IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i18p7988-d1476961.html
   My bibliography  Save this article

The City as a Power Hub for Boosting Renewable Energy Communities: A Case Study in Naples

Author

Listed:
  • Giuseppe Aruta

    (Department of Industrial Engineering, Università degli Studi di Napoli Federico II, Piazzale Tecchio 80, 80125 Napoli, Italy)

  • Fabrizio Ascione

    (Department of Industrial Engineering, Università degli Studi di Napoli Federico II, Piazzale Tecchio 80, 80125 Napoli, Italy)

  • Romano Fistola

    (Department of Civil, Building and Environmental Engineering, Università degli Studi di Napoli Federico II, Piazzale Tecchio 80, 80125 Napoli, Italy)

  • Teresa Iovane

    (Department of Industrial Engineering, Università degli Studi di Napoli Federico II, Piazzale Tecchio 80, 80125 Napoli, Italy)

Abstract

This study introduces an innovative methodology for designing sustainable urban energy districts using Geographic Information Systems (GIS). The scope is to identify specific parts of the urban fabric, suitable for becoming energy districts that can meet the energy needs of dwellings and activities and produce an energy surplus for the city. The method uses building archetypes to characterize the districts and perform simulations through an algorithm based on correction coefficients considering variables such as total building height, exposure, year of construction, and building typology. By leveraging GIS, this approach supports the creation of urban energy maps, which help identify and address potential energy-related issues in various urban contexts. Additionally, the research explores different scenarios for developing energy communities within the district, aiming to optimize energy use and distribution. A case study in Naples, Southern Italy, demonstrates that installing photovoltaic panels on the roofs of buildings can allow a complete electrical supply to the building stock. The final goal is to provide a robust tool that enhances confidence in urban energy planning decisions, contributing to more sustainable and efficient energy management at the district level. This approach may support the urban and territorial governance towards sustainable solutions by developing strategies for the creation of energy communities and optimizing the potential of specific sites.

Suggested Citation

  • Giuseppe Aruta & Fabrizio Ascione & Romano Fistola & Teresa Iovane, 2024. "The City as a Power Hub for Boosting Renewable Energy Communities: A Case Study in Naples," Sustainability, MDPI, vol. 16(18), pages 1-22, September.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:18:p:7988-:d:1476961
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/18/7988/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/18/7988/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aruta, Giuseppe & Ascione, Fabrizio & Bianco, Nicola & Mauro, Gerardo Maria, 2023. "Sustainability and energy communities: Assessing the potential of building energy retrofit and renewables to lead the local energy transition," Energy, Elsevier, vol. 282(C).
    2. Perwez, Usama & Yamaguchi, Yohei & Ma, Tao & Dai, Yanjun & Shimoda, Yoshiyuki, 2022. "Multi-scale GIS-synthetic hybrid approach for the development of commercial building stock energy model," Applied Energy, Elsevier, vol. 323(C).
    3. Wang, Meng & Yu, Hang & Liu, Yupeng & Lin, Jianyi & Zhong, Xianzhun & Tang, Yin & Guo, Haijin & Jing, Rui, 2024. "Unlock city-scale energy saving and peak load shaving potential of green roofs by GIS-informed urban building energy modelling," Applied Energy, Elsevier, vol. 366(C).
    4. 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).
    Full references (including those not matched with items on IDEAS)

