Add timeline inspector — print hour-by-hour LP schedule for any week
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Michiel Berger
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scripts/timeline_inspect.py
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scripts/timeline_inspect.py
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#!/usr/bin/env python3
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"""Print the hour-by-hour LP schedule for a battery over a chosen 7 days.
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Same LP we use everywhere else — the "shortcut" is just the per-day LP
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optimum aggregated to a yearly number. This script shows the disaggregated
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trajectory so you can read the actual decisions.
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Default scenario matches the web app's defaults (dad's P1, retail €0.25,
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terugleveringskosten −€0.106). Pick a different week with --start.
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Usage:
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uv run python scripts/timeline_inspect.py
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uv run python scripts/timeline_inspect.py --start 2024-06-17 --days 7
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"""
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from __future__ import annotations
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import argparse
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import datetime as dt
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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, apply_nl_tariff, load_hourly, oracle_daily_schedule, simulate,
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)
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def main() -> None:
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p = argparse.ArgumentParser(description=__doc__)
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p.add_argument("--start", type=lambda s: dt.date.fromisoformat(s),
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default=dt.date(2024, 6, 17),
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help="First UTC day to print (yyyy-mm-dd). Default: a sunny mid-June week.")
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p.add_argument("--days", type=int, default=7)
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p.add_argument("--retail", type=float, default=0.25)
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p.add_argument("--export-rate", type=float, default=-0.106)
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p.add_argument("--demand-kwh", type=float, default=2350.0)
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# Zendure SolarFlow 800 Plus
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p.add_argument("--cap", type=float, default=1.92)
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p.add_argument("--power", type=float, default=0.8)
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p.add_argument("--eta", type=float, default=0.88)
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p.add_argument("--cost", type=float, default=479.0)
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args = p.parse_args()
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# ─── Build the same df the web app sees ───
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base = apply_nl_tariff(load_hourly("data/raw"))
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df = base.copy()
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annual = base["demand_kwh"].sum() * 8766 / len(base)
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df["demand_kwh"] = base["demand_kwh"] * (args.demand_kwh / annual)
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scale = args.retail / base["eur_per_kwh"].mean()
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df["eur_per_kwh"] = base["eur_per_kwh"] * scale
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df["epex_eur_per_kwh"] = base["epex_eur_per_kwh"] * scale
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df["export_eur_per_kwh"] = float(args.export_rate)
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bat = Battery(args.cap, args.power, args.power, args.eta, 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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# Pre-compute import / export split for clarity.
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g = out["grid_kwh_with_battery"]
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out = out.assign(
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import_kwh=np.maximum(0.0, g),
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export_kwh=np.maximum(0.0, -g),
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no_bat_grid=out["demand_kwh"], # equals the original net since pv=0
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cost_no_battery_eur=np.where(out["demand_kwh"] > 0,
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out["demand_kwh"] * out["eur_per_kwh"],
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out["demand_kwh"] * out["export_eur_per_kwh"]),
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cost_with_battery_eur=np.where(g > 0, g * out["eur_per_kwh"],
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g * out["export_eur_per_kwh"]),
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)
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out["savings_eur"] = out["cost_no_battery_eur"] - out["cost_with_battery_eur"]
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# ─── Slice the chosen window ───
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start = pd.Timestamp(args.start, tz="UTC")
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end = start + pd.Timedelta(days=args.days)
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win = out.loc[(out.index >= start) & (out.index < end)].copy()
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if win.empty:
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raise SystemExit(f"No data in range {start} .. {end}; window is "
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f"{out.index[0].date()} .. {out.index[-1].date()}")
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# ─── Print per-day blocks ───
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print(f"Battery: {args.cap} kWh / {args.power} kW / €{args.cost:.0f} ; "
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f"η_rt = {args.eta}; retail €{args.retail}; export €{args.export_rate}")
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print(f"Window: {start.date()} → {end.date()-pd.Timedelta(days=1)} ({len(win)} hours)\n")
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for date, day in win.groupby(win.index.date):
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print(f"── {date} ────────────────────────────────────────────────────────────────")
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print(f" {'hr':>2s} {'price':>5s} {'net (no bat)':>13s} "
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f"{'charge':>7s} {'disch':>7s} {'SoC':>5s} {'grid':>7s} {'sav':>6s}")
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for ts, r in day.iterrows():
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net_no = r["demand_kwh"]
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mark = "↓ surplus" if net_no < -0.01 else "↑ import" if net_no > 0.01 else "·"
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grid = r["grid_kwh_with_battery"]
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grid_str = f"{grid:+6.2f}"
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print(f" {ts.hour:02d} €{r['eur_per_kwh']:.3f} "
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f"{net_no:+8.2f} {mark:5s} "
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f"{r['charge_kwh']:>6.2f} {r['discharge_kwh']:>6.2f} "
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f"{r['soc_kwh']:>4.2f} {grid_str:>7s} €{r['savings_eur']:+5.2f}")
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d_imp_no = max(0.0, day['demand_kwh'].clip(lower=0).sum())
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d_exp_no = -day['demand_kwh'].clip(upper=0).sum()
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d_imp_yes = day['import_kwh'].sum()
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d_exp_yes = day['export_kwh'].sum()
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d_sav = day['savings_eur'].sum()
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d_charge = day['charge_kwh'].sum()
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d_discharge = day['discharge_kwh'].sum()
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print(f" day total imports: {d_imp_no:5.1f} → {d_imp_yes:5.1f} "
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f"exports: {d_exp_no:5.1f} → {d_exp_yes:5.1f} "
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f"battery in/out: {d_charge:5.2f}/{d_discharge:5.2f} kWh "
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f"SAVED €{d_sav:+.2f}\n")
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# ─── Window aggregate vs annual rate ───
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week_savings = win["savings_eur"].sum()
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week_charge = win["charge_kwh"].sum()
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week_disch = win["discharge_kwh"].sum()
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week_surplus = (-win["demand_kwh"].clip(upper=0)).sum()
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week_imports = win["demand_kwh"].clip(lower=0).sum()
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annual_savings = out["savings_eur"].sum()
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annualised = week_savings * 365.25 / args.days
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print(f"=== {args.days}-day totals ===")
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print(f" imports without bat : {week_imports:6.1f} kWh")
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print(f" surplus without bat : {week_surplus:6.1f} kWh")
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print(f" battery throughput : charge {week_charge:.1f} / discharge {week_disch:.1f} kWh "
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f"({week_disch/args.cap:.1f} cycles)")
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print(f" savings this {args.days:>2d} days: €{week_savings:6.2f}")
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print(f" annualised → 365 days : €{annualised:6.2f}")
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print(f" full-year LP savings : €{annual_savings:6.2f} "
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f"(this week is {week_savings/annual_savings*100:.1f}% of annual)")
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if __name__ == "__main__":
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main()
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