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Dynamic Partition Method for Distributed Energy Cluster with Combined Heat and Power Unit

1.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing 102206, China;2.Electric Power Research Institute of China Southern Power Grid Company Limited, Guangzhou 510080, China


The cluster partition is the basic part of realizing the optimal dispatching of distributed energy networks. A cluster partition method for distributed energy networks with combined heat and power unit is proposed in this paper. Firstly, a cluster partition index system considering the structural and functional properties is proposed. The modular index that takes into account the characteristics of electric and heat network is used on the structural property to describe the connection strength between different network nodes. On the functional property, the indices of supply-demand matching degree and heat-electricity coupling degree are introduced to reflect the balance degree between the internal supply and actual demand as well as source-side coupling output and actual consumption in a single energy network cluster. Secondly, based on the cluster partition index system, the optimization objectives at the two levels of the power grid and heat network are designed as the basis for cluster partition. Thus, an electricity-heat cluster centered on combined heat and power generation is formed by Louvain community detection algorithm. Finally, the effectiveness of the proposed method is verified through the case study.



This work is supported by National Natural Science Foundation of China (No. 51877076) and China Southern Power Grid Company Limited (No. ZBKJXM20170049).

Get Citation
[1]PAN Mingyi, LIU Nian, LEI Jinyong. Dynamic Partition Method for Distributed Energy Cluster with Combined Heat and Power Unit[J]. Automation of Electric Power Systems,2021,45(1):168-176. DOI:10.7500/AEPS20200217013
  • Received:February 17,2020
  • Revised:September 08,2020
  • Adopted:
  • Online: January 05,2021
  • Published: