IDEAS home Printed from https://ideas.repec.org/a/bla/wireae/v9y2020i5ne374.html
   My bibliography  Save this article

City‐scale urban sustainability: Spatiotemporal mapping of distributed solar power for New York City

Author

Listed:
  • Job Taminiau
  • John Byrne

Abstract

Research into urban sustainability increasingly deploys advanced analytics and large‐scale data to investigate possible sustainable energy options. This paper reviews several available data‐driven approaches that answer urban sustainability questions. One such approach is applied to identify energy consumption and rooftop photovoltaic (PV) generation potential in New York City at the electricity network level with the objective of improving the city's resilience to expected impacts of climate change. Electric system resilience is, in part, dependent on the spatial and temporal distribution of consumption and potential sustainable energy generation which is investigated here by separating the city into 68 electricity networks and evaluating their generation‐consumption interaction pattern. The analysis reveals that New York City could be home to about 10 GWp of rooftop solar installations—sufficient to cover approximately 25% of annual city electricity consumption and 53% of daylight hour consumption. Localized electricity import–export dimensions are explored for each of the 68 electricity networks and we identify an excess 3.1 TWh electricity supply per year if the entire technical potential of rooftop solar PV is deployed. This excess electricity supply is roughly equivalent to an annual $734 million value which could benefit low‐income areas in the city. This article is categorized under: Energy Infrastructure ≥ Economics and Policy Photovoltaics ≥ Systems and Infrastructure Energy Infrastructure ≥ Systems and Infrastructure Photovoltaics ≥ Economics and Policy

Suggested Citation

  • Job Taminiau & John Byrne, 2020. "City‐scale urban sustainability: Spatiotemporal mapping of distributed solar power for New York City," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(5), September.
    • Handle: RePEc:bla:wireae:v:9:y:2020:i:5:n:e374
    DOI: 10.1002/wene.374
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/wene.374
    Download Restriction: no
    File URL: https://libkey.io/10.1002/wene.374?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
    ---><---

    References listed on IDEAS

    as
    1. John Byrne & Job Taminiau & Jeongseok Seo & Joohee Lee & Soojin Shin, 2017. "Are solar cities feasible? A review of current research," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(3), pages 239-256, September.
    2. Araos, Malcolm & Berrang-Ford, Lea & Ford, James D. & Austin, Stephanie E. & Biesbroek, Robbert & Lesnikowski, Alexandra, 2016. "Climate change adaptation planning in large cities: A systematic global assessment," Environmental Science & Policy, Elsevier, vol. 66(C), pages 375-382.
    3. Meng, Ting & Hsu, David & Han, Albert, 2017. "Estimating energy savings from benchmarking policies in New York City," Energy, Elsevier, vol. 133(C), pages 415-423.
    4. Perez, Richard & Rábago, Karl R. & Trahan, Mike & Rawlings, Lyle & Norris, Ben & Hoff, Tom & Putnam, Morgan & Perez, Marc, 2016. "Achieving very high PV penetration – The need for an effective electricity remuneration framework and a central role for grid operators," Energy Policy, Elsevier, vol. 96(C), pages 27-35.
    5. John Byrne & Job Taminiau & Kyung Nam Kim & Jeongseok Seo & Joohee Lee, 2016. "A solar city strategy applied to six municipalities: integrating market, finance, and policy factors for infrastructure‐scale photovoltaic development in Amsterdam, London, Munich, New York, Seoul, an," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 5(1), pages 68-88, January.
    6. Nyangon, Joseph & Byrne, John, 2018. "Diversifying Electricity Customer Choice: REVing Up the New York Energy Vision for Polycentric Innovation," MPRA Paper 91486, University Library of Munich, Germany.
    7. Olivo, Y. & Hamidi, A. & Ramamurthy, P., 2017. "Spatiotemporal variability in building energy use in New York City," Energy, Elsevier, vol. 141(C), pages 1393-1401.
    8. Perez, Richard & Zweibel, Ken & Hoff, Thomas E., 2011. "Solar power generation in the US: Too expensive, or a bargain?," Energy Policy, Elsevier, vol. 39(11), pages 7290-7297.
    9. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    10. Thomas Hale, 2016. "“All Hands on Deck”: The Paris Agreement and Nonstate Climate Action," Global Environmental Politics, MIT Press, vol. 16(3), pages 12-22, August.
    11. Chen, Yixing & Hong, Tianzhen & Piette, Mary Ann, 2017. "Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis," Applied Energy, Elsevier, vol. 205(C), pages 323-335.
    12. Waite, Michael & Modi, Vijay, 2014. "Potential for increased wind-generated electricity utilization using heat pumps in urban areas," Applied Energy, Elsevier, vol. 135(C), pages 634-642.
    13. Hong, Tianzhen & Yang, Le & Hill, David & Feng, Wei, 2014. "Data and analytics to inform energy retrofit of high performance buildings," Applied Energy, Elsevier, vol. 126(C), pages 90-106.
    14. Hsu, David, 2015. "Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data," Applied Energy, Elsevier, vol. 160(C), pages 153-163.
    15. Chris Bataille & Henri Waisman & Michel Colombier & Laura Segafredo & Jim Williams & Frank Jotzo, 2016. "The need for national deep decarbonization pathways for effective climate policy," Climate Policy, Taylor & Francis Journals, vol. 16(sup1), pages 7-26, June.
    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. Job Taminiau & John Byrne & Jongkyu Kim & Min‐whi Kim & Jeongseok Seo, 2021. "Infrastructure‐scale sustainable energy planning in the cityscape: Transforming urban energy metabolism in East Asia," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(5), September.
    2. Liu, Junling & Li, Mengyue & Xue, Liya & Kobashi, Takuro, 2022. "A framework to evaluate the energy-environment-economic impacts of developing rooftop photovoltaics integrated with electric vehicles at city level," Renewable Energy, Elsevier, vol. 200(C), pages 647-657.
    3. Ram, Manish & Gulagi, Ashish & Aghahosseini, Arman & Bogdanov, Dmitrii & Breyer, Christian, 2022. "Energy transition in megacities towards 100% renewable energy: A case for Delhi," Renewable Energy, Elsevier, vol. 195(C), pages 578-589.
    4. Aurel Pera, 2020. "Assessing Sustainability Behavior and Environmental Performance of Urban Systems: A Systematic Review," Sustainability, MDPI, vol. 12(17), pages 1-19, September.

