As a power electronic device, Soft Open Point (SOP) offers increasingly valuable flexible and accurate power flow control for electricity distribution networks. In this paper, SOPs are optimized to minimize energy curtailment of distributed generation. The optimal operating set-points of SOPs are determined by using a multi-period non-linear optimization model. The optimization model adopts minimum energy curtailment of distributed generation as the objective, while considering the constraints of power losses and physical limits of SOPs and power output limits of distributed generation simultaneously. At the input of the model, load variation is considered by generating random power loading conditions via Monte Carlo simulation. As such, the results of minimum energy curtailment of the model can be analyzed statistically. The methodology is demonstrated on a modified IEEE 33-bus system with different SOP cases. The performance of SOP is evaluated comparing to the case without SOP, and the results show that an SOP can effectively reduce minimum energy curtailment by 84% on average. The impacts of location, capacity and number of SOPs on the performance are also analyzed respectively.
This study is the first to assess the historical carbon mitigation and simulate the energy and emission peaks of Chinaâ€™s commercial building sector using a dynamic emission scenario. It shows that the emission mitigation of the commercial building sector during 2000â€“2016 is 1221.50 (Â± 486.89) million tons of carbon dioxide (MtCO2), and the scenario simulation demonstrates that the commercial building sector will achieve its carbon emission peak in year 2039 (Â± 8) with a peak value of 1154.88 (Â± 191.05) MtCO2. The sensitivity analysis reveals that the impacts of emission factor and GDP per capita are the most significant for the uncertainty of emission peaks. A strict energy demand benchmark of the commercial building sector suggests a control at 465.99 million tons of standard coal equivalent (Mtce), and its peaking year is estimated for 2035, which is 13 years ahead of the business-as-usual scenario, with energy savings of 112.90 Mtce. For the earliest peaking time, if the commercial building sector aims to achieve its emission peak before 2030, the emission peak should be controlled at 958.03 MtCO2. Overall, this paper can assist the government in more accurate and feasible building emission mitigation strategies. Moreover, the results provide a more powerful decision-making reference in issuing targeted and feasible strategies for future commercial building emission mitigation.
Occupants behaviour (OB) has significant impacts on building energy performance and promoting sustainability. Imprecision of evaluating the impacts of the occupant behaviour brings about excess energy waste. On the other hand, the increased quantity and quality of the various building energy data collected promotes the use of data-driven approaches, while recognising the potential for building energy prediction as innovative choices. It is significant to conduct research on the data-driven methods for occupantsâ€™ behaviour in building energy management while considering the different impacts. In this regard, this paper aims to provide a literature review of the current research on data-driven methods for modelling, simulating and predicting the occupantâ€™s behaviour and its impacts on building energy, highlighting the opportunities for further research in this context.
With the rapid development of self-driving cars, automatic parking technologies have been widely concerned. However, extreme conditions such as different illuminations and incomplete features bring huge challenges for the parking slot detection, which is a key to automatic parking systems. To accurately and quickly detect parking slot features in such unfavorable conditions, an efficient park slot feature detection method based on the Convolutional Neural Network (CNN) is proposed in this paper. We collect simulated parking slot images under various extreme conditions which are taken as the input of the network as dataset. For each image in dataset, the parking slot feature points are carefully labeled. The YOLO v3 is applied as the basic neural network framework and the transfer learning is employed to train the network so as to accurately detect the parking slot features. Experimental results show that the parking slot feature recognition accuracy of the proposed method exceeds 96%, and the detection speed reaches 26 Frames per Second (FPS).
Urban carbon emissions are greatly influenced by land use patterns in urban areas, which are largely decided by the urban planning program. The procedure for planning program adjustment aim for low carbon is urgently needed in the actual planning process. This paper establishes a land use-based method (LU-BM) using land-based emission factor (LB-EF) approach to evaluate and restrain carbon emissions for urban planning. Through the three-step processes of â€œEstablishment of quantitative approachâ€, â€œAnalysis of the pre-planning programâ€, and â€œAdjustment of the pre-planning programâ€, the total carbon emissions of 2035 planning of the central urban district in Changxing county, China was evaluated, and the land-use composition and carbon emission intensity in different functional zones were adjusted using land-use planning map and control indexes as the adjustment tools. By calculation, the comprehensive adjustments proposed by LU-BM in total cut 39.67 percent CO2 emission equivalents compared with the original planning, the effects were significant. The results indicate that land use planning and management can be a valuable tool to restrain carbon emissions in planning stage.
