Stochastic energy management strategy for microgrid-connected electric vehicle charging infrastructure

Abstract

Ensuring the reliability and stability of standalone microgrids (MGs) is fundamental to the effective integration of renewable energy sources, which are inherently uncertain. This work presents a stochastic optimization model using mixed-integer linear programming (MILP) to determine the optimal operation of electric vehicle charging stations (EVCS) with transactive control, emphasizing the balance between economic efficiency and system reliability. As a result, deploying EVCS will become a vital strategy for integrating renewable energy. An innovative method for supplying electric power from EV fleets involves using transportation networks as additional infrastructure. This article proposes that transportation networks, EVCS, and MGs can be optimally scheduled under uncertain photovoltaic (PV) generation using transactive control. The stochastic optimization problem is formulated as a mixed-integer nonlinear program and implemented in a moving-horizon framework for real-time onboard operation. The framework is tested on the IEEE 30-bus transmission network. The results show the efficiency of the proposed framework as an improvement tool for economic performance and operational stability in renewable-integrated power markets, and it reduces peak loads through the coordinated charging and discharging of vehicles.