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Computer Science > Machine Learning

arXiv:2108.03236 (cs)
[Submitted on 7 Aug 2021 (v1), last revised 24 Jan 2022 (this version, v2)]

Title:Efficient Representation for Electric Vehicle Charging Station Operations using Reinforcement Learning

Authors:Kyung-bin Kwon, Hao Zhu
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Abstract:Effectively operating electrical vehicle charging station (EVCS) is crucial for enabling the rapid transition of electrified transportation. To solve this problem using reinforcement learning (RL), the dimension of state/action spaces scales with the number of EVs and is thus very large and time-varying. This dimensionality issue affects the efficiency and convergence properties of generic RL algorithms. We develop aggregation schemes that are based on the emergency of EV charging, namely the laxity value. A least-laxity first (LLF) rule is adopted to consider only the total charging power of the EVCS which ensures the feasibility of individual EV schedules. In addition, we propose an equivalent state aggregation that can guarantee to attain the same optimal policy. Based on the proposed representation, policy gradient method is used to find the best parameters for the linear Gaussian policy . Numerical results have validated the performance improvement of the proposed representation approaches in attaining higher rewards and more effective policies as compared to existing approximation based approach.
Subjects: Machine Learning (cs.LG); Optimization and Control (math.OC)
Cite as: arXiv:2108.03236 [cs.LG]
  (or arXiv:2108.03236v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2108.03236

Submission history

From: Kyung-Bin Kwon [view email]
[v1] Sat, 7 Aug 2021 00:34:48 UTC (325 KB)
[v2] Mon, 24 Jan 2022 07:08:28 UTC (312 KB)
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