Scholarlink

<p>This manuscript proposes an innovative approach to optimize the operation of a community-based microgrid integrating Electric Vehicles (EVs), Energy Storage Systems (ESSs), and Photovoltaic (PV) installations. The proposed technique is the Ladder Spherical Evolution Search (LSE) technique. It harnesses the stored energy in EVs for residential and grid power needs, strategically managing bidirectional power flow by adjusting charge and discharge rates based on factors like EV availability, day-ahead electricity prices, and transformer load. A predictive method using hourly vehicle usage data forecasts EV availability, aiming to maximize economic benefits while minimizing battery degradation. This manuscript thoroughly addresses charge, discharge regulation, and battery health for EVs and ESS, with the LSE optimizer defining optimal solutions for maximum economic gain. This research offers a holistic strategy to enhance the performance of community microgrids including EVs, ESS, and PV, optimizing economic benefits, and promoting grid independence. The proposed control technique is run in MATLAB, and its performance is contracted to other existing techniques. The proposed method attains a 9.9% reduction in 50% incentives and a 33.3% reduction in operating costs with 100% incentives compared to 0% incentives. The proposed method has the maximum efficiency compared to all other existing methods, like Cuckoo Search Algorithm (CSA), Latent Semantic Analysis (LSA), and Salp Swarm Algorithm (SSA). Additionally, the findings underscore the importance of incentive-based pricing strategies in reducing grid dependence, offering valuable insights for policymakers in designing regulations that encourage prosumer participation and sustainable energy management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimized energy regulation of EVs for effective operation in incentive-based prosumer microgrids

  • 期刊论文

  • 发表日期：

    2025年6月4日

查看全文
  • S. Arulkumar,
  • G. Arunsankar,
  • Kannan Palanisamy

摘要

This manuscript proposes an innovative approach to optimize the operation of a community-based microgrid integrating Electric Vehicles (EVs), Energy Storage Systems (ESSs), and Photovoltaic (PV) installations. The proposed technique is the Ladder Spherical Evolution Search (LSE) technique. It harnesses the stored energy in EVs for residential and grid power needs, strategically managing bidirectional power flow by adjusting charge and discharge rates based on factors like EV availability, day-ahead electricity prices, and transformer load. A predictive method using hourly vehicle usage data forecasts EV availability, aiming to maximize economic benefits while minimizing battery degradation. This manuscript thoroughly addresses charge, discharge regulation, and battery health for EVs and ESS, with the LSE optimizer defining optimal solutions for maximum economic gain. This research offers a holistic strategy to enhance the performance of community microgrids including EVs, ESS, and PV, optimizing economic benefits, and promoting grid independence. The proposed control technique is run in MATLAB, and its performance is contracted to other existing techniques. The proposed method attains a 9.9% reduction in 50% incentives and a 33.3% reduction in operating costs with 100% incentives compared to 0% incentives. The proposed method has the maximum efficiency compared to all other existing methods, like Cuckoo Search Algorithm (CSA), Latent Semantic Analysis (LSA), and Salp Swarm Algorithm (SSA). Additionally, the findings underscore the importance of incentive-based pricing strategies in reducing grid dependence, offering valuable insights for policymakers in designing regulations that encourage prosumer participation and sustainable energy management.