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Improving Deployment Availability of Energy Storage With Data-Driven AGC Signal Models

Energy Storage (ES) provides great flexibility and large benefits to power system operations and control. When providing ancillary services (e.g., regulation, reserve, etc.), the real-time (RT) deployment of ES is uncertain, and it is important to manage state of charge accordingly. Aiming to improve the ES performance for providing energy and regulation service in the electricity market, we propose two data-driven Automatic Generation Control (AGC) signal models. The first one is a historical-data-driven AGC signal model, which is based on the analysis of the historical AGC signals, and is designed for ES participation in the day-ahead (DA) market. The second one is a prediction-data-driven AGC signal model, which is based on the prediction of the AGC signals, and is designed for ES participation in the RT market. We also develop a deployment availability check model and solution algorithm. The proposed framework is applied to an ES bidding problem in the DA and RT markets. The results indicate that deployment availability and operational performance of the ES are improved with the proposed data-driven AGC models compared to traditional benchmarks.