IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v206y2020ics0360544220312305.html
   My bibliography  Save this article

Operation performance comparison of CCHP systems with cascade waste heat recovery systems by simulation and operation optimisation

Author

Listed:
  • Wang, Xuan
  • Shu, Gequn
  • Tian, Hua
  • Wang, Rui
  • Cai, Jinwen

Abstract

The waste heat recovery system (WHRS) is a critical part of the combined cooling, heating, and power (CCHP) system and can evidently improve its primary energy utilisation rate. However, few researchers have investigated the operation performance of the WHRS with the entire CCHP system to reveal its effects on the CCHP system efficiency. Therefore, dynamic simulation models of an entire CCHP system with a novel cascade WHRS named ‘electricity-cooling cogeneration system (ECCS)’ and a dual-effect absorption refrigeration system (DARS) are established in this study. The operation performances of the CCHP system–ECCS and CCHP system–DARS are compared based on dynamic simulations, thereby focusing on the effects of different WHRSs. In addition, the dynamic simulation results are compared with the results of operation optimisation and static simulation to reveal the applicability of the different methods. The optimisation and dynamic simulation results of the CCHP system operation agree well when the off-design performance is considered. Furthermore, the dynamic simulation results enable a more detailed analysis, thereby showing that the CCHP system–ECCS requires 0.29% less primary energy. Besides, under slow variations in load, the simulation results of the static and dynamic models have no evident difference, and the maximal error is only approximately 0.5%.

