Data-Driven Stochastic Robust Optimization for Industrial Energy System Considering Renewable Energy Penetration
Feifei Shen, Liang Zhao, Wenli Du, Weimin Zhong, Xin Peng, Feng Qian
Abstract
Achieving carbon neutrality has been one of the main tasks these decades. In this study, renewable energies were introduced to reduce the greenhouse gas emissions of industrial energy systems. Considering solar heat and wind energy uncertainties, a data-driven stochastic robust optimization framework was proposed. Machine learning methods were applied to derive data information: a data mining method to classify the high-volume uncertain data; a kernel-based method to construct the uncertainty sets for each data class. The stochastic robust optimization model of the industrial energy system was developed as a bilevel optimization procedure: the outer level is a two-stage stochastic programming problem to optimize the expected objective value of different data clusters, and the robust optimization is nested internally to ensure robustness. A case study on the practical industrial energy system was performed, and the results are that the total annual cost is reduced by 1 507 730 $/a and 7.62% GHG emissions are decreased by introducing renewable energies; the proposed method is superior to the traditional ones in terms of PoR (2.91%) and robustness (99.79%). The results of multiobjective optimization considering economic and environmental revenues can provide multipreference schemes for decision-makers.