Search

• Digitalization become a new driving force to improve energy-saving and emission reduction performance (EEP). • The relationship between digitalization and EEP has spatial and temporal heterogeneity. • There is a synergy between digitalization and industrialization. • Some constructive policy suggestions are proposed from the perspective of digital development. In the digital era, enterprises actively use digital technology to enable resource conservation and pollution reduction, which has important practical significance. However, the existing research has not fully clarified the link between digitalization and energy-saving and emission reduction. This paper measures the digital development level and energy-saving and emission reduction performance (EEP) of 271 Chinese cities from 2003 to 2019 and empirically explores the impact of digitalization on EEP. The empirical results are: (1) digitalization can significantly improve EEP in Chinese cities, and become a new driving force to save energy and reduce emissions in the new era. After solving the endogeneity problems and replacing the explained variables, the conclusion remains robust. (2) The relationship between digitalization and EEP has significant spatiotemporal heterogeneity. Digitalization helps to promote EEP in China's eastern cities, but it has not shown a positive effect in western cities. The positive effect of digitalization began to emerge after 2011. (3) There is a synergy between digitalization and industrialization, which enables cities to save energy and reduce emissions. This study helps clarify the relationship between digitalization and EEP, which provides important insights into how digitalization can empower cities' energy-saving and emission reduction. [ABSTRACT FROM AUTHOR]

The energy transition towards sustainable resources is more urgent than ever given the environmental and geopolitical challenges. Being one of the major energy users, cities need to understand their energy sector to accomplish its transition, by means of data. However, data are not easily accessible and have their own challenges. This paper presents a joint effort between researchers, city representatives and industry to provide an urban system service that supports research, accelerates urban innovation, and involves the community. An energy data portal, "NRGYHUB", has been developed, where hourly data from thousands of energy meters are available. These meters were collected from neighborhoods in the city of Västerås, Sweden, and they measure electrical and heating energy. In addition, the data are complemented by geometrical and non-geometrical information of the buildings, as well as demographic statistics of the areas. The paper describes the process of data collection, preprocessing, and visualization, in addition to the main challenges and limitations of the project. This dataset can be used for energy use benchmarking, prediction, and analysis. • The creation and implementation of an energy data portal is detailed and made available on nrgyhub.mdu.se. • Legal and licensing issues in data sharing are explored, offering insights for those interested in Sweden and beyond • By showcasing real-world problems, the impact and role of this data sharing initiative are demonstrated. [ABSTRACT FROM AUTHOR]

This paper explores how green fiscal expenditure affects G reen T otal F actor E nergy E fficiency (GTFEE) from both non-spatial and spatial perspectives based on selected panel data of 254 cities in China. The results of the non-spatial panel model indicate that green fiscal expenditure has a significant positive impact on GTFEE. A comparative study of different econometric models shows that the main parts of the results are largely consistent, which indicates that the conclusions are robust. The spatial autocorrelation findings indicate a statistically significant presence of positive spatial spillover effects in the GTFEE of Chinese cities. Spatial Durbin Model (SDM) and Dynamic Spatial Durbin Model (DSDM) indicate that green fiscal expenditure has a similarly significant positive effect on GTFEE, which will increase over time. This paper considers different spatial weight matrices and changing measures to measure GTFEE, and the conclusions remain robust. Furthermore, industrial structure upgrading, and digital economy are vital in strengthening the relationship between local governments' green fiscal expenditure and GTFEE. According to heterogeneity analysis, green fiscal expenditure has a more significant effect on the GTFEE in mature resource-based cities and non-resource-based cities. • Influence factors of green total factor energy efficiency (GTFEE) is explored. • Green fiscal expenditure (GFE) has a significant positive impact on GTFEE. • Significant positive spatial spillover effects exist in the GTFEE of Chinese cities. • Industrial structure upgrading and digital economy are vital for GFE and GTFEE. [ABSTRACT FROM AUTHOR]

