Model-free reinforcement learning-based energy management for plug-in electric vehicles in a cooperative multi-agent home microgrid with consideration of travel behavior

Abstract

The rise in popularity of plug-in electric vehicles (PEVs) and the increasing use of renewable energy sources (RESs) have paved the way for advanced energy management systems (EMSs) that optimize energy usage and distribution in home microgrids (H-MGs). This study focuses on the integration of PEVs in H-MGs using an EMS and analyzes its impact on electrical power grids (EPGs). We examine the effect of PEV charging and discharging patterns on H-MG energy flow, considering various scenarios such as high renewable energy generation, different levels of PEV penetration, and EPG connection status. Our findings indicate that an efficient EMS can significantly enhance H-MGs’ overall efficiency by intelligently scheduling PEV charging and discharging, maximizing the use of locally generated renewable energy, and reducing peak load on the EPG. We demonstrate that a cooperative multi-agent system EMS, driven by a robust continuous and real-time fuzzy Q-learning (FQL) method, can reduce electricity market prices by 15% by increasing the use of renewable energy generation by 25%. To fully realize the benefits of EMS, we address challenges such as reducing dependence on EPG by 30%, improving battery state by 12%, and ensuring EPG stability in the face of uncertainties.