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The solar furnace is key equipment for the thermochemical reaction research which uses focused solar energy to drive the reactor, and the solar flux distribution on the reactor surface plays a decisive role in the regulation of the thermochemical reaction process and formation of target products. In this paper, a reflective solar furnace with a thermal power of 30 kWth in Yanqing, Beijing, is taken as the research objective. Firstly, the Monte Carlo Ray Tracing (MCRT) method is used to establish the mathematical model for the optical calculation, and the distribution characteristics of solar flux are deeply analyzed. The accuracy of the model is verified through the comparison with the SolTrace software and experimental measurements respectively. Secondly, an improved four-quadrant and adjustable focusing solar furnace experimental equipment is developed, and a new solar flux distribution regulation strategy is proposed by using the inward crossover and outward expansion offset of the four-quadrant focusing points coupled with the heat-absorbing platform movement. The results show that the non-uniform factor and the peak value of the solar flux distribution in the set target area can be optimized by selecting an appropriate inward crossover offset and movement of the heat-absorbing platform, respectively. Moreover, when the DNI varies between 480 W/m2 and 800 W/m2, through the optimized control strategy, a non-uniformity factor with the value of less than 0.10 can be achieved in the target area of a square reactor surface with a side length of 10 cm, and the peak flux can also be controlled in the range from 3000 kW/m2 to 3300 kW/m2 with a fluctuation range of less than 10.0% as set by the thermochemical reaction. The optimized control strategy can effectively meet the uniformity and stability requirements of solar flux distribution in solar thermochemical reaction and finally achieve the goal of regulating the target products. • An improved four-quadrant adjustable focusing solar furnace is developed. • An innovative solar flux distribution regulation strategy is proposed. • A non-uniformity factor with the value of less than 0.10 can be achieved. • The peak flux can be controlled in the range from 3000 kW/m2 to 3300 kW/m2. [ABSTRACT FROM AUTHOR]

The district heating system (DHS) is one of the essential carriers for coordinating renewable energy with fossil energy and achieving flexible energy consumption. Considering the uncertainty of both the renewable energy units' power and the consumers' aggregate loads impact the transport process of the DHS, the thermal characteristics of the DHS transportation and the node temperature response under the source-load uncertainty need to be quantified and analyzed. This paper proposed a method to calculate the nodes' temperature dynamic response of the DHS under multiple source-load uncertainty scenarios. The prediction and combination methods of supply and demand probability intervals of source and load nodes under varying credibility levels were investigated. Additionally, the effects of source-load uncertainty boundary conditions on the DHS transport process were analyzed. The temperature response of the nodes was calculated based on the established dynamic DHS model under multiple source-load uncertainty scenarios. A DHS in Beijing was selected for case validation and analysis, and it has 90 nodes and 109 pipes. The simulation results showed that the relative average error of node flow rate in the dynamic DHS model was 3.02%, and the average difference in node temperature was 1 °C. The probability distribution semi-analytical method and the Bayesian credible interval calculation method can accurately describe the uncertainty on both source and load sides. The models and algorithms proposed in this paper could effectively quantify the nodes' dynamic temperature response under the source-load uncertainty. • Proposed uncertainty quantification models for the source and load nodes of DHS. • Developed a novel credible interval calculation method for nodes' thermal power. • Quantified nodes' temperature dynamic response considering source-load uncertainty. [ABSTRACT FROM AUTHOR]

In addition to its important economic value, carbon trading is also an important tool in international politics and diplomacy. Carbon price forecast accuracy has far-reaching implications for economic development and the environment. Few existing studies have been more accurate in predicting carbon prices over longer periods. In this paper, the incremental Gaussian nonlinear trend fuzzy granulation method and the Gaussian nonlinear trend fuzzy granulation method are innovatively proposed to predict carbon prices. This study first converts carbon prices into incremental granulation time series. A time-varying core line is added to produce nonlinear trend granulation on the foundation of linear trend granulation. The nonlinear trend and residual information are then predicted using the DeeAR network, respectively, and the final prediction result is obtained by adding the predicted values for each. Moreover, a new evaluation index, the comprehensive evaluation of RMSE, MAE, and MAPE as three indicators, is proposed to consider the accuracy of the evaluation index more comprehensively and reliably. The results show that the prediction method has the smallest error in long-term prediction compared with other models. The daily closing price datasets of carbon exchanges published in Shenzhen and Beijing are used to validate the efficacy of this methodology. • A modified Gaussian nonlinear trend that can better fit the actual data is proposed. • Incremental Gaussian nonlinear trend fuzzy granulation method with F test is proposed. • A new evaluation index is proposed to make the prediction result more interpretable. [ABSTRACT FROM AUTHOR]

