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The household is the basic unit of a residential community or building. High-resolution, long-term open data are necessary to help study residential electricity consumption, smart home technologies, and electricity flexibility technologies at household level. This paper introduces an IoT-based data collection platform (IDCP) consisting of gateways, sensors, and cloud servers. This platform can collect data on the occupant presence, indoor environment, window-opening states, and appliance electricity consumption simultaneously. This study deployed the IDCP in a single-family apartment in Beijing, China, and compiled a dataset, namely, CN-OBEE, including data on the per-minute occupant behavior, thermal environment, and appliance electricity use of the apartment for an entire year (from May 31, 2021, to May 31, 2022), and hourly meteorological data collected by the nearest national weather station during the same period. This dataset is the first detailed and publicly available occupant behavior and electricity use dataset for Chinese homes. As a regional feature, the dataset compiled by this study includes window-opening behavior and the use of split air conditioners (ACs). [ABSTRACT FROM AUTHOR]

The coal-to-electricity project (CTEP) using electricity instead of coal for heating is a significant measure to cope with climate change and air pollution in China. After years of development, the CTEP has been implemented on a large scale of areas in Beijing. An evaluation model is proposed in this paper to analyze the environmental benefits and assist in determining whether the CTEP should be continued. Firstly, the heating system and framework of this paper are introduced. We also analyzed the feasibility of CTEP, illuminating how electric heating benefits the environment. Secondly, the evaluations models of CTEP are built, including reduction of coal, pollutant emission, and overall benefit. Finally, based on the models results are obtained and discussed and prove environmental benefits of CTEP. It is calculated that 18 kg of coal per unit heating area can be saved in the CTEP, which effectively reduces 98% of PM2.5, 95% of SO2, 44% of NOX, and 81% of CO2 emissions. The CTEP has increasing contribution to the reductions of atmospheric pollutants. The considerable environmental benefits demonstrate the significance of the CTEP in the north of China, which also facilitate its future development in areas outside of Beijing. [ABSTRACT FROM AUTHOR]

Electricity consumption of metro stations increases sharply with expansion of a metro network and this has been a growing cause for concern. Based on relevant historical data from existing metro stations, this paper proposes a support vector regression (SVR) model to estimate daily electricity consumption of a newly constructed metro station. The model considers some major factors influencing the electricity consumption of metro station in terms of both the interior design scheme of a station (e.g., layout of the station and allocation of facilities) and external factors (e.g., passenger volume, air temperature and relative humidity). A genetic algorithm with five-fold cross-validation is used to optimize the hyper-parameters of the SVR model in order to improve its accuracy in estimating the electricity consumption of a metro station (ECMS). With the optimized hyper-parameters, results from case studies on the Beijing Subway showed that the estimating accuracy of the proposed SVR model could reach up to 95% and the correlation coefficient was 0.89. It was demonstrated that the proposed model could outperform the traditional methods which use a back-propagation neural network or multivariate linear regression. The method presented in this paper can be an adequate tool for estimating the ECMS and should further assist in the delivery of new, energy-efficient metro stations. [ABSTRACT FROM AUTHOR]

Fractional order grey model is effective in describing the uncertainty of the system. In this paper, we propose a novel variable-order fractional discrete grey model (short for VOFDGM(1,1)) by combining the discrete grey model and variable-order fractional accumulation, which is a more general form of the DGM(1,1). The detailed modeling procedure of the presented model is first systematically studied, in particular, matrix perturbation theory is used to prove the validity in terms of the stability of the model, and then, the model parameters are optimized by the whale optimization algorithm. The accuracy of the proposed model is verified by comparing it with classical models on six data sequences with different forms. Finally, the model is applied to predict the electricity consumption of Beijing and Liaoning Province of China, and the results show that the model has a better prediction performance compared with the other four commonly-used grey models. To the best of our knowledge, this is the first time that the variable-order fractional accumulation is introduced into the discrete grey model, which greatly increases the prediction accuracy of the DGM(1,1) and extends the application range of grey models. [ABSTRACT FROM AUTHOR]

