IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v179y2019icp392-406.html
   My bibliography  Save this article

An urbanization algorithm for districts with minimized emissions based on urban planning and embodied energy towards net-zero exergy targets

Author

Listed:
  • Kılkış, Şiir
  • Kılkış, Birol

Abstract

The realization of net-zero exergy districts can be supported by urbanization options for district density and building characteristics. This research work formulates an urbanization algorithm to minimize the carbon dioxide emissions responsibility of districts based on energy usage and aspects of embodied energy to tackle multiple drivers of urban emissions. The combined method is implemented to scenarios that contribute to a net-zero exergy district target with 6 options for district density and the selection of building materials. Based on a comparison of scenarios for a case study in the province of Ankara, Turkey, the scenario in which on-site exergy production is about 9.5% of the annual exergy consumption will be responsible for about 13,731 ktonnes of carbon dioxide emissions in a timeframe of 30 years. Conversely, a near net-zero exergy district in which on-site exergy production is 75% of the annual exergy consumption will have about 2967 ktonnes of carbon dioxide emissions during the same timeframe, including embodied energy in buildings. The sensitivity analysis with 9 different combinations provides differences in trade-offs based on timeframes and scenarios. The research work has ramifications for local decision-making to avoid locking-in of carbon dioxide emissions by prioritizing an integrated approach to urban energy solutions on the supply and demand sides, district density, and building materials while reaching net-zero targets in the future.

