Load Profile and Load Flow Analysis for a Grid System with Electric Vehicles Using a Hybrid Optimization Algorithm

Abstract

As they become more widespread, electric vehicles (EVs) will require more electricity to charge. It is expected that a range of grid transportation solutions that complement one another and considerable transmission infrastructure changes will be needed to achieve this goal. Strategic planning and control, including economic models and strategies to engage and reward users, can reduce energy loss on the power network. This would eliminate grid upgrades. Bidirectional charging of EVs can help transmission systems cope with EV allocation. Power loss and voltage instability are the transmission network’s biggest issues. Adding EV units to the transmission network usually solves these problems. Therefore, EVs need the right layout and proportions. This study determined where and how many radial transmission network EVs there should be before and after the adjustment. To discover the best EV position and size before and after the dial network modification, a hybrid genetic algorithm for particle swarm optimization (HGAIPSO) was utilized. Electric vehicles coordinated in an active transmission network reduce power losses, raise voltage profiles, and improve system stability. Electric vehicles are responsible for these benefits. The simulation showed that adding EVs to the testing system reduced power waste. The system’s minimum bus voltage likewise increased. The proposed technology reduced transmission system voltage fluctuations and power losses, according to the comparison analysis. The IEEE-30 bus test system reduced real power loss by 40.70%, 36.24%, and 42.94% for the type A, type B, and type C EV allocations, respectively. The IEEE-30 bus voltage reached 1.01 pu.