The accurate and stable prediction of electricity consumption is essential for intelligent power systems in rapidly developing countries. Grey prediction model is one of choices for prediction under the condition of limited historical data. Nonetheless, it seems rather sceptical using single-variable grey prediction model to predict the dynamics of a complex system. This paper presents a novel multivariable grey prediction model based on first-order linear difference equation for long-term electricity consumption prediction. The proposed model solves the problem of parameter estimation and variable prediction deriving from different approaches through rewriting the whitenization equation of multivariable grey model (MGM(1, m)). To validate the effectiveness of the proposed hybrid model, the electricity consumption is estimated and predicted over the data from Shanxi province and Beijing city in China from 1999 to 2018. The results show that the hybrid model provides a better estimation and prediction performance compared with other prediction model for predicting electricity consumption. [ABSTRACT FROM AUTHOR]

In recent years, the grey-based models with fractional accumulation have received extensive attention by scholars and have been widely used in various fields. However, the existing rough construction of the background value in the fractional grey model impairs its predictive performance to some extent. To address this problem, this paper reconstructs a dynamic background value for the fractional grey model by the composite integral median theorem, as a result, a novel fractional grey model-based variable background value (denoted as OFAGM(1,1) for short) is proposed. In particular, the Particle Swarm Optimization algorithm (PSO) is then employed to determine the optimum for the fractional order and the background value coefficients. Based on electricity consumption of two regions of China (i.e. Beijing, Inner Mongolia), the superiority of the proposed model has been verified in comparison with other benchmark models, the electricity consumption of Beijing is predicted to reach 1 3 8 5. 3 5 × 1 0 8 Kwh in 2022 and that of Inner Mongolia will reach 5 0 6 3. 3 9 × 1 0 8 Kwh in 2023. This study also takes the relative growth rate and the doubling time into account for these two regions that facilities comparison of the regions' performance. [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]

With the rising urge to mitigate the green house effect, reducing the energy consumption of the freight train attracts much attention. Multiple constraints should be taken into account to solve the energy-efficient control problem, which can be reformulated as the multi-objective optimization. This paper proposes a Reinforcement Learning (RL) method for the multi-objective speed trajectory optimization with the goal of the energy-efficiency, punctuality and accurate parking simultaneously. Since the solution space for the optimization problem in this paper is large and discrete, a Gated Recurrent Unit (GRU)-based network is proposed to achieve the fast approximation of the optimal value function instead of the lookup Q-table. Meanwhile, a new architecture, including the embedding matrix, is used to model the control sequence that generates the speed trajectory. Besides, this paper constructs a Deep Q-Network (DQN) framework to train the GRU network without relying on the prior knowledge of the freight train model. Finally, the Intelligent Train Operation (ITO) algorithm is proposed and verified, using the data of Beijing–Guangzhou Railway Line and HXD1B electric locomotive. The case studies indicate that the reward function for the ITO algorithm converges rapidly and the energy consumption monotonically decreases with the trip time, which satisfies the multiple optimization objectives. In terms of saving the energy consumption, the ITO algorithm performs better than Fuzzy Predictive Control (FPC), Genetic Algorithm (GA) and the field test data. The computation time of different speed trajectories demonstrates that the ITO algorithm is applicable to generating the optimal speed trajectory off-line. • A novel multi-objective optimization model based on the reinforcement learning is proposed to search for the optimal control sequence. The embedding matrix is used to formulate the control sequence. • An intelligent train operation algorithm based on the gated recurrent unit network is proposed to compute the optimal speed trajectory without the precise train dynamics or the prior knowledge from the experienced drivers. • The energy consumption is reduced by 3.17%, 0.71% and 6.77% respectively comparing with fuzzy predictive control, genetic algorithm and field test data. [ABSTRACT FROM AUTHOR]

