文献类型：期刊文献

英文题名：Stochastic dual dynamic programming for multi-stage stochastic programming of sustainable utility systems

作者：Liu, Nianxin[2];Yang, Kangyuan[2];Zhao, Liang[1,2,3];Ye, Zhencheng[2]

机构：[1]East China Univ Sci & Technol, State Key Lab Ind Control Technol, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[3]Huzhou Inst Ind Control Technol, Huzhou 313099, Peoples R China

年份：2025

卷号：338

外文期刊名：ENERGY

收录：;EI(收录号：20254319362630);WOS:【SCI-EXPANDED(收录号:WOS:001604236800019)】；

基金：The work was supported by National Natural Science Foundation of China (62394343, 22178103, 62373154) , the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, and Fundamental Research Funds for the Central Universities (222202517006) , China.

语种：英文

外文关键词：Sustainable utility system; Renewable energy; Multi-stage stochastic programming; Stochastic dual dynamic programming; Optimization under uncertainty

摘要：Fossil fuels remain a predominant energy source in traditional industrial utility systems, resulting in significant carbon emissions. This study proposes a sustainable utility system that integrates renewable energy and energy storage. A Multi-stage Stochastic Programming (MSSP) model is developed to accommodate system flexibility in dynamic environments characterized by multiple uncertainties, including wind speed, solar irradiance, and multi-level steam demand. To address the computational challenges of large-scale stochastic optimization, a Stochastic Dual Dynamic Programming (SDDP) algorithm is employed, enhanced with scenario sampling, reduction techniques, and Benders decomposition. Case studies from real industrial utility systems demonstrate that the proposed method reduces total operational costs and carbon-related costs by 2.3 % and 5.7 %, respectively, while achieving up to 90 % reductions in problem size under the same scenario tree. As a result, the proposed approach for optimizing sustainable utility systems is both effective and scalable.