Suggested Citation

  • Wang, Xuan & Shu, Gequn & Tian, Hua & Wang, Rui & Cai, Jinwen, 2020. "Operation performance comparison of CCHP systems with cascade waste heat recovery systems by simulation and operation optimisation," Energy, Elsevier, vol. 206(C).
  • Handle: RePEc:eee:energy:v:206:y:2020:i:c:s0360544220312305
    DOI: 10.1016/j.energy.2020.118123
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220312305
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2020.118123?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Xuan & Shu, Gequn & Tian, Hua & Wang, Rui & Cai, Jinwen, 2020. "Dynamic performance comparison of different cascade waste heat recovery systems for internal combustion engine in combined cooling, heating and power," Applied Energy, Elsevier, vol. 260(C).
    2. Jannelli, E. & Minutillo, M. & Cozzolino, R. & Falcucci, G., 2014. "Thermodynamic performance assessment of a small size CCHP (combined cooling heating and power) system with numerical models," Energy, Elsevier, vol. 65(C), pages 240-249.
    3. Manente, Giovanni & Toffolo, Andrea & Lazzaretto, Andrea & Paci, Marco, 2013. "An Organic Rankine Cycle off-design model for the search of the optimal control strategy," Energy, Elsevier, vol. 58(C), pages 97-106.
    4. Al Moussawi, Houssein & Fardoun, Farouk & Louahlia, Hasna, 2017. "Selection based on differences between cogeneration and trigeneration in various prime mover technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 491-511.
    5. Ren, Hongbo & Gao, Weijun, 2010. "A MILP model for integrated plan and evaluation of distributed energy systems," Applied Energy, Elsevier, vol. 87(3), pages 1001-1014, March.
    6. Ying Ji & Jianhui Wang & Jiacan Xu & Xiaoke Fang & Huaguang Zhang, 2019. "Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning," Energies, MDPI, vol. 12(12), pages 1-21, June.
    7. Zheng, C.Y. & Wu, J.Y. & Zhai, X.Q. & Yang, G. & Wang, R.Z., 2016. "Experimental and modeling investigation of an ICE (internal combustion engine) based micro-cogeneration device considering overheat protection controls," Energy, Elsevier, vol. 101(C), pages 447-461.
    8. Horst, Tilmann Abbe & Rottengruber, Hermann-Sebastian & Seifert, Marco & Ringler, Jürgen, 2013. "Dynamic heat exchanger model for performance prediction and control system design of automotive waste heat recovery systems," Applied Energy, Elsevier, vol. 105(C), pages 293-303.
    9. Asensio, F.J. & San Martín, J.I. & Zamora, I. & Garcia-Villalobos, J., 2017. "Fuel cell-based CHP system modelling using Artificial Neural Networks aimed at developing techno-economic efficiency maximization control systems," Energy, Elsevier, vol. 123(C), pages 585-593.
    10. Maraver, Daniel & Sin, Ana & Royo, Javier & Sebastián, Fernando, 2013. "Assessment of CCHP systems based on biomass combustion for small-scale applications through a review of the technology and analysis of energy efficiency parameters," Applied Energy, Elsevier, vol. 102(C), pages 1303-1313.
    11. Yu, Guopeng & Shu, Gequn & Tian, Hua & Wei, Haiqiao & Liu, Lina, 2013. "Simulation and thermodynamic analysis of a bottoming Organic Rankine Cycle (ORC) of diesel engine (DE)," Energy, Elsevier, vol. 51(C), pages 281-290.
    12. Han, Jie & Ouyang, Leixin & Xu, Yuzhen & Zeng, Rong & Kang, Shushuo & Zhang, Guoqiang, 2016. "Current status of distributed energy system in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 288-297.
    13. Sun, Liuli & Han, Wei & Jing, Xuye & Zheng, Danxing & Jin, Hongguang, 2013. "A power and cooling cogeneration system using mid/low-temperature heat source," Applied Energy, Elsevier, vol. 112(C), pages 886-897.
    14. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    15. Fang, Fang & Wei, Le & Liu, Jizhen & Zhang, Jianhua & Hou, Guolian, 2012. "Complementary configuration and operation of a CCHP-ORC system," Energy, Elsevier, vol. 46(1), pages 211-220.
    16. Wang, Xuan & Jin, Ming & Feng, Wei & Shu, Gequn & Tian, Hua & Liang, Youcai, 2018. "Cascade energy optimization for waste heat recovery in distributed energy systems," Applied Energy, Elsevier, vol. 230(C), pages 679-695.
    17. Wang, Yaodong & Huang, Ye & Chiremba, Elijah & Roskilly, Anthony P. & Hewitt, Neil & Ding, Yulong & Wu, Dawei & Yu, Hongdong & Chen, Xiangping & Li, Yapeng & Huang, Jincheng & Wang, Ruzhu & Wu, Jingyi, 2011. "An investigation of a household size trigeneration running with hydrogen," Applied Energy, Elsevier, vol. 88(6), pages 2176-2182, June.
    18. Jradi, M. & Riffat, S., 2014. "Tri-generation systems: Energy policies, prime movers, cooling technologies, configurations and operation strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 396-415.
    19. Barelli, L. & Bidini, G. & Gallorini, F. & Ottaviano, A., 2012. "Dynamic analysis of PEMFC-based CHP systems for domestic application," Applied Energy, Elsevier, vol. 91(1), pages 13-28.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhou, Yuan & Wang, Jiangjiang & Liu, Yi & Yan, Rujing & Ma, Yanpeng, 2021. "Incorporating deep learning of load predictions to enhance the optimal active energy management of combined cooling, heating and power system," Energy, Elsevier, vol. 233(C).
