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Indoor environmental parameters are closely related to the energy consumption and indoor thermal comfort of office buildings. Predicting these parameters, especially indoor temperature, can contribute to the management of energy consumption and thermal comfort levels in office buildings. An accurate indoor temperature prediction model is the basis for implementing this process. To this end, this paper first discusses the input and output parameters of the model, and then it compares the prediction effects of mainstream prediction model algorithms based on data mining under the same data conditions. The superiority of the XGBoost integrated learning algorithm is verified, and a further XGBoost-based indoor temperature online prediction method is designed. The effectiveness of the method is validated using actual data from a commercial office building in Haidian District, Beijing. Finally, optimization methods for the prediction method are discussed with regard to the scheduler mechanism proposed in this paper. Overall, this work can assist building operators in optimizing HVAC equipment running strategies, thus improving the indoor thermal comfort and energy efficiency of the building. [ABSTRACT FROM AUTHOR]

A solar heat pump based on the photovoltaic photothermal (PV/T) module is a new technology that can improve the photovoltaic efficiency and recovery of waste heat in photovoltaic conversion. The comprehensive efficiency of a system can thus be greatly improved. At present, there is little research on the simulation of a solar heat pump based on the Simulink 2018 software. In this paper, PV/T modules are modeled and simulated using the Simulink software based on the typical meteorological parameters in Beijing city during summer and winter conditions. Considering the system characteristics of a simultaneous operation, the models of all components such as the compressor, condenser, evaporator, expansion valve, and heat storage tanks contained in a system are established based on the system's working principle, energy conservation equations, and some empirical formulas and then simulated. As PV/T modules are the key influencing factor for system performance, the model and simulation process are introduced in detail in this paper. The results show that the surface temperature of PV/T modules under different operating conditions are all significantly lower than that of ordinary photovoltaic panels. The average temperature of PV/T modules is only 15.2 °C, which is 20.2 °C lower than that of ordinary panels under summer conditions, and the average temperature of PV/T modules is only 0.5 °C, 17.8 °C lower than that of ordinary panels under winter conditions. The average photoelectric efficiency can be improved by 15.4% and 8.9%. And, the temperature change amplitude of PV/T modules is lower, which weakens the temperature fluctuation of the modules. The photovoltaic efficiency is therefore further increased. As a result, the power generation and photovoltaic efficiency are both improved significantly. [ABSTRACT FROM AUTHOR]

The traction load of electrified railways is a special user of the power system, which will have a significant impact on and pose a challenge to the power system after grid connection. Considering the impact and fluctuation of traction load connected to the power grid, this paper proposed a method to study the influence of traction load on the transient stability of the power grid based on parameter identification. Firstly, according to the operation characteristics of traction load, a comprehensive traction load model based on the measurement-based method of induction motor parallel power function model is selected, and the objective function of parameter identification is determined. Then, the specific steps of using the improved gray wolf optimizer (IGWO) algorithm to achieve the parameter identification of the traction comprehensive load model are introduced, including chaotic map, nonlinear convergence factor, and individual position update. Next, with the IEEE39 bus system as the network background, the load parameters identified by the measured data of a line of the Beijing–Shanghai high-speed railway are imported into the Power System Analysis Software Package (PSASP) 7.0 for transient simulation to analyze the impact of traction load on the transient stability of the power grid. Finally, three typical load models are compared with the traction load model to draw relevant conclusions, and it is verified that the traction load characteristics are different from the general load model, which provides reference for the connection of electrified railway traction load to the power grid and the economic and technical construction of the power system. [ABSTRACT FROM AUTHOR]

