Abstract
Dense urban morphologies further amplify extreme climate events due to the urban heat island phenomenon, rendering cities more vulnerable to extreme climate events. Here we develop a modelling framework using multi-scale climate and energy system models to assess the compound impact of future climate variations and urban densification on renewable energy integration for 18 European cities. We observe a marked change in wind speed and temperature due to the aforementioned compound impact, resulting in a notable increase in both peak and annual energy demand. Therefore, an additional cost of 20‒60% will be needed during the energy transition (without technology innovation in building) to guarantee climate resilience. Failure to consider extreme climate events will lower power supply reliability by up to 30%. Energy infrastructure in dense urban areas of southern Europe is more vulnerable to the compound impact, necessitating flexibility improvements at the design phase when improving renewable penetration levels.
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Data availability
The data generated or analysed during this study are included in the published article and its Supplementary Information. The raw climate data are available through the Coordinated Regional Climate Downscaling Experiment (http://www.cordex.org/). For each of the building simulation models created, specific physical properties (U values for roof, walls, windows and ground surfaces, and solar heat gain coefficients) for the building envelopes were extracted from the TABULA database (https://episcope.eu/welcome/).The data relevant to the energy and climate models not found in Supplementary Notes1–3 are available from the corresponding author upon reasonable request. Data used for Fig. 2 are available at https://doi.org/10.6084/m9.figshare.22058849. Source data are provided with this paper.
Code availability
The computational code is not publicly available due to intellectual property and patenting process but is available from the corresponding author for academic purposes upon reasonable request.
References
Zhou, Y., Varquez, A. C. G. & Kanda, M. High-resolution global urban growth projection based on multiple applications of the SLEUTH urban growth model. Sci. Data 6, 34 (2019).
Cities and climate change: an urgent agenda. World Bank https://openknowledge.worldbank.org/handle/10986/17381 (2010).
Umezawa, T. et al. Statistical characterization of urban CO2 emission signals observed by commercial airliner measurements. Sci. Rep. 10, 7963 (2020).
Romanello, M. et al. The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future. Lancet 398, 1619–1662 (2021).
UNICEF. The United Nations International Children’s Emergency Fund. Reimagining our Future: Building Back Better from COVID-19 https://www.unicef.org/media/73326/file/COVID-Climate-Advocacy-Brief.pdf (2020).
Takakura, J. et al. Dependence of economic impacts of climate change on anthropogenically directed pathways. Nat. Clim. Change 9, 737–741 (2019).
IPCC. Fifth Assessment Synthesis Report http://ar5-syr.ipcc.ch/ (2014).
Panteli, M. & Mancarella, P. Influence of extreme weather and climate change on the resilience of power systems: impacts and possible mitigation strategies. Electr. Power Syst. Res. 127, 259–270 (2015).
Nik, V. M., Perera, A. T. D. & Chen, D. Towards climate resilient urban energy systems: a review. Natl Sci. Rev. 8, nwaa134 (2021).
Nik, V. M. Making energy simulation easier for future climate—synthesizing typical and extreme weather data sets out of regional climate models (RCMs). Appl. Energy 177, 204–226 (2016).
Pauliuk, S., Arvesen, A., Stadler, K. & Hertwich, E. G. Industrial ecology in integrated assessment models. Nat. Clim. Change 7, 13–20 (2017).
Oke, T. R. The energetic basis of the urban heat island. Q. J. R. Meteorol. Soc. 108, 1–24 (1982).
Heat island effect. US EPA https://www.epa.gov/heatislands (2014).
Moonen, P., Defraeye, T., Dorer, V., Blocken, B. & Carmeliet, J. Urban physics: effect of the micro-climate on comfort, health and energy demand. Front. Archit. Res. 1, 197–228 (2012).
Mauree, D. et al. A review of assessment methods for the urban environment and its energy sustainability to guarantee climate adaptation of future cities. Renew. Sustain. Energy Rev. 112, 733–746 (2019).
Hong, T. et al. Urban microclimate and its impact on building performance: a case study of San Francisco. Urban Clim. 38, 100871 (2021).
Perera, A. T. D., Nik, V. M., Chen, D., Scartezzini, J.-L. & Hong, T. Quantifying the impacts of climate change and extreme climate events on energy systems. Nat. Energy 5, 150–159 (2020).
