论文标题
致力于联合电力和数据交易：可扩展的合作游戏理论方法
Towards Joint Electricity and Data Trading: A Scalable Cooperative Game Theoretic Approach
论文作者
论文摘要
本文首次提出了基于合作游戏理论的联合电力和数据交易机制。所有生产商首先向市场运营商提交与电力和数据相关的参数。操作员利用公共和制造商的私人数据来预测分布式可再生生成器（DRG），并根据降低的不确定性集来量化由生产商的私人数据驱动的改进。然后，考虑到不确定的DRG产生，运营商最大程度地提高了大联盟的总收益，并根据他们对电力和数据共享的贡献将收益授予每位生产商的收益。大联盟的数学公式是通过使用基于杂志的强大方法来开发并转化为二阶锥编程问题的。这种大联盟的稳定是数学证明的，即，所有生产商都愿意合作。此外，为了解决合作游戏中现有回报归档方法的可伸缩性挑战，提出了一种基于两阶段优化的方法，该方法将转换为混合整数二阶圆锥编程并由Benders分解解决。案例研究说明了所有生产商在联合交易框架下进行电力和数据的动机，而拟议的插补方法显着提高了可扩展性。
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.
