IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v265y2020ics030626192030297x.html
   My bibliography  Save this article

Neural-network-based optimization for economic dispatch of combined heat and power systems

Author

Listed:
  • Kim, Min Jae
  • Kim, Tong Seop
  • Flores, Robert J.
  • Brouwer, Jack

Abstract

One of the major research areas in combined heat and power (CHP) systems is optimal dispatch, which involves the minimization of the operating cost. In economic dispatch, it is important to use a model that accurately simulates the performance of the power and heat generation equipment. However, physics-based characteristic models require considerable time for the analysis, so it is hard to apply them to the optimization of dispatch schedules. This study introduced a neural network model, which was built based upon the simulation results of a physics-based model, to optimize a CHP system. The novel method was used to optimize the operation schedule of a system consisting of a gas turbine, steam turbine bottoming cycle, compressed air energy storage, and a boiler. The schedule was optimized to minimize the operation cost per day and according to the power and heating demand of users. The results showed that the introduction of the neural network reduced the time required for the system analysis by more than 7000 times. Furthermore, the optimization results confirmed the importance of accurately predicting the performance of each device using the physics-based model. This study contributes to the reduction in computation time and improvement of optimization accuracy.

Suggested Citation

  • Kim, Min Jae & Kim, Tong Seop & Flores, Robert J. & Brouwer, Jack, 2020. "Neural-network-based optimization for economic dispatch of combined heat and power systems," Applied Energy, Elsevier, vol. 265(C).
    • Handle: RePEc:eee:appene:v:265:y:2020:i:c:s030626192030297x
    DOI: 10.1016/j.apenergy.2020.114785
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626192030297X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.114785?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. 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.
    2. Sameti, Mohammad & Haghighat, Fariborz, 2019. "Optimization of 4th generation distributed district heating system: Design and planning of combined heat and power," Renewable Energy, Elsevier, vol. 130(C), pages 371-387.
    3. Samanthi, Ranadeera G.M. & Sepanski, Jungsywan, 2019. "Methods for generating coherent distortion risk measures," Annals of Actuarial Science, Cambridge University Press, vol. 13(2), pages 400-416, September.
    4. Zou, Dexuan & Li, Steven & Kong, Xiangyong & Ouyang, Haibin & Li, Zongyan, 2019. "Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy," Applied Energy, Elsevier, vol. 237(C), pages 646-670.
    5. Zidan, Aboelsood & Gabbar, Hossam A. & Eldessouky, Ahmed, 2015. "Optimal planning of combined heat and power systems within microgrids," Energy, Elsevier, vol. 93(P1), pages 235-244.
    6. Anand, Himanshu & Narang, Nitin & Dhillon, J.S., 2019. "Multi-objective combined heat and power unit commitment using particle swarm optimization," Energy, Elsevier, vol. 172(C), pages 794-807.
    7. Seijo, Sandra & del Campo, Inés & Echanobe, Javier & García-Sedano, Javier, 2016. "Modeling and multi-objective optimization of a complex CHP process," Applied Energy, Elsevier, vol. 161(C), pages 309-319.
    8. Wang, Jiawei & You, Shi & Zong, Yi & Cai, Hanmin & Træholt, Chresten & Dong, Zhao Yang, 2019. "Investigation of real-time flexibility of combined heat and power plants in district heating applications," Applied Energy, Elsevier, vol. 237(C), pages 196-209.
