Mitigating unbalance and improving voltage considering higher penetration of EVs and DG using hybrid optimization technique

Abstract

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.

A Systematic Review on Recent Advancement in Electric Vehicle Technologies

“…Furthermore, the depletion of conventional energy resources and the associated environmental impacts have led to the rapid transformation of the transportation sector to adopt electric vehicles [5]. Electric vehicles are emerging as a positive strategy for addressing growing environmental concerns and energy shortages, and this trend is likely to continue in the future [6]. The automotive industry is facing significant challenges due to the depletion of fossil resources and rising crude oil costs [7].…”

Phase Balance Optimization of Customers and Electric Vehicles in a Real Low Voltage Electricity Distribution Network

“…An efficient hybrid method is proposed in the work [4], which aims at mitigating voltage unbalance and 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.…”

“…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. The contributions of the proposed work is that the network unbalancing and voltage profile are successfully mitigated, with the loss of network energy, utilizing obtainable network capacity and better comfort level for EV consumers [4].…”