With the accelerating urbanization process in China, the extensive economic growth model with high energy consumption and high pollution aggravates atmospheric environmental pollution and restricts the sustainable development of the social economy. The Beijing–Tianjin–Hebei region has the most severe haze pollution and the most acute contradiction between resources, environment and economic development. Based on the physical coupling theory, this paper constructs a coupling coordination degree model of the economy, environment and high haze pollution industries, such as the thermal power industry. Through literature analysis and expert investigation of the economic, industrial and environmental data of the Beijing–Tianjin–Hebei region from 2006 to 2018, the coordinated development of the economy, environment and high haze pollution industries, such as the thermal power industry, in the above region, was dynamically monitored. The monitoring results show that the development level of the Beijing, Tianjin and Hebei region is significantly different, and the three subregions have not entered the stage of coordinated development. Moreover, since 2015, they have been in a bottleneck period of development and have not achieved a higher level of progress. The three subregions have different advantages and restraining factors. For example, the backward level of Hebei's economy and high haze pollution industries, such as the thermal power industry, restrict the coordinated development of the region as a whole. Only by accelerating the high-quality development of the Hebei economy can we break through the bottleneck of the development of Beijing, Tianjin and Hebei. [ABSTRACT FROM AUTHOR]

Nowadays, sustainability-related issues have attracted growing attention due to fossil fuel depletion and environmental concerns. Considering many cities have gradually replaced taxis with electric vehicles (EVs), to reduce greenhouse gas emissions and traditional energy consumption, this paper studies the optimization strategy of battery swapping for electric taxis (ETs), and it is not only to ease congestion in the battery swapping station (BSS) but also for electric taxis to address their range anxiety and maximize their benefits. Firstly, based on the road network, the Dijkstra algorithm is adopted to provide the optimal path for ETs to BSSs with the minimum energy consumption. Then, this paper proposes the optimization objective function with minimum cost, which contains the battery service cost based on the battery's state of charge, waiting cost caused by waiting for swapping battery in BSSs and the carbon emission reduction benefit generated during ETs driving to BSSs, and uses a mixed-integer linear programming (MILP) algorithm to solve this function. Finally, taking the Leisure Park of Laoshan City in Beijing as an example, the numerical simulation is carried out and the proposed battery swapping strategy is efficient to alleviate the congestion of BSSs and maximize the total benefit of ETs, and the cost based on the proposed strategy is 14.21% less than that of disorderly swapping. [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]

To address the severity of the wind and light abandonment problem and the economics of hydrogen energy production and operation, this paper explores the problem of multi-cycle resource allocation optimization of hydrogen storage systems for coal–wind–solar power generation. In view of the seriousness of the problem of abandoning wind and photovoltaic power and the economy of hydrogen production and operation, the node selection and scale setting issues for hydrogen production and storage, as well as decision-making problems such as the capacity of new transmission lines and new pipelines and route planning, are studied. This research takes the satisfaction of energy supply as the basic constraint and constructs a multi-cycle resource allocation optimization model for an integrated energy system, aiming to achieve the maximum benefit of the whole system. Using data from Inner Mongolia, where wind abandonment and power limitation are severe, and Beijing and Shanxi provinces, where hydrogen demand is high, this paper analyzes the benefits of the hydrogen storage system for coal–wind–solar power generation, and explores the impact of national subsidy policies and technological advances on system economics. [ABSTRACT FROM AUTHOR]

In recent years, Beijing has been facing serious environmental problems. As an important cause of environmental problems, a further study of the factors influencing CO2 emissions in Beijing has important significance for the social and economic development of Beijing. In this paper, Cointegration and Granger causality test were proposed to select influencing factors of CO2 emissions prediction in Beijing, the influencing factors with different leading lengths were checked as well, and the genetic algorithm (GA) was used to optimize the initial weight and threshold values of a support vector machine (SVM) and the new SVM optimized by GA (GA-SVM) was established to predict the CO2 emissions of Beijing from 2016-2020 with scenario analysis. Through the comparison of 36 kinds of development scenarios, we found that economic growth, resident population growth and energy intensity enhancement were the major growth factors of carbon emissions, of which the contributions exceed 0.5% in all kinds of development scenarios. Finally, this paper put forward some reasonable policy recommendations for the control of CO2 emissions. [ABSTRACT FROM AUTHOR]

