Distributed optimization strategy for networked microgrids based on network partitioning

Jingjing Wang, Liangzhong Yao, Jun Liang, Jun Wang and Fan Cheng

Applied Energy, 2025, vol. 378, issue PB, No S0306261924022177

Abstract: The integration of a large number of distributed resources into an active distribution network presents significant challenges, including high control dimensionality, strong output uncertainty, and low utilization of renewable energy. This paper introduces a distributed optimization strategy for networked microgrids based on network partitioning to alleviate the computational burden, reduce operating costs, and enhance the utilization of renewable energy. The active distribution network is partitioned into networked microgrids, and a two-layer distributed optimization model is developed for their management. The first layer focuses on intra-day distributed optimal dispatch, balancing power and load by managing various flexible resources and the exchange power between virtual microgrids. The second layer, real-time distributed power tracking optimization, coordinates flexible resources within virtual microgrids to mitigate photovoltaic power fluctuations and track intra-day dispatch instructions. Simulation results demonstrate that the proposed network partitioning method reduces dispatch costs by 5.3 % and increases the utilization of distributed PV by 3 %, compared to the NP method that only considering modularity. Moreover, calculation times for intra-day dispatch and real-time power tracking are reduced by approximately 26 % and 50 %, respectively, compared to centralized control.

Keywords: High penetration photovoltaic integration; Network partitioning; Networked microgrids; Distributed optimization (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2024.124834

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