GRID INTEGRATED ELECTRIC CAR CHARGING OPTIMIZATION USING TOPSIS AND GREY WOLF OPTIMIZATION

Abstract

Electric cars are becoming popular these days and the adoption is on the rise. It is crucial to figure out a smart way to schedule when they can charge and discharge. This scheduling should consider the technical limitations of power grids while meeting the economic and environmental goals. For improving the management of power usage of electric cars, a new approach has been proposed in this paper. The proposed approach includes a charging plan that incorporates a vehicle-to-grid (V2G) method with an objective to reduce the variation in power usage and to cut down the cost of charging for electric cars in the residential areas. TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) has been used to address the complex scheduling problem and Grey Wolf Optimization (GWO) has been applied for optimizing the schedules. This paper compares the suggested strategy with both single and multi-objective scheduling, focusing on factors like energy losses, peak load on transformers, and the load on power lines. To test the effectiveness of this approach, the authors have applied it to a 38-node distribution feeder in an experiment. The results show that the solutions obtained using TOPSIS are very helpful for smoothing out peaks in power demand and reducing costs. In simpler terms, this approach would help make electric car charging more efficient and economical while also benefiting the power grid. Integrating EV charging stations with the power grid presents challenges like managing changing demand, balancing the load, and keeping energy costs low. To solve these problems, this paper introduces an innovative approach that combines the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with Grey Wolf Optimization (GWO). Our method uses the strengths of both techniques to find the best charging strategies based on several factors, such as cost, grid stability, and user convenience. The GWO algorithm imitates the hunting strategy of grey wolves to search for optimal solutions, while TOPSIS ranks these solutions by their closeness to the ideal outcome. This combination provides a more effective and flexible way to manage complex charging scenarios than traditional methods. By improving the efficiency of the charging process and minimizing its impact on the grid, this approach supports a smarter, greener future where EVs can be charged more intelligently and affordably.