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Dynamic Pricing for Demand Response Considering Market Price Uncertainty

Author

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  • Mohammad Ali Fotouhi Ghazvini

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development-Polytechnic of Porto (IPP), R. Dr. António Bernardino de Almeida 431, 4200-072 Porto, Portugal)

  • João Soares

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development-Polytechnic of Porto (IPP), R. Dr. António Bernardino de Almeida 431, 4200-072 Porto, Portugal)

  • Hugo Morais

    (Automation and Control Group, Department of Electrical Engineering, Technical University of Denmark (DTU), Elektrovej, Building 326, DK-2800 Kgs. Lyngby, Denmark)

  • Rui Castro

    (Instituto de Engenharia de Sistemas e Computadores—Investigação e Desenvolvimento/Instituto Superior Técnico (INESC-ID/IST), University of Lisbon, 1049-001 Lisbon, Portugal)

  • Zita Vale

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development-Polytechnic of Porto (IPP), R. Dr. António Bernardino de Almeida 431, 4200-072 Porto, Portugal)

Abstract

Retail energy providers (REPs) can employ different strategies such as offering demand response (DR) programs, participating in bilateral contracts, and employing self-generation distributed generation (DG) units to avoid financial losses in the volatile electricity markets. In this paper, the problem of setting dynamic retail sales price by a REP is addressed with a robust optimization technique. In the proposed model, the REP offers price-based DR programs while it faces uncertainties in the wholesale market price. The main contribution of this paper is using a robust optimization approach for setting the short-term dynamic retail rates for an asset-light REP. With this approach, the REP can decide how to participate in forward contracts and call options. They can also determine the optimal operation of the self-generation DG units. Several case studies have been carried out for a REP with 10,679 residential consumers. The deterministic approach and its robust counterpart are used to solve the problem. The results show that, with a slight decrease in the expected payoff, the REP can effectively protect itself against price variations. Offering time-variable retail rates also can increase the expected profit of the REPs.

Suggested Citation

  • Mohammad Ali Fotouhi Ghazvini & João Soares & Hugo Morais & Rui Castro & Zita Vale, 2017. "Dynamic Pricing for Demand Response Considering Market Price Uncertainty," Energies, MDPI, vol. 10(9), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1245-:d:109486
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    References listed on IDEAS

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    Cited by:

    1. Kamalanathan Ganesan & João Tomé Saraiva & Ricardo J. Bessa, 2019. "On the Use of Causality Inference in Designing Tariffs to Implement More Effective Behavioral Demand Response Programs," Energies, MDPI, vol. 12(14), pages 1-20, July.
    2. Savelli, Iacopo & Morstyn, Thomas, 2021. "Electricity prices and tariffs to keep everyone happy: A framework for fixed and nodal prices coexistence in distribution grids with optimal tariffs for investment cost recovery," Omega, Elsevier, vol. 103(C).
    3. 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).
    4. Tahir, Muhammad Faizan & Chen, Haoyong & Khan, Asad & Javed, Muhammad Sufyan & Cheema, Khalid Mehmood & Laraik, Noman Ali, 2020. "Significance of demand response in light of current pilot projects in China and devising a problem solution for future advancements," Technology in Society, Elsevier, vol. 63(C).
    5. Nikos Kampelis & Elisavet Tsekeri & Dionysia Kolokotsa & Kostas Kalaitzakis & Daniela Isidori & Cristina Cristalli, 2018. "Development of Demand Response Energy Management Optimization at Building and District Levels Using Genetic Algorithm and Artificial Neural Network Modelling Power Predictions," Energies, MDPI, vol. 11(11), pages 1-22, November.
    6. Mahmood Hosseini Imani & Shaghayegh Zalzar & Amir Mosavi & Shahaboddin Shamshirband, 2018. "Strategic Behavior of Retailers for Risk Reduction and Profit Increment via Distributed Generators and Demand Response Programs," Energies, MDPI, vol. 11(6), pages 1-24, June.
    7. Iacopo Savelli & Thomas Morstyn, 2020. "Electricity prices and tariffs to keep everyone happy: a framework for fixed and nodal prices coexistence in distribution grids with optimal tariffs for investment cost recovery," Papers 2001.04283, arXiv.org, revised Jun 2021.
    8. Feihu Hu & Xuan Feng & Hui Cao, 2018. "A Short-Term Decision Model for Electricity Retailers: Electricity Procurement and Time-of-Use Pricing," Energies, MDPI, vol. 11(12), pages 1-18, November.
    9. Roberto Casado-Vara & Zita Vale & Javier Prieto & Juan M. Corchado, 2018. "Fault-Tolerant Temperature Control Algorithm for IoT Networks in Smart Buildings," Energies, MDPI, vol. 11(12), pages 1-17, December.

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