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The design of low-carbon energy systems towards sustainable cities requires correctly quantifying uncertainties in future climate variations , since they affect both energy demand of urban cities and energy system performance. However, quantifying climate uncertainties is challenging due to the stochastic and unpredictable nature of future climate events. This paper develops a novel and general framework to quantify climate uncertainties for urban energy system design using scenario-based stochastic optimization. Climate models are coupled with building performance simulation to generate scenarios for uncertainty quantification and energy system optimization. We apply this optimization framework to US cities for demonstration. We find that climate uncertainties in renewable generation and energy demand can lead to significant performance differences in urban energy systems. Neglecting climate uncertainties results in an optimistic energy system; however, it is unable to provide stable power supply to urban cities. Moreover, we observe that maintaining a certain degree of grid integration is beneficial for urban energy systems and can alleviate the economic burden of urban cities. Finally, we provide some valuable insights on urban energy systems for system designer and policymakers. The designers should fully consider uncertainties associated with various climate events during system design and deploy proper backup systems in case of contingency. The policymakers should thoroughly account for climate uncertainties and their resulting costs when stipulating energy policies for promoting low-carbon energy systems. Both designers and policymakers need to cooperate together and coordinate their actions on urban energy systems towards sustainable cities. Introduction Nowadays, climate change is no longer debatable as evidenced by the adverse and extreme weather conditions and more and more frequent natural disasters. These climate events cause urban energy systems more vulnerable, and failing to properly address them incurs energy systems malfunction and energy supply disruption (The Global Risks Report 2022, 2022). The latter can be very costly or even catastrophic to urban cities as many facilities there require stable energy supplies. Currently, more and more population is living in urban cities, leading to a continuous increase in their energy consumption and greenhouse gas (GHG) emissions. This will certainly accelerate climate change, and the resulting consequences and costs will be significantly magnified and thus hardly affordable (World Urbanization Prospects: The 2018 Revision, 2019). Hence, decarbonizing urban cities is crucial for mitigating climate change. Renewable energy has been considered as an effective means for reducing carbon emissions (International Renewable Energy Agency, 2019). Integrating renewables with urban energy systems enables low-carbon transition of urban cities and thus helps realize city-level decarbonization (United Nations, 2020). However, renewable generation is entangled with climate change, while urban energy demand is affected by weather conditions. In this context, designing renewable energy systems that are adaptive and robust to climate change for urban cities is of great importance but quite challenge. Urban energy system design is normally addressed using energy system modeling frameworks. Some research works focus on developing such frameworks to balance urban energy supply and demand. van Beuzekom et al. developed a multi-period planning framework for studying urban energy systems. Zwickl-Bernhard and Auer soft-linked an open source framework to explore how to use renewable generation to cover urban energy demand. Quantitative evaluation of climate change’s impacts on urban energy systems via deterministic or stochastic optimization is a great concern in these frameworks. Mixed-integer linear programming (MILP) has been a popular formulation used for deterministic optimization (DO) (Keirstead et al., 2012, Allegrini et al., 2015, Lopion et al., 2018, Scheller and Bruckner, 2019). For example, Ameri et al. proposed an MILP formulation to determine the optimal capacity installation and summer and winter day operation of a multi-energy system for urban cities (Ameri and Besharati, 2016). Liu et al. developed a novel MILP model for optimal design and seasonal operation of combined cooling, heating, and power systems (Liu et al., 2020a, Liu et al., 2020b). Moreover, Liu et al. applied MILP to address the optimal daily operation of a hydrogen-based multi-energy system in a typical year (Liu et al., 2021b). These contributions address urban energy system design well; however, their shortcoming in quantifying the impacts of climate change is that they all employ a few typical design days (TDD) or a typical meteorological year (TMY) to represent future weather conditions. TDD and TMY are ad hoc weather profiles for a single projection of future weather conditions. Having in mind that future climate change is uncertain and volatile, TDD and TMY are unable to fully capture all the possible climate change scenarios, where future weather conditions can have a number of potential variation patterns (Schlott et al., 2018, Nik et al., 2021), for urban energy system design. Furthermore, they will miss some unexpected or extreme climate events such that urban energy systems designed by DO cannot satisfy future energy demand in the context of climate change, which leads to severe consequences and huge economic losses (Zeyringer et al., 2018). Therefore, capturing uncertainties in future climate change is of importance for urban energy system design. Stochastic optimization (SO) has been adopted to address this issue. By using a set of potential scenarios, SO is able to capture various uncertainties pertinent to energy systems, including but not limited to climate change. McCollum et al. assessed how uncertainties in fuel prices influenced carbon emissions (McCollum et al., 2016), while Spyrou et al. and Pantankar et al. considered conflict-related risks and consequences on power system planning in polity-fragile regions (Patankar et al., 2019, Spyrou et al., 2019). The impacts of climate uncertainties on urban energy systems has been widely studied in recent years (Ioannou et al., 2017, Mavromatidis et al., 2018b, Firouzmakan et al., 2019). Mavromatidis et al. quantified uncertainties in solar irradiance