IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v276y2020ics0306261920310011.html
   My bibliography  Save this article

A framework for analyzing city-wide impact of building-integrated renewable energy

Author

Listed:
  • Song, Jeonghun
  • Song, Seung Jin

Abstract

As the building-integrated renewables increase in the urban energy mix, it is important to assess the collective effects of building-integrated renewables at city scale. Estimation of the total energy supply and capacity of renewables, variations in the energy from the grid, and CO2 emission would aid renewable energy policy evaluation. However, analyzing the collective effects considering the optimal energy system for each building in a city is difficult due to a large number of buildings (on the order of 105). Therefore, this study proposes a new framework for analyzing the city-wide impact of increased building-integrated renewables. To reduce the number of optimization, clusters of buildings with similar characteristics and one virtual representative building for each cluster are generated. For apartment buildings, the characteristics are the floor area and roof area per household. For non-residential buildings, the characteristics are the shapes of the monthly electricity and gas usages, the ratio between the annual gas and electricity usages, and normalized roof area. To generate clusters of similar non-residential buildings, k-Means Clustering Algorithm and Genetic Algorithm have been applied. The proposed framework has been validated by comparing the collective results from i) optimization of 4,425 actual apartment buildings and 2,779 actual non-residential buildings in an urban district; and ii) optimization of corresponding 957 representative apartment buildings and 176 representative non-residential buildings. Total energy supply and capacity of each renewable energy source, total monthly electricity and gas from the grid, and total hourly electricity from the grid show good agreement. As a demonstration, the proposed framework has been applied to the city of Seoul, Korea for a future scenario of building energy obligation – i) to estimate the total capacities and energy supply of the building-integrated renewables and the change in the energy from the grid; and ii) to evaluate the cost-effectiveness of the obligation based on the unit cost of CO2 reduction for varying renewable energy requirements for the buildings.

