Chance-constrained economic dispatch with renewable energy and storage

Jianqiang Cheng, Richard Li Yang Chen, Habib N. Najm, Ali Pinar, Cosmin Safta, Jean Paul Watson

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Increasing penetration levels of renewables have transformed how power systems are operated. High levels of uncertainty in production make it increasingly difficulty to guarantee operational feasibility; instead, constraints may only be satisfied with high probability. We present a chance-constrained economic dispatch model that efficiently integrates energy storage and high renewable penetration to satisfy renewable portfolio requirements. Specifically, we require that wind energy contribute at least a prespecified proportion of the total demand and that the scheduled wind energy is deliverable with high probability. We develop an approximate partial sample average approximation (PSAA) framework to enable efficient solution of large-scale chance-constrained economic dispatch problems. Computational experiments on the IEEE-24 bus system show that the proposed PSAA approach is more accurate, closer to the prescribed satisfaction tolerance, and approximately 100 times faster than standard sample average approximation. Finally, the improved efficiency of our PSAA approach enables solution of a larger WECC-240 test system in minutes.

Original languageEnglish (US)
Pages (from-to)479-502
Number of pages24
JournalComputational Optimization and Applications
Volume70
Issue number2
DOIs
StatePublished - Jun 1 2018

Keywords

  • Chance constraints
  • Economic dispatch
  • Energy storage
  • Partial sample average approximation
  • Renewable energy integration
  • Sample average approximation

ASJC Scopus subject areas

  • Control and Optimization
  • Computational Mathematics
  • Applied Mathematics

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