Data-Driven Distributionally Robust Energy-Reserve-Storage Dispatch

Chao Duan, Lin Jiang, Wanliang Fang*, Jun Liu, Shiming Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

This paper proposes distributionally robust energy-reserve-storage co-dispatch model and method to facilitate the integration of variable and uncertain renewable energy. The uncertainties of renewable generation forecasting errors are characterized through an ambiguity set, which is a set of probability distributions consistent with observed historical data. The proposed model minimizes the expected operation costs corresponding to the worst case distribution in the ambiguity set. Distributionally robust chance constraints are employed to guarantee reserve and transmission adequacy. The more historical data are available, the smaller the ambiguity set is and the less conservative the solution is. The formulation is finally cast into a mixed integer linear programming whose scale remains unchanged as the number of historical data increases. Inactive constraint identification and convex relaxation techniques are introduced to reduce the computational burden. Numerical results and Monte Carlo simulations on IEEE 118-bus systems demonstrate the effectiveness and efficiency of the proposed method.

Original languageEnglish (US)
Pages (from-to)2826-2836
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Volume14
Issue number7
DOIs
StatePublished - Jul 2018

Keywords

  • Chance constraints
  • distributionally robust optimization (DRO)
  • economic dispatch
  • energy storage
  • reserve scheduling

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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