Suggested Citation

  • Song, Jeonghun & Song, Seung Jin, 2020. "A framework for analyzing city-wide impact of building-integrated renewable energy," Applied Energy, Elsevier, vol. 276(C).
  • Handle: RePEc:eee:appene:v:276:y:2020:i:c:s0306261920310011
    DOI: 10.1016/j.apenergy.2020.115489
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920310011
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2020.115489?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. Best, Robert E. & Flager, Forest & Lepech, Michael D., 2015. "Modeling and optimization of building mix and energy supply technology for urban districts," Applied Energy, Elsevier, vol. 159(C), pages 161-177.
    2. Nagpal, Shreshth & Hanson, Jared & Reinhart, Christoph, 2019. "A framework for using calibrated campus-wide building energy models for continuous planning and greenhouse gas emissions reduction tracking," Applied Energy, Elsevier, vol. 241(C), pages 82-97.
    3. Chung, Mo & Park, Hwa-Choon, 2015. "Comparison of building energy demand for hotels, hospitals, and offices in Korea," Energy, Elsevier, vol. 92(P3), pages 383-393.
    4. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    5. Li, Xiwang & Wen, Jin & Malkawi, Ali, 2016. "An operation optimization and decision framework for a building cluster with distributed energy systems," Applied Energy, Elsevier, vol. 178(C), pages 98-109.
    6. 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.
    7. Chung, Mo & Park, Chuhwan & Lee, Sukgyu & Park, Hwa-Choon & Im, Yong-Hoon & Chang, Youngho, 2012. "A decision support assessment of cogeneration plant for a community energy system in Korea," Energy Policy, Elsevier, vol. 47(C), pages 365-383.
    8. Evins, Ralph, 2013. "A review of computational optimisation methods applied to sustainable building design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 230-245.
    9. Baek, Seoin & Park, Eunil & Kim, Min-Gil & Kwon, Sang Jib & Kim, Ki Joon & Ohm, Jay Y. & del Pobil, Angel P., 2016. "Optimal renewable power generation systems for Busan metropolitan city in South Korea," Renewable Energy, Elsevier, vol. 88(C), pages 517-525.
    10. Garshasbi, Samira & Kurnitski, Jarek & Mohammadi, Yousef, 2016. "A hybrid Genetic Algorithm and Monte Carlo simulation approach to predict hourly energy consumption and generation by a cluster of Net Zero Energy Buildings," Applied Energy, Elsevier, vol. 179(C), pages 626-637.
    11. Aikins, Kojo Atta & Choi, Jong Min, 2012. "Current status of the performance of GSHP (ground source heat pump) units in the Republic of Korea," Energy, Elsevier, vol. 47(1), pages 77-82.
    12. Seljom, Pernille & Lindberg, Karen Byskov & Tomasgard, Asgeir & Doorman, Gerard & Sartori, Igor, 2017. "The impact of Zero Energy Buildings on the Scandinavian energy system," Energy, Elsevier, vol. 118(C), pages 284-296.
    13. Ashouri, Araz & Fux, Samuel S. & Benz, Michael J. & Guzzella, Lino, 2013. "Optimal design and operation of building services using mixed-integer linear programming techniques," Energy, Elsevier, vol. 59(C), pages 365-376.
    14. Soares, N. & Bastos, J. & Pereira, L. Dias & Soares, A. & Amaral, A.R. & Asadi, E. & Rodrigues, E. & Lamas, F.B. & Monteiro, H. & Lopes, M.A.R. & Gaspar, A.R., 2017. "A review on current advances in the energy and environmental performance of buildings towards a more sustainable built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 845-860.
    15. Hart, Elaine K. & Jacobson, Mark Z., 2011. "A Monte Carlo approach to generator portfolio planning and carbon emissions assessments of systems with large penetrations of variable renewables," Renewable Energy, Elsevier, vol. 36(8), pages 2278-2286.
    16. Chung, Mo & Park, Hwa-Choon, 2012. "Building energy demand patterns for department stores in Korea," Applied Energy, Elsevier, vol. 90(1), pages 241-249.
    17. Mathew, Paul A. & Dunn, Laurel N. & Sohn, Michael D. & Mercado, Andrea & Custudio, Claudine & Walter, Travis, 2015. "Big-data for building energy performance: Lessons from assembling a very large national database of building energy use," Applied Energy, Elsevier, vol. 140(C), pages 85-93.
    18. Iturriaga, E. & Aldasoro, U. & Campos-Celador, A. & Sala, J.M., 2017. "A general model for the optimization of energy supply systems of buildings," Energy, Elsevier, vol. 138(C), pages 954-966.
    19. Hori, Keiko & Matsui, Takanori & Hasuike, Takashi & Fukui, Ken-ichi & Machimura, Takashi, 2016. "Development and application of the renewable energy regional optimization utility tool for environmental sustainability: REROUTES," Renewable Energy, Elsevier, vol. 93(C), pages 548-561.
    20. Oh, Si-Doek & Kim, Ki-Young & Oh, Shuk-Bum & Kwak, Ho-Young, 2012. "Optimal operation of a 1-kW PEMFC-based CHP system for residential applications," Applied Energy, Elsevier, vol. 95(C), pages 93-101.
    21. Fonseca, Jimeno A. & Schlueter, Arno, 2015. "Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts," Applied Energy, Elsevier, vol. 142(C), pages 247-265.
