Paul et al. introduced a quantum particle swarm optimization (QPSO) framework for optimizing grid-connected microgrids by minimizing both operational costs and carbon emissions
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The model is solved using a multi-objective Particle Swarm Optimization (MOPSO) algorithm, which is well-suited for its fast convergence and ability to efficiently identify the Pareto
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In this paper, particle swarm optimization and genetic algorithms are applied for the optimization of a network of microgrids based on renewable energy and dies
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The Particle Swarm Optimization (PSO) algorithm stands out in the realm of optimization techniques due to its remarkable computational efficiency, swift execution pace, and reduced
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In this review, the capabilities of swarm intelligence-based algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC), and fish swarm
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This study introduces a quantum particle swarm optimization (QPSO)-based framework to address the dual challenges of operational cost minimization and emission reduction in grid-connected microgrids.
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To address the aforementioned issues, this paper proposes a Multi-Objective Particle Swarm Optimization with Multi-Strategy (IMOPSO) for solving microgrid optimization dispatch models
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To offer an optimal solution for managing microgrids with hybrid renewable energy sources (HRESs) while taking microgrid reserve margins into account, the particle swarm
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As a crucial component of smart grid optimization, the optimal dispatch of microgrids is of great significance for reducing energy consumption and environmental pollution.
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