Multi‐timescale coordinated optimization of hybrid three‐phase/single‐phase multimicrogrids. (28th December 2017)

• Record Type: Journal Article
• Authors: Xu, Zhirong; Zhang, Yujia; Liang, Yingqi; Zeng, Zhiji; Yang, Ping; Peng, Jiajun; He, Ting; Chen, Jingfeng
• Is Part Of: International transactions on electrical energy systems. Volume 28:Number 3(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-12-28
• Subjects: coordinated optimization -- multimicrogrids -- multitimescale -- particle swarm optimization; Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/etep.2499
• Languages: English
• ISSNs: 2050-7038

Abstract

Summary: With the increasing amount and type of connected microgrids in the near‐term future power networks, how to optimize the operation of multimicrogrids efficiently and reliably has become essential for taking full advantage of the complex systems. In this paper, a multitimescale coordinated optimization strategy for hybrid three‐phase/single‐phase multimicrogrids is proposed. The multitimescale strategy is implemented for day‐ahead economic optimization on a long‐time scale and real‐time tracking optimization on a short‐time scale. With the consideration of the economy of microgrids and three‐phase unbalance constraints in multimicrogrids, a strategy of collecting‐distributing fuzzy modified adaptive particle swarm economic optimization is introduced. In this day‐ahead strategy, a bilayer rolling optimizing structure is designed, where the lower layer conducts economic optimization at all time slots and the upper layer balances the tie‐line power at each time slot based on the results obtained from the lower layer. On this basis, by taking a day‐ahead optimized tie‐line power as baseline values, the real‐time power of energy storage systems is obtained by adopting an improved nondominated sorting genetic algorithm, which achieves distributed real‐time optimization for the multimicrogrid. Simulation results verify that the proposed strategy is feasible.