• Energy Research

The measured data of the power system with voltage source converter (VSC) depending on data communication network, are monitored and analyzed by Dynamic state estimation (DSE), which can provide predictive data to control system. However, information communication technology may contain unknown risks and be vulnerable to cyber-attacks, resulting in inaccurate predictions of DSE. For example, the communication network of wide area measurement system (WAMS) will provide wrong data to DSE after being attacked. Thus, in this paper, an optimized dynamic state estimation method for voltage source converter considering cyber-attack is proposed. First, in order to consider more dynamic characteristics of VSC and provide a more complete model for DSE, the dynamic model of VSC which is affected by cyber-attacks is derived. Then, base on the model which is linearized around an equilibrium point, the Kalman filter considering anomalous events caused by false data injection attack (FDIA) is improved to obtain the optimal state estimated value. Finally, to verify the effectiveness of the proposed method and the accuracy of prediction values, a simulation has been carried out on a single-VSC infinite-bus power system. The simulation results show that the proposed method can predict dynamic state values effectively and the Root Mean Squared Error (RMSE) is around 4.6×10−5in the power system with VSC considering cyber-attack.

For transmission systems to operate safely and reliably, fault identification and classification are essential. However, power network physical architecture and data information cannot be fully utilized by conventional intelligent approaches. This study, therefore, presents a fault localization, detection, and classification model for transmission systems that concentrate on the key distribution nodes. The model makes use of a deep graph neural network with multi-scale attention and multi-linear perceptron block which accounts for the power network's structural composition during learning. The model's capacity to manage unusual data input and unidentified application situations is improved by the inclusion of multi-scale attention. Furthermore, it enables the model to precisely pinpoint fault areas by identifying patterns and connections among system parts, concentrating on specific areas or nodes. In addition, a multi-linear perceptron block is designed to enhance the capturing of amplitude information and increase comprehension. The efficiency and generalizability of the proposed model are improved by the implementation of a multi-task training approach for locating faults and their type. With the use of two IEEE 13-Bus systems and the PSS/E 23-Bus system, the proposed fault diagnosis model is tested. Examining various setups for fault analysis allows for a more thorough evaluation of the model's ability to generalize and disturbance resilience. Experimental findings show that the proposed model outperforms existing cutting-edge techniques in terms of efficacy with a balanced accuracy of 0.8204 for classification, 0.556 for localization, and a Macro MAE of 38.780 for detection.

In recent years, the developed, emerging, and developing economies have prioritized environmental sustainability attainment. In an attempt to offer some potential policy choices towards the achievement of sustainable development, this paper shifts emphasis from the popularly discussed economic development and carbon emissions nexus. Instead, we examine the impact of the banking and financial system’s activities on carbon emissions for a sample of 45 countries. These are comprised of developed, emerging, and developing countries between 1990 and 2017. To fill the gap in the literature, the nexus is examined in seven different phases. This study exposes robust and reliable empirical results with the use of Feasible General Least Squares, random effects with regards to the Durbin–Wu–Hausman test, and Difference General Method of Moments panel data estimation models. Our findings indicate that the increase of domestic credit to the private sector and commercial bank lending consistently contributes towards aggravated carbon emissions in all economic types. Additionally, increased deposit rates in developing economies, increased lending rates in developed economies, and increased deposit rates in emerging economies contribute towards the overall reduction of carbon emissions. The decrease in lending to high GHG emitting members of the private sector by financial institutions in all economies is recommended based on the results of this study.

The conversion of different waste materials into biogas through anaerobic digestion process has been presented in different literatures. However, studies about the application of these biomass ener...

In this study, photovoltaic (PV) technology development over the years is reviewed. The use of PV power plants to solve inadequate power supply in Kurdistan is also analysed. PV power application is one of the most developed renewable energy applications but still not commercialized in somw countries. In this paper, three different locations will be evaluated in Kurdistan for PV plant installation. The research will investigate the best location for PV plant installation in Kurdistan, check the viability of the proposed plants and compare the performance of a fixed and a double axis tracking system. A 10 MW PV plant is developed and simulated based on economic terms. The results from the analysis shows that the simple payback period for a 10 MW PV plant in all the locations considered is between 6.8 and 7.2 years. Also, the installation with two-axis tracking system gave the lowest simple payback period (6.8 years). The PV plant is viable considering other economic indicators like; IRR, NPV, annual life cycle savings and BCR. The yearly savings of the system for one of the locations considered is US$1,573,327 with a dual axis tracking system. ©2019. CBIORE-IJRED. All rights reserved

Please read abstract in the article. ; http://www.elsevier.com/locate/seta ; 2024-01-21 ; hj2023 ; Mechanical and Aeronautical Engineering

Despite the global push for net-zero emission and the power system’s decarbonization, there are still a lot of developing countries (especially in sub-Saharan Africa) with electricity crises or inadequate electrification. Coincidentally, these countries have enormous renewable energy potential. Therefore, this paper presents a brief summary of the electricity incapacitation and renewable energy potential in many sub-Saharan African countries. This study is novel as it presents a techno-analysis of 100% renewable electrification in Chad which is one of the countries with the least electricity access globally. Also, the models developed are simulated using the EnergyPLAN tool with targeted implementation years set as 2030, 2040, and 2050 based on different global decarbonization targets. From the result of this study, the use of biomass power plants is the only scenario that does not have critical excess electricity production (CEEP) in the power system. Also, these scenarios require the least power capacities (270 MW for 2030, 650 MW for 2040, and 1600 MW for 2050) out of all the scenarios in this study. Solar PV and wind power can be categorized as the most probable options as they are matured technologies and in application in many countries, however, they will require pumped hydro storage integration.