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Beijing is a typical energy-imported city, and the study of energy consumption factors in Beijing is important for the sustainable development of Beijing. There are many influencing factors of energy consumption in Beijing, and the influence degree of each factor differs in different development periods. By constructing the IPAT model of energy consumption change in Beijing, this paper breaks down the factors that cause the change in energy consumption in Beijing into population, affluence, and technological progress. The results show that Beijing is a highly energy-consuming city and the change in energy consumption in Beijing is the superposition of multiple effects. The energy consumption change caused by population growth in Beijing continues to increase through data from 2006 to 2020, but begins to show a downward trend, with a maximum change in energy consumption of 19.8 million tons of standard coal. The largest change in energy consumption is caused by per capita GDP growth, which reached 50.3 million tons of standard coal in 2018–2019, and the effect caused by affluence is greater than the impact of population changes. Technological advances always reduce energy consumption and are increasingly effective. To reduce energy consumption and improve environmental quality, the following two things need to be done: first, to continuously control the population of Beijing, and second, to enhance the development and introduction of innovative technologies for energy conservation. [ABSTRACT FROM AUTHOR]

Urban energy issues have attracted global attentions due to the key role in controlling climate change. As one of the world's largest metropolises, Beijing has been focusing on improving urban energy performances to achieve green transition and sustainable development during the past decades, for which purpose the measurement and decomposition of energy performance is of vital importance. In this paper, exergy is introduced as one of the indicators under the frame of index decomposition analysis. An hierarchical framework is constructed to reveal the contributions from different level of urban energy system to overall energy performance. Beijing's energy and exergy consumption and intensity are then analyzed and compared from the city and sector level during 2008–2017. Results suggest that Beijing's city and sector-level energy performance has been improving during the last 10 years and technical improvements contribute significantly from both energy and exergy perspectives. Meanwhile, changes from Beijing's industrial structure and energy supply structure display neutral contributions, which probably indicate the underestimation of structural contribution is caused under the IDA framework. [ABSTRACT FROM AUTHOR]

In urban railway systems, the timetable guides the section operation of the single train and the arrangement of the train group to meet the dual needs of cost and passengers. This paper proposes a two‐objective train operation optimization based on eco‐driving and timetabling to restore a more realistic scene, including a method level and an objective level. For the method level, the speed curve optimization of the single train and the timetable optimization of the train group are adopted jointly. For the objective level, both the total energy consumption of the train group and the consuming time of passengers are considered. A hybrid solution strategy based on quadratic programming and improved artificial bee colony algorithm is proposed. A hardware‐in‐the‐loop platform is built to carry out validation experiments. Both the cases in general hours and special hours are verified based on the actual data from Beijing Metro Line 15. The results show that both the energy consumption and the passenger consuming time are reduced simultaneously. Correspondingly, the speed curve and the time distribution of the timetable are individually optimized based on the fluctuating passenger flow. [ABSTRACT FROM AUTHOR]

Taking the high and large space of the University of Science and Technology Beijing Gymnasium as this research object, this paper analyzes the influence of different window positions, window-to-wall ratio (WWR), solar heat gain coefficient (SHGC), heat transfer coefficient (K), and visible light transmittance (VT) on the total indoor energy consumption in winter and summer and obtains the relationship between the daylight factor and VT formed when the window is opened per unit area. Through energy consumption simulation, the variation law and calculation formula for indoor total energy consumption are obtained. The results show that the SHGC and K of the exterior window have a significant influence on the total energy consumption. By using the energy consumption simulation of different types of exterior windows, it is concluded that the SHGC of the south-facing window is negatively correlated with the variation of air conditioning energy consumption per unit area Δe1,w, while the others are positively correlated. Moreover, the SHGC and K of the skylight have the most significant influence on the Δe1,w. The total energy consumption decreases and then increases with the increase in the window area, and there is a lowest point, so the right side of the lowest point is less than or equal to 105% of the lowest total energy consumption as a reasonable window area zone. Finally, a progressive optimization method for weighing daylighting and energy consumption in university gymnasiums in Beijing is proposed. [ABSTRACT FROM AUTHOR]

Temperature-control loads, such as residential air conditioners (ACs) and electric water heaters (EWHs), have become important demand response resources in the power system. However, due to the impact of various factors on users' response behavior, it has been difficult for power grid operators to accurately evaluate the response potential under complex factor relationships to derive optimal incentive strategy. Therefore, it cannot achieve a win-win economic benefit between the grid and users. In this paper, a method combining Analytic Network Process (ANP) and Fuzzy logical inference is proposed to predict the user's willingness firstly by taking residential AC load as an example. The weight of each factor affecting users' willingness is analyzed, and main factors are selected as inputs of fuzzy logic inference to derive the willingness of the resident to actively regulate the AC. Then, this method is applied in evaluating the response potential of certain residential area in Beijing according to the survey. By further considering users' house size and the sacrificed comfort temperature under the incentive strategy, the power potential curve of the AC load under different incentives is obtained by using the first-order equivalent thermal parameter (ETP) model and the regulation willingness. Finally, with the objective of maximizing the consumption of the photovoltaic (PV) power, the optimal operation is achieved through the coordinated regulation of residential ACs and EWHs based on the potential curve, and the corresponding optimal incentive strategy for the flexible temperature-control loads is obtained. Simulation results show that the optimal incentive strategy proposed not only increases the PV consumption ratio to 98.35% with an increase of 24.71%, but also maximizes the economic benefits of both sides of the power grid and users. This method of deriving incentive strategy can be used as a reference for grid companies to formulate the incentive strategy to realize optimal operation, such as the maximum new energy consumption. [ABSTRACT FROM AUTHOR]

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This paper introduces a new type of gypsum-based humidity-control material. The material combines gypsum–silica gel humidity-control material with 20% sepiolite powder activated by calcium chloride. Both experimental and simulation studies were conducted to assess its humidity-control performance. The experimental results indicate that gypsum-based humidity-control material has the property of absorbing moisture in high-humidity environments and releasing moisture in low-humidity environments. Moreover, both environmental temperature and relative humidity (RH) have an impact on the material's humidity-control performance. At a relative humidity of 97.4%, the maximum equilibrium moisture content of the material is 0.225 g/g, which is 1.4 times that of the gypsum–silica gel humidity-control material and 4.5 times that of pure gypsum material. The simulation results indicate that gypsum-based humidity-control material effectively mitigates indoor relative humidity fluctuations and maintains indoor air relative humidity within a narrow range. Furthermore, the material has the potential to reduce building energy consumption. This is especially evident under climate conditions with large temperature and relative humidity differences between day and night, such as in Beijing, Paris, and Atlanta. The maximum potential energy-saving rate in Beijing can reach up to 19.31%. [ABSTRACT FROM AUTHOR]

