pluginbattery/scripts/timeline_inspect.py

#!/usr/bin/env python3
"""Print the hour-by-hour LP schedule for a battery over a chosen 7 days.

Same LP we use everywhere else — the "shortcut" is just the per-day LP
optimum aggregated to a yearly number. This script shows the disaggregated
trajectory so you can read the actual decisions.

Default scenario matches the web app's defaults (dad's P1, retail €0.25,
terugleveringskosten −€0.106). Pick a different week with --start.

Usage:
    uv run python scripts/timeline_inspect.py
    uv run python scripts/timeline_inspect.py --start 2024-06-17 --days 7
"""
from __future__ import annotations

import argparse
import datetime as dt

import numpy as np
import pandas as pd

from pluginbattery.sim import (
    Battery, apply_nl_tariff, load_hourly, oracle_daily_schedule, simulate,
)


def main() -> None:
    p = argparse.ArgumentParser(description=__doc__)
    p.add_argument("--start", type=lambda s: dt.date.fromisoformat(s),
                   default=dt.date(2024, 6, 17),
                   help="First UTC day to print (yyyy-mm-dd). Default: a sunny mid-June week.")
    p.add_argument("--days",  type=int, default=7)
    p.add_argument("--retail",      type=float, default=0.25)
    p.add_argument("--export-rate", type=float, default=-0.106)
    p.add_argument("--demand-kwh",  type=float, default=2350.0)

    # Zendure SolarFlow 800 Plus
    p.add_argument("--cap",   type=float, default=1.92)
    p.add_argument("--power", type=float, default=0.8)
    p.add_argument("--eta",   type=float, default=0.88)
    p.add_argument("--cost",  type=float, default=479.0)

    args = p.parse_args()

    # ─── Build the same df the web app sees ───
    base = apply_nl_tariff(load_hourly("data/raw"))
    df = base.copy()
    annual = base["raw_demand_kw"].sum() * 8766 / len(base)
    df["raw_demand_kw"] = base["raw_demand_kw"] * (args.demand_kwh / annual)
    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
    df["export_eur_per_kwh"] = float(args.export_rate)

    bat = Battery(args.cap, args.power, args.power, args.eta, allows_export=False)
    schedule = oracle_daily_schedule(df, bat)
    out = simulate(df, bat, schedule)

    # Pre-compute import / export split for clarity.
    g = out["grid_kwh_with_battery"]
    out = out.assign(
        import_kwh=np.maximum(0.0, g),
        export_kwh=np.maximum(0.0, -g),
        no_bat_grid=out["raw_demand_kw"],   # equals the original net since pv=0
        cost_no_battery_eur=np.where(out["raw_demand_kw"] > 0,
                                     out["raw_demand_kw"] * out["eur_per_kwh"],
                                     out["raw_demand_kw"] * out["export_eur_per_kwh"]),
        cost_with_battery_eur=np.where(g > 0, g * out["eur_per_kwh"],
                                       g * out["export_eur_per_kwh"]),
    )
    out["savings_eur"] = out["cost_no_battery_eur"] - out["cost_with_battery_eur"]

    # ─── Slice the chosen window ───
    start = pd.Timestamp(args.start, tz="UTC")
    end = start + pd.Timedelta(days=args.days)
    win = out.loc[(out.index >= start) & (out.index < end)].copy()
    if win.empty:
        raise SystemExit(f"No data in range {start} .. {end}; window is "
                         f"{out.index[0].date()} .. {out.index[-1].date()}")

    # ─── Print per-day blocks ───
    print(f"Battery: {args.cap} kWh / {args.power} kW / €{args.cost:.0f} ; "
          f"η_rt = {args.eta}; retail €{args.retail}; export €{args.export_rate}")
    print(f"Window: {start.date()} → {end.date()-pd.Timedelta(days=1)} ({len(win)} hours)\n")

    for date, day in win.groupby(win.index.date):
        print(f"── {date} ────────────────────────────────────────────────────────────────")
        print(f"   {'hr':>2s}  {'price':>5s}  {'no-bat meter':>15s}  "
              f"{'charge':>7s}  {'disch':>7s}  {'SoC':>5s}  {'with-bat meter':>16s}  {'sav':>6s}")
        for ts, r in day.iterrows():
            net_no = r["raw_demand_kw"]
            mark = "↓ exp" if net_no < -0.01 else "↑ imp" if net_no > 0.01 else "·"
            grid = r["grid_kwh_with_battery"]
            grid_mark = "↓ exp" if grid < -0.01 else "↑ imp" if grid > 0.01 else "·"
            print(f"   {ts.hour:02d}  €{r['eur_per_kwh']:.3f}  "
                  f"{net_no:+7.2f} {mark:5s}  "
                  f"{r['charge_kwh']:>6.2f}  {r['discharge_kwh']:>6.2f}  "
                  f"{r['soc_kwh']:>4.2f}  {grid:+8.2f} {grid_mark:5s}  €{r['savings_eur']:+5.2f}")
        d_imp_no = max(0.0, day['raw_demand_kw'].clip(lower=0).sum())
        d_exp_no = -day['raw_demand_kw'].clip(upper=0).sum()
        d_imp_yes = day['import_kwh'].sum()
        d_exp_yes = day['export_kwh'].sum()
        d_sav = day['savings_eur'].sum()
        d_charge = day['charge_kwh'].sum()
        d_discharge = day['discharge_kwh'].sum()
        print(f"   day total  imports: {d_imp_no:5.1f} → {d_imp_yes:5.1f}   "
              f"exports: {d_exp_no:5.1f} → {d_exp_yes:5.1f}   "
              f"battery in/out: {d_charge:5.2f}/{d_discharge:5.2f} kWh   "
              f"SAVED €{d_sav:+.2f}\n")

    # ─── Window aggregate vs annual rate ───
    week_savings = win["savings_eur"].sum()
    week_charge  = win["charge_kwh"].sum()
    week_disch   = win["discharge_kwh"].sum()
    week_surplus = (-win["raw_demand_kw"].clip(upper=0)).sum()
    week_imports = win["raw_demand_kw"].clip(lower=0).sum()

    annual_savings = out["savings_eur"].sum()
    annualised = week_savings * 365.25 / args.days

    print(f"=== {args.days}-day totals ===")
    print(f"  imports without bat   : {week_imports:6.1f} kWh")
    print(f"  surplus without bat   : {week_surplus:6.1f} kWh")
    print(f"  battery throughput    : charge {week_charge:.1f} / discharge {week_disch:.1f} kWh "
          f"({week_disch/args.cap:.1f} cycles)")
    print(f"  savings this {args.days:>2d} days: €{week_savings:6.2f}")
    print(f"  annualised → 365 days : €{annualised:6.2f}")
    print(f"  full-year LP savings  : €{annual_savings:6.2f}  "
          f"(this week is {week_savings/annual_savings*100:.1f}% of annual)")


if __name__ == "__main__":
    main()