Carbon efficiency evaluation method for urban energy system with multiple energy complementary

Urban energy systems(UESs)play a pivotal role in the consumption of clean energy and the promotion of energy cascade utilization.In the context of the construction and operation strategy of UESs with multiple complementary energy resources,a comprehensive assessment of the energy efficiency is of paramount importance.First,a multi-dimensional evaluation system with four primary indexes of energy utilization,environmental protection,system operation,and economic efficiency and 21 secondary indexes is constructed to comprehensively portray the UES.Considering that the evaluation system may contain a large number of indexes and that there is overlapping information among them,an energy efficiency evaluation method based on data processing,dimensionality reduction,integration of combined weights,and gray correlation analysis is proposed.This method can effectively reduce the number of calculations and improve the accuracy of energy efficiency assessments.Third,a demonstration project for a UES in China is presented.The energy efficiency of each scenario is assessed using six operational scenarios.The results show that Scenario 5,in which parks operate independently and investors build shared energy-storage equipment,has the best results and is best suited for green and low-carbon development.The results of the comparative assessment methods show that the proposed method provides a good energy efficiency assessment.This study provides a reference for the optimal planning,construction,and operation of UESs with multiple energy sources.

DEVELOPMENT OF THE ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC URBAN BUSES

A novel parallel hybrid electrical urban bus （PHEUB） configuration consisting of an extra one-way clutch and an automatic mechanical transmission （AMT） is taken as the study subject. An energy management strategy combining a logic threshold approach and an instantaneous optimization algorithm is proposed for the investigated PHEUB. The objective of the energy management strategy is to achieve acceptable vehicle performance and drivability requirements while simultaneously maximizing the engine fuel consumption and maintaining the battery state of charge in its operation range at all times. Under the environment of Matlab/Simulink, a computer simulation model for the PHEUB is constructed by using the model building method combining theoretical analysis and bench test data. Simulation and experiment results for China Typical Bus Driving Schedule at Urban District （CTBDS_UD） are obtained, and the results indicate that the proposed control strategy not only controls the hybrid system efficiently but also improves the fuel economy significantly.

Calculation and decomposition of regional household energy consumption in China:based on perspectives of urbanization and residents' consumption

Demographic urbanization caused great changes in scale of residents' consumption and residents' lifestyle and then impacted changes of regional household energy consumption. This paper expanded Logarithmic Mean Decomposition Index method through introducing variables of urbanization and residential consumption into the model. It also analyzed the influences of six factors as energy structure, energy intensity, population scale, urbanization, residential consumption, and consumption inhibit on regional household energy consumption. Results showed that in 2003-2012, impact of urbanization on regional household energy consumption of Chinese three areas was significantly higher than population size. The "population gathered in eastern region" phenomenon caused eastern region getting the largest population scale effect. Driving force of residential consumption on regional household energy consumption was much higher than the other five effects. Due to the comparative advantage of residential consumption compared with government consumption, investment, and net export, the decrease of consumption ratio promoted the growth of regional household energy consumption. Energy intensity in Chinese three regions kept reducing in 2003-2012. The progress of energy utilization technology slowed the growth of regional household energy consumption, and energy intensity effect was most significant in the central region.

Developing web-based urban energy environmental database

Unbalanced energy consumption distribution caused by the concentration of facilities and population topples the natural energy equilibrium of a city and causes environmental problems such as urban tropical night,heat island phenomenon,global warming deterioration.Therefore,to secure eco-friendliness and sustainability of a city,it is necessary to introduce measures to alleviate the unequal distribution phenomenon of urban energy consumption from the city planning stage.For this purpose,the first step is to understand the current energy environment.The urban energy environment is affected by many factors in addition to gathering of buildings.Therefore,there is a limit to fully understanding advanced urban energy environment with only simple statistical urban information management technique.Research on methods of analyzing urban energy environment through simulation of recent urban scale is underway.There is not enough discussion about basic informaion databases for environmental analysis simulation of urban energy.This study presents a method using GIS(geographic information system) and web-based environmental information database as a way to improve the simulation accuracy.First,environmental information factors used for urban simulation were derived,and a web-based environmental information database targeting Daegu metropolitan city of Korea was built.Then,the urban energy environment was analyzed on a trial basis by linking the database with GIS.

