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The Chinese electric power industry, including its coal industry and other energy industries that are not efficient, contributes to China’s serious energy shortages and environmental contamination. The governing authority considers energy conservation to be one of the most prominent national targets, and has formulated various plans for decarbonizing the power system. Applying the trans-log cost function, this paper examined the trans-log cost function to analyze the potential inter-factor substitution among energy, capital and labor. We also investigated what role human capital played in energy substitution for the electric power sector during the period from 1981 to 2017. Three key results were derived: (1) energy is price-insensitive, (2) there exists large substitution sustainability between both capital and labor with energy, and (3) human capital input not only enhances the extent of energy substitutability with capital and labor but also is a substitute to energy itself. These findings imply that the liberalization of the electric price mechanism is conducive to lessening energy use and augmenting non-energy intensiveness, and that energy conservation technology could become more sustainable by investing more capital in the electricity sector, thereby achieving a capital–energy substitution and a decrease of CO2 emissions. We further suggest that the priority for the Chinese electric power industry should be to attach more importance to increasing human capital input.

We researched China's climate and sustainable development goal with relevant and susceptible instruments capable of inducing and mitigating carbon emissions. Amidst the contributor to the global carbon emissions, China is caught in between mitigating its carbon emission and aiming towards placing its national contribution of emissions to the acceptable levels of 1.5 °C and below 2 °C. Following the intricacies surrounding China's sustainable development as it contains its economic and environmental performance, we adopt China's data of 1980 and 2018 with different scientific approaches (nonlinear autoregressive distributed lag (NARDL), dynamic ordinary least square test, and bootstrap Granger causality) with different instruments (such as economic growth, financial development, renewable energy, and innovation policies) to research China's sustainable development. For clear exposition and insight into our findings with policies attached, we draw a conclusion from the outcomes of the mentioned approaches. From NARDL and dynamic ordinary least squares (DOLS), we find that economic growth through economic activities is statistically significant in determining the trend (increase) of carbon emissions in China in both periods (short run and long run). However, other selected instruments (financial, renewable, and innovation policies) tend towards controlling and moderating the carbon emissions in China. Thus, China has good prospects to mitigate its carbon emissions if considered tailoring its policies towards favorable instruments. From bootstrap Granger causality, we find similar inferential results that support previous findings thereby confirming the positive implication of the selected instruments to China's sustainable development. Hence, the nexus that is established among the selected instruments clearly show the importance of technological innovation and renewable energy in mitigating carbon emissions.

In recent years, the energy consumption structure has been accelerating towards clean and low-carbon globally, and China has also set positive goals for new energy development, vigorously promoting the development and utilization of renewable energy, accelerating the implementation of renewable energy substitution actions, and focusing on improving the consumption capacity of new energy. However, due to the intermittent and unstable characteristics of renewable energy, it is difficult to meet the demands of the power load side in practical applications. Energy storage is an important link for the grid to efficiently accept new energy, which can significantly improve the consumption of new energy electricity such as wind and photovoltaics by the power grid, ensuring the safe and reliable operation of the grid system, but energy storage is a high-cost resource. Therefore, this paper focuses on the energy storage scenarios for a big data industrial park and studies the energy storage capacity allocation plan and business model of big data industrial park. Firstly, based on the characteristics of the big data industrial park, three energy storage application scenarios were designed, which are grid center, user center, and market center. On this basis, an optimal energy storage configuration model that maximizes total profits was established, and financial evaluation methods were used to analyze the corresponding business models. Finally, taking an actual big data industrial park as an example, the economic viability of energy storage configuration schemes under two scenarios was discussed, and an energy storage system construction plan was proposed to promote the zero-carbon target of the big data industrial park.

