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Cited by 132Electric Vehicles and InfrastructureEnergy Load and Power ForecastingTraffic Prediction and Management Techniques

Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach

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Li, Yuanzheng, He, Shangyang, Li, Yang, Ge, Leijiao, Lou, Suhua, Zeng, Zhigang·2023·IEEE Transactions on Intelligent Vehicles
DOI 10.1109/tiv.2022.3168577
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Abstract

The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely deployed with the development of large-scale transportation electrifications. However, since charging behaviors of EVs show large uncertainties, the forecasting of EVCS charging power is non-trivial. This paper tackles this issue by proposing a reinforcement learning assisted deep learning framework for the probabilistic EVCS charging power forecasting to capture its uncertainties. Since the EVCS charging power data are not standard time-series data like electricity load, they are first converted to the time-series format. On this basis, one of the most popular deep learning models, the long short-term memory (LSTM) is used and trained to obtain the point forecast of EVCS charging power. To further capture the forecast uncertainty, a Markov decision process (MDP) is employed to model the change of LSTM cell states, which is solved by our proposed adaptive exploration proximal policy optimization (AePPO) algorithm based on reinforcement learning. Finally, experiments are carried out on the real EVCSs charging data from Caltech, and Jet Propulsion Laboratory, USA, respectively. The results and comparative analysis verify the effectiveness and outperformance of our proposed framework.

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  • Introducing a mandatory requirement for EVCS reactive power control will require the use of an active front end in fast EVCSs, which may lead to an increase in their cost.

    “In addition, the introduction of this requirement will require the use of an active front end in fast EVCSs, which may lead to an increase in their cost [49,50].”

    From Determining Volt/Var Characteristics of Electric Vehicle Charging Station Inverters for Voltage Regulation in Distribution Networks (2024) · cites this paper as [49]

    The Introduction (p. 1) discusses uncertainty in EVCS charging power and the need to forecast it, not requirements for reactive power control. Searches for active front ends, reactive power control, and converter costs found no passage addressing whether such a requirement would increase fast-EVCS costs. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 30

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Cited by

Sorted by citations

Authors
Li, Y., He, S., Li, Y., Ge, L., Lou, S., & Zeng, Z.
Affiliations
  • Northeast Electric Power University (CN)
  • China-EU Institute for Clean and Renewable Energy (CN)
  • Tianjin University (CN)
  • Huazhong University of Science and Technology (CN)
ORCID
Venue
IEEE Transactions on Intelligent Vehicles
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2023-01
Volume
8
Issue
1
Pages
344-357
ISSN
  • 2379-8858 (ISSN-L)
  • 2379-8904
DOI
10.1109/tiv.2022.3168577
OpenAlex
W4224295330
Cited by
132
References
1
Citations by year
2026: 33 · 2025: 30 · 2024: 39 · 2023: 25 · 2022: 5
FWCI
6.78
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Intelligent Vehicles (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China (62073148)
  • Tencent Rhinoceros Foundation of China (CCF-Tencent RAGR20210102)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
  • Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
Keywords
electric vehicle charging stations, probabilistic forecasting, forecast uncertainty, reinforcement learning, deep learning, LSTM, Markov decision process, proximal policy optimization, adaptive exploration, EV charging behavior

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