Data-driven two-stage stochastic programming for utility system optimization under uncertainty

Abstract

The utility system is a popular research field in process optimization. At the same time, widespread uncertainties pose new challenges to this issue. This paper presents a data-driven two-stage stochastic programming (TSSP) to hedge against uncertainty. A kernel density estimation (KDE) method is used to calculate the probability density function from uncertain data. Based on the derived probability density function, Latin Hypercube Sampling (LHS) samples 8-dimension uncertain data to generate different scenarios. Lastly, a real-world case study is conducted to demonstrate the effectiveness of the approach.