    9. Akpini K. A. Michael & Assui K Richard & Yoro Gozo & Bailly Bale, 2019. "Optimal Method of Runge-Kutta of Order 5," Journal of Mathematics Research, Canadian Center of Science and Education, vol. 11(1), pages 93-101, February.
    10. Jingjing Ye & Keping Li & Jing Li, 2019. "An improved clustering method for uncertain system," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 30(09), pages 1-13, September.
    11. Philip M. E. Garboden & Prentiss A. Dantzler, 2019. "A Methodological Critique of Wassmer and Wahid," Housing Policy Debate, Taylor & Francis Journals, vol. 29(2), pages 359-362, March.
    12. Moon, Seong Won & Kwon, Hyun Min & Kim, Tong Seop & Kang, Do Won & Sohn, Jeong Lak, 2018. "A novel coolant cooling method for enhancing the performance of the gas turbine combined cycle," Energy, Elsevier, vol. 160(C), pages 625-634.
    13. Kim, Min Jae & Kim, Tong Seop, 2017. "Feasibility study on the influence of steam injection in the compressed air energy storage system," Energy, Elsevier, vol. 141(C), pages 239-249.
    14. Vishwanathan, Gokul & Sculley, Julian P. & Fischer, Adam & Zhao, Ji-Cheng, 2018. "Techno-economic analysis of high-efficiency natural-gas generators for residential combined heat and power," Applied Energy, Elsevier, vol. 226(C), pages 1064-1075.
    15. Li, Bingxin, 2019. "Pricing dynamics of natural gas futures," Energy Economics, Elsevier, vol. 78(C), pages 91-108.
    16. Haisheng Chen & Xinjing Zhang & Jinchao Liu & Chunqing Tan, 2013. "Compressed Air Energy Storage," Chapters, in: Ahmed F. Zobaa (ed.), Energy Storage - Technologies and Applications, IntechOpen.
    17. Ersoz, Ibrahim & Colak, Uner, 2016. "Combined cooling, heat and power planning under uncertainty," Energy, Elsevier, vol. 109(C), pages 1016-1025.
    18. Fengwei Li & Xiuqing Gao, 2019. "Optimal Methodologies," Information Management and Computer Science (IMCS), Zibeline International Publishing, vol. 2(1), pages 1-3, February.
    19. Kang, Do Won & Kim, Tong Seop, 2018. "Model-based performance diagnostics of heavy-duty gas turbines using compressor map adaptation," Applied Energy, Elsevier, vol. 212(C), pages 1345-1359.
    20. Kim, Min Jae & Kim, Tong Seop, 2019. "Integration of compressed air energy storage and gas turbine to improve the ramp rate," Applied Energy, Elsevier, vol. 247(C), pages 363-373.
    21. Patacchini, Eleonora & Hsieh, Chih-Sheng & Lin, Xu, 2019. "Social Interaction Methods," CEPR Discussion Papers 14141, C.E.P.R. Discussion Papers.
    22. Santos, Maria Izabel & Uturbey, Wadaed, 2018. "A practical model for energy dispatch in cogeneration plants," Energy, Elsevier, vol. 151(C), pages 144-159.
    23. Adam, Alexandros & Fraga, Eric S. & Brett, Dan J.L., 2015. "Options for residential building services design using fuel cell based micro-CHP and the potential for heat integration," Applied Energy, Elsevier, vol. 138(C), pages 685-694.
    24. Sadeghi, Saber & Askari, Ighball Baniasad, 2019. "Prefeasibility techno-economic assessment of a hybrid power plant with photovoltaic, fuel cell and Compressed Air Energy Storage (CAES)," Energy, Elsevier, vol. 168(C), pages 409-424.
    25. Nikpey, H. & Assadi, M. & Breuhaus, P., 2013. "Development of an optimized artificial neural network model for combined heat and power micro gas turbines," Applied Energy, Elsevier, vol. 108(C), pages 137-148.