    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. Yuanfan Zheng & Liang Chen & Haipeng Zhao, 2024. "Assessing Building Energy Savings and the Greenhouse Gas Mitigation Potential of Green Roofs in Shanghai Using a GIS-Based Approach," Sustainability, MDPI, vol. 16(18), pages 1-23, September.
    2. Ye, Zhongnan & Cheng, Kuangly & Hsu, Shu-Chien & Wei, Hsi-Hsien & Cheung, Clara Man, 2021. "Identifying critical building-oriented features in city-block-level building energy consumption: A data-driven machine learning approach," Applied Energy, Elsevier, vol. 301(C).
    3. Yamaguchi, Yohei & Shoda, Yuto & Yoshizawa, Shinya & Imai, Tatsuya & Perwez, Usama & Shimoda, Yoshiyuki & Hayashi, Yasuhiro, 2023. "Feasibility assessment of net zero-energy transformation of building stock using integrated synthetic population, building stock, and power distribution network framework," Applied Energy, Elsevier, vol. 333(C).
    4. Jeeyoung Lim & Joseph J. Kim & Sunkuk Kim, 2021. "A Holistic Review of Building Energy Efficiency and Reduction Based on Big Data," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    5. Yijie Lin & Canyichen Cui & Xiaojun Liu & Gang Mao & Jianwu Xiong & Yin Zhang, 2023. "Green Renovation and Retrofitting of Old Buildings: A Case Study of a Concrete Brick Apartment in Chengdu," Sustainability, MDPI, vol. 15(16), pages 1-19, August.
    6. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    7. Pagliaro, Francesca & Hugony, Francesca & Zanghirella, Fabio & Basili, Rossano & Misceo, Monica & Colasuonno, Luca & Del Fatto, Vincenzo, 2021. "Assessing building energy performance and energy policy impact through the combined analysis of EPC data – The Italian case study of SIAPE," Energy Policy, Elsevier, vol. 159(C).
    8. Abdul Mateen Khan & Muhammad Abubakar Tariq & Sardar Kashif Ur Rehman & Talha Saeed & Fahad K. Alqahtani & Mohamed Sherif, 2024. "BIM Integration with XAI Using LIME and MOO for Automated Green Building Energy Performance Analysis," Energies, MDPI, vol. 17(13), pages 1-36, July.
    9. Liu, Zhengguang & Guo, Zhiling & Song, Chenchen & Du, Ying & Chen, Qi & Chen, Yuntian & Zhang, Haoran, 2023. "Business model comparison of slum-based PV to realize low-cost and flexible power generation in city-level," Applied Energy, Elsevier, vol. 344(C).
    10. Hu, Yuqing & Cheng, Xiaoyuan & Wang, Suhang & Chen, Jianli & Zhao, Tianxiang & Dai, Enyan, 2022. "Times series forecasting for urban building energy consumption based on graph convolutional network," Applied Energy, Elsevier, vol. 307(C).
    11. Thebault, Martin & Desthieux, Gilles & Castello, Roberto & Berrah, Lamia, 2022. "Large-scale evaluation of the suitability of buildings for photovoltaic integration: Case study in Greater Geneva," Applied Energy, Elsevier, vol. 316(C).
    12. Wenfei Wang & Ning Kang & Fang He & Xiaoping Li, 2023. "Analysis of the Influence of Office Building Operating Characteristics on Carbon Emissions in Cold Regions," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
    13. Agbonaye, Osaru & Keatley, Patrick & Huang, Ye & Ademulegun, Oluwasola O. & Hewitt, Neil, 2021. "Mapping demand flexibility: A spatio-temporal assessment of flexibility needs, opportunities and response potential," Applied Energy, Elsevier, vol. 295(C).
    14. Perwez, Usama & Yamaguchi, Yohei & Ma, Tao & Dai, Yanjun & Shimoda, Yoshiyuki, 2022. "Multi-scale GIS-synthetic hybrid approach for the development of commercial building stock energy model," Applied Energy, Elsevier, vol. 323(C).
    15. Edgar A. Martínez-Sarmiento & Jose Manuel Broto & Eloi Gabaldon & Jordi Cipriano & Roberto García & Stoyan Danov, 2024. "Linked Data Generation Methodology and the Geospatial Cross-Sectional Buildings Energy Benchmarking Use Case," Energies, MDPI, vol. 17(12), pages 1-24, June.
    16. Fan, Cheng & Lei, Yutian & Sun, Yongjun & Piscitelli, Marco Savino & Chiosa, Roberto & Capozzoli, Alfonso, 2022. "Data-centric or algorithm-centric: Exploiting the performance of transfer learning for improving building energy predictions in data-scarce context," Energy, Elsevier, vol. 240(C).
    17. Nutkiewicz, Alex & Mastrucci, Alessio & Rao, Narasimha D. & Jain, Rishee K., 2022. "Cool roofs can mitigate cooling energy demand for informal settlement dwellers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    18. Zhang, Yuhang & Zhang, Yi & Yi Zhang, & Zhang, Chengxu, 2022. "Effect of physical, environmental, and social factors on prediction of building energy consumption for public buildings based on real-world big data," Energy, Elsevier, vol. 261(PB).
    19. Angeliki Kitsopoulou & Evangelos Bellos & Christos Tzivanidis, 2024. "An Up-to-Date Review of Passive Building Envelope Technologies for Sustainable Design," Energies, MDPI, vol. 17(16), pages 1-55, August.
    20. Hongwen Dou & Radu Zmeureanu, 2023. "Transfer Learning Prediction Performance of Chillers for Neural Network Models," Energies, MDPI, vol. 16(20), pages 1-16, October.

    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:gam:jsusta:v:16:y:2024:i:18:p:7988-:d:1476961. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.