Robust optimization for electricity generation

Haoxiang Yang, David P. Morton, Chaithanya Bandi, Krishnamurthy Dvijotham

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We consider a robust optimization problem in an electric power system under uncertain demand and availability of renewable energy resources. Solving the deterministic alternating current (AC) optimal power flow (ACOPF) problem has been considered challenging since the 1960s due to its nonconvexity. Linear approximation of the AC power flow system sees pervasive use, but does not guarantee a physically feasible system configuration. In recent years, various convex relaxation schemes for the ACOPF problem have been investigated, and under some assumptions, a physically feasible solution can be recovered. Based on these convex relaxations, we construct a robust convex optimization problemwith recourse to solve for optimal controllable injections (fossil fuel, nuclear, etc.) in electric power systems under uncertainty (renewable energy generation, demand fluctuation, etc.).We propose a cutting-planemethod to solve this robust optimization problem, and we establish convergence and other desirable properties. Experimental results indicate that our robust convex relaxation of the ACOPF problem can provide a tight lower bound.

Original languageEnglish (US)
Pages (from-to)336-351
Number of pages16
JournalINFORMS Journal on Computing
Volume33
Issue number1
DOIs
StatePublished - Dec 2021

Keywords

  • ACOPF
  • Convex relaxation
  • Cutting-plane method
  • Robust optimization

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research

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