With increasing energy consumption in urban rail transit systems, researchers have paid significant attention to energy-saving train control. In this paper, we propose an effective train trajectory optimization method to reduce the energy consumption based on coasting control, in which coasting control regimes are added to balance running time and energy consumption. For better determining the starting points of coasting control regimes, the whole train running process is divided into several subintervals. Then, aiming to achieve energy efficiency, coasting regimes are added to the subintervals with high energy-saving effects, in which more energy consumption can be reduced with the same running time addition. Based on this, a coasting control method is proposed to generate energy-saving trajectories considering train dynamics, safety, and punctuality. In addition, the proposed method can solve the multisection energy-saving train trajectory optimization problem to obtain optimal running time schemes and related trajectories. Finally, numerical examples based on one of the Beijing metro lines are implemented to verify the effectiveness of the proposed method. The results show that, for the single-section train control problem, the proposed coasting control algorithm can achieve significant energy-saving effects compared to the practical trajectory and calculate energy-saving trajectory in shorter computation times compared to the dynamic programming method. Meanwhile, for the multisection train control problem, energy consumption can be further reduced by optimizing trajectories and running times integratedly. [ABSTRACT FROM AUTHOR]

Wastewater treatment and reuse are important means of addressing water scarcity and protecting the aquatic environment in urban areas. However, it comes at the cost of energy consumption and greenhouse gas emissions. However, the issues of governance and provincial-scale research have largely been ignored in current urban wastewater treatment and reuse studies. This paper summarizes the current status of 175 wastewater treatment plants (WWTPs) in Beijing, explores energy-intensive processes, energy consumption ratios, and the overall energy intensity of WWTPs, and maps the structure of urban wastewater treatment and reuse governance. The results indicate that most WWTPs in Beijing are medium or small in scale, treating wastewater at less than 200 thousand tons/day. Then, five energy-intensive subprocesses are identified, and their energy consumption ratios vary with treatment technologies and management factors, which calls for individual WWTP analysis and plant-specific strategies. The energy intensity of WWTPs in Beijing varies with treatment capacity and membrane bioreactor treatment technology used. Large-scale WWTPs employing MBR technology have a higher average energy intensity. Furthermore, the current coordination group and the four-layer policy system provide sufficient executive power and promote efficiency in departmental collaborations. Finally, inconsistent data, reductions in energy consumption, and the normalization of the governance structure are discussed, and policy suggestions are proposed. [ABSTRACT FROM AUTHOR]

With the traffic congestion problem deteriorating, people increasingly choose urban rail transit (URT) to travel. Although URT alleviates traffic congestion, the long-term operation of a large number of trains leads to huge energy consumption. In order to adapt the major social development concept of "Low carbon", a multi-train energy-saving control collaborative optimization method is proposed in this paper. First, the composition of single train operating conditions is determined by the conversion of operating conditions between stations and the force changes under the premise of ensuring safe and on-time train operation. A single-train energy consumption calculation combinatorial optimization model with the dual control objectives of reducing passengers' average waiting time as well as train traction energy consumption is established. The energy saving control strategy of a single train is investigated by ARMA-Radial Basis Function Neural Network (ARMA-RBFNN) and Genetic Algorithm (GA). Next, the queuing theory is introduced to analyze the variation in passenger waiting time for multiple trains at different arrival intervals. A Deep Reinforcement Learning (DRL) algorithm is designed to obtain the correlation among passenger waiting time, arrival interval and train stopping time. The optimization objective is to minimize the multi-train traction energy consumption and the average passenger waiting time while considering conditions such as train operating safety interval, speed limit, multiple operating state and single train energy-saving models, etc. Then, a multi-train cooperative energy-saving control model is proposed based on the Dragonfly Algorithm (DA). Finally, a case study of Beijing Metro Line 4 is conducted to illustrate the effectiveness of the proposed method. The results demonstrate that the total traction energy consumption and passenger waiting time are reduced by 3.1% and 5 s, respectively, compared with the method of independently optimizing the single-train control strategy. The findings can aid in the development of energy-saving strategies and also provide a basis for energy-saving operation control of multiple trains. [ABSTRACT FROM AUTHOR]

In this paper, the feasibility and applicability of the test and evaluation methods for the temperature and energy consumption coefficient in the cold storage system are tested and verified which is specified in the Beijing local standard "Energy Conservation Monitoring of Refrigeration Storage." It also summarizes and analyses the test verification results of the evaluation index of the location of measuring points of temperature in refrigeration storage, data-collecting time interval of temperature in refrigeration storage, energy efficiency coefficient of the refrigeration unit, and energy consumption coefficient of the refrigeration. The study work provides technical support for making the monitor and evaluation methods which applies to the standard. [ABSTRACT FROM AUTHOR]

Using the calculation method of the United Nations World Tourism Organization (UNWTO), this paper measured the carbon emissions of tourism traffic and their evolution process in Beijing from 2005 to 2017. The Tapio model and Logarithm Mean Divisia Index (LMDI) approach were used to discuss the decoupling relationship between economic development and the change of tourism traffic carbon emissions and the influencing factors. There are six major indicators in our analysis, including (1) tourists scale, (2) per capita tourism consumption level, (3) contribution rate of tourism industry to Gross Domestic Product (GDP), (4) passenger traffic volume per unit GDP, (5) energy consumption per unit passenger traffic volume, and (6) energy structure. The results showed that: the main positive drivers are tourists scale, per capita tourism consumption level and energy consumption per unit of passenger traffic volume. In addition, per unit GDP passenger traffic volume is an effective factor to restrain the growth of carbon emissions. And the economic development of Beijing is developing in step with the change of carbon emissions in Beijing's tourism traffic. The research results have important theoretical and practical significance for Beijing to formulate emission reduction policies and develop low-carbon economy. [ABSTRACT FROM AUTHOR]

Operation optimization for modern subway trains usually requires the speed curve optimization and speed curve tracking simultaneously. For the speed curve optimization, a multi-objective seeking issue should be addressed by considering the requirements of energy saving, punctuality, accurate parking, and comfortableness at the same time. But most traditional searching methods lack in efficiency or tend to fall into the local optimum. For the speed curve tracking, the widely applied proportional integral differential (PID) and fuzzy controllers rely on complicated parameter tuning, whereas robust adaptive methods can hardly ensure the finite-time convergence strictly, and thus are not suitable for applications in fixed time intervals of trains. To address the above-mentioned two problems, this paper presents a novel approach for speed curve seeking and tracking control. First, we present the random reinforcement genetic algorithm (GA) algorithm to avoid the local optimum efficiently. Then, a sliding mode controller is developed for speed curve tracking with bounded disturbance. The Lyapunov theory is adopted to prove that the system can be stabilized in the finite time. Finally, using the real datasets of Yizhuang Line in Beijing Subway, the proposed approach is validated, demonstrating its effectiveness and superiorities for the operation optimization. [ABSTRACT FROM AUTHOR]

China has set a goal to achieve peak CO2 emissions before 2030 and carbon neutrality by 2060. To achieve the goals of carbon peak and carbon neutrality, China needs to address the challenge of the large and still growing CO2 emission base. This paper investigated the energy consumption and CO2 emission in Beijing from 2020–2035 based on the energy elasticity coefficient and contribution value of the sub-energy increment (CVSI) method. Beijing is one of the first cities in China to propose the "carbon peak" target as of 2020. From 2020 Beijing will strive to achieve the goal of carbon neutrality. The results show that in 2035 the CO2 emission in Beijing may drop to 50% of 2020. This decline would be affected by economic growth, energy efficiency and the proportion of renewable energy use. Beijing's energy supply mainly comes from outside the region. Therefore, for Beijing, in addition to increasing the proportion of renewable energy sources outside the region, its own energy acceptance also needs to be strengthened, including strengthening energy storage construction, actively researching and promoting carbon capture and utilization of gas-fired units, which are effective ways to achieve carbon neutrality target. [ABSTRACT FROM AUTHOR]

