Battery Storage Arbitrage Calculator
What could a grid-scale battery actually earn trading real day-ahead electricity prices? Every number below comes from a live linear program solved against a full year of real NYISO market data — not a lookup table, not a forecast.
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N.Y.C. zone, NYISO
No forecasting — day-ahead prices are already known 24h out
Revenue per kW-year
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standard storage economics benchmark
Equivalent full cycles / year
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Day-ahead price — one representative week
Battery dispatch — same week
Methodology: for each hour, the battery's charge/discharge power is a decision variable subject to power and energy limits, with state of charge tracked across the full year; the objective maximizes discharge revenue minus charge cost. Solved with SciPy's HiGHS LP solver (~0.4s per run). Because NYISO publishes all 24 hours of next-day prices at once, this is a perfect-information optimization over realized prices, not a prediction — no forecasting model is involved anywhere in this tool. Results are an idealized upper bound: energy-market arbitrage only, no degradation cost, no ancillary-services or capacity-market revenue stacking.
Built by Jack Schiavo as a technical case study in energy-market optimization. Full methodology, code, and a companion electricity-price-forecasting project (comparing quantum machine learning kernels against classical baselines) available on request.