Decentralized Model Predictive Control for Offshore Wind-Powered Seaport DC Microgrids With Electric Vehicle Stations

Abstract

The rapid adoption of electric vehicles (EVs) has catalyzed the urgency of a resilient and sustainable charging infrastructure. This study addresses this issue by harnessing offshore wind energy resources to meet the charging demands, particularly in remote coastal regions. The primary challenge tackled in this research involves the complex management of power flow dynamics due to the inherent variability of wind energy and the stochastic nature of EV charging demands, modeled using probabilistic distributions to represent varying arrival times, charging durations, and power requirements of EVs. This work introduces a pioneering framework centered on the development of a wind-powered electric vehicle charging system (EVCS) that utilizes a medium-voltage direct current (MVDC) bus. An enhanced decentralized model predictive control (MPC) strategy was employed that distinguishes itself from conventional control paradigms due to its heightened adaptability and proficient management of the dynamic interactions among wind energy generation, energy storage systems (ESS), EV charging demands, and grid interactions. Rigorous simulations and real-time hardware-in-loop studies underscore the efficacy of the MPC strategy in preserving the voltage stability within the MVDC bus while optimizing the power flow, thereby minimizing energy losses and ensuring grid resilience. These results validate the viability of the proposed wind energy-integrated EVCS as an integral component of seaport grid infrastructure.