and wind speed by sampling their probability distribution functions (Mavromatidis et al., 2018a, Mavromatidis et al., 2018c). Their approaches are straightforward and easy for implementation; however, in practice, it is quite difficult to obtain accurate distribution functions of uncertain parameters. Another widely used alternative is to utilize historical climate data to generate potential scenarios for urban energy systems. Yu et al. and Bhavsar et al. applied clustering methods to annual historical climate data to identify representative scenarios with specific occurrence probabilities for urban energy system design (Yu et al., 2019, Bhavsar et al., 2021). However, it is worth noting that clustering annual historical data can only produce scenarios to covers a limited number of design days instead of a whole year, which are not sufficient to capture the potential climate change in future decades. Moreover, electrifying residential and transportation sectors by renewable energy can significantly reduce carbon emissions from fossil fuel consumption, and thus has been considered as an essential pathway for realizing sustainable urban cities (Mokhtara et al., 2020, Isik et al., 2021, Jager-Waldau et al., 2020). Gulagi et al. found that direct and indirect electrification of these sectors led to significant emission mitigation and notable economic benefits (Gulagi et al., 2021). Under this circumstance, urban cities are more prone to deploy renewable technologies and increase renewable penetration during urban energy system optimization (Torabi Moghadam et al., 2017, Delmastro and Gargiulo, 2020). Nonetheless, renewable generation and urban energy demand are highly coupled with climate change, urban energy systems designed using design days from clustering historical climate data via SO are not capable of adapting to climate change during their lifetime. Hence, it is necessary to properly quantify the impacts of climate uncertainties, translate them into relevant system parameters, and use them to design resilient urban energy systems. Many contributions have considered the impacts of climate change on urban energy system design; however, these approaches are unable to capture the possible evolution of long-term climate change during system lifetime. This may incur that the designed urban energy systems cannot provide stable power to consumers. This research gap needs to be addressed for enabling robust urban energy systems. Here, we develop an optimization framework that considers climate change for low-carbon urban energy system design. The impact of climate uncertainties is quantified using a set of potential scenarios that capture hourly variations of weather conditions and energy demand during energy system lifetime. The scenarios are generated by dynamically downscaling general circulation models (GCMs) using regional climate models (RCMs) under given representative concentration pathways (RCPs). The energy demand of urban cities under these climate change scenarios is obtained through building performance simulation. A clustering method is used to obtain an optimal number of scenarios that capture future climate uncertainties and demand variations as well as balance computation burden and economic performance for urban energy system design. We apply our optimization framework to urban cities in eight climate zones in the US for demonstration. We perform both DO and SO to showcase the impacts of climate uncertainties on urban energy system design and operation, then carry out a sensitivity analysis to identify important system design parameters, and finally provide some valuable insights and conclusions on urban energy systems for designers and policymakers. Section snippets Methods We present here our methods for generating scenarios to quantify climate uncertainties as well as model formulation for urban energy systems. The assumptions we adopt in this work are as follows: • Each energy technology is run at a constant efficiency (Liu et al., 2021a); • Heating and cooling demands of urban cities are fully electrified (Gulagi et al., 2021); • An hourly modeling resolution is used to capture climate change profiles and technology operation details in a year (Brown and Botterud, 2021 Optimization of urban energy systems Our optimization framework is applied to US urban cities to showcase how urban energy systems should be designed for climate change adaptation. The US is divided into eight climate zones based on the International Energy Conservation Code (IECC) to provide consistent approaches for climate definition as shown in Fig. 3 (IECC Climate zone map | building america solution center, 2012). To uncover how climate change impacts urban energy systems in these climate zones, a representative urban city Discussion and insights for designers and policymakers Urban energy systems are required to supply stable power to urban cities. Any disruptions in power supply will cause huge economic loss to urban cities. Climate-induced uncertainties significantly impact renewable energy generation, energy demand, as well as energy system operation. Failing to address these uncertainties may incur power outage and result in unaffordable losses. For example, in 2021, the state of Texas, US suffers from a major power crisis, in which unexpected low air Conclusions and future works Climate change affects energy demand of urban cities and energy system performance. To ensure a stable power supply under climate change scenarios, it is necessary to properly translate climate uncertainties into relevant parameters for energy system design. This work developed a general framework to quantify climate uncertainties for urban energy system design using scenario-based stochastic optimization. Through applying our optimization framework to US urban cities for demonstration, we CRediT authorship contribution statement Zuming Liu: Conceptualization, Methodology, Software, Formal analysis, Writing – original draft, Writing – review & editing, Visualization. Lanyu Li: Writing – review & editing, Visualization. Shukun Wang: Writing – review & editing, Visualization. Xiaonan Wang: Writing – review & editing, Supervision, Visualization. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References (57) Yu J. et al. A stochastic optimization approach to the design and operation planning of a hybrid renewable energy system Appl. Energy (2019) Wei M. et al. 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