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A framework for the optimal integration of solar assisted district heating in different urban sized communities: A robust machine learning approach incorporating global sensitivity analysis

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  • Abokersh, Mohamed Hany
  • Vallès, Manel
  • Cabeza, Luisa F.
  • Boer, Dieter

Abstract

A promising pathway towards sustainable transaction to clean energy production lies in the adoption of solar assisted district heating systems (SDHS). However, SDHS technical barriers during their design and operation phases, combined with their economic limitation, promote a high variation in quantifying SDHS benefits over their lifetime. This study proposes a complete multi-objective optimization framework using a robust machine learning approach to inherent sustainability principles in the design of SDHS. Moreover, the framework investigates the uncertainty in the context of SDHS design, in which the Global Sensitivity Analysis (GSA) is combined with the heuristics optimization approach. The framework application is illustrated through a case study for the optimal integration of SHDS at different urban community sizes (10, 25, 50, and 100 buildings) located in Madrid. The results reveal a substantial improvement in economic and environmental benefits for deploying SDHS, especially with including the seasonal storage tank (SST) construction properties in the optimization problem, and it can achieve a payback period up to 13.7 years. In addition, the solar fraction of the optimized SDHS never falls below 82.1% for the investigated community sizes with an efficiency above 69.5% for the SST. Finally, the GSA indicates the SST investment cost and its relevant construction materials, are primarily responsible for the variability in the optimal system feasibility. The proposed framework can provide a good starting point to solve the enormous computational expenses drawbacks associated with the heuristics optimization approach. Furthermore, it can function as a decision support tool to fulfill the European Union energy targets regarding clean energy production.

