Towards Joint Electricity and Data Trading: A Scalable Cooperative Game Theoretic Approach

TL;DR

This paper introduces a scalable cooperative game theory-based mechanism for joint electricity and data trading among prosumers, improving cooperation stability and scalability through advanced optimization techniques.

Contribution

It presents the first joint trading mechanism combining electricity and data, with a robust, scalable payoff imputation method using second order cone programming.

Findings

Prosumers are motivated to trade electricity and data under the proposed framework.

The payoff imputation method significantly enhances scalability.

The grand coalition stability is mathematically proven.

Abstract

This paper, for the first time, proposes a joint electricity and data trading mechanism based on cooperative game theory. All prosumers first submit the parameters associated with both electricity and data to the market operator. The operator utilizes the public and prosumers' private data to forecast the distributed renewable generators (DRGs) and quantify the improvement driven by prosumers' private data in terms of reduced uncertainty set. Then, the operator maximizes the grand coalition's total payoff considering the uncertain generation of DRGs and imputes the payoff to each prosumer based on their contribution to electricity and data sharing. The mathematical formulation of the grand coalition is developed and converted into a second order cone programming problem by using an affinepolicy based robust approach. The stability of such a grand coalition is mathematically proved, i.e., all prosumers are willing to cooperate. Furthermore, to address the scalability challenge of existing payoff imputation methods in the cooperative game, a two stage optimization based approach is proposed, which is converted into a mixed integer second order cone programming and solved by the Benders decomposition. Case studies illustrate all prosumers are motivated to trade electricity and data under the joint trading framework and the proposed imputation method significantly enhances the scalability.