A Novel Weighted Ebola Vector Optimization Based Charging Scheduling of Electric Vehicle Integrated with PV Charging Stations

Abstract

Electric vehicle (EV) charging scheduling integrated with photovoltaic (PV)-based charging stations is an important aspect of smart energy management. This paper presents an optimal EV charging scheduling framework considering solar-powered charging infrastructure and bidirectional vehicle-to-grid (V2G) and grid-to-vehicle (G2V) power transfer. A hybrid weighted ebola vector algorithm is proposed to improve charging coordination and minimize operational cost. In addition, photovoltaic power generation is forecasted using an artificial neural network (ANN) for accurate 24-hour solar energy prediction. The objective function considers peak shaving, valley filling, power loss, charging coordination, and charging cost minimization under varying load conditions. The proposed method is evaluated using a 24-hour load duration curve with forecasted PV power and power loss is compared with the Ebola Optimization Search Algorithm and Weighted Mean Vector Optimization (WMVO) technique under Normal, High EV plug-in, Low Solar power availability, and Peak Hour scenarios. Simulation results demonstrate that the proposed algorithm achieves superior performance in convergence speed, scheduling accuracy, and power loss reduction. The proposed method reduces charging cost by 28%, whereas the Ebola and WMVO methods achieve reductions of 17.7% and 8.8%, respectively. The results confirm the effectiveness of the proposed optimization approach for smart EV charging management with renewable energy integration.