Learning Reachability of Energy Storage Arbitrage
For power system operators and storage owners, this work addresses the problem of myopic battery operation that can compromise reliability during critical hours.
The paper introduces a stopping-time reward and state-of-charge range penalty to align energy storage arbitrage with system reliability, and embeds it in an end-to-end learning framework. The method enhances reachability of target SoC ranges, improves profit under volatile conditions, and reduces profit standard deviation.
Power systems face increasing weather-driven variability and, therefore, increasingly rely on flexible but energy-limited storage resources. Energy storage can buffer this variability, but its value depends on intertemporal decisions under uncertain prices. Without accounting for the future reliability value of stored energy, batteries may act myopically, discharging too early or failing to preserve reserves during critical hours. This paper introduces a stopping-time reward that, together with a state-of-charge (SoC) range target penalty, aligns arbitrage incentives with system reliability by rewarding storage that maintains sufficient SoC before critical hours. We formulate the problem as an online optimization with a chance-constrained terminal SoC and embed it in an end-to-end (E2E) learning framework, jointly training the price predictor and control policy. The proposed design enhances reachability of target SoC ranges, improves profit under volatile conditions, and reduces its standard deviation.