    26. Kim, Min Jae & Kim, Jeong Ho & Kim, Tong Seop, 2018. "The effects of internal leakage on the performance of a micro gas turbine," Applied Energy, Elsevier, vol. 212(C), pages 175-184.
    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. Shahenda Sarhan & Abdullah Shaheen & Ragab El-Sehiemy & Mona Gafar, 2022. "A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies," Mathematics, MDPI, vol. 10(13), pages 1-26, June.
    2. Hye-Rim Kim & Tong-Seop Kim, 2021. "Optimization of Sizing and Operation Strategy of Distributed Generation System Based on a Gas Turbine and Renewable Energy," Energies, MDPI, vol. 14(24), pages 1-28, December.
    3. Xiong, Guojiang & Shuai, Maohang & Hu, Xiao, 2022. "Combined heat and power economic emission dispatch using improved bare-bone multi-objective particle swarm optimization," Energy, Elsevier, vol. 244(PB).
    4. 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).
    5. Sharf, Miel & Romm, Iliya & Palman, Michael & Zelazo, Daniel & Cukurel, Beni, 2022. "Economic dispatch of a single micro gas turbine under CHP operation with uncertain demands," Applied Energy, Elsevier, vol. 309(C).
    6. Ragab El-Sehiemy & Abdullah Shaheen & Ahmed Ginidi & Mostafa Elhosseini, 2022. "A Honey Badger Optimization for Minimizing the Pollutant Environmental Emissions-Based Economic Dispatch Model Integrating Combined Heat and Power Units," Energies, MDPI, vol. 15(20), pages 1-22, October.
    7. Ondřej Putna & Jakub Kůdela & Martin Krňávek & Martin Pavlas & Kamil Ondra, 2022. "Modelling of Change in Fuel Mix within a District Heating Network," Energies, MDPI, vol. 15(8), pages 1-13, April.
    8. Liu, Haizhou & Shen, Xinwei & Guo, Qinglai & Sun, Hongbin, 2021. "A data-driven approach towards fast economic dispatch in electricity–gas coupled systems based on artificial neural network," Applied Energy, Elsevier, vol. 286(C).
    9. He, Yi & Guo, Su & Zhou, Jianxu & Wu, Feng & Huang, Jing & Pei, Huanjin, 2021. "The many-objective optimal design of renewable energy cogeneration system," Energy, Elsevier, vol. 234(C).
    10. Yang, Lijun & Jiang, Yaning & Chong, Zhenxiao, 2023. "Optimal scheduling of electro-thermal system considering refined demand response and source-load-storage cooperative hydrogen production," Renewable Energy, Elsevier, vol. 215(C).
    11. Wang, Xinlin & Flores, Robert & Brouwer, Jack & Papaefthymiou, Marios, 2022. "Real-time detection of electrical load anomalies through hyperdimensional computing," Energy, Elsevier, vol. 261(PA).
    12. Kumar Jadoun, Vinay & Rahul Prashanth, G & Suhas Joshi, Siddharth & Narayanan, K. & Malik, Hasmat & García Márquez, Fausto Pedro, 2022. "Optimal fuzzy based economic emission dispatch of combined heat and power units using dynamically controlled Whale Optimization Algorithm," Applied Energy, Elsevier, vol. 315(C).
    13. Chen, Yu-Zhi & Tsoutsanis, Elias & Xiang, Heng-Chao & Li, Yi-Guang & Zhao, Jun-Jie, 2022. "A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions," Applied Energy, Elsevier, vol. 317(C).
    14. Chen, Yu-Zhi & Tsoutsanis, Elias & Wang, Chen & Gou, Lin-Feng, 2023. "A time-series turbofan engine successive fault diagnosis under both steady-state and dynamic conditions," Energy, Elsevier, vol. 263(PD).
    15. Fei Chen & Zhiyang Wang & Yu He, 2023. "A Deep Neural Network-Based Optimal Scheduling Decision-Making Method for Microgrids," Energies, MDPI, vol. 16(22), pages 1-17, November.
    16. Seong Won Moon & Tong Seop Kim, 2020. "Advanced Gas Turbine Control Logic Using Black Box Models for Enhancing Operational Flexibility and Stability," Energies, MDPI, vol. 13(21), pages 1-23, October.

