IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v257y2023ics0925527322003413.html
   My bibliography  Save this article

Designing electricity tariffs in the retail market: A stochastic bi-level approach

Author

Listed:
  • Beraldi, Patrizia
  • Khodaparasti, Sara

Abstract

This paper presents a stochastic bi-level approach for tariff design in the electricity market where the leader is represented by a retailer and the follower by a residential prosumager, i.e. a consumer equipped with an energy system consisting of photovoltaic panels and a battery storage device. Both players solve an optimization problem subject to uncertainty in market prices, weather-related variables and electricity demand. To account for the retailer’s attitude towards risk, the upper level problem includes a safety measure to maximize. The model allows to determine a dynamic pricing scheme with time-variant rates delivering the average profit that can be gained in a given percentage of unfavorable realizations of the uncertain parameters and the optimal load pattern that minimizes the expected prosumager’s electricity bill.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:proeco:v:257:y:2023:i:c:s0925527322003413
    DOI: 10.1016/j.ijpe.2022.108759
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527322003413
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijpe.2022.108759?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Di Somma, M. & Graditi, G. & Heydarian-Forushani, E. & Shafie-khah, M. & Siano, P., 2018. "Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects," Renewable Energy, Elsevier, vol. 116(PA), pages 272-287.
    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. Michael Bussieck & Alexander Meeraus, 2007. "Algebraic modeling for IP and MIP (GAMS)," Annals of Operations Research, Springer, vol. 149(1), pages 49-56, February.
    4. 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).
    5. 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.
    6. Benoît Colson & Patrice Marcotte & Gilles Savard, 2007. "An overview of bilevel optimization," Annals of Operations Research, Springer, vol. 153(1), pages 235-256, September.
    7. M. Hosein Zare & Juan S. Borrero & Bo Zeng & Oleg A. Prokopyev, 2019. "A note on linearized reformulations for a class of bilevel linear integer problems," Annals of Operations Research, Springer, vol. 272(1), pages 99-117, January.
    8. 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.
    9. Thomas Kleinert & Martine Labbé & Fr¨ank Plein & Martin Schmidt, 2020. "Technical Note—There’s No Free Lunch: On the Hardness of Choosing a Correct Big-M in Bilevel Optimization," Operations Research, INFORMS, vol. 68(6), pages 1716-1721, November.
    10. Dadashi, Mojtaba & Haghifam, Sara & Zare, Kazem & Haghifam, Mahmoud-Reza & Abapour, Mehdi, 2020. "Short-term scheduling of electricity retailers in the presence of Demand Response Aggregators: A two-stage stochastic Bi-Level programming approach," Energy, Elsevier, vol. 205(C).
    11. Escudero, Laureano F. & Monge, Juan F. & Rodríguez-Chía, Antonio M., 2020. "On pricing-based equilibrium for network expansion planning. A multi-period bilevel approach under uncertainty," European Journal of Operational Research, Elsevier, vol. 287(1), pages 262-279.
    12. Aven, Terje, 2016. "Risk assessment and risk management: Review of recent advances on their foundation," European Journal of Operational Research, Elsevier, vol. 253(1), pages 1-13.
    13. Ferrara, Massimiliano & Violi, Antonio & Beraldi, Patrizia & Carrozzino, Gianluca & Ciano, Tiziana, 2021. "An integrated decision approach for energy procurement and tariff definition for prosumers aggregations," Energy Economics, Elsevier, vol. 97(C).
    14. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    15. Goutam Dutta & Krishnendranath Mitra, 2017. "A literature review on dynamic pricing of electricity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1131-1145, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chargui, Kaoutar & Zouadi, Tarik & Sreedharan, V. Raja & El Fallahi, Abdellah & Reghioui, Mohamed, 2023. "A novel robust exact decomposition algorithm for berth and quay crane allocation and scheduling problem considering uncertainty and energy efficiency," Omega, Elsevier, vol. 118(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Martin Weibelzahl & Alexandra Märtz, 2020. "Optimal storage and transmission investments in a bilevel electricity market model," Annals of Operations Research, Springer, vol. 287(2), pages 911-940, April.
    4. James Ming Chen, 2018. "On Exactitude in Financial Regulation: Value-at-Risk, Expected Shortfall, and Expectiles," Risks, MDPI, vol. 6(2), pages 1-28, June.
    5. Juan S. Borrero & Oleg A. Prokopyev & Denis Sauré, 2019. "Sequential Interdiction with Incomplete Information and Learning," Operations Research, INFORMS, vol. 67(1), pages 72-89, January.
    6. Hélène Le Cadre & Bernardo Pagnoncelli & Tito Homem-De-Mello & Olivier Beaude, 2018. "Designing Coalition-Based Fair and Stable Pricing Mechanisms Under Private Information on Consumers' Reservation Prices," Working Papers hal-01353763, HAL.
    7. Böttger, T. & Grimm, V. & Kleinert, T. & Schmidt, M., 2022. "The cost of decoupling trade and transport in the European entry-exit gas market with linear physics modeling," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1095-1111.
    8. Bucarey, Víctor & Labbé, Martine & Morales, Juan M. & Pineda, Salvador, 2021. "An exact dynamic programming approach to segmented isotonic regression," Omega, Elsevier, vol. 105(C).
    9. Prékopa, András & Lee, Jinwook, 2018. "Risk tomography," European Journal of Operational Research, Elsevier, vol. 265(1), pages 149-168.
    10. Le Cadre, Hélène & Pagnoncelli, Bernardo & Homem-de-Mello, Tito & Beaude, Olivier, 2019. "Designing coalition-based fair and stable pricing mechanisms under private information on consumers’ reservation prices," European Journal of Operational Research, Elsevier, vol. 272(1), pages 270-291.
    11. 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.
    12. Hélène Le Cadre & Bernardo Pagnoncelli & Tito Homem-De-Mello & Olivier Beaude, 2018. "Designing Coalition-Based Fair and Stable Pricing Mechanisms Under Private Information on Consumers' Reservation Prices," Post-Print hal-01353763, HAL.
    13. Yuan, Guanxiu & Gao, Yan & Ye, Bei, 2021. "Optimal dispatching strategy and real-time pricing for multi-regional integrated energy systems based on demand response," Renewable Energy, Elsevier, vol. 179(C), pages 1424-1446.
    14. Yao, Leyi & Liu, Zeyuan & Chang, Weiguang & Yang, Qiang, 2023. "Multi-level model predictive control based multi-objective optimal energy management of integrated energy systems considering uncertainty," Renewable Energy, Elsevier, vol. 212(C), pages 523-537.
    15. Tamás Kis & András Kovács & Csaba Mészáros, 2021. "On Optimistic and Pessimistic Bilevel Optimization Models for Demand Response Management," Energies, MDPI, vol. 14(8), pages 1-22, April.
    16. 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).
    17. M. Hosein Zare & Oleg A. Prokopyev & Denis Sauré, 2020. "On Bilevel Optimization with Inexact Follower," Decision Analysis, INFORMS, vol. 17(1), pages 74-95, March.
    18. Qi Zhang & Shaohua Zhang & Xian Wang & Xue Li & Lei Wu, 2020. "Conditional-Robust-Profit-Based Optimization Model for Electricity Retailers with Shiftable Demand," Energies, MDPI, vol. 13(6), pages 1-19, March.
    19. Shen, Ziqi & Wei, Wei & Wu, Lei & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Economic dispatch of power systems with LMP-dependent demands: A non-iterative MILP model," Energy, Elsevier, vol. 233(C).
    20. 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).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:257:y:2023:i:c:s0925527322003413. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.