The government in Beijing has established a platform that covered the monthly electricity-use data of 11,370 public buildings, carried out a series of measures to control the quantity of buildings' electricity-consumption and its growth since 2013. Presently, it is trying to find out which buildings that will more probably have a rising trend or higher energy-saving potential than the others, so as to promote the energy-saving momentum and make its management more refine and targeted. Common methods used by the government for these tasks are traditional statistical charts and plots, the analysis results of which always highly rely on the experts' subjective judgment. Moreover, identifying the right objects from such a large database by manpower is also a big challenge. Therefore, this study proposed a series of data-mining methods, including k -means clustering, C4.5 decision tree, 2-dimension scatter diagram and outlier detection, to investigate and model the change patterns as well as compare the levels of the buildings' electricity-consumption. Results indicated that the information wanted by the government was successfully explored from the massive data. Finally, the paper gave some policy proposals on energy-use supervision and data management to help achieve the goal of further energy conservation in public buildings. • Data mining methods are developed to assist management on building energy-use. • The samples include electricity-use data from 11,370 public buildings in Beijing. • Buildings with rising electricity-use & high energy-saving potential are identified. • Policies on further energy-use supervision and data management are proposed. [ABSTRACT FROM AUTHOR]

Nowadays, increased attention is being paid to the causal relationship between electricity consumption and economic growth. This paper attempts to examine the causal relationship between electricity consumption and economic growth for China's Beijing-Tianjin-Hebei agglomeration, using annual data covering the period 1982-2008. In this study, unit root tests, the Johansen co-integration test, and the Granger causality test are applied. The empirical results indicate that the two series (electricity consumption and economic growth) of the three locales (Beijing, Tianjin, and Hebei) are non-stationary. But first differences of the two series are stationary. The results of the Johansen co-integration test indicate that electricity consumption and economic growth are co-integrated in Hebei and Tianjin while this is not the case in Beijing. The Granger causality test implies that there is causality running from electricity consumption to economic growth in all of the three locales. Causality running from economic growth to electricity consumption is found in Hebei and Beijing while this is not the case in Tianjin. This means that an increase in electricity consumption directly affects economic growth and that economic growth also stimulates further electricity consumption in Hebei and Beijing. But in Tianjin, an increase in electricity consumption directly affects economic growth while economic growth cannot affect electricity consumption. These findings can provide useful information for local governments of the three locales to formulate sustainable energy and economic policies. The study is of great significance for circular economy and building a resource-conserving society. [ABSTRACT FROM AUTHOR]

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]

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]

Abstract This paper investigates the potential expansion and impacts of Electric Vehicle (EV) market in Beijing, China at the micro level with an agent-based integrated urban model (SelfSim-EV), considering the interactions, feedbacks and dynamics found in the complex urban system. Specifically, a calibrated and validated SelfSim-EV Beijing model was firstly used to simulate how the EV market might expand in the context of urban evolution from 2016 to 2020, based on which the potential impacts of EV market expansion on the environment, power grid system and transportation infrastructures were assessed at the multiple resolutions. The results suggest that 1) the adoption rate of Battery Electric Vehicle (BEV) increases over the period, whereas the rate of Plug-in Hybrid Electric Vehicle (PHEV) almost remains the same; Furthermore, the so-called neighbour effects appear to influence the uptake of BEVs, based on the spatial analyses of the residential locations of BEV owners; 2) the EV market expansion could eventually benefit the environment, as evident from the slight decrease in the amounts of HC, CO and CO 2 emissions after 2017; 3) Charging demand accounting for around 4% of total residential electricity demand in 2020 may put slight pressure on the power grid system; 4) the EV market expansion could influence several EV-related transport facilities, including parking lots, refuelling stations, and charging posts at parking lots, in terms of quantity, layout and usage. These results are expected to be useful for different EV-related stakeholders, such as local authorities and manufacturers, to shape polices and invest in technologies and infrastructures for EVs. [ABSTRACT FROM AUTHOR]

