Reserve zone determination based on statistical clustering methods

Fengyu Wang, Kory Hedman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

Due to contingencies, uncertainties, and congestion both the amount and the location of operating reserve (spinning and non-spinning) are essential to ensure system reliability and market efficiency. Today's reserve zone determination methods are mainly based on ad-hoc rules, such as utilities ownership, geographical boundaries, or key transmission lines. Thus, there is a need for more systematic, theoretical ways to determine reserve zones. The mathematical representation of reserves within day-ahead unit commitment models does not account for congestion, which is why reserve zones are important: to ensure the deliverability of reserve. Even with reserve zones, reserves within a zone are assumed to have no deliverability problem, i.e., it is assumed that congestion will not prevent reserve to be delivered where needed within that zone. With the advent of variable renewable resources and other causes of uncertainty (load, electric vehicles), there is a need to improve the determination of reserve zones in order to ensure a reliable system. Statistical clustering techniques are employed to determine the reserve zones based on the power transfer distribution factors (PTDF) and electrical distances (ED).

Original languageEnglish (US)
Title of host publication2012 North American Power Symposium, NAPS 2012
DOIs
StatePublished - Dec 10 2012
Event2012 North American Power Symposium, NAPS 2012 - Champaign, IL, United States
Duration: Sep 9 2012Sep 11 2012

Publication series

Name2012 North American Power Symposium, NAPS 2012

Other

Other2012 North American Power Symposium, NAPS 2012
CountryUnited States
CityChampaign, IL
Period9/9/129/11/12

Keywords

  • Expected load not served
  • K-means
  • electrical distance
  • power transfer distribution factors
  • reserve requirements
  • reserve zones
  • unit commitment
  • value of lost load

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Fuel Technology

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  • Cite this

    Wang, F., & Hedman, K. (2012). Reserve zone determination based on statistical clustering methods. In 2012 North American Power Symposium, NAPS 2012 [6336318] (2012 North American Power Symposium, NAPS 2012). https://doi.org/10.1109/NAPS.2012.6336318