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  • Abokersh, Mohamed Hany & Vallès, Manel & Cabeza, Luisa F. & Boer, Dieter, 2020. "A framework for the optimal integration of solar assisted district heating in different urban sized communities: A robust machine learning approach incorporating global sensitivity analysis," Applied Energy, Elsevier, vol. 267(C).
    • Handle: RePEc:eee:appene:v:267:y:2020:i:c:s0306261920304153
    DOI: 10.1016/j.apenergy.2020.114903
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    1. Tulus, Victor & Abokersh, Mohamed Hany & Cabeza, Luisa F. & Vallès, Manel & Jiménez, Laureano & Boer, Dieter, 2019. "Economic and environmental potential for solar assisted central heating plants in the EU residential sector: Contribution to the 2030 climate and energy EU agenda," Applied Energy, Elsevier, vol. 236(C), pages 318-339.
    2. Allouhi, A. & Agrouaz, Y. & Benzakour Amine, Mohammed & Rehman, S. & Buker, M.S. & Kousksou, T. & Jamil, A. & Benbassou, A., 2017. "Design optimization of a multi-temperature solar thermal heating system for an industrial process," Applied Energy, Elsevier, vol. 206(C), pages 382-392.
    3. Lu, L. & Yang, H.X., 2010. "Environmental payback time analysis of a roof-mounted building-integrated photovoltaic (BIPV) system in Hong Kong," Applied Energy, Elsevier, vol. 87(12), pages 3625-3631, December.
    4. Antoniadis, Christodoulos N. & Martinopoulos, Georgios, 2019. "Optimization of a building integrated solar thermal system with seasonal storage using TRNSYS," Renewable Energy, Elsevier, vol. 137(C), pages 56-66.
    5. Li, Qiyuan & Tehrani, S. Saeed Mostafavi & Taylor, Robert A., 2017. "Techno-economic analysis of a concentrating solar collector with built-in shell and tube latent heat thermal energy storage," Energy, Elsevier, vol. 121(C), pages 220-237.
    6. Li, Danny H.W. & Lam, Tony N.T. & Chan, Wilco W.H. & Mak, Ada H.L., 2009. "Energy and cost analysis of semi-transparent photovoltaic in office buildings," Applied Energy, Elsevier, vol. 86(5), pages 722-729, May.
    7. Aven, T., 2011. "Interpretations of alternative uncertainty representations in a reliability and risk analysis context," Reliability Engineering and System Safety, Elsevier, vol. 96(3), pages 353-360.
    8. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
    9. Carpenter, Joseph & Woodbury, Keith A. & O'Neill, Zheng, 2018. "Using change-point and Gaussian process models to create baseline energy models in industrial facilities: A comparison," Applied Energy, Elsevier, vol. 213(C), pages 415-425.
    10. Karl Widerquist, 2018. "The Bottom Line," Exploring the Basic Income Guarantee, in: A Critical Analysis of Basic Income Experiments for Researchers, Policymakers, and Citizens, chapter 0, pages 93-98, Palgrave Macmillan.
    11. Rehman, Hassam ur & Hirvonen, Janne & Sirén, Kai, 2018. "Performance comparison between optimized design of a centralized and semi-decentralized community size solar district heating system," Applied Energy, Elsevier, vol. 229(C), pages 1072-1094.
    12. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    13. Rehman, Hassam ur & Hirvonen, Janne & Sirén, Kai, 2017. "A long-term performance analysis of three different configurations for community-sized solar heating systems in high latitudes," Renewable Energy, Elsevier, vol. 113(C), pages 479-493.
    14. Bornatico, Raffaele & Hüssy, Jonathan & Witzig, Andreas & Guzzella, Lino, 2013. "Surrogate modeling for the fast optimization of energy systems," Energy, Elsevier, vol. 57(C), pages 653-662.
    15. Mavrotas, George & Florios, Kostas & Vlachou, Dimitra, 2010. "Energy planning of a hospital using Mathematical Programming and Monte Carlo simulation for dealing with uncertainty in the economic parameters," MPRA Paper 105754, University Library of Munich, Germany.
    16. Mirkhani, Sh. & Saboohi, Y., 2012. "Stochastic modeling of the energy supply system with uncertain fuel price – A case of emerging technologies for distributed power generation," Applied Energy, Elsevier, vol. 93(C), pages 668-674.
    17. Rad, Farzin M. & Fung, Alan S., 2016. "Solar community heating and cooling system with borehole thermal energy storage – Review of systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1550-1561.
    18. Bustos, F. & Toledo, A. & Contreras, J. & Fuentes, A., 2016. "Sensitivity analysis of a photovoltaic solar plant in Chile," Renewable Energy, Elsevier, vol. 87(P1), pages 145-153.
    19. Reed, A.L. & Novelli, A.P. & Doran, K.L. & Ge, S. & Lu, N. & McCartney, J.S., 2018. "Solar district heating with underground thermal energy storage: Pathways to commercial viability in North America," Renewable Energy, Elsevier, vol. 126(C), pages 1-13.
    20. Tulus, Victor & Boer, Dieter & Cabeza, Luisa F. & Jiménez, Laureano & Guillén-Gosálbez, Gonzalo, 2016. "Enhanced thermal energy supply via central solar heating plants with seasonal storage: A multi-objective optimization approach," Applied Energy, Elsevier, vol. 181(C), pages 549-561.
    21. Dahash, Abdulrahman & Ochs, Fabian & Janetti, Michele Bianchi & Streicher, Wolfgang, 2019. "Advances in seasonal thermal energy storage for solar district heating applications: A critical review on large-scale hot-water tank and pit thermal energy storage systems," Applied Energy, Elsevier, vol. 239(C), pages 296-315.
    22. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Uncertainty and global sensitivity analysis for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 214(C), pages 219-238.
    23. Welsch, Bastian & Göllner-Völker, Laura & Schulte, Daniel O. & Bär, Kristian & Sass, Ingo & Schebek, Liselotte, 2018. "Environmental and economic assessment of borehole thermal energy storage in district heating systems," Applied Energy, Elsevier, vol. 216(C), pages 73-90.
    24. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
    25. Petropoulos, G. & Wooster, M.J. & Carlson, T.N. & Kennedy, M.C. & Scholze, M., 2009. "A global Bayesian sensitivity analysis of the 1d SimSphere soil–vegetation–atmospheric transfer (SVAT) model using Gaussian model emulation," Ecological Modelling, Elsevier, vol. 220(19), pages 2427-2440.
    26. Yang, Yun & Zhang, Shijie & Xiao, Yunhan, 2015. "Optimal design of distributed energy resource systems coupled with energy distribution networks," Energy, Elsevier, vol. 85(C), pages 433-448.
    27. Østergård, Torben & Jensen, Rasmus Lund & Maagaard, Steffen Enersen, 2018. "A comparison of six metamodeling techniques applied to building performance simulations," Applied Energy, Elsevier, vol. 211(C), pages 89-103.
    28. Ciampi, Giovanni & Rosato, Antonio & Sibilio, Sergio, 2018. "Thermo-economic sensitivity analysis by dynamic simulations of a small Italian solar district heating system with a seasonal borehole thermal energy storage," Energy, Elsevier, vol. 143(C), pages 757-771.
    29. Wang, Zeyu & Srinivasan, Ravi S., 2017. "A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 796-808.
    30. Buoro, Dario & Pinamonti, Piero & Reini, Mauro, 2014. "Optimization of a Distributed Cogeneration System with solar district heating," Applied Energy, Elsevier, vol. 124(C), pages 298-308.
    31. Bava, Federico & Furbo, Simon, 2017. "Development and validation of a detailed TRNSYS-Matlab model for large solar collector fields for district heating applications," Energy, Elsevier, vol. 135(C), pages 698-708.
    32. Xia, Lei & Ma, Zhenjun & Kokogiannakis, Georgios & Wang, Zhihua & Wang, Shugang, 2018. "A model-based design optimization strategy for ground source heat pump systems with integrated photovoltaic thermal collectors," Applied Energy, Elsevier, vol. 214(C), pages 178-190.
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    10. Hiris, Daniel P. & Pop, Octavian G. & Dobrovicescu, Alexandru & Dudescu, Mircea C. & Balan, Mugur C., 2023. "Modelling of solar assisted district heating system with seasonal storage tank by two mathematical methods and with two climatic data as input," Energy, Elsevier, vol. 284(C).
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    15. Dahash, Abdulrahman & Ochs, Fabian & Tosatto, Alice, 2021. "Techno-economic and exergy analysis of tank and pit thermal energy storage for renewables district heating systems," Renewable Energy, Elsevier, vol. 180(C), pages 1358-1379.

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