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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]

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]

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]

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]

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]

Abstract Chinese energy consumption structure has been dominated by coal for a long time, which makes China facing serious environmental problems. To achieve the target of CO2 emission reduction, a carbon emission trading (CET) market and promoting the development of new energy should be worthy choices. The issues of dynamic linkages and spillover effects between the CET market, the coal market and the stock market of new energy companies (NEC) are important topics for studies. The paper applies a VAR(1)-DCC-GARCH(1,1) model and a VAR(1)-BEKK-AGARCH(1,1) model to obtain the following conclusions: (1) There exists significant time-varying correlations and long-run persistence of shocks to the DCCs between Beijing CET market, the coal market and the stock market of NEC, and the coal market and the new energy stock market have higher volatility persistence; (2) There is a bi-directional spillover effects between the coal market and the stock market of NEC, which is consistent with the results of the Granger causality; (3) The optimal weight is 82.65% for new energy stock and 17.35% for coal. The optimal hedge ratio is 49.98% for coal, while the optimal hedge ratio is 17.34% for new energy stock. Highlights • There are significant time-varying correlations and long-run persistence of shocks to the DCCs between three markets. • There is a bi-directional volatility spillover between the coal market and the new energy stock market. • The optimal weight is 82.65% for new energy stock and 17.35% for coal. • The optimal hedge ratio is 17.34% for new energy stock and 49.98% for coal. [ABSTRACT FROM AUTHOR]

Abstract Battery electric vehicles (BEVs) have positive effects on the reduction of petroleum dependence and vehicle emissions. However, limited driving range of BEVs contributes to the range anxiety of drivers. Therefore, accurately estimating remaining driving range is a critical issue for BEV manufacturers to help drivers alleviating range anxiety. In this paper, by using the real-world data collected from a BEV operating in Beijing, China, the nonlinear estimation models for remaining driving range under different temperature conditions are established based on the data-driven method. The models consider the State of Charge (SOC), speed and temperature conditions as the impacting factors for remaining driving range. The significant nonlinear relationship between speed and driving distance per SOC is explored and considered in the model. The robust nonlinear regression method is used to determine the parameters of the models. Model verification results confirm the accuracy of the model. Moreover, the models are used to explore the economical driving speeds for the BEV under different temperature conditions. The results indicate that the economical driving speeds have an increasing trend as temperature increases. The economical driving speeds under low, moderate and high temperate conditions equal to 48.97 km/h, 50.89 km/h and 51.37 km/h, respectively. Highlights • The data from Beijing are used to model the remaining driving range of BEVs. • Energy consumption under different speeds and temperature conditions is explored. • Robust nonlinear regression method is adopted to establish the models. • Economical driving speeds are explored by using the models. [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]

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]

Beijing, as the capital of China, is under the high pressure of climate change and pollution. The consumption of non-renewable energy is one of the most important sources of the CO 2 emissions, which cause climate changes. This paper presents a study on the energy system modelling towards renewable energy and low carbon development for the city of Beijing. The analysis of energy system modelling is organized in two steps to explore the alternative renewable energy system in Beijing. Firstly, a reference energy system of Beijing is created based on the available data in 2014. The EnergyPLAN , an energy system analysis tool, is chosen to develop the reference energy model. Secondly, this reference model is used to investigate the alternative energy system for integrating renewable energies. Three scenarios are developed towards the energy system of Beijing in 2030, which are: (i) reference scenario 2030 , (ii) BAU ( business as usual ) scenario 2030 , and (iii) RES ( renewable energies ) scenario 2030 . The 100% renewable energy system with zero CO 2 emissions can be achieved by increasing solar energy, biomass and municipal solid waste (MSW) and optimizing heating system. The primary fuel consumption is reduced to 155.9 TWh in the RES scenario, which is 72% of fuel consumption in the reference scenario 2030. [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]

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]

The national and municipal government of China enacted a series of regulations and policies to stimulate/promote the development of new energy vehicles, in order to mitigate the increasingly serious carbon emissions, environmental pollution, and energy shortage. As a large metropolitan and populated city subject to the notorious air pollution, Beijing has been making a remarkable progress in the large-scale demonstration of new energy vehicles in recent years, which could result in a significant impact on both transport and electricity sectors. As a result, there is an urgent necessity to study the characteristics of the large-scale new energy vehicles adoption for a deep understanding of operational status (e.g., energy consumption and battery charging patterns) and benefits, as well as charging facilities. Based on the operational data collected from realistic electric-taxi demonstration in Beijing, the driver behavior and charging characteristics are examined in this paper. The energy consumption and efficiency of two representative electric-taxi platforms are compared, and the influence of the driving schedules is discussed. The results show that the average driving distance per day of these electric taxes is 117.98 km, and 92% of drivers recharge their cars twice per day. Further study shows that the drivers make two trips per day, and the two peaks in the distribution of departure and arrival times coincide with the rush hour in the morning and evening. The taxi recharge duration is largely influenced by the charging power. Generally, the associated battery SOC (state of charge) swing is between 40% and 100%. By evaluating the energy consumption of 282 trips recorded in 2013 and 2014, we find that the two platforms have similar energy efficiency. The micro-trips method is utilized to probe the correlation of energy consumption and average speed. [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]