To better make use of the waste heat in distributed data centers, a new design of integrated waste heat recovery system that can meet different needs including heating for users and data center cooling is introduced. The system can switch among different operation modes including waste heat recovery mode, natural cooling mode, composite cooling mode and refrigeration mode based on the ambient temperature. The integrated waste heat recovery system is experimented in the enthalpy-difference laboratory, and the experimental results show well consistency compared with the modeling results. The operation strategies to switch among different modes are determined based on the experimental and modeling results. The paper introduced PUE (Power Usage Effectiveness) and ERE (Energy Reuse effectiveness) to comprehensively evaluate the cooling and energy reutilization effectiveness of the system. According to the evaluation result, the integrated waste heat recovery system has well performed energy effectiveness, environmental and economic effect. The ERE values of using the integrated waste heat recovery system in Chinese typical climate cities including Harbin, Beijing, Xi'an, Shanghai and Guangzhou are 0.87, 1.02, 1.04, 1.17 and 1.29 respectively. Using this system at 96 kW distributed data center instead of CRAC (computer room air conditioning) system can save 152.7 MWh electricity, 56.4 tons of standard coal, and 244.7 tons of CO 2 emission in Beijing annually. The payback periods of this new system in different climate regions in China are all within 3 years. • An integrated data center cooling and waste heat recovery system is proposed. • Operation performance of the system is verified by experiment and modeling. • Energy Reuse Factor in Beijing is 1.02, and payback period is within 3 years. [ABSTRACT FROM AUTHOR]

The waterlogging disaster has become a major threat to the resilient development of cities, which needs a unified and systematic accounting framework to estimate potential waterlogging effects in terms of forming disaster prevention strategies. In this paper, we developed an emergy-based waterlogging footprint accounting framework applying depth-damage functions, input–output (IO) analysis and ecological footprint, considering both economic and environmental factors. Taking the old town Beijing as a case study, we simulated the urban waterlogging distribution with a hydrodynamic model (InfoWorks ICM) over different return periods (1, 2, 3, 5, 10, 20, and 50 years) at high resolution. This study first considered the economic impact caused by urban waterlogging to evaluate the direct economic loss and indirect economic loss by using the depth-damage function and IO model, respectively. The environmental loss was assessed with an ecological footprint and was unified by emergy with economic loss. Then, four emergy-based indicators were established to quantify the specific impacts of waterlogging on urban resilience. The results show that the waterlogging footprints grew constantly by 284.57% from 1 year to 50 years. The waterlogging footprints driven by environmental loss, which were overlooked, accounted for 13.02% of the total. The waterlogging footprint density and waterlogging resilience index showed high spatial heterogeneity, indicating that the sensitivity of subdistricts to waterlogging varies greatly. Our study provides a useful energy-based tool for assessing urban waterlogging losses and supports decision-making for waterlogging disaster mitigation and risk zoning management. • An emergy assessment of hydrological-based urban waterlogging loss was first conducted. • Losses of natural capital due to urban waterlogging disasters were quantified. • Economic and environmental losses were incorporated. • Urban waterlogging resilience was explained in context of metabolism. [ABSTRACT FROM AUTHOR]

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

• Comparing BEV and HFCV from operational, economic, and environmental perspectives. • Proposing a data-driven and simulation-based multi-objective optimization method. • Using LCA to estimate GHG emissions and energy consumption in C2G life cycle. • Using a real-world trajectory dataset to quantify energy demand at the micro-scale. • Different combinations of fuel and facility have benefits and downsides. In order to evaluate suitability of battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (HFCVs) in freight transport systems, this paper proposes a data-driven and simulation-based multi-objective optimization method to deploy charging/refueling facilities for BEVs/HFCVs. The model considers three objectives, namely minimizing total system cost, maximizing service reliability, and minimizing greenhouse gas (GHG) emissions. In particular, a data-driven micro-simulation approach is developed to simulate the operation of freight transport systems with different vehicle and facility types based on the analysis of a one-week Global Positioning System (GPS) trajectory dataset containing 63,000 freight vehicles in Beijing. With the model, we compare the suitability of BEVs and HFCVs within three typical scenarios, i.e., BEVs coupled with Charging Stations (BEV-CS), BEVs coupled with Battery Swap Stations (BEV-BSS), and HFCVs coupled with Hydrogen Refueling Stations (HFCV-HRS). The results suggest that BEV-CS has the lowest total system cost: its system cost is 62.5% and 90.3% of the costs in BEV-BSS and HFCV-HRS, respectively. BEV-BSS has the lowest delay time: its delay time is 62.1% and 86.0% of the delay times in BEV-CS and HFCV-HRS, respectively. HFCV-HRS has the lowest GHG emissions: its emissions are 37.3% and 46.9% of the emissions in BEV-CS and BEV-BSS, respectively. The results are expected to be helpful for policy making and infrastructure planning in promoting the development of alternative fuel vehicles. [ABSTRACT FROM AUTHOR]

