Open AccessDistributed model predictive control strategy based on block‐wised alternating direction multiplier method for microgird clusters
Author(s) -
Zhu Pengcheng,
Liu Zhaoyu,
Sun Ke,
Wang Lei,
Hu Pengfei,
Zhu Naixuan,
Jiang Daozhuo
Publication year - 2022
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12627
Subject(s) - microgrid , computer science , scheduling (production processes) , model predictive control , mathematical optimization , cluster (spacecraft) , renewable energy , control theory (sociology) , control (management) , engineering , mathematics , artificial intelligence , programming language , electrical engineering
Microgrid clusters (MGC) can improve the consumption of renewable energy and the system reliability. The control of microgrid cluster with large‐scale microgrids is the focus of microgrid cluster research. In order to better realize the coordination control and energy scheduling among microgrids, this paper proposes a distributed optimal strategy for complex MGC based on model predictive control (MPC) and block‐wised alternating direction multiplier method (BADMM). Firstly, an optimization model is established based on a honeycomb active distribution network (HADN); then the network is decomposed into several regions according to BADMM. The global optimization goal of the cluster is achieved by iterating the optimization results of each region in a time domain. Finally, the case study results verify the effectiveness and convergence of the proposed distributed optimization strategy, and provide a reference for energy management of MGC.