Systematic review of the efficacy of data-driven urban building energy models during extreme heat in cities:Current trends and future outlook

Energy demand fluctuations due to low probability high impact(LPHI)micro-climatic events such as urban heat island effect(UHI)and heatwaves,pose significant challenges for urban infrastructure,particularly within urban built-clusters.Mapping short term load forecasting(STLF)of buildings in urban micro-climatic setting(UMS)is obscured by the complex interplay of surrounding morphology,micro-climate and inter-building energy dynamics.Conventional urban building energy modelling(UBEM)approaches to provide quantitative insights about building energy consumption often neglect the synergistic impacts of micro-climate and urban morphology in short temporal scale.Reduced order modelling,unavailability of rich urban datasets such as building key performance indicators for building archetypes-characterization,limit the inter-building energy dynamics consideration into UBEMs.In addition,mismatch of resolutions of spatio-temporal datasets(meso to micro scale transition),LPHI events extent prediction around UMS as well as its accurate quantitative inclusion in UBEM input organization step pose another degree of limitations.This review aims to direct attention towards an integrated-UBEM(i-UBEM)framework to capture the building load fluctuation over multi-scale spatio–temporal scenario.It highlights usage of emerging data-driven hybrid approaches,after systematically analysing developments and limitations of recent physical,data-driven artificial intelligence and machine learning(AI-ML)based modelling approaches.It also discusses the potential integration of google earth engine(GEE)-cloud computing platform in UBEM input organization step to(i)map the land surface temperature(LST)data(quantitative attribute implying LPHI event occurrence),(ii)manage and pre-process high-resolution spatio-temporal UBEM input-datasets.Further the potential of digital twin,central structed data models to integrate along UBEM workflow to reduce uncertainties related to building archetype characterizations is explored.It has also found that a trade-off between high-fidelity baseline simulation models and computationally efficient platform support or co-simulation platform integration is essential to capture LPHI induced inter-building energy dynamics.

Coordinated Dispatch Based on Distributed Robust Optimization for Interconnected Urban Integrated Energy and Transmission Systems

To improve the economic efficiency of urban integrated energy systems(UIESs)and mitigate day-ahead dispatch uncertainty,this paper presents an interconnected UIES and transmission system(TS)model based on distributed robust optimization.First,interconnections are established between a TS and multiple UIESs,as well as among different UIESs,each incorporating multiple energy forms.The Bregman alternating direction method with multipliers(BADMM)is then applied to multi-block problems,ensuring the privacy of each energy system operator(ESO).Second,robust optimization based on wind probability distribution information is implemented for each ESO to address dispatch uncertainty.The column and constraint generation(C&CG)algorithm is then employed to solve the robust model.Third,to tackle the convergence and practicability issues overlooked in the existing studies,an external C&CG with an internal BADMM and corresponding acceleration strategy is devised.Finally,numerical results demonstrate that the adoption of the proposed model and method for absorbing wind power and managing its uncertainty results in economic benefits.

Vertical axis wind turbine with omni-directional-guide-vane for urban high-rise buildings

A novel shrouded wind-solar hybrid renewable energy and rain water harvester with an omni-directional-guide-vane(ODGV) for urban high-rise application is introduced.The ODGV surrounds the vertical axis wind turbine(VAWT) and enhances the VAWT performance by increasing the on-coming wind speed and guiding it to an optimum flow angle before it interacts with the rotor blades.An ODGV scaled model was built and tested in the laboratory.The experimental results show that the rotational speed of the VAWT increases by about 2 times.Simulations show that the installation of the ODGV increases the torque output of a single-bladed VAWT by 206% for tip speed ratio of 0.4.The result also reveals that higher positive torque can be achieved when the blade tangential force at all radial positions is optimized.In conclusion,the ODGV improves the power output of a VAWT and this integrated design promotes the installation of wind energy systems in urban areas.

