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

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-user...

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Published inEnergies (Basel) Vol. 17; no. 11; p. 2491
Main Authors Pan, Weiqi, Yu, Xiaorong, Guo, Zishan, Qian, Tao, Li, Yang
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2024
Subjects
Algorithms
Alternative energy
Alternative energy sources
Analysis
Batteries
Case studies
Costs
Decision making
Deep learning
Demand side management
Electric vehicles
Electricity
Energy consumption
Energy industry
Energy management
Energy resources
Energy storage
Gas turbines
Markov processes
multi-energy microgrids deep reinforcement learning
Neural networks
Optimization techniques
Power
Profitability
Profits
Scheduling
vehicle-to-grid
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en17112491