Improving BA Control Performance Through Advanced Regulation Requirements Prediction

The paper presents a comprehensive approach to predict balancing authority (BA) regulation requirements in order to improve BAs control performance. The proposed probabilistic approach takes into account multiple uncertain and time dependent factors affecting the system balance (e.g., wind and solar generation, electrical loads). A set of methods to predict the regulation has been developed: (1) probabilistic analysis of regulation requirements with respect to time of the day, (2) linear or nonlinear regression models linking regulation requirements to different influencing factors, (3) adopting time series forecasting techniques (e.g., autoregressive integrated moving average (ARIMA)) to account for temporal continuity and auto-correlation in the regulation requirements data. The proposed methodology has been tested and validated using actual California Independent System Operator (ISO) data.