Summary
In this manuscript, an efficient hybrid method is proposed aimed at mitigating imbalance, improving the voltage in the higher penetration of electric vehicles (EVs) and distributed generation (DG). The proposed hybrid method is the combination of the mayfly algorithm (MA) and the radial basis function neural network (RBFNN), called the mayfly algorithm‐radial basis function neural network (MA‐RBFNN) method. These major contributions of the proposed work mitigate network unbalancing and voltage profile with the loss of network energy, utilize obtainable network capacity and enlarge the comfort level for EV consumers. The MA is utilized for solving the multi‐objective optimization issue of coordinating EV together with DG in the distribution grid. The RBFNN agrees with the distribution system operators to mutually enhance the phase sequence with the optimum dispatch of DGs for improving the performance of the network. The proposed system allows distribution system operators to mutually enhance the phase sequence with an optimum DG dispatch for recovering the performance of the network and solve the multi‐objective optimization issue of coordinating EV and DG on the distribution network. This proposed technique is implemented on the MATLAB/Simulink platform as well as evaluated for efficiency, likened to the existing methods such as genetic algorithm (GA), particle swarm optimization (PSO) and MA technique. The performance of the proposed method was analysed in terms of quality of solution comparison, integral of the time‐weighted absolute error (ITAE), integral squared error (ISE) and loss reduction (%). These comparative outcomes display the eminence of the proposed method more than any other existing technique.