Optimization on trajectory planning is significant offering the driver or automatic train operation system a guidance to drive the train efficiently. An optimal trajectory is subject to operational, geographic, physical and dynamic constraints. In previous studies, researchers have put much effort into offline optimization. However, operational errors in real situation are rarely taken into consideration. Thus, this paper proposes a dynamic trajectory planning to address the deviation. Specifically, first, we design an online optimization framework to display model predictive control-based train trajectory planning theory. Various constraints are set for optimization problem. Second, taking energy consumption and punctuality into consideration, we propose a moving window method to search the optimal variables, which are applied to evaluate the fitness function. Plus, to improve global search ability and convergence rate, we present a multi-swarm particle swarm optimization by dividing the population into several parts. On the basis of operational time error at reallocation positions, we design a novel weight allocation mechanism for the revaluation of fitness function. At last, taking real data from Beijing-Shanghai high-speed railway as an example, the robustness and effectiveness of the proposed algorithm is proved by comparing multiple parameters with some numerical results of simulations. The results show that the proposed method can adjust operating strategy dynamically so that the train runs in an energy-efficient way. Additionally, in delay scene, the proposed method can also guide the train to catch up with the scheduled time and save energy consumption. [ABSTRACT FROM AUTHOR]

Because the new energy is intermittent and uncertain, it has an influence on the system's output power stability. A hydrogen energy storage system is added to the system to create a wind, light, and hydrogen integrated energy system, which increases the utilization rate of renewable energy while encouraging the consumption of renewable energy and lowering the rate of abandoning wind and light. Considering the system's comprehensive operation cost economy, power fluctuation, and power shortage as the goal, considering the relationship between power generation and load, assigning charging and discharging commands to storage batteries and hydrogen energy storage, and constructing a model for optimal capacity allocation of wind–hydrogen microgrid system. The optimal configuration model of the wind, solar, and hydrogen microgrid system capacity is constructed. A particle swarm optimization with dynamic adjustment of inertial weight (IDW-PSO) is proposed to solve the optimal allocation scheme of the model in order to achieve the optimal allocation of energy storage capacity in a wind–hydrogen storage microgrid. Finally, a microgrid system in Beijing is taken as an example for simulation and solution, and the results demonstrate that the proposed approach has the characteristics to optimize the economy and improve the capacity of renewable energy consumption, realize the inhibition of the fluctuations of power, reduce system power shortage, and accelerate the convergence speed. [ABSTRACT FROM AUTHOR]

Input-output analysis has been proven to be a powerful instrument for estimating embodied (direct plus indirect) energy usage through economic sectors. Using 9 economic input-output tables of years 1987, 1990, 1992, 1995, 1997, 2000, 2002, 2005, and 2007, this paper analyzes energy flows for the entire city of Beijing and its 30 economic sectors, respectively. Results show that the embodied energy consumption of Beijing increased from 38.85 million tonnes of coal equivalent (Mtce) to 206.2 Mtce over the past twenty years of rapid urbanization; the share of indirect energy consumption in total energy consumption increased from 48% to 76%, suggesting the transition of Beijing from a production-based and manufacturing-dominated economy to a consumption-based and service-dominated economy. Real estate development has shown to be a major driving factor of the growth in indirect energy consumption. The boom and bust of construction activities have been strongly correlated with the increase and decrease of system-side indirect energy consumption. Traditional heavy industries remain the most energy-intensive sectors in the economy. However, the transportation and service sectors have contributed most to the rapid increase in overall energy consumption. The analyses in this paper demonstrate that a system-wide approach such as that based on input-output model can be a useful tool for robust energy policy making. [ABSTRACT FROM AUTHOR]

The normal supply of energy is related to the stable development of the economy and society. Forecasting energy consumption helps prepare for the normal supply of energy. In the study of energy consumption forecasting, different scholars have used different forecasting models. This paper uses five-year energy consumption data in the Beijing-Tianjin-Hebei region and uses the grey fractional FGM(1, 1) model to analyze the next six years. Then, the energy consumption of three places is predicted. The advantage of the grey score FGM(1, 1) model is that it can get more accurate prediction results based on a small amount of information. In this study, relatively outdated information affects the accuracy of prediction results. However, other prediction models have great limitations on data. Choosing the grey number fractional model for prediction research can get a more reasonable prediction result. We use the FGM(1, 1) model to make predictions and get the prediction results. In Beijing, the growth rate of natural gas consumption has slowed down and will be basically stable by 2023. The average annual deceleration of coal consumption is 32%. The average annual deceleration of coke consumption is 10%. Crude oil consumption decreased by 6.3% annually. Gasoline consumption is slowly increasing. The consumption of kerosene increased about 8% annually. Diesel consumption is slowly decreasing. Fuel oil consumption is reduced by 17% annually. The average annual growth rate of power consumption exceeds 6%. In Tianjin, the annual growth rate of natural gas consumption is about 5%. Coal consumption is reduced by about 8% every year. The average annual deceleration of coke consumption is 7%. Crude oil consumption decreased by 2.4% annually. Gasoline consumption is slowly decreasing. The consumption of kerosene has increased by about 20% annually. Diesel consumption is slowly decreasing. Fuel oil consumption is reduced by 20% annually. Electricity consumption is slowly increasing. In Hebei Province, the annual growth rate of natural gas consumption is about 15%. Annual coal consumption is reduced by about 3%. Coke consumption remained stable. Crude oil consumption is reduced by 3% annually. Gasoline consumption is slowly increasing, and kerosene consumption has increased by about 31% annually. Diesel consumption is reduced by about 3% annually. Fuel oil consumption remained stable. Electricity consumption is slowly increasing. [ABSTRACT FROM AUTHOR]

Railway transport becomes a more popular transportation in many countries due to its large transport capacity, low energy consumption, and benign environment. The passenger train service planning is the key of the rail operations system to balance the transport service and the passenger demand. In this paper, we propose a mixed binary linear programming formulation for the passenger train service planning to optimize the train route, frequency, stop schedule, and passenger assignment simultaneously. In addition, we analyze the computational complexities of the model and develop a Benders decomposition algorithm with valid inequalities to solve this problem. Finally, our model and algorithm are tested on a real-world instance of the Beijing-Shanghai high-speed railway line. The computational results show that our approach can solve these problems within reasonable solution time and small optimality gaps (less than 2.5%). [ABSTRACT FROM AUTHOR]

This paper constructs a system dynamics model for simulating the impact of different strategies on urban traffic's energy consumption and carbon emissions. Based on a case study in Beijing, the model includes three subsystems: (1) urban traffic, (2) population and economy, and (3) energy consumption and carbon emissions. First, the model is used to decompose the impact of different vehicles on energy consumption and carbon emissions. Decomposition results show that private cars have the most significant impact on urban traffic's energy consumption and carbon emissions; however, total vehicle kilometers traveled by private cars are the smallest among four trip modes. Then, the model is used to simulate different urban traffic policies. Policies are categorized as follows: (a) driving restrictions on vehicle registration numbers, (b) a scheme for vehicle registrations via a lottery system, and (c) development of public transportation infrastructures. Scenario simulation results show that all those measures can reduce energy consumption and carbon emissions. Though the last strategy (c) contains several delays, its effect is more stable and far-reaching. Finally, some recommendations about easing traffic pressure and reducing traffic emissions are given. [ABSTRACT FROM AUTHOR]

