A Novel on Energy Management Strategy with Maximum Exploitation of Renewables and EV Storage in Distribution Networks

Abstract

Electric vehicles (EVs) are widely recognized for their environmentally friendly attributes and superior performance. They offer considerable potential for managing energy in low-voltage distribution networks through the use of vehicle-to-grid (V2G) and grid-to-vehicle (G2V) technologies. This article provides a detailed investigation into the management of energy in distribution networks using a combination of EVs, solar photovoltaic (PV), and diesel generators (DG). The water filling algorithm (WFA) is utilized to distribute the storage of EVs in each energy zone in an optimal manner, thereby achieving load flattening, minimizing energy costs, and reducing grid reliance. A multiobjective genetic algorithm (MOGA) is employed to solve a formulated multiobjective optimization problem for load flattening and voltage regulation, with optimal power transaction (OPT) serving as the decision variable. An adaptive neuro-fuzzy inference system (ANFIS) based EV ranking technique is employed to prioritize EVs based on their ability to provide the required services and determine optimal energy distribution (OED) for different scenarios. This study investigates the impact of OED in several scenarios and examines the influence of ANFIS prioritization on overall EV power availability and cost of charging (CoC). The findings of this study are crucial for developing effective energy management strategies that minimize energy costs and reduce grid reliance.