The Beijing-Tianjin-Hebei region is subject to the most severe haze condition in China. Against the backdrop of the coordinated development of Beijing, Tianjin, and Hebei, it is of great significance to explore the space-time distribution characteristics of high haze pollution industries in the above region. The purpose of this article is to find high haze pollution industries scientifically, analyze the spatial distribution characteristics of high haze pollution industries in Beijing-Tianjin-Hebei correctly, and formulate effective reduction measures for the spatial distribution of high haze pollution industries in different regions of Beijing-Tianjin-Hebei. It hopes that the measures provide a basis for effective to reduce high haze pollution in the Beijing-Tianjin-Hebei region. Using the Gini coefficient and location entropy, this paper explores the space-time characteristics of high haze pollution industries in 13 cities of the Beijing-Tianjin-Hebei region. The results show that those aforesaid high haze pollution industries present an obvious clustering trend in Beijing, Tianjin, Shijiazhuang, Handan, and some areas along the economic axis of China, overall flooding into Eastern coastal and Southern inland areas. By industry, the petroleum processing and coking processing industry, chemical raw materials and chemical products industry, manufacture of non-metallic mineral products, and ferrous metal smelting and rolling processing industry witness a declining clustering trend, as opposed to the electricity, heat production, and supply industry. Meanwhile, core cities of the Beijing-Tianjin-Hebei region have developed high haze pollution industries, and the hinterland lags in economic development. It is necessary to face up to the differences in economic development among areas in the Beijing-Tianjin-Hebei region, promote the staggered development of high haze pollution industries, and build a high haze pollution industry chain with coordinated economic and environmental development, thus reducing the economic gap with each area, and realizing the coordinated development of economy, industry, and environment. [ABSTRACT FROM AUTHOR]

Recently, "power cuts" and "coal price surges" have been significant concerns of individuals and societies. The main reasons for a power cut are a recent rapid increase in power consumption, shortage of thermal coal or the large shutdown capacity of thermal power units, resulting in a tight power supply in the power grid. In recent years, the shortage of fossil resources has led to frequent energy crises. In the context of carbon peaks and carbon neutralization, how to better develop electric-energy substitution and eliminate the dependence on fossil energy has become a problem that needs to be solved at present. In this paper, the influencing factors of electric-energy substitution in Beijing are analyzed, and the indexes affecting the electric-energy substitution are outlined. By constructing various machine-learning models, the prediction is performed. The results show that the Gaussian kernel support vector machine model based on a grid search has a good prediction effect on the electric-energy substitution potential in Beijing, which has certain guiding significance for electric-energy substitution potential analysis. [ABSTRACT FROM AUTHOR]

Ultra-short-term load demand forecasting is significant to the rapid response and real-time dispatching of the power demand side. Considering too many random factors that affect the load, this paper combines convolution, long short-term memory (LSTM), and gated recurrent unit (GRU) algorithms to propose an ultra-short-term load forecasting model based on deep learning. Firstly, more than 100,000 pieces of historical load and meteorological data from Beijing in the three years from 2016 to 2018 were collected, and the meteorological data were divided into 18 types considering the actual meteorological characteristics of Beijing. Secondly, after the standardized processing of the time-series samples, the convolution filter was used to extract the features of the high-order samples to reduce the number of training parameters. On this basis, the LSTM layer and GRU layer were used for modeling based on time series. A dropout layer was introduced after each layer to reduce the risk of overfitting. Finally, load prediction results were output as a dense layer. In the model training process, the mean square error (MSE) was used as the objective optimization function to train the deep learning model and find the optimal super parameter. In addition, based on the average training time, training error, and prediction error, this paper verifies the effectiveness and practicability of the load prediction model proposed under the deep learning structure in this paper by comparing it with four other models including GRU, LSTM, Conv-GRU, and Conv-LSTM. [ABSTRACT FROM AUTHOR]

