Optimization Strategy for Coordinated Operation of Electric Vehicles and Power Grid Driven by MPC and DP Fusion

Abstract

The potential of electric vehicles (EVs) as an important flexible resource in the electricity market is increasingly prominent, but existing dispatch strategies often overlook the coordination mechanism between distribution network physical constraints and multilevel market signals. To address this issue, this paper aims to develop a real-time intelligent charging optimization strategy for EV-grid collaboration to balance the profits of charging operators, the preferences of vehicle owners, and grid safety. This paper integrates model predictive control (MPC) and dynamic programming (DP) to construct an MPC local control + DP global planning framework for multiperiod charging and discharging optimization, and designs four integrated scheduling strategies based on the EV2Gym platform. The main novel contributions of this study are twofold: first, a three-level price linkage model is designed to clarify the interaction rules between the regional day-ahead market and the local real-time market; second, grid factors such as transformer power limits and photovoltaic accommodation constraints are quantitatively integrated into the optimization framework. Experiments demonstrate that compared with existing technologies, the proposed strategy increases profits by 8%, decreases the risk of transformer overload by 15%, and improves the PV accommodation rate by 20%, while successfully controlling battery degradation within an acceptable range. This study provides an effective and practical solution for the implementation of large-scale intelligent charging.