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Optimization of 4th generation distributed district heating system: Design and planning of combined heat and power

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  • Sameti, Mohammad
  • Haghighat, Fariborz

Abstract

This study applies a mathematical programming procedure to model the optimal design and planning of a new district which satisfies two features of the 4th generation district heating systems: energy reciprocity and on-site generation. The aim of the computational model is to investigate the effect of energy reciprocity (energy exchange among the buildings) as well as to find the best way to select the equipment among various candidates (capacities), the pipeline network among the buildings, and their electrical connections. The objective function includes the annualized overall capital and operation costs for the district along with the benefits of selling electricity to the grid. The distributed energy supply consists of heating, cooling, and power networks, several CHP technologies, solar array, chillers, and auxiliary boilers. The performance of the model for poly-generation was evaluated for designing the new part of Suurstoffi district situated in Risch Rotkreuz, Switzerland with seven residential and office complexes under four different scenarios. Allowing heat exchange among the buildings leads to 25% reduction in total annualized cost and 5% reduction in emission compared to the conventional districts. Removing the network and installation of PV and CHPs results in 9% reduction in emission and 11% reduction in cost. Simultaneous heat and electricity exchange results in a higher reduction in total annualized cost equal to 40% of the base scenario.

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  • 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.
    • Handle: RePEc:eee:renene:v:130:y:2019:i:c:p:371-387
    DOI: 10.1016/j.renene.2018.06.068
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    6. Volpe, R. & Catrini, P. & Piacentino, A. & Fichera, A., 2022. "An agent-based model to support the preliminary design and operation of heating and power grids with cogeneration units and photovoltaic panels in densely populated areas," Energy, Elsevier, vol. 261(PB).
    7. Sorknæs, Peter & Østergaard, Poul Alberg & Thellufsen, Jakob Zinck & Lund, Henrik & Nielsen, Steffen & Djørup, Søren & Sperling, Karl, 2020. "The benefits of 4th generation district heating in a 100% renewable energy system," Energy, Elsevier, vol. 213(C).
    8. Pang, Kang Ying & Liew, Peng Yen & Woon, Kok Sin & Ho, Wai Shin & Wan Alwi, Sharifah Rafidah & Klemeš, Jiří Jaromír, 2023. "Multi-period multi-objective optimisation model for multi-energy urban-industrial symbiosis with heat, cooling, power and hydrogen demands," Energy, Elsevier, vol. 262(PA).
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    10. 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.
    11. Ren, Hongbo & Wu, Qiong & Li, Qifen & Yang, Yongwen, 2020. "Optimal design and management of distributed energy network considering both efficiency and fairness," Energy, Elsevier, vol. 213(C).
    12. Meena, Nand K. & Yang, Jin & Zacharis, Evan, 2019. "Optimisation framework for the design and operation of open-market urban and remote community microgrids," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    13. Arabkoohsar, Ahmad & Alsagri, Ali Sulaiman, 2020. "A new generation of district heating system with neighborhood-scale heat pumps and advanced pipes, a solution for future renewable-based energy systems," Energy, Elsevier, vol. 193(C).
    14. Donghun Lee & Seok Mann Yoon & Jaeseung Lee & Kwanho Kim & Sang Hwa Song, 2020. "Applying Deep Learning to the Heat Production Planning Problem in a District Heating System," Energies, MDPI, vol. 13(24), pages 1-17, December.
    15. 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).
    16. Millar, Michael-Allan & Yu, Zhibin & Burnside, Neil & Jones, Greg & Elrick, Bruce, 2021. "Identification of key performance indicators and complimentary load profiles for 5th generation district energy networks," Applied Energy, Elsevier, vol. 291(C).
    17. Finkenrath, Matthias & Faber, Till & Behrens, Fabian & Leiprecht, Stefan, 2022. "Holistic modelling and optimisation of thermal load forecasting, heat generation and plant dispatch for a district heating network," Energy, Elsevier, vol. 250(C).
    18. Pietro Catrini & Tancredi Testasecca & Alessandro Buscemi & Antonio Piacentino, 2022. "Exergoeconomics as a Cost-Accounting Method in Thermal Grids with the Presence of Renewable Energy Producers," Sustainability, MDPI, vol. 14(7), pages 1-27, March.
    19. Ashouri, Milad & Fung, Benjamin C.M. & Haghighat, Fariborz & Yoshino, Hiroshi, 2020. "Systematic approach to provide building occupants with feedback to reduce energy consumption," Energy, Elsevier, vol. 194(C).
    20. Egging-Bratseth, Ruud & Kauko, Hanne & Knudsen, Brage Rugstad & Bakke, Sara Angell & Ettayebi, Amina & Haufe, Ina Renate, 2021. "Seasonal storage and demand side management in district heating systems with demand uncertainty," Applied Energy, Elsevier, vol. 285(C).
    21. Miao Li & Yiran Feng & Maojun Zhou & Hailin Mu & Longxi Li & Yajun Wang, 2019. "Economic and Environmental Optimization for Distributed Energy System Integrated with District Energy Network," Energies, MDPI, vol. 12(10), pages 1-19, May.

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