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A Short-Term Decision Model for Electricity Retailers: Electricity Procurement and Time-of-Use Pricing

Author

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  • Feihu Hu

    (School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Xuan Feng

    (School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Hui Cao

    (School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

This paper establishes a short-term decision model, based on robust optimization, for an electricity retailer to determine the electricity procurement and electricity retail prices. The electricity procurement process includes purchasing electricity from generation companies and from the spot market. The selling prices of electricity for the customers are based on time-of-use (TOU) pricing which is widely employed in modern electricity market as a demand response program. The objective of the model is to maximize the expected profit of the retailer through optimizing the electricity procurement strategy and electricity pricing scheme. A price elasticity matrix (PEM) is adopted to model the demand response. Also, uncertainty in spot prices is modeled using a robust optimization approach, in which price bounds are considered instead of predicted values. Using a robust optimization approach, the retailer can adjust the level of robustness of its decisions through a robust control parameter. A case study is presented to illustrate the performance of the model. The simulation results demonstrate that the developed model is effective in increasing the expected profit of the retailer and flattening the load profiles of customers.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3258-:d:184878
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    3. Duan, Jiandong & Liu, Fan & Yang, Yao, 2022. "Optimal operation for integrated electricity and natural gas systems considering demand response uncertainties," Applied Energy, Elsevier, vol. 323(C).
    4. 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.
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    6. Wanlei Xue & Xin Zhao & Yan Li & Ying Mu & Haisheng Tan & Yixin Jia & Xuejie Wang & Huiru Zhao & Yihang Zhao, 2023. "Research on the Optimal Design of Seasonal Time-of-Use Tariff Based on the Price Elasticity of Electricity Demand," Energies, MDPI, vol. 16(4), pages 1-17, February.
    7. Alexander Baranovsky & Nataliia Tkachenko & Vladimer Glonti & Valentyna Levchenko & Kateryna Bogatyrova & Zaza Beridze & Larisa Belinskaja & Iryna Zelenitsa, 2020. "Non-Price Criteria for the Evaluation of the Tender Offers in Public Procurement of Ukraine," IJFS, MDPI, vol. 8(3), pages 1-15, July.
    8. Catanzaro, Daniele & Pesenti, Raffaele & Ronco, Roberto, 2021. "Job Scheduling under Time-of-Use Energy Tariffs for Sustainable Manufacturing: A Survey," LIDAM Discussion Papers CORE 2021019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. Thibaut Théate & Sébastien Mathieu & Damien Ernst, 2020. "An Artificial Intelligence Solution for Electricity Procurement in Forward Markets," Energies, MDPI, vol. 13(23), pages 1-17, December.
    10. Catanzaro, Daniele & Pesenti, Raffaele & Ronco, Roberto, 2023. "Job scheduling under Time-of-Use energy tariffs for sustainable manufacturing: a survey," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1091-1109.
    11. Zhang, Yang & Yang, Qingyu & Li, Donghe & An, Dou, 2022. "A reinforcement and imitation learning method for pricing strategy of electricity retailer with customers’ flexibility," Applied Energy, Elsevier, vol. 323(C).
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