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An integrated model for assessing electricity retailer’s profitability with demand response

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  • Dagoumas, Athanasios S.
  • Polemis, Michael L.

Abstract

This paper introduces a model that integrates a Unit Commitment (UC) model, which performs the simulation of the day-ahead electricity market, combined with an econometric model that estimates the income and price elasticities of electricity demand. The integrated model is further extended to estimate the retailers’ profitability with demand responsive consumers. The applicability of the proposed model is illustrated in the Greek day-ahead electricity market. The model is designed to identify the effects of demand responsiveness to the fluctuations of spot prices, based on their short-term price elasticities. It provides price signals on the profitability of retailers/demand aggregators, when forming their tariffs. We argue that the non-linearity between demand response and evolution of wholesale price, inherits risk for retailers. This finding could lead even to losses for some time periods, affecting strongly their viability. The model provides useful insights into the risk of retailers from their price responsive customers and therefore acts as a pivotal study to policy makers and government officials (i.e. regulators, transmission and distribution system operators) active in the electricity market.

Suggested Citation

  • Dagoumas, Athanasios S. & Polemis, Michael L., 2017. "An integrated model for assessing electricity retailer’s profitability with demand response," Applied Energy, Elsevier, vol. 198(C), pages 49-64.
    • Handle: RePEc:eee:appene:v:198:y:2017:i:c:p:49-64
    DOI: 10.1016/j.apenergy.2017.04.050
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    Cited by:

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    3. Lu, Renzhi & Hong, Seung Ho & Zhang, Xiongfeng, 2018. "A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach," Applied Energy, Elsevier, vol. 220(C), pages 220-230.
    4. Dagoumas, Athanasios & Polemis, Michael, 2018. "Analysing Carbon Pass-Through Rate Mechanism in the Electricity Sector: Evidence from Greece," MPRA Paper 91067, University Library of Munich, Germany.
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    6. Allard, Stéphane & Debusschere, Vincent & Mima, Silvana & Quoc, Tuan Tran & Hadjsaid, Nouredine & Criqui, Patrick, 2020. "Considering distribution grids and local flexibilities in the prospective development of the European power system by 2050," Applied Energy, Elsevier, vol. 270(C).
    7. Grimm, Veronika & Orlinskaya, Galina & Schewe, Lars & Schmidt, Martin & Zöttl, Gregor, 2021. "Optimal design of retailer-prosumer electricity tariffs using bilevel optimization," Omega, Elsevier, vol. 102(C).
    8. Nikolaos Koltsaklis & Athanasios Dagoumas, 2018. "Policy Implications of Power Exchanges on Operational Scheduling: Evaluating EUPHEMIA’s Market Products in Case of Greece," Energies, MDPI, vol. 11(10), pages 1-26, October.
    9. Athanasios Dagoumas, 2019. "Assessing the Impact of Cybersecurity Attacks on Power Systems," Energies, MDPI, vol. 12(4), pages 1-23, February.
    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. Ioannis Panapakidis & Nikolaos Asimopoulos & Athanasios Dagoumas & Georgios C. Christoforidis, 2017. "An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures," Energies, MDPI, vol. 10(9), pages 1-42, September.
    12. Panagiotis Adraktas & Athanasios Dagoumas, 2019. "Integration of Electric Vehicles in the Unit Commitment Problem with Uncertain Renewable Electricity Generation," International Journal of Energy Economics and Policy, Econjournals, vol. 9(2), pages 315-333.
    13. Michael L. Polemis, 2018. "A mixed integer linear programming model to regulate the electricity sector," Letters in Spatial and Resource Sciences, Springer, vol. 11(2), pages 183-208, July.
    14. Vallés, Mercedes & Bello, Antonio & Reneses, Javier & Frías, Pablo, 2018. "Probabilistic characterization of electricity consumer responsiveness to economic incentives," Applied Energy, Elsevier, vol. 216(C), pages 296-310.
    15. Zhou, Yue & Wu, Jianzhong & Long, Chao, 2018. "Evaluation of peer-to-peer energy sharing mechanisms based on a multiagent simulation framework," Applied Energy, Elsevier, vol. 222(C), pages 993-1022.
    16. Hui Wang & Congcong Wang & Wenhui Zhao, 2022. "Decision on Mixed Trading between Medium- and Long-Term Markets and Spot Markets for Electricity Sales Companies under New Electricity Reform Policies," Energies, MDPI, vol. 15(24), pages 1-23, December.
    17. Komorowska, Aleksandra & Benalcazar, Pablo & Kaszyński, Przemysław & Kamiński, Jacek, 2020. "Economic consequences of a capacity market implementation: The case of Poland," Energy Policy, Elsevier, vol. 144(C).
    18. Stéphane Allard & Vincent Debusschere & Silvana Mima & Tuan Tran Quoc & Nouredine Hadjsaid & Patrick Criqui, 2020. "Considering distribution grids and local flexibilities in the prospective development of the European power system by 2050," Post-Print hal-03133109, HAL.
    19. Guo, Bowei & Weeks, Melvyn, 2022. "Dynamic tariffs, demand response, and regulation in retail electricity markets," Energy Economics, Elsevier, vol. 106(C).
    20. Tian, Xiaoge & Chen, Weiming & Hu, Jinglu, 2023. "Game-theoretic modeling of power supply chain coordination under demand variation in China: A case study of Guangdong Province," Energy, Elsevier, vol. 262(PA).

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