In the context of China's dual carbon target, Beijing, as the capital of China, should play an exemplary role in carbon emission reduction. On the premise of optimizing high-emission sectors such as coal and industry, Beijing is still a certain distance from the goal of carbon neutrality. Therefore, on the basis of Beijing's energy resource endowment, considering Beijing's economic development and carbon neutrality goals and scientifically and reasonably planning Beijing's carbon emission reduction path are important tasks. We construct an energy structure optimization model to achieve the goal of carbon neutrality by 2050. The model analysis concludes that the residents and transportation sectors will account for a large proportion of Beijing's total carbon emissions in the future. To achieve the goal of carbon neutrality, the electricity substitution of fossil energy and the high proportion of external power are two necessary measures, and the optimal path of carbon emission reduction is proposed. [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]

Due to the massive congestion in ground transportation in Beijing, underground rail transit has gradually become the main mode of travel for residents of large urban areas. Because the average daily traffic of the Beijing subway is over 12 million passengers, ensuring the safety of underground rail transit is particularly important. Big data shows that more than 4000 passengers participate in Long-term Stay in the Subway every day. However, the behaviors of these passengers have not been characterized. This paper proposes a method for identifying the Long-term Staying in Subway System (LSSS) in the subway based on the shortest path and analyze its travel mode. In combination with the past research of scholars, we try to quantify the suspected behavior with a database of assumed suspected behavior records. Finally, we extract the spatial-temporal travel characteristics of passengers and we propose a SAE-DNN algorithm to identify suspected anomalies; the accuracy of the training set can reach 95.7%, and the accuracy of the test set can also reach 93.5%, which provides a reference for the subway operators and the public security system. [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]

The purchase subsidy policy gives powerful support in battery electric vehicles' (BEVs) market penetration. However, the purchase subsidy is also a huge financial burden for the government, so it can only be considered as a transitional measure and will be canceled gradually. This paper aims to investigate the impact of purchase subsidy phase-out on BEV adoptions and explore alternative incentive policies to continue stimulating BEV adoptions. A stated preference (SP) survey is conducted in Beijing, and a binary logit (BL) model is established to describe how various factors affect BEV adoption preferences. In addition to the factors related to vehicle techniques, the policies of license plate restrictions and driving restrictions are focused due to Beijing's unique external policy environment. The vehicle use subsidy and bus line driving permit are tested as alternative incentive policies against the purchase subsidy decrease. The results show that incentive policies can significantly influence BEV adoption intentions. If the purchase subsidy policy is canceled in Beijing, the BEV choice probability will be reduced from 45.94% to 16.62%. In this case, the vehicle use subsidy needs to be set at the level of 4966 CNY/year (714.3 USD/year) to maintain the original BEV choice probability. [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]

To relieve the peak operating power of the electric grid for an electric bus fast-charging station, this paper proposes to install a stationary energy storage system and introduces an optimization problem for obtaining the optimal sizes of an energy buffer. The charging power demands of the fast-charging station are uncertain due to arrival time of the electric bus and returned state of charge of the onboard energy storage system can be affected by actual traffic conditions, ambient temperature and other factors. The introduced optimization is formulated as a stochastic program, where the power matching equality of the total charging demands of connected electric buses is described as a chance constraint by denoting a satisfaction probability, then a stochastic supremum for the operating power of the electric grid is defined by actual data and the problem finally can be solved by convex programming. A case study for an existing electric bus fast-charging station in Beijing, China was utilized to verify the optimization method. The result shows that the operation capacity cost and electricity cost of the electric grid can be decreased significantly by installing a 325 kWh energy storage system in the case of a 99% satisfaction probability. [ABSTRACT FROM AUTHOR]

