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<p>This paper presents a rapid and dispatchable energy storage strategy that integrates electric vehicles (EVs) with energy storage systems (ESS) into smart grids to reduce load, minimize costs, and optimize energy management during periods of varying demand. The mobility and dynamic usage patterns of EVs present challenges in managing energy flow within smart grids. To address these challenges and enhance EV participation in smart grid operations, this paper introduces an improved particle swarm optimization (PSO) algorithm. The algorithm incorporates an adjustable weight factor, enhancing its ability to efficiently search for optimal solutions. Simulations confirm that this strategy significantly improves the coordination of EVs, leveraging their energy storage potential to boost the overall efficiency of smart grid operations. Additionally, the improved PSO algorithm reduces operational costs by up to 22% compared to conventional methods, demonstrating its practicality and cost-effectiveness.</p>

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A Rapidly Dispatchable Energy Strategy Utilizing Electric Vehicles with Energy Storage Systems (EV-ESS) to Enhance Grid Redundancy

  • 期刊论文

  • 发表日期：

    2025年1月14日

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  • Qasim Ajao,
  • Lanre Sadeeq

摘要

This paper presents a rapid and dispatchable energy storage strategy that integrates electric vehicles (EVs) with energy storage systems (ESS) into smart grids to reduce load, minimize costs, and optimize energy management during periods of varying demand. The mobility and dynamic usage patterns of EVs present challenges in managing energy flow within smart grids. To address these challenges and enhance EV participation in smart grid operations, this paper introduces an improved particle swarm optimization (PSO) algorithm. The algorithm incorporates an adjustable weight factor, enhancing its ability to efficiently search for optimal solutions. Simulations confirm that this strategy significantly improves the coordination of EVs, leveraging their energy storage potential to boost the overall efficiency of smart grid operations. Additionally, the improved PSO algorithm reduces operational costs by up to 22% compared to conventional methods, demonstrating its practicality and cost-effectiveness.