• Energy Research
• 2016-2025close
• engineering and technology close
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This paper demonstrates the capabilities of the Simulink simulation environment for tuning the parameters of Power system stabilizers (PSS), which play a key role in damping the low-frequency oscillations in the electric power systems. First, the theory and algorithms for optimization of the parameters are examined, focusing on the phase compensation (P-Vr) method, which was selected and implemented in Simulink. The different steps of the tuning algorithm are explained, as well as the formulation of the objective function. The theoretically described steps were practically applied to a single-machine system, as well as to the well-known four-machine system of Kundur. The available solvers were also tested and compared in terms of speed and efficiency.

Developing a low-carbon economy is the only way for countries to achieve sustainable development. Carbon emission reduction policies and low-carbon technology (LCT) innovation play key roles in developing low-carbon economy. Under government reward and punishment regulations, based on the bilateral matching and evolutionary theories, this paper constructs an evolution model consisting of a manufacturer investing LCT and a supplier offering LCT to analyze multi-phase LCT investment strategies. Firstly, the profit optimization model of a green supply chain is constructed from the perspectives of centralized-matching (CM), decentralized-matching (DM), and mismatching (MM), and the spatial information internal evolution law of multi-phase LCT investment is described by the Markov chain. Then, a bilateral matching algorithm is proposed to solve the equilibrium solutions, and the evolution process of the three modes is analyzed by numerical simulation. Finally, based on the product green degree, we analyze the impact of subsidies and taxes on investment-production decisions. Analytical results show that the matching mechanism proposed in this paper can help supply chain firms to obtain stable matching and has a significant effect on the realization of "triple wins" of society, economy, and environment. The investment utility of CM is higher than that of DM and MM. Manufacturers are inclined to adopt LCT, and the investment level tends to be stable over time. Government reward and punishment regulations are helpful to motivate supply chain firms to invest in LCT, and the synergistic effect of subsidies and taxes is better than that of a single policy.

The object of the study is the water supply and sanitation system of an industrial enterprise. Currently, many enterprises do not use the treated industrial waste water in the water supply cycle. Therefore, the use of treated wastewater is of practical importance. The scheme of thermal treatment of waste water for obtaining pure condensate by evaporation is proposed. The main results of the study are the technical solution for the development of the enterprise's wastewater treatment complex. Pure condensate is obtained in an instant boiling evaporator. The research method is based on the calculation of energy costs in wastewater treatment for different types of waste water. The results of the study allow us to determine the optimal set of energy equipment for obtaining pure condensate and its further use. The specific practical significance lies in the development of a technical solution that helps the treatment of wastewater and the reduction of emissions into the water basin.

Abstract Mitigating the effects of environmental deterioration requires a focus on not just CO2 emissions from energy consumption, but also environmental pollution from industry sectors. To reach this goal, recent studies have extended ecological footprint (EF) analysis to identify the ecological drivers of various key industry sectors. The role of the phosphorus (P) industry on the EF within the environmental Kuznets curve (EKC) framework for China is the emphasis of this study. Autoregressive distributive lag (ARDL) as well as the impulse response function and robustness analysis were used to consider a time from 1985 to 2018. The study verifies the EKC hypothesis for China in both the long and the short run, and indispensable determinants are proposed to be included to assure the model’s fitness and robustness when conducting EF analysis of industry sectors. Energy consumption–based carbon emissions have been verified as the dominant contributor to EF, but P use and urbanization have a significant lagged positive influence on EF in the short run. P exports, in particular, have been highlighted as a critical driver of the EF of China’s P industry. The conducted frequency domain causality test reinforced the above findings and demonstrated bidirectional causality at different frequencies. This work suggests that formulating plausible P export policies to alleviate the conflict between the output of China’s P industry and the environmental sustainability of this industry are necessary. In this context, “multidisciplinary, multidimensional, and practical solutions” are most desirable for sustainable P management.

As a financial innovation model to promote sustainable economic development, carbon finance promotes green and inclusive economic development by reshaping the financial system. As a big energy consumer, China has been committed to the green transformation of energy structure, and carbon finance has been regarded as an important tool to support carbon emission reduction. This paper divides carbon finance from three levels, financial macro, environmental governance, and technological development, and selects 17 indexes to measure the development level of carbon finance in China. Time series regression model and the entropy weight method (EWM) are used to analyze the impact of carbon finance on China's energy consumption structure. The empirical results show that carbon finance plays a positive role in promoting China's energy consumption structure, and developing carbon finance is conducive to the use of clean energy and the optimization of energy consumption structure in China; carbon finance can further optimize China's energy consumption structure by investing capital, resources, and technology in industries related to promoting clean energy consumption. Therefore, China should continue to develop carbon finance and promote the establishment of a unified national carbon trading market; at the same time, China should use the CCER trading mechanism to promote the development of renewable energy and enhance the endogenous driving force of energy transformation by promoting technological progress.

Human reliability analysis (HRA) is a proactive approach to model and evaluate human systematic errors, and has been extensively applied in various complicated systems. Dependence assessment among human errors plays a key role in the HRA, which relies heavily on the knowledge and experience of experts in real-world cases. Moreover, there are ofthen different types of uncertainty when experts use linguistic labels to evaluate the dependencies between human failure events. In this context, this paper aims to develop a new method based on linguistic hesitant fuzzy sets and the technique for human error rate prediction (THERP) technique to manage the dependence in HRA. This method handles the linguistic assessments given by experts according to the linguistic hesitant fuzzy sets, determines the weights of influential factors by an extended best-worst method, and confirms the degree of dependence between successive actions based on the THERP method. Finally, the effectiveness and practicality of the presented linguistic hesitant fuzzy THERP method are demonstrated through an empirical healthcare dependence analysis.

Accelerating the development of renewable energy is seen as an effective way for achieving the goals of carbon peak and carbon neutrality. The polices of Renewable Electricity Standard (RES) and Renewable Energy Certificates (REC) play increasing and important roles in developing renewable energy. In this paper, we develop an analytical model to analyze the impacts of the interaction of RES and REC polices on the renewable energy investment levels of an electricity generation firm and the carbon emissions. Our analysis reveals several interesting insights. First, we find that the green tags price under REC policy has a non-monotonic effect on the renewable energy investment, which highly depends on the quota (i.e., the required percentage of renewable electricity consumption on total electricity consumption) under the RES policy. Specifically, when the quota in RES policy is set too high, an increase in the green tags price will increase renewable energy investment; otherwise it will reduce the electricity generation firm's incentive to invest in renewable energy. Second, we show that the green tags price also has a non-monotonic effect on the carbon emissions. Specifically, when the quota in RES policy is set small enough, an increase in the green tags price will decrease the carbon emission. However, when the quota in RES policy is high enough, an increase in the green tags price will increase the carbon emission.