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To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide. • This paper attempts to provide a feasible framework for analyzing urban building energy use by utilizing real-time urban semantic perception information and building features. • The proposed framework have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings. • The proposed methodology can obtain regional energy consumption stocks and produce spatial distribution results, thus improving the interpretability of the data. • The results show that energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphoning effect is obvious in regions with high energy consumption. [ABSTRACT FROM AUTHOR]

The rapid adoption of electric vehicles (EVs) has led to dramatic increase in charging demands that poses great challenges for efficient charging infrastructure rollout and operation. It is crucial to accurately assess charging demand in urban areas to optimize the siting and sizing of charging infrastructure. This paper proposes a novel urban charging load forecasting model for private passenger EVs based on massive operating data of EVs in Beijing. First, the characteristics of travel patterns for private passenger EVs, urban road network, functional area distribution and charging infrastructure distribution within the entire Beijing area are identified. Then a charging load forecasting model that can simultaneously simulate trip chains for EVs is constructed by considering the occupancy states of public charging piles and the interactions among different EVs. Finally, the effectiveness of the proposed charging load forecasting model is verified based on comprehensive test data. Our findings imply that the number of EVs at recharge and the charging power can be reliably predicted with the accuracy of over 84.73 % and 81.92 %, respectively. It provides the foundation for optimal charging infrastructure planning and charging scheduling. [ABSTRACT FROM AUTHOR]

The widespread adoption of electric vehicles has become pivotal in attaining emissions reduction objectives, and the rationale behind designing and situating charging stations determines the unfurling of electric vehicles. This scholarly work fully mines the geographical situational particulars underlying the functioning statistics of charging stations in Beijing during the period from January to March 2016. By incorporating the urban road network, we construct a charging stations accessibility index to investigate the influence of stations accessibility on utilization rates. The findings reveal a substantial positive relationship between the overall accessibility of charging stations and their utilization rates. However, the limited number of charging stations hinders the agglomeration effect from significantly boosting utilization rates. Moreover, the impact of charging station accessibility on utilization rates varies significantly across regions with different levels of development, indicating pronounced heterogeneity. The research conclusions of this paper provide useful suggestions for promoting the rational layout of charging stations. [Display omitted] • Charging station accessibility depends on driving distance to important city areas. • The accessibility of charging stations significantly impacts their utilization rate. • Charging station accessibility's impact on utilization rate varies significantly across regions. • Insufficient charging stations limit agglomeration's impact on utilization rates. [ABSTRACT FROM AUTHOR]

More accurate multivariate load forecasting is conducive to integrated energy system planning optimization and economic operation. In this paper, based on the consideration of exogenous factors such as weather, endogenous factors are added into the prediction, and a short-term prediction model for integrated energy multivariate load based on endo-exogenous uncertainty is established using fine-tuned transfer learning. The LSTM pre-training model is first initially established using genetic algorithm and exogenous factors such as weather. Then the exogenous uncertainty of the pre-training model and the knowledge of endogenous uncertainty in the target model are combined using fine-tuned transfer learning to continuously optimize the basic parameters of the prediction model to obtain the final multivariate load prediction model. The performance of the four models is comprehensively evaluated after the historical load validation of the integrated energy system in Beijing, China. Compared with the traditional LSTM multivariate load forecasting model, the MAPE of the proposed model in this paper is reduced by 21.03 %, 13.00 %, and 24.39 % for electricity, heat, and cooling load forecasting, respectively. • The coupling mechanism between multiple energy sources of the integrated energy system is analyzed. • An endogenous-exogenous uncertainty method is proposed to extract the dynamic coupling relationship between multiple loads. • An endogenous-exogenous uncertainty-based GA-LSTM multivariate load prediction model is developed. • Optimizing model parameters using fine-tuned migration learning to deepen the impact of endo-exogenous uncertainty. [ABSTRACT FROM AUTHOR]

