commitments vary considerably among countries. This study explores the cross-country difference in climate change policy stringency and its association with respective cultural differences. Particularly, we hypothesize that more religious countries incline to have lesser stringent climate change policies. Our empirical evidence using ordinary least square estimates provide support for this supposition. Estimates using instrumental variables and further evidence from individual-level analysis with a panel data of up-to 220758 observations over the past three decades confirm our main findings. The results hold up to a bunch of robustness checks. Our findings may be of relevance to policymakers looking to design climate change policy reforms.
The free-piston engine linear generator (FPELG) has the high thermal efficiency and simply structure. Thus, it is investigated by many researcher groups. However, many researchers main focused on the FPELG characteristics from the simulation results. Therefore, in the paper, the piston dynamics and the combustion characteristics of the gasoline FPELG from the experimental results were investigated. The experimental results demonstrated that the piston TDC is 32.2mm, the peak velocity is 5.3m/s and the frequency is 32.8Hz. And it is found that the optimal ignition timing of the free-piston engine is between 27.5 mm and 28 mm.
Due to the introduced spatial-temporal uncertainty and flexibility of the increasing Electric vehicle (EV) charging load, distribution network operation will be greatly impacted by the large-scale EV charging power. This paper proposes a reliability assessment approach considering the stochastic EV charging and movement in an integrated power and traffic system. The improved sequential Monte Carlo method is applied to evaluate the reliability of distribution network. Based on a spatial-temporal charging load model, the influence of different factors on the reliability for distribution network is analyzed in a case, including permeability and the ratio of trip chain, which provides a theoretical basis for the formulation of orderly charging strategies and the planning of charging stations. Furthermore, the reliability analysis considering the future distributed generators (DGs) and EVs development mode is given.
Vehicle exhaust pollution and traffic congestion are plaguing the daily life of the citizens. Although electric vehicles represent green travel, the problem of mileage anxiety still troubles electric occupants. Aiming at the existing problems, an electric vehicle energy consumption prediction based on LSTM deep learning technology combined with traffic information is proposed to plan the economical driving path with the best coupling of energy consumption and driving distance. The method has the ability to integrate multidimensional data of heterogeneous heads, solves the problem that electric vehicle energy consumption estimation cannot take into account real traffic information. And getting rid of the shortcomings of path planning relying only on driving distance, effectively improving the driving feeling of electric vehicles and bettering travel efficiency to optimize urban traffic conditions.
Compression ignition (CI) engines have evolved into one of the world’s most capable and reliable forms of motive power for transportation due to high fuel efficiency and high-power output. However, to cope with stringent emission standards, improving the combustion processes, make use of cleaner combustion and implement exhaust gas cleaning systems is necessary. The gasoline biodiesel fuel (GB) blends have the potential to reduce soot formation during the combustion process and will be deeply investigated in this paper. Experiments were performed using 10%, 20%, and 40% blend ratios by volume where both the fuels possess distinct fuel properties to investigate the ignition and soot formation for gasoline biodiesel fuel (GB) blends using an optically accessible constant volume combustion chamber (CVCC). The fuel blends were injected into the CVCC to combust under elevated high pressure-temperature conditions using a singlehole research grade injector. Broadband chemiluminescence technique is utilized to determine ignition characteristics. Natural soot luminous images from the combustible flame were captured by a CMOS camera to determine soot particles during combustion. A wide range of experimental conditions from 800 K to 1200 K and the oxygen concentration 21% was investigated. The experimental observations showed that a higher gasoline content produced a significantly longer ignition delay, thus improving and extending the evaporation process. The combustion properties of gasoline-biodiesel blends are significantly improved with the decrease in gasoline content, and this has the great potential for power generation in the GDI engine.
This study investigates a fuel spray development process of gasoline–biodiesel blended fuel (GB) in macroscopic and microscopic scales. Long-distance microscopy and shadowgraph were utilized as optical methods to capture the highly transient spray development. Different injection pressures were tested, which ranged from 40 to 120 MPa with a fuel temperature of 323K. Tested four fuels were neat gasoline and biodiesel addition (5%, 20%, 40% by volume) to gasoline in three different ratios. The results regarding the development process for the initial spray near the nozzle show that the spray penetration and the spray tip velocity both decreased with decreasing biodiesel blending ratio. This relationship appears to be due to the associated differences in the mass flow rate and the radial direction velocity vector of the spray. In addition, the different spray tip velocities at the start of spraying result in different atomization regimes between the fuels. The GB fuels with the low biodiesel blending ratio were disadvantaged in spray atomization due to their lower spray penetration and tip velocity. However, as the injection pressure increased, the differences in microscopic spray penetrations between the fuels became smaller, along and there were changes in the atomization characteristics.
Accurate and robust real-time state estimation is essential to the reliable and safe operation of the hybrid energy storage system. This paper handles a closed-loop method for state-of-charge estimation of lithium-ion battery and ultracapacitor hybrid system. In this work, a fractional-order model is developed to approximate the dynamic behavior of the lithium-ion battery and ultracapacitor. Then, a closed-loop method is proposed for model parameter and state-of-charge estimation. Experiments under dynamic load profiles are used to verify the proposed method. The experimental results indicate that the proposed method can obtain robust estimation results for the hybrid energy storage system, and is appropriate for real-time systems.
Phase change material board (PCMB) is considered high potential as an efficient passive solution to energy saving in building applications, especially in hot weather. A numerical investigation is conducted on PCMB, with both sides subjected to periodical temperature variations to examine its thermal behaviour. The experimentally validated model is based on the enthalpy method. The inner surface temperature variation is used as a comparison factor, further with two newly introduced parameters, thermal comfort ratio (TCR) and energy saving potential (ESP), to parametrically analyse the influencing factors in terms of both thermal comfort and energy saving aspect. Melting range, latent heat capacity, convective heat transfer coefficients for inner/outer surfaces, thermal conductivity and PCMB thickness are studied parametrically. Furthermore, the optimal heat storage capacity of a PCMB placed on the inner side of a traditional brick-concrete exterior wall is theoretically obtained.
To support home energy management, users or operators prefer appliance-level energy consumption information than the house monthly electricity bill report. Two methods exist for appliance energy usages recognition: Non-intrusive Load Monitoring (NILM) and Intrusive Load Monitoring (ILM). Both have not been widely used due to either insufficient performance or high cost. This paper proposed a practical socket-level non-intrusive load monitoring method. First, through socket submeters, the load disaggregation accuracy can be improved by reducing occurrences of indistinguishable appliances when using simple power features; Second, by involving users’ feedback, the load classification accuracy can be enhanced by feature registration and match. An unsupervised hierarchical clustering algorithm was used for load disaggregation, and the dynamic time wrapping algorithm was used for appliance feature match. This method was validated through a public dataset and showed a great promise.
This study use a dynamic multi-sectoral CGE model with different nested structure and substitution elasticity for electricity sectors with different power sources to capture the effects of reducing renewable electricity curtailment across all economic sectors. We found that the reduction of renewable electricity curtailment would lead to a significant increase in renewable electricity generation, and a moderate decrease in non-renewable electricity generation. Among the renewable powers, wind power has the largest increase in activity level. Secondly, the reduction of renewable electricity curtailment would bring green co-benefits that carbon dioxide and air pollutant emissions from power sectors fall significantly, meantime national GDP and employment have slight increases. Third, without the cost-neutrality assumption, the impacts of reducing electricity curtailment would be largely over-estimated with CGE model. Fourth, if with multi-value simulations of CES substitution elasticity, the disparity on nested structure of power sectors would not cause serious disagreement on simulation results.