Energy Informatics最新文献

Investigating the impact of carbon emissions trading policy and elucidating the underlying mechanisms are crucial for enhancing policy effectiveness and refining related systems. This study examines the impact of carbon emissions trading policy by constructing a difference-in-difference model utilizing unbalanced panel data from China’s provinces spanning the period from 2005 to 2019. Additionally, a mediating effect model is employed to delve into the underlying mechanisms. The key findings are as follows: Firstly, the implementation of carbon emissions trading policy has a notable inhibitory impact on carbon emissions. Secondly, both the upgrading of industrial structure and the reduction of energy intensity play mediating roles in carbon emissions reduction. However, the development of clean energy industries does not exhibit a significant mediating effect. In conclusion, this study offers policy recommendations aimed at facilitating carbon reduction. These include enhancing the market-based trading mechanism for carbon emissions, optimizing and upgrading industrial structures, fostering innovation in green and low-carbon technologies, and promoting the development and utilization of clean energy.

The basic requirements for the grid connection of the generator motor of the gravity energy storage system are: the phase sequence, frequency, amplitude, and phase of the voltage at the generator end and the grid end must be consistent. However, in actual working conditions, there will always be errors in the voltage indicators of the generator and grid terminals, resulting in transient impulse currents. In addition, due to the difference between gravity energy storage systems and conventional power generation units, frequent switching between charging and discharging operating conditions is required according to the needs of the power grid. Each switching requires the completion of the generator motor startup and grid connection. If there is always a significant error in the voltage indicators between the generator and grid terminals during frequent grid connection, stable transient surge currents will be generated. Without human intervention, long-term operation will bring hidden dangers to the safety of the grid connected system, leading to a series of consequences such as equipment aging and even damage. In response to the above issues, this article establishes a gravity energy storage power generation/motor grid connection model. Through simulation analysis, the variation law of the weight of the impact of different terminal voltage indicators on the grid connected transient impulse current is summarized. A grid connection method for gravity energy storage systems based on sensitivity analysis of voltage grid connection indicators is proposed. Through simulation verification, this method can significantly reduce the grid connected transient impulse current while improving the success rate of grid connection, The correctness and practicality of the proposed method have been fully verified.

Public institutions, emblematic of public infrastructure, exhibit extensive reach and operational scope, positioning them as vanguards in China’s dual carbon initiatives and serving as exemplars. Electricity and natural gas predominantly fuel the operations of public institutions. Notably, the fixed commute routes and consistent procurement patterns of office personnel yield a standardized energy consumption profile within these entities. Researching carbon emissions related to commuting and evaluating procurement strategies for reducing carbon footprints in public institutions demonstrate a precision-tailored approach. This paper, through an analysis of the energy consumption characteristics, utilization structure of public institutions, and the commuting behaviors and procurement practices of office personnel, establishes a bespoke carbon accounting model specifically designed for public institutions, seamlessly embedded within a comprehensive platform. By providing fundamental methodological frameworks and advanced technological foundations for carbon accounting in public institutions across China, this work propels the nation’s efforts towards carbon peak and ultimately carbon neutrality.

Non intrusive load monitoring belongs to the key technologies of intelligent power management systems, playing a crucial role in smart grids. To achieve accurate identification and prediction of electricity load, intelligent optimization algorithms are introduced into deep learning optimization for improvement. A load recognition model combining sparrow search algorithm and deep confidence network is designed, as well as a gated recurrent network prediction model on the grounds of particle swarm optimization. The relevant results showed that the sparrow search algorithm used in the study performed well on the solution performance evaluation metrics with a minimum value of 0.209 for the inverse generation distance and a maximum value of 0.814 for the hyper-volume. The accuracy and recall values of the optimized load identification model designed in the study were relatively high. When the accuracy was 0.9, the recall rate could reach 0.94. The recognition accuracy of the model on the basis of the test set could reach up to 0.924. The lowest classification error was only 0.05. The maximum F1 value of the harmonic evaluation index of the bidirectional gated recurrent network optimized by particle swarm optimization converged to 90.06%. The loss function had been optimized by particle swarm optimization, and both the convergence value and convergence speed had been markedly enhanced. The average absolute error and root mean square error of the prediction model were both below 0.3. Compared to the bidirectional gated recurrent model before optimization, the particle swarm optimization strategy had a significant improvement effect on prediction details. In addition, the research method had superior recognition response speed and adaptability in real application environments. This study helps to understand the load demand of the power system, optimize the operation of the power grid, and strengthen the reliability, efficiency, and sustainability of the power system.

Focusing on the Russia–Ukraine war, this paper investigates natural gas futures volatilities. Applying several hybrid GARCH and EGARCH models, which innovatively incorporate both fat-tailed distribution errors and structural breaks, we derive the following new evidence. First, our hybrid modeling approach is effective in timely capturing the natural gas futures volatility spike when tensions simmered on the Russia–Ukraine border. Second, the hybrid modeling approach is effective for not only GARCH modeling but also EGARCH modeling. Third, the volatility estimates from our hybrid models have predictive power for the volatilities of nonhybrid models. Fourth, the volatility estimates from the nonhybrid models lag behind the volatilities of our hybrid models.

