• Energy Research
• 7. Clean energy close
• 4. Education close
• KR close

Wind turbine generators (WTGs) in a wind power plant (WPP) contain different levels of releasable kinetic energy (KE) because of the wake effects. This paper proposes a releasable KE-based inertial control scheme for a doubly fed induction generator (DFIG) WPP that differentiates the contributions of the WTGs depending on their stored KE. The proposed KE-based gain scheme aims to make use of the releasable KE in a WPP to raise the frequency nadir. To achieve this, two additional loops for the inertial control are implemented in each DFIG controller: the rate of change of frequency and droop loops. The proposed scheme adjusts the two loop gains in a DFIG controller depending on its rotor speed so that a DFIG operating at a higher rotor speed releases more KE. The performance of the proposed scheme was investigated under various wind conditions. The results clearly indicate that the proposed scheme successfully improves the frequency nadir more than the conventional same gain scheme by releasing more KE stored in a WPP, and it helps all WTGs to ensure stable operation during inertial control by avoiding the rotor speed reaching the minimum speed limit.

Abstract Hydrogen is considered a potential game changer for world energy systems and a solution to climate change concerns, as it generates zero waste and it is suited for power generation and transportation. Despite its several advantages, there are significant technical challenges in deploying a stable hydrogen economy including improving its process efficiencies, lowering production costs, maintaining cost-effective transmission and distribution, and exploiting inexpensive and sustainable feedstocks. In this context, a detailed study was conducted to analyze the production sources, technologies, storage and transport systems, and global potential exportable feedstocks to produce hydrogen. A comprehensive analysis of current hydrogen production technologies with their energy efficiencies and hydrogen selling prices was reported in this study. Various hydrogen production technologies with their capital investments and CO2 emissions were also presented. Potential feedstocks for hydrogen production were identified and analyzed through a product space model, which characterizes a network of global exportable products based on their similarities and productive knowledge. It was established that the hydrogen production feedstocks and sources currently used are primarily available in six countries: the United States of America, France, Russia, Sweden, the Netherlands, and Spain. Broadly, the results revealed that the United States of America and Russia shared the highest hydrogen feedstock exports, indicating a higher probability of hydrogen production in these countries. Except for Russia, all the studied countries fell in the most desired quadrant, indicating that they can move in all product space directions to exploit unexplored hydrogen feedstocks for better sustainable economic growth.

Large-size woody biomass is a valuable renewable resource to replace fossil fuels in biorefinery processes. The preprocessing of wood chips and briquettes is challenging to manage, especially in an industrial setting, as it generates a significant amount of dust and noise and occasionally causes unexpected accidents. As a result, a substantial amount of resources, energy, labor, and space are needed. The thermochemical conversion behavior of large-size woody biomass was studied to reduce energy consumption for chipping. Large-size wood was 1.5 m in length, 0.1 m in breadth, and stacked 90 cm in height. This strategy has many benefits, including increased effectiveness and reduced CO2 emissions. The target of this paper presents the thermochemical process, and large-size wood was chosen because it provides high-quality product gas while reducing the preprocessing fuel cost. This review examines the benefits of thermochemical conversion technologies for assessing the likelihood of carbon neutrality.

Thermomagnetic heat engines were designed, constructed, and tested, where numbers of gadolinium (Gd) blocks were used to exploit low temperature waste heat. Gadolinium is a rare earth material whose magnetic property changes with temperature, altering between ferromagnetic and paramagnetic. A motion develops in the thermomagnetic heat engine as Gd blocks are exposed to different temperatures causing changes in their magnetic property. A change in the magnetic property of any Gd block is directly related to the resultant torque driving the thermomagnetic heat engine for power production. Among heat engines studied to date, the cylindrical thermomagnetic heat engine was able to develop a maximum mechanical power of 1.1 W at a temperature difference of 45 °C between hot and cold thermal resources. Furthermore, depending on the effectiveness of an electromagnetic generator (EMG) combined with a triboelectric nanogenerator (TENG), the electric power output can be notably improved.

