ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Smart Grids
Volume 13 - 2025 | doi: 10.3389/fenrg.2025.1593938
Multi-Objective Coordinated Control and Optimization for Photovoltaic Microgrid Scheduling
Provisionally accepted
- 1Power Dispatching and Control Center of Guangdong Power Grid, Guangzhou, China
- 2NARI Technology Co., Ltd., Nanjing, China
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This paper proposes a multi-objective coordinated control and optimization system for photovoltaic (PV) microgrids. To address the challenges of slow convergence and local optima in traditional PV microgrid scheduling methods, this study introduced an improved multiple objective particle swarm optimization (IMOPSO) algorithm that integrates an adaptive inertia weight adjustment strategy based on optimal similarity and a multi-directional iterative Pareto solution archive update mechanism. A triobjective optimization model is formulated to minimize operational costs, environmental pollution, and grid output fluctuation variance, with decision-making supported by the TOPSIS method. The proposed algorithm is validated through a practical case study of a PV microgrid located in Suzhou, China, and the results demonstrate that IMOPSO achieves a 2.5% reduction in total operational costs under time-of-use pricing (from 49.77 USD to 48.51 USD) and a 1.0% reduction under fixed pricing (from 52.94 USD to 52.39 USD), alongside a maximum safety variance reduction of 45% (from 22.16 to 12.15). The Pareto front distribution exhibits enhanced diversity and uniformity compared to the original MOPSO. While single-objective optimization yields lower costs in isolated scenarios (e.g., 28.50 USD for economic cost minimization), it significantly compromises environmental performance (20.44 USD) and grid stability (14.05 variance). In contrast, IMOPSO ensures coordinated control and effectively balances economic efficiency, environmental sustainability, and operational safety, reducing fossil fuel dependency by 32% during peak hours through coordinated battery-grid interactions. This study provides a robust framework for multiobjective coordinated control and microgrid scheduling, advancing sustainable energy transition.
Keywords: Coordinated control, Optimal scheduling, Distributed energy sources, Photovoltaic microgrid, Improved PSO algorithm, Multiple objective functions
Received: 14 Mar 2025; Accepted: 28 Apr 2025.
Copyright: © 2025 Yu, Hou, Lin, Cai, Shan and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Kai Hou, NARI Technology Co., Ltd., Nanjing, China
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