Typical public institutions such as government offices, hospitals, and schools play important leading and exemplary roles in the green and low-carbon development of the whole society. This paper analyzes the business and energy consumption characteristics of public institutions, as well as the characteristics of personnel travel, and constructs a carbon accounting model for public institutions. It innovatively proposes a methodology for carbon accounting of personnel travel related to public institutions and validates and analyzes it using a comprehensive hospital as an example. The paper analyzes the carbon emission characteristics of various energy types, systems, and types of travelers in hospitals, provides schemes for the transformation of main energy systems, and corresponding emission reduction effects, thereby providing technical support for the full-chain carbon accounting of public institutions. Additionally, this paper explores the carbon reduction pathways for hospitals to support the peak carbon and carbon neutrality goals of public institutions, and promote the high-quality development of public institutions in China.

This paper focuses on enhancing the energy extraction efficiency of photovoltaic (PV) modules through the use of a straightforward power converter and control algorithm. This research delves into the electrical characteristics of PV modules, explaining the concepts of global maximum power point, and local maximum power points. By integrating maximum power point tracking algorithms and differential power processing technology, an innovative scheme for power equalization and optimization of PV modules is introduced. The scheme is based on a single-switch multi-winding forward-flyback converter. Using the STP-340-72-Vfh-type PV module as a case study, a simulation model is developed with PLECS simulation software. The simulations cover 30 different irradiance scenarios. The findings illustrate the effectiveness of the proposed PV module power optimization system in achieving maximum power output under different irradiance conditions, achieving an average efficiency of 94.61%. This efficiency rate is 13.95% greater than that of existing global maximum power tracking schemes.

Amidst the industrial transformation and upgrade, the new energy vehicle industry is at a crucial juncture. Power batteries, a vital component of new energy vehicles, are currently at the forefront of industry competition with a focus on technological innovation and performance enhancement. The operational temperature of a battery significantly impacts its efficiency, making the design of a reliable Thermal Management System (TMS) essential to ensure battery safety and stability. Cylindrical power batteries are widely utilized in the industry. This article outlines the four main structures and their drawbacks of TMS for cylindrical power batteries. Among these structures, air cooling falls short in meeting high heat dissipation requirements. Liquid cooling is expensive, intricate, and adds considerable weight. Phase Change Materials (PCM) are not yet prevalent in practical applications. Similarly, heat pipes are relatively uncommon in large high-power battery packs. To better align with the new energy vehicle industry’s demands for top-notch performance, cost-effectiveness, eco-friendliness, and reliability, this paper strongly recommends delving deeper into composite cooling solutions. The construction of an economically viable and fully optimized composite cooling method is poised to become a significant scientific challenge for future research endeavors.

Amidst the pressing need to combat climate change and curb greenhouse gas (GHG) emissions, the building sector emerges as a pivotal sector, substantially impacting worldwide emissions. Despite efforts to improve energy efficiency and incorporate non-fossil energy sources, the sector still lags in achieving the necessary decarbonization goals. Existing Building Energy Management Systems primarily prioritize economic criteria, overlooking the vital aspect of emissions reduction. Energy Informatics and Information Systems hold the potential to bridge this gap by enabling precise and verifiable GHG emissions accounting, end-to-end real-time tracking, and automated verification within Energy Management Systems (EMS). This paper presents research on designing the advancement of EMSs in the form of a Building Energy Emission Management System (BEEMS) leveraging verifiable emission data for emission-based actions. The central research question revolves around designing BEEMS to facilitate emission-based actions based on verifiable data. Following a multi-step approach, the research methodology encompasses a comprehensive literature review and iterative evaluation of our design principles through a workshop and semi-structured interviews with experts from industry and research. The contributions include a conceptual architecture of a BEEMS and six design principles for future BEEMS development. Ultimately, this research strives to facilitate end-to-end verifiable GHG emissions management in the building sector to enable emission-based energy consumption decisions, contributing to the existing body of knowledge of the Energy Informatics field on BEEMS.

As traditional energy reserves continue to decline, the importance of new energy sources increases. However, the current traditional power system often fails to consider new energy sources, particularly in power supply systems that integrate multiple new energy sources. The cost, efficiency, and environmental factors seriously affect the energy system’s efficiency. Therefore, this proposal presents a multi-objective optimization discrete assignment pathfinder algorithm. The algorithm can handle multi-objective optimization problems and adapt to various constraints, providing a more precise optimization scheme for new energy systems. The experimental results indicated that the proposed research method exhibits better performance compared to other algorithms of the same type. Compared with the multi-objective multivariate universe optimization algorithm and the multi-objective sparrow search algorithm, the research method was ahead in terms of fitness value by 9.54% and 14.67%, respectively. Meanwhile, in the grid simulation, the research method achieved an average efficiency of 96.16%, which is better than the comparative algorithms by 6.57–14.02%. The study not only improves the optimization efficiency of new energy consumption, but also provides a powerful decision support tool for the planning and operation of wind farms. It is of great significance for the improvement of power system efficiency and decarbonization, and helps to promote the large-scale integration and sustainable development of new energy.