In the traditional PV/T system, there is a certain contradiction in the collection process of heat and electric energy. This paper describes a spectral splitting PV/T system, which effectively alleviates the negative effects of high temperature on monocrystalline silicon cells, and separates the soalr radiation so as to improve the utilization efficiency of full spectrum energy. Then, through the all-day experimental study, the results show that the maximum temperature of the spectral splitting medium can reach 38.21 °C, the maximum thermal and electrical efficiency were 38.7 % and 11.77 %, respectively. The output power of the PV cell is more affected by solar radiation, and the filling factor is more affected by the temperature of the battery. In addition, the mathematical model is established and verified. The results show that the optimal nanofluid concentration of the system is 1*107 μg/m3 and 5*106 μg/m3 in Xining and Hefei, respectively. When applied in Beijing area, the optimal nanofluid concentration is 1*106 μg/m3 in summer and 1*107 μg/m3 in winter. Finally, we calculated the annual electric energy and heat output in the three regions using the best nanofluids, namely 126.6 MJ/m2 and 1186.68 MJ/m2 in Xining, 154.44 MJ/m2 and 948.96 MJ/m2 in Hefei, 164.46 MJ/m2 and 1062.43 MJ/m2 in Beijing. • Experimental and numerical investigation on spectral splitting PV/T system under natural conditions. • Excellent thermal and electrical performance, loss coefficient of heat energy. • The applicability of spectrally selective nanofluids in the system were evaluated. • Optimal nanofluid concentration for different regions based on annual performance analysis. [ABSTRACT FROM AUTHOR]

The "Reverse power flow" (RPF) behavior of distributed photovoltaics (DPV) promotes the new energy consumption and has a negative impact on the adaptability of the distribution network (DN). Researching and constructing a DN adaptability assessment model considering the DPV's RPF behavior is an important method to explore the weaknesses of DN adaptability from the perspective of management decision-making, and then propose improvement solutions. Firstly, this paper constructs a set of hierarchical DN adaptability assessment index systems from the three dimensions of security, economy and low carbon. Secondly, the combination of ordered weighted averaging improved the Analytic Hierarchy Process and the Entropy weight method is used to weigh the indicators. Then, an assessment method based on the combination of credibility theory and cloud model is proposed to solve the ambiguity and randomness in the evaluation process. Finally, the model was applied practically in the DN of Qiantuan Village in Beijing, and the adaptation of the DN is compared and analyzed under three scenarios: DPV not grid-connected, grid-connected and fully local consumption, grid-connected and RPF behavior. The results show that the adaptability levels of Qiantan Village DN are classified as "Medium", "Good", and "Good", with scores of 78.839, 86.312, and 88.287, respectively, under the three different scenarios. Furthermore, regarding the first-level indicators, the DPV's RPF behavior reduces the security of the DN but improves the adaptability in general. This paper's research can accurately locate the weak points based on reflecting the level of DN adaptability, thereby providing a reference for the formulation of a DN adaptability improvement plan and the construction of a clean and low-carbon DN. • Constructed a set of distribution network adaptability assessment index systems. • Propose a Combined weighting method by combining Ordered Weighted Averaging improved AHP and the Entropy weight method. • Established a distribution network adaptability assessment model based on the credibility theory and cloud model. • The adaptability evaluation model is applied to the distribution network of Qiantuan village in Beijing. [ABSTRACT FROM AUTHOR]

The scale-up of urban electric vehicle (EV) fleets, driven by environmental benefits, is resulting in surging aggregate energy demands that may reshape a city's power supply. This paper establishes an integrated data-driven assessment model for investigating the energy use (kWh) patterns and charging load (kW) profiles of urban-scale EV fleets. To this end, urban EV operating and operational datasets are linked with climate data and vehicle specifications. Four vehicle fleet types are distinguished: private, taxi, rental, and business fleets. Statistical models regarding distribution analysis, spectrum analysis, and identical distribution tests are employed to analyze the patterns of driving distances, energy consumption, and shares of active charging EVs. The minute-level changes in charging EV numbers and aggregate charging power are examined to reflect the grid load impact. The results show that private light-duty EVs in Beijing consume an average of 9.1 kWh/day, with more charging activities on Fridays. The primary load peaks of light-duty EVs in Beijing usually occur between 11 p.m. and 1 a.m., attributable chiefly to the private fleet's midnight peak load estimated at 28 % of the total daily charging private EV count multiplied by 5.5 kW/EV. Secondary peaks occur between 8 a.m. and 10 a.m. on weekdays for private fleets, and at 4 p.m. for public fleets. Our work can be extensively used for analyses on transport emissions, urban power supply, infrastructure build-ups, and policymaking. • An integrated data-driven model for large-scale EV operation analysis is proposed. • Real-world energy use patterns and load profiles of urban EV fleets are unveiled. • Urban-scale EV big data are utilized for fact-based assessments. • Diverse data sources are linked through spatiotemporal information. [ABSTRACT FROM AUTHOR]

