Multi-timescale optimization strategy for three-phase/single-phase multi-microgrids

Abstract

With the increasing amount and type of connected microgrids in the near-term future power networks, how to optimize the operation of a multi-microgrid system efficiently and reliably has become essential for taking full advantage of the complex systems. In this paper, we propose a multi-timescale coordinated optimization strategy for hybrid three-phase/single-phase multi-microgrids. With the consideration of the economy of microgrids and three-phase unbalance constraints of multi-microgrids, a strategy of collecting-distributing fuzzy modified adaptive particle swarm economic optimization is provided. In this day-ahead strategy, the system is constructed as a bi-layer rolling optimizing structure, where the lower layer sets objectives for economic optimization, and the upper layer balances constrained tie-line power of single-phase microgrids based on the optimized results obtained from lower layer. On this basis, by taking day-ahead optimized tie-line power as baseline values, we obtain the real-time power of energy storage systems using an improved nondominated sorting genetic algorithm, which achieves distributed real-time optimization for the multi-microgrid. Simulation results verify that the proposed strategy is feasible.