Cost allocation model for distribution networks considering high penetration of distributed energy resources

Tiago Soares, Fábio Pereira, Hugo Morais, Zita Vale

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

The high penetration of distributed energy resources (DER) in distribution networks and the competitive environment of electricity markets impose the use of new approaches in several domains. The network cost allocation, traditionally used in transmission networks, should be adapted and used in the distribution networks considering the specifications of the connected resources. The main goal is to develop a fairer methodology trying to distribute the distribution network use costs to all players which are using the network in each period. In this paper, a model considering different type of costs (fixed, losses, and congestion costs) is proposed comprising the use of a large set of DER, namely distributed generation (DG), demand response (DR) of direct load control type, energy storage systems (ESS), and electric vehicles with capability of discharging energy to the network, which is known as vehicle-to-grid (V2G). The proposed model includes three distinct phases of operation. The first phase of the model consists in an economic dispatch based on an AC optimal power flow (AC-OPF); in the second phase Kirschen's and Bialek's tracing algorithms are used and compared to evaluate the impact of each resource in the network. Finally, the MW-mile method is used in the third phase of the proposed model. A distribution network of 33 buses with large penetration of DER is used to illustrate the application of the proposed model. [All rights reserved Elsevier].
Original languageEnglish
JournalElectric Power Systems Research
Volume124
Pages (from-to)120-132
ISSN0378-7796
DOIs
Publication statusPublished - 2015

Keywords

  • Bialek’s tracing method
  • Distributed energy resources
  • Kirschen’s tracing method
  • MW-mile
  • Network tariffs allocation
  • Topological distribution factors

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