The building sector accounts for nearly 1/3 of global energy end-use and associated carbon emissions. Fulfilling buildings' thermal demand in an efficient way is critical for decarbonizing the building sector. Therefore, this paper introduces the thermally activated building system (TABS) as an energy retrofit solution. A TABS floor-by-floor return water temperature control approach is proposed to reduce buildings' energy use and carbon emissions. The approach includes the topology optimization of TABS and flow control. The bypass pipe connections of the TABS to each floor, namely, the floor-by-floor pipe network topology, have been optimized by using genetic algorithms to achieve the lowest total cost. A case study of a high-rise building located in Beijing demonstrates that the proposed approach can improve the system's energy efficiency compared to the conventional TABS without optimization. The reduction of energy cost can be up to 11.21% with a 13.53% annual emission reduction. In general, the proposed TABS floor-by-floor return water temperature control approach can significantly improve the building efficiency comprehensively, therefore can be considered as a promising energy retrofit solution for the decarbonization of high-rise buildings. [Display omitted] • Fulfill building's thermal demand by TABS – an efficient way with less emissions. • Propose a floor-by-floor return water temperature control approach of TABS. • Proposed method can reduce energy use and emissions even without optimization. • Optimize the topology of TABS by using genetic algorithm. • Optimized TABS can save 11.21% energy cost and reduce 13.53% emissions. [ABSTRACT FROM AUTHOR]

A heat pump driven atmospheric water harvesting system, which can be operated under direct cooling mode and desiccant wheels assisted cooling mode, was proposed in this paper. Investigations were carried out to find out the optimal operation mode and operation parameters under different ambient conditions and daily water harvesting requirements. Optimization studies were firstly carried out on each operation mode to find the recommended operation parameters, namely evaporation temperature and daily operation hours, for different ambient conditions, daily water harvesting rate and maximum operating hours. Then, the optimal operation modes, which has lower daily power consumption under their own recommended operation parameters, were suggested on the psychrometric chart. The results showed that, for different combinations of daily water harvesting rate and maximum operating hours, there exist a critical humidity ratio and a critical relative humidity ratio for each case. When ambient air relative humidity and humidity are both higher than the critical values, it is recommended to use the direct cooling mode. Otherwise, the desiccant wheels assisted cooling mode should be used. Lastly, performances of the system in different climate zones were analyzed. Under the optimal operation mode, the annual average water harvesting efficiency was from 1.08 to 1.22 kg/kWh in Urumqi, from 1.50 to 1.78 kg/kWh in Beijing, and from 2.22 to 3.72 kg/kWh in Haikou, respectively, when daily water harvesting rate is from 50 to 200 kg/day and maximum operating hours is from 6 to 10 h. • Propose a water harvesting system that can be operated under two operating modes. • Study the optimized operating parameters and performances for each operating mode. • Suggest optimal mode based on ambient conditions and water harvesting requirements. • Study annual performances in three typical cities with different climate conditions. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) charging demand is an essential input of charging facility location models. However, charging demand may vary across seasons. In response, this paper first provided insights into the seasonal variance in charging demand using a unique GPS trajectory dataset which contained travel, parking, and charging information of 2,658 private EVs in Beijing. The dataset was collected in January, April, July, and October 2018, which were representative months in winter, spring, summer, and autumn, respectively. Through statistical and spatiotemporal analyses, we found that in winter, EVs got recharged when their state of charge (SOC) was lower: the average SOCs on working days were 51.96%, 48.39%, 50.86%, and 43.50%, in spring, summer, autumn, and winter, respectively. Furthermore, the central urban areas tended to have a higher charging demand in winter. To further explore how the seasonal variance in charging demand may influence infrastructure deployment, we used the classical p-median model to deploy charging facilities with the charging demands in the four seasons, considering the modifiable areal unit problem (MAUP). The results suggested that the seasonal variance did influence the layout of charging facilities under different spatial analysis units (SAUs). The deployment of charging facilities in the central urban areas and outer suburbs tended to be more sensitive to seasonal variance in charging demand. The findings are expected to be useful for charging infrastructure planning in both the transport and power sectors. • Explore seasonal variance in Electric Vehicle (EV) charging demand with big data. • EVs got recharged when their state of charge (SOC) was lower in winter. • The central urban areas tended to have a higher charging demand in winter. • Significant seasonal effects on infrastructure deployment in central and outer areas. • More noticeable seasonal effects with fewer charging infrastructure to be deployed. [ABSTRACT FROM AUTHOR]

