A Comparative Analysis of International and Chinese Electronic Commerce Research

Due to the growth of the Internet and e-commerce, both practitioners and researchers are in the midst of a social, business and culture revolution. Internet and e-commerce related research has been developed and grown up by United States, but China has become one of the most exciting research areas. This literature review consists of 1044 journal articles published between 1993 and 2003 in fourteen International and Chinese journals. The articles are classified by a scheme that consists of four main categories: application areas, technological issues, support and implementation and others. Based on the classification and analysis of e-commerce related researches, we present the current state of International and Chinese research and discuss the differences between them

Comparing the Effects of Perceived Enjoyment and Perceived Risk on Hedonic/Utilitarian Smartphone Applications

Despite the widespread adoption of smartphone applications, empirical research that examines the user acceptance on different application types is still scare. This paper empirically compares the effects of perceived enjoyment and perceived risk on hedonic and utilitarian smartphone applications. Our analyses show that perceived enjoyment is a stronger determinant of intention to use a hedonic smartphone application than a utilitarian application. Perceived risk has a significant negative influence on intention to use utilitarian smartphone applications, while it does not have a significant impact on intention to use hedonic applications. Surprisingly, perceived risk has an insignificant effect on perceived usefulness both in utilitarian and hedonic smartphone applications

An explicit formula based estimation method for distribution network reliability

An improved explicit estimation algorithm is proposed for reliability estimation of distribution network. Firstly, hierarchical clustering is used to identify and cluster typical feeders based on topology structure. Secondly, the explicit formula of reliability indices under each typical feeder topology is derived by regression analysis, to establish the model for network reliability estimation. Numerical simulations show the suitability of the proposed method in obtaining accurate reliability index for diversified network topology

Formulation of locational marginal electricity-carbon price in power systems

Decarbonisation of power systems is essential for realising carbon neutrality, in which the economic cost caused by carbon is needed to be qualified. Based on the formulation of locational marginal price (LMP), this paper proposes a locational marginal electricity-carbon price (EC-LMP) model to reveal carbon-related costs caused by power consumers. A carbon-price-integrated optimal power flow (C-OPF) is then developed to maximise the economic efficiency of the power system considering the costs of electricity and carbon. Case studies are presented to demonstrate the new formulation and the results demonstrate the efficacy of the EC-LMP-based C-OPF on decarbonisation and economy

Agent-Based Modeling for Scale Evolution of Plug-In Electric Vehicles and Charging Demand

Scale evolution pattern recognition of plug-in electric vehicles (PEVs) and charging demand modeling are essential for various involved sectors to promote PEV proliferation and integration into power systems. Considering that the market penetration development of PEVs will drive the evolution of charging demand, an integrated dynamic method based on an agent-based modeling technology is proposed in this paper by combining scale evolution model with charging demand model to jointly detect the possible PEVs evolution patterns and long-term charging demand profiles. Heterogeneous consumers presenting different preferences in making vehicle purchase decisions and the interactions with other consumers via social dynamics are taken into consideration in the scale evolution model. After obtain the scale of PEVs by aggregating individual consumer agents purchase behavior, the driving patterns, charging behavior habits, and charging strategies are systematically incorporated into the charging demand model. Case studies demonstrate the feasibility and effectiveness of the proposed methodology by taking an urban area as an example. Furthermore, the factors that affect the market evolution of PEVs and the charging demand are also simulated and analyzed.</p

Formulation of Locational Marginal Electricity-Carbon Price in Power Systems

Decarbonisation of power systems is essential for realising carbon neutrality, in which the economic cost caused by carbon is needed to be qualified. Based on the formulation of locational marginal price (LMP), this paper proposes a locational marginal electricity-carbon price (EC-LMP) model to reveal carbon-related costs caused by power consumers. A carbon-price-integrated optimal power flow (C-OPF) is then developed to maximise the economic efficiency of the power system considering the costs of electricity and carbon. Case studies are presented to demonstrate the new formulation and the results demonstrate the efficacy of the EC-LMP-based C-OPF on decarbonisation and economy

Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction

Wind power generation rapidly grows worldwide with declining costs and the pursuit of decarbonised energy systems. However, the utilization of wind energy remains challenging due to its strong stochastic nature. Accurate wind power forecasting is one of the effective ways to address this problem. Meteorological data are generally regarded as critical inputs for wind power forecasting. However, the direct use of numerical weather prediction in forecasting may not provide a high degree of accuracy due to unavoidable uncertainties, particularly for areas with complex topography. This study proposes a hybrid short-term wind power forecasting method, which integrates the corrected numerical weather prediction and spatial correlation into a Gaussian process. First, the Gaussian process model is built using the optimal combination of different kernel functions. Then, a correction model for the wind speed is designed by using an automatic relevance determination algorithm to correct the errors in the primary numerical weather prediction. Moreover, the spatial correlation of wind speed series between neighbouring wind farms is extracted to complement the input data. Finally, the modified numerical weather prediction and spatial correlation are incorporated into the hybrid model to enable reliable forecasting. The actual data in East China are used to demonstrate its performance. In comparison with the basic Gaussian process, in different seasons, the forecasting accuracy is improved by 7.02%–29.7% by using additional corrected numerical weather prediction, by 0.65–10.23% after integrating with the spatial correlation, and by 10.88–37.49% through using the proposed hybrid method.</p