Optimizing Distribution Grid Performance through Electric Vehicle Integration and Stochastic Modeling in Extreme Weather Conditions

This paper presents an innovative method for operational planning of micro grids, focusing on improving economic performance and enhancing resilience. The proposed approach addresses key uncertainties, including weather conditions, probabilistic charging/discharging behavior of electric vehicles (EVs), and integration of renewable energy sources, energy price fluctuations, and load conditions. Additionally, it considers EV owners' satisfaction and demand-side management. A key innovation of this research is the development of a comprehensive framework for simultaneously managing network topology reconfiguration, EV movement within the network, and mitigating the impacts of adverse weather conditions. Monte Carlo simulation is employed to model uncertainties, while a multi-objective optimization algorithm is used to solve the problem. This algorithm aims to maximize the profits of network operators and the private sector while minimizing unsupplied energy and its associated penalties. The proposed method demonstrates significant improvements, including a 37.1% reduction in unsupplied energy costs, a 5% increase in network operators' profits, and a 23.1% boost in EV charging station profits. Overall, the method outperforms existing approaches by approximately 8%. The proposed method offers an effective and robust solution for improving micro grid resilience and operational efficiency under extreme weather conditions, showcasing its superiority over traditional approaches.