The management of the distribution network is becoming increasingly important as the penetration of distributed energy resources is increasing. Reliable knowledge of the real-time status of the network is essential if algorithms are to be used to help distribution system operators define network configurations. State Estimation (SE) algorithms are capable of producing such an accurate snapshot of the network state but, in turn, require a wide range of information, e.g., network topology, real-time measurement and power profiles from customers/productions. Those profiles which may, in principle, be provided by smart meters are not always available due to technical limitations of existing Advanced Metering Infrastructure (AMI) in terms of communication, storage and computing power. That means that power profiles are only available for a subset of customers. The paper proposes an approach that can overcome these limitations: the remaining profiles, required by SE algorithms, are generated on the basis of customer-related information, identifying clusters of customers with similar features, such as the same contract and pattern of energy consumption. For each cluster, a power profile estimator is generated using long-term power profiles of a limited sub-set of customers, randomly selected from the cluster itself. The synthesized full power profile, representing each customer of the distribution network, is then obtained by scaling the power profile estimator of the cluster to which the customer belongs, by the monthly energy exchanged by that customer, data that are easily available. The feasibility of the proposed approach was validated considering the distribution grid of Unareti SpA, an Italian Distribution System Operator (DSO), operating in northern Italy and serving approximately one million customers. The application of the proposed approach to the actual infrastructure shows some limitations in terms of the accuracy of the estimation of the power profile of the customer. In particular, the proposed methodology is not fully able to properly represent clusters composed of customers with a large variability in terms of power exchange with the distribution network. In any case, the root mean square error of the synthesized full power profile with the respect to validation power profiles belonging to the same cluster is, in the worst case, on the order of 6.3%, while in the rest of cases is well below 5%. Thus, the proposed approach represents a good compromise between accuracy in representing the behavior of customers on the network and resources (in terms of computational power, data storage and communication resources) to achieve that results.

This paper proposes a novel consensus-based distributed control algorithm for solving the economic dispatch problem of distributed generators. A legacy central controller can be eliminated in order to avoid a single point of failure, relieve computational burden, maintain data privacy, and support plug-and-play functionalities. The optimal economic dispatch is achieved by allowing the iterative coordination of local agents (consumers and distributed generators). As coordination information, the local estimation of power mismatch is shared among distributed generators through communication networks and does not contain any private information, ultimately contributing to a fair electricity market. Additionally, the proposed distributed algorithm is particularly designed for easy implementation and configuration of a large number of agents in which the distributed decision making can be implemented in a simple proportional-integral (PI) or integral (I) controller. In MATLAB/Simulink simulation, the accuracy of the proposed distributed algorithm is demonstrated in a 29-node system in comparison with the centralized algorithm. Scalability and a fast convergence rate are also demonstrated in a 1400-node case study. Further, the experimental test demonstrates the practical performance of the proposed distributed algorithm using the VOLTTRON platform and a cluster of low-cost credit-card-size single-board PCs. Comment: 16 Pages, 13 figures Figures order and references are corrected!

Since the turn of the century, there has been a growth in the use of e-business by both large and small companies worldwide, a trend that has been given further impetus by the move to online trading in the COVID-19 pandemic era. For small companies, there are the potential benefits of increased efficiencies and market share gain, associated with the re-engineering of selling and marketing processes; but in developing countries, such as Nigeria, research into how small businesses are using e-business systems and technologies is limited. This article builds upon earlier case study research in the Nigerian small business sector to develop a framework for e-business strategy development, implementation and review. Using an inductive approach, data was collected from six small businesses, using interviews and questionnaires, to profile the e-business operations of these companies. This study found that e-business strategy was generally lacking in these companies, but interview material was used to support the development and validation of the strategy framework, which provides a process and a checklist for small businesses pursuing e-business initiatives in developing world environments.

Mass finishing processes have widely industrial application, and in most conditions these processes are used with empiricism and trial and error. Based on basic concept of mass finishing, its category is presented. Main aspects of motion and force analysis, material removal model, equipment and consumables, process experiments and industrial application are induced. Finally, development trends are recommended from quantified models, process database, new mass finishing processes and surface integrity. This will provide full investigation and further development and research to improvement part surface integrity.

Telecommunication is a prerequisite for the Smart Grid. Several technologies are available on the market, but there is not a general agreement about which of them, or combination of them, that can enable most of the services for the distribution grid automation. The paper describes the approach of 7th Framework Program European Project INTEGRIS which relies on the hypothesis that there is not a single technology with an optimal trade-off between cost of investment and performances for all use cases and topologies of a distribution grid. It rather suggests to mesh several technologies (with focus on the Broadband Power Line, the Wi-Fi and the Fiber Optics) to cover all the segment of the distribution grid. This concept has been experimentally validated in two field demonstrators in Italy and in Spain. This paper presents the results of tests on the latency and the throughput.