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Optimal setting of time-and-level-of-use prices for an electricity supplier

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  • Anjos, Miguel F.
  • Brotcorne, Luce
  • Gomez-Herrera, Juan A.

Abstract

This paper presents a novel price setting optimization problem for an electricity supplier in the smart grid. In this framework the supplier provides electricity to a residential load aggregator using Time-and-Level-of-Use prices (TLOU). TLOU is an energy pricing structure recently introduced in the literature, where the prices vary depending on the time and the level of consumption. This problem is formulated as a bilevel optimization problem, in which the supplier sets the prices that maximize the profit in a demand response context, anticipating the reaction of a residential load aggregator that minimizes total cost. These decisions are made in a competitive environment, while explicitly considering the aggregator’s load shifting preferences and the level of consumption, and ensuring a user-friendly price structure. The optimization problem is reformulated as a single-level problem to be solved using off-the-shelf solvers. We present computational experiments to validate the performance of TLOU, and provide insights on the relationship between the user’s demand flexibility, the capacity profile and the resulting structure of prices. We show that the supplier’s economical benefit is increased up to 10% through the implementation of this type of demand response program, while providing savings of up to 6% for the consumers.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:225:y:2021:i:c:s0360544221007660
    DOI: 10.1016/j.energy.2021.120517
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    Cited by:

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    2. 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).
    3. Jiang, Qian & Mu, Yunfei & Jia, Hongjie & Cao, Yan & Wang, Zibo & Wei, Wei & Hou, Kai & Yu, Xiaodan, 2022. "A Stackelberg Game-based planning approach for integrated community energy system considering multiple participants," Energy, Elsevier, vol. 258(C).
    4. Dewangan, Chaman Lal & Vijayan, Vineeth & Shukla, Devesh & Chakrabarti, S. & Singh, S.N. & Sharma, Ankush & Hossain, Md. Alamgir, 2023. "An improved decentralized scheme for incentive-based demand response from residential customers," Energy, Elsevier, vol. 284(C).
    5. Ivan Eryganov & Radovan Šomplák & Dušan Hrabec & Josef Jadrný, 2023. "Bilevel programming methods in waste-to-energy plants' price-setting game," Operational Research, Springer, vol. 23(2), pages 1-37, June.
    6. Klaus Rheinberger & Peter Kepplinger & Markus Preißinger, 2021. "Flexibility Control in Autonomous Demand Response by Optimal Power Tracking," Energies, MDPI, vol. 14(12), pages 1-14, June.
    7. Emad M. Ahmed & Rajarajeswari Rathinam & Suchitra Dayalan & George S. Fernandez & Ziad M. Ali & Shady H. E. Abdel Aleem & Ahmed I. Omar, 2021. "A Comprehensive Analysis of Demand Response Pricing Strategies in a Smart Grid Environment Using Particle Swarm Optimization and the Strawberry Optimization Algorithm," Mathematics, MDPI, vol. 9(18), pages 1-24, September.

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