Electric Vehicles to Grid Technology-Load Flow Analysis Using Hybrid Optimization Algorithm

For the successful integration of a number of electric vehicles (EVs) into the electric power grid, it is important to conduct comprehensive analysis and observation of the economic implications, operational benefits, and control advantages in ideal conditions. The observed trend indicates a progressive increase in the proportion of travel undertaken utilizing electric vehicles. Based on recent trends, it is anticipated that this particular mode of transportation will rapidly replace vehicles that are propelled by internal combustion engines. The objective of this research is to optimize power efficiency by minimizing power losses within the power system. This will be achieved by regulating the voltage levels within each bus to adhere to specific permissible limits. Additionally, the paper aims to ensure the system's radial architecture can support the anticipated grid modification caused by the integration of electric vehicles. In this investigation, a hybrid approach is used by combining Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA), resulting in a novel artificial intelligence methodology. The analysis was performed using Matlab and a standard IEEE 33-bus transmission network to validate the proposed solution and assess its efficiency. The results indicate the feasibility of the proposed approach and illustrate its effectiveness in reducing power loss during individual operations, while also reducing processing time. Upon comparative analysis of the findings, it becomes evident that the hybrid genetic particle swarm optimisation (HGAPSO) algorithm yields a superior solution in comparison to the GA and PSO algorithms.