Capacity configuration optimization of energy storage for microgrids considering source–load prediction uncertainty and demand response

The fluctuation of renewable energy resources and the uncertainty of demand-side loads affect the accuracy of the configuration of energy storage (ES) in microgrids. High peak-to-valley differences on the load side also affect the stable operation of the microgrid. To improve the accuracy of capacity configuration of ES and the stability of microgrids, this study proposes a capacity configuration optimization model of ES for the microgrid, considering source–load prediction uncertainty and demand response (DR). First, a microgrid, including electric vehicles, is constructed. Second, the uncertainty of renewable energy resources and electric demand is handled by Monte Carlo scenario generation techniques and K-means-based scenario reduction techniques. Then, a DR model combining price-based demand response and incentive-based demand response is constructed to achieve a better match between electricity demand and supply. Finally, the results of the ES capacity configuration are determined with the objective of minimizing the total daily cost of the microgrid. The simulation results show that the optimal configuration of ES capacity and DR promotes renewable energy consumption and achieves peak shaving and valley filling, which reduces the total daily cost of the microgrid by 22%. Meanwhile, the DR model proposed in this paper has the best optimization results compared with a single type of the DR model. It is verified through comparative analysis that under a certain proportion of flexible loads, the total daily cost of the microgrid is the lowest when the time-shiftable loads and the curtailable loads are both 10%.