文献类型：会议论文

英文题名：Intelligent scheduling and flexible operation for the commercial-scale coal-fired power plant integrated with post-combustion carbon capture

作者：Liao, Peizhi[1];Wu, Xiao[2];Wang, Meihong[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Southeast Univ, Sch Energy & Environm, Key Lab Energy Thermal Convers & Control, Ofillinistry Educ, Nanjing 210096, Peoples R China

会议论文集：11th IFAC Symposium on Control of Power and Energy Systems (CPES)

会议日期：JUN 21-23, 2022

会议地点：ELECTR NETWORK

语种：英文

外文关键词：Coal-fired power plant; Post-combustion carbon capture; Deep belief network; Optimal scheduling; Model predictive control

摘要：This paper develops a two-level control structure for commercial-scale coal-fired power plant integrated with post-combustion carbon capture (CFPP-PCC). In high-level scheduler, an economic objective function with the consideration of grid demand, price conditions and system constraints is developed. Deep belief network is utilized to develop the steady-state model for CFPP-PCC process. Intelligent scheduling of electricity generation and capture level is then implemented using Bayesian optimization. Given the optimal setpoints, model predictive controller (MPC) is applied to achieve flexible operation in the low-level supervisory control. The simulation results show that the proposed control structure specifies an economically attractive operation profile for the CFPP-PCC process.