Abstract This study provides new insights into the comprehensive energy and economic performances of Photovoltaic Shading Systems (PVSS) in multi-story buildings. A numerical shading model was developed to evaluate the shading effect from an upper PVSS row on its subjacent row. Simulation models based on EnergyPlus were developed to analyze the Net Electricity Consumption (NEC) of PVSS with different tilt angles and widths in different climates. Benefit per Capacity (BC) and the Cost of Benefit (CB) indicators were used to analyze the economic performances of PVSS. Finally, the optimum PVSS tilt angles and widths in different cities were obtained. Harbin, Beijing, Changsha, Kunming, and Guangzhou, were selected as representative cities for different geographical and climatic conditions. The results indicate that the optimum tilt angles for PVSS installed in Harbin, Beijing, Changsha, Kunming and Guangzhou are 55°, 50°, 40°, 40° and 30°, respectively. Optimum PVSS width for all five cities is 1.156 m (7 columns of standard solar cells). PVSS installed, using the optimal design scheme, in multi-story buildings have better energy-saving potentials than either rooftop photovoltaic systems or traditional power supply modes for commercial buildings in China. Highlights • This paper focuses on the optimal design of photovoltaic shading systems. • A special PV module configuration was presented to reduce shading effect. • Numerical model embodying a profile angle was developed to analyze shading effect. • Optimum tilt angles and widths were obtained by analyzing benefit per capacity. [ABSTRACT FROM AUTHOR]

The large airport terminal is the city's transportation hub and logistics center, and its building electricity consumption is twice or even higher than that of general large public buildings. Energy-saving and consumption reduction of the terminal building is not only the need to improve the operational efficiency and reduce the operation cost of the airport but also the need to realize the goal of "double carbon" in China. In this study, a large airport terminal in Beijing is taken as the research object. According to the characteristics of its main electrical equipment system, and the measured data of passenger flow from January 2020 to February 2023, combined with Pearson correlation analysis, K-means cluster analysis and multiple regression analysis, the influence and correlation characteristics of building scale, passenger flow and outdoor meteorological parameters on its electricity consumption are analyzed. The results show that: 1) The daily electricity consumption of the terminal can be analyzed by five levels according to the daily passenger flow, namely, 10,000 < N ≤ 30,000,30,000 < N ≤ 50,000, 50,000 < N ≤ 70,000, 70,000 < N ≤ 100,000, and 100,000 < N ≤ 130,000; 2) The electricity consumption of the terminal can be divided into three categories: basic electricity consumption related to the building scale of the terminal, variable electricity consumption I related to passenger flow, and variable electricity consumption II related to outdoor air temperature and passenger flow. Based on this, the terminal's daily electricity consumption prediction model is constructed, and the measured data verify the model's effectiveness. 3) Put forward the evaluation reference value of the daily electricity consumption of different passenger flow in this terminal, When the year passenger flow reaches the design value of 45 million, the normal daily electricity consumption level ranges from 0.44 to 0.48 kW·h/(m2·day). The research results can provide methods and evaluation reference for electricity consumption prediction, and accurate control of electricity consumption of main equipment systems in airport terminal. [ABSTRACT FROM AUTHOR]

This paper proposes a system dynamics model to develop a real-time charge pricing (RCP) mechanism of electric vehicles (EVs). The model includes six modules: power consumption of EVs, generator set dispatching, charge pricing, user response, benefit evaluation of all stakeholders and charging stations' life-cycle net income. We consider four charge pricing scenarios and design a RCP mechanism for Beijing according to the simulation results. Sensitivity analysis proves that the model is robust, and the increased charging power of EVs is beneficial for charging service operators. The empirical results indicate that RCP based on the peak-valley time-of-use tariff is propitious for the existing development scale of EVs. In addition, the government subsidies are important to drive EV development in the initial period. However, it should be phased out to reduce the financial burden accompanying the amplification of the scale of EVs. [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]