Air pollution has become an increasingly serious environmental problem in China. Especially in winter, the air pollution in northern China becomes even worse due to winter heating. The "coal to gas" policy, which uses natural gas to replace coal in the heating system in winter, was implemented in Beijing in the year 2013. However, the effects of this policy reform have not been examined. Using a panel dataset of 16 districts in Beijing, this paper employs a first difference model to examine the impact of the "coal to gas" policy on air quality. Strong evidence shows that the "coal to gas" policy has significantly improved the air quality in Beijing. On average, the "coal to gas" policy reduced sulfur dioxide ( SO 2 ), nitrogen dioxide ( NO 2 ), particulate matter smaller than 10 µm (PM10), particulate matter smaller than 2.5 µm (PM2.5) and carbon monoxide (CO) by 12.08%, 4.89%, 13.07%, 11.94% and 11.10% per year, respectively. We find that the "coal to gas" policy is more effective in areas with less energy use efficiency. The finding of this paper suggests that the government should continue to implement the "coal to gas" policy, so as to alleviate the air pollution in Beijing, China. [ABSTRACT FROM AUTHOR]

The distributed renewable energy system, integrating various renewable energy resources, is a significant energy supply technology within energy internet. It is an effective way to meet increasingly growing demand for energy conservation and environmental damage reduction in energy generation and energy utilization. In this paper, the life cycle assessment (LCA) method and fuzzy rough sets (FRS) theory are combined to build an environmental evaluation model for a distributed renewable energy system. The ReCiPe2016 method is selected to calculate the environmental effect scores of the distributed energy system, and the FRS is utilized to identify the crucial activities and exchanges during its life cycle from cradle to grave. The generalized evaluation method is applied to a real-world case study, a typical distributed energy system located in Yanqing District, Beijing, China, which is composed of wind power, small-scale hydropower, photovoltaic, centralized solar thermal power plant and a biogas power plant. The results show that the environmental effect of per kWh power derived from the distributed renewable energy system is 2.06 × 10−3 species disappeared per year, 9.88 × 10−3 disability-adjusted life years, and 1.75 × 10−3 USD loss on fossil resources extraction, and further in the uncertainty analysis, it is found that the environmental load can be reduced effectively and efficiently by improving life span and annual utilization hour of power generation technologies and technology upgrade for wind turbine and photovoltaic plants. The results show that the proposed evaluation method could fast evaluate the environmental effects of a distributed energy system while the uncertainty analysis with FRS successfully and effectively identifies the key element and link among its life span. [ABSTRACT FROM AUTHOR]

In this paper, an optical structure design for a solar furnace is described. Based on this configuration, Monte Carlo ray tracing simulations are carried out to analyze the influences of four optical factors on the concentrated solar heat flux distribution. According to the practical mirror shape adjustment approach, the curved surface of concentrator facet is obtained by using the finite element method. Due to the faceted reflector structure, the gaps between the adjacent mirror arrays and the orientations of facets are also considered in the simulation model. It gives the allowable error ranges or restrictions corresponding to the optical factors which individually effect the system in Beijing: The tilt error of heliostat should be less than 4 mrad; the tilt error of the concentrator in the orthogonal directions should be both less than 2 mrad; the concentrator facets with the shape most approaching paraboloid would greatly resolve slope error and layout errors arising in the concentrator. Besides, by comparing the experimentally measured irradiance with the simulated results, the optical performance of the facility is evaluated to investigate their comprehensive influence. The results are useful to help constructors have a better understanding of the solar furnace's optical behavior under conditions of multiple manufacture restrictions. [ABSTRACT FROM AUTHOR]

With the coming of the 2022 Beijing Winter Olympic Games, China's artificial ice rink construction will be in rapid development. A parametric evaluation of the cooling pipe in a direct evaporation rink was performed by numerical simulation. The results showed that the influence of the temperature of the antifreeze pipe on the ice surface temperature can be ignored. The evaporation temperature of the working medium in the cooling pipe is between −32 °C and −22.4 °C to ensure the ice surface temperature is between −5 °C and −3 °C. With the increase in the cooling pipe spacing, the required evaporation temperature of the working medium in the cooling pipe and the uniformity of the ice surface temperature decreased. The required evaporation temperature of the working medium in the cooling pipe decreases by 1.2–1.5 °C for every 10 mm increment of spacing. With the increase in the cooling pipe diameter, the required evaporation temperature of the working medium in the cooling pipe and the uniformity of the ice surface temperature increase. The required evaporation temperature of the working medium in the cooling pipe increases by 2.2–2.9 °C for every 5 mm increment of diameter. The maximum temperature difference of ice surface temperature ranged from 0.004 °C to 0.111 °C. [ABSTRACT FROM AUTHOR]

