Multi-population Cooperative Evolutionary Algorithm for Electric Vehicles and Renewable Energy Systems

To address the issues of significant renewable energy output fluctuations and high electric vehicles (EVs) user benefits in the coordinated scheduling of EVs and renewable energy systems, this paper proposes an innovative multi-objective coordinated scheduling algorithm aimed at simultaneously reducing the impact of renewable energy output fluctuations on the grid and accelerating the adoption of EVs. To achieve these objectives, a multi-objective scheduling model has been developed that integrates EVs, wind power, and photovoltaic systems. This model includes key constraints such as battery storage capacity, charging and discharging power limits, and charging/discharging cycle constraints. To solve this model, this paper proposes a constraint multi-objective evolutionary optimization algorithm based on multi-population collaboration(MCEA), which incorporates the ϵ method for handling constraints within the algorithm. Experimental results demonstrate that the improved algorithm shows significant advantages in mitigating renewable energy output fluctuations and enhancing EV user benefits.