This article presents an efficient algorithm based on particle swarm optimization (PSO) for energy and operation management (EOM) of a microgrid including different
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This study proposes a novel multi-objective optimization framework for grid-connected microgrids using quantum particle swarm optimization (QPSO) to address the dual challenges of
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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 order to tackle these difficulties, a framework for dual-objective optimization was developed with the aim of improving both economic efficiency and environmental sustainability in
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In the article, you will find the examples on how to use swarm algorithms to choose the storage characteristics and photovoltaic generator for them to work as the elements of the microgrid.
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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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In view of this situation, a particle swarm optimization method for microgrid dispatch is presented.
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To minimize the environmental and total operating costs of the micro-grid intelligent scheduling system during grid connection, this study proposes a micro-grid intelligent scheduling
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Abstract The control over microgrid energy transaction committing demand flexibility has been designed in this article by a new reward based generalized stochastic Petri nets (RGSPNs)
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By merging particle swarm optimisation (PSO) and the gravitational search algorithm (GSA), a hybrid modified GSA-PSO (MGSAPSO) method is proposed to optimise the load dispatch
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