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]

• Synergistic between renewable energy and cogeneration is an ideal transition path. • Imported and exported power could reduce the CO₂ emissions of the heating system. • Investment costs mainly affect the economy of the future heating systems. • Taken Beijing as a case study, the proposed method can be applied to other regions. In response to climate change, China has set the target to achieve carbon neutrality by 2060. The large-scale development of renewable energy and the decarbonization transition of the heating system are crucial for achieving this target. Is there a synergistic effect between the heating system's decarbonization transition and the use of large-scale renewable energy sources? What effect does this synergistic development have on the CO₂ emissions and the economic costs of an energy system? This paper conducts a case study of Beijing, China, and designs three technical path scenarios for heating system decarbonization transition in the future, namely, business as usual scenario where existing policies and regulations are applied, the electricity substitution scenario where the traditional fossil fuel heating system is replaced by electric heat pump, and the synergistic development scenario where the existing thermal power units are retrofitted to replace the consumption of fossil fuels with renewable energy sources. Using the EnergyPLAN model, the hourly operation of Beijing's power and heating systems under the three scenarios are simulated. The research results are: Firstly, the proportion of local renewable energy will be over 70% in all three scenarios, and in the synergistic development scenario, the electricity and heating demand can be met by 100% renewable energy, reducing CO₂ emissions resulted from fossil fuel combustion; secondly, in view of the real-time match of power supply and demand in the power system, at times renewable energy supply would exceed demand. The rational use of the surplus renewable energy can promote further the synergistic development of decarbonization transition of heating system and renewable energy, reducing the primary energy consumption and CO₂ emissions by over 16% and 28% respectively in the future heating system; Thirdly to improve the economic benefit of a synergistic electric power and heating system, focus should be on the reduction of infrastructure construction cost rather than the reduction of fuel consumption. The proposed method can be extended to regional energy systems with more energy sectors, and can be used to formulate decarbonization transition schemes for industry, agriculture, etc. [ABSTRACT FROM AUTHOR]

• Waste biomass and rooftop photovoltaic is integrated into the hybrid power system. • The effects of renewable energy policy and demand response are investigated. • Sensitivity analysis is performed with the consideration of uncertainties. • Demand response program is helpful to reduce both system cost and carbon emission. Modern urban energy system with renewable energy sources has become an effective solution to enhance energy security and promote carbon neutrality. This paper presents a holistic framework for the optimal design and management of such a flexible hybrid power system at the community scale. This framework integrates the potential assessment of rooftop PV and waste biomass, a general optimization approach with multi-perspective performance evaluation, as well as the impacts analysis of energy policy, demand response, and uncertain economic parameters. The proposed framework was applied to a small neighborhood area in Beijing, China. It was found that 100% utilization of available biomass and rooftop PV would satisfy 73% of local electricity demand with the levelized cost of electricity of 0.1030 $/kWh and the embodied carbon emission of 0.5416 kg/kWh. The flexible shiftable load could lead to lower peak load and less electricity supply cost, and help to match the energy demand with the dispatchable rooftop PV output. However, the economic benefits caused by implementing demand response are more obvious than the environmental benefits. Furthermore, a higher electricity tariff and lower capital cost of PV technology would facilitate the investment in rooftop PV and achieve carbon emission reduction targets. [ABSTRACT FROM AUTHOR]