    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. Job Taminiau & John Byrne & Jongkyu Kim & Min‐whi Kim & Jeongseok Seo, 2021. "Infrastructure‐scale sustainable energy planning in the cityscape: Transforming urban energy metabolism in East Asia," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(5), September.
    2. Kobashi, Takuro & Choi, Younghun & Hirano, Yujiro & Yamagata, Yoshiki & Say, Kelvin, 2022. "Rapid rise of decarbonization potentials of photovoltaics plus electric vehicles in residential houses over commercial districts," Applied Energy, Elsevier, vol. 306(PB).
    3. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    4. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    5. Fernández, María Eugenia & Gentili, Jorge Osvaldo & Campo, Alicia María, 2022. "Solar access: Review of the effective legal framework for an average argentine city," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    6. Guglielmina Mutani & Valeria Todeschi & Simone Beltramino, 2020. "Energy Consumption Models at Urban Scale to Measure Energy Resilience," Sustainability, MDPI, vol. 12(14), pages 1-31, July.
    7. Dario Cottafava & Giulia Sonetti & Paolo Gambino & Andrea Tartaglino, 2018. "Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms," Energies, MDPI, vol. 11(5), pages 1-18, May.
    8. Ali Movahedi & Sybil Derrible, 2021. "Interrelationships between electricity, gas, and water consumption in large‐scale buildings," Journal of Industrial Ecology, Yale University, vol. 25(4), pages 932-947, August.
    9. Deb, Chirag & Dai, Zhonghao & Schlueter, Arno, 2021. "A machine learning-based framework for cost-optimal building retrofit," Applied Energy, Elsevier, vol. 294(C).
    10. Papadopoulos, Sokratis & Bonczak, Bartosz & Kontokosta, Constantine E., 2018. "Pattern recognition in building energy performance over time using energy benchmarking data," Applied Energy, Elsevier, vol. 221(C), pages 576-586.
    11. 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.
    12. 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.
    13. Hribar, Rok & Potočnik, Primož & Šilc, Jurij & Papa, Gregor, 2019. "A comparison of models for forecasting the residential natural gas demand of an urban area," Energy, Elsevier, vol. 167(C), pages 511-522.
    14. Roth, Jonathan & Lim, Benjamin & Jain, Rishee K. & Grueneich, Dian, 2020. "Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective," Energy Policy, Elsevier, vol. 139(C).
    15. Huang, Xiaodan & Zhang, Hongyu & Zhang, Xiliang, 2020. "Decarbonising electricity systems in major cities through renewable cooperation – A case study of Beijing and Zhangjiakou," Energy, Elsevier, vol. 190(C).
    16. Zhan, Sicheng & Liu, Zhaoru & Chong, Adrian & Yan, Da, 2020. "Building categorization revisited: A clustering-based approach to using smart meter data for building energy benchmarking," Applied Energy, Elsevier, vol. 269(C).
    17. Gerhard Zucker & Usman Habib & Max Blöchle & Florian Judex & Thomas Leber, 2015. "Sanitation and Analysis of Operation Data in Energy Systems," Energies, MDPI, vol. 8(11), pages 1-19, November.
    18. Wang, Wei & Hong, Tianzhen & Xu, Xiaodong & Chen, Jiayu & Liu, Ziang & Xu, Ning, 2019. "Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm," Applied Energy, Elsevier, vol. 248(C), pages 217-230.
    19. Chou, Jui-Sheng & Ngo, Ngoc-Tri, 2016. "Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns," Applied Energy, Elsevier, vol. 177(C), pages 751-770.
    20. Wenliang Li, 2020. "Quantifying the Building Energy Dynamics of Manhattan, New York City, Using an Urban Building Energy Model and Localized Weather Data," Energies, MDPI, vol. 13(12), pages 1-22, June.

    More about this item

    Statistics

    Access and download statistics

    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:bla:wireae:v:9:y:2020:i:5:n:e374. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=2041-8396 .

    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.