Bennett, J. A. et al. Extending energy system modelling to include extreme weather risks and application to hurricane events in Puerto Rico. Nat. Energy 6, 240–249 (2021).
Craig, M. T. et al. Overcoming the disconnect between energy system and climate model-ing. Joule 6, 1405–1417 (2022).
Turner, S. W. D., Voisin, N., Fazio, J., Hua, D. & Jourabchi, M. Compound climate events transform electrical power shortfall risk in the Pacific Northwest. Nat. Commun. 10, 8 (2019).
Moon, W. & Wettlaufer, J. S. A unified nonlinear stochastic time series analysis for climate science. Sci. Rep. 7, 44228 (2017).
Fischer, E. & Schär, C. Future changes in daily summer temperature variability: driving processes and role for temperature extremes. Clim. Dyn. 33, 917–935 (2009).
Nik, V. M., Sasic Kalagasidis, A. & Kjellström, E. Statistical methods for assessing and analysing the building performance in respect to the future climate. Build. Environ. 53, 107–118 (2012).
Chen, D. & Chen, H. W. Using the Köppen classification to quantify climate variation and change: an example for 1901–2010. Environ. Dev. 6, 69–79 (2013).
Perera, A. T. D., Nik, V. M., Wickramasinghe, P. U. & Scartezzini, J.-L. Redefining energy system flexibility for distributed energy system design. Appl. Energy 253, 113572 (2019).
Florczyk, A. et al. GHS-UCDB R2019A—GHS Urban Centre Database 2015, Multitemporal and Multidimensional Attributes http://data.europa.eu/89h/53473144-b88c-44bc-b4a3-4583ed1f547e (2019).
Melchiorri, M., Pesaresi, M., Florczyk, A. J., Corbane, C. & Kemper, T. Principles and applications of the global human settlement layer as baseline for the land use efficiency indicator—SDG 11.3.1. ISPRS Int. J. Geo-Inf. 8, 96 (2019).
de Dear, R. J. et al. Progress in thermal comfort research over the last twenty years. Indoor Air 23, 442–461 (2013).
Zhang, H., Huizenga, C., Arens, E. & Yu, T. Considering individual physiological differences in a human thermal model. J. Therm. Biol. 26, 401–408 (2001).
Perera, A. T. D. & Hong, T. Vulnerability and resilience of urban energy ecosystems to extreme climate events: a systematic review and perspectives. Renew. Sustain. Energy Rev. 173, 113038 (2023).
Perera, A. T. D., Khayatian, F., Eggimann, S., Orehounig, K. & Halgamuge, S. Quantifying the climate and human-system-driven uncertainties in energy planning by using GANs. Appl. Energy 328, 120169 (2022).
Perera, A. T. D., Nik, V. M., Mauree, D. & Scartezzini, J.-L. Electrical hubs: an effective way to integrate non-dispatchable renewable energy sources with minimum impact to the grid. Appl. Energy 190, 232–248 (2017).
Levi, P. J. et al. Macro-energy systems: toward a new discipline. Joule 3, 2282–2286 (2019).
Demuzere, M., Bechtel, B., Middel, A. & Mills, G. Mapping Europe into local climate zones. PLoS ONE 14, e0214474 (2019).
EUcities. GitHub https://github.com/vertragus/EUcities (2020).
EU-DEM v1.1. Copernicus Land Monitoring Service https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1i (2020).
Open Street Map. https://www.openstreetmap.org/ (2020).
Building Height 2012 Copernicus Land Monitoring Service https://land.copernicus.eu/local/urban-atlas/building-height-2012 (2020).
R. McNeel & Associates. Rhinoceros 3D. https://www.rhino3d.com/ (2020).
Grasshopper 3D, algorithmic modeling for Rhino. http://www.grasshopper3d.com/ (2020).
DeCoding Spaces Toolbox. https://toolbox.decodingspaces.net/#lab (2020).
Wallacei—an evolutionary multi-objective optimization and analytic engine for Grasshopper 3D. https://www.wallacei.com/ (2020).
Mostapha Sadeghipour Roudsari, M. P. & Adrian Smith + Gordon Gill Architecture, Chicago, USA. Ladybug: a parametric environmental plugin for grasshopper to help designers create an environmentally-conscious design. 13th Conference of International building Performance Simulation Association, 3129–3135 (2013).
