Online EVs Vehicle-to-Grid Scheduling Coordinated with Multi-Energy Microgrids: A Deep Reinforcement Learning-Based Approach

Weiqi Pan, Xiaorong Yu, Zishan Guo, Tao Qian () and Yang Li
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Weiqi Pan: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Xiaorong Yu: State Grid Jiangsu Electric Vehicle Service Co., Ltd., Nanjing 320105, China
Zishan Guo: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Tao Qian: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Yang Li: School of Electrical Engineering, Southeast University, Nanjing 210096, China

Energies, 2024, vol. 17, issue 11, 1-20

Abstract: The integration of electric vehicles (EVs) into vehicle-to-grid (V2G) scheduling offers a promising opportunity to enhance the profitability of multi-energy microgrid operators (MMOs). MMOs aim to maximize their total profits by coordinating V2G scheduling and multi-energy flexible loads of end-users while adhering to operational constraints. However, scheduling V2G strategies online poses challenges due to uncertainties such as electricity prices and EV arrival/departure patterns. To address this, we propose an online V2G scheduling framework based on deep reinforcement learning (DRL) to optimize EV battery utilization in microgrids with different energy sources. Firstly, our approach proposes an online scheduling model that integrates the management of V2G and multi-energy flexible demands, modeled as a Markov Decision Process (MDP) with an unknown transition. Secondly, a DRL-based Soft Actor-Critic (SAC) algorithm is utilized to efficiently train neural networks and dynamically schedule EV charging and discharging activities in response to real-time grid conditions and energy demand patterns. Extensive simulations are conducted in case studies to testify to the effectiveness of our proposed approach. The overall results validate the efficacy of the DRL-based online V2G scheduling framework, highlighting its potential to drive profitability and sustainability in multi-energy microgrid operations.

Keywords: electric vehicles; vehicle-to-grid; multi-energy microgrids deep reinforcement learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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