Mean‐Guided Elite Selection Genetic Algorithm for Multi‐Objective Optimization of Operational Costs and Voltage Control in Grid‐Connected Microgrids
Journal
IET Renewable Power Generation
Date Issued
2025-12-29
Author(s)
DOI
10.1049/rpg2.70178
Abstract
<jats:title>ABSTRACT</jats:title>
<jats:p>This paper presents a bi‐objective optimisation approach for grid‐connected microgrids, aiming to minimise operational costs and voltage deviation at the connection nodes of distributed energy resources and loads. Existing research typically addresses these objectives separately, and the simultaneous consideration of economic performance and voltage deviation in grid‐connected community microgrids with multiple generation resources remains in an early stage of development. To advance the research in this area, a novel mean‐guided elite selection genetic algorithm (MGES‐GA) is proposed to enhance the balance between convergence and diversity in multi‐objective optimisation. The proposed algorithm enhances the selection process by re‐evaluating low‐performing individuals through gene mixing with elite solutions, thereby preserving diversity and avoiding premature convergence. Comparative analysis of the MGES‐GA with the enhanced genetic algorithm, differential evolution with heuristic, and improved differential evolutionary optimisation algorithms demonstrates its superior performance in optimising the economic dispatch of a grid‐connected microgrid. In a bi‐objective comparison with state‐of‐the‐art algorithms, tested on a modified IEEE European low‐voltage test feeder and IEEE 33‐bus network, MGES‐GA demonstrates its effectiveness in balancing conflicting objectives by producing lower voltage deviations at comparable or lower costs.</jats:p>
<jats:p>This paper presents a bi‐objective optimisation approach for grid‐connected microgrids, aiming to minimise operational costs and voltage deviation at the connection nodes of distributed energy resources and loads. Existing research typically addresses these objectives separately, and the simultaneous consideration of economic performance and voltage deviation in grid‐connected community microgrids with multiple generation resources remains in an early stage of development. To advance the research in this area, a novel mean‐guided elite selection genetic algorithm (MGES‐GA) is proposed to enhance the balance between convergence and diversity in multi‐objective optimisation. The proposed algorithm enhances the selection process by re‐evaluating low‐performing individuals through gene mixing with elite solutions, thereby preserving diversity and avoiding premature convergence. Comparative analysis of the MGES‐GA with the enhanced genetic algorithm, differential evolution with heuristic, and improved differential evolutionary optimisation algorithms demonstrates its superior performance in optimising the economic dispatch of a grid‐connected microgrid. In a bi‐objective comparison with state‐of‐the‐art algorithms, tested on a modified IEEE European low‐voltage test feeder and IEEE 33‐bus network, MGES‐GA demonstrates its effectiveness in balancing conflicting objectives by producing lower voltage deviations at comparable or lower costs.</jats:p>
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