The quick growth of energy consumption in urban rail transit has drawn much attention due to the pressure of both operational cost and environmental responsibilities. In this paper, the timetable is optimized with respect to the system cost of urban rail transit, which pays more attention to energy consumption. Firstly, we propose a Mixed-Integer Non-Linear Programming (MINLP) model including the non-linear objective and constraints. The objective and constraints are linearized for an easier process of solution. Then, a Mixed-Integer Linear Programming (MILP) model is employed, which is solved using the commercial solver Gurobi. Furthermore, from the viewpoint of system cost, we present an alternative objective to optimize the total operational cost. Real Automatic Fare Collection (AFC) data from the Changping Line of Beijing urban rail transit is applied to validate the model in the case study. The results show that the designed timetable could achieve about a 35% energy reduction compared with the maximum energy consumption and a 6.6% cost saving compared with the maximum system cost. [ABSTRACT FROM AUTHOR]

To avoid a sharp rise in temperature in the cabin of parked vehicles exposed to solar radiation, experienced drivers leave some windows partly open when the vehicle is parked in the sunlight to achieve cooling through natural ventilation. However, the effectiveness of this measure to reduce the temperature under different weather conditions has not been verified. To this end, this study investigates the effect of open windows on the thermal environment of a vehicle under different environmental conditions. A field measurement, in which two identical vehicles with and without window gaps were used, was carried out in Daxing District, Beijing. The measurements were conducted for 15 days under different window gaps and ambient conditions. The results revealed that open windows resulted in a maximum temperature reduction of 6.7 °C in cabin air temperature under high temperature and high solar radiation, while only 0.6 °C can be reduced under low temperature and low solar radiation. The results also showed that when window gaps effectively reduce the air temperature, lower air temperature can be obtained with larger open-window areas. [ABSTRACT FROM AUTHOR]

With the advantage of integrating distributed energy, storage and DC load with high efficiency, the DC distribution network recently attracted wide attention in the field of nearly zero energy buildings. Considering the large number of buildings and the enormous energy-saving potential, the distribution form and the revenue distribution are key factors affecting energy efficiency and operational economy. Aiming at the characteristics of AC and DC distribution topologies in nearly zero energy buildings, an energy efficiency evaluation method is proposed based on time-sequential power flow simulation, where the piecewise linear efficiency function of converters and dynamic line power loss model are established. Based on a DC distribution demonstration project, an AC power distribution system with the same boundary conditions is designed. The results show that the efficiency of DC distribution topology is about 6% higher than the AC system, while the efficiency advantage in office, hotel, commercial, educational and residential buildings in Beijing, Shenzhen and Shanghai varies from 0.6–4.96%, which shows a high correlation with the proportion of a high-power load, with Pearson correlation coefficient of 0.794. With the increase of DC load ratio and distributed power supply access capacity, the efficiency of the system will be improved accordingly. On the basis of this, a method to equalize DC distribution income in the nearly zero energy buildings power market is proposed. In addition, 15 typical scenarios of different cities were selected to evaluate efficiency and influencing factors. Finally, assessing the multi-stakeholder impact of DC retrofitting to balance the revenue generated by the DC distribution model suggests that transport costs should be raised by 8.2–13.1% to balance revenue with the equipment prices decreased by about 0.1 yuan/W. [ABSTRACT FROM AUTHOR]