Research on ecological total-factor energy efficiency (ETFEE) is conducive to energy conservation, emission reduction, and ecological protection. This paper focuses on the measurement and decomposition of the ETFEE in the Beijing-Tianjin-Hebei (BTH) region in China. In order to measure the ETFEE values, the window technology is combined with a nonradial and nonoriented SBM-undesirable model considering undesirable outputs to overcome the defect of insufficient data of research objects and ensure the calculation process to be implemented. The findings show that Beijing and Tianjin are DEA-efficient, while Hebei is not. The technological progress rates of Beijing and Tianjin reach up to 11.92% and 14.96%, while that of Hebei retrogresses by 4.47%. The scale efficiencies of Beijing, Tianjin, and Hebei are 97.75%, 86.60%, and 93.81%, respectively, which means that there are potentials for further optimization in the energy structures. The impulse response results between the energy structure and the ETFEE show that the proportions of coal and petroleum have negative effects on the ETFEE, while that of natural gas has a positive effect. The research results can provide reference for decision makers to formulate regional development plans. [ABSTRACT FROM AUTHOR]

Highlights • The majority of carbon emissions derived from indirect fraction of energy consumption in Beijing. • Life expectancy is reduced in areas where a higher amount of coal is burnt for household consumption. • Socio-economic structure changes play a major role in the observed energy and emission trends. • Search of optimal solutions for energy-efficient and low carbon private will continue to be crucial. Abstract Household carbon emissions are major contributors to global emissions that is increasing recognized. In China, approximately 20 million rural populations transfer to the urban areas annually, which results in a lot of new infrastructure construction and consumer requirements. This paper quantifies the numbers and trends of urban and rural resident on energy use and carbon footprint in Beijing from 1996 to 2011 by analysis of Consumer Lifestyle Approach (CLA) and carbon footprint. It provides a better understanding of complete energy consumption and carbon emissions profile in Beijing. The results show that for the Beijing residents, when direct and indirect energy consumption and CO 2 emissions are taken into account, the total household energy consumption proportion compared to the city increases from 22.7% to 59.2%, and the total household CO 2 emissions proportion increases from 32.2% to 68.8% in the research period. In the urbanization process, total energy consumption and carbon emissions of Beijing urban residents are significantly higher than those of rural residents. However, the transformation of a rural Beijing resident into an urban Beijing resident would not create a large difference between urban and rural areas in the per-capita energy consumption and per-capita carbon footprint. Results are assessed with respect to the available scientific literature, as well as potential available technological and policy solutions. Since the implementation that different policy instruments can mitigate carbon emissions, governments are gradually combining policy measures for the same technology. The results in this paper may help promote alternative consumption strategies in household sector which could entail more reasonable resource distribution and effective reduction of both urban and rural environmental influences. [ABSTRACT FROM AUTHOR]

Energy consumption has promoted a continuous development for economy and society, but it has also brought serious environmental pollution problems which endanger public health. In order to focus on the impact of PM 2.5 emissions from future energy consumption on human health better, this paper estimated PM 2.5 emissions caused by energy consumption in Beijing in 2020,2025 and 2030 by setting the baseline scenario, emission reduction scenario, intensified emission reduction scenario and the data of the base year 2015, which will be 162.6 thousand tons, 195.9 thousand tons and 235.9 thousand tons in the baseline scenario, 143.1 thousand tons, 151.8 thousand tons and 160.9 thousand tons under the emission reduction scenario, and 132.6 thousand tons, 130.2 thousand tons, 127.9 thousand tons under the intensified emission reduction scenario. Finally the paper measured the number of different health effect ends caused by PM 2.5 emissions and the economic loss. The results showed that there was a rapid increase, a mild increase and a moderate decrease in energy consumption and PM 2.5 emissions in Beijing under the three scenarios from 2015 to 2030. Besides, under the same scenario and the same concentration level baseline, the number of the internal medicine (over the age of 15) was the largest, and the economic loss caused by total death rate was the biggest. This article provides suggestions to reduce PM 2.5 emissions and public health effect ends for Beijing in the future. [ABSTRACT FROM AUTHOR]

Haze is a pollution phenomenon that has become increasingly frequent in recent years, primarily composed of SO2, NOx, and particulate matter. Since the “PM2.5 Crisis” in 2013, the Beijing-Tianjin-Hebei region has been plagued by haze. After a series of high-intensity management activities, the air quality in Beijing has continued to improve. To investigate the effectiveness of the haze control measures in Beijing, a dynamic management model was constructed using the system dynamics approach and implemented using Stella software. The model is simulated and evaluated the contribution to different governance strategies (increasing investment in science and technology innovation, reducing motor vehicle ownership, reducing coal consumption, and a combination policy) to reduce haze in Beijing from 2010 to 2025. The validity and robustness of the model were verified through model testing and validation. The simulation results showed that: (1) the combination policy has the most significant effect on reducing energy consumption and pollution emissions, which would reduce energy consumption by 38.93 million tons and pollution emissions by 128.33 thousand tons in 2025, (2) among the single treatment measures, reducing industrial coal consumption is the most effective measure to reducing haze, (3) it is necessary to strengthen the joint prevention and control of the Beijing-Tianjin-Hebei region in the future to promote the sustainable development of the regional environment. [ABSTRACT FROM AUTHOR]

An integrated energy system (IES) plays a key role in transforming energy consumption patterns and solving serious environmental and economic problems. However, the abundant optional schemes and the complex coupling relationship among each piece of equipment make the optimization of an IES very complicated, and most of the current literature focuses on optimization of a specific system. In this work, a simulation-based two-step decoupling method is proposed to simplify the optimization of an IES. The generalized IES is split into four subsystems, and a two-layer optimization method is applied for optimization of the capacity of each piece of equipment. The proposed method enables fast comparison among abundant optional configurations of an IES, and it is applied to a hospital in Beijing, China. The optimized coupling system includes the gas-fired trigeneration system, the GSHP, and the electric chiller. Compared with the traditional distributed systems, the emission reduction rate of CO2 and NOX for the coupling system reaches 153.8% and 314.5%, respectively. Moreover, the primary energy consumption of the coupling system is 82.67% less than that of the traditional distributed energy system, while the annual cost is almost at the same level. [ABSTRACT FROM AUTHOR]

This study focuses on analyzing the provincial CO2 emission efficiency of China using data on energy consumption and carbon emissions from 2013 to 2019. Specifically, the game cross-equilibrium efficient frontier data envelopment analysis (DEA) approach, consisting of a game cross-efficiency model and a DEA fixed-sum output model, was proposed. In this method, CO2 is considered a fixed-sum undesirable output, and the decision-making units (DMUs) affect each other to achieve the efficiency result. The results show that: (1) CO2 emissions efficiency varied significantly between provinces during the study period, with Beijing performing the best and Ningxia the worst. (2) The overall national average CO2 emission efficiency showed a slow increasing trend over time. The eastern region performed best, followed by the northeastern, central, and western regions. (3) By comparing the existing DEA fixed-sum output methods, the CO2 efficiency obtained by our method, considering the DMU game relationship, is more acceptable. Based on these results, several policies and improvements were recommended to the provinces, such as reducing energy utilization in high-carbon emission industries. [ABSTRACT FROM AUTHOR]

