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Siting and Sizing of Electric Vehicle Charging Stations Considering Users' Responses to Time-of-Use Pricing.

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Title: Siting and Sizing of Electric Vehicle Charging Stations Considering Users' Responses to Time-of-Use Pricing.
Authors: Huang, Hui1 (AUTHOR), Zhao, Zetian1 (AUTHOR) m15369940818@163.com, Liu, Dunnan1 (AUTHOR), Cai, Wantong1 (AUTHOR), Li, Zongxing1 (AUTHOR)
Source: Energies (19961073). Oct2026, Vol. 19 Issue 19, p4503. 35p.
Subject Terms: *Electric vehicle charging stations, *Time-based pricing, *Supply & demand, *Energy demand management, *Human behavior models
Abstract: Electric vehicle (EV) users may actively adjust their charging times in response to time-of-use (TOU) prices, reshaping the spatiotemporal distribution of charging demand and consequently affecting EVCS siting and sizing. To capture this effect, this paper proposes an EV charging station planning method that explicitly accounts for users' responses to TOU pricing. First, charging demand and adjustable charging windows are identified based on vehicle travel characteristics and state-of-charge (SOC) evolution. Within each fixed parking event, users may adjust the charging start time, while the underlying trip chain and event-level charging energy requirement remain unchanged. A charging time choice model is then developed by jointly considering charging time preferences, delay inconvenience, SOC urgency, TOU prices, and aggregate charging load conditions, thereby generating the spatiotemporal charging demand under TOU-responsive charging. The resulting demand is further incorporated into the EVCS siting and sizing model, which determines station locations and capacities while accounting for infrastructure investment, user charging service, and distribution network operational constraints. Case studies are conducted on a coupled system consisting of a 29-node transportation network and the IEEE 33-bus distribution system. An ablation analysis further distinguishes the respective effects of the TOU price signal and load-state feedback. The TOU price signal alone shifts charging away from high-price periods but may create a rebound concentration when users respond synchronously, whereas load-state feedback suppresses such aggregate charging concentrations. Under the combined TOU-responsive charging mechanism with load-state feedback, the peak charging load decreases by 20.4%, total installed charging capacity by 10.3%, total annualized cost by 6.9%, and required distribution network expansion capacity by 81.8%. These findings demonstrate that the planning benefits arise from the combined mechanism, which both restructures charging demand and reduces charging simultaneity, thereby altering the appropriate scale and spatial configuration of charging infrastructure. Incorporating such coordinated TOU-responsive charging behavior into demand forecasting and EVCS planning can therefore improve planning economics and better align infrastructure deployment with actual charging behavior. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 197944480
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Siting and Sizing of Electric Vehicle Charging Stations Considering Users' Responses to Time-of-Use Pricing.
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Huang%2C+Hui%22">Huang, Hui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Zetian%22">Zhao, Zetian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> m15369940818@163.com</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Dunnan%22">Liu, Dunnan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cai%2C+Wantong%22">Cai, Wantong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Zongxing%22">Li, Zongxing</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Oct2026, Vol. 19 Issue 19, p4503. 35p.
– Name: Subject
  Label: Subject Terms
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  Data: *<searchLink fieldCode="DE" term="%22Electric+vehicle+charging+stations%22">Electric vehicle charging stations</searchLink><br />*<searchLink fieldCode="DE" term="%22Time-based+pricing%22">Time-based pricing</searchLink><br />*<searchLink fieldCode="DE" term="%22Supply+%26+demand%22">Supply & demand</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+demand+management%22">Energy demand management</searchLink><br />*<searchLink fieldCode="DE" term="%22Human+behavior+models%22">Human behavior models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Electric vehicle (EV) users may actively adjust their charging times in response to time-of-use (TOU) prices, reshaping the spatiotemporal distribution of charging demand and consequently affecting EVCS siting and sizing. To capture this effect, this paper proposes an EV charging station planning method that explicitly accounts for users' responses to TOU pricing. First, charging demand and adjustable charging windows are identified based on vehicle travel characteristics and state-of-charge (SOC) evolution. Within each fixed parking event, users may adjust the charging start time, while the underlying trip chain and event-level charging energy requirement remain unchanged. A charging time choice model is then developed by jointly considering charging time preferences, delay inconvenience, SOC urgency, TOU prices, and aggregate charging load conditions, thereby generating the spatiotemporal charging demand under TOU-responsive charging. The resulting demand is further incorporated into the EVCS siting and sizing model, which determines station locations and capacities while accounting for infrastructure investment, user charging service, and distribution network operational constraints. Case studies are conducted on a coupled system consisting of a 29-node transportation network and the IEEE 33-bus distribution system. An ablation analysis further distinguishes the respective effects of the TOU price signal and load-state feedback. The TOU price signal alone shifts charging away from high-price periods but may create a rebound concentration when users respond synchronously, whereas load-state feedback suppresses such aggregate charging concentrations. Under the combined TOU-responsive charging mechanism with load-state feedback, the peak charging load decreases by 20.4%, total installed charging capacity by 10.3%, total annualized cost by 6.9%, and required distribution network expansion capacity by 81.8%. These findings demonstrate that the planning benefits arise from the combined mechanism, which both restructures charging demand and reduces charging simultaneity, thereby altering the appropriate scale and spatial configuration of charging infrastructure. Incorporating such coordinated TOU-responsive charging behavior into demand forecasting and EVCS planning can therefore improve planning economics and better align infrastructure deployment with actual charging behavior. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19194503
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
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        PageCount: 35
        StartPage: 4503
    Subjects:
      – SubjectFull: Electric vehicle charging stations
        Type: general
      – SubjectFull: Time-based pricing
        Type: general
      – SubjectFull: Supply & demand
        Type: general
      – SubjectFull: Energy demand management
        Type: general
      – SubjectFull: Human behavior models
        Type: general
    Titles:
      – TitleFull: Siting and Sizing of Electric Vehicle Charging Stations Considering Users' Responses to Time-of-Use Pricing.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Huang, Hui
      – PersonEntity:
          Name:
            NameFull: Zhao, Zetian
      – PersonEntity:
          Name:
            NameFull: Liu, Dunnan
      – PersonEntity:
          Name:
            NameFull: Cai, Wantong
      – PersonEntity:
          Name:
            NameFull: Li, Zongxing
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          Dates:
            – D: 01
              M: 10
              Text: Oct2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
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              Value: 19
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              Value: 19
          Titles:
            – TitleFull: Energies (19961073)
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