Improved-performance day-ahead electricity demand forecast is important to deliver necessary information for right decision of energy management of microgrids. It supports microgrid operators and stakeholders to have better decisions on microgrid flexibility, stability and control. The available conventional forecasting methods for electricity demand at national or regional level are not effective for electricity demand forecasting in microgrids. This is due to the fact that the electricity consumption in microgrids is many times less than the regional or national demands and it is highly volatile. In this paper, an integrated Artificial Intelligence (AI) based approach consisting of Wavelet Transform (WT), Simulated Annealing (SA) and Feedforward Artificial Neural Network (FFANN) is devised for day-ahead prediction of electric power consumption in microgrids. The FFANN is the basic forecasting engine of the proposed model. The WT is utilized to extract relevant features of the target variable (electric load data series) to obtain a cluster of enhanced-feature subseries. The extracted subseries of the past values of the electric load demand data are employed as the target variables to model the FFANN. The SA optimization technique is employed to obtain the optimal values of the FFANN weight parameters during the training process. Historical information of actual electricity consumption, meteorological variables, daily variations, weekly variations, and working/non-working day indicators have been employed to develop the forecasting tool of the devised integrated AI based approach. The approach is validated using electricity demand data of an operational microgrid in Beijing, China. The prediction results are presented for future testing days with one-hour time interval. The validation results demonstrated that the devised approach is capable to forecast the microgrid electricity demand with acceptably small error and reasonably short computation time. Moreover, the prediction performance of the devised approach has been evaluated relative to other four approaches and resulted in better prediction accuracy. [ABSTRACT FROM AUTHOR]

Vehicle-to-Grid (V2G) is a promising technology in electrification of transportation. Previous studies of V2G tended to be focused on private Electric Vehicles (EVs), paying little attention to rental EVs. In response, this paper proposed a data-driven approach to quantify the potential contribution of rental EVs to smart grid through V2G, using a one-month GPS trajectory dataset containing 967 rental EVs in Beijing in January 2019. The data-driven approach was tested within several "what-if" scenarios in Beijing, which considered availability of infrastructures with different charging/discharging speeds. The results suggested that the increased availability of infrastructure and charging/discharging speed could increase both charging and discharging demands. On overage, a 10% increase in the probability of getting EVs charged gives rise to a 13% increase in charging demand, and a 28% increase in discharging demand. Given that rental EV users can get access to infrastructure anytime and anywhere with a charging/discharging speed of 3.0 km/min (i.e., 3 km electric range per minute), it was estimated that the total daily charging and discharging demands, on average, were 1,860,234 kWh and 1,467,044 kWh, respectively, which were around 3.12% and 2.46% of the total daily residential electricity demand. Furthermore, the increased availability and charging/discharging speed can also lead to a smaller gap between the charging cost per vehicle per day and discharging reward per vehicle per day, bringing more economic benefits to rental EV users through V2G. [ABSTRACT FROM AUTHOR]

Abstract: The better contrast sensitivity and resolution of neutron radiographs require higher neutron flux and enough L/D. The neutron yield of compact accelerator-driven neutron sources is usually lower than reactor and spallation neutron sources. It is difficult to obtain the high L/D and the high neutron flux simultaneously. The deuteron beam and Be target were selected for Peking University neutron imaging facility (PKUNIFTY), which can give higher neutron yield at lower beam energy. That means smaller facility size and lower power consumption in the accelerator, and therefore the lower cost. But that reaction also gives higher yield, so how to realize higher n/ratio is another issue. For intense deuteron beam the deuterons lost in the structure materials may become target atoms and generate neutrons under the bombardment of the succeeded beam. This paper will discuss the above problems, and introduce the PKUNIFTY design, which tried to reach a compromise among the various conflicting demands and meet the basic requirements of neutron radiography. [Copyright &y& Elsevier]

Abstract: The high electric power consumption of air conditioning in communication base station needs to be solved urgently. This paper presents a new technology to discharge excess heat from a communication plant by utilizing the heat exchanger when the outdoor temperature is low enough. Then it compares the applied effects between two types of heat exchanger in a typical base station located in Beijing of China. It is found that this technology is feasible in Beijing of China. The plate heat exchanger can cool the station instead of air conditioning for about 5014hours each year, and the corresponding electronic power saving rate reaches up to 29%. The plate heat exchanger applied effects are superior to the heat pipe heat exchanger. [Copyright &y& Elsevier]