[Display omitted] • Estimated relationship between nighttime stable light data and carbon emissions. • Reference to shared social economy and representative concentration to set scenarios. • Classification of cities based on industry output and GDP. • The carbon emission pathway of the city from 2021 to 2060 was dynamically simulated. • Service cities are more likely to have autonomous carbon peaks. As cities are critical actors in mitigating climate change and achieving the "3060″ target, multi-scenario studies on urban carbon emissions can provide a scientific basis for formulating urban carbon peaking action plans. To remedy the problems of missing regional statistics, inconsistent caliber, and lack of city-scale studies in carbon emission research, this paper uses the sparrow optimization neural network algorithm to fit carbon emission data with nighttime stable light for training. Carbon emission data were obtained for 281 cities in China during 2000–2020. The rates of change of influencing factors are set based on shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs) for different periods and different scenarios. The carbon emission and carbon peaking evolution paths of service, industrial and comprehensive cities from 2021 to 2060 are dynamically simulated. The results show that (1) service cities are significantly higher than industrial and comprehensive cities in population, GDP, secondary industry output, and energy consumption. (2) The economic development effect, as the primary driver of carbon emission growth, increases and then decreases in all five categories of cities, with 2010 as the inflection point. Industrial structure improvement has an increasingly strong offsetting effect on carbon emissions and is one of the critical directions for future carbon emission reduction. (3) Service cities such as Beijing and Shanghai are already at the completion stage of urban transformation and are more likely to reach the carbon peak on their own than other types of cities. In the low carbon following scenario, comprehensive cities such as Kaifeng, Rizhao, and Jilin can achieve their carbon peaking targets efficiently. The findings of this paper can provide valid theoretical support for carbon peaking action programs in China and other countries. [ABSTRACT FROM AUTHOR]

Projecting future urban CO 2 emissions is of paramount importance in comprehending and addressing climate change, as well as evaluating the impact of various policies on future greenhouse gas emissions. While previous studies on projecting CO 2 emissions have primarily focused on large administrative scales, fine-resolution mapping of CO 2 emissions is also vital for identifying CO 2 emission hotspots and delineating emission reduction responsibilities. In this paper, we projected the future CO 2 distribution at a fine-resolution (1 km) under the shared socioeconomic pathways (SSPs) scenario framework, based on which we assessed the peak time, peak volume, spatial distribution, and CO 2 emission intensity (emissions per unit building volume) for cities in the Pearl River Delta (PRD) region. We first simulated the future three-dimensional (3D) spatial patterns of cities by using the FLUS-3D model, and then projected fine-resolution CO 2 emissions by combining the 3D information and socioeconomic factors. Results show that the total CO 2 emissions in the PRD region will reach 180–345 million tons (Mt) by 2030, and 67% of cities in the PRD region will peak CO 2 emissions before 2030 under all five SSP scenarios. Three cities (Jiangmen, Zhongshan and Foshan) will witness a continual rise in CO 2 emissions after 2030 under the SSP5 scenario. Additionally, we found that the time (2010) to reach maximum CO 2 emission intensity of different urban functional types precedes the peak time (2010−2030) of city-level CO 2 emissions. This suggests that the escalation in CO 2 emissions since 2010 will be primarily driven by urban expansion and population growth, rather than an increase in the CO 2 emission intensity of buildings. These findings provide extensive knowledge for future spatial distribution of CO 2 emissions and contribute to the construction of low-carbon cities. • Future gridded CO 2 emissions (1 km resolution) are projected under the shared socioeconomic pathways (SSPs). • The future evolution of three-dimensional urban structures is considered in CO 2 emission projection. • Most cities in the PRD will peak CO 2 emissions before 2030 under all five SSPs. • The CO 2 emission intensity of urban lands declines since 2010 for most of cities. [ABSTRACT FROM AUTHOR]