• A seawater-filling type planting system based on solar distillation is proposed. • The optical performance of the solar concentrator is simulated. • Heat and mass transfer in the system are analysed and modeled. • Under a sunny weather condition, the freshwater yield reaches 2.1 kg/m2·day. • The system is demonstrated with soil humidity control and planting of tomatoes. This paper proposes and describes the design of a novel solar seawater distillation and planting system which can use seawater directly in agricultural irrigation. It combines solar energy concentration, seawater desalination, and agricultural cultivation in a novel way, in which the produced freshwater is transported passively and directly to the root system of plants, avoiding the addition of traditional agricultural irrigation facilities. Energy balance analysis of heat and mass transfer is presented and the numerical calculation is compared with the experimental data regarding temperature change and freshwater production. Design and construction of a small-scale experimental solar seawater distillation and planting system are described. According to the experimental results under a real weather condition in Beijing in May, the experimental device has a freshwater production 2.1 kg/m2·day. In particular, the soil humidity and plant growing within the device have been monitored. Under a sunny weather condition, the soil humidity in the planting section with a mulching film cover reached 25% on the 16th day of operating the device, and the average height of tomatoes was 30 cm on the 20th day of planting in the device. [ABSTRACT FROM AUTHOR]

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

As an environmentally friendly and convenient transport mode, bike-sharing has been fast developed in major cities around the world. More bike-sharing firms have been formed and intensified market competition. The government has stipulated regulations to intervene in bike-sharing firm operations and to maintain orderly competition. This paper establishes an integrated economic model to examine duopoly bike-sharing firm competition in both prices and the number of deployed bikes. Specifically, more deployed bikes in the urban area reduce the time cost for the users to find an available bike (i.e., the search cost), but they can also impose a negative social externality on the urban management. Two government policies to control the number of deployed bikes have been examined and benchmarked. One is the limit on the total number of the deployed bikes (i.e., the supply-side regulation), and the other is the restriction to allow the users to only register or use certain firms' services (i.e., the demand-side regulation of single-homing). Our model is also further calibrated by using the bike-sharing market data of Beijing for policy implications. It is found that, when the negative social externality of bike deployment is sufficiently small, the government should allow users' multi-homing (i.e., registration on both firms' platforms). However, when the negative social externality is high, the government should strictly control the number of bikes through both the supply-side and demand-side restrictions. Our analyses also suggest that it is never socially optimal to only adopt the demand-side restriction (i.e., forcing the single-homing). That is, the supply-side policy is more socially preferred to correct the negative social externality in most cases. When two bike-sharing firms offer quite heterogeneous services (i.e., low service substitutability), it is more socially optimal to only implement the supply-side policy. • Dockless bike-sharing firms compete in both price and number of deployed bikes. • The government adopts two alternative policies to control the number of deployed bikes. • With small negative externality, the supply-side regulation to limit deployed bikes is preferred. • It is never socially optimal to only adopt demand-side regulation by imposing single-homing. • The analytical model is also calibrated with real-world market data of Beijing for more insights. [ABSTRACT FROM AUTHOR]

• A deep learning approach to predicting the electric vehicle (EV) charging demand. • A real-world large EV trajectory dataset was used for model training and validation. • The deep learning approach outperformed traditional time series prediction models. • Input data structures, and sample sizes were not influential to prediction accuracy. Short-term prediction of the Electric Vehicle (EV) charging demand is of great importance to the operation of EV fleets and charging stations. This paper develops a Long Short-Term Memory (LSTM) neural network to predict the EV charging demand at the station level for the next few hours (e.g., 1–5 h), using a unique trajectory dataset containing over 76,000 private EVs in Beijing in January 2018. To explore the performance of the LSTM model, we set up four scenarios by 1) comparing LSTM against two typical time series prediction models, i.e., the Auto-Regressive Moving Average model (ARIMA), and the Multiple Layer Perceptron model (MLP), 2) and investigating how different input data structures, sample sizes, and time spans and intervals would influence model accuracy. The results suggest that the LSTM model outperformed the ARIMA, and MLP models, and their MAPE1 values are 6.83 %, 21.58 %, and 18.31 %, respectively. In addition, we find that the time span and interval tend to be more influential to the LSTM model's prediction accuracy than input data structures, and sample sizes. In general, the LSTM model with a shorter time span or interval (e.g., 1 h) would perform better. [ABSTRACT FROM AUTHOR]