    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. Seong Won Moon & Tong Seop Kim, 2020. "Advanced Gas Turbine Control Logic Using Black Box Models for Enhancing Operational Flexibility and Stability," Energies, MDPI, vol. 13(21), pages 1-23, October.
    2. Ali Sulaiman Alsagri & Abdulrahman A. Alrobaian, 2022. "Optimization of Combined Heat and Power Systems by Meta-Heuristic Algorithms: An Overview," Energies, MDPI, vol. 15(16), pages 1-34, August.
    3. Kim, Min Jae & Kim, Tong Seop, 2019. "Integration of compressed air energy storage and gas turbine to improve the ramp rate," Applied Energy, Elsevier, vol. 247(C), pages 363-373.
    4. Bartela, Łukasz, 2020. "A hybrid energy storage system using compressed air and hydrogen as the energy carrier," Energy, Elsevier, vol. 196(C).
    5. Hye-Rim Kim & Tong-Seop Kim, 2021. "Optimization of Sizing and Operation Strategy of Distributed Generation System Based on a Gas Turbine and Renewable Energy," Energies, MDPI, vol. 14(24), pages 1-28, December.
    6. Yang, Dechang & Wang, Ming & Yang, Ruiqi & Zheng, Yingying & Pandzic, Hrvoje, 2021. "Optimal dispatching of an energy system with integrated compressed air energy storage and demand response," Energy, Elsevier, vol. 234(C).
    7. Fan, Jinyang & Liu, Wei & Jiang, Deyi & Chen, Junchao & Ngaha Tiedeu, William & Chen, Jie & JJK, Deaman, 2018. "Thermodynamic and applicability analysis of a hybrid CAES system using abandoned coal mine in China," Energy, Elsevier, vol. 157(C), pages 31-44.
    8. Cheayb, Mohamad & Marin Gallego, Mylène & Tazerout, Mohand & Poncet, Sébastien, 2022. "A techno-economic analysis of small-scale trigenerative compressed air energy storage system," Energy, Elsevier, vol. 239(PA).
    9. Donghui Wang & Chunming Liu, 2019. "Combination Optimization Configuration Method of Capacitance and Resistance Devices for Suppressing DC Bias in Transformers," Energies, MDPI, vol. 12(9), pages 1-13, May.
    10. Antonucci, V. & Branchini, L. & Brunaccini, G. & De Pascale, A. & Ferraro, M. & Melino, F. & Orlandini, V. & Sergi, F., 2017. "Thermal integration of a SOFC power generator and a Na–NiCl2 battery for CHP domestic application," Applied Energy, Elsevier, vol. 185(P2), pages 1256-1267.
    11. Azimian, Mahdi & Amir, Vahid & Javadi, Saeid, 2020. "Economic and Environmental Policy Analysis for Emission-Neutral Multi-Carrier Microgrid Deployment," Applied Energy, Elsevier, vol. 277(C).
    12. Yang, Lichao & Cai, Zuansi & Li, Cai & He, Qingcheng & Ma, Yan & Guo, Chaobin, 2020. "Numerical investigation of cycle performance in compressed air energy storage in aquifers," Applied Energy, Elsevier, vol. 269(C).
    13. Asensio, F.J. & San Martín, J.I. & Zamora, I. & Oñederra, O., 2018. "Model for optimal management of the cooling system of a fuel cell-based combined heat and power system for developing optimization control strategies," Applied Energy, Elsevier, vol. 211(C), pages 413-430.
    14. Tong, Zheming & Cheng, Zhewu & Tong, Shuiguang, 2021. "A review on the development of compressed air energy storage in China: Technical and economic challenges to commercialization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    15. Maciej Bujalski & Paweł Madejski, 2021. "Forecasting of Heat Production in Combined Heat and Power Plants Using Generalized Additive Models," Energies, MDPI, vol. 14(8), pages 1-15, April.
    16. Young-Kwang Park & Seong-Won Moon & Tong-Seop Kim, 2021. "Advanced Control to Improve the Ramp-Rate of a Gas Turbine: Optimization of Control Schedule," Energies, MDPI, vol. 14(23), pages 1-23, December.
    17. Fragiacomo, Petronilla & Lucarelli, Giuseppe & Genovese, Matteo & Florio, Gaetano, 2021. "Multi-objective optimization model for fuel cell-based poly-generation energy systems," Energy, Elsevier, vol. 237(C).
    18. Panda, Ambarish & Mishra, Umakanta & Aviso, Kathleen B., 2020. "Optimizing hybrid power systems with compressed air energy storage," Energy, Elsevier, vol. 205(C).
    19. Guo, Huan & Xu, Yujie & Zhang, Xuehui & Liang, Qi & Wang, Shurui & Chen, Haisheng, 2021. "Dynamic characteristics and control of supercritical compressed air energy storage systems," Applied Energy, Elsevier, vol. 283(C).
    20. Lucrezia Manservigi & Mattia Cattozzo & Pier Ruggero Spina & Mauro Venturini & Hilal Bahlawan, 2020. "Optimal Management of the Energy Flows of Interconnected Residential Users," Energies, MDPI, vol. 13(6), pages 1-21, March.

    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:appene:v:265:y:2020:i:c:s030626192030297x. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.