In the past years various methods have been developed to estimate high-resolution solar potential in urban areas, by simulating solar irradiance over surface models that originate from remote sensing data. In general, this requires discretisation of solar irradiance models that estimate direct, reflective, and diffuse irradiances. The latter is most accurately estimated by an anisotropic model, where the hemispherical sky dome from arbitrary surface's viewpoint consists of the horizon, the circumsolar and sky regions. Such model can be modified to incorporate the effects of shadowing from obstruction with a view factor for each sky region. However, state-of-the-art using such models for estimating solar potential in urban areas, only considers the sky view factor, and not circumsolar view factor, due to high computational load. In this paper, a novel parallelisation of solar potential estimation is proposed by using General Purpose computing on Graphics Processing Units (GPGPU). Modified anisotropic Perez model is used by considering diffuse shadowing with all three sky view factors. Moreover, we provide validation based on sensitivity analysis of the method's accuracy with independent meteorological measurements, by changing circumsolar sky region's half-angle and resolution of the hemispherical sky dome. Finally, the presented method using GPPGU was compared to multithreaded Central Processing Unit (CPU) approach, where on average a 70x computational speedup was achieved. Finally, the proposed method was applied over a ∼ 21 km 2 urban area, obtained from Light Detection And Ranging (LiDAR) data, where the computation of solar potential was performed in a reasonable time. • A new method for high-resolution solar potential estimation using GPGPU. • Novel incorporation of the modified Perez anisotropic diffuse irradiance model. • The method considers sky, circumsolar, and horizon view factors with shadowing. • In comparison to a multithreaded CPU approach, a 70x speedup can be achieved. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to design energy consumption modeling systems to predict the energy consumption value due to today's energy use in cities increasing. A new Modified Fish Migration Optimization Algorithm-based Numerical Moment Matching (MFMOA-NMM) technique is applied as an initial indeterminacy evaluation method to forecast the electrical power use in large-scale datasets of single-family residential homes with the application of main characteristics. To develop energy modeling, the DesignBuilder is used. The energy performance of the homes is assessed by combining these techniques. With the application of the information from the energy audit and data of the assessor (8368 single-family homes), four effective parameters including attic insulation R-value, the efficiency of air conditioners (SEER), type of the window, and the area of homes are inputted into the model improvement. Waterloo in Iowa is selected as the case study. The results showed that the yearly electrical power use expected is equal to 10,195 kWh which is within 5% of the experimental electrical power use. Also, the monthly expected electrical power use Average Bias Error is equal to 2.4% and the Coefficient of Variation with the Root Average Square Error is equal to 6.7%. This technique can be applied to create a small group of typical buildings to show the energy performance of a bigger group of buildings. • Energy consumption modeling to predict energy consumption for single-family homes in Waterloo. • Energy performance optimization by a Modified Fish Migration Optimization Algorithm. • Combining the introduced new algorithm with Numerical Moment Matching technique. [ABSTRACT FROM AUTHOR]

PV windows are considered to be a promising building-integrated PV technology due to their excellent electrical, daylighting and thermal performance. However, the shading of the PV cells increases the heating load in winter and the high temperature of PV cells exacerbates the cooling load in summer. Moreover, a significant portion of the heat absorbed by the PV modules is usually wasted. To address these issues, this paper proposed a multi-functional PV/T window. This system could recover the heat on PV cells for producing warm air in winter and hot water in other seasons. As a result, the proposed system can realize the combined PV/T utilization of solar energy and reduce the seasonal thermal needs of the building. First, the mathematical model of the proposed window was developed and validated. Next, the developed model was integrated into building energy software to evaluate the overall energy performance of the proposed window in four different cities of Yangtze River region: Shanghai, Nanjing, Wuhan, and Chongqing in China. These cities experience the subtropical humid climate (Cfa in the Köppen-Geiger classification) and belong to the zones with medium global horizontal irradiation. The sensitivity analysis revealed that the optimal slat angles are between 0o and 40o, while the optimal orientation angles range from 30o to 60o. Selecting low-iron glass as the inner glass resulted in the lowest overall energy consumption. By adopting the MFPV/T window, the annual operation cost of the studied case could be reduced by 34% (6.4 $/m2), 36% (7.0 $/m2), 28% (5.7 $/m2), and 16% (2.9 $/m2) in these four cites, respectively. Additionally, the annual CO 2 emissions could be curtailed by 53.5, 58.5, 47, and 22.3 kg/m2, respectively. • A novel PV/T window was proposed for addressing the common issues of PV windows. • The mathematical model of proposed window was developed and validated. • The parameter sensitivity was analyzed. • The overall energy performance was predicted in Yangtze River region. [ABSTRACT FROM AUTHOR]

