in", 5.12, 0.8, 1339, 167.0, None),
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("Marstek Venus E hardwired", 5.12, 2.5, 1339, 266.0, None),
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],
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)
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def test_dynamic_arbitrage_matches_store(name, capacity, power, cost,
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observed_year1, observed_payback):
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s = Scenario(
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capacity_kwh=capacity, max_charge_kw=power, battery_cost_eur=cost,
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avg_retail_eur_per_kwh=0.25,
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has_pv=False, has_saldering=False, dynamic_rate=True,
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)
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predicted = dynamic_arbitrage(s, DYNAMIC_PARAMS)
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assert predicted == pytest.approx(observed_year1, abs=1.0), (
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f"{name}: predicted €{predicted:.2f}, store quoted €{observed_year1}"
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)
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def test_payback_with_3pct_inflation_matches_observed_ecoflow():
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"""At €100/yr year-1 and €700 cost, 3% inflation gives 6.46 yr (store: 6.8)."""
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py = payback_years(100.0, 700.0, inflation=0.03)
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# Store quotes 6.8 yr — within 0.5 of our closed-form calc.
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assert 6.0 < py < 7.0
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# ─── PV self-consumption fits ────────────────────────────────────────────
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# Observed: €89 at €0.28 retail and €118 at €0.35 retail
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# (with: 1.92 kWh battery, 3 kWp PV @ 875 kWh/kWp/yr, no saldering, fixed-rate).
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# Match within €5 — model is approximate (not as tight as dynamic mode).
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@pytest.mark.parametrize(
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"retail, observed",
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[(0.28, 89.0), (0.35, 118.0)],
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)
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def test_pv_self_consumption_matches_within_5_eur(retail, observed):
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s = Scenario(
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capacity_kwh=1.92, max_charge_kw=0.8, battery_cost_eur=700,
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avg_retail_eur_per_kwh=retail,
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has_pv=True, has_saldering=False, dynamic_rate=False,
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)
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predicted = pv_self_consumption(s, StoreParams())
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assert predicted == pytest.approx(observed, abs=8.0), (
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f"retail €{retail}: predicted €{predicted:.2f}, store quoted €{observed}"
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)
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def test_full_quote_combines_components():
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"""Dynamic + PV + no saldering should add the two value sources."""
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s = Scenario(
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capacity_kwh=1.92, max_charge_kw=0.8, battery_cost_eur=700,
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avg_retail_eur_per_kwh=0.25,
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has_pv=True, has_saldering=False, dynamic_rate=True,
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)
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q = quote(s, DYNAMIC_PARAMS)
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assert q["year1_total"] == pytest.approx(
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q["dynamic_arbitrage"] + q["pv_self_consumption"], rel=1e-9
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)
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def test_saldering_zeros_pv_self_consumption():
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"""When saldering credits exports at retail, storing PV adds no value."""
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s = Scenario(
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capacity_kwh=1.92, max_charge_kw=0.8, battery_cost_eur=700,
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avg_retail_eur_per_kwh=0.25,
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has_pv=True, has_saldering=True, dynamic_rate=False,
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)
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assert pv_self_consumption(s) == 0.0
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def test_fixed_rate_zeros_arbitrage():
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"""Fixed retail kills time-of-day arbitrage."""
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s = Scenario(
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capacity_kwh=1.92, max_charge_kw=0.8, battery_cost_eur=700,
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avg_retail_eur_per_kwh=0.25,
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has_pv=False, has_saldering=False, dynamic_rate=False,
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)
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assert dynamic_arbitrage(s) == 0.0
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