Taking Chinese solar greenhouse as the research object, a greenhouse heat and humidity control system combining Narrow-Trough Solar Collector (NTC) and solid desiccant is proposed in this paper. The system is a day and night double-cycle mode, which can realize multiple adsorption and desorption by using solid desiccant. On the basis of solar energy as the main energy supply, a sustainable regulation means is realized. The optical analysis and geometric relationship analysis are carried out. When the inclination angle of NTC is 30°, it can provide more than 80% optical efficiency in Beijing winter at 40° north latitude. The system can regulate the relative humidity of greenhouse at night from high humidity range to normal crop growth range in winter (94.2%–81.3%), the average relative humidity decreased by 12.9% and the indoor average temperature increased by 1.2 °C.The water removal rate of the system is 11.6 g/s m−2. Combined with solar medium temperature heat collection technology and recyclable solid desiccant, the temperature and humidity environment in the solar greenhouse in winter is adjusted to the appropriate range for crop growth. • A heat and humidity control system of solar greenhouse with solid desiccant was proposed. • The thermal and optical characteristics of the narrow-trough collector in the system are analyzed. • The average dehumidification rate per unit area of the system is 11.6 g/s · m−2. • The average relative humidity at night in the test greenhouse decreased from 94.2% to 81.3%. [ABSTRACT FROM AUTHOR]

Demand-side decarbonization and electrification promote a strong coupling between electric vehicles (EVs) and buildings. From a building-centric perspective, this paper established a survey-based simulation platform for charging system design, which supports annual simulations with a 15-min interval. Field survey data of private vehicles in two office buildings located in Beijing (a metropolis) and Ruicheng (a county in China) were used as inputs and for validation. The platform was applied to analyze workplace charging and give suggestions for charging system design. First, the city size greatly influences vehicle behavior and charging demands. This study indicates an unusual double-peak charging demand on weekdays in the county case due to different vehicle behavior from the urban cases. Second, slow charging at workplaces can almost meet the intra-urban travel demand of private EVs. Specifically, chargers with a rated power of 3–4 kW can achieve a guarantee level of EV use of SOC 0.999 = 20%. Third, the low solar self-consumption rate of current PV charging systems (generally 18%∼42%) mainly results from significant PV surpluses on weekends and in summer. These findings emphasize building-centric planning of EV-related infrastructures and proper charging strategies for energy interaction between EVs and buildings. [Display omitted] • Electric vehicle (EV) behavior is investigated from a building-centric perspective. • A simulation platform is developed for charging system design (τ = 1 yr, Δ τ = 15 min). • City size greatly influences vehicle behavior and charging demands. • Slow charging at workplace can meet the intra-urban travel demand of private EVs. • PV surpluses on weekends and in summer result in low solar power self-consumption. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are playing a key role in supporting transportation electrification and reducing air pollution and greenhouse gas emissions. The increased number of EVs may also bring about some issues concerning energy system structure optimization and efficiency enhancement. User behavior analysis and simulation is an important method to solve these issues. A stochastic model for describing the usage of vehicle is essential to handle simulation models and behavior models. Therefore, a more comprehensive understanding of EV usage patterns is necessary for the model establishment. The paper focuses on the 2,047,222 charging events and 8,382,032 travel events collected from 26,606 battery electric vehicles operating in Beijing, China, in 2018, based on the open lab of National Big Data Alliance of New Energy Vehicles. With the large-scale data resource rather than limited samples, we provide some robust statistical results and some multi-dimensional comparative analysis in the paper, which can be applied in large-scale deployment environments and large population cities. The results can also provide information for charging infrastructures construction, gird management, vehicle charging scheduling, and so forth in Beijing and even other metropolises with similar situations. • Operational data of 26,606 battery electric vehicles in Beijing are collected. • The usage pattern including charging pattern and travel pattern is investigated. • Probability density function is used to analyze usage pattern characteristics. • The characteristics of vehicles in different application scenarios are compared. [ABSTRACT FROM AUTHOR]

