Mechanism design for data sharing: An electricity retail perspective

Bohong Wang, Qinglai Guo and Yang Yu

Applied Energy, 2022, vol. 314, issue C, No S0306261922003038

Abstract: Information incompletion always forces market participants to make decisions under uncertainty in energy transactions, while obtaining related data is a feasible way for them to reduce uncertainty and gain profits. However, the application of data transactions is not yet mature. To extend the application range of data transactions and concretize the data transaction model, a novel framework of electricity-side and data-side transactions linked with data sharing is proposed from the electricity retail perspective in this paper. The necessity and processes of data sharing between the electricity retailer and data suppliers are elaborately illustrated in the framework. Data revenues and data costs are analyzed according to uncertainty reduction and information provision. Considering the widely used two-settlement system of electricity markets, data revenues and data costs can be expressed in closed forms and their differences are net profits that are regarded to drive the data flow. Furthermore, an ex-post profit allocation mechanism is matched to appropriately allocate the net profits between the electricity retailer and data suppliers in the data sharing model. By comparison with the Shapley value method, the mechanism is less time-consuming and will ensure the profitability of the electricity retailer. Finally, a practical case with real data is employed to realize the results proposed in the theoretical analysis, and the feasibility of the data sharing model and profit allocation mechanism is validated.

Keywords: Data sharing; Uncertainty; Electricity retail; Profit allocation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.apenergy.2022.118871

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