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A Bilevel Programming Framework for Demand Response Incentive Design with Non-Intrusive Load Monitoring-Based Flexibility Estimation.

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Title: A Bilevel Programming Framework for Demand Response Incentive Design with Non-Intrusive Load Monitoring-Based Flexibility Estimation.
Authors: Ding, Ye1 (AUTHOR), Zhou, Kai1,2 (AUTHOR) jhtechnical@126.com, He, Xiuming3 (AUTHOR), Sun, Yuan1 (AUTHOR) yuansun@suda.edu.cn
Source: Energies (19961073). Jun2026, Vol. 19 Issue 12, p2818. 21p.
Subject Terms: *Bilevel programming, *Energy demand management, *Incentive (Psychology), *Mixed integer linear programming, *Hidden Markov models, *Marginal pricing
Abstract: Demand response (DR) plays a key role in enhancing power system flexibility under increasing renewable penetration, yet most existing approaches rely on aggregate demand models that fail to capture appliance-level heterogeneity. A bilevel programming framework for DR incentive design incorporating non-intrusive load monitoring (NILM)-based flexibility estimation is proposed. A conditional factorial hidden Markov model (CFHMM) is used to disaggregate smart meter data and recover appliance-level consumption patterns, which are then mapped to willingness-to-accept (WTA) values to construct device-informed DR potential functions. These estimates are embedded in a bilevel optimization model, where a retailer determines optimal incentives while accounting for the endogenous impact of demand response on locational marginal prices through market clearing. The model is reformulated as a single-level mixed-integer linear program using Karush–Kuhn–Tucker (KKT) conditions. Case studies using real-world data and the IEEE test system show that the proposed framework produces more effective incentive strategies than aggregate DR modeling, leading to improved DR utilization and higher retailer profitability. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194909267
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Bilevel Programming Framework for Demand Response Incentive Design with Non-Intrusive Load Monitoring-Based Flexibility Estimation.
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Ding%2C+Ye%22">Ding, Ye</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Kai%22">Zhou, Kai</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> jhtechnical@126.com</i><br /><searchLink fieldCode="AR" term="%22He%2C+Xiuming%22">He, Xiuming</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Yuan%22">Sun, Yuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yuansun@suda.edu.cn</i>
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  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 12, p2818. 21p.
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  Data: *<searchLink fieldCode="DE" term="%22Bilevel+programming%22">Bilevel programming</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+demand+management%22">Energy demand management</searchLink><br />*<searchLink fieldCode="DE" term="%22Incentive+%28Psychology%29%22">Incentive (Psychology)</searchLink><br />*<searchLink fieldCode="DE" term="%22Mixed+integer+linear+programming%22">Mixed integer linear programming</searchLink><br />*<searchLink fieldCode="DE" term="%22Hidden+Markov+models%22">Hidden Markov models</searchLink><br />*<searchLink fieldCode="DE" term="%22Marginal+pricing%22">Marginal pricing</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Demand response (DR) plays a key role in enhancing power system flexibility under increasing renewable penetration, yet most existing approaches rely on aggregate demand models that fail to capture appliance-level heterogeneity. A bilevel programming framework for DR incentive design incorporating non-intrusive load monitoring (NILM)-based flexibility estimation is proposed. A conditional factorial hidden Markov model (CFHMM) is used to disaggregate smart meter data and recover appliance-level consumption patterns, which are then mapped to willingness-to-accept (WTA) values to construct device-informed DR potential functions. These estimates are embedded in a bilevel optimization model, where a retailer determines optimal incentives while accounting for the endogenous impact of demand response on locational marginal prices through market clearing. The model is reformulated as a single-level mixed-integer linear program using Karush–Kuhn–Tucker (KKT) conditions. Case studies using real-world data and the IEEE test system show that the proposed framework produces more effective incentive strategies than aggregate DR modeling, leading to improved DR utilization and higher retailer profitability. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/en19122818
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      – Code: eng
        Text: English
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        PageCount: 21
        StartPage: 2818
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      – SubjectFull: Bilevel programming
        Type: general
      – SubjectFull: Energy demand management
        Type: general
      – SubjectFull: Incentive (Psychology)
        Type: general
      – SubjectFull: Mixed integer linear programming
        Type: general
      – SubjectFull: Hidden Markov models
        Type: general
      – SubjectFull: Marginal pricing
        Type: general
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      – TitleFull: A Bilevel Programming Framework for Demand Response Incentive Design with Non-Intrusive Load Monitoring-Based Flexibility Estimation.
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            NameFull: Ding, Ye
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            NameFull: He, Xiuming
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            NameFull: Sun, Yuan
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            – D: 15
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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