Multi-objective optimization of power networks integrating electric vehicles and wind energy

•Integration of Diverse Energy Sources: The study focuses on the integration of various energy sources, including electric vehicles (EVs) and renewable energies like wind power, into power networks.•Optimization of Plug-In Electric Vehicles (PEVs): The optimal utilization of PEVs is discussed, emphasizing the balance between vehicle-to-grid (V2 G) connectivity and integration into the broader energy ecosystem.•Multi-Purpose Planning for Wind Resources: The integration of wind resources into power networks necessitates multi-purpose planning to optimize production and reduce costs, highlighting the complexity and need for comprehensive strategies.•Innovative Multi-Purpose Method: The study introduces a multi-purpose method grounded in collective competition, which reduces computational complexity and enhances performance using a Pareto front optimality point.•Application and Testing: The proposed method is tested on two well-established IEEE power networks (30- and 118-bus) in various scenarios with windmills and PEV producers, demonstrating its effectiveness in managing complexity and uncertainty. In the ever-evolving landscape of power networks, the integration of diverse sources, including electric vehicles (EVs) and renewable energies like wind power, has gained prominence. With the rapid proliferation of plug-in electric vehicles (PEVs), their optimal utilization hinges on reconciling conflicting and adaptable targets, including facilitating vehicle-to-grid (V2 G) connectivity or harmonizing with the broader energy ecosystem. Simultaneously, the inexorable integration of wind resources into power networks underscores the critical need for multi-purpose planning to optimize production and reduce costs. This study tackles this multifaceted challenge, incorporating the presence of EVs and a probabilistic wind resource model. Addressing the complexity of the issue, we devise a multi-purpose method grounded in collective competition, effectively reducing computational complexity and creatively enhancing the model's performance with a Pareto front optimality point. To discern the ideal response, fuzzy theory is employed. The suggested pattern is rigorously tested on two well-established IEEE power networks (30- and 118-bus) in diverse scenarios featuring windmills and PEV producers, with outcomes showcasing the remarkable excellence of our multi-purpose framework in addressing this intricate issue while accommodating uncertainty.