Promoting the development and utilization of solar energy is a practical way to alleviate the energy crisis and achieve the goal of carbon neutrality. Recently, interest has arisen in the dual-functional active solar thermal façade (ASTF) system that produces hot water throughout the whole year and reduces cooling and heating load as a function of the building façade. Here, a mathematical model of the ASTF system is built and validated by the experimental data, and the annual performance of the ASTF system in representative cities in three climate regions is evaluated. The results are that compared with the common solar water system, the ASTF system adds passive energy savings, which accounts for 5.8%, 7.2% and 11.4% of the total primary energy savings of the system for Shanghai, Beijing and Lanzhou. Compared with the traditional wall, the ASTF saves 16.4% and 23.0% of cooling energy consumption and 102.3% and 92.4% of heating energy consumption for Shanghai and Beijing, respectively. Additionally, it saves 74.7% of heating energy consumption for Lanzhou. Lastly, the impact of the design parameters and operation parameters of the system are investigated, respectively. This study demonstrates a viable path to promoting cost-effective active solar thermal façades in different climates, and the results can be beneficial to further research. [ABSTRACT FROM AUTHOR]

Rammed earth (RE) is a low-tech recyclable building material with good heat storage and moisture absorption performance that can better maintain the stability of the indoor thermal environment and improve indoor comfort. With innovations in and the development of new technology, the field of rammed earth construction technology is gradually expanding. However, deficiencies in the thermal insulation of traditional rammed earth structures make it impossible for them to meet China's building energy codes in cold regions. This study constructs a comprehensive evaluation index of the thermal performance of rammed earth walls that is based on the heat transfer mechanism, optimizing the thickness of the boundary conditions of the building interior's design temperature, as well as the energy demand and economic efficiency. This research also offers a new design for the thermal insulation of rammed earth construction by combining the building energy savings design code with WUFI Pro software. This study demonstrates that the optimum thickness of rammed earth construction in Beijing is about 360 mm, the thickness of extruded polystyrene board (XPS) is 50 mm (for public buildings) and 70 mm (for residential buildings), and the structural form of external insulation offers the highest performance benefit. In addition, this work also evaluates the risk of condensation inside composite rammed earth construction, finding that there is a risk of condensation on the exterior side of the wall and at the interface between the insulation panels and rammed earth wall, thus requiring an additional moisture-proof layer. In this study, thermal mass and insulation are fully considered and a design strategy for rammed earth construction given quantitatively, providing a theoretical basis for the application of rammed earth materials in cold regions. [ABSTRACT FROM AUTHOR]

Asynchronous motors are widely used in industry and agriculture because of their simple structure, low cost, and easy maintenance. However, due to the coupling and uncertain factors of the actual operation of the motor, a traditional controller cannot achieve a satisfactory control effect. A linear active disturbance rejection controller (LADRC), featuring good robustness and adaptability, was proposed to improve the control efficiency of a nonlinear, uncertain plant. A linear extended state observer (LESO) is the core part of a L. The accuracy of the observation of state variables and unknown disturbances is related to the control effect of the controller. The performance of a traditional LESO is not high enough, and thus an error differential is introduced by analyzing the principle of LESO to improve its observation performance. The improved LADRC applies to the vector speed control of the induction motor. Additionally, low-speed and high-speed no-load starting, sudden load, electromagnetic torque, and three-phase stator current of the induction motor was simulated using MATLAB (Developed by MathWorks in Natick, MA, USA, and dealt by MathWorks Software (Beijing) Co., Ltd. in Beijing, China). Theoretical analysis and simulation results show that the ADRC based on the improved linear expansion observer was better than the traditional linear ADRC in terms of the dynamic and static performance and robustness. [ABSTRACT FROM AUTHOR]