Suggested Citation

  • Kılkış, Şiir & Kılkış, Birol, 2019. "An urbanization algorithm for districts with minimized emissions based on urban planning and embodied energy towards net-zero exergy targets," Energy, Elsevier, vol. 179(C), pages 392-406.
  • Handle: RePEc:eee:energy:v:179:y:2019:i:c:p:392-406
    DOI: 10.1016/j.energy.2019.04.065
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219306887
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2019.04.065?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. Kaczorowska, Anna & Kain, Jaan-Henrik & Kronenberg, Jakub & Haase, Dagmar, 2016. "Ecosystem services in urban land use planning: Integration challenges in complex urban settings—Case of Stockholm," Ecosystem Services, Elsevier, vol. 22(PA), pages 204-212.
    2. Paola Caputo & Costa Gaia & Valentina Zanotto, 2013. "A Methodology for Defining Electricity Demand in Energy Simulations Referred to the Italian Context," Energies, MDPI, vol. 6(12), pages 1-19, December.
    3. Wali, Qamar & Elumalai, Naveen Kumar & Iqbal, Yaseen & Uddin, Ashraf & Jose, Rajan, 2018. "Tandem perovskite solar cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 89-110.
    4. Wiesmann, Daniel & Lima Azevedo, Inês & Ferrão, Paulo & Fernández, John E., 2011. "Residential electricity consumption in Portugal: Findings from top-down and bottom-up models," Energy Policy, Elsevier, vol. 39(5), pages 2772-2779, May.
    5. Somogyi, Viola & Sebestyén, Viktor & Domokos, Endre, 2018. "Assessment of wastewater heat potential for district heating in Hungary," Energy, Elsevier, vol. 163(C), pages 712-721.
    6. Mörtberg, Ulla & Goldenberg, Romain & Kalantari, Zahra & Kordas, Olga & Deal, Brian & Balfors, Berit & Cvetkovic, Vladimir, 2017. "Integrating ecosystem services in the assessment of urban energy trajectories – A study of the Stockholm Region," Energy Policy, Elsevier, vol. 100(C), pages 338-349.
    7. Weber, C. & Shah, N., 2011. "Optimisation based design of a district energy system for an eco-town in the United Kingdom," Energy, Elsevier, vol. 36(2), pages 1292-1308.
    8. Suciu, Raluca & Girardin, Luc & Maréchal, François, 2018. "Energy integration of CO2 networks and power to gas for emerging energy autonomous cities in Europe," Energy, Elsevier, vol. 157(C), pages 830-842.
    9. Engelken, Maximilian & Römer, Benedikt & Drescher, Marcus & Welpe, Isabell, 2016. "Transforming the energy system: Why municipalities strive for energy self-sufficiency," Energy Policy, Elsevier, vol. 98(C), pages 365-377.
    10. Lund, Henrik & Werner, Sven & Wiltshire, Robin & Svendsen, Svend & Thorsen, Jan Eric & Hvelplund, Frede & Mathiesen, Brian Vad, 2014. "4th Generation District Heating (4GDH)," Energy, Elsevier, vol. 68(C), pages 1-11.
    11. Möller, Bernd & Wiechers, Eva & Persson, Urban & Grundahl, Lars & Connolly, David, 2018. "Heat Roadmap Europe: Identifying local heat demand and supply areas with a European thermal atlas," Energy, Elsevier, vol. 158(C), pages 281-292.
    12. Liu, Xiaoping & Ou, Jinpei & Chen, Yimin & Wang, Shaojian & Li, Xia & Jiao, Limin & Liu, Yaolin, 2019. "Scenario simulation of urban energy-related CO2 emissions by coupling the socioeconomic factors and spatial structures," Applied Energy, Elsevier, vol. 238(C), pages 1163-1178.
    13. Blázquez, Leticia & Boogen, Nina & Filippini, Massimo, 2013. "Residential electricity demand in Spain: New empirical evidence using aggregate data," Energy Economics, Elsevier, vol. 36(C), pages 648-657.
    14. Frangopoulos, Christos A., 2012. "A method to determine the power to heat ratio, the cogenerated electricity and the primary energy savings of cogeneration systems after the European Directive," Energy, Elsevier, vol. 45(1), pages 52-61.
    15. Joshi, Sandeep S. & Dhoble, Ashwinkumar S., 2018. "Photovoltaic -Thermal systems (PVT): Technology review and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 92(C), pages 848-882.
    16. Mohajeri, Nahid & Perera, A.T.D. & Coccolo, Silvia & Mosca, Lucas & Le Guen, Morgane & Scartezzini, Jean-Louis, 2019. "Integrating urban form and distributed energy systems: Assessment of sustainable development scenarios for a Swiss village to 2050," Renewable Energy, Elsevier, vol. 143(C), pages 810-826.
    17. Fleischhacker, Andreas & Lettner, Georg & Schwabeneder, Daniel & Auer, Hans, 2019. "Portfolio optimization of energy communities to meet reductions in costs and emissions," Energy, Elsevier, vol. 173(C), pages 1092-1105.
    18. Wu, Qiong & Ren, Hongbo & Gao, Weijun & Weng, Peifen & Ren, Jianxing, 2018. "Coupling optimization of urban spatial structure and neighborhood-scale distributed energy systems," Energy, Elsevier, vol. 144(C), pages 472-481.
    19. Stephan, André & Crawford, Robert H., 2016. "The relationship between house size and life cycle energy demand: Implications for energy efficiency regulations for buildings," Energy, Elsevier, vol. 116(P1), pages 1158-1171.
