Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Cloud-edge-based We-Market: Autonomous Bidding and Peer-to-peer Energy Sharing Among Prosumers
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1.College of Information Science and Engineering, Northeastern University, Shenyang, China,2.State Key Laboratory of Power System and Generation Equipment, Department of Electrical Engineering, Tsinghua University, Beijing, China;2.School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, China;3.School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, China

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This work was supported in part by the National Natural Science Foundation of China (No. U1908213), in part by Colleges and Universities in Hebei Province Science Research Program (No. QN2020504), and in part by Fundamental Research Funds for the Central Universities (No. N2223001).

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    Abstract:

    With the extensive penetration of distributed renewable energy and self-interested prosumers, the emerging power market tends to enable user autonomy by bottom-up control and distributed coordination. This paper is devoted to solving the specific problems of distributed energy management and autonomous bidding and peer-to-peer (P2P) energy sharing among prosumers. A novel cloud-edge-based We-Market is presented, where the prosumers, as edge nodes with independent control, balance the electricity cost and thermal comfort by formulating a dynamic household energy management system (HEMS). Meanwhile, the autonomous bidding is initiated by prosumers via the modified Stone-Geary utility function. In the cloud center, a distributed convergence bidding (CB) algorithm based on consistency criterion is developed, which promotes faster and fairer bidding through the interactive iteration with the edge nodes. Besides, the proposed scheme is built on top of the commercial cloud platform with sufficiently secure and scalable computing capacity. Numerical results show the effectiveness and practicability of the proposed We-Market, which achieves 15% cost reduction with shorter running time. Comparative analysis indicates better scalability, which is more suitable for larger-scale We-Market implementation.

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History
  • Received:September 01,2021
  • Revised:February 21,2022
  • Adopted:
  • Online: July 25,2023
  • Published:
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