Sample average approximation for risk-averse problems: A virtual power plant scheduling application

EURO Journal on Computational Optimization

Antonio J. Conejo¹  Loïc Giraldi²  Omar M. Knio²  Ibrahim Hoteit³  Ricardo M. Lima⁴  Olivier Le Maître⁵ 

[1] Computer, Electrical and Mathematical Sciences &Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia;;Computer, Electrical and Mathematical Sciences &Corresponding author.;Integrated Systems Engineering-Electrical and Computer Engineering, The Ohio State University, OH, USA;

关键词: Sample average approximation;    Risk-averse stochastic programming;    Virtual power plant;   

DOI  :  

来源: DOAJ

【 摘 要 】

In this paper, we address the decision-making problem of a virtual power plant (VPP) involving a self-scheduling and market involvement problem under uncertainty in the wind speed and electricity prices. The problem is modeled using a risk-neutral and two risk-averse two-stage stochastic programming formulations, where the conditional value at risk is used to represent risk. A sample average approximation methodology is integrated with an adapted L-Shaped solution method, which can solve risk-neutral and specific risk-averse problems. This methodology provides a framework to understand and quantify the impact of the sample size on the variability of the results. The numerical results include an analysis of the computational performance of the methodology for two case studies, estimators for the bounds of the true optimal solutions of the problems, and an assessment of the quality of the solutions obtained. In particular, numerical experiences indicate that when an adequate sample size is used, the solution obtained is close to the optimal one.

【 授权许可】

Unknown