Station-skipping and deadheading for subway operation regulations are two effective ways to reduce the effects of train variations, such as large passenger flow and unexpected incidents. These variations, if not properly eliminated through strategies, will lead to a gap in the train and eventually increase passenger waiting time and energy consumption. This paper addresses the last-train station-skipping, transfer-accessible, and energy-efficient scheduling problem for the subway system by optimizing the subway schedule and the last train station-skipping scheme. First, an integrated last train operational model was developed to achieve energy savings and better performance of transfer waiting and in-train travel times by adjusting train acceleration, cruising, coasting, and braking times on each rail segment. Second, a heuristic evaluation-based optimization algorithm was designed to solve a real-life case study of the Beijing Subway to demonstrate the effectiveness of our methods. Two operational strategies (station-skipping and deadheading) for the last trains were designed and compared quantitatively. The results indicate that the station-skipping plan shows an advantage in minimizing the in-train travel time and energy consumption. (1) This paper proposes the integrated last-train station-skipping, transfer-accessible, and energy-efficient scheduling problem for the subway system. (2) By optimizing train acceleration, cruising, coasting, and braking times on each rail segment, energy consumptions and train operating times are reduced. (3) Station-skipping and deadheading strategies for the last train operations are designed. (4) A heuristic evaluation-based optimization algorithm is designed to solve a real-life case study of the Beijing Subway. [ABSTRACT FROM AUTHOR]

Firstly, this paper introduces a novel multi-functional BIPV/T wall system in order to satisfy building's seasonal energy demand in China. This system can generate electricity during the whole year. In heating season, hot air is created to decrease heating load for the building. In non-heating season, hot water is generated to supply the household demand. Meanwhile temperature of PV panel is decreased by water cycle, therefore higher electrical performance is able to be achieved. Secondly, mathematical model is established and verified by the experimental results. Thirdly, annual performance of the system in three different typical cities are evaluated. The annual electrical generation in Beijing, Hefei and Xining are 247.7 kWh, 152.6 kWh and 268.4 kWh respectively. The solar fraction in Beijing, Hefei and Xining are 49.9%, 38.7% and 41.3% respectively. The system is able to satisfy 79.1%, 66.8% and 60.4% hot water energy demand in these cities respectively. The overall annual energy saving in Beijing, Hefei and Xining are 2661.8 kWh, 1908.4 kWh and 2412.3 kWh respectively. Fourthly, the impact of external/internal PV structures, Si/CdTe solar cells and aspect ratio of the system are investigated respectively. • This paper introduces a novel multi-functional BIPV/T wall system for building in cold winter and hot summer area. • Mathematical models are established and verified by experimental results. • Annual performance of the system in three different typical cities are evaluated. • System's performance under different parameters are conducted. [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]

Abstract: The paper highlights the energy dilemma in China’s modernization process. It explores the technological and policy options for the transition to a sustainable energy system in China with Tsinghua University’s Low Carbon Energy Model (LCEM). China has already taken intensive efforts to promote research, development, demonstration and commercialization of sustainable energy technologies over the past five year. The policy actions cover binding energy conservation and environmental pollution control targets, economic incentives for sustainable energy, and public R&D supports. In order to achieve the sustainable energy system transformation eventually, however, China needs to take further actions such as strengthening R&D of radically innovative sustainable energy technologies and systems such as poly-generation, enhancing the domestic manufacturing capacity of sustainable energy technologies and systems, creating stronger economic incentives for research, development, demonstration and commercialization of sustainable energy technologies, and playing a leading role in international technology collaborations. [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]