    20. Huang, Wen-Hsiu, 2015. "The determinants of household electricity consumption in Taiwan: Evidence from quantile regression," Energy, Elsevier, vol. 87(C), pages 120-133.
    21. Mohajeri, Nahid & Upadhyay, Govinda & Gudmundsson, Agust & Assouline, Dan & Kämpf, Jérôme & Scartezzini, Jean-Louis, 2016. "Effects of urban compactness on solar energy potential," Renewable Energy, Elsevier, vol. 93(C), pages 469-482.
    22. Silva, Mafalda C. & Horta, Isabel M. & Leal, Vítor & Oliveira, Vítor, 2017. "A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand," Applied Energy, Elsevier, vol. 202(C), pages 386-398.
    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. Balta, Münevver Özge & Balta, Mustafa Tolga, 2022. "Development of a sustainable hydrogen city concept and initial hydrogen city projects," Energy Policy, Elsevier, vol. 166(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. Kachirayil, Febin & Weinand, Jann Michael & Scheller, Fabian & McKenna, Russell, 2022. "Reviewing local and integrated energy system models: insights into flexibility and robustness challenges," Applied Energy, Elsevier, vol. 324(C).
    2. Małgorzata Sztorc, 2022. "The Implementation of the European Green Deal Strategy as a Challenge for Energy Management in the Face of the COVID-19 Pandemic," Energies, MDPI, vol. 15(7), pages 1-21, April.
    3. Sayegh, M.A. & Danielewicz, J. & Nannou, T. & Miniewicz, M. & Jadwiszczak, P. & Piekarska, K. & Jouhara, H., 2017. "Trends of European research and development in district heating technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 1183-1192.
    4. Persson, Urban & Wiechers, Eva & Möller, Bernd & Werner, Sven, 2019. "Heat Roadmap Europe: Heat distribution costs," Energy, Elsevier, vol. 176(C), pages 604-622.
    5. Chambers, Jonathan & Narula, Kapil & Sulzer, Matthias & Patel, Martin K., 2019. "Mapping district heating potential under evolving thermal demand scenarios and technologies: A case study for Switzerland," Energy, Elsevier, vol. 176(C), pages 682-692.
    6. Prasanna, Ashreeta & Dorer, Viktor & Vetterli, Nadège, 2017. "Optimisation of a district energy system with a low temperature network," Energy, Elsevier, vol. 137(C), pages 632-648.
    7. Javier Bueno & Desiderio Romero-Jordán & Pablo del Río, 2020. "Analysing the Drivers of Electricity Demand in Spain after the Economic Crisis," Energies, MDPI, vol. 13(20), pages 1-18, October.
    8. Capuder, Tomislav & Mancarella, Pierluigi, 2014. "Techno-economic and environmental modelling and optimization of flexible distributed multi-generation options," Energy, Elsevier, vol. 71(C), pages 516-533.
    9. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Salari, Mahmoud & Javid, Roxana J., 2016. "Residential energy demand in the United States: Analysis using static and dynamic approaches," Energy Policy, Elsevier, vol. 98(C), pages 637-649.
    11. Zhu, Penghu & Lin, Boqiang, 2022. "Do the elderly consume more energy? Evidence from the retirement policy in urban China," Energy Policy, Elsevier, vol. 165(C).
    12. Shigeru Matsumoto, 2015. "Electric Appliance Ownership and Usage: Application of Conditional Demand Analysis to Japanese Household Data," Proceedings of International Academic Conferences 3105452, International Institute of Social and Economic Sciences.
    13. Chen, Chien-fei & Xu, Xiaojing & Adua, Lazarus & Briggs, Morgan & Nelson, Hannah, 2022. "Exploring the factors that influence energy use intensity across low-, middle-, and high-income households in the United States," Energy Policy, Elsevier, vol. 168(C).
    14. Javanroodi, Kavan & Mahdavinejad, Mohammadjavad & Nik, Vahid M., 2018. "Impacts of urban morphology on reducing cooling load and increasing ventilation potential in hot-arid climate," Applied Energy, Elsevier, vol. 231(C), pages 714-746.
    15. Jann Michael Weinand, 2020. "Reviewing Municipal Energy System Planning in a Bibliometric Analysis: Evolution of the Research Field between 1991 and 2019," Energies, MDPI, vol. 13(6), pages 1-18, March.
    16. Verschelde, Tars & D'haeseleer, William, 2021. "Methodology for a global sensitivity analysis with machine learning on an energy system planning model in the context of thermal networks," Energy, Elsevier, vol. 232(C).
    17. Matsumoto, Shigeru, 2016. "How do household characteristics affect appliance usage? Application of conditional demand analysis to Japanese household data," Energy Policy, Elsevier, vol. 94(C), pages 214-223.
    18. Jones, Rory V. & Fuertes, Alba & Lomas, Kevin J., 2015. "The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 901-917.
    19. Mengting Jiang & Camilo Rindt & David M. J. Smeulders, 2022. "Optimal Planning of Future District Heating Systems—A Review," Energies, MDPI, vol. 15(19), pages 1-38, September.
    20. Salari, Mahmoud & Javid, Roxana J., 2017. "Modeling household energy expenditure in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 822-832.

    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:179:y:2019:i:c:p:392-406. 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.