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A flexible and deep peak shaving scheme for combined heat and power plant under full operating conditions

Author

Listed:
  • Hou, Guolian
  • Huang, Ting
  • Jiang, Hao
  • Cao, Huan
  • Zhang, Tianhao
  • Zhang, Jianhua
  • Gao, He
  • Liu, Yong
  • Zhou, Zhenhua
  • An, Zhenyi

Abstract

Improving the flexible and deep peak shaving capacity of combined heat and power (CHP) plant under full operating conditions to facilitate renewable energy consumption is the main choice of novel power system. Accordingly, a dry/wet state automatic conversion control scheme fuses the precise mechanism structure, reinforcement learning and multi-objective model predictive control (MPC) algorithms is designed to promote the deep peak shaving ability of CHP plant in this paper. Firstly, the detailed mechanism models of once-through boiler at dry and wet state are presented by analyzing the dynamic characteristics of steam-water flow. Secondly, the large-scale unknown parameters in mechanism models are identified via the Takagi-Sugeno fuzzy modeling, reinforcement learning algorithms and actual dry/wet state conversion data. Thanks to the proposed hybrid modeling strategy, the high-precision boiler-steam unit models at dry and wet state are rapid obtained. Then, to meet different peak shaving demands, the multi-objective MPC algorithm is respectively constructed under dry and wet state with the comprehensive consideration of operational constraints, load tracking error, algorithm stability, power generation cost, CO2 and NOx emission costs. Aiming at maximizing the peak shaving efficiency and flexible operation ability of plant, a dry/wet automatic control scheme combined the designed multi-objective MPC algorithms and identified dry/wet state models is proposed. Finally, the load variation rate of 5 % and 2 % RCM/min is respectively achieved in the dry/wet state conversion tests on a 350 MW CHP plant based on the proposed control scheme. Thus, the rapid and thorough dry/wet state conversion performance of once-through boiler has successfully improved the operational flexibility of CHP plant under full operating conditions.

Suggested Citation

  • Hou, Guolian & Huang, Ting & Jiang, Hao & Cao, Huan & Zhang, Tianhao & Zhang, Jianhua & Gao, He & Liu, Yong & Zhou, Zhenhua & An, Zhenyi, 2024. "A flexible and deep peak shaving scheme for combined heat and power plant under full operating conditions," Energy, Elsevier, vol. 299(C).
  • Handle: RePEc:eee:energy:v:299:y:2024:i:c:s0360544224011757
    DOI: 10.1016/j.energy.2024.131402
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    1. Romero-Anton, N. & Martin-Escudero, K. & Portillo-Valdés, L.A. & Gómez-Elvira, I. & Salazar-Herran, E., 2018. "Improvement of auxiliary BI-DRUM boiler operation by dynamic simulation," Energy, Elsevier, vol. 148(C), pages 676-686.
    2. Gimelli, A. & Mottola, F. & Muccillo, M. & Proto, D. & Amoresano, A. & Andreotti, A. & Langella, G., 2019. "Optimal configuration of modular cogeneration plants integrated by a battery energy storage system providing peak shaving service," Applied Energy, Elsevier, vol. 242(C), pages 974-993.
    3. Gong, Linjuan & Hou, Guolian & Li, Jun & Gao, Haidong & Gao, Lin & Wang, Lin & Gao, Yaokui & Zhou, Junbo & Wang, Mingkun, 2023. "Intelligent fuzzy modeling of heavy-duty gas turbine for smart power generation," Energy, Elsevier, vol. 277(C).
    4. Zhu, Hengyi & Tan, Peng & He, Ziqian & Zhang, Cheng & Fang, Qingyan & Chen, Gang, 2022. "Nonlinear model predictive control of USC boiler-turbine power units in flexible operations via input convex neural network," Energy, Elsevier, vol. 255(C).
    5. Trojan, Marcin, 2019. "Modeling of a steam boiler operation using the boiler nonlinear mathematical model," Energy, Elsevier, vol. 175(C), pages 1194-1208.
    6. Zhang, Kezhen & Zhao, Yongliang & Liu, Ming & Gao, Lin & Fu, Yue & Yan, Junjie, 2021. "Flexibility enhancement versus thermal efficiency of coal-fired power units during the condensate throttling processes," Energy, Elsevier, vol. 218(C).
    7. Han, Zhonghe & Xiang, Peng, 2020. "Modeling condensate throttling to improve the load change performance of cogeneration units," Energy, Elsevier, vol. 192(C).
    8. Taler, Jan & Zima, Wiesław & Ocłoń, Paweł & Grądziel, Sławomir & Taler, Dawid & Cebula, Artur & Jaremkiewicz, Magdalena & Korzeń, Anna & Cisek, Piotr & Kaczmarski, Karol & Majewski, Karol, 2019. "Mathematical model of a supercritical power boiler for simulating rapid changes in boiler thermal loading," Energy, Elsevier, vol. 175(C), pages 580-592.
    9. Liu, Yikui & Wu, Lei & Li, Jie, 2019. "Towards accurate modeling of dynamic startup/shutdown and ramping processes of thermal units in unit commitment problems," Energy, Elsevier, vol. 187(C).
    10. De Lorenzi, Andrea & Gambarotta, Agostino & Marzi, Emanuela & Morini, Mirko & Saletti, Costanza, 2022. "Predictive control of a combined heat and power plant for grid flexibility under demand uncertainty," Applied Energy, Elsevier, vol. 314(C).
    11. Pipicelli, Michele & Muccillo, Massimiliano & Gimelli, Alfredo, 2023. "Influence of the control strategy on the performance of hybrid polygeneration energy system using a prescient model predictive control," Applied Energy, Elsevier, vol. 329(C).
    12. Hou, Guolian & Gong, Linjuan & Hu, Bo & Huang, Ting & Su, Huilin & Huang, Congzhi & Zhou, Guiping & Wang, Shunjiang, 2022. "Flexibility oriented adaptive modeling of combined heat and power plant under various heat-power coupling conditions," Energy, Elsevier, vol. 242(C).
    13. Fan, He & Su, Zhi-gang & Wang, Pei-hong & Lee, Kwang Y., 2021. "A dynamic nonlinear model for a wide-load range operation of ultra-supercritical once-through boiler-turbine units," Energy, Elsevier, vol. 226(C).
    14. Al-Momani, Ahmad & Mohamed, Omar & Abu Elhaija, Wejdan, 2022. "Multiple processes modeling and identification for a cleaner supercritical power plant via Grey Wolf Optimizer," Energy, Elsevier, vol. 252(C).
    15. Farahani, Yaser & Jafarian, Ali & Mahdavi Keshavar, Omid, 2022. "Dynamic simulation of a hybrid once-through and natural circulation Heat Recovery Steam Generator (HRSG)," Energy, Elsevier, vol. 242(C).
    16. Ma, Tingshan & Li, Zhengkuan & Lv, Kai & Chang, Dongfeng & Hu, Wenshuai & Zou, Ying, 2024. "Design and performance analysis of deep peak shaving scheme for thermal power units based on high-temperature molten salt heat storage system," Energy, Elsevier, vol. 288(C).
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