To meet carbon emission reduction goals in line with the Paris agreement, planning resilient and sustainable energy systems has never been more important. In the building sector, particularly, strategic urban energy planning engenders large optimization problems across multiple spatiotemporal scales leading to necessary system scope simplifications. This has resulted in disconnected system scales, namely, building occupants (bottom layer) and smart-city energy networks (top layer). This paper intends on bridging these disjointed scales to secure both resilient and more energy-efficient urban planning. To assess the aggregated impact of user behavior stochasticities on optimal urban energy planning, a stochastic energy community sizing and operation problem is designed, encompassing multi-level utilities founded on energy hub concepts for improved energy and carbon emission efficiencies. The problem is solved through an organic spatial problem distribution suitable for field deployment, validated by a proof of concept. We examine uncertainty factors affecting urban energy planning through a local sensitivity analysis, namely, economic, climate, and occupant-behavior uncertainties. Founded on this modeling setup, an energy community of 41 Dutch residential buildings is optimally designed using historical measurements. Results disclose a fast-converging distributed stochastic problem, showcasing boilers as the preferred heating utility. Distributed renewable energy and storage systems are identified as unprofitable for the community. Occupant behavior is particularly exposed as the leading uncertainty factor impacting energy community planning. This demonstrates the relevance and value of our approach in connecting occupants to cities for improved, and more resilient, urban energy planning strategies. • Stochastic optimization for resilient planning of energy communities. • Uncertainty factor impact assessment on optimal system design through local sensitivity analysis. • Scalable urban planning, and control-oriented real-world deployment readiness through a distributed optimization approach. • Case study investigation encompassing 41 Dutch residential buildings. • Results reveal occupant behavior to be the leading uncertainty factor affecting the system design, behind climate and economic conditions. [ABSTRACT FROM AUTHOR]

Accurate mid-term gas demand forecasting plays a crucial role for gas companies and policymakers to achieve reliable gas supply plans, supply contracts management, and efficient operation to meet the increasing gas demand. However, mid-term gas demand forecasting faces the problems of data paucity caused by the low frequency of collecting monthly data and heterogeneous consumption patterns of various usage categories. This paper proposes a novel Fed erated Con trastive pretraining - L ocal C lustered F inetuning paradigm (FedCon-LCF) by integrating federated learning, deep contrastive learning, and clustering approaches. The proposed method can utilize data from multiple gas companies to overcome data paucity issues in a privacy-preserving way, and high-performance forecasting can be achieved by local clustered regression considering the heterogeneous patterns. An improved hierarchical contrastive loss and multi-scale regression loss are integrated to develop the F orecasting- O riented C ontrastive L earning model (FOCL), which can effectively extract information and generate fine-grained representations of time series for accurate forecasting. The proposed method is evaluated on a dataset collected from 11 gas companies in 11 different Chinese cities with a total of 17648 clients over 10 usage categories. The proposed method outperforms the benchmark LSTM model with an average improvement of 25.30% in MSE and 16.52% in MAE for 3-month-ahead, 6-month-ahead, 9-month-ahead, and 12-month-ahead gas demand forecasting. • Propose a new paradigm to address data scarcity and diverse demand patterns problems. • Develop a new method to generate fine-grained representations from gas demand data. • Provide new findings for mid-term gas demand forecasting of different ranges. [ABSTRACT FROM AUTHOR]

