Optimal Schedule of Energy Storage and Wind Farms in the power spot market under Risk-constrained

In China, a growing number of energy storage systems (ESS) are installed inside wind farms to smooth out the volatility of wind generation and increase revenue in the power market. In the paper, a two-stage optimal scheduling model for coordination control of energy storage and wind power based on risk constraints is proposed. Maximizing the expected overall net revenue of the wind farm from the day-ahead market, the real-time market and the frequency regulation market is the objective of this model. The curtailment reduction of wind generation using ESS is considered in the model. A linearized energy storage cycle cost model is applied to simulate the effect of decline of cycle life of energy storage battery due to charging and discharging operation. For the two stages of the model, control decisions are optimized in the first stage of the day-ahead market, the real-time market and frequency regulation market for the second stage. A Monte Carlo approach is used to simulate the uncertainties faced by wind farms into a set of random scenarios. To avoid the major revenue losses of wind farms under severe scenarios, the conditional value-at-risk (CVaR) and its linearized expression are embedded in the model as constraints. Finally, the validity of the above model is verified by case studies.