International Journal of Global Energy Issues (12 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.
  
  

Special Issue on: The Analysis of Energy Efficiency Perspectives and Policies towards Sustainable Development

• Robust and efficient hybrid autoencoder-ADAM (HAA) algorithm for analysing anomalies in Indian electricity consumption data  
  by M. Ravinder, Vikram Kulkarni 
  Abstract: Anomaly detection in electricity-consumption data plays a crucial role in ensuring the reliability and stability of modern smart-grid systems. In this study, we propose the Hybrid Autoencoder-ADAM (HAA) algorithm, specifically designed for anomaly detection in Indian electricity consumption data from 2014 to 2023, considering distinct seasonal patterns. The HAA algorithm combines autoencoders with adaptive optimisation (ADAM) to effectively capture and reconstruct normal consumption patterns. Comparative analysis show that the HAA algorithm outperforms Long Short-Term Memory (LSTM) and XGBoost in accuracy and robustness for anomaly detection. It demonstrates adaptability across different seasons, regions and periods, offering valuable insights for advancing smart grid analytics and energy conservation strategies. Future research includes hyper-parameter optimisation and exploring ensemble methods to enhance its real-world applicability in operational smart-grid scenarios. The HAA algorithm presents a promising approach for large-scale smart grid anomaly detection, emphasising its efficiency and effectiveness in improving energy management and resource optimisation.
  Keywords: anomaly detection; HAA algorithm; smart grid; electricity consumption; LSTM; XGBoost; seasonal patterns.
  DOI: 10.1504/IJGEI.2024.10062681
   
  
• A data-driven energy consumption prediction method for building electrical equipment based on data-driven  
  by Xiulan Yin, Huiting Liang 
  Abstract: A data-driven energy consumption prediction method for building electrical equipment based on data-driven is proposed to address the issues of unstable prediction results and low accuracy in existing methods. Multiple sensors are selected to collect voltage, power, temperature and humidity data of electrical equipment. The mean filling method is used to fill in the missing values of the collected data. The K-means algorithm is used to detect anomalies in the filled data, identify and remove abnormal clusters or samples. Based on the data processing results, particle swarm optimisation algorithm is used to train energy consumption data, construct an energy consumption prediction model and achieve energy consumption detection through this model. The experimental results show that the highest prediction accuracy of this method is 98.5%, and the difference between the predicted results and actual energy consumption is small, indicating that the stability and robustness of this method are strong.
  Keywords: data-driven; electrical equipment; energy consumption prediction; multiple sensors; K-means algorithm; particle swarm optimisation.
  DOI: 10.1504/IJGEI.2024.10063315
   
  
• Optimisation of solar thermal photovoltaic heating systems for buildings considering stability  
  by Hongwei Jia 
  Abstract: In order to improve its power generation efficiency and output power, and ensure the sustainability and stability of the system, the optimisation study of building solar thermal photovoltaic heating system considering stability is carried out this time. This method first analyses the operating principle of photovoltaic heating systems used in buildings, then constructs an output power model of the photovoltaic heating system, and fully considers the stability of the heating system operation to design an objective function. Finally, based on the improved Particle Swarm Optimisation with Adaptive Elite Strategy Algorithm (PSO-AESA), the output power model of the photovoltaic heating system is solved, and realise optimal control of photothermal photovoltaic heating systems for buildings. The experimental results show that the total power generation efficiency and output power of the heating system are higher after the proposed method is used to optimise the control. The system control is better than the comparison method, and has high-application value.
  Keywords: stability; photothermal photovoltaics; heating system; control optimisation; source load energy storage.
  DOI: 10.1504/IJGEI.2024.10063316
   
  
• Capacity configuration method for new energy storage system based on segmented peak shaving  
  by Zesen Li, Bingjie Li, Guojing Liu 
  Abstract: To overcome the problems of low accuracy in capacity estimation, low balancing degree and low utilisation rate in traditional methods, a capacity configuration method for new energy storage system based on segmented peak shaving is proposed. The battery's internal resistance and terminal voltage signals of the new energy storage system are taken as inputs, and the capacity estimation is the output. A capacity estimation model based on an improved fuzzy neural network is established. The capacity configuration objective function is constructed by combining segmented peak shaving and economic cost. Hybrid frog-leaping algorithm is used to obtain the optimal parameters for segmented peak shaving and economic cost through population initialisation, position updates and frog swarm sorting to determine the optimal configuration scheme. Experimental results show that the average accuracy of capacity estimation using this method is 97.31%, the maximum balancing degree is 0.98 and the minimum utilisation rate is only 90.9%.
  Keywords: segmented peak shaving; new energy storage system; capacity configuration; improved fuzzy neural network; hybrid frog-leaping algorithm.
  DOI: 10.1504/IJGEI.2024.10063318
   
