Resilience Pathways for Climate Extremes: Key to Urban Energy Transition and Climate Change Adaptation
Cities, as complex entities, are defined by their interconnected infrastructures as well as their multivariate and heterogeneous systems. Cities are the economic and societal development hubs, which are responsible for over 75% of global primary energy consumption and more than 70% of global CO2 emissions 1,2. Increased rates of urbanisation, combined with climate change and extreme weather variations, are posing several challenges to cities which could lead to costly damages. Densification could lead to:
- a convoluted microclimate (i.e., heatwaves, cold snaps) 3,4
- increased exposure to environmental stressors5
- higher pressure on energy infrastructure 6
Accelerated electrification and decarbonisation of urban energy infrastructure through increasing the share of renewable energy sources (RES) could also exacerbate the pressure on the energy sector, particularly during extreme climate events.7
Integrating Smart Solutions for Sustainable and Resilient Cities
The transformation of the energy sector by adopting more sustainable and resilient urban solutions is crucial to address these compound and diverse impacts. However, it is important to implement smart solutions that account for mismatches between supply and demand as well as uncertainties related to intermittent energy sources and climate variations 8.
To address these challenges in the urban energy infrastructure, we need to develop integrated and coherent urban energy system decision-making platforms that account for the interconnectivity of urban sectors (i.e., building, energy, and transport). Such frameworks should facilitate densification and improve the sustainability and resilience of the energy sector 9. This requires the integration of multi-sector urban models and multi-scale climate models with diverse spatial and temporal resolutions as well as high-quality datasets.
To consider the probable future climate conditions, both bottom-up approaches, which model system interactions with the climate, and top-down approaches, which explore various climate scenarios and uncertainties, are necessary. A seamless connection between climate data and urban models is also crucial when it comes to assessing whether the system or buildings can withstand climate variations, particularly those that result in extreme weather events 10.
Such an integrated platform 11 should be able to develop an integrated model of interconnected urban infrastructures considering relevant urban sectors (Step 1). Additionally, a multi-scale climate model(Step 2) should be developed to count for mesoscale and micro scale variations of climate variables, ensuring that historical and future climate data are seamlessly incorporated into the integrated urban models (Step 3). Accordingly, the demand profile distribution, and clustering, as well as qualitative and quantitative mapping of RES needs to be conducted (Step 4). The next phase involves conducting a resilience assessment using relevant indicators to quantify risk sand propose feasible remedies (Step 5). This is followed by optimising and designing the energy grid based on identified criteria (Step 6), or repeating steps 4-6 until the desired threshold is reached.
Conclusion
By adopting such an integrated modeling platform, we can ensure climate change adaptation and resilience in energy infrastructure while enhancing their sustainability. It is also recommended that future research explores the synergies between urban development strategies and climate resilience during energy/urban planning as well as grid reliability studies to sustain current economic growth in cities.
References
1. Umezawa, T. et al. Statistical characterization of urban CO2 emission signals observed by commercial airliner measurements. SciRep 10, 7963 (2020).
2. Romanello,M. et al. The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels. The Lancet 400,1619–1654 (2022).
3. Javanroodi,K., Nik, V. M., Giometto, M. G. & Scartezzini, J.-L. Combining computational fluid dynamics and neural networks to characterize microclimate extremes: Learning the complex interactions between meso-climate and urban morphology. Science of The Total Environment 829, 154223 (2022).
4. Javanroodi,K. & Nik, V. M. Interactions between extreme climate and urban morphology:Investigating the evolution of extreme wind speeds from mesoscale to micro scale. Urban Climate 31, (2020).
5. Todeschi,V. et al. Impact of the COVID-19 pandemic on the energy performance of residential neighborhoods and their occupancy behavior. Sustainable Cities and Society 82, 103896 (2022).
6. Perera,A. T. D., Javanroodi, K., Wang, Y. & Hong, T. Urban cells: Extending the energy hub concept to facilitate sector and spatial coupling. Advances inApplied Energy 3, 100046 (2021).
7. Nik,V. M., Perera, A. T. D. & Chen, D. Towards climate resilient urban energy systems: a review. National Science Review 8, nwaa134 (2021).
8. Nik,V. M. & Hosseini, M. CIRLEM: a synergic integration of CollectiveIntelligence and Reinforcement learning in Energy Management for enhanced climate resilience and lightweight computation. Applied Energy 350,121785 (2023).
9. Perera,A. T. D. et al. Challenges resulting from urban density and climate change for the EU energy transition. Nat Energy 8, 397–412(2023).
10. Perera,A. T. D., Javanroodi, K. & Nik, V. M. Climate resilient interconnected infrastructure: Co-optimization of energy systems and urban morphology. AppliedEnergy 2021;285:116430 doi:10.1016/j.apenergy.2020.116430.
11. Javanroodi,K., Perera, A. T. D., Hong, T. & Nik, V. M. Designing climate resilient energy systems in complex urban areas considering urban morphology: A technical review. Advances in Applied Energy 12, 100155 (2023).
Guest blog post by Kavan Javanroodi Assistant Professor| Ph.D. in High-Performance Buildings, Division of Building Physics, Department of Building and Environmental Technology Faculty of Engineering, LTH Lund University, Sweden, & Vahid Nik, Professor, Department of Building and Environmental Technology Faculty of Engineering, LTH Lund University, Sweden
