Overarching Sustainable Energy Management of PV Integrated EV Parking Lots in Reconfigurable Microgrids Using Generative Adversarial Networks

Abstract

In recent years, environmental issues have motivated the wide usage of electric vehicles (EVs) due to their zero tailpipe emission. However, this trend can pose severe challenges to power systems, such as decreasing equipment lifetime. Moreover, the CO2 emission of EVs is closer to that of internal combustion engine vehicles in some cases due to the carbon footprint of EV charging. Modeling the uncertain nature of EV users' behavior is another obstacle due to the complex dynamics of these uncertainties. To overcome these problems, an overarching day-ahead smart charging method is proposed in this paper from the perspective of distribution system operators (DSOs), EV users, and governments simultaneously. The aim of the proposed method is to minimize the operating cost of microgrids, the degradation cost of EV batteries, and emission cost by scheduling the active and reactive power of EV parking lots integrated with photovoltaic (PV) systems as well as finding the optimum network configuration. Previous model-based methods cannot appropriately model uncertainties in EV users' behavior because of some statistical assumptions. Nevertheless, this paper employs data-driven methods based on generative adversarial networks (GAN) to represent these uncertainties. The performance of the proposed method is evaluated by implementing it on a real reconfigurable microgrid. The results show that using the proposed method, the DSO and emission costs can reduce by 11.96% and 337% compared to the uncoordinated charging of EVs, respectively. Furthermore, the share of sustainable energy in EV charging increases by 9% using the proposed method.