Multilayer Iterative Stochastic Dynamic Programming for Optimal Energy Management of Residential Loads with Electric Vehicles

Abstract

This work introduces a multilayer iterative stochastic dynamic programming (MISDP) framework for optimizing energy management in smart residential settings, incorporating electric vehicles to reduce energy costs while enhancing operational efficiency. The study investigates the complexities of managing residential loads with integrated EV batteries, set against the backdrop of unpredictable charging demands and fluctuating energy prices. The proposed method is designed to optimize charging and discharging schedules, ensuring cost‐effective energy consumption without compromising the longevity of EV’s battery operations. The proposed MISDP strategy encompasses multi‐iteration processes, both at internal and external levels, that not only highlight the method’s capacity for precise, real‐time decision‐making but also underscore its adaptability to the dynamic nature of energy systems. The external iteration primarily focuses on adapting to broader operational variables, such as fluctuating prices and demand patterns, setting a framework for optimization. Concurrently, the internal iteration updates the details of EV battery operation, fine‐tuning charging and discharging strategies to refine the control law sequence for each operational period, ensuring optimal energy management. Throughout the iteration process, the framework ensures the performance index function remains bounded, adhering strictly to the evolving control law sequence. Through comparative analysis, the MISDP framework is evaluated against different optimization techniques, demonstrating its superior capability in achieving significant energy cost savings and operational effectiveness while ensuring convergence under stochastic conditions.