Industrial transformation is the key to urban transformation. It is also considered to be one of the important ways to reduce resource consumption and improve environmental quality. However, industrial transformation is often constrained by local resource endowments. Based on the panel dataset of 283 Chinaâ€™s cities. This paper analyzed the effect of resource endowment on industrial transformation under the background of market-oriented reform by using several econometric methods. We found that resource endowments have a nonlinear impact on the industrial structure under different marketization levels. We confirm that resource endowment has lock-in effect on industrial transformation, and Chinaâ€™s market-oriented reform can alleviate such lock-in effect. Based on these findings, we propose several target policy suggestions to promote industrial transformation.
Usually the energy matching between building load and PV generation is rigid for photovoltaic direct-driven air conditioners (PVAC). The utilization of thermal comfort can improve the flexibility of building loads to increase the real-time energy matching for PVACs. This study aims to propose a dynamic zero energy evaluation method considering the thermal comfort temperatures for PVAC. The interaction between the flexible building load and rigid PV generation is investigated using different machine learning models with an one-minute time resolution. The indoor temperatures conditioned by PVAC are simulated under actual operations. Indicators such as hourly self-consumption (SC), hourly self-sufficiency (SS), hourly zero energy time (ZET), and real-time zero energy ratio (RZER) are used to evaluate the dynamic energy performance of PVAC in different seasons. With fixed indoor setting temperature selected from standard, the RZER is only 27.87% in summer for hot-summer and cold-winter zone. While taking the thermal comfort temperature into account, the corresponding RZER for PVACs reaches 51.31% with 100% SC of PV generation. Moreover, zero energy points always appear at times of large cooling demand, which can reduce the burden on the utility grid. An optimization for PV capacity is also conducted and is found that an increase of PV capacity helps to raise RZER but results in the excessive energy output. The real-time zero energy evaluation method with indoor comfort taking into account is useful for evaluating the zero-energy potential and designing more flexible PVAC systems.
Due to the superior hydrothermal properties and CO2 geological storage, Super-critical carbon dioxide (ScCO2) has superior performance as working fluid in Enhanced geothermal system (EGS). In this paper, a three-dimensional fracture grid model is established to obtain the flow and heat transfer characteristics of ScCO2 in Hot dry rock (HDR). The influence of mass flow rate on heat extraction performance and flow characteristics in EGS is evaluated, and outlet temperature and flow resistance with operation time is obtained. The results indicate that the fluid temperature of production well begins to decrease when heat extraction operates two years. The greater the mass flow rate, the greater the decline speed and range. After ten years of operation, the maximum drop of the fluid temperature of production well is 65 K. With the increase of mass flow rate, the heat extraction capacity increases, the heat extraction of unit mass flow ScCO2 decreases, and both of them decrease with operation time. With the increase of mass flow rate, the flow resistance increases, which causes larger consumption of circulation pump power. In addition, the power consumption per unit heat extraction increase.
The promotion of electric vehicles (EVs) in the transport sector is critical to achieve the urban sustainable goals, while the EV diffusion process varies in different cities mainly due to regional heterogeneities in consumer preferences. This research intends to reveal the EV consumer heterogeneities in the city level by applying conjoint analysis on the survey data from 1251 questionnaires collected in twelve EV promoting cities of China, and tries to simulate the possible EV diffusion process in the twelve cities by further constructing a system dynamic model. Through the study, it is found that the investigated cities could be categorized into three types from the perspective of consumer EV preference, which includes performance-driven, social-driven, and privilege policy-driven EV promoting cities. In addition, the demand heterogeneities may result in differentiated urban EV diffusion processes, which could be clustered into natural diffusion independent of policy, fast diffusion depending on appropriate policy, and lagged diffusion insensitive to policy. Based on our analysis, policy suggestions are put forward for accelerating EV diffusion in different types of cities respectively.
Traditional NO removal technology was restricted in many special industries and fields. In this study, electrochemically-activated persulfate (EC/PS) system was selected to carry out NO removal research. Influencing factors such as anode, gas flow, reaction temperature, current density, PS concentration, etc. have been studied. It was found that the EC/PS system has a stable removal effect of NO, and the removal rate can be stabilized above 60% under optimal conditions. The Ti/SnO2 anode shows excellent oxidation performance in the EC/PS system. Reaction temperature and gas flow are the main factors that affect the NO removal effect of the EC/PS system. The findings of this research might provide a practical reference for promoting the removal of NO.