    22. Song, Jeonghun & Song, Seung Jin & Oh, Si-Deok & Yoo, Yungpil, 2015. "Evaluation of potential fossil fuel conservation by the renewable heat obligation in Korea," Renewable Energy, Elsevier, vol. 79(C), pages 140-149.
    23. Song, Jeonghun & Oh, Si-Doek & Song, Seung Jin, 2019. "Effect of increased building-integrated renewable energy on building energy portfolio and energy flows in an urban district of Korea," Energy, Elsevier, vol. 189(C).
    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. Dalia Štreimikienė & Vidas Lekavičius & Gintare Stankūnienė & Aušra Pažėraitė, 2022. "Renewable Energy Acceptance by Households: Evidence from Lithuania," Sustainability, MDPI, vol. 14(14), pages 1-17, July.
    2. Zhang, Sheng & Ocłoń, Paweł & Klemeš, Jiří Jaromír & Michorczyk, Piotr & Pielichowska, Kinga & Pielichowski, Krzysztof, 2022. "Renewable energy systems for building heating, cooling and electricity production with thermal energy storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    3. Chen, Yuzhu & Hu, Xiaojian & Xu, Wentao & Xu, Qiliang & Wang, Jun & Lund, Peter D., 2022. "Multi-objective optimization of a solar-driven trigeneration system considering power-to-heat storage and carbon tax," Energy, Elsevier, vol. 250(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. Song, Jeonghun & Oh, Si-Doek & Song, Seung Jin, 2019. "Effect of increased building-integrated renewable energy on building energy portfolio and energy flows in an urban district of Korea," Energy, Elsevier, vol. 189(C).
    2. Ferrari, Simone & Zagarella, Federica & Caputo, Paola & D'Amico, Antonino, 2019. "Results of a literature review on methods for estimating buildings energy demand at district level," Energy, Elsevier, vol. 175(C), pages 1130-1137.
    3. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    4. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    5. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    6. Halhoul Merabet, Ghezlane & Essaaidi, Mohamed & Ben Haddou, Mohamed & Qolomany, Basheer & Qadir, Junaid & Anan, Muhammad & Al-Fuqaha, Ala & Abid, Mohamed Riduan & Benhaddou, Driss, 2021. "Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    7. Fuentes-Cortés, Luis Fabián & Flores-Tlacuahuac, Antonio, 2018. "Integration of distributed generation technologies on sustainable buildings," Applied Energy, Elsevier, vol. 224(C), pages 582-601.
    8. Urban, Kristof L. & Scheller, Fabian & Bruckner, Thomas, 2021. "Suitability assessment of models in the industrial energy system design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    9. Ang, Yu Qian & Berzolla, Zachary Michael & Reinhart, Christoph F., 2020. "From concept to application: A review of use cases in urban building energy modeling," Applied Energy, Elsevier, vol. 279(C).
    10. Kim, Min-Hwi & Kim, Deukwon & Heo, Jaehyeok & Lee, Dong-Won, 2020. "Energy performance investigation of net plus energy town: Energy balance of the Jincheon Eco-Friendly energy town," Renewable Energy, Elsevier, vol. 147(P1), pages 1784-1800.
    11. Ascione, Fabrizio & De Masi, Rosa Francesca & de Rossi, Filippo & Ruggiero, Silvia & Vanoli, Giuseppe Peter, 2016. "Optimization of building envelope design for nZEBs in Mediterranean climate: Performance analysis of residential case study," Applied Energy, Elsevier, vol. 183(C), pages 938-957.
    12. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    13. 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.
    14. Alaia Sola & Cristina Corchero & Jaume Salom & Manel Sanmarti, 2018. "Simulation Tools to Build Urban-Scale Energy Models: A Review," Energies, MDPI, vol. 11(12), pages 1-24, November.
    15. Evins, Ralph, 2015. "Multi-level optimization of building design, energy system sizing and operation," Energy, Elsevier, vol. 90(P2), pages 1775-1789.
    16. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    17. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Purcell, Karl & Hoare, Cathal & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making," Applied Energy, Elsevier, vol. 279(C).
    18. Kazas, Georgios & Fabrizio, Enrico & Perino, Marco, 2017. "Energy demand profile generation with detailed time resolution at an urban district scale: A reference building approach and case study," Applied Energy, Elsevier, vol. 193(C), pages 243-262.
    19. Sergio Ortega Alba & Mario Manana, 2017. "Characterization and Analysis of Energy Demand Patterns in Airports," Energies, MDPI, vol. 10(1), pages 1-35, January.
    20. Soares, N. & Bastos, J. & Pereira, L. Dias & Soares, A. & Amaral, A.R. & Asadi, E. & Rodrigues, E. & Lamas, F.B. & Monteiro, H. & Lopes, M.A.R. & Gaspar, A.R., 2017. "A review on current advances in the energy and environmental performance of buildings towards a more sustainable built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 845-860.

    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:276:y:2020:i:c:s0306261920310011. 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.