22 Scopus citations


In this paper we address the optimal operation of a virtual power plant using stochastic programming. We consider one risk-neutral and two risk-averse formulations that rely on the conditional value at risk. To handle large-scale problems, we implement two decomposition methods with variants using single- and multiple-cuts. We propose the utilization of wind ensembles obtained from the European Centre for Medium Range Weather Forecasts (ECMWF) to quantify the uncertainty of the wind forecast. We present detailed results relative to the computational performance of the risk-averse formulations, the decomposition methods, and risk management and sensitivities analysis as a function of the number of scenarios and risk parameters. The implementation of the two decomposition methods relies on the parallel solution of subproblems, which turns out to be paramount for computational efficiency. The results show that one of the two decomposition methods is the most efficient.
Original languageEnglish (US)
Pages (from-to)350-373
Number of pages24
JournalComputers & Operations Research
StatePublished - Dec 15 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The authors acknowledge the support of the Center for Uncertainty Quantification in Computational Science & Engineering at KAUST.


Dive into the research topics of 'Risk-averse formulations and methods for a virtual power plant'. Together they form a unique fingerprint.

Cite this