Stable feedback linearization-based economic MPC scheme for thermal power plant

被引:13
作者
Kong, Xiaobing [1 ]
Abdelbaky, Mohamed Abdelkarim [1 ]
Liu, Xiangjie [1 ]
Lee, Kwang Y. [2 ]
机构
[1] North China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewable, Beijing 102206, Peoples R China
[2] Baylor Univ, Elect & Comp Engn Dept, Waco, TX 76798 USA
基金
中国国家自然科学基金;
关键词
Boiler -turbine system; Input; output feedback linearization; Economic optimization; Constraints mapping algorithm; Model predictive control; MODEL-PREDICTIVE CONTROL; BOILER-TURBINE UNIT; DYNAMIC MATRIX CONTROL; STEP-RESPONSE MODEL; COORDINATE CONTROL; NONLINEAR-SYSTEMS; OPERATION; STABILITY; STATE;
D O I
10.1016/j.energy.2023.126658
中图分类号
O414.1 [热力学];
学科分类号
摘要
The major concern of modern power plants is changing from tracking control to environmental and economic issues. The economic model predictive control (EMPC) scheme, which incorporates the boiler-turbine unit's dynamic tracking and economic optimization into one online framework, can well enhance the dynamic economic performance. Considering the strong nonlinearity that existed in the boiler-turbine system, this paper presents an advanced EMPC scheme based on the input/output feedback linearization (IOFL) approach. The boiler-turbine dynamics are converted via the IOFL method to a linear form, which can be readily used to constitute the standard EMPC scheme. A dual-mode method is adopted in this paper to guarantee the stability of the IOFL EMPC strategy for the boiler-turbine system, in which the first-mode optimizes the economic objective function while preserving the states of the system within a feasible region, and the second-mode moves the system state to the optimum operating point using an auxiliary controller. The simulations under the MATLAB environment demonstrate that the application of the IOFL-based EMPC scheme enhances the economic and dynamic output performance under load demand changes in comparison with fuzzy hierarchical MPC and fuzzy economic MPC schemes.
引用
收藏
页数:13
相关论文
共 47 条
  • [1] Abdelbaky MA, 2021, CHIN CONTR CONF, P2703, DOI 10.23919/CCC52363.2021.9549729
  • [2] Economic Model Predictive Control of Nonlinear Process Systems Using Empirical Models
    Alanqar, Anas
    Ellis, Matthew
    Christofides, Panagiotis D.
    [J]. AICHE JOURNAL, 2015, 61 (03) : 816 - 830
  • [3] Alessandretti A, 2016, IEEE DECIS CONTR P, P3196, DOI 10.1109/CDC.2016.7798749
  • [4] Optimizing process economics online using model predictive control
    Amrit, Rishi
    Rawlings, James B.
    Biegler, Lorenz T.
    [J]. COMPUTERS & CHEMICAL ENGINEERING, 2013, 58 : 334 - 343
  • [5] Bell R.D., 1987, DYNAMIC MODELS BOILE
  • [6] Caluya KF, 2020, P AMER CONTR CONF, P3577, DOI [10.13140/rg.2.2.12995.55848, 10.23919/ACC45564.2020.9147847]
  • [7] Deep-Neural-Network-Based Economic Model Predictive Control for Ultrasupercritical Power Plant
    Cui, Jinghan
    Chai, Tianyou
    Liu, Xiangjie
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2020, 16 (09) : 5905 - 5913
  • [8] Enerdata, 2022, WORLD EN CONS STAT Y
  • [9] Multiobjective optimal power plant operation through coordinate control with pressure set point scheduling
    Garduno-Ramirez, R
    Lee, KY
    [J]. IEEE TRANSACTIONS ON ENERGY CONVERSION, 2001, 16 (02) : 115 - 122
  • [10] Design & application of adaptive variable structure & H∞ robust optimal schemes in nonlinear control of boiler-turbine unit in the presence of various uncertainties
    Ghabraei, Soheil
    Moradi, Hamed
    Vossoughi, Gholamreza
    [J]. ENERGY, 2018, 142 : 1040 - 1056