Data valuation for decision-making with uncertainty in energy transactions: A case of the two-settlement market system. (15th April 2021)

• Record Type: Journal Article
• Authors: Wang, Bohong; Guo, Qinglai; Yang, Tianyu; Xu, Luo; Sun, Hongbin
• Is Part Of: Applied energy. Volume 288(2021)
• Publication Date: 2021-04-15
• Subjects: Data -- Valuation -- Decision-making -- Uncertainty -- Two-settlement market system; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.116643
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Data products can reduce uncertainty in decision-making problems with uncertainty. Data value is defined to describe profit enhancement through uncertainty reduction. Closed-form formulas of data value rates can be directly used for data valuation. Parametric and nonparametric estimation can meet different needs in data valuation. Abstract: Decision-making in energy transactions should consider inherent uncertainty. Obtaining data from data transactions and sharing can reduce the uncertainty, therefore mitigate risks in decision-making, and enhance the economic benefits of market participants. In this paper, the quantitative relationships between the uncertainty reduction and the profit enhancement are proposed and solved, which could be regarded as the contribution of the data. Regarding the electricity retailers as the core market participants, a typical optimization model with load demand uncertainty according to the two-settlement market system is constructed, the optimal solution is analyzed under different standard deviations of the load. Considering that data products, which come from end users' private data, such as their historical electricity consumption data and future electricity consumption schedule, can contribute to the uncertainty reduction, a data valuation paradigm and an explicit expression of data value are proposed. Hence, closed-form formulas of data value rate are derived, and their guidance and assistance in the decision-making of electricity retailers are illustrated. To improve the comprehensiveness and accuracy of the valuation, parametric estimation and nonparametric estimation methods are investigated in the distribution fitting of load forecast errors, and the corresponding data valuation can be conducted directly and quickly. Finally, a numerical case is studied using load data from the Commission for Energy Regulation to demonstrate the generality and feasibility of the data valuation and the effectiveness of theoretical results.