International Journal of Global Energy Issues (11 papers in press)

Special Issue on: Development and Application of Distributed Energy Systems in Smart Grids

• Research on intelligent charging method of electric vehicles based on virtual power plants  
  by Lianrong Pan, Jiayi Yang, Peikai Li 
  Abstract: The rising number of Electric Vehicles (EVs) requires new charging options to address grid integration and environmental concerns. Innovative charging methods are needed to reduce the burden on conventional infrastructure from electric automobiles. By applying these methods to VPPs, we can improve grid efficiency, electric car charging and energy sustainability. This research proposes the VPP-EV Charging Optimisation Framework (VPECOF) to evaluate the necessity and feasibility of an intelligent charging strategy for electric cars. A solution to the increased demand for electric vehicle charging infrastructure that meets grid stability and sustainable energy objectives is being developed using VPP technology and Smart Charging Optimisation Algorithms (SCOA). The suggested technique considers grid capacity, renewable energy availability and user preferences to improve charge schedules. This research simulates and analyses the intelligent charging strategy in the VPP framework, proving its efficacy and feasibility. The data may illuminate the benefits of synchronised electric car charging, such as peak demand reduction, grid resilience and renewable power integration. The study impacts transportation and energy regulators, utilities and stakeholders. The work improves electric car intelligent charging techniques and has consequences for their evolution.
  Keywords: electric vehicles; virtual power plant; VPP-EV charging optimisation framework; grid efficiency; renewable energy sources; smart charging optimisation algorithm.
  
  
• Design of network security risk warning for a power market monitoring system based on cloud computing technology  
  by Jinyin Peng, Xiangjin Zhu 
  Abstract: To improve the network security level of power market monitoring systems, this article optimised the structural scheme of a power market monitoring system from aspects such as physical environment, network access, and communication process according to actual application background conditions. To verify the reliability of the optimised power market monitoring system, this article conducted comparative experiments on the application of traditional power market monitoring systems and the proposed optimised power market monitoring system. Experimental verification found that the optimised power market monitoring system had a shorter system response time and faster data transmission speed compared with traditional power market monitoring systems. In the risk response assessment experiment, it showed an average improvement of 17.8% in four evaluation indicators. This article briefly discussed the problems that arise in applying traditional power market monitoring systems and proposed a solution for optimising network security based on cloud computing technology. The reliability of the optimised power market monitoring system was verified through experiments. Findings show that the optimised system achieved timely transmission and sharing of monitoring information, and improved accuracy and efficiency.
  Keywords: cloud computing; electricity market; monitoring systems; network security.
  
  
• Utilisation of cloud computing and internet of things technology in power distribution automation  
  by Xiaoguang Liu, Xi Li, Zuohu Chen, Hu Zhou, Zhenfen Zhang 
  Abstract: This study introduced cloud computing and Internet of Things (IoT) technology into distribution automation for application. Firstly, real-time collection of power distribution system data was achieved through IoT devices; data filtering was carried out using Flume, and efficient data transmission and processing were achieved through Kafka. Subsequently, the AWS IoT (Amazon Web Services Internet of Things) platform was utilised to achieve registration, communication, and remote control of smart devices, enabling real-time monitoring of power grid loads. Then, Spark was applied for offline analysis to train Recurrent Neural Network (RNN) models. At the same time, real-time flow data processing from IoT devices was carried out through Flink, combined with trained models for distribution prediction, ultimately achieving distribution automation. The automation performance of the system was evaluated based on data collection speed, transmission accuracy, device registration efficiency, and distribution prediction accuracy.
  Keywords: distribution automation; cloud computing; internet of things; data processing; current neural network.
  DOI: 10.1504/IJGEI.2025.10071319
   