To enhance the energy saving, emission reduction, and economic feasibility of battery swapping stations (BSSs), this paper develops a BSS configuration and operation model with three charging strategies for Beijing. The model dynamically and holistically analyzes the configuration of chargers, swappers, and reserve batteries, as well as the different annual battery rental fees of BSSs to satisfy the battery swapping demand of users and enhance the profitability of BSSs. The simulation results show that the valley charging strategy can achieve the lowest peak-valley grid load difference (reducing the peak-valley load difference by 156.02 MW) and the greatest emission reductions (2,683,078.49 tons). However, higher investments in reserve batteries and chargers are required to enable service of all demands through valley charging. Based on the current technology, policy, and BSS plan, the BSSs cannot be profitable regardless of their charging strategies. The battery costs and the battery swapping price are the key factors that influence the life-cycle net income of BSSs. Business model innovation, such as charging taxi companies an annual battery rental fee (in the range of 10,300 to 12,160 yuan per battery), can help improve the financial feasibility of the BSSs. • Propose a system dynamics model to improve BSSs' environmental and economic effects. • The model includes five modules and evaluates three charging strategies. • Valley charging can reduce the peak-valley load difference by 156.02 MW. • Charging an annual battery rental fee of at least 12,160 yuan per battery can make BSS profitable. • Policy suggestions are made for battery swapping station development in Beijing. [ABSTRACT FROM AUTHOR]

The choice of battery range (all-electric driving range) for battery electric vehicles (BEVs) is an important issue for both BEV adopters and BEV makers. This paper proposes a model to identify the minimum BEV battery range that can satisfy given travel demands, considering the opportunities to charge at existing public charging stations and the uncertainties in charging decision making. We conducted a stated preference survey to study the charging decision making and analyzed the data using the Latent Class model to generate the model coefficients for charging decisions making. The proposed approach can better identify the needed battery range than the often-used simplified charging rules. We applied the model to a case study of Beijing to evaluate the needed battery range for taxis and private vehicles. For taxis, BEVs with 220-mile battery range are able to satisfy the travel demands for about 90% of the drivers. For private vehicles, a 300-mile range is needed to cover the travel demands of 90% of the drivers, while a 100-mile range battery is able to satisfy the need for 80% of the private drivers. Simplified charging rules tend to underestimate the range needs for taxis but overestimate the range needs for private vehicles. Image 1 • Proposed a Battery Range Optimization Model (BROM). • Integrated heterogeneous charging behaviors into battery range optimization. • Evaluated the battery range needs for taxis and private vehicles in Beijing as case studies. • Simple charging rules may lead to overestimated battery needs for taxis. • Simple charging rules may lead to underestimated battery range needs for private vehicles. [ABSTRACT FROM AUTHOR]

Through the coordination and complementarity of multiple energy sources, the optimal capacity planning of integrated energy system under limited financial constraints can promote the local absorption of renewable energy, realize the optimal utilization of resources and improve the utilization rate of comprehensive energy. Aiming at the integrated energy system formed by multi-energy coupling, this paper adopts three investment restraint schemes, simulates the economic operation of the system based on typical daily load characteristic curves in different seasons, and establishes an optimal capacity allocation model of the integrated energy system taking into account the investment cost restraint and minimizing the total annual cost and carbon dioxide emissions. Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Technology for Order Preference by Similarity to an Ideal Solution (TOPSIS) are used to optimize the solution. Finally, a park in Beijing is taken as an example to verify the model optimization results and the actual results. The deviation of the target results is less than 5%. This study realizes the scientific capacity allocation of integrated energy system, and provides theoretical basis and technical support for the planning and design of integrated energy system. • Capacity planning optimization under different equipment investment constraints. • Bi-level model framework for universal capacity planning optimization. • The model of multi-objective capacity planning considering economic-environmental cost. • Three schemes are simulated and compared, and sensitivity analysis is carried out. [ABSTRACT FROM AUTHOR]

In this paper a novel solar combined heat and power (CHP) system incorporating absorption heat pump (AHP) driven by mid-temperature solar heat and exhaust heat is proposed to improve solar energy utilization and electricity power generation. To validate thermodynamic performance of the proposed system, a case study is carried out based on 1 MW solar tower power plant located in Beijing. Depending on the well-developed mathematical model in accordance with actual equipment, the relevant module is coded in TRNSYS application environment. Besides, control and operation strategy is deeply analyzed to efficiently utilize solar energy with principal of cascade utilization. The dynamic simulation effectively predicts thermodynamic performance under the different operation modes in a typical winter day. Results indicate that flexible heating supply of the proposed system can meet well with user-generated heating loads. The AHP system recovers the condensate heat of 914 MJ with the average COP of 1.52. The proposed system generates the additional power of 217 kWh due to the regenerative cycle. Accompanying with power generation of 6214 kWh and useable thermal energy of 14502 MJ of the proposed system, the overall utilization efficiency of solar energy is up to 15.96% with the increase of 4.55 percentages. • Propose a novel CHP system with heat recovery process based on STP tower plant. • Develop mathematical models and simulation modules in TRNSYS application. • Develop control and operation strategy for efficient utilization of solar energy. • Simulate thermodynamic performances of the CHP system at 1 min intervals. [ABSTRACT FROM AUTHOR]