Optimization of a Bayesian game for Peer-to-Peer trading among prosumers under incomplete information via a CNN-LSTM-ATT  

作　　者：Hongjie Jia Wanxin Tang Xiaolong Jin Yunfei Mu Dengxin Ai Xiaodan Yu Wei Wei 

机构地区：[1]Key Laboratory of Smart Grid of Ministry of Education,Tianjin University,Tianjin 300072,China [2]Key Laboratory of Smart Energy&Information Technology of Tianjin Municipality,Tianjin University,Tianjin 300072,China [3]National Industry-Education Platform for Energy Storage(Tianjin University),Tianjin University,Tianjin 300072,China [4]State Grid Tianjin Electric Power Company,Tianjin300392,China

出　　处：《Energy and AI》2024年第4期550-561,共12页能源与人工智能(英文)

基　　金：supported by the project of Science and Technology Project of the State Grid Corporation of China(1400-202312333A-1-1-ZN).

摘　　要：In modern low-carbon industrial parks,various distributed renewable energy resources are employed to fulfill production needs.Despite the growing capacity of renewable energy generation,a significant portion of the power produced by these renewable resources remains unconsumed,resulting in a waste of resources.Within an industrial park,microgrids that both generate and consume energy resources act as energy prosumers.Peer-topeer(P2P)trading provides an efficient means of utilizing renewable energy among these energy prosumers,who possess both power generation and consumption capabilities.However,within the current market mechanism,each prosumer retains private information that is not disclosed on the network.To address the issue of incomplete information among multiple prosumers during the decision-making process,we develop a Bayesian game model based on the CNN-LSTM-ATT prediction method for P2P electricity transactions among multiple prosumers.The energy prosumers in each industrial park aim to minimize their energy consumption costs by adjusting strategies that include P2P energy trading and managing thermal loads.Prosumers make decisions on the basis of their own characteristics and estimates of other prosumer characteristics,which are obtained from the joint probability distribution predicted by the CNN-LSTM-ATT method.These decisions are aimed at mini-mizing each prosumer’s electricity costs.The simulation results demonstrate the effectiveness of the Bayesian game model proposed in this study.

关 键 词：Bayesian game Electricity energy price forecasting Peer-to-peer transaction Renewable energy consumption Thermal dynamics 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]