Distributionally Robust Optimal Power Flow in Multi-Microgrids With Decomposition and Guaranteed Convergence

Abstract

Multi-microgrids (MMGs) are emerging as a cost-effective solution to provide ancillary services. To reconcile external reserve provision and internal risk hedging for MMGs, a novel comprehensive multi-area dynamic optimal power flow (MADOPF) model is established, where energy-reserve co-optimization, three-phase unbalanced network intrinsics and dual control time-scales are all addressed. To better hedge the uncertainties of distributed generation and loads, distributionally robust model predictive control (MPC) is applied to the MADOPF problem. To preserve operational independence and information privacy for each microgrid, decomposition of the nonconvex model is devised with guaranteed convergence. Numerical tests on a two-area system and a real large-scale 16-area system derived from Shandong Power Grid validate the effectiveness of the proposed method. The advantages are demonstrated by the comparison with the conventional MPC, stochastic and robust methods.