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Monetizing Customer Load Data for an Energy Retailer: A Cooperative Game Approach

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  • Liyang Han
  • Jalal Kazempour
  • Pierre Pinson

Abstract

When energy customers schedule loads ahead of time, this information, if acquired by their energy retailer, can improve the retailer's load forecasts. Better forecasts lead to wholesale purchase decisions that are likely to result in lower energy imbalance costs, and thus higher profits for the retailer. Therefore, this paper monetizes the value of the customer schedulable load data by quantifying the retailer's profit gain from adjusting the wholesale purchase based on such data. Using a cooperative game theoretic approach, the retailer translates their increased profit in expectation into the value of cooperation, and redistributes a portion of it among the customers as monetary incentives for them to continue providing their load data. Through case studies, this paper demonstrates the significance of the additional profit for the retailer from using the proposed framework, and evaluates the long-term monetary benefits to the customers based on different payoff allocation methods.

Suggested Citation

  • Liyang Han & Jalal Kazempour & Pierre Pinson, 2020. "Monetizing Customer Load Data for an Energy Retailer: A Cooperative Game Approach," Papers 2012.05519, arXiv.org, revised Aug 2021.
    • Handle: RePEc:arx:papers:2012.05519
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    References listed on IDEAS

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    Cited by:

    1. Alonso Pedrero, Raquel & Crespo del Granado, Pedro, 2023. "Assessing the impact of energy communities on retailers’ balancing positions in the power market," Energy, Elsevier, vol. 283(C).
    2. Wang, Bohong & Guo, Qinglai & Yu, Yang, 2022. "Mechanism design for data sharing: An electricity retail perspective," Applied Energy, Elsevier, vol. 314(C).

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