This paper investigates the win-win commercialization mode of aggregating electric vehicles (EVs) in demand side for ancillary service. We have conducted a half-year-long incentive verification experiment covering 10,066 electric vehicle owners in Beijing. Based on the experimental results, we develop an incentive-based mechanism that enables electric vehicles to participate the wholesale capacity market through an aggregator. The aggregator, which is held by charging service operators can make a profit by designing a smart pricing policy. In this process, not only the electric vehicle owners but also the utility can gain benefits. [ABSTRACT FROM AUTHOR]

Rural energy consumption significantly affects the economy of a country and plays a vital role in the rural residents’ lives. This paper analyzed the association of the rural household energy consumption with their demands on renewable energy systems and contributions to the reduction of CO 2 , TSP (total suspended particulate), and PM 2.5 emissions in Zhangziying town of Daxing district of Beijing. This system analysis aimed to evaluate the sustainability of energy supply and economic development in rural area of Beijing by taking Zhangziying town as an example. Specifically, in this paper energy consumed by the rural households was investigated in terms of energy sources (coal, biogas, straw gas, LPG, electricity and firewood), utilization, choices, costs and systems. The results show that coal is still the major energy source for heating during winter, which constitutes that largest portion of the household energy consumption. With the development of renewable energy, the utilization of coal, LPG and firewood for cooking has been replaced by renewable energy such as biogas and straw gas, which are coupled with a higher heating value and lower price. Meanwhile, this study also suggests that the renewable energy generated in the surveyed villages is equivalent to 32,863.68 kgce and could annually reduce 81,929.15 kgCO 2 emissions, 22,347.30 kg TSP, and 8938.92 kg PM 2.5 emissions. The structure of rural household energy consumption of Zhangziying town is sustainable and environmentally sound. The structure of rural household energy consumption is undergoing a transformation from traditional low-efficiency biomass domination to integrated consumption of traditional and renewable energy sources. Renewable energy is not only a solution to the energy shortage problem in rural areas, but also helps conserve fossil resources and protect the environment. [ABSTRACT FROM AUTHOR]

PM2.5 concentration in Beijing China has become a focus for government and common people. It has been explored through source apportionment that heating generates significant influence on PM2.5 concentration in Beijing. However, there has still been great difficulty of quantitatively measuring the impact. In this paper, the author, taking PM2.5 in Beijing as research object, establishes a model of heating influence on PM2.5 emission. Data about the city's boiler number, distribution, fuel consumption as well as rural fuel consumption have been obtained through questionnaire. With the adoption of coefficients for producing and emitting pollutants, influence of heating factors in Beijing on PM2.5 concentration is evaluated quantitatively. High accuracy of the model in the paper has been proved through comparison with data of 2014 Beijing PM2.5 concentration monitored by Chinese Ministry of Environmental Protection. According to evaluation result 10.9, heating contributes to 13.7% of PM2.5 emission in Beijing, 9.9% and 3.8% from the six urban districts and rural areas respectively. It has been suggested that measures including decreasing heating temperature should be adopted to alleviate PM2.5 pollutions in Beijing. [ABSTRACT FROM AUTHOR]

A novel two-axis solar tracker with parallel mechanism is proposed in this paper. A dynamic model is derived by using the virtual work principle and the consumed energy including the mechanical energy and motor energy loss is computed. Taking Beijing as the working location of the solar tracker, the energy consumptions of the parallel solar tracker and its corresponding serial solar tracker are compared based on the premise that the proposed solar tracker and its corresponding serial solar tracker have similar static stiffness. Mechanical energy consumption of the proposed tracker is reduced by 7.55% compared to the serial solar tracker. The motor energy loss of the parallel solar tracker is also significantly lower. This simple and low-energy consumption solar tracker is a good alternative to the traditional solar tracker with large energy consumption. [ABSTRACT FROM AUTHOR]

The combined cooling, heating and power (CCHP) system is a promising energy-efficient technology to realize energy cascade utilization. With the integration of photovoltaic/thermal panels and thermal energy storage, the comprehensive performance of the system can be further improved. However, the performance is also affected by the operation strategy. This paper proposes an effective operation strategy to deal with the energy flow of the system well to achieve a better performance. The mathematical model of a CCHP system hybridized with photovoltaic/thermal panels and thermal energy storage is established in this paper. The optimal size of key components of the CCHP system is determined by the particle swarm optimization (PSO) algorithm. Case studies of four scenarios of a residential zone in Beijing are conducted to verify the effectiveness of the system structure and efficiency of the proposed method. The results show that by adding photovoltaic/thermal (PV/T) panels and thermal energy storage, the economic and energetic benefits can be effectively improved and the proposed operation strategy is effective. [ABSTRACT FROM AUTHOR]

Energy conservation is attracting more attention to achieve a reduced lifecycle system cost level while enabling environmentally friendly characteristics. Conventional research mainly concentrates on energy-saving speed profiles, where the energy level evaluation of the timetable is usually considered separately. This paper integrates the train driving control optimization and the timetable characteristics by analyzing the achievable tractive energy conservation performance and the corresponding boundaries. A calculation method for energy efficient driving control solution is proposed based on the Bacterial Foraging Optimization (BFO) strategy, which is utilized to carry out batch processing with timetable. A boundary identification solution is proposed to detect the range of energy conservation capability by considering the relationships with average interstation speed and the passenger volume condition. A case study is presented using practical data of Beijing Metro Batong Line and two timetable schemes. The results illustrate that the proposed optimized energy efficient driving control approach is capable of saving tractive energy in comparison with the conventional traction calculation-based train operation solution. With the proposed boundary identification method, the capability space of the energy conservation profiles with respect to the energy reduction and energy saving rate is revealed. Moreover, analyses and discussions on effects from different passenger load conditions are given to both the weekday and weekend timetables. Results of this paper may assist the decision making of rail operators and engineers by enhancing the cost effectiveness and energy efficiency. [ABSTRACT FROM AUTHOR]

With the increase in energy demand, environmental issues such as carbon emissions are becoming more and more prominent. China will scale its intended nationally determined contributions by adopting more vigorous policies and measures. China aims to have CO2 emissions peak before 2030 and achieve carbon neutrality before 2060. The current challenge and priority of China's high-quality development is to ensure a harmonious balance between the ecological environment and the economy. The South-to-North Water Diversion Project passes through Beijing, Tianjin, Henan, and Hebei, which were chosen as the study sites. The carbon emission data was from the China Carbon Emission Database 2000–2019. Decoupling modeling using statistical yearbook data from four provinces and municipalities. KMO and Bartlett's test used SPSS 27 software. The selection of indicators was based on relevance. Analyses were performed using the extended STIRPAT model and ridge regression. Moreover, projections of carbon peaks in the study area for 2020–2035 under different rates of change were simulated by the scenario analysis method. The results show that: (1) The decoupling analysis of the four provinces and cities from 2000-2019 gradually shifts to strong decoupling; (2) Resident population, energy structure, and secondary industry as a proportion of GDP significantly impact carbon emissions; (3) From 2000–2035, Beijing and Henan experienced carbon peaks. The peak time in Beijing was 96.836 million tons in 2010. The peak time in Henan was 654.1004 million tons in 2011; (4) There was no peak in Hebei from 2000–2035. [ABSTRACT FROM AUTHOR]

