Energy Allocation for Vehicle-to-Grid Settings: A Low-Cost Proposal Combining DRL and VNE
Document Type
Article
Publication Title
IEEE Transactions on Sustainable Computing
Abstract
As electric vehicle (EV) ownership becomes more commonplace, partly due to government incentives, there is a need also to design solutions such as energy allocation strategies to more effectively support sustainable vehicle-to-grid (V2G) applications. Therefore, this work proposes an energy allocation strategy, designed to minimize the electricity cost while improving the operating revenue. Specifically, V2G is abstracted as a three-domain network architecture to facilitate flexible, intelligent, and scalable energy allocation decision-making. Furthermore, this work combines virtual network embedding (VNE) and deep reinforcement learning (DRL) algorithms, where a DRL-based agent model is proposed, to adaptively perceives environmental features and extracts the feature matrix as input. In particular, the agent consists of a four-layer architecture for node and link embedding, and jointly optimizes the decision-making through a reward mechanism and gradient back-propagation. Finally, the effectiveness of the proposed strategy is demonstrated through simulation case studies. Specifically, compared to the used benchmarks, it improves the VNR acceptance ratio, Long-term average revenue, and Long-term average revenue-cost ratio indicators by an average of 3.17%, 191.36, and 2.04%, respectively. To the best of our knowledge, this is one of the first attempts combining VNE and DRL to provide the energy allocation strategy for V2G.
First Page
1
Last Page
13
DOI
10.1109/TSUSC.2023.3307551
Publication Date
8-22-2023
Keywords
Computer science, Costs, Decision making, Deep Reinforcement Learning, Energy Allocation, Intelligent Transportation Systems, Optimization, Petroleum, Resource management, Vehicle-to-Grid, Vehicle-to-grid, Virtual Network Embedding
Recommended Citation
P. Zhang et al., "Energy Allocation for Vehicle-to-Grid Settings: A Low-Cost Proposal Combining DRL and VNE," in IEEE Transactions on Sustainable Computing, Aug 2023. doi: 10.1109/TSUSC.2023.3307551.
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