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Incentive-Based Demand Response Scheduling of Air-Conditioning Loads in Load-Type Virtual Power Plants: Balancing User Revenue and Satisfaction.

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Title: Incentive-Based Demand Response Scheduling of Air-Conditioning Loads in Load-Type Virtual Power Plants: Balancing User Revenue and Satisfaction.
Authors: Yang, Ting1 (AUTHOR) tyang@njit.edu.cn, Cheng, Qi1 (AUTHOR), Chen, Butian1 (AUTHOR), Lu, Danhong1 (AUTHOR), Wu, Han1 (AUTHOR), Zhu, Yiming1 (AUTHOR), Wu, Dongwei1 (AUTHOR)
Source: Energies (19961073). May2026, Vol. 19 Issue 9, p2028. 28p.
Subject Terms: *Cooling loads (Mechanical engineering), *Incentive (Psychology), *Energy demand management, *Mathematical optimization, *Peak load, *Satisfaction
Abstract: Large-scale and widely distributed air-conditioning (AC) loads can be aggregated into load-type Virtual Power Plants (VPPs) to participate in peak-shaving ancillary services, thereby improving the allocation of demand-side electricity resources. However, current AC aggregation methods primarily focus on meeting peak-shaving instructions and generally employ fixed incentive pricing and proportional capacity allocation, making it difficult to balance user revenue and satisfaction and thereby constraining the flexibility of VPP demand-side regulation. This paper proposes a unified incentive-based demand response scheduling framework for both fixed- and variable-frequency AC loads across industrial, commercial, and residential scenarios. Based on the Equivalent Thermal Parameter model, AC loads are classified into curtailable and shiftable types, with their adjustable boundaries characterized by a Time-of-Use (TOU) elasticity-based interaction willingness model and a fuzzy load transfer rate model, respectively. A three-objective optimization model is established to maximize user revenue while minimizing user dissatisfaction and scheduling error, with incentive pricing and capacity allocation jointly optimized via Non-dominated Sorting Genetic Algorithm III (NSGA-III). Case studies are conducted on a load-type VPP covering three scenarios, namely a large industrial zone, a commercial zone, and a residential zone, under weekday and non-weekday TOU tariffs and three representative 1 h peak-shaving periods. Compared with a fixed-pricing benchmark, the proposed strategy increases total user revenue by 9.4% to 11.4% and reduces weighted average dissatisfaction by 0.27 to 1.92%. The case study results demonstrate that the proposed method can improve the trade-off between user revenue and satisfaction. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 193715924
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: Incentive-Based Demand Response Scheduling of Air-Conditioning Loads in Load-Type Virtual Power Plants: Balancing User Revenue and Satisfaction.
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Yang%2C+Ting%22">Yang, Ting</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tyang@njit.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Cheng%2C+Qi%22">Cheng, Qi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Butian%22">Chen, Butian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Danhong%22">Lu, Danhong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Han%22">Wu, Han</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Yiming%22">Zhu, Yiming</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Dongwei%22">Wu, Dongwei</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 9, p2028. 28p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Cooling+loads+%28Mechanical+engineering%29%22">Cooling loads (Mechanical engineering)</searchLink><br />*<searchLink fieldCode="DE" term="%22Incentive+%28Psychology%29%22">Incentive (Psychology)</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+demand+management%22">Energy demand management</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Peak+load%22">Peak load</searchLink><br />*<searchLink fieldCode="DE" term="%22Satisfaction%22">Satisfaction</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Large-scale and widely distributed air-conditioning (AC) loads can be aggregated into load-type Virtual Power Plants (VPPs) to participate in peak-shaving ancillary services, thereby improving the allocation of demand-side electricity resources. However, current AC aggregation methods primarily focus on meeting peak-shaving instructions and generally employ fixed incentive pricing and proportional capacity allocation, making it difficult to balance user revenue and satisfaction and thereby constraining the flexibility of VPP demand-side regulation. This paper proposes a unified incentive-based demand response scheduling framework for both fixed- and variable-frequency AC loads across industrial, commercial, and residential scenarios. Based on the Equivalent Thermal Parameter model, AC loads are classified into curtailable and shiftable types, with their adjustable boundaries characterized by a Time-of-Use (TOU) elasticity-based interaction willingness model and a fuzzy load transfer rate model, respectively. A three-objective optimization model is established to maximize user revenue while minimizing user dissatisfaction and scheduling error, with incentive pricing and capacity allocation jointly optimized via Non-dominated Sorting Genetic Algorithm III (NSGA-III). Case studies are conducted on a load-type VPP covering three scenarios, namely a large industrial zone, a commercial zone, and a residential zone, under weekday and non-weekday TOU tariffs and three representative 1 h peak-shaving periods. Compared with a fixed-pricing benchmark, the proposed strategy increases total user revenue by 9.4% to 11.4% and reduces weighted average dissatisfaction by 0.27 to 1.92%. The case study results demonstrate that the proposed method can improve the trade-off between user revenue and satisfaction. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19092028
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 28
        StartPage: 2028
    Subjects:
      – SubjectFull: Cooling loads (Mechanical engineering)
        Type: general
      – SubjectFull: Incentive (Psychology)
        Type: general
      – SubjectFull: Energy demand management
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Peak load
        Type: general
      – SubjectFull: Satisfaction
        Type: general
    Titles:
      – TitleFull: Incentive-Based Demand Response Scheduling of Air-Conditioning Loads in Load-Type Virtual Power Plants: Balancing User Revenue and Satisfaction.
        Type: main
  BibRelationships:
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      – PersonEntity:
          Name:
            NameFull: Yang, Ting
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          Name:
            NameFull: Cheng, Qi
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            NameFull: Chen, Butian
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            NameFull: Lu, Danhong
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            NameFull: Wu, Han
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            NameFull: Zhu, Yiming
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            NameFull: Wu, Dongwei
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          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
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            – Type: volume
              Value: 19
            – Type: issue
              Value: 9
          Titles:
            – TitleFull: Energies (19961073)
              Type: main
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