• An agent-based spatial integrated model was used to simulate electric vehicle (EV) diffusion. • Cost of on-board battery was influential to the uptake of plug-in hybrid EVs. • Battery capacity and fast charging infrastructures had little influence on EV penetration rate. • Battery and charging technologies would heavily influence the EV diffusion path at the micro level. Technology innovations are expected to overcome several barriers to the uptake of Electric Vehicles (EVs). This paper explored the role of battery and charging technologies in the diffusion of EVs. Specifically, four groups of "what-if" scenario in Beijing were set up to assess the potential impacts of battery cost (i.e., EV price), battery capacity (i.e., driving range), battery swap stations and fast charging posts on the expansion of EV market. An agent-based spatial integrated model (SelfSim-EV) was used to simulate how vehicle consumers might respond to these technological innovations over time. The results suggested that 1) Plug-in Hybrid Electric Vehicle (PHEV) became competitive when its sale price decreased over time at a yearly rate of 8%, due to the decrease in battery cost; 2) Increasing the driving range of Battery Electric Vehicle (BEV) had little influence on the total number of vehicle purchasers, but did increase electricity consumption; 3) Deploying fast charging infrastructures, i.e., battery swap stations and fast charging posts, had little influence on the uptake of EVs at the macro level, suggesting that fast charging facilities might not be necessary at the early stage of EV development. [Display omitted] [ABSTRACT FROM AUTHOR]

With Beijing's energy demand shifting to the household side in the past decade, uncovering the driving factors of Beijing's energy consumption plays an essential role for policy design. To this end, the structural changes in Beijing's residential and productive energy consumption are decomposed using the logarithmic mean Divisia index (LMDI) method in this paper. Empirical analysis is conducted focusing on Beijing's energy changes during 2009–2018. Results suggest that: (1) The contribution of residential energy consumption has exceeded that of the productive sector, meanwhile the expected decrease in both sectors' energy consumption are not observed considering the energy efficiency improvements; (2) The optimization of Beijing's energy consumption cannot solely rely on the technical development, the influence due to the upgrade of industrial structure in Beijing also matters, especially after 2011; (3) In the residential sector, all factors display stronger impacts on energy consumption changes in urban areas than in rural areas, among which the living standard is the most significant. Based on these results, suggestions are provided to promote the decrease of Beijing's energy consumption. • The structural changes in Beijing's residential and productive energy consumption are decomposed. • The phenomenon of "Jevons paradox" appear in both Beijing's productive and residential sectors. • The energy optimization in Beijing cannot solely rely on technical improvement due to the rebound effect. • All factors display stronger impacts on energy consumption changes in urban areas than in rural areas. [ABSTRACT FROM AUTHOR]

Cost-related factors (e.g., subsides) play a vital role in the diffusion of Electric Vehicle (EV). However, it remains unclear how these factors would influence the diffusion and further the associated urban elements (e.g., infrastructures) at the micro scale. In response, this paper tried to quantify the influence of two types of cost-related factors on the adoption of Electric Vehicle (EV), namely upfront cost and usage-related cost, using purchase subsides and fuel prices as examples, respectively. An agent-based integrated micro-simulation model (SelfSim-EV) was used here to simulate how the EV market in Beijing might evolve from 2016 to 2020, within several "what-if" scenarios considering different Plug-in Hybrid Electric Vehicle (PHEV) subsides, petrol prices and electricity prices. The results suggested that 1) doubling the PHEV subsidy would make PHEV price competitive and thus increase the PHEV sale from around zero to 2500 in 2019. The PHEV sale price increases by around 3500 RMB (from around 261,000 to 264,500 RMB) due to the increase in the PHEV penetrate rate. This further gives rise to the changes in those urban elements connected with the EV market, including the urban environment, electricity and infrastructure systems, especially at the disaggregate level; 2) both electricity and petrol prices have little influence on the adoption of EVs at the macro level (i.e. the city level), but they do influence the spatial distributions of both CV and EV owners (based on the analyses of their residential locations) and further geographical distributions of vehicular emissions, EV-related facilities (e.g., charging posts) and electricity demand of EV at multiple resolutions, ranging from the facility level to district level. • Exploring influence of cost-related factors on the uptake of electric vehicle (EV). • An agent-based spatial integrated urban model, SelfSim-EV, is used. • Doubling the subsidy would increase the plug-in hybrid EV sale. • Energy price has little influence on the adoption of EVs at the macro level. • Varying cost-related factors could influence spatial distribution of EV elements. [ABSTRACT FROM AUTHOR]

