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Designing time-of-use tariffs in electricity retail markets using a bi-level model – Estimating bounds when the lower level problem cannot be exactly solved

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  • Soares, Inês
  • Alves, Maria João
  • Antunes, Carlos Henggeler

Abstract

Time-of-use tariffs are a pricing strategy for a product or service in which the supplier establishes time-differentiated prices. Dynamic (e.g., day-ahead) time-differentiated electricity prices can contribute to increase the retailer's profit, allow end-users to reduce the consumption costs and enhance grid efficiency.

Suggested Citation

  • Soares, Inês & Alves, Maria João & Antunes, Carlos Henggeler, 2020. "Designing time-of-use tariffs in electricity retail markets using a bi-level model – Estimating bounds when the lower level problem cannot be exactly solved," Omega, Elsevier, vol. 93(C).
    • Handle: RePEc:eee:jomega:v:93:y:2020:i:c:s0305048318306819
    DOI: 10.1016/j.omega.2019.01.005
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    References listed on IDEAS

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    1. Lunday, Brian J. & Robbins, Matthew J., 2019. "Collaboratively-developed vaccine pricing and stable profit sharing mechanisms," Omega, Elsevier, vol. 84(C), pages 102-113.
    2. Martine Labbé & Alessia Violin, 2016. "Bilevel programming and price setting problems," Annals of Operations Research, Springer, vol. 240(1), pages 141-169, May.
    3. Benoît Colson & Patrice Marcotte & Gilles Savard, 2007. "An overview of bilevel optimization," Annals of Operations Research, Springer, vol. 153(1), pages 235-256, September.
    4. Soares, Ana & Gomes, Álvaro & Antunes, Carlos Henggeler, 2014. "Categorization of residential electricity consumption as a basis for the assessment of the impacts of demand response actions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 490-503.
    5. Zugno, Marco & Morales, Juan Miguel & Pinson, Pierre & Madsen, Henrik, 2013. "A bilevel model for electricity retailers' participation in a demand response market environment," Energy Economics, Elsevier, vol. 36(C), pages 182-197.
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    Cited by:

    1. Qiu, Rui & Xu, Jiuping & Zeng, Ziqiang & Chen, Xin & Wang, Yinhai, 2022. "Carbon tax policy-induced air travel carbon emission reduction and biofuel usage in China," Journal of Air Transport Management, Elsevier, vol. 103(C).
    2. Soares, Inês & Alves, Maria João & Henggeler Antunes, Carlos, 2021. "A deterministic bounding procedure for the global optimization of a bi-level mixed-integer problem," European Journal of Operational Research, Elsevier, vol. 291(1), pages 52-66.
    3. 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.
    4. Bucarey, Víctor & Labbé, Martine & Morales, Juan M. & Pineda, Salvador, 2021. "An exact dynamic programming approach to segmented isotonic regression," Omega, Elsevier, vol. 105(C).
    5. 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).
    6. Beraldi, Patrizia & Khodaparasti, Sara, 2023. "Designing electricity tariffs in the retail market: A stochastic bi-level approach," International Journal of Production Economics, Elsevier, vol. 257(C).
    7. Fokkema, Jan Eise & uit het Broek, Michiel A.J. & Schrotenboer, Albert H. & Land, Martin J. & Van Foreest, Nicky D., 2022. "Seasonal hydrogen storage decisions under constrained electricity distribution capacity," Renewable Energy, Elsevier, vol. 195(C), pages 76-91.
    8. Karaca, Orcun & Delikaraoglou, Stefanos & Hug, Gabriela & Kamgarpour, Maryam, 2022. "Enabling inter-area reserve exchange through stable benefit allocation mechanisms," Omega, Elsevier, vol. 113(C).
    9. Oggioni, Giorgia & Schwartz, Alexandra & Wiertz, Ann-Kathrin & Zöttl, Gregor, 2024. "Dynamic pricing and strategic retailers in the energy sector: A multi-leader-follower approach," European Journal of Operational Research, Elsevier, vol. 312(1), pages 255-272.
    10. Cai, Qiran & Xu, Qingyang & Qing, Jing & Shi, Gang & Liang, Qiao-Mei, 2022. "Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities," Energy, Elsevier, vol. 261(PB).
    11. 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.
    12. Henggeler Antunes, Carlos & Alves, Maria João & Soares, Inês, 2022. "A comprehensive and modular set of appliance operation MILP models for demand response optimization," Applied Energy, Elsevier, vol. 320(C).
    13. Juan Sebastian Roncancio & José Vuelvas & Diego Patino & Carlos Adrián Correa-Flórez, 2022. "Flower Greenhouse Energy Management to Offer Local Flexibility Markets," Energies, MDPI, vol. 15(13), pages 1-20, June.

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