Demand-side and Utility-side Management Techniques for Increasing EV Charging Load

Electricity authorities need capacity assessment and expansion plans for efficiently charging the growing Electric vehicle (EV) fleet. Specifically, distribution grid needs significant as it faces most impact to accommodate high variant residential EV load. Utility companies employ different scheduling policies maintenance of their transformers (hereinafter, XFR). However, they lack scenario-based cope with varying penetration across locations time. The contributions this paper are twofold. First, we propose a customer feedback-based simultaneously minimize peak load XFR satisfy needs. Second, present deep reinforcement learning (DRL) method maintenance, which focuses on XFR’s effective age loading periodically choose best candidate replacement. Our case study feeder shows adaptability success our in reducing demand extend life. Furthermore, DRL-based replacement policy outperforms existing rule-based policies. Together, two approaches provide complete planning tool efficient increasing