KAUST DepartmentApplied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Earth Fluid Modeling and Prediction Group
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Red Sea Research Center (RSRC)
Online Publication Date2017-12-15
Print Publication Date2018-08
Permanent link to this recordhttp://hdl.handle.net/10754/626422
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AbstractIn 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.
CitationLima RM, Conejo AJ, Langodan S, Hoteit I, Knio OM (2017) Risk-averse formulations and methods for a virtual power plant. Computers & Operations Research. Available: http://dx.doi.org/10.1016/j.cor.2017.12.007.
SponsorsThe authors acknowledge the support of the Center for Uncertainty Quantification in Computational Science & Engineering at KAUST.
JournalComputers & Operations Research