Simulating train movement in an urban railway based on an improved car-following model

Based on the optimal velocity car-following model, in this paper, we propose an improved model for simulating train movement in an urban railway in which the regenerative energy of a train is considered. Here a new additional term is introduced into a traditional car-following model. Our aim is to analyze and discuss the dynamic characteristics of the train movement when the regenerative energy is utilized by the electric locomotive. The simulation results indicate that the improved car-following model is suitable for simulating the train movement. Further, some qualitative relationships between regenerative energy and dynamic characteristics of a train are investigated, such as the measurement data of regenerative energy presents a power-law distribution. Our results are useful for optimizing the design and plan of urban railway systems.

The potential of remote sensing and GIS in urban building energy modelling

As the world continues to urbanize at an unprecedented rate,the energy demand in cities is rising.Buildings account for over 75%of all the energy consumed in cities and are responsible for over two-thirds of the emissions.Assessment of energy demand in buildings is a highly integrative endeavour,bringing together the interdisciplinary fields of energy and urban studies,along with a host of technical domains namely,geography,engineering,economics,sociology,and planning.In the last decade,several urban building energy modelling tools(UBEMs)have been developed for estimation as well as prediction of energy demand in cities.These models are useful in policymaking as they can evaluate future urban energy scenarios.However,data acquisition for generating the input database for UBEM has been a major challenge.In this review,a comprehensive assessment of the potential of remote sensing and GIS techniques for UBEM has been presented.Firstly,the most common input variables of UBEM have been identified by reviewing recent publications on UBEM and then studies related to the acquisition of data corresponding to these variables have been explored.More than 140 research papers and review articles relevant to remote sensing and GIS applications for building level data extraction in urban areas and UBEM applications have been investigated for this purpose.After going through level of details required for each of the input components of UBEM and studying the possibility of acquiring some of those data using remote sensing,it has been inferred that satellite remote sensing and Unmanned Aerial Vehicles(UAVs)have a strong potential in enhancing the input data space for UBEM but their applicability has been limited.Further,the challenges of the usage of these technologies and the possible solutions have also been presented in this study.It is recommended to utilise the existing methodologies of extracting information from remote sensing and GIS for UBEM,along with newer techniques such as machine learning and artificial intelligence.

A Data-driven Distributionally Robust Operational Model for Urban Integrated Energy Systems

A multi-energy conversion can effectively increase the utilization of renewable energy in the urban integrated energy system(UIES).Meanwhile,the uncertainties of renewable energy resources(e.g.,wind energy)also bring increased challenges to the operation of UIES.In this study,a typical two-stage datadriven distributionally robust operation(DDRO)model based on finite scenarios is proposed for UIES including power,gas and heat networks to obtain a salient strategy from both an economic and robustness perspective.In the first stage,the forecasted information for wind power is especially included to improve the economic aspect of robust decisions.The worst probability distribution for the selected known real-time wind power scenarios can be identified in the second stage where the power differences caused by the real-time uncertainties of wind power can be mitigated by flexible regulation of energy purchasing and coupling units(such as gas turbine,power to gas equipment,electric boiler and gas boiler).Moreover,norm-1 and norm-inf co-constraints are utilized to construct a confidence set for the probability distributions of uncertain wind power.The whole two-stage model is solved by the column-and-constraint generation(CCG)algorithm.Finally,case studies are conducted to show the performance of the proposed model and various approaches.Index Terms-Data-driven methods,distributionally robust optimization,urban integrated energy system,wind power.

