Stochastic dual dynamic programming for multi-stage stochastic programming of sustainable utility systems - Liu, Nianxin; Yang, Kangyuan; Zhao, Liang; Ye, Zhencheng (Energy (Oxford), 2025)
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. •Multi-stage stochastic programming method is used to handle multi-scale uncertainties.•Stochastic dual dynamic programming is used to for solving the MSSP model.•Emission reduction of sustainable utility systems is quantified.