• A regional division method of municipal solid waste incinerators based on incineration characteristics is proposed. • The material balance equation of municipal solid waste incineration (MSWI) process is established. • The energy balance equation of MSWI process is established. • A multi-input and multi-output controlled object model is constructed, which lays the foundation for optimal control. Municipal solid waste incineration (MSWI) is an important part of energy recovery and sustainable development, constructing the model of controlled object is the basis of studying its optimal control. MSWI is a typical multi-input and multi-output industrial process, which has many uncertain characteristics such as strong nonlinearity and multivariable coupling. To solve this problem, a model of controlled object in MSWI process is proposed in this paper. Firstly, the MSWI process based on grate incinerator is analyzed to summarize its relevant variables and control requirements. Secondly, the solid phase and gas phase of MSWI process are divided to construct material balance equations of subsystems. Next, the energy transfer process in incinerator is deduced and calculated through thermodynamic laws. Finally, the validity of the model is verified by the process data of a MSWI plant in Beijing, China, which lays a model foundation for the study of the optimal control of MSWI process. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) adversely affects air quality, human health and the climate, and it is more prevalent in urban areas. Few efforts have been made to quantify the impact of trade on PM concentrations in an urban economy. This paper presents an analysis of the impacts of domestic and international trade on PM emissions in Beijing using a three-scale input–output model, supported by the national and global embodied energy-related PM 2.5 , PM 10 and TSP (total suspended particulate) emission intensities. The results found that the total energy-related PM 2.5 , PM 10 and TSP emissions (production-based) in Beijing were 106 Gg, 163 Gg and 347 Gg, respectively, in 2010. Of these amounts, 48% (51 Gg, 74 Gg and 146 Gg, respectively) was associated with local demand, 42% (44 Gg, 73 Gg and 168 Gg, respectively) was associated with domestic exports, and 10% (11 Gg, 16 Gg and 33 Gg, respectively) was associated with international exports. From a consumption perspective, Beijing’s PM 2.5 , PM 10 and TSP emissions were more than double the production-based PM emissions. Approximately 75% (172 Gg, 311 Gg and 786 Gg, respectively) of the consumption-based PM emissions were domestically outsourced to other provinces, primarily via the import of metal (32 Gg, 58 Gg and 151 Gg), construction (26 Gg, 36 Gg and 91 Gg) and chemical products (16 Gg, 30 Gg and 75 Gg), and 3% (8 Gg, 10 Gg and 24 Gg, respectively) of consumption-based PM emissions were outsourced abroad. Our results indicate that domestic trade plays a dominant role in Beijing’s PM 2.5 emissions. These findings suggest that more national and sub-national government should co-ordinate design and implement effective mechanisms to alleviate urban air pollution because of the significant effects of interprovincial and international trade on local emissions. [ABSTRACT FROM AUTHOR]

Over the past decades, China has been facing severe airborne pollution associated with atmospheric fine particulate matter (PM 2.5 ). Much attention has been paid to the physical transport of PM 2.5 emissions. However, the embodied emissions, namely the emissions transferred through economic activities, have seldom been investigated. In this paper, embodied emission of PM 2.5 from each sector of Beijing is quantified based on input–output analysis (IOA). Forty-two economic sectors from the input–output table are aggregated into fifteen components. Furthermore, the mutual interactions and control relationship within those sectors have been revealed by using ecological network analysis (ENA) to identify the dominant sectors. The results show that, in 2010, 34% of the total PM 2.5 emissions, or 59.4 kt PM 2.5 , were indirect emissions traded through economic sectors within Beijing. According to the results of ENA, we found that “Smelting & Pressing of Metals“, “Metal Products” and “Nonmetal Mineral Products“ are the top three sectors with the highest control levels while “Agriculture”, “Catering Services” and “Residential Services” are the lowest-ranking sectors among the system. The network confirms that sectors related to heavy industry are the dominant sectors driving the embodied PM 2.5 emissions in the whole system. Compared to the conventional approaches for tracking PM 2.5 emissions, ENA may provide a practical way to reveal the mechanisms of embodied PM 2.5 emission flows via socioeconomic activities from a holistic perspective. [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 reliability and economy of UMES integrated with PTH are assessed. • Modeling of PTH flexibility considering both energy- and service-related constraints. • A system operation model combining energy management, vehicle dispatch and V2G is proposed. • A holistic evaluation framework embedded with multi-modal operation simulation is developed. • Influential factors for the contribution of PTH flexibility are analyzed. Growing penetration of renewable energy sources (RES) and emerging electrified loads (EEL) are bringing about increased difficulties for the power balancing and efficient operation of energy system, due to the impact of remarkable volatilities introduced. Public transport hub (PTH), as a new-style infrastructure of traffic service carriers, is regarded to offer a cogent solution to this problem, in terms of their potential operational flexibilities permitted by energy regulation, vehicular dispatch, and vehicle-to-grid (V2G) programs. As such, this paper carries out a comprehensive study to investigate the implication of harnessing PTH-enabled flexibility in a context of urban multi-energy system (UMES). The proposed methodology is established on a holistic reliability/economic analysis framework which is designed to indicate how UMES's performances would vary with different utilization of PTH resources. In order to portray the real-time controllability of PTH during operation, a PTH model that takes into account the impacts of both energy- and service-related aspects has been developed, with particular focus on the interdependencies between the energy and transportation sector. The operational simulation of UMES in presence of PTHs is implemented by using a multi-modal-based optimization model, which captures the effects of PTH flexibility under both normal and contingency scenarios integratedly. By embedding the above formulation into a sequential Monte Carlo simulation-based assessment framework, the contribution of PTH to the reliability and economy of UMES can be determined. Numerical studies are conducted based on an illustrative electricity-gas-heat test case and the real PTH datasets in Beijing. The simulation results confirm the significance of PTH-enabled flexibility in improving the performances of UMES. Also, it is demonstrated that the reserving strategy adopted, the composition of vehicle model, and the travel demand profile of passengers are the noteworthy factors that influence the profitability of PTH exploitation. [ABSTRACT FROM AUTHOR]

