Artificial intelligence-based optimal EVCS integration with stochastically sized and distributed PVs in an RDNS segmented in zones

• Published in: Journal of Electrical Systems and Information Technology Vol. 11; no. 1; pp. 1 - 22
• Main Authors: Rene, Ebunle Akupan, Fokui, Willy Stephen Tounsi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-12-2024; Springer Nature B.V; SpringerOpen
• Subjects: Alternative energy sources; Artificial Intelligence; Charging station; Deviation; Electric potential; Electric power systems; Electric vehicle; Electric vehicle charging; Electric vehicle charging stations; Electric vehicles; Electrical Engineering; Electrical Machines and Networks; Electricity distribution; Emissions; Energy consumption; Energy management; Energy resources; Engineering; Environmental impact; Environmental legislation; Genetic algorithm; Genetic algorithms; Information Systems and Communication Service; Infrastructure; Mechanical Engineering; Optimization techniques; Particle swarm optimization; Photovoltaic; Photovoltaic cells; Placement; Power Electronics; Radial distribution; Swarm intelligence; Systems stability; Voltage; Zones; Photovoltaic; Genetic algorithm; Charging station; Power losses; Optimization techniques; Electric vehicle; Voltage stability margin; Artificial intelligence; Particle swarm optimization; Zones
• ISSN: 2314-7172; 2314-7172
• DOI: 10.1186/s43067-023-00126-w

The growing interest in electric vehicles (EVs) for transportation has led to increased production and government support through legislation since they offer environmental benefits such as reduced air pollution and carbon emissions compared to conventional combustion engine vehicles. This shift toward EV technology aligns with the goal of preserving the natural environment. To fully utilize EVs, effective management of the power grid is crucial, particularly in radial distribution network systems (RDNS) as they pose stress and deviation of power system parameters from their normal. This study proposes a novel strategy for maximizing EV utilization through EV charging stations (EVCSs) in an RDNS by considering factors such as load voltage deviation, line losses, and the presence of distributed solar photovoltaic systems at load centers. The research begins by segmenting the RDNS into zones, followed by the application of an artificial intelligence-based hybrid genetic algorithm (GA) and particle swarm optimization (PSO) approach known as hybrid GA–PSO. This approach identifies optimal locations for EVCSs integrated with photovoltaics within the network. Subsequently, the employment of individual GA and PSO algorithms to optimize EVCS placement focuses on minimizing power loss and enhancing voltage. The effectiveness of the hybrid GA–PSO algorithm is compared to that of separate GA and PSO methods. Extensive simulations using the IEEE 33-node test feeders validate the proposed techniques, demonstrating the usefulness of the hybrid GA–PSO algorithm in identifying optimal EVCS placement within each zone. The results also highlight the advantages and novelty of hybrid GA–PSO in achieving optimal EVCS placement with stochastically sized and distributed photovoltaic in an RDNS.