Most of existing photovoltaic (PV)-Trombe wall systems use a single coolant which has the drawbacks of low utilization efficiency and unreasonable heat distribution of space heating. In order to address these problems, a novel micro-channel heat pipe PV-Trombe wall system is proposed in this paper. The system uses heat pipe and Trombe wall to cool the PV collector decreasing its operating temperature and thus improving the overall efficiency. Moreover, an air-cooled condenser is used to control the thermal energy transfer in the room to improve the thermal comfort. The mathematical model of the system under different operation modes is verified to investigate the performance of the system in Lhasa, Beijing and Nanjing. The results show that: (ⅰ) during the space heating mode, the average room temperature of 20.36 °C, 19.80 °C and 20.18 °C can be reached while the daily total power generation of 0.86 kWh, 0.81 kWh, 0.72 kWh can be achieved in Lhasa, Beijing and Nanjing, respectively; (ⅱ) during the hot water mode, the water temperature in the tanks increases only by 8.08°C–12.76 °C and the total power generation is around 0.20 kWh-0.23 kWh in the three cities while the indoor temperature increases by less than 0.1 °C due to the lowest facade solar radiation in summer. • Proposing a novel micro-channel heat pipe photovoltaic-Trombe wall system. • The system can realize multiple functions under different running modes without power equipment. • Establishing a dynamic mathematical model. • Investigating the performance of the system in representative cities in China. [ABSTRACT FROM AUTHOR]

Controlling Heating, Ventilation and Air Conditioning (HVAC) systems is critical to improving energy efficiency of demand-side. In this paper, a model-free optimal control method based on deep reinforcement learning is proposed to control the heat pump start/stop and room temperature setting in residential buildings. The optimization goal of this method is to obtain the highest comprehensive reward which considering thermal comfort and energy cost. Firstly, the randomness, learning process, thermal comfort and energy consumption of the model-free controller are systematically investigated by a simulation system based on measured data. The results show that randomness has a significant impact on the initial performance and convergence speed of the model-free controller; The model-free controller has a linear accumulation of comprehensive rewards during the learning process, and the slope of the accumulated comprehensive rewards can be used to determine whether the controller converges; The model-free controller coordinates monitoring data, weather forecasts and building thermal inertia to achieve the highest comprehensive reward. Afterwards, the model-free controller was verified in a nearly zero energy residential building in Beijing, China. The results show that model-free controller improves the comprehensive reward by 15.3% compared to rule-based method. • A model-free controller based on deep reinforcement learning is developed. • Problem formulation including the state, action, and reward is carefully designed. • Randomness affects the initial performance and convergence speed of the controller. • The controller improved the comprehensive reward by 15.3% over the baseline. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is one of the most effective ways to reduce energy waste and achieve carbon neutrality, but it may also bring the energy rebound effect and thus fail to achieve the desired energy conservations. In this paper, we examine the energy rebound effect of Beijing at the level of overall economy, industry and sector based on the Cobb-Douglas production function and the idea of Solow residual, so as to clarify the true impact of technological progress on energy-saving target. Results suggest that: (1) The overall economy of Beijing shows partial rebound effect in most years and appears backfire effect in some years, but the degree of the rebound effect has been gradually mitigated; (2) The energy rebound degree of the tertiary industry is higher than that of the secondary industry on the whole, but the rebound effect of the secondary industry fluctuates more obviously; (3) Energy savings at the sector level is also not ideal, and in most cases, sectors with low energy efficiency have a higher rebound effect than sectors with high energy efficiency. Based on these results, suggestions are provided to alleviate the energy rebound effect of Beijing. • The energy rebound effect of Beijing are quantified at the level of overall economy, industry and sector. • The overall economy of Beijing appears backfire effect and partial rebound effect. • The energy rebound effect of the tertiary industry is higher than that of the secondary industry in most years. • Sectors with low energy efficiency may have higher rebound effects. [ABSTRACT FROM AUTHOR]

