Risk-Averse Stochastic Programming vs. Adaptive Robust Optimization: A Virtual Power Plant Application

Ricardo M. Lima*, Antonio J. Conejo, Loïc Giraldi, Olivier Le Maître, Ibrahim Hoteit, Omar M. Knio

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


This paper compares risk-averse optimization methods to address the self-scheduling and market involvement of a virtual power plant (VPP). The decision-making problem of the VPP involves uncertainty in the wind speed and electricity price forecast. We focus on two methods: risk-averse two-stage stochastic programming (SP) and two-stage adaptive robust optimization (ARO). We investigate both methods concerning formulations, uncertainty and risk, decomposition algorithms, and their computational performance. To quantify the risk in SP, we use the conditional value at risk (CVaR) because it can resemble a worst-case measure, which naturally links to ARO. We use two efficient implementations of the decomposition algorithms for SP and ARO; we assess (1) the operational results regarding first-stage decision variables, estimate of expected profit, and estimate of the CVaR of the profit and (2) their performance taking into consideration different sample sizes and risk management parameters. The results show that similar first-stage solutions are obtained depending on the risk parameterizations used in each formulation. Computationally, we identified three cases: (1) SP with a sample of 500 elements is competitive with ARO; (2) SP performance degrades comparing to the first case and ARO fails to converge in four out of five risk parameters; (3) SP fails to converge, whereas ARO converges in three out of five risk parameters. Overall, these performance cases depend on the combined effect of deterministic and uncertain data and risk parameters.

Original languageEnglish (US)
Pages (from-to)1795-1818
Number of pages24
JournalINFORMS Journal on Computing
Issue number3
StatePublished - May 2022

Bibliographical note

Publisher Copyright:
Copyright: © 2022 INFORMS


  • risk management
  • robust optimization
  • stochastic programming
  • virtual power plant

ASJC Scopus subject areas

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


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