• Optimal design of multi-vector energy hubs in energy communities. • MILP model using a multi-objective function. • Application to a real case represented by a university campus. One of the main ways to reach the important targets of the European Commission in the energy field set by 2030 is to implement sustainable energy systems to cover electrical and thermal energy demands, as well as transportation needs, in urban areas. In the aforesaid scenario, multi-vector energy hubs consist of installing small and medium size power plants, mainly fed by renewable sources, and charging infrastructures for electric vehicles in sustainable urban districts. The optimal design of such systems can be faced from different points of view and using different methods, one of the most adopted being the development of mathematical programming algorithms typically aimed to minimize global costs and emissions. The contribution of this paper is to propose an innovative methodology to optimally design a multi-vector energy hub able to supply electricity and thermal energy, for space heating and cooling, to a set of buildings within a sustainable district acting as a local energy community. The developed mixed integer linear programming model is characterized by very detailed models of the technologies and by a multi-objective function which is proposed to evaluate the impact of the optimal solution on global costs and emissions. The proposed mathematical model is very comprehensive as it involves a wide range of energy technologies with detailed modelling by considering the impact of external ambient conditions and energy vector temperature values on the plant conversion efficiency, thus showing high replicability potential in real contexts. In the case study, the model is applied to a university campus in the North of Italy. [ABSTRACT FROM AUTHOR]

• A resilience improvement process of UIES is proposed. • A resilience index is defined to quantify load restoration of UIES. • Minimum diameter spanning tree is employed to separate UIES islands. • A multi-energy coordination approach is developed to improve UIES resilience. • Influence of interdependenc in UIES on resilience improvement is revealed. The urban integrated energy system (UIES)'s ability to deliver power, heat and gas to users uninterruptedly even under a high impact and low probability event is critical to the safety and living quality of city residents and also the economy of the urban area. This paper proposes a load restoration strategy based on multi-energy coordination to improve the resilience of UIES, with the goal of restoring important loads as much as possible. The proposed restoration strategy introduces the minimum diameter spanning tree (MDST) method to UIES, and changes the topology of the system accordingly to reorganize the various "islands" after the extreme event, so as to ensure that the operating radius of the area is the smallest, and to alleviate the drops of voltage, air pressure and temperature. On this basis, the proposed restoration strategy also includes a method to better utilize the complementarity of energy sources by coordinating all power, gas and heat sources in the area to ensure the uninterrupted energy supply for important loads in the integrated electricity/gas/heating system. Finally, this paper verifies the feasibility and effectiveness of the proposed method through UIES E33-G14-H6 test system and UIES E123-G48-H32 test system. [ABSTRACT FROM AUTHOR]

• Agent-Based Model to emulate real-world performance of microgrid with P2P trading. • Holistic simulation of economic, technical, and ecological metrics. • Validation of Model with urban area of medium-sized German city. • Comparison of microgrid metrics before and during the European energy crisis. • Conclusions for research, practice, and energy policy. The shift towards renewable energy sources (RES) in energy systems is becoming increasingly important. Residential energy generation and storage assets, smart home energy management systems, and peer-to-peer (P2P) electricity trading in microgrids can help integrate and balance decentralized renewable electricity supply with an increasingly electrified power, heat, and transport demand, reducing costs and CO 2 emissions. However, these microgrids are difficult to model because they consist of autonomous and interacting entities, leading to emergent phenomena and a high degree of complexity. Agent-based modeling is an established technique to simulate the complexity of microgrids. However, the existing literature still lacks real-world implementation studies and, as a first step, models capable of validating the existing results with real-world data. To this end, we present an agent-based model and analyze the corresponding microgrid performance with real-world data. The model quantifies economic, technical, and environmental metrics to simulate microgrid performance holistically and, in line with state-of-the-art research, consists of self-interested, autonomous agents with specific load profiles, RES generation, and demand-response potential. The model can simulate a P2P marketplace where electricity is traded between agents. In the second part of the paper, we validate the model with data from a medium-sized German city. In this case study, we also compare microgrid performance in 2022, during the energy market crisis in Europe, with historical data from 2019 to assess the effects of energy market shocks. Our results show how microgrids with P2P trading can reduce electricity costs and CO 2 emissions. However, our trading mechanism illustrates that the benefits of energy-community trading are almost exclusively shared among prosumers, highlighting the need to consider distributional issues when implementing P2P trading. [ABSTRACT FROM AUTHOR]

