Long-run marginal cost pricing based on analytical method for revenue reconciliation

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Incremental and marginal approaches are two different types of methods to price the use of networks. The major difference between them is in the way they evaluate the costs imposed by network users. The former calculates network charges through simulation and the latter derives charges with a sensitivity- based analytical approach. Both charging models aim to send cost-reflective economic signals to customers, providing an economic climate for the cost-effective development of networks. In this paper, a novel long-run marginal cost (LRMC) pricing methodology based on analytical method is proposed to reflect the impacts on the long-run costs imposed by a nodal injection through sensitivity analysis. The sensitivity analysis consists of three partial differentiations: 1) the sensitivity of circuit power flow with respect to nodal power increment, 2) the sensitivity of the time to reinforce network with respect to changes in circuit power flows, and 3) the sensitivity of present value of future reinforcement with respect to changes in time to reinforce. Two test systems are employed to illustrate the principles and implementation of the proposed method. Results from incremental and marginal approaches under different system conditions are compared and contrasted in terms of charges and tariffs. The proposed method, as demonstrated in the test systems, can produce forward-looking charges that reflect the extent of network utilization levels in addition to the distance that power must travel from points of generation to points of consumption. Furthermore, the proposed method is able to provide further insights into factors influencing network charges.
Original languageEnglish
Pages (from-to)103-110
Number of pages8
JournalIEEE Transactions on Power Systems
Issue number1
Early online date28 Apr 2010
Publication statusPublished - Feb 2011


  • long-run marginal cost
  • load growth rate
  • long-run incremental cost
  • network charging


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