• Characterizing Electric Vehicle (EV) users' mobility patterns using trajectory data. • Exploring the association between mobility patterns and the built environment. • Private EV users tended to have regular travel and activity patterns. • EV users performed activities at a small number of places and within a small area. • Gymnasia tended to be more statistically associated with the mobility patterns. Human mobility pattern analysis has received rising attention. However, little is known about the mobility patterns of private Electric Vehicle (EV) users. In response, this paper characterized mobility patterns of private EV users using a unique one-month dataset containing moving trajectories of 76,774 actual private EVs in January 2018 in Beijing. Specifically, we first explored the diversity, regularity, spatial extent, and uniqueness of EV users' mobility patterns. The results suggested that most EV users had both regular travel and activity patterns (the mean travel and activity entropies were 2.17 and 1.83, respectively) with special preferences towards some specific activity locations relative to all the locations they visited (the mean number of activity locations visited was 13.57 in one month). Furthermore, they tended to perform activities within a small geographical area (the mean radius of gyration was 7.60 km) and have a short daily travel distance (the mean value was 37.35 km) relative to their electric driving range. Further, we associated EV users' mobility patterns with the built environment through ordinary least squares and geographically weighted regression models, particularly considering the so-called modifiable areal unit problem (MAUP). Due to the MAUP, most of the statistically significant built environment variables varied across spatial analysis units (SAUs). Gymnasia was the only variable statistically associated with the mobility patterns for all SAUs; while the variables related to residence and workplace were not statistically associated. [ABSTRACT FROM AUTHOR]

• A multi-energy absorption system is proposed for combined cooling and heating. • Multiple sets of indoor terminals are driven by a single set of absorption machine. • Annual operating cost for a typical household is reduced by 25.5–46.8%. • Carbon reduction ratio reaches 41–44% compared with traditional methods. • Annual carbon emission per household for cooling and heating reduces below 3000 kg. Under the background of carbon neutrality, efficient and low-carbon cooling and heating in rural areas away from the centralized network is a meaningful topic. In this paper, a multi-energy complementary absorption system is proposed to provide space heating in winter and cooling in summer. The system utilizes renewable energy that is abundant in rural areas, including solar and biomass, and balances the energy supply through natural gas replenishment. By adopting the cooling/heating medium circulation, multiple sets of indoor terminals are driven by a single set of absorption machine and outdoor unit. It is found that as long as the renewable energy ratio is higher than 0.2, the economic performance of the proposed system will be outstanding over the existing methods. When applied in Beijing, the annual operating cost for a typical household is reduced by 25.5–46.8% compared with traditional gas furnace/compression heat pump combined with air conditioner. Moreover, even when the proposed system is driven solely by natural gas, the carbon emission is reduced by 16–20% compared with traditional methods. When the renewable energy ratio is 0.4, the carbon reduction ratio reaches 41–44%, and the annual carbon emission for cooling and heating of a typical household reduces below 3000 kg. It is expected that by solving the problem of load and concentration variation during the year-round operation, the proposed system has great application potential in cold winter and hot summer areas away from the centralized cooling or heating network. [ABSTRACT FROM AUTHOR]