• Urban and rural energy rebound effects in Beijing are studied by LA-AIDS model. • Energy rebound from substitution and income effects is decomposed by Slutsky equation. • Energy rebound effect of rural residents exceeded that of urban residents after 2010. • The income effect is the main source of the energy rebound effect in Beijing. • Energy rebound caused by various commodity groups presents obvious heterogeneity. With Beijing facing greater pressure for energy conservation, improving energy efficiency becomes more necessary. However, the energy rebound effect may weaken this energy-saving effect. Using the LA-AIDS (Linear Approximation of the Almost Ideal Demand System) model and simulation analysis, this paper evaluates the energy rebound effects of different commodity groups in Beijing's urban and rural households during 2000–2019. Then, the Slutsky decomposition equation is introduced to decompose the energy rebound caused by the substitution and income effects. Results suggest that: (1) The energy consumption of urban and rural residents shows a partial rebound in most years, and the energy rebound effect of rural residents exceeded that of urban residents after 2010. (2) The income effect from commodity price changes is the main source of the energy rebound effect both in urban and rural households. (3) The commodity groups Residence, Household facilities & Services, Transportation & Communication and Health care & Medical services have the same influencing direction on the energy rebound effect in urban and rural residents, while the commodity groups Food, Tobacco & Alcohol, Clothing and Culture, Education & Entertainment have different influencing direction. Finally, suggestions are provided for alleviating the energy rebound effect of households in Beijing. [ABSTRACT FROM AUTHOR]

• LNG point supply has the advantages of safety, economy, and environmental protection. • Incomplete construction regulations are the challenges of LNG point supply method. • LNG supply chain optimization model aims to minimize economic & environmental costs. • This paper employs the actual data of towns in the optimization model for research. • The sensitivity of construction cost is relatively high. Liquefied natural gas (LNG) point supply is one of the most flexible and convenient methods advocated by China for distributing energy to end-users such as towns and villages where it is difficult or uneconomical to construct gas pipelines. This study sorts out the advantages and challenges and designs the optimization model for LNG point supply in China based on a mixed-integer linear programming (MILP) model. In this model, the goal of optimization is to minimize economic and environmental costs in the construction and operation stages. By optimizing location-allocation, gas storage capacity, vaporization capacity, equipment selection, and pipeline distribution, the model meets the fluctuating seasonal demand for natural gas. This model design uses the actual construction and operation-related parameters of the vaporization station and pipeline, and applies the model to four different types of towns. Through sensitivity analysis, this study finds that the sensitivity of construction cost is relatively high. [ABSTRACT FROM AUTHOR]

The advancement of fixed charging stations (FCS) has facilitated electric vehicle (EV) charging and increased EV adoption. FCSs are especially important in dense urban areas, where home charging is not an option. However, FCSs' construction is limited by budget, fragile power grid infrastructure, and site constraints, among others. Therefore, suitable locations for building FCSs are limited. Even in the presence of FCSs, EV drivers face challenges such as long charging times and inconvenient charging locations. Mobile charging stations (MCS), which allow for scheduling and convenient charging, can be a supplementary charging technology to address these shortcomings and allow rapid expansion of charging infrastructure, assuming the MCS can be scheduled efficiently. This paper develops an optimization framework for the optimal charging management of EVs considering a multi-charger system integrating different types of FCSs and MCSs. Considering the EV driver's travel trajectory and time value, the proposed framework finds the optimal charging technology and location to minimize the EV user's overall charging cost and time. The model is implemented in Chattanooga, TN, USA, where the evaluation results demonstrated that MCSs are the optimal charging method for many EV users. Moreover, the results indicated that MCS mitigates the power grid stress caused by EV charging during peak hours. • Optimal charging management of Electric vehicles using multiple charging technologies. • Studying mobile charging stations' impact on the EV users' charging time and costs. • Investigating mobile charging technology impacts on mitigating power grid stress. • Evaluating the proposed charging framework of a real case study (Chattanooga). [ABSTRACT FROM AUTHOR]

