AUTHOR=Yu Da , Hou Kai , Lin Xu , Cai Guoyang , Shan Xin , Wang Weihua 

TITLE=Multi-objective coordinated control and optimization for photovoltaic microgrid scheduling

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2025.1593938

DOI=10.3389/fenrg.2025.1593938

ISSN=2296-598X

ABSTRACT=The stability and economic dispatch efficiency of photovoltaic (PV) microgrids is influenced by various internal and external factors, and they require a well-designed optimization plan to enhance their operation and management. This paper proposes a multi-objective coordinated control and optimization system for 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 tri-objective optimization model is formulated to minimize operational costs, environmental pollution, and grid output fluctuation variance, with decision-making supported by the Entropy Weight 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 4.4% reduction in total operational costs under time-of-use pricing (from 50.73 USD to 48.49 USD) and a 4.6% reduction under fixed pricing (from 54.93 USD to 52.38 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. This study provides a robust framework for multi-objective coordinated control and microgrid scheduling, advancing sustainable energy transition.