Data-Driven Scheduling of Energy Storage in Day-Ahead Energy and Reserve Markets With Probabilistic Guarantees on Real-Time Delivery

Energy storage systems (ESS) may provide the required flexibility to cost-effectively integrate weather-dependent renewable generation, in particular by offering operating reserves. However, since real-time deployment of these services is uncertain, ensuring their availability requires merchant ESS fully reserve associated energy capacity day-ahead schedule. To improve such conservative policies, we propose a data-driven probabilistic characterization balancing stage inform scheduling problem an owner. This distributional information used enforce tailored guarantee on scheduled via chance constrained programming, which allows profit-maximizing participation energy, and markets. The merit order-based competition with rival resources markets captured bi-level model, reformulated as computationally efficient mixed-integer linear problem. Results show that owner leverage effect avoid violations its limits, proposed risk-aware method sourcing more capacity, thus value, from storage, without jeopardizing reliability power system.