Load forecasting analysis plays an important role for regional electric power project planning as well as consumption management. For improving the long-term load forecasting accuracy and usability, this paper proposes a new residual-type combined Grey Model-Least Squares Support Vector Machine forecasting model for the component loads by extracting the load characteristics. In this model, each component decomposed load is forecasted using characteristic-matched model. Meanwhile, Moran's I and α convergence are used to identify the spatial-temporal distribution characteristics and levels of electric consumption intensity from the perspective of spatial econometrics. The results show that the proposed model can improve the forecasting accuracy, and can avoid the poor usability in the application phase. According to a case study for Beijing, the electric consumption intensity of the city has a positive spatial correlation, and the low electric consumption intensity districts are mainly concentrated in the central region, which reveals a trend of spreading from the center to periphery. The overall electric consumption intensity shows a downward trend, but the internal differences increase with time. The results may help to carry out electric construction planning, and to formulate regionally energy-saving policies. • Electric load characteristics are captured with Empirical Mode Decomposition. • The component decomposed loads are forecasted by characteristic-matched models. • Building a residual-type combined Grey Model-Least Squares Support Vector Machine model for load forecasting. • Electric anomalies and substitution potential districts are identified from spatial econometrics. • Analyzing convergence trend and level of electric consumption intensity. [ABSTRACT FROM AUTHOR]

Urban-scale energy model can be a crucial tool for evaluating and in turn propelling the implementation of energy efficiency and low carbon programs for buildings and cites. A variety of disputes on integrity and correctness lie in basic statistical data of China's building energy use. Inadequate information and performance gap caused by input uncertainties are the common obstacles to produce reliable energy modelling results. This paper develops a bottom-up approach to depict energy use intensity (EUI) for space heating of building in the urban level accurately, even if the data resource is inadequate and imprecise or the data are influenced by uncertain issues. A hybrid probability model is developed and the model parameters are calibrated by Bayesian inference and Markov Chain Monte Carlo simulation using data set of meter reading from 2062 stochastic-sampled heating substations in Beijing. Preparation and simulation efforts of using this energy model are discussed, following this framework and adjusting according to local climate conditions, typological building characteristics and morphological urban-scale parameters. The results show that the approach is reliable and efficient to calibrate parameters in building energy models. The urban-scale benchmarks of EUI for space heating in Beijing is successfully performed to demonstrate the proposed methodology. • Reliability of top-down and bottom-up approach are discussed in urban-scale modelling. • An urban-scale energy model is proposed even if the data are affected by uncertainty. • The framework of selection, calibration and validation of modelling are presented. • Study on urban-scale Benchmark for heating in Beijing shows how well the methodology. [ABSTRACT FROM AUTHOR]