To evaluate the effect of phase change materials (PCMs) on the indoor thermal environment of greenhouses under different weather conditions and over a long time in the heating season, a study was carried out using both experimental method and numerical method. The study was conducted in a typical greenhouse located in Beijing, China, and important parameters have been monitored continuously for 61 days, including indoor air temperature, outdoor air temperature, solar radiation, surface temperature of greenhouse envelopes and soil temperature. Based on these parameters, a number of indicators, namely, operative temperature, daily effective accumulative temperature, irradiated surface temperature of the north wall, average temperature of PCMs, and daily heat storage and release, have been used to evaluate the performance of PCMs in greenhouses. All indicators have provided consistent results that confirm the positive effect of PCMs on improving the indoor thermal environment of greenhouses over a long time. Additionally, the paper has demonstrated that a sunny weather could help to promote the efficiency of PCMs, comparing to a cloudy weather. [ABSTRACT FROM AUTHOR]

Energy consumption has always been a central issue for sustainable urban assessment and planning. Different forms of energy analysis can provide various insights for energy policy making. This paper brought together three approaches for energy consumption accounting, i.e., energy flow analysis (EFA), input–output analysis (IOA) and ecological network analysis (ENA), and compared their different perspectives and the policy implications for urban energy use. Beijing was used to exemplify the different energy analysis processes, and the 42 economic sectors of the city were aggregated into seven components. It was determined that EFA quantifies both the primary and final energy consumption of the urban components by tracking the different types of fuel used by the urban economy. IOA accounts for the embodied energy consumption (direct and indirect) used to produce goods and services in the city, whereas the control analysis of ENA quantifies the specific embodied energy that is regulated by the activities within the city’s boundary. The network control analysis can also be applied to determining which economic sectors drive the energy consumption and to what extent these sectors are dependent on each other for energy. So-called “controlled energy” is a new concept that adds to the analysis of urban energy consumption, indicating the adjustable energy consumed by sectors. The integration of insights from all three accounting perspectives further our understanding of sustainable energy use in cities. [ABSTRACT FROM AUTHOR]

• Develop an agent-based model of electric vehicle (EV) and vehicle-to-grid (V2G) adoption. • The EV-V2G model can explicitly simulate social influences on the EV and V2G adoption. • Only 3.3% of survey participants were familiar with V2G. • V2G selling price and advertisement were not very influential to the diffusion of V2G. The potential widespread adoption of Electric Vehicles (EVs) has received considerable attention across the globe. However, as a promising technology for both EVs and smart grid, Vehicle-to-Grid (V2G) tended to receive much less attention. This paper developed an agent-based joint EV and V2G model to simultaneously simulate how EVs and V2G might diffuse across space and over time, with empirical findings from a questionnaire survey in Beijing. In particular, random forest models were developed with the survey data to generate each agent's preferences and attitudes towards EVs and V2G. The joint model also considered three typical levels of social influence, i.e., global influence, neighbor effect, and friendship effect, in the diffusion of EVs and V2G. Finally, the joint model was tested through several "what-if" scenarios, considering different V2G prices, EV/V2G advertisement intensities, and vehicle purchase restrictions. The survey results suggested that 67.7% of the respondents were familiar with EVs, but only 3.3% of them were familiar with V2G. However, over 70% of them would/might try V2G given that they had an EV. The model results suggested that the number of CV applicants was 6.19 times that of BEV applicants in 2030 in the baseline scenario, and only 27.8% of BEV users adopted V2G. Furthermore, V2G selling price, EV/V2G advertisement, and dedicated PHEV purchase permits were not very influential to the diffusion of V2G. The outcomes would be helpful for EV- and V2G-related stakeholders in policy making and technology investment. [ABSTRACT FROM AUTHOR]

• Synchronous multi-parameter prediction for battery systems is investigated. • All-climate and all-state application in electric vehicles can be obtained. • A developed pre-dropout technique is introduced to prevent LSTM from overfitting. • Different models can be intelligently applied in driving/charging states. • The predicted horizons are adjustable to provide adequate emergency time. Voltage, temperature, and state of charge (SOC) are the main characterizing parameters for various battery faults that can cause these parameters' abnormal fluctuations. Accurate prediction for these parameters is critical for the safe, durable, and reliable operation of battery systems in electric vehicles. This paper investigates a new deep-learning-enabled method to perform accurate synchronous multi-parameter prediction for battery systems using a long short-term memory (LSTM) recurrent neural network. A year-long dataset of an electric taxi was retrieved at the Service and Management Center for electric vehicles (SMC-EV) in Beijing to train the LSTM model and verify the model's validity and stability. By taking into account the impacts of weather and driver's behaviors on a battery system's performance to improve the prediction accuracy, a Weather-Vehicle-Driver analysis method is proposed, and a developed pre-dropout technique is introduced to prevent LSTM from overfitting. Besides, the many-to-many(m-n) model structure using a developed dual-model-cooperation prediction strategy is applied for offline training the LSTM model after all hyperparameters pre-optimized. Additionally, the stability and robustness of this method have been verified through 10-fold cross-validation and comparative analysis of multiple sets of hyperparameters. The results show that the proposed model has powerful and precise online prediction ability for the three target parameters. This paper also provides feasibility for synchronous multiple fault prognosis based on accurate parameter prediction of the battery system. This is the first of its kind to apply LSTM to the synchronous multi-parameter prediction of the battery system. [ABSTRACT FROM AUTHOR]

