Two-Stage Stochastic Programming for Sustainable Utility Systems Optimization with Energy Storage

Utility systems consume fuels to provide steam and energy for industrial processes, resulting in significant carbon dioxide emissions. To enhance the economic and environmental efficiency of the system, a sustainable utility system integrating energy storage has been proposed. The scheduling optimization of sustainable utility systems involves multiple energy sources and various uncertainties, posing a challenging mathematical programming problem. An approach utilizing a two-stage stochastic programming method has been introduced to tackle the challenge of uncertainty. The optimal planning stage aims to minimize economic and environmental costs. In the first stage, deterministic programming is employed to solve the capacity planning problem for renewable energy and energy storage devices. In the second stage, weather scenarios are generated based on historical data, and stochastic programming methods are used to transform the uncertainty issue into a mixed-integer programming problem, addressing the operational scheduling optimization of the systems. Finally, the effectiveness of this method is validated using a real factory case study.