Analysis of observations on the urban surface energy balance in Beijing

The 1-year（2009-2010） measurements are analyzed of the urban surface energy balance（SEB） obtained from the sensors located at three vertical layers of a 325-m tower in downtown Beijing.Results show that：（1） The measurements from the 325-m tower represent the SEB characteristics of the cities located in semi-humid warm-temperate continental monsoon climate zone.In a typical hot and rainy summer,cold and dry winter,the measured Bowen ratio is minimum in summer and maximum in winter.The Bowen ratio measured at 140 m for spring,summer,autumn,and winter are 2.86,0.82,1.17,and 4.16 respectively.（2） At the height of 140-m（in the constant flux layer）,the noontime albedo is ~0.10 for summer,~0.12 for spring and autumn,and ~0.14 for winter.The ratios of daytime sensible heat flux,latent heat flux,and storage heat flux to net radiation are 0.25,0.16,and 0.59 for clear-sky days,and 0.33,0.19,and 0.48 for cloudy days respectively.（3） Under clear-sky days,the nighttime sensible heat flux is almost zero,but the latent heat flux is greater than zero.For cloudy days,the nighttime sensible heat flux is slightly greater than the latent heat flux in winter.The nighttime upward heat flux is presumably due to the anthropogenic release（mainly latent heat for summer,while latent and sensible heat for winter）.

Archetype identification and urban building energy modeling for city-scale buildings based on GIS datasets

Urban building energy modeling has become an efficient way to understand urban building energy use and explore energy conservation and emission reduction potential.This paper introduced a method to identify archetype buildings and generate urban building energy models for city-scale buildings where public building information was unavailable.A case study was conducted for 68,966 buildings in Changsha city,China.First,clustering and random forest methods were used to determine the building type of each building footprint based on different GIS datasets.Then,the convolutional neural network was employed to infer the year built of commercial buildings based on historical satellite images from multiple years.The year built of residential buildings was collected from the housing website.Moreover,twenty-two building types and three vintages were selected as archetype buildings to represent 59,332 buildings,covering 87.4%of the total floor area.Ruby scripts leveraging on OpenStudio-Standards were developed to generate building energy models for the archetype buildings.Finally,monthly and annual electricity and natural gas energy use were simulated for the blocks and the entire city by EnergyPlus.The total electricity and natural gas use for the 59,332 buildings was 13,864 GWh and 23.6×10^(6) GJ.Three energy conservation measures were evaluated to demonstrate urban energy saving potential.The proposed methods can be easily applied to other cities in China.

Impact of household transitions on domestic energy consumption and its applicability to urban energy planning

The household sector consumes roughly 30% of Earth's energy resources and emits approximately 17% of its carbon dioxide. As such, developing appropriate policies to reduce the CO_2 emissions, which are associated with the world's rapidly growing urban population, is a high priority. This, in turn, will enable the creation of cities that respect the natural environment and the well-being of future generations. However, most of the existing expertise focuses on enhancing the thermal quality of buildings through building physics while few studies address the social and behavioral aspects. In fact, focusing on these aspects should be more prominent, as they cause between 4% and 30% of variation in domestic energy consumption.Premised on that, the aim of this study was to investigate the effect in the context of the UK of household transitions on household energy consumption patterns. To achieve this, we applied statistical procedures(e.g., logistic regression) to official panel survey data comprising more than 5500 households in the UK tracked annually over the course of 18 years. This helped in predicting future transition patterns for different household types for the next 10 to 15 years. Furthermore, it enabled us to study the relationship between the predicted patterns and the household energy usage for both gas and electricity. The findings indicate that the life cycle transitions of a household significantly influence its domestic energy usage. However, this effect is mostly positive in direction and weak in magnitude. Finally, we present our developed urban energy model "Evo Energy" to demonstrate the importance of incorporating such a concept in energy forecasting for effective sustainable energy decision-making.