• A novel tri-stage online dispatch framework for a microgrid is proposed. • The proposed framework coordinates the EV charging behavior at different timescales. • Multiple uncertainties introduced by EV, PV, and load are taken into account. • The real-world data of eight EVs in Beijing is collected to model EV charging behavior. This paper proposes a novel tri-stage online dispatch framework that coordinates the charging behaviors of electrical vehicles (EVs) within an AC/DC hybrid microgrid in the presence of uncertainties. A day-ahead scheduling model is proposed as the first stage to optimize the total operational cost for the next day (24 h). In the second stage, a receding horizon manner is adopted to adjust the day-ahead scheduling results, which can compensate for unpredictable disturbances. Both the first and second stages are operated with a time scale of one hour, which is, however, insufficient in capturing the operations of EVs. Hence, the third stage is introduced with the stochastic model predictive control (SMPC) method in a time scale of 10 min to further consider uncertainties of load, PV, EVs. The real-world behavioral data of eight private EVs in Beijing are used to evaluate the performance of our dispatch model. The simulation results show that compared with some traditional online dispatch methods, the total operational cost of the proposed dispatch framework is significantly reduced. [ABSTRACT FROM AUTHOR]

With the tide of electrifying urban transportation systems by introducing electric vehicles, the differences between fuel vehicles and electric vehicles in driving styles and strategies to achieve eco-driving have become a burden for efficient operations of urban transportation systems. Most of the previous energy management strategies have sought to achieve system optimisation at a single-vehicle or multi-vehicles level, and failed to consider the vehicle-to-vehicle and vehicle-to-infrastructure effects in a global optimisation manner. Furthermore, as a typical human-in-the-loop cyber–physical system, the mobility behaviours of road users undoubtedly play a vital role in the cooperative and green operations of urban transportation systems. Yet little research has dedicated to develop means to incentivise energy-saving behaviours in transportation systems. Hence, in this paper, we propose a unifying queueing and neural network model to calculate the time and energy efficient course of actions and routes for different types of road users within an urban road network in a real time manner. The lower-level queueing model captures the interactive dynamics of road users and solves the optimal flow ratio at each intersection while the upper-level neural network model further customises desired routes for different types of road users. In addition, an incentive mechanism is proposed to encourage road users to follow the optimal actions via publishing various types of reward-gaining tasks. A case study in a designated area of Beijing shows that the use of the bi-level optimisation algorithm can reduce the average travel time by approximately 20% and decrease the energy consumption by 10% in comparison with the realistic trip data. • An overall energy management framework for urban road networks is built in detail. • A heterogeneous queueing network model is utilised to derive eco-driving strategies. • Random Neural Network is applied to search desired routes for heterogeneous users. • SUMO-based simulation experiment is conducted to validate the performance. • The framework improves time and energy efficiency at the cost of monetary incentives. [ABSTRACT FROM AUTHOR]

• A mathematic model is built to evaluate the air pollution of heating consumption. • The coal substitution policy can reduce the PM2.5 concentrations by about 20 μg/m3. • Effect of renewable energy and its development to air quality is considered. • PM2.5 concentration in 2030 will drop by about 27% in Northern China compared to 2016. The Air Pollution Prevention and Control Action Plan proposed in 2013 has contributed to the fossil fuel reduction in central heating, but the consumption of scattered coal in rural areas should not be overlooked as it accounts for nearly 50% pollutant emissions during the heating season. Air Pollution Prevention and Control Action Plan for Beijing-Tianjin-Hebei and Surrounding Areas was issued to solve the problem of scattered coal consumption and improve the heating energy structure in 2017. In this paper, an air quality-heating model is established to evaluate the air quality improvement potential and far-reaching influence of scattered coal consumption reduction in rural areas. Distinct from other air quality evaluation models, the proposed model is to evaluate the regional air quality related to the heating factors. The results indicate that the coal substitution policy can reduce PM2.5 concentrations in the heating season by about 20 μg/m3 in Beijing-Tianjin-Hebei and surrounding areas. Furthermore, predictions about the variation of PM2.5 concentration in the heating season are conducted, which indicate that the PM2.5 concentration of Beijing, Tianjin, Hebei and surrounding areas in 2030 will drop by 48%, 35%, 29% and 23% respectively compared to that in 2016. The reduction could be further enhanced to be 55%, 40%, 32% and 27% with the consideration of clean power development. This study is conducive to evaluate the clean heating technologies role in improving the environment and air quality. [ABSTRACT FROM AUTHOR]