Abstract The South Korean government has been actively promoting an educational-facility improvement program as part of its energy-saving efforts. This research seeks to develop a decision support model for selecting the facility expected to be effective in generating energy savings and making the facility improvement program more effective. In this research, project characteristics and electric-energy consumption data for the year 2009 were collected from 6282 elementary schools located in seven metropolitan cities in South Korea. In this research, the following were carried out: (i) a group of educational facilities was established based on electric-energy consumption, using a decision tree; (ii) a number of similar projects were retrieved from the same group of facilities, using case-based reasoning; and (iii) the accuracy of prediction was improved, using the combination of genetic algorithms, the artificial neural network, and multiple regression analysis. The results of this research can be useful for the following purposes: (i) preliminary research on the systematic and continuous management of educational facilities’ electric-energy consumption; (ii) basic research on electric-energy consumption prediction based on the project characteristics; and (iii) practical research for selecting an optimum facility that can more effectively apply an educational-facility improvement program as a decision support model.

Capture of trace amounts (parts per trillion or ppt level) of 90Sr from highly Na+-rich (5 M or 115 000 parts per million) liquid wastes produced from reprocessing of spent nuclear fuel rods is crucial for continuous operation of nuclear power plants.

This study introduces a novel methodology to calculate the carbon dioxide (CO2) emission reduction related to residual emissions, calculating the CO2 emission reduction through a 2 MW (40 tCO2/day) carbon capture and utilization (CCU) plant installed at a 500 MW coal-fired power plant in operation, to evaluate the accuracy, maintainability, and reliability of the quantified reduction. By applying the developed methodology to calculate the CO2 emission reduction, the established amount of CO2 reduction in the mineral carbonation was evaluated through recorded measurement and monitoring data of the 2 MW CCU plant at the operating coal-fired plant. To validate the reduction, the accuracy, reproducibility, consistency, and maintainability of the reduction should be secured, and based on these qualifications, it is necessary to evaluate the contribution rate of nationally determined contributions (NDCs) in each country. This fundamental study establishes the concept of CCU CO2 reduction and quantifies the reduction to obtain the validation of each country for the reduction. The established concept of the CCU in this study can also be applied to other CCU systems to calculate the reduction, thereby providing an opportunity for CCU technology to contribute to the NDCs in each country and invigorate the technology.

Abstract Due to the various restrictions on the energy performance of public office buildings, it is essential to obtain occupancy information for not only evaluating but also regulating the building energy performance. There is still a lack of information and standard, however, for occupancy density due to the limitations on data collection and the lack of reliable data. Therefore, this study aimed to determine the optimal occupancy density for reducing the energy consumption in public office buildings. Towards this end, this study used various statistical methods, such as correlation analysis, decision tree, and Mann-Whitney U test, based on the actual occupancy data from public office buildings in South Korea. This study was conducted in three steps: (i) establishment of the database; (ii) determination of the optimal occupancy density using the statistical approach; and (iii) application of the proposed occupancy density using building energy policies. As a result, it was shown that buildings with an occupancy density above 31.41 m2/person could save up to 50.3% energy on average compared to those with an occupancy density below 31.41 m2/person. The analysis results showed that the proposed occupancy density could help in deciding the appropriate occupancy density for reducing the energy consumption of public office buildings.

Abstract In this paper, a hybrid features based support vector machine (SVM) model is proposed using infrared thermography technique for hotspots detection and classification of photovoltaic (PV) panels. A novel hybrid feature vector consisting of RGB, texture, the histogram of oriented gradient (HOG), and local binary pattern (LBP) as features is formed using a data fusion approach. A machine learning algorithm SVM is employed to classify the obtained thermal images of PV panels into three different classes (i.e., healthy, non-faulty hotspot, and faulty). The comparison of different machine learning algorithms and datasets is also carried out to validate the superiority of the proposed model and hybrid feature dataset. The experimental results reveal that the proposed hybrid features with SVM resulted in 96.8% training accuracy and 92% testing accuracy with lesser computational complexity and storage space than other machine learning algorithms. The proposed approach is easily implementable for efficient monitoring and fault diagnosis of PV panels.