DeepRadiation:An intelligent augmented reality platform for predicting urban energy performance just through 360 panoramic streetscape images utilizing various deep learning models

Urban energy simulation is critical for understanding and managing energy performance in cities.In this research,we design a novel framework called DeepRadiation,to enable automatic urban environmental performance prediction.By incorporating deep learning strategies,DeepRadiation predicts solar radiation on an urban scale using just panoramic streetscape images without any 3D modeling and simulation.New York City was chosen as the case study for this research.DeepRadiation is comprised of three different deep learning models organized into two stages.The first stage,named DeepRadiation modeling,serves as the framework's brain.At this stage,solar radiation analysis was performed using a Pix2Pix model,a type of conditional generative adversarial networks(GANs).After extracting GIS data and performing energy simulation analysis to prepare the dataset,the Pix2Pix model was trained on 10000 paired panoramic depth images of streetscapes with only building blocks and related panoramic images of streetscapes with only solar radiation analysis.Two GAN generator evaluation measures named qualitative evaluation and quantitative evaluation were used to validate the trained Pix2Pix model.Both demonstrated high levels of accuracy(qualitative evaluation:93%,quantitative evaluation:89%).DeepRadiation application as the DeepRadiation's sescond stage is the framework's eyes.At this stage,two convolutional neural network(CNN)models(DeepLabv3 and MiDaS)were used to perform computer vision tasks on panoramic streetscape images,such as semantic segmentation and depth estimation.The DeepRadiation application stage allows urban designers,architects,and urban policymakers to use the DeepRadiation framework and experience the final output via augmented reality.

Using urban building energy modeling to quantify the energy performance of residential buildings under climate change

The building sector is facing a challenge in achieving carbon neutrality due to climate change and urbanization.Urban building energy modeling(UBEM)is an effective method to understand the energy use of building stocks at an urban scale and evaluate retrofit scenarios against future weather variations,supporting the implementation of carbon emission reduction policies.Currently,most studies focus on the energy performance of archetype buildings under climate change,which is hard to obtain refined results for individual buildings when scaling up to an urban area.Therefore,this study integrates future weather data with an UBEM approach to assess the impacts of climate change on the energy performance of urban areas,by taking two urban neighborhoods comprising 483 buildings in Geneva,Switzerland as case studies.In this regard,GIS datasets and Swiss building norms were collected to develop an archetype library.The building heating energy consumption was calculated by the UBEM tool—AutoBPS,which was then calibrated against annual metered data.A rapid UBEM calibration method was applied to achieve a percentage error of 2.7%.The calibrated models were then used to assess the impacts of climate change using four future weather datasets out of Shared Socioeconomic Pathways(SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5).The results showed a decrease of 22%–31%and 21%–29%for heating energy consumption,an increase of 113%–173%and 95%–144%for cooling energy consumption in the two neighborhoods by 2050.The average annual heating intensity dropped from 81 kWh/m^(2) in the current typical climate to 57 kWh/m^(2) in the SSP5-8.5,while the cooling intensity rose from 12 kWh/m^(2) to 32 kWh/m^(2).The overall envelope system upgrade reduced the average heating and cooling energy consumption by 41.7%and 18.6%,respectively,in the SSP scenarios.The spatial and temporal distribution of energy consumption change can provide valuable information for future urban energy planning against climate change.

Big Data to support sustainable urban energy planning:The EvoEnergy project

Energy sustainability is a complex problem that needs to be tackled holistically by equally addressing other aspects such as socio-economic to meet the strict CO emission targets.This paper builds upon our previous work on the effect of household transition on residential energy consumption where we developed a 3D urban energy prediction system(EvoEnergy)using the old UK panel data survey,namely,the British household panel data survey(BHPS).In particular,the aim of the present study is to examine the validity and reliability of EvoEnergy under the new UK household longitudinal study(UKHLS)launched in 2009.To achieve this aim,the household transition and energy prediction modules of EvoEnergy have been tested under both data sets using various statistical techniques such as Chow test.The analysis of the results advised that EvoEnergy remains a reliable prediction system and had a good prediction accuracy(MAPE;5%)when compared to actual energy performance certificate data.From this premise,we recommend researchers,who are working on data-driven energy consumption forecasting,to consider merging the BHPS and UKHLS data sets.This will,in turn,enable them to capture the bigger picture of different energy phenomena such as fuel poverty;consequently,anticipate problems with policy prior to their occurrence.Finally,the paper concludes by discussing two scenarios of EvoEnergy development in relation to energy policy and decision-making.