  
• Condition monitoring and fault warning of a ground network of hydropower station based on power internet of things  
  by Renjie Liu, Zhiping Cheng, Haotian Wu 
  Abstract: This study proposed the Power Internet of Things and Deep Learning-assisted Condition Monitoring and Fault Detection Model (PIoT-DL-CMFD) for effectively monitoring faults in the ground network of hydropower stations. Data segmentation is used first to reconstruct the raw vibration data, which may enhance training efficiency. Secondly, Long-Short Term Memory (LSTM) can train the reconstruction data efficiently and adaptively under diverse operational conditions and fault factors. LSTM may then use network inference to detect the information fault classifications. Using the IoT, users can monitor storage conditions and control the devices by sending commands from any place in the world. The numerical findings illustrate that the recommended PIoT-DL-CMFD model enhances the fault prediction rate of 96.8%, accuracy ratio of 98.5%, overall performance ratio of 95.6%, water flow monitoring ratio of 94.5% and energy generation ratio of 97.2% compared to other popular methods.
  Keywords: hydropower station; deep learning; condition monitoring; fault detection; power internet of things; monitoring and control system.
  DOI: 10.1504/IJGEI.2025.10071379
   
  
• Exploration and practice of energy efficiency under natural gas well drainage and gas production process method  
  by Chengxiang Yao, Haijian Li, Junhan Li, Yang Li, Yunjie Cai 
  Abstract: Natural gas, as a key fossil fuel, plays a vital role in the energy industry. This paper explores three drainage and gas production technologies foam drainage, beam pumping unit deep well pump drainage, and gas lift drainage with a focus on improving energy efficiency. By analysing experimental data, the study compares their performance in terms of cost and efficiency. At a drainage depth of 200 m, foam drainage was found to be the most cost-effective, reducing gas production costs by 170.02 yuan and 183.9 yuan compared to the other two methods. While energy efficiency among the three methods showed little difference, foam drainage is recommended for its lower cost, contributing to energy savings and technological advancement in natural gas production.
  Keywords: natural gas wells; drainage gas recovery; energy efficiency; deep well pump; foam drainage gas production process.
  DOI: 10.1504/IJGEI.2025.10071450
   
  
• Efficient management of transmission network construction business process under data centres and ECA rules in cloud computing systems  
  by Weizhen Yang, Wei Fu, Yujun Liang 
  Abstract: This study addresses inefficiencies in power grid construction management by integrating data centres and ECA (Event-Condition-Action) rules within cloud computing systems. Data centres enhanced computational capabilities for process optimisation, while the ECA rule engine dynamically captured critical equipment events, enabling real-time adjustments to project schedules and resource allocation. The system was validated through simulations and a real-world case study of a transmission network in Changsha. Results demonstrated a 21.1% improvement in management efficiency (reaching 99.3%) and a reduced system response time of 14.5 ms under medium load, significantly enhancing both operational efficiency and real-time responsiveness for transmission network construction processes.
  Keywords: transmission network construction; business process management; cloud computing system; data centre; ECA rules; management efficiency; response speed.
  DOI: 10.1504/IJGEI.2025.10071497
   
  
• Intelligent distribution equipment status monitoring and fault warning based on the power internet of things  
  by Xingting Liu, Ke Ning, Bin Hou, Jin Zhang, Shanshan Gao, Jing Ma 
  Abstract: This paper used the power Internet of Things (IoT) technology to monitor the status of the intelligent DR and give early warning of faults. First, the existing problems of the current DR were introduced. This paper then analysed the status monitoring requirements and system configuration of the DR and then discussed the monitoring technology of the intelligent DR to deal with the corresponding data faults. At the end of this paper, the effect of condition monitoring and fault early warning of intelligent DR was analysed, and finally, the conclusion was drawn. After adopting the IoT, the monitoring accuracy of each intelligent DR has improved compared with the previous one. The timeliness of fault early warning in DR has been greatly improved after the adoption of power distribution network technology, and the timeliness of fault early warning in DR 5 can reach 92%.
  Keywords: intelligent distribution room; condition monitoring; fault warning; power IoT; monitoring accuracy.
  