Energy consumption and pollutant emissions from buildings have caused serious impacts on the environment. Currently, research on building environmental costs is quite insufficient. Based on life cycle inventory of building materials, fossil fuel and electricity power, a calculating model for environmental costs during different stages is presented. A single-objective optimization model is generated by converting environmental impact into environmental cost, with the same unit with direct cost. Two residential buildings, one located in Beijing and another in Xiamen, China, are taken as the case studies and analyzed to test the proposed model. Moreover, data uncertainty and sensitivity analysis of key parameters, including the discount rate and the unit virtual abatement costs of pollutants, are also conducted. The analysis results show that the environmental cost accounts for about 16% of direct cost. The environmental degradation cost accounts for about 70% of the total environmental cost. According to the probabilistic uncertainty analysis results, the coefficient of variation of material production stage is the largest. The sensitivity analysis results indicate that the unit virtual abatement cost of CO2 has the largest influence on the final environmental cost. [ABSTRACT FROM AUTHOR]

The peak-valley characteristic of electrical load brings high cost in power supply coming from the adjustment of generation to maintain the balance between production and demand. Distributed energy storage system (DESS) technology can deal with the challenge very well. However, the number of devices for DESS is much larger than central energy storage system (CESS), which brings challenges for solving the problem of location selection and capacity allocation with large scale. We formulate the charging/discharging model of DESS and economic analysis. Then, we propose a simulation optimization method to determine the locations to equip with DESSs and the storage capacity of each location. The greedy algorithm with Monte Carlo simulation is applied to solve the location and capacity optimization problem of DESS over a large scale. Compared with the global optimal genetic algorithm, the case study conducted on the load data of a district in Beijing validates the efficiency and superiority of our method. [ABSTRACT FROM AUTHOR]

The energy consumption of urban central heating in northern China is two to four times that in northern Europe and other countries. Beijing has adopted measures, such as 'coal to gas' and 'coal to electricity', to reduce environmental pollution caused by central heating. Given a peak-to-valley difference in the electricity supply of power plant, which is uneven day and night, this study proposes to store the night-time off-peak electricity in the form of heat energy and drive the ammonia absorption system in the form of steam or hot water during peak or flat electricity. Simulation results of ammonia absorption cooling and heating dual-supply system show that heat source temperature increases, evaporation temperature increases, and cooling water temperature decreases are all beneficial to improve the refrigeration coefficient in the summer cooling condition. In the meantime, heat source temperature increases, evaporation temperature increases, and cooling water temperature decreases are beneficial for increasing the heating coefficient in the winter heating condition. The heating and cooling coefficients of the system are 1.38 and 0.65 in the optimal working condition in winter and summer. Benefit analysis shows that, compared with central heating and cold storage air conditioning, the system can save 576,000 tons of standard coal and 1.417 million tons of carbon dioxide if used in 12% of the cooling and heat supply areas in Beijing. The potential for energy saving and emission reduction is large. [ABSTRACT FROM AUTHOR]

The increasing importance of lignocellulosic biomass based energy production has led to an urgent need to conduct a reliable resource supply assessment. This study analyses and estimates the availability of agricultural residue biomass in Beijing, where biomass energy resources are relatively rich and is mainly distributed in the suburbs. The major types of crops considered across Beijing include food crops (e.g., maize, winter wheat, soybean, tubers and rice), cotton crops and oil-bearing crops (e.g., peanuts). The estimates of crop yields are based on historical data between 1996 and 2017 collected from the Beijing Municipal Bureau of Statistics. The theoretical and collectable amount of agricultural residues was calculated on the basis of the agricultural production for each crop, multiplied by specific parameters collected from the literature. The assessment of current and near future agricultural residues from crop harvesting and processing resources in Beijing was performed by employing three advanced modeling methods: the Time Series Analysis Autoregressive moving average (ARMA) model, Least Squares Linear Regression and Gray System Gray Model (GM) (1,1). The results show that the time series model prediction is suitable for short-term prediction evaluation; the least squares fitting result is more accurate but the factors affecting agricultural waste production need to be considered; the gray system prediction is suitable for trend prediction but the prediction accuracy is low. [ABSTRACT FROM AUTHOR]