A Data-Driven Reserve Response Set Policy for Power Systems with Stochastic Resources

Nikita G. Singhal, Nan Li, Kory Hedman

Research output: Contribution to journalArticle

Abstract

This paper modifies the traditional security-constrained unit commitment (UC) model to include a reserve policy that aims to preemptively anticipate post-contingency congestion patterns and account for uncertainty, simultaneously. The policy uses post-contingency transmission constraints to predict the influence of recourse actions under critical generator contingencies and to cover a range of uncertain scenarios by defining reserve response factors, which are determined offline using a data-mining algorithm. The main motive is to address both the locational and the deliverability issues that are usually associated with reserve. The performance of the proposed data-driven reserve response set policy is compared against two sets of deterministic reserve policies and an extensive form stochastic UC model. All simulations are conducted on a modified 2383-bus Polish test system. Test results illustrate that the proposed model consistently outperforms the benchmark policies by improving the market efficiency and enhancing the reliability of the market solution while maintaining scalability and transparency. The proposed model can be employed by contemporary solvers with minimal disruption to existing market procedures.

Original languageEnglish (US)
JournalIEEE Transactions on Sustainable Energy
DOIs
StateAccepted/In press - Jun 8 2018

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Keywords

  • Ancillary service
  • data-mining
  • electricity market design
  • Generators
  • Mathematical model
  • Power transmission lines
  • Production
  • Reliability
  • reserve policies
  • scheduling models
  • Stochastic processes
  • Uncertainty
  • uncertainty

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

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