The focus of this study is an intense heat episode that occurred on 9–13 July 2017 in Beijing, China, that resulted in severe impacts on natural and human variables, including record-setting daily electricity consumption levels. This event was observed and analyzed with a suite of local and mesoscale instruments, including a high-density automated weather station network, soil moisture sensors, and ground-based vertical instruments (e.g., a wind profiler, a ceilometer, and three radiometers) situated in and around the city, as well as electric power consumption data and analysis data from the U.S. National Centers for Environmental Prediction. The results show that the heat wave originated from dry adiabatic warming induced by the dynamic downslope and synoptic subsidence. The conditions were aggravated by the increased air humidity during subsequent days, which resulted in historically high records of the heat index (i.e., an index representing the apparent temperature that incorporates both air temperature and moisture). The increased thermal energy and decreased boundary layer height resulted in a highly energized urban boundary layer. The differences between urban and rural thermal conditions throughout almost the entire boundary layer were enhanced during the heat wave, and the canopy-layer urban heat island intensity (UHII) reached up to 8°C at a central urban station at 2300 local standard time 10 July. A double-peak pattern in the diurnal cycle of UHIIs occurred during the heat wave and differed from the single-peak pattern of the decadal average UHII cycles. Different spatial distributions of UHII values occurred during the day and night. [ABSTRACT FROM AUTHOR]

This article proposes a hybrid technique for very short-term load forecasting in microgrids. The proposed technique integrates genetic algorithm, particle swarm optimization, and adaptive neuro fuzzy inference systems. An accurate electricity demand forecasting in microgrids is crucial task for efficient operation of power systems and to ensure system reliability in general. In this article, binary genetic algorithm is used to select important predictors that significantly influence the load pattern among a number of candidate input variables; and particle swarm optimization algorithm is used to optimize an adaptive neuro fuzzy inference systems-based model for very short-term forecasting of load. The effectiveness of the proposed technique is demonstrated using a historical load demand dataset of a case study microgrid system in Beijing. Performance of the proposed approach is compared with backpropagation neural network, auto regressive integrated moving average, and persistence models. The proposed approach outperformed all three methods for 10 min ahead forecasting of load, demonstrating its effectiveness and applicability for very short-term load forecasting in microgrids. The average execution time for one-step-ahead forecasting is reduced by over 40% compared to a backpropagation neural network-based approach. [ABSTRACT FROM AUTHOR]

Based on the monthly micro-level data of Beijing urban households from 2002 to 2009, we estimate the price and income elasticities of residential electricity demand among income groups with both the almost-ideal-demand-system (AIDS) and the linear double-logarithmic (LDL) model specifications. The estimated price elasticity is close to unit elasticity and increases as income grows, and income elasticity is low and positive and approximates to zero as income increases. Thus, we conclude that it might be effective by using pricing policies for demand-side management to adjust the electricity consumption of high-income groups, while increasing price will severely hit low-income groups. In addition, low-income groups will consume more electricity if their income increases. This suggests that while tariff reform is necessary, supporting policies are needed. In this regard, either directly subsidizing low-income families or rationally setting the price levels of different tariff blocks can be helpful. [ABSTRACT FROM AUTHOR]