Mauree, D., Blond, N., Kohler, M. & Clappier, A. On the coherence in the boundary layer: development of a canopy interface model. Front. Earth Sci. 4, 109 (2017).
Robinson, D. Computer Modelling for Sustainable Urban Design: Physical Principles, Methods and Applications (Routledge, 2012).
Corrado, V., Ballarini, I. & Corgnati, S. P. National Scientific Report on the Tabula Activities in Italy (Politecnico di Torino, 2012).
Lesosai 2017: certification and thermal balance calculation for buildings. http://www.lesosai.com (2017).
Mauree, D., Coccolo, S., Kaempf, J. & Scartezzini, J.-L. Multi-scale modelling to evaluate building energy consumption at the neighbourhood scale. PLoS ONE 12, e0183437 (2017).
Perera, A., Coccolo, S., Scartezzini, J.-L. & Mauree, D. Quantifying the impact of urban climate by extending the boundaries of urban energy system modeling. Appl. Energy 222, 847–860 (2018).
Javanroodi, K. & Nik, V. M. Interactions between extreme climate and urban morphology: investigating the evolution of extreme wind speeds from mesoscale to microscale. Urban Climate 31, 100544 (2020).
Javanroodi, K., Mahdavinejad, M. & Nik, V. M. Impacts of urban morphology on reducing cooling load and increasing ventilation potential in hot-arid climate. Appl. Energy 231, 714–746 (2018).
Javanroodi, K., Nik, V. M., Giometto, M. & Scartezzini, J.-L. Combining computational fluid dynamics and neural networks to characterize microclimate extremes: learning the complex interactions between meso-climate and urban morphology. Sci.Total Environ. 829, 154223 (2022).
Geidl, M. & Andersson, G. Optimal power flow of multiple energy carriers. IEEE Trans. Power Syst. 22, 145–155 (2007).
Cohen, S. M. et al. How structural differences influence cross-model consistency: an electric sector case study. Renew. Sustain. Energy Rev. 144, 111009 (2021).
Oikonomou, K., Tarroja, B., Kern, J. & Voisin, N. Core process representation in power system operational models: gaps, challenges, and opportunities for multisector dynamics research. Energy 238, 122049 (2022).
Mohammadi, M., Noorollahi, Y., Mohammadi-ivatloo, B. & Yousefi, H. Energy hub: from a model to a concept—a review. Renew. Sustain. Energy Rev. 80, 1512–1527 (2017).
Schiavina, M. et al. Land use efficiency of functional urban areas: Global pattern and evolution of development trajectories. Habitat Int. 123, 102543 (2022).
Acknowledgements
The research presented in this paper is a contribution to the strategic research area Modelling the Regional and Global Earth system, MERGE. V.M.N. is supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement for the COLLECTiEF (Collective Intelligence for Energy Flexibility) project (101033683) (V.M.N.) and the joint programming initiative ‘ERA-Net Smart Energy Systems’ with support from the European Union’s Horizon 2020 research and innovation programme under grant agreement for the Flexi-Sync project (775970) (V.M.N.). Support from the Centre for Innovation Research at Lund University (CIRCLE), Sweden’s innovation agency (VINNOVA - MIRAI) and The Crafoord Foundation to V.M.N. are acknowledged.
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A.T.D.P.: conceptualization, methodology, formal analysis for climate and energy systems, and writing. K.J.: methodology, formal analysis for climate, energy demand, urban climate and microclimate, and writing. D.M.: conceptualization, formal analysis for urban climate and energy demand, and writing—original draft. V.M.N.: methodology, formal analysis for climate, and writing. P.F.: methodology, formal analysis for urban data and writing. T.H.: writing/reviewing. D.C.: conceptualization, methodology and writing/reviewing.
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Nature Energy thanks Mingxing Chen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Source Data Fig. 3
Energy demand data.
Source Data Fig. 4
Temperature and wind speed data.
Source Data Fig. 5
Energy demand data.
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Energy demand data.
Source Data Fig. 7
Pareto points.
Source Data Fig. 8
Pareto points, population data and performance gaps.
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Perera, A.T.D., Javanroodi, K., Mauree, D. et al. Challenges resulting from urban density and climate change for the EU energy transition. Nat Energy 8, 397–412 (2023). https://doi.org/10.1038/s41560-023-01232-9
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DOI: https://doi.org/10.1038/s41560-023-01232-9
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