In light of reducing train operation energy consumption while maintaining the passenger service level for creating sustainable urban rail transit systems, we address a non-parallel train timetabling problem considering regenerative braking energy utilization and the non-parallel operation of multiple trains on a metro line via a newly proposed multi-objective timetable (MOT) optimization model and an evolutionary algorithm based on NSGA-II. The optimization objectives of the MOT model are to find satisfactory energy-saving timetables on the Pareto frontier by minimizing the total travel time of passengers and minimizing the net energy consumption of trains. An improved multi-objective evolutionary algorithm based on NSGA-II is constructed to generate the optimal arrival and departure times at each station for each train running in a non-parallel operation mode. This study tests the feasibility of the proposed optimization method via an empirical case using the data collected from the Yizhuang Line of the Beijing metro systems in China. The simulation results show that the proposed optimization method satisfies both the energy utilization and passenger service levels along a Pareto front. The MOT improves the overall effectiveness of regenerative braking energy utilization by 29.88% in comparison with the original timetable; it reduces the net operation energy consumption by 44.86% relative to the travel-oriented timetable (TOT); and it reduces the total passenger travel time by 27.18% compared with the energy-oriented timetable (EOT). [ABSTRACT FROM AUTHOR]

Abstract: This paper describes the development of a forecasting model, named the emergy-based urban dynamic model capable of accurately simulating the observed resource consumption, economic growth and environmental impact of Beijing. This model differs from previous urban emergy models by monitoring the negative effects to human well-being and ecosystem integrity in the developing urban system. A fresh perspective focuses on urban comprehensive performance linking such impacts to a supply-side environmental cost evaluation (including ecological service supply, ecological and economic losses and investment for treatment). In this introductory paper, we present the conceptual considerations of the most important components of the emergy-based urban dynamic model: the urban assets, lands, capitals, population, water resources, economic and environmental loss. Each subsystem in this model will enrich the feedback dynamics, policy levers and post-scenario analyses. Statistical Information and calibration are also considered in this dynamic emergy accounting. This study advances the temporal dynamic principles of emergy accounting through integrating upstream and downstream evaluation methods to quantify the environmental impact by addressing specific damages to human health and ecosystem''s integrity and by linking such impacts to a supply-side environmental cost evaluation. [Copyright &y& Elsevier]

Even though renewable energy development has gained momentum in China, thermal power generation still accounts for approximately 70% of the county's total power generation serving as the major source of carbon dioxide (CO 2) emissions in China. Facing the challenges of meeting 2030 peak target of CO 2 emission and realizing the coordinated development of thermal power generation in Beijing-Tianjin-Hebei region, this paper applies generalized Divisia Index Method (GDIM) to decompose the dynamics in the relevant CO 2 emission. The effects of five factors including electricity demand, energy consumption, technology, energy efficiency and energy-mix are considered. The decomposition suggests that electricity demand is the primary factor driving the CO 2 emission up, whereas technology effect decreases CO 2 emission the most. Given the significant roles of technology, energy-mix and energy efficiency in CO 2 emissions reduction, seven scenarios are designed to identify the optimal coordinated development pathway for thermal power generation in Beijing-Tianjin-Hebei region. Through upgrading energy structure and/or enhancing energy efficiency, the thermal power generation in Beijing-Tianjin-Hebei region can achieve coordinated development and realize the 2030 peak target under four scenarios. The detailed development pathways for CO 2 emissions and specific policy implications for Beijing, Tianjin and Hebei are provided to further govern CO 2 emissions and maintain sustainable development. • Proposed a five-factor Generalized Divisia Index Model (GDIM) for CO 2 emission analysis. • Analyzed the CO 2 from thermal power generation in China's Beijing-Tianjin-Hebei region. • Electricity demand and technology contributed the most to the positive and negative changes of CO 2 , respectively. • Designed seven thermal generation development scenarios to study the CO 2 change until 2030. • The 2030 CO 2 peak target can be achieved under four scenarios. [ABSTRACT FROM AUTHOR]

Abstract The residential building sector consumes a large amount of energy and is recognized as a high potential area where urban energy consumption and carbon emission can be reduced. To reduce the residential energy consumption, relevant policies and measures have been formulated in different places. However, due to the lack of adequate information in the process of policy formulation, the effectiveness of most energy-saving policies for residential buildings seems to be unsatisfactory. Although previous studies have analyzed the energy consumption for a single building or for the whole construction industry at the national level, few of them investigated the life-cycle energy consumption of residential buildings at the city level. Since municipal governments are the key implementers for the reduction of urban energy consumption, a city-level analysis on the energy consumption of residential buildings is highly needed for the policy making. To fill the research gap, this paper develops a life-cycle dynamic simulation model to fully reveal the characteristics of energy consumption of residential buildings at the city level. This easy-to-use model is validated by a case study of Beijing, which demonstrates its applicability and capability to help analyze multiple indicators of residential energy consumption and the dynamic trends of these indicators for municipal governments. Furthermore, the model can be used under the condition of limited input data, and can provide the situation analysis and simulation results of energy consumption of urban residential buildings for facilitating the policy making by municipal governments. Graphical abstract Image 1 Highlights • The proposed model can analyze city-level energy consumption of residential buildings. • The model can be used with relatively limited amounts of input data. • Operation stage accounts for 67% of total residential energy consumption in Beijing. • Heating is the most energy-intensive activity of operation stage in Beijing. • Policy implications for residential energy saving are provided based on the model. [ABSTRACT FROM AUTHOR]

The establishment of low-carbon cities has been suggested all over the World, since cities are key drivers of energy usage and the associated carbon emissions. This paper presents a scenario analysis of future energy consumption and carbon emissions for the city of Beijing. The Long-range Energy Alternatives Planning (LEAP) model is used to simulate a range of pathways and to analyze how these would change energy consumption and carbon emissions from 2007 to 2030. Three scenarios have been designed to describe future energy strategies in relation to the development of Beijing city, namely a reference scenario (RS), control scenario (CS), and integrated scenario (IS). The results show that under the IS the total energy demand in Beijing is expected to reach 88.61 million tonnes coal equivalent (Mtce) by 2030 (59.32 Mtce in 2007), 55.82% and 32.72% lower than the values under the RS and the CS, respectively. The total carbon emissions in 2030 under the IS, although higher than the 2007 level, will be 62.22% and 40.27% lower than under the RS and the CS, respectively, with emissions peaking in 2026 and declining afterwards. In terms of the potential for reduction of energy consumption and carbon emissions, the industrial sector will continue to act as the largest contributor under the IS and CS compared with the RS, while the building and transport sectors are identified as promising fields for achieving effective control of energy consumption and carbon emissions over the next two decades. The calculation results show that an integrated package of measures is the most effective in terms of energy savings and carbon emissions mitigation, although it also faces the largest challenge to achieve the related targets. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we give an overview of the situation of CO2 emissions in both Beijing and Tokyo and discuss and compare the causes for their emission changes according to decomposition based on both social activities and fuel varieties. We find that in order to enjoy a sustainable development, Beijing needs to learn from Tokyo especially in energy consumption area. At the end of the paper, the cooperation between these two metropolises is summarized. [Copyright &y& Elsevier]

