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This work introduces a methodology for integrating renewable energy sources (RESs) and electric vehicle charging stations (EVCSs) into radial distribution networks, leveraging a novel Hybrid Whale Optimization Algorithm (HWOA). The efficacy of this algorithm is thoroughly examined through the analysis of IEEE 33 and IEEE 69 bus radial distribution systems (RDSs), which serve as test cases to evaluate its performance and suitability for real-world applications. The HWOA is specifically tailored to tackle the intricate optimization tasks associated with RESs and EVCSs integration, with the primary objectives of minimizing power losses, improving voltage stability, and guaranteeing adequate coverage of EVCSs across the distribution network. In pursuit of these objectives, the algorithm synergistically integrates the Genetic Whale Optimization Algorithm (GWOA) and Thermal Exchange Optimization (TEO) techniques, harnessing their capabilities to optimize the placement and sizing of RESs and EVCSs components. Extensive analysis validates the proposed algorithm’s impressive efficacy in achieving its intended outcomes. Through strategic placement of RESs at optimal sites and determination of appropriate capacities, the HWOA efficiently mitigates power losses while concurrently enhancing the voltage profile of the RDSs. The algorithm identifies optimal positions for EVCS units, guaranteeing comprehensive coverage to address the growing demand for electric vehicle charging stations. Results obtained through applying the HWOA underscore its capacity to substantially improve the overall performance and dependability of RDSs. By incorporating multiple optimization objectives and leveraging sophisticated optimization techniques, the proposed algorithm presents a promising solution for seamlessly integrating RESs and EVCSs, thereby facilitating the evolution of more sustainable and resilient energy systems. This research represents a significant contribution to the advancement of distribution system optimization, laying the foundation for the practical implementation of renewable energy and electric vehicle technologies within modern power grids.

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Optimizing Renewable Energy Integration and Electric Vehicle Charging Stations in Distribution Networks Using Hybrid Whale Optimization Algorithm

  • 章节

  • 发表日期：

    2025年

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  • Varun Krishna Paravasthu,
  • Balasubbareddy Mallala,
  • B. Mangu,
  • Surender Reddy Salkuti

摘要

This work introduces a methodology for integrating renewable energy sources (RESs) and electric vehicle charging stations (EVCSs) into radial distribution networks, leveraging a novel Hybrid Whale Optimization Algorithm (HWOA). The efficacy of this algorithm is thoroughly examined through the analysis of IEEE 33 and IEEE 69 bus radial distribution systems (RDSs), which serve as test cases to evaluate its performance and suitability for real-world applications. The HWOA is specifically tailored to tackle the intricate optimization tasks associated with RESs and EVCSs integration, with the primary objectives of minimizing power losses, improving voltage stability, and guaranteeing adequate coverage of EVCSs across the distribution network. In pursuit of these objectives, the algorithm synergistically integrates the Genetic Whale Optimization Algorithm (GWOA) and Thermal Exchange Optimization (TEO) techniques, harnessing their capabilities to optimize the placement and sizing of RESs and EVCSs components. Extensive analysis validates the proposed algorithm’s impressive efficacy in achieving its intended outcomes. Through strategic placement of RESs at optimal sites and determination of appropriate capacities, the HWOA efficiently mitigates power losses while concurrently enhancing the voltage profile of the RDSs. The algorithm identifies optimal positions for EVCS units, guaranteeing comprehensive coverage to address the growing demand for electric vehicle charging stations. Results obtained through applying the HWOA underscore its capacity to substantially improve the overall performance and dependability of RDSs. By incorporating multiple optimization objectives and leveraging sophisticated optimization techniques, the proposed algorithm presents a promising solution for seamlessly integrating RESs and EVCSs, thereby facilitating the evolution of more sustainable and resilient energy systems. This research represents a significant contribution to the advancement of distribution system optimization, laying the foundation for the practical implementation of renewable energy and electric vehicle technologies within modern power grids.