Ranking parameters in urban energy models for various building forms and climates using sensitivity analysis

Urban Building Energy Modelling(UBEM)allows us to simulate buildings’energy performances at a larger scale.However,creating a reliable urban-scale energy model of new or existing urban areas can be difficult since the model requires overly detailed input data,which is not necessarily publicly unavailable.Model calibration is a necessary step to reduce the uncertainties and simulation results in order to develop a reliable and accurate UBEM.Due to the concerns over computational resources and the time needed for calibration,a sensitivity analysis is often required to identify the key parameters with the most substantial impact before the calibration is deployed in UBEM.Here,we study the sensitivity of uncertain input parameters that affect the annual heating and cooling energy demand by employing an urban-scale energy model,CitySim.Our goal is to determine the relative influence of each set of input parameters and their interactions on heating and cooling loads for various building forms under different climates.First,we conduct a global sensitivity analysis for annual cooling and heating consumption under different climate conditions.Building upon this,we investigate the changes in input sensitivity to different building forms,focusing on the indices with the largest Total-order sensitivity.Finally,we determine First-order indices and Total-order effects of each input parameter included in the urban building energy model.We also provide tables,showing the important parameters on the annual cooling and heating demand for each climate and each building form.We find that if the desired calibration process require to decrease the number of the inputs to save the computational time and cost,calibrating 5 parameters;temperature set-point,infiltration rate,floor U-value,avg.walls U-value and roof U-value would impact the results over 55%for any climate and any building form.

Evaluation of the environmental impact of the urban energy lifecycle based on lifecycle assessment

Energy resources have environmental impact through their entire lifecycle. The evaluation of the environmental impacts of the energy lifecycle can contribute to decision making regarding energy manage- ment. In this paper, the lifecycle assessment （LCA） method is introduced to calculate the environmental impact loads of different types of energy resources （including coal, oil, natural gas, and electricity） used in urban regions. The scope of LCA includes the production, transportation, and consumption processes. The pollutant emission inventory is listed, and the environmental impact loads are acquired through the calculation of environmental impact potentials, normalization, and weighted assessment. The evaluation method is applied to Beijing, China, revealing that photochemical oxidant formation and acidification are the primary impact factors in the lifecycle of all energy resources and that the total environmental impact load increased steadily from 132.69 million person equivalents （PE） in 1996 to 208.97 million PE in 2010. Among the energy types, coal contributes most to the environmental impact, while the impacts caused by oil, natural gas, and electricity have been growing. The evaluation of the environmental impact of the urban energy lifecycle is useful for regulating energy structures and reducing pollution, which could help achieve sustainable energetic and environmental development.

Optimal City Size in China:An Extended Empirical Study from the Perspective of Energy Consumption

The optimal city size has always been a heated topic for debate in China. Given the background of global warming and fossil fuel crisis, it is argued that the issue should be considered from not only the perspective of economic benefits of a city but should also consider the energy consumption efficiency of the city. On the basis of the energy consumption data of 286 cities at the prefectural level and above in Chinese mainland except Lasa, which are obtained from the EU Emission Database for Global Atmospheric Research(EDGAR), this paper carries out an empirical analysis on the relationship between the city size and the energy consumption efficiency of the city. Then based on this analysis, the paper further examines the economic benefits, social benefits, and environment quality of cities in different scales, and the findings reveal that large cities with 2 – 5 million population have the highest efficiency in all these aspects.