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

• The capacity optimization model of the integrated photovoltaic- energy storage-charging station was built. • The case study bases on the data of 21 charging stations in Beijing. • The construction of the integrated charging station shows the maximum economic and environment benefit in hospital and minimum in residential. • A decline in energy storage costs increases the benefits of all-scale investments, an increase in electric vehicles promotes the benefits of small-scale investments, expansion of the peak-to-valley price distance increases the benefits of large-scale investments. The coupled photovoltaic-energy storage-charging station (PV-ES-CS) is an important approach of promoting the transition from fossil energy consumption to low-carbon energy use. However, the integrated charging station is underdeveloped. One of the key reasons for this is that there lacks the evaluation of its economic and environmental benefits. Based on the electricity load of different types of buildings and the data of electric vehicle charging stations in Beijing, this paper analyzes the economic and environmental benefits of integrated charging station developed over different scales by the capacity optimization model. Said benefits are shown to differ considerably with different locations (building types): the construction of the integrated charging station in places where the electricity load is high at night, and where the curve of daytime electricity consumption is consistent with the PV generation curve (e.g., hospitals), shows maximal economic benefits and carbon dioxide emissions reduction. In other (e.g., residential) areas, where power consumption is lower across both day and nighttime hours, the economic benefits and carbon dioxide emissions reduction are lowest. The economic and environmental benefits of the integrated charging station also markedly differ on different scales: with scale expansion, the rate of return on investment and the carbon dioxide emissions reduction first increase and then decrease. A decline in energy storage costs increases the economic benefits of all integrated charging station scales, an increase in EVs increases the economic benefits of small-scale investments, and expansion of the peak-to-valley price difference increases the economic benefits of large-scale investments. [ABSTRACT FROM AUTHOR]

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

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

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

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