60e0706736
- 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.
102 lines
4 KiB
Python
102 lines
4 KiB
Python
"""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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