Two-Time-Scale Energy Management for Microgrids With Data-Based Day-Ahead Distributionally Robust Chance-Constrained Scheduling

The uncertainties arising from both renewable generation and load demand have brought challenges to the reliable and efficient operation of power systems. This paper presents a two-time-scale (i.e., day-ahead and intraday) microgrid energy management model for scheduling with low operational costs and high reliability against uncertainties. For the day-ahead scheduling, we propose a data-based distributionally robust chance-constrained (DRCC) energy dispatch model for grid-connected microgrids, to trade off the economic efficiency and operational risk. This model gains a robust and low conservative day-ahead scheduling solution against uncertainties by formulating the chance-constraint based on Wasserstein ambiguity set into a tractable convex constraint with conditional value-at-risk (CVaR) approximation. For the intraday scheduling, we blend the shorter-time scale prediction with a robust day-ahead scheduling plan as well as the model predictive control (MPC) rolling optimization method. This ensures accurate intraday dispatch solution and balanced supply-demand. Finally, the effectiveness and performance of the proposed method are verified via case studies.