Abstract: Energy consumption is a technical issue or a spatial structure issue? Aim of this paper is to study the impact of population density and energy consuming density to space together on energy consuming with urban districts of Beijing. The author of this paper puts two kinds of factors including economic and technological ones, and urban space context factors with energy consuming together into a model. LMDI method will be employed to do the analysis. It is concluded that non-economic and non-technological factors were also significant to reduce energy consumption. Even population density did not contribute to reduction of energy consuming, when it was combined with energy consuming density to space together, the contribution was efficient. It indicates that compacted urban space with low-carbon city is the most efficient way to reduce energy consuming in urban areas. © 2011 Published by Elsevier Ltd. Selection and peer-review under responsibility of RIUDS [Copyright &y& Elsevier]

Integrated energy system (IES) is characterized by high self-consumption ratio of on-site generated renewable energy, high efficiency of conventional energy utilization and possesses a significant flexibility in its operation. This overall, constitute to the foundation of low-carbon communities. Considering economic and environmental benefits, this paper proposes a two-layer co-optimization model with the upper layer optimizing the IES configuration and the lower layer optimizing IES operation. A community in Beijing is introduced as a case study to analyze the benefits of IES and compared with the conventional energy system. Multiple scenarios are researched, including (a) IES for low-carbon communities with PV roof area limitation, (b) IES for each of the four building types with PV roof area limitation, (c) IES for low-carbon communities without PV roof area limitation. The results indicate that the optimized IES for community can reduce the cost by 25% and CO 2 emissions by 32% per year by considering the limitation of roof area for the PV system. Without this constraint, the costs and emissions could be reduced by 20% and 62%, respectively. In addition, IES is more appropriate for office and commercial buildings since their load characteristics allow for load shifting and community-level IES costs and emissions are 8% and 10% lower than building-level due to the complementary of community load. This study provides suggestions for the IES planning and application in low-carbon community. [Display omitted] • An IES with multiple renewable energy and energy storage subsystems is proposed. • A two-layer co-optimization model of IES design and operation is developed. • IES optimization results for building-level and community-level are compared. • Impact of roof area limitation for PV on IES optimization results is investigated. [ABSTRACT FROM AUTHOR]

Promoting the coupled development of water, energy, and food (WEF) subsystems is a critical step to enhance synergies and increase efficiencies in the WEF nexus. However, the evolution and obstacles of coupled development are largely ignored. This study developed a framework dividing links in the nexus to select indicators and integrated models for the degree of coordination and obstacle diagnosis to explore the coupled development of the WEF nexus from 2000 to 2020 in the water-scarce megacities of Beijing and Tianjin. The results show that the average coordination degree of the WEF nexus in Beijing (0.315) and Tianjin (0.317) is at a low level, indicating a limited interaction between the WEF subsystems, while the coordinated development degree of the WEF nexus is increasing. The order degree of the WEF subsystems shows a two-level hierarchical structure, indicating that both similarities and differences are included in place-specific characteristics. Furthermore, obstacle factors are identified from water and energy subsystems, in which environmental water use (W3) and the energy consumption per gross domestic production unit (E4) played the most significant roles and require a higher priority in policy response. The results in this paper complement obstacle factor analysis in WEF nexus practice, and provide operational indicators for nexus governance. [Display omitted] • Linkages are divided into one-, two-, and three-resource scales. • The number of related official documents is used to define the weight of subsystems. • The water subsystem dominated the coupled development of the water-energy-food nexus. • The order degree of subsystems show a two-level hierarchical structure. • Key obstacle factors are identified from water and energy subsystems. [ABSTRACT FROM AUTHOR]

In recent years, China has been vigorously carrying out the construction and development of a sponge city. To prove that the material and energy consumption involved in the implementation of a sponge city is much less than that of the integrated urban drainage system (IUDS) in addition to saved energy and reduced carbon in the sponge city, it was important to calculate the corresponding carbon source and sink and analyze its key influence factors. The emission factor method was used to calculate carbon emissions. In view of this, based on the Intergovernmental Panel on Climate Change (IPCC) guidelines and life cycle assessment (LCA), this research established a systematic accounting method for carbon emissions from the IUDS and the sponge city, which focused on improving the calculation method of the carbon sink stage. A case study was conducted in Beijing, China, and the carbon emission reduction effect of the construction of the sponge city was discussed. The results showed that the carbon emission reduction potential (CRP) of sponge facilities in this project for 50 years was 612.45 tons of CO2 equivalent after the renovation. Compared with IUDS, sponge city construction had a positive effect on carbon emission reduction and reduced carbon emissions by 87.08% on average. For the IUDS and the sponge city, the stormwater pipe network had the largest contribution of carbon emission, and its material, transportation, pipeline laying, and maintenance of stormwater pipe networks had important influences. Morris global analysis method was used to analyze the sensitivity of LCA results and obtained that the influence degree of sensitivity factors on carbon emissions in the life cycle was in the order of annual rainfall > carbon sequestration rate of green space > high-density polyethylene (HDPE) > transport distance > fertilization and insecticide. This study can provide a positive contribution to the construction of a sponge city and planning the low-carbon development of the city in the future in China. [ABSTRACT FROM AUTHOR]

The primary energy use in cities accounts for a large share of global energy consumption, leading to significant carbon emissions. Exploring the city's current characteristics and future trends in energy demand has policy implications for carbon reduction policy-making. We establish a city-level energy forecast demand model to predict Beijing's future energy demand. Results show that economic growth has strong positive correlations with energy consumption. Beijing's energy demand is predicted to stay at a pretty high level by the end of 2020, between 98.72 and 121.60 Mtce, or between 4.12 and 5.08 tce per capita under three distinctive GDP growth (High, Medium and Low) scenarios. Comparing with the start of historical time series 1980, this is a huge increase of 563.50% (High), 489.90% (Medium) and 423.91% (Low) in terms of total demand, while is increases of 147.98%, 120.47% and 95.81% per capita. Based on an energy structure model estimated by a Markov-chain method, we find that our predictions would follow the current trend of energy structure, making Beijing's coal demand decline towards zero share of energy consumption, natural gas rises approaching 50%, oil stays almost constant around 32.3%, electricity decreases below 20%, and other fuels keep in a small amount in 2035. We would expect to see almost 8000 kg per capita carbon emissions in Beijing if the GDP growth rate is high, and still over 6000 kg per capita if GDP growth is low. In terms of total carbon emissions, we show that though coal consumption will keep decreasing towards zero in the next years, total carbon emission still rises by 36.5–72.8% comparing with 2010. These findings can provide references for policymakers to implement some incentive policies to promote renewable and clean energy. [ABSTRACT FROM AUTHOR]

