Analysis of Load Flow with a Hybrid Optimization Algorithm for Connecting Electric Vehicles to the Grid

EVs will require more electricity to charge as their popularity rises. This necessitates the installation of several complementary grid-transportation technologies and significant adjustments to the distribution infrastructure. If power loss on the power network is decreased by strategic planning and control, which includes economic models and techniques to engage and reward users, then grid upgrades are no longer necessary. Voltage instability and power outages are the transmission network's biggest issues. As a result, great thought should be given to the design and dimensions of EV charging stations (CS). In order to reduce power losses resulting from the addition of a new load to the system, this research offers an optimization method for the placement and sizing of CS in the power network using the hybrid genetic algorithm particle swarm optimization (HGAIPSO) artificial intelligence (AI) algorithm. In an active transmission network, power losses are reduced, voltage profiles are enhanced, and system stability is maintained when CS are coordinated. Additionally, the system now requires a higher minimum bus voltage. The comparison's findings demonstrated how well the suggested strategy reduces power losses and voltage variations in the transmission system.