• Long-term energy use and CO 2 emissions from urban buildings are estimated. • System dynamics modeling is performed to assess the holistic effects of multiple policies. • Potentials of different policies in reducing CO 2 emissions from urban buildings are investigated. • Inefficiency and interference due to the simultaneous application of multiple policies are identified. • Suggestions for systematically improving and enforcing multiple policies are provided. To reduce energy-related CO 2 emissions from the urban building sector, multiple diverse energy efficiency policies are often simultaneously implemented by different government entities. Thus, evaluating and quantifying the effects of these policies are important for improving their efficacy and prioritizing them. In this paper, a projection of the long-term CO 2 emissions from city-scale urban buildings whose energy use and construction are governed by multiple diverse policies is presented. A system dynamics (SD) model is established to simulate the causal path through which different policies directly and indirectly affect the energy use of and CO 2 emissions from buildings in the urban building stock, and the model is applied to Beijing, China. The results show that under the existing set of policies, the primary energy use by urban buildings in Beijing will peak around 2025 and that energy-related CO 2 emissions will peak around 2022. The model also reveals interference between the policy for promoting green building construction and the policy limiting the urban building stock area. Targeted measures for improving and prioritizing these policies are suggested, and implications for policy design and implementations are proposed. The framework developed in this study can be used not only to project long-term energy-related emissions from building sectors but also to evaluate the effects of policies on emissions. Suggestions for adapting the model to more general cases are provided. [ABSTRACT FROM AUTHOR]

• A 3-level fragment division method is proposed for actual vehicle data processing. • The influencing factors of electric vehicle energy consumption are discussed. • A novel data-driven energy consumption prediction framework is proposed. • The driving conditions prediction is integrated in the proposed framework. With increasing mass-adoption of electric vehicles, the energy consumption has become a key performance index to electric vehicle drivers, automakers and policy-makers. Accurate and real-time energy consumption prediction under real-world driving conditions is essential for alleviating the 'range anxiety' and can provide support for optimal battery sizing, energy-efficient route planning and charging infrastructures operation. In this paper, real-world driving data collected from fifty-five electric taxis in Beijing city are obtained and divided into three-level driving fragments. The influencing factors of energy consumption, including vehicle-, environment-, and driver-related factors, are extracted and studied. With the extracted key influencing factors, a novel machine learning-based energy consumption prediction framework integrated with driving condition prediction is proposed and used in actual energy consumption prediction. The real-world trip test results show that a root mean squared error of 0.159kWh (RMSE) and a mean absolute percentage error 12.68% (MAPE) are reached, the RMSE and the MAPE are respectively reduced by 32.05% and by 30.14% compared to the conventional method. [ABSTRACT FROM AUTHOR]

• A new comprehensive control method based on Model Predictive Control is proposed. • A new dynamic equation of Continuously Variable Transmission is proposed. • Take engine torque and clamping force as control objectives. • The method can improve transmission efficiency by 8.92% and fuel economy by 4.9%. This paper proposes an optimal comprehensive control framework to maximize the powertrain transmission efficiency and fuel economy meeting vehicle owners' travel needs. The proposed control framework consists of vehicle layers and verification. In the vehicle layer, creatively, each set of automotive Continuously Variable Transmission (CVT) supply equipment is equipped with original comprehensive controller that estimates engine fuel consumption and CVT efficiency based on a newly dynamic equation of CVT, engine torque, clamping force, and upcoming trip information. The controller obtains the optimal input of engine torque and clamping force by repeatedly solving the optimization problem of each sampling point in real-time. In the verification layer, with the powertrain system model received from the comprehensive controller, the robustness was verified by bench and hardware-in-the-loop simulation platform. The proposed comprehensive optimization approach can enhance accuracy and solve the multi-objective control problems compared with traditional methods. It is also scalable to the expanding CVT fleet and robust to uncertainties future system condition and in further to meet the vehicle owner's travel comfort and economy. The proposed method is studied using a prototypical driving platform developed at State Key Laboratory of Automotive Safety and Energy of Tsinghua University and detailed trip information extracted from 2019 BEIJING Transport Annual Report Survey. The main contribution of this study is to explore a novel way to optimize the comprehensive transmission efficiency of engine and CVT, compared with the Proportional Integral Derivative (PID) control, the comprehensive controller can improve the transmission efficiency by 8.92%, and reduce the fuel consumption by 4.9%, it also can save about 90 million liters of fuel and 70 million US dollar for CVT vehicles in Beijing. [ABSTRACT FROM AUTHOR]