To reduce traffic congestion and protect the environment, license plate control (LPC) policy has been implemented in Beijing since 2011. In 2019, 100,000 vehicle license plates were distributed, including 60,000 for electric vehicles (EVs) and 40,000 for gasoline vehicle (GVs). However, whether the current license plate allocation is optimal from a social welfare maximization perspective remains unclear. This paper proposes a two-level Stackelberg game, which portrays the interaction between vehicle applicants and the government to quantify the optimal number of EV license plates under the LPC policy in Beijing. The equilibrium number of EV license plates derived from the Stackelberg model is 58,800, which could increase the social welfare by 0.38%. Sensitivity analysis is conducted to illustrate the impact of important influential factors — total license plate quota, vehicle rental fee, and energy price — on EV adoption. The LPC policy under COVID-19 is also studied through a scenario analysis. If the government additionally increases the total quota by 20,000, 24% could be allocated to GV and 76% to EV. One third reduction of the current vehicle rental fee could increase EV license plates by 10.5%. In terms of energy prices, when gasoline price is low, reducing electricity prices could contribute to EV adoption significantly, while that effect tapers off as gasoline prices increase. • The optimal license plate allocation between EVs and GVs in Beijing is analyzed. • A Stackelberg game is proposed to model stakeholders' interaction under LPC policy. • The optimal license plate allocation policy under COVID-19 is also studied. • One third reduction of vehicle rental fee could increase EV adoption by 10.5%. [ABSTRACT FROM AUTHOR]

In order to get the utmost out of the thermal energy stored in the general liquid air energy storage (LAES) system and improve the cycle efficiency of the energy storage system, this paper proposes a novel multi-generation LAES system. The thermodynamic model and economic model of the novel multi-generation LAES system are constructed. The thermodynamic performance and economy of the novel multi-generation system are analyzed, the cycle efficiency and economic indexes are used as evaluation indicators to compare with the general LAES system. The results show that the novel multi-generation LAES system can provides 325.02 kW of thermal energy, 177.43 kW of cold energy and 3.01 m3/s of fresh air except meets the requirements of 1.5 MW power output. Compared with the general LAES system of 56.48% cycle efficiency, the cycle efficiency of the novel multi-generation LAES system can reach 75.40%. The leveled cost of electricity (LCOE) of the multi-generation LAES system in Beijing is 0.79 ¥/kWh, the rate of return on investment is 20.5%, and the internal rate of return is 27%. The air liquefaction rate reaches 84.52% and the volume of liquid air storage tank is only 121.91 m3. Therefore, the novel multi-generation LAES system with high energy storage density can play a better role in the energy storage scenarios, and not restricted by the geographical environment. In addition, the influence rules of the key parameters, such as unit stages, temperature, pressure and adiabatic efficiency, on the thermodynamic characteristic of the system are also obtained. The novel multi-generation LAES system and related analysis results proposed in this study can provide references for the user side energy storage. • The novel multi-generation LAES system was proposed. • The thermodynamic model of the novel multi-generation system was established. • The cycle efficiency of the novel multi-generation LAES system is 75.40%. • The economy of the system under different energy supply modes was studied. [ABSTRACT FROM AUTHOR]

While coal consumption has played an essential role in fueling the industrialization and urbanization of China, it is also responsible for serious atmospheric pollution. Great efforts have been made by Chinese governments to restrict coal consumption in metropolitan areas. Taking Beijing as a case, this paper investigated the dynamics of coal consumption from 1985 to 2017 with regard to quantity, proportion, and sectoral distribution. A structural decomposition analysis model was developed to identify the key driving forces of the quantity decline in coal consumption. Results showed that Beijing has experienced an evident de-coal process with two stages of relative decline (1985–2005) and absolute decline (2005–2017). Coal intensity and final demand composition changes, mainly associated with technological renovation, energy substitution, and adjustment of economic structure, had positive effects on curbing coal consumption. Results obtained in this study could shed light on energy transition in other regions of China, by building a diversified energy portfolio, optimizing regional industrial structure, and improving technology levels of production and consumption systems. Nevertheless, the de-coal process model of Beijing cannot be fully copied to other regions due to its unique political position and developmental stage, and appropriate adjustments based on local circumstances must be made. • De-coal process and underlying driving forces are investigated for urban Beijing. • Two stages of de-coal process with relative decline and absolute decline were observed. • Coal intensity plays a dominant role against that of population and final demand. • Sectoral level has the similar but more diversified pattern of driving forces. [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]