• Stakeholders see great potential in PEDs for energy poverty reduction. • Energy poverty mitigation needs to be included in PEDs from the onset. • PED replication can synergistically address both decarbonization and energy poverty mitigation. • Increasing levels of energy poverty makes PEDs more financially viable as mitigation tools. • More consideration needs to be given to the social dimension in decisions on new PED creation. 100 Positive Energy Districts (PEDs) are to be created in Europe by 2025, with a stated goal of urban decarbonization. These are highly energy efficient residential urban areas, powered entirely through renewables. PED creation is to be guided by principles of quality of life, sustainability, and inclusiveness (specifically focusing on affordability and energy poverty prevention). Although there is research into the decarbonization aspects of PEDs, there has been little focus on the guiding principles, and their potential to reduce energy vulnerability. Using energy vulnerability factors and an energy justice framework, this article examines how the topic of energy vulnerability mitigation is perceived by professional PED stakeholders. Stakeholders from multiple countries were interviewed in order to determine how and to what extent they approached the topic of inclusivity and energy vulnerability. The contribution of this paper to academic research is in helping to frame energy vulnerability in European smart city urban areas, focusing on the perceptions of key stakeholders. This contributes to research on the identification and evaluation of innovations such as PEDs which offer a potential model for an inclusive transition. Furthermore, this article offers a contribution for policymakers, informing PED replication policies with a focus on the synergistic aims of decarbonization and energy vulnerability mitigation. [ABSTRACT FROM AUTHOR]

[Display omitted] • Explore the relative optimization potential for UES multi-objective optimization. • Quantify the difference in sensitivity of sub-objectives of the trade-off process. • Provide objective decision basis for the trade-off features of different scenarios. • Propose a partial a posteriori decision method for multi-objective trade-offs. • Introduce an evaluation methods that are consistent with the decision perspective. Urban energy systems often contain multiple optimization sub-objectives with different dimensions. They often need to be normalized in many multi-objective optimization problems. This process may result in the loss of trade-off characteristics of each sub-objectives. This paper introduces a novel indicator, relative optimization potential, to quantify the difference in sensitivity of sub-objectives to the trade-off process, which is easily ignored in previous studies. An integrated multi-objective optimization framework for urban energy systems with flexible decision-making approaches is further proposed to match the differentiated requirements of various scenarios. This framework includes a partial a posterior approach to tackle high-dimensional multi-objective optimization problems, which reduces the computational cost while obtaining equivalent or better results through a reasonable simplification of the decision process. Moreover, a results evaluation method consistent with the perspective of decision-making is proposed to improve the interpretability and consistency of the entire trade-off process. The case studies illustrate the preference characteristics of the proposed approach under different scenarios. And its feasibility is verified by the cross-sectoral planning problem of integrated energy system and building retrofit in different urban areas and scenarios. The proposed approach favors different sub-objectives under different building environments, demand profiles and grid-connection conditions while seeking the maximization of overall relative benefits. Overall, the proposed approach can be applied flexibly in various optimization scenarios to rationally trade-off multi-objective according to different optimization conditions without causing a significant increase in computational cost. This study provides a new tool for multi-objective optimization of urban energy systems with different perspectives. [ABSTRACT FROM AUTHOR]