文献类型：期刊文献

英文题名：Data-Driven Robust Optimization for Industrial Utility System Integrating Wind and Solar Energies

作者：Huang, Liqian[1]; Zheng, Xuechun[1]; Wang, Pengyu[1]; Wang, Wenkai[2]; Zhang, Yaowei[1]; Li, Renjie[1]; Wang, Qipeng[3]; Zhao, Liang[3]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China; [2] School of Materials Science and Engineering, East China University of Science and Technology, Shanghai, China; [3] East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Shanghai, China

年份：2023

起止页码：8947

外文期刊名：Proceedings - 2023 China Automation Congress, CAC 2023

收录：EI(收录号：20241515852933)

语种：英文

外文关键词：Budget control - Carbon - Chemical industry - Chemical plants - Digital storage - Greenhouse gases - Multiobjective optimization - Wind power

摘要：Utility systems provide power and heat for chemical plants and drive production processes, while large amounts of greenhouse gases are emitted. The introduction of renewable energy sources can help reduce carbon emissions from traditional utility systems. A data-driven robust optimization for utility systems that introduce wind and solar energy is presented in this paper. The paper proposes a superstructure model for utility systems with renewable energy equipment, as well as physical models for wind turbines, photovoltaic power generation, and electrical energy storage. A robust optimization method is employed to solve the optimization problem in case of uncertainty in wind and solar energy. Then, an environmental and economic multi-objective optimization model was formulated to balance economic costs and carbon emissions. Finally, the feasibility of the proposed methods was demonstrated by a case study of a real industrial chemical plant. The optimization results indicate that a data-driven robust optimization approach overcomes the uncertainty within wind and solar energy by setting reasonable robust budget parameters, which reflects the ability to withstand system risk. ? 2023 IEEE.