A Novel Stochastic Hierarchical Framework for Smart Management of Electric Vehicle Aggregation with Demand Response Mechanism

This research paper introduces a novel stochastic hierarchical framework to enhance the efficiency of automated grids in the context of electric vehicles (EVs) and a demand response program (DRP). The framework leverages the unscented transform (UT) to address uncertainties arising from fluctuating loads. Three distinct charging strategies are introduced to optimize EV management within the grid: coordinated charging, uncoordinated charging, and intelligent charging. Additionally, automated switches are strategically placed to maximize power flow direction, mitigating the strain caused by increased EV-related demand. To address the complexity of this multifaceted challenge, an advanced optimization approach based on grey wolf optimization (GWO) is employed. The effectiveness of this model is rigorously evaluated using an IEEE standard case system, showcasing its exceptional performance in enhancing automated grid management in the presence of EVs and DRP, with potential implications for the broader energy industry.