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Locational Marginal Price Model and Solution Considering Network Loss and Load Deviation

1.State Key Laboratory of Smart Grid Protection and Control, Nanjing 211106, China;2.NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China


This paper analyzes the difficulty of accurately calculating the load deviation of power generation and consumption and system loss in market clearing and calculation of locational marginal price (LMP) in China. First, two kinds of LMP calculation models are proposed, which are both involved in the methods of allocating the network loss by using loss distribution factors and allocating load deviation by using load deviation distribution factors. The accurate calculation of system network loss and load deviation is realized. Then, the iteration-based LMP model uses the bus load as the initial point to prepare a power generation plan. After each solution, the deviation between the total generation and the system load is allocated to all nodes to correct their loads. The LMP is solved by multiple iterations. The two-stage LMP model establishes a coupling model that considers the system network loss and load deviation based on the system load. In the first stage, the load deviation is calculated; in the second stage, the deviation is fixed to calculate the LMP. Finally, the effectiveness of the proposed model is verified by comparing the LMP of the proposed method and the direct allocation method of network loss, and analyzing the influence of different load deviation levels and load deviation distribution factors on the LMP.



This work is supported by State Grid Corporation of China (No. 1200-201940420A-0-0-00).

Get Citation
[1]ZHANG Bingjin, DING Qia, CAO Rongzhang, et al. Locational Marginal Price Model and Solution Considering Network Loss and Load Deviation[J/OL]. Automation of Electric Power Systems, http://doi. org/10.7500/AEPS20200513005.
  • Received:May 13,2020
  • Revised:November 19,2020
  • Adopted:September 01,2020
  • Online:
  • Published: