文献类型：期刊文献

英文题名：Two-Stage Stochastic Programming for Multi-Objective Optimization of Sustainable Utility Systems Integrating with Combined Heat and Power Units

作者：Yang, Kangyuan[1]; Wang, Qipeng[1]; Zhao, Liang[1,2]

机构：[1] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China; [2] Engineering Research Center of Process System Engineering, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China

年份：2023

外文期刊名：SSRN

收录：EI(收录号：20230359709)

语种：英文

外文关键词：Carbon dioxide - Intelligent systems - Monte Carlo methods - Multiobjective optimization - Natural resources - Probability density function - Stochastic models - Stochastic programming - Stochastic systems - Wind

摘要：Utility systems provide thermal energy and power for industrial processes while emitting significant amounts of carbon dioxide. This study investigates the integration of Combined Heat and Power (CHP) units and renewable energy sources into utility systems in the context of diverse energy demands and low-carbon operations. A two-stage stochastic model (TSSP) is proposed for sustainable utility systems. The main objective of the model was to minimize the annual total cost and the other two objectives were to maximize renewable energy penetration rate and minimize grid net interaction level. The model tackles renewable energy sources uncertainty and evaluates environmental metrics by computing carbon emission costs. In TSSP, the first stage involves new equipment capacity planning. The second stage involves considering uncertainties related to wind speed and solar radiation. Monte Carlo simulations and probability density functions were used to generate a variety of scenarios for the optimization of operational scheduling decisions. Finally, a case study was conducted to validate the effectiveness of the proposed model. Sustainable utility systems reduced carbon emissions by 5.6 percent and reduced total costs by 0.78 percent compared to traditional utility systems. ? 2023, The Authors. All rights reserved.