A DRO-SDDP Decentralized Algorithm for Economic Dispatch of Multi Microgrids With Uncertainties

This article proposes a decentralized optimization algorithm for the economic dispatch of multimicrogrids (MMGs) with the uncertainties of renewable energy sources. The Wasserstein metric-based distributionally robust optimization is extensively applied to handle the uncertainties in MMGs, and the original centralized problem is decomposed into a series of nested subproblems by stochastic dual dynamic programming. Compared with traditional decentralized optimization algorithm, the proposed algorithm can provide tractable and favorable robust dispatch strategies while preserve information privacy of each individual microgrid. Moreover, a bilevel parallel technique is further proposed to accelerate the algorithm in both scenario and stage levels. Case studies on several test systems and a real 265-bus system illustrate that the proposed algorithm is favorable in terms of computational accuracy and efficiency.