View in EDS HTML Full Text PDF Full Text

Electric Vehicle Charging and Discharging Scheduling Method Based on Clustering and Deep Reinforcement Learning.

Saved in:
Bibliographic Details
Title: Electric Vehicle Charging and Discharging Scheduling Method Based on Clustering and Deep Reinforcement Learning.
Authors: He, Chunqi1 (AUTHOR) hcq561@mail.shiep.edu.cn, Li, Jiang1 (AUTHOR)
Source: Energies (19961073). May2026, Vol. 19 Issue 9, p2238. 36p.
Subject Terms: *Reinforcement learning, *Clustering algorithms, *Scheduling, *Mixed integer linear programming, *Electric power system management, *Electric vehicle charging stations, *Markov processes
Abstract: With the large-scale integration of electric vehicles (EVs) into the power grid, uncoordinated charging behavior has aggravated load fluctuations in the power system. Deep reinforcement learning can optimize EV charging and discharging strategies through dynamic decision-making, thereby alleviating the operational pressure imposed on the grid by load variations. However, under large-scale EV integration scenarios, challenges still remain, including the excessively high dimensionality of the state space and the resulting decline in training efficiency. In addition, the coupling between existing clustering methods and dynamic scheduling mechanisms is still insufficiently tight. To address these issues, this study proposes a cluster-based deep reinforcement learning method for EV charging and discharging scheduling, referred to as CDRL. First, a probabilistic behavioral model is constructed based on EV charging transaction data to characterize the stochasticity of user charging behavior. A Density–Centroid Hybrid Clustering (DCHC) method is then adopted to cluster the charging behavior characteristics of EVs. Subsequently, at the cluster level, a day-ahead base load forecasting model is introduced, and the forecasting results are fed into a mixed-integer linear programming (MILP) model to generate the charging and discharging power allocation tasks for each cluster. At the individual level, the EV charging and discharging process is formulated as a Markov decision process (MDP), and a deep Q-network (DQN) is employed for policy learning, thereby achieving the decomposition of cluster-level tasks into individual scheduling decisions. The simulation results demonstrate that the proposed method can effectively reduce charging costs and smooth system load fluctuations while improving training convergence speed and policy stability. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: enr
DbLabel: Energy & Power Source
An: 193716134
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Electric Vehicle Charging and Discharging Scheduling Method Based on Clustering and Deep Reinforcement Learning.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22He%2C+Chunqi%22">He, Chunqi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hcq561@mail.shiep.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Jiang%22">Li, Jiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 9, p2238. 36p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br />*<searchLink fieldCode="DE" term="%22Mixed+integer+linear+programming%22">Mixed integer linear programming</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+power+system+management%22">Electric power system management</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+vehicle+charging+stations%22">Electric vehicle charging stations</searchLink><br />*<searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: With the large-scale integration of electric vehicles (EVs) into the power grid, uncoordinated charging behavior has aggravated load fluctuations in the power system. Deep reinforcement learning can optimize EV charging and discharging strategies through dynamic decision-making, thereby alleviating the operational pressure imposed on the grid by load variations. However, under large-scale EV integration scenarios, challenges still remain, including the excessively high dimensionality of the state space and the resulting decline in training efficiency. In addition, the coupling between existing clustering methods and dynamic scheduling mechanisms is still insufficiently tight. To address these issues, this study proposes a cluster-based deep reinforcement learning method for EV charging and discharging scheduling, referred to as CDRL. First, a probabilistic behavioral model is constructed based on EV charging transaction data to characterize the stochasticity of user charging behavior. A Density–Centroid Hybrid Clustering (DCHC) method is then adopted to cluster the charging behavior characteristics of EVs. Subsequently, at the cluster level, a day-ahead base load forecasting model is introduced, and the forecasting results are fed into a mixed-integer linear programming (MILP) model to generate the charging and discharging power allocation tasks for each cluster. At the individual level, the EV charging and discharging process is formulated as a Markov decision process (MDP), and a deep Q-network (DQN) is employed for policy learning, thereby achieving the decomposition of cluster-level tasks into individual scheduling decisions. The simulation results demonstrate that the proposed method can effectively reduce charging costs and smooth system load fluctuations while improving training convergence speed and policy stability. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193716134
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19092238
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 36
        StartPage: 2238
    Subjects:
      – SubjectFull: Reinforcement learning
        Type: general
      – SubjectFull: Clustering algorithms
        Type: general
      – SubjectFull: Scheduling
        Type: general
      – SubjectFull: Mixed integer linear programming
        Type: general
      – SubjectFull: Electric power system management
        Type: general
      – SubjectFull: Electric vehicle charging stations
        Type: general
      – SubjectFull: Markov processes
        Type: general
    Titles:
      – TitleFull: Electric Vehicle Charging and Discharging Scheduling Method Based on Clustering and Deep Reinforcement Learning.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: He, Chunqi
      – PersonEntity:
          Name:
            NameFull: Li, Jiang
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
          Numbering:
            – Type: volume
              Value: 19
            – Type: issue
              Value: 9
          Titles:
            – TitleFull: Energies (19961073)
              Type: main
ResultId 1