  
• Optimisation of power grid equipment fault prediction model based on machine learning and high-performance computing  
  by Ke Ning, Yang Bai, Bin Hou, Jin Zhang, Shanshan Gao, Xingting Liu 
  Abstract: This paper optimised the power grid equipment fault prediction model based on ML and high-performance computing, analysed the application of high-performance computers in online fault prediction and designed the overall structure of the mechanical equipment fault prediction and detection model. It explains the data classification and prediction in ML, describes how to establish prediction models and applies different ML algorithms to power grid equipment fault prediction models. Through experiments, comparing the optimisation effects of varying ML algorithms on power grid equipment fault prediction models, it was found that the Least Squares Support Vector Machine (LS-SVM) prediction algorithm has the highest accuracy and the best optimisation effect on power grid equipment fault prediction models. After using the LS-SVM prediction algorithm, the entire fault prediction time has been shortened.
  Keywords: predictive model; power grid equipment failure; high-performance computing; machine learning; LS-SVM prediction algorithm.
  DOI: 10.1504/IJGEI.2025.10071746
   
  
• Utilisation of fuzzy logic control in self-healing of power systems: improved fuzzy C-means clustering method  
  by Zhongqiang Zhou, Jianwei Ma, Yusong Huang, Ling Liang, Zhiqi Chen 
  Abstract: This paper used a fuzzy logic controller based on an enhanced fuzzy C-means clustering method to address the current issues with power system self-healing. The augmented fuzzy C-means algorithm based on learning automata (LAFCMA) was produced by analysing the conventional fuzzy C-means algorithm (FCMA) and integrating and referencing the research methodologies of other academics. A Fuzzy Logic Controller (FLC) was built using LAFCMA, which enhanced the controllers clustering impact by analysing and grouping power data. The accuracy of using LAFCMA-FLC for power system fault detection was above 96.73%, and the average accuracy of detecting 20 fault points was 97.95%. The fuzzy logic control based on the improved fuzzy C-means clustering method had broad application prospects and research value in the self-healing of power systems. By achieving self healing of power systems, the frequency of manual intervention and maintenance can be reduced, thereby reducing operation and maintenance costs and improving economic benefits.
  Keywords: fuzzy C-means algorithm; fuzzy C-means algorithm based on learning automata; self-healing of power system; fuzzy logic control; load forecasting.
  DOI: 10.1504/IJGEI.2025.10071795
   
  
• Dynamic response analysis and control of power systems by combining differential equation models with power data  
  by Jing Wang, Qiong Wang, Fangjun Li, Zhenfen Zhang, Jianyong Gao 
  Abstract: In response to the complex situation where the accuracy of describing the dynamic behaviour of the power system (PS) is low and the control strategy is difficult to cope with dynamic changes, this article combines differential equation models and power data to study the dynamic response analysis and control of the PS. Firstly, electricity data was collected from a certain power company, and the data was cleaned and standardised. Then, differential equation models were constructed for the generators, loads, and transmission lines in the PS, describing their dynamic behaviour and discretising the model. The MPC (Model Predictive Control) algorithm was used to define the objective function, set constraints, and solve the problem. The combination of differential equation modelling and MPC algorithm has improved the accuracy of describing the dynamic behaviour of the PS, and has good adaptability to complex dynamic changes, ensuring the safe and stable operation of the PS.
  Keywords: power system; power data; differential equation model; MPC algorithm; dynamic response analysis and control; description accuracy.
  DOI: 10.1504/IJGEI.2025.10071818
   
  
• Differential equation modelling in dynamic modelling and prediction of power index data  
  by Xiaomeng Liu, Zuohu Chen, Wenlei Shi, Zhenguo Peng, Wenxia Li 
  Abstract: This paper combined differential equation models to study the dynamic modelling and prediction of power index data. Firstly, it collected electricity data from a certain power company from 1 June to 15 June 2023, and selected electricity index data to construct a partial differential equation model. The boundary and initial conditions were defined, and nonlinear effects and coupling relationships were established. Then, the least squares method and gradient descent algorithm were combined to estimate the parameters of the partial differential equation model. It then solved the model using the fourth-order Runge-Kutta method. Finally, based on the results of differential equations, this paper introduced the LSTM model for the dynamic prediction of power indicators. It demonstrates that the combined differentiable equation model-LSTM method performs well in power indicator prediction, enhances prediction accuracy, and guarantees steady power system operation. It also demonstrates a good capacity for capturing data changes in indicators.
  Keywords: power system; power indicators; differential equation model; dynamic modelling; forecast accuracy; LSTM model.
  DOI: 10.1504/IJGEI.2025.10071819