To recycle regenerative braking energy (RBE) while reducing demand charge in electrified railway, a co‐phase power supply system with hybrid energy storage system (HESS) is implemented. However, the dynamical degradation characteristic of battery is necessary to be considered in optimal operation of HESS. A bi‐level model considering battery degradation is proposed to obtain optimal sizing and operation of HESS. The proposed model includes a novel real‐time energy management strategy (EMS) using average power as thresholds, to effectively reduce the demand charge and energy consumption charge. Thresholds are dynamically adjusted with the consideration of battery capacity degradation. A real measured load profile from Beijing‐Shanghai high speed railway is studied. The results demonstrate that the proposed EMS performs better than previous EMS in cost saving. The dynamically adjusted thresholds of EMS are proved to be essential under the consideration of battery degradation. With the optimal sizing of the HESS, the traction substation can achieve 8.69% annual saving of demand charge and recycle 52.33% of the RBE. The results also show that a traction substation equipped with the HESS yields higher economic benefit than the energy storage systems equipped with only a battery or a supercapacitor. [ABSTRACT FROM AUTHOR]

Optimizing battery swapping station (BSS) configuration is essential to enhance BSS's energy savings and economic feasibility, thereby facilitating energy refueling efficiency of electric taxis (ETs). This study proposes a novel modular battery swapping mode (BSM) that allows ET drivers to choose the number of battery blocks to rent according to their driving range requirements and habits, improving BSS's economic profitability and operational flexibility. We further develop a data-driven approach to optimizing the configuration of modular BSS considering the scheduling of battery charging at the operating stage under a scenario of time-of-use (ToU) price. We use the travel patterns of taxis extracted from the GPS trajectory data on 12,643 actual taxis in Beijing, China. Finally, we test the effectiveness and performance of our data-driven model and modular BSM in a numerical experiment with traditional BSM as the benchmark. Results show that the BSS with modular BSM can save 38% on the investment cost of purchasing ET battery blocks and is better able to respond to the ToU price than to the benchmark. The results of the sensitivity analysis suggest that when the peak electricity price is too high, additional battery blocks must be purchased to avoid charging during those peak periods. [ABSTRACT FROM AUTHOR]

The ever-increasing electricity price and energy consumption in high-speed railway industry push railway companies to seek a promising way to realize their sustainable developments. Making full use of the solar resource along with high-speed railways can be a potential solution to cut the electricity bill, bring more profit to railway companies and realize the decarbonization of high-speed railway industry. This paper studies the optimal planning of distributed photovoltaic generation (DPVG) and energy storage system (ESS) for the traction power supply system (TPSS) of high-speed railway. A quantitative method is proposed to study the time and space characteristics of photovoltaic generation and electricity demand of high-speed trains. An integrated cost-benefit analysis framework is developed to evaluate the effect of DPVG and ESS on the economy of TPSS. To derive the optimal planning scheme and energy management strategy of DPVG and ESS, a mathematical programming model with the objective of minimizing the total cost is proposed to seek the most economical solution. A hybrid global optimal solution approach is developed to solve the model. A real-world case of Beijing-Baoding high-speed railway in China is used to illustrate the capability and characteristics of the proposed model. The computational results show that DPVG is able to supply 32.5 % electricity demand of high-speed trains. The integration of DPVG and ESS can help railway company save 4.2 million CNY each year in Beijing-Baoding high-speed railway. This paper demonstrates the potential and applicability of DPVG and ESS in high-speed railway industry. • A novel distributed photovoltaic generation model is proposed for the traction power supply system of high-speed railway. • A global optimal algorithm is provided to derive the most economical planning scheme and energy-management strategy. • Photovoltaic generation can help Beijing-Baoding high-speed railway save 4.2 million China Yuan each year. • Solar energy is able to supply 32.5% electricity demand of Beijing-Baoding high-speed railway. [ABSTRACT FROM AUTHOR]

• The capacity optimization model of the integrated photovoltaic- energy storage-charging station was built. • The case study bases on the data of 21 charging stations in Beijing. • The construction of the integrated charging station shows the maximum economic and environment benefit in hospital and minimum in residential. • A decline in energy storage costs increases the benefits of all-scale investments, an increase in electric vehicles promotes the benefits of small-scale investments, expansion of the peak-to-valley price distance increases the benefits of large-scale investments. The coupled photovoltaic-energy storage-charging station (PV-ES-CS) is an important approach of promoting the transition from fossil energy consumption to low-carbon energy use. However, the integrated charging station is underdeveloped. One of the key reasons for this is that there lacks the evaluation of its economic and environmental benefits. Based on the electricity load of different types of buildings and the data of electric vehicle charging stations in Beijing, this paper analyzes the economic and environmental benefits of integrated charging station developed over different scales by the capacity optimization model. Said benefits are shown to differ considerably with different locations (building types): the construction of the integrated charging station in places where the electricity load is high at night, and where the curve of daytime electricity consumption is consistent with the PV generation curve (e.g., hospitals), shows maximal economic benefits and carbon dioxide emissions reduction. In other (e.g., residential) areas, where power consumption is lower across both day and nighttime hours, the economic benefits and carbon dioxide emissions reduction are lowest. The economic and environmental benefits of the integrated charging station also markedly differ on different scales: with scale expansion, the rate of return on investment and the carbon dioxide emissions reduction first increase and then decrease. A decline in energy storage costs increases the economic benefits of all integrated charging station scales, an increase in EVs increases the economic benefits of small-scale investments, and expansion of the peak-to-valley price difference increases the economic benefits of large-scale investments. [ABSTRACT FROM AUTHOR]

As the capital of China, Beijing is regarded as a major metropolis in the world. Study of the variation in temporal energy intensity generated by the driving forces in Beijing can provide guidance for policy decisions on energy consumption in global metropolises. In this paper, we established Beijing energy-input-output tables from 1997 to 2012 in 2002 constant price. Based on the tables we use the structural decomposition analysis (SDA) to decompose energy intensity into five determining factors: Energy input coefficient, technology coefficient (Leontief inverse coefficient), final demands structure by product, final demands by category and final energy consumption coefficient. The results show that (1) During 1997 and 2012, Beijing energy intensity had continued to decline, and the energy input coefficient and final demands by category are the main factor help to decrease the energy intensity. (2) Energy input coefficient plays a vitally important role to energy intensity decrease, which may be due to the increase of the proportion of the tertiary industry. (3) The change of final demands structure by product is a driving force to make energy intensity rising except for 2002-2007, and technology coefficient (Leontief inverse) has impact on energy intensity increase except for 2007-2012. (4) The effect of final energy consumption coefficient is gradually weakened role on energy intensity. This study showed how the “top-down” IO-SDA methodology was implemented on a city scale. Policy implications from this study would be helpful for addressing energy consumption in global capital cities and metropolises. [ABSTRACT FROM AUTHOR]

The facilitation of regional trade has boosted the fragmentation in global supply chain, integrating the production, energy and other ecological factors across the regional boundaries by virtue of vertical specialization. The consequences of this trend on urban energy consumption are firstly analyzed by constructing multiregional input–output table and decomposing the value added export in Beijing. Energy consumption per unit of GDP in Beijing is only half of that per unit of value added after adjusting the export by virtue of decomposition techniques and Beijing displays no obvious advantages in energy usage when considering the value added processes. Energy intensive parts of production are relocated outside the metropolis by the downstream and upstream participation in global areas. The results of value added export for energy consumption reveal that the vertical specialization in this metropolis slashes the amount of energy consumption in the city but raises that in the outside. The unified framework by integration of the value added export and energy consumption in this paper could be extended to other related topics. [ABSTRACT FROM AUTHOR]