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Limiting gaming opportunities on incentive-based demand response programs

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  • Vuelvas, José
  • Ruiz, Fredy
  • Gruosso, Giambattista

Abstract

Incentive-based demand response is a program where participant users are paid to reduce energy consumption from an established baseline. Counter-factual models to estimate the baselines are vulnerable for gaming. In this paper, a novel demand response contract between a user and an aggregator is developed to induce individual rationality (voluntary participation) and asymptotic incentive-compatibility (truthfulness) through the probability of call, which is the chance of a consumer to be selected by the aggregator to serve as demand response resource at a given period. In this approach, a consumer self-reports his baseline and reduction capacity, given a payment scheme that includes cost of electricity, incentive price, and penalty caused by any deviation between self-reported and actual energy consumption. Another important feature of this approach, different from the classic solutions, is that a participant agent does not require reporting marginal utility (energy preference), and only announces information in terms of energy. A two-stage stochastic programming problem is proposed from the demand side to understand the consumer rational decisions under this contract. As result, the aggregator decides randomly what users are called to perform the energy reduction in order to manage the truth-telling behavior of each agent through the probability of call. Mathematical proofs and numerical studies are provided to demonstrate the properties and advantages of this contract in limiting gaming opportunities and in terms of its implementation.

Suggested Citation

  • Vuelvas, José & Ruiz, Fredy & Gruosso, Giambattista, 2018. "Limiting gaming opportunities on incentive-based demand response programs," Applied Energy, Elsevier, vol. 225(C), pages 668-681.
    • Handle: RePEc:eee:appene:v:225:y:2018:i:c:p:668-681
    DOI: 10.1016/j.apenergy.2018.05.050
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    References listed on IDEAS

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    2. Diaz-Londono, Cesar & Enescu, Diana & Ruiz, Fredy & Mazza, Andrea, 2020. "Experimental modeling and aggregation strategy for thermoelectric refrigeration units as flexible loads," Applied Energy, Elsevier, vol. 272(C).
    3. Xu, Fangyuan & Wu, Wanli & Zhao, Fei & Zhou, Ya & Wang, Yongjian & Wu, Runji & Zhang, Tao & Wen, Yongchen & Fan, Yiliang & Jiang, Shengli, 2019. "A micro-market module design for university demand-side management using self-crossover genetic algorithms," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    4. Fontecha, John E. & Nikolaev, Alexander & Walteros, Jose L. & Zhu, Zhenduo, 2022. "Scientists wanted? A literature review on incentive programs that promote pro-environmental consumer behavior: Energy, waste, and water," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    5. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
    6. Abedrabboh, Khaled & Al-Fagih, Luluwah, 2023. "Applications of mechanism design in market-based demand-side management: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    7. Khaled Abedrabboh & Luluwah Al-Fagih, 2021. "Applications of Mechanism Design in Market-Based Demand-Side Management," Papers 2106.14659, arXiv.org.
    8. Ziras, Charalampos & Heinrich, Carsten & Bindner, Henrik W., 2021. "Why baselines are not suited for local flexibility markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    9. Pedro Nel Ovalle & José Vuelvas & Arturo Fajardo & Carlos Adrián Correa-Flórez & Fredy Ruiz, 2021. "Optimal Portfolio Selection Methodology for a Demand Response Aggregator," Energies, MDPI, vol. 14(23), pages 1-24, November.
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    11. Vicente Gutiérrez González & Lissette Álvarez Colmenares & Jesús Fernando López Fidalgo & Germán Ramos Ruiz & Carlos Fernández Bandera, 2019. "Uncertainy’s Indices Assessment for Calibrated Energy Models," Energies, MDPI, vol. 12(11), pages 1-18, May.
    12. Konstantakopoulos, Ioannis C. & Barkan, Andrew R. & He, Shiying & Veeravalli, Tanya & Liu, Huihan & Spanos, Costas, 2019. "A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure," Applied Energy, Elsevier, vol. 237(C), pages 810-821.
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