Robust Optimization for Electricity Generation

Haoxiang Yang (), David P. Morton (), Chaithanya Bandi () and Krishnamurthy Dvijotham ()
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Haoxiang Yang: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208;
David P. Morton: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208;
Chaithanya Bandi: Kellogg School of Management, Northwestern University, Evanston, Illinois 60208;
Krishnamurthy Dvijotham: Google Deepmind, London N1C 4AG, United Kingdom

INFORMS Journal on Computing, 2021, vol. 33, issue 1, 336-351

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 problem with 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-plane method 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.

Keywords: ACOPF; robust optimization; convex relaxation; cutting-plane method (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:33:y:2021:i:1:p:336-351

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