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Achieving an optimal trade-off between revenue and energy peak within a smart grid environment

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  • Afşar, Sezin
  • Brotcorne, Luce
  • Marcotte, Patrice
  • Savard, Gilles

Abstract

In this paper, we consider an energy provider whose goal is to simultaneously set revenue-maximizing prices and meet a peak load constraint. The problem is cast within a bilevel setting where the provider acts as a leader (upper level) that takes into account a smart grid (lower level) that minimizes the sum of users' disutilities. The latter bases its actions on the hourly prices set by the leader, as well as the preference schedules set by the users for each task. We consider both the monopolistic and competitive situations, and validate numerically the potential of this approach to achieve an ‘optimal’ trade-off between three conflicting objectives: revenue, user cost and peak demand.

Suggested Citation

  • Afşar, Sezin & Brotcorne, Luce & Marcotte, Patrice & Savard, Gilles, 2016. "Achieving an optimal trade-off between revenue and energy peak within a smart grid environment," Renewable Energy, Elsevier, vol. 91(C), pages 293-301.
    • Handle: RePEc:eee:renene:v:91:y:2016:i:c:p:293-301
    DOI: 10.1016/j.renene.2016.01.055
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    References listed on IDEAS

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    Cited by:

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    2. Anjos, Miguel F. & Brotcorne, Luce & Gomez-Herrera, Juan A., 2021. "Optimal setting of time-and-level-of-use prices for an electricity supplier," Energy, Elsevier, vol. 225(C).
    3. Aussel, Didier & Brotcorne, Luce & Lepaul, Sébastien & von Niederhäusern, Léonard, 2020. "A trilevel model for best response in energy demand-side management," European Journal of Operational Research, Elsevier, vol. 281(2), pages 299-315.
    4. Lu, Tianguang & Ai, Qian & Wang, Zhaoyu, 2018. "Interactive game vector: A stochastic operation-based pricing mechanism for smart energy systems with coupled-microgrids," Applied Energy, Elsevier, vol. 212(C), pages 1462-1475.
    5. Viana, Matheus Sabino & Manassero, Giovanni & Udaeta, Miguel E.M., 2018. "Analysis of demand response and photovoltaic distributed generation as resources for power utility planning," Applied Energy, Elsevier, vol. 217(C), pages 456-466.
    6. Carlos Henggeler Antunes & Maria João Alves & Billur Ecer, 2020. "Bilevel optimization to deal with demand response in power grids: models, methods and challenges," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 814-842, October.
    7. Kyritsis, A. & Voglitsis, D. & Papanikolaou, N. & Tselepis, S. & Christodoulou, C. & Gonos, I. & Kalogirou, S.A., 2017. "Evolution of PV systems in Greece and review of applicable solutions for higher penetration levels," Renewable Energy, Elsevier, vol. 109(C), pages 487-499.
    8. Cl'emence Alasseur & Ivar Ekeland & Romuald Elie & Nicol'as Hern'andez Santib'a~nez & Dylan Possamai, 2017. "An adverse selection approach to power pricing," Papers 1706.01934, arXiv.org, revised Sep 2019.
    9. Arega Getaneh Abate & Rosana Riccardi & Carlos Ruiz, 2021. "Dynamic tariffs-based demand response in retail electricity market under uncertainty," Papers 2105.03405, arXiv.org, revised Feb 2024.
    10. Liu, Yixin & Guo, Li & Wang, Chengshan, 2018. "A robust operation-based scheduling optimization for smart distribution networks with multi-microgrids," Applied Energy, Elsevier, vol. 228(C), pages 130-140.
    11. Khan, Saad Salman & Ahmad, Sadiq & Naeem, Muhammad, 2023. "On-grid joint energy management and trading in uncertain environment," Applied Energy, Elsevier, vol. 330(PB).

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