Optimal Planning Approaches under Uncertain and Seasonal Variations across an Active Distribution Grid encapsulating Large-scale Electrical Vehicle Fleets and Renewable Generation /

Abstract

With the emergence of the smart grid, the distribution network is facing various problems, such as power limitations, voltage uncertainty, and many others. Apart from the power sector, with the growth of electric vehicles (EVs), power demand is rising day by day. These problems can potentially lead to blackouts. This paper presents three meta-heuristic techniques: grey wolf optimization (GWO), whale optimization algorithm (WOA), and dandelion optimizer (DO) for optimal allocation (sitting and sizing) of solar photovoltaic (SPV), wind turbine generation (WTG), and electric vehicle charging stations (EVCSs). The aim of implementing these techniques is to optimum allocation of renewable energy distributed generation (RE-DG) for reducing active power losses, reactive power losses, total voltage deviation and to improve the voltage stability index in radial distribution networks (RDNs). MATLAB 2022 is used for the simulation of meta-heuristic techniques. The proposed techniques are implemented on IEEE 33-bus RDN for optimal allocation of RE-DGs and EVCSs while considering seasonal variations and uncertainty modeling. The results validate the efficiency of meta-heuristic techniques with a substantial reduction of active power loss, reactive power loss, and improvement of voltage profile with optimal allocation across all considered scenarios. The corelative analysis reveals that the proposed techniques with existing literature validate the better execution of proposed techniques.