    2. Yuan, Yu & Bai, Zhang & Zhou, Shengdong & Zheng, Bo & Hu, Wenxin, 2022. "Potential of applying the thermochemical recuperation in combined cooling, heating and power generation: Flexible demand response characteristics," Applied Energy, Elsevier, vol. 325(C).
    3. Zhu, Yilin & Xu, Yujie & Chen, Haisheng & Guo, Huan & Zhang, Hualiang & Zhou, Xuezhi & Shen, Haotian, 2023. "Optimal dispatch of a novel integrated energy system combined with multi-output organic Rankine cycle and hybrid energy storage," Applied Energy, Elsevier, vol. 343(C).
    4. Ou, Kai & Yuan, Wei-Wei & Kim, Young-Bae, 2021. "Development of optimal energy management for a residential fuel cell hybrid power system with heat recovery," Energy, Elsevier, vol. 219(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Xuan & Shu, Gequn & Tian, Hua & Wang, Rui & Cai, Jinwen, 2020. "Dynamic performance comparison of different cascade waste heat recovery systems for internal combustion engine in combined cooling, heating and power," Applied Energy, Elsevier, vol. 260(C).
    2. Al Moussawi, Houssein & Fardoun, Farouk & Louahlia, Hasna, 2017. "Selection based on differences between cogeneration and trigeneration in various prime mover technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 491-511.
    3. Wang, Xuan & Jin, Ming & Feng, Wei & Shu, Gequn & Tian, Hua & Liang, Youcai, 2018. "Cascade energy optimization for waste heat recovery in distributed energy systems," Applied Energy, Elsevier, vol. 230(C), pages 679-695.
    4. Xuan Wang & Hua Tian & Gequn Shu, 2016. "Part-Load Performance Prediction and Operation Strategy Design of Organic Rankine Cycles with a Medium Cycle Used for Recovering Waste Heat from Gaseous Fuel Engines," Energies, MDPI, vol. 9(7), pages 1-21, July.
    5. Ren, Fukang & Wei, Ziqing & Zhai, Xiaoqiang, 2022. "A review on the integration and optimization of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    6. Shi, Rongqi & He, Tianqi & Peng, Jie & Zhang, Yangjun & Zhuge, Weilin, 2016. "System design and control for waste heat recovery of automotive engines based on Organic Rankine Cycle," Energy, Elsevier, vol. 102(C), pages 276-286.
    7. Wegener, Moritz & Malmquist, Anders & Isalgué, Antonio & Martin, Andrew, 2018. "Biomass-fired combined cooling, heating and power for small scale applications – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 392-410.
    8. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    9. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    10. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    11. Kang, Ligai & Yang, Junhong & An, Qingsong & Deng, Shuai & Zhao, Jun & Wang, Hui & Li, Zelin, 2017. "Effects of load following operational strategy on CCHP system with an auxiliary ground source heat pump considering carbon tax and electricity feed in tariff," Applied Energy, Elsevier, vol. 194(C), pages 454-466.
    12. Chang, Huawei & Wan, Zhongmin & Zheng, Yao & Chen, Xi & Shu, Shuiming & Tu, Zhengkai & Chan, Siew Hwa & Chen, Rui & Wang, Xiaodong, 2017. "Energy- and exergy-based working fluid selection and performance analysis of a high-temperature PEMFC-based micro combined cooling heating and power system," Applied Energy, Elsevier, vol. 204(C), pages 446-458.
    13. Yang, Cheng & Huang, Zhifeng & Ma, Xiaoqian, 2018. "Comparative study on off-design characteristics of CHP based on GTCC under alternative operating strategy for gas turbine," Energy, Elsevier, vol. 145(C), pages 823-838.
    14. Di Somma, M. & Graditi, G. & Heydarian-Forushani, E. & Shafie-khah, M. & Siano, P., 2018. "Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects," Renewable Energy, Elsevier, vol. 116(PA), pages 272-287.
    15. Zhang, Na & Wang, Zefeng & Lior, Noam & Han, Wei, 2018. "Advancement of distributed energy methods by a novel high efficiency solar-assisted combined cooling, heating and power system," Applied Energy, Elsevier, vol. 219(C), pages 179-186.
    16. Yujian Ye & Dawei Qiu & Huiyu Wang & Yi Tang & Goran Strbac, 2021. "Real-Time Autonomous Residential Demand Response Management Based on Twin Delayed Deep Deterministic Policy Gradient Learning," Energies, MDPI, vol. 14(3), pages 1-22, January.
    17. Wang, Jiangjiang & Sui, Jun & Jin, Hongguang, 2015. "An improved operation strategy of combined cooling heating and power system following electrical load," Energy, Elsevier, vol. 85(C), pages 654-666.
    18. Li, C.Y. & Deethayat, T. & Wu, J.Y. & Kiatsiriroat, T. & Wang, R.Z., 2018. "Simulation and evaluation of a biomass gasification-based combined cooling, heating, and power system integrated with an organic Rankine cycle," Energy, Elsevier, vol. 158(C), pages 238-255.
    19. Zhang, Lijun & Chennells, Michael & Xia, Xiaohua, 2018. "A power dispatch model for a ferrochrome plant heat recovery cogeneration system," Applied Energy, Elsevier, vol. 227(C), pages 180-189.
    20. Bahlouli, K. & Khoshbakhti Saray, R. & Sarabchi, N., 2015. "Parametric investigation and thermo-economic multi-objective optimization of an ammonia–water power/cooling cycle coupled with an HCCI (homogeneous charge compression ignition) engine," Energy, Elsevier, vol. 86(C), pages 672-684.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:206:y:2020:i:c:s0360544220312305. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.