Improving the realism of hydropower and hydropower–battery hybrid plant optimization: A forecast-aware rolling horizon model

This paper introduces HydroBoost, a forecast-aware rolling-horizon optimization model for co-optimizing hydropower and battery energy storage system dispatch under realistic electricity market conditions. HydroBoost integrates a mixed-integer linear programming formulation with reservoir dynamics, ancillary service provision, and multi-day ahead electricity price forecasts. Unlike conventional techno-economic planning tools that assume perfect foresight of future market prices, HydroBoost explicitly distinguishes between perfect foresight, defined as full knowledge of realized future prices, and imperfect foresight, in which dispatch decisions are based on forecasted prices with uncertainty. Case studies using California Independent System Operator market data demonstrate that seven-day imperfect forecasts, implemented using a mean persistence model, capture most of the economic benefits of seven-day perfect foresight while revealing important operational differences. Across two representative years, imperfect foresight results in 1%–2% lower total revenues, approximately 25% more water spillage, around 15% more hydro unit starts, and roughly 3% more battery cycling relative to perfect seven-day foresight. These results show that even modest forecast errors can materially influence operational behavior and asset wear. By explicitly incorporating forecast uncertainty and extended planning horizons, HydroBoost provides a practical, data-driven decision-support tool for evaluating and optimizing hybrid hydropower–BESS systems in real-world markets.