MSAPSO-based Optimal Placement of EV Charging Stations and Capacitors in IEEE-34 Distribution System

Abstract

The quick growth in the use of electric vehicles (EVs) has increased the need for public charging facilities as well as the operation complexities associated with electricity distribution systems. Poorly located public charging stations (PCSs) will add up feeder loading, active power loss, voltage fluctuation, and cost of investment. In this paper, a new two level process referred to as Public Charging Station Owner Index-Multi-Stage Adaptive Particle Swarm Optimization (PCSOI-MSAPSO) is developed for the optimal placement of public charging stations for electric vehicles and shunt capacitors. In the first step of the new model, the newly introduced Public Charging Station Owner Index (PCSOI) which considers both Land Value Index (LVI) and Electric Vehicle Population Index (EVPI) to come up with economically viable locations will reduce the optimization problem scope. The second step involves the new MSAPSO approach that finds out the best amongst the various charging stations and capacitors simultaneously to minimize active power loss under distribution system operating conditions. Unlike PSO, The proposed algorithm makes use of adaptive inertia weight, elite learning, adaptive exploration and exploitation, mutation and diversity maintenance to enhance convergence while preventing premature convergence. The proposed methodology is tested on the updated IEEE 34 bus radial distribution network with the use of Backward Forward Sweep (BFS) power flow calculation algorithm. The results of the simulations founded that that the proposed algorithm lowers the active power losses from 40.145 kW to 37.575 kW (approximately 6.40%), lowers the reactive power losses, enhances the minimum bus voltage and ensures stable convergence by achieving a best fitness of 0.43148. The proposed PCSOI-MSAPSO framework is a reliable and useful decision-making tool for EV charging infrastructure design and can be extended to larger smart distribution grids with renewable energy sources and V2G applications.