  
• Intelligent scheduling optimisation strategy for comprehensive energy systems  
  by Kun Yan, Hongwei Dong, Tao Han, Jin Zhu 
  Abstract: In order to improve the output of integrated energy system and reduce the high-operation cost, a coordinated optimal scheduling method based on improved genetic algorithm was proposed. Aiming at the problems of poor output and high operating cost after the application of existing energy system scheduling methods, an integrated coordinated optimal scheduling method based on improved genetic algorithm is proposed. Firstly, the structure of the integrated energy system is analysed, and then the output of wind turbine, incentive demand response, gas turbine, etc., are analysed, and the output model of the integrated energy system is built. Finally, the optimal scheduling model of the energy system is established, and the improved genetic algorithm is used to solve it, and the optimal scheduling of the integrated energy system is realised. The experimental results show that the system output is the best, the operation cost is the lowest, and it can meet the operation requirements of the integrated energy system.
  Keywords: improved genetic algorithm; integrated energy system; coordination and optimisation; scheduling algorithm.
  DOI: 10.1504/IJGEI.2024.10063317
   
  
• Collaborative planning method for integrated energy system based on improved compressed sensing algorithm  
  by Yan Li, Xiaojun Zhu, Qingshan Wang, Qiong Wang, Na Li, Yinzhe Xie, Zhu Chen 
  Abstract: Aiming at the problems of high-energy cost, high-energy consumption and environmental pollution in existing methods, a collaborative planning method for integrated energy systems based on improved compressed sensing algorithm is proposed. Build a comprehensive energy system architecture that includes modules for energy production, storage and conversion, transmission and distribution, consumption and management. Establish a collaborative planning mathematical model based on the characteristics of the architecture, set three objective functions: total energy consumption, total cost and total pollutant emissions, and set corresponding energy consumption, cost and environmental protection constraints. The improved compressed sensing algorithm is used for the integrated energy system collaborative planning, and the optimal solution is output, which is the optimal integrated energy system collaborative planning scheme. The experimental results show that the proposed method effectively reduces energy costs and energy consumption, and significantly reduces carbon dioxide emissions, indicating that the proposed method has practical value.
  Keywords: improved compressed sensing algorithm; integrated energy system; search for updates; constraint condition.
  DOI: 10.1504/IJGEI.2024.10063314
   
  
• A demand side energy scheduling method for energy-saving buildings based on priority weights  
  by Yining Sun 
  Abstract: In order to improve the voltage stability of energy scheduling and shorten scheduling time, a priority weighted demand side energy scheduling method for energy-saving buildings is proposed. Firstly, analyse the demand side incentive response behaviour of energy-saving buildings through load superposition. Secondly, construct priority decision-making indicators for energy scheduling. Construct a hesitant fuzzy matrix and calculate the membership score, and calculate the correlation coefficient between priority indicators. Finally, based on the correlation coefficient of the indicators, priority weights are calculated, and on the basis of normalised calculation of priority weights, an energy scheduling function is constructed to transform the scheduling engineering problem into a mathematical problem, solving the scheduling function to complete the demand side energy scheduling of energy-saving buildings. The experimental results show that the proposed method can shorten the time of energy scheduling, and the maximum scheduling time of the proposed method does not exceed 4 minutes.
  Keywords: priority weight; energy-saving building; demand side; energy scheduling.
  DOI: 10.1504/IJGEI.2024.10063319
   
  
• An evaluation of low-carbon collaborative emission reduction effect of new energy wind and solar power generation based on set pair analysis  
  by Peidong He, Xiaojun Li, Shijiong Yuan, Keli Liu, Xiaoxiao Yang 
  Abstract: In order to overcome the problems of poor accuracy and poor evaluation of emission reduction effects, this article introduces set pair analysis to evaluate the low-carbon collaborative emission reduction effect of new energy solar power generation. Firstly, an evaluation index system is constructed. Then, the subjective weight of the indicators is calculated using the intuitive fuzzy analytic hierarchy process, the objective weight is calculated using the entropy weight method and the comprehensive weight value of the indicators is calculated using game theory. Finally, the emission reduction effect evaluation function is constructed using set pair analysis theory, and the evaluation level is determined using confidence to obtain the final evaluation result. The results show that the evaluation accuracy of the method in this paper can reach 99.5%, and this method can accurately evaluate the low-carbon collaborative emission reduction effect of new energy wind power generation.
  Keywords: set pair analysis; new energy; wind and solar power generation; low carbon; collaborative emission reduction; effect evaluation.
  DOI: 10.1504/IJGEI.2024.10063320
   
  
• High proportion new energy grid voltage fluctuation tracking method based on double layer master slave game  
  by Yiming Peng, Mingdong Guan, Dongxu Li, Jia Wang, Hanzhi Zhang 
  Abstract: In order to reduce the error of voltage fluctuation tracking and shorten the tracking time, a high proportion new energy medium voltage power grid voltage fluctuation tracking method based on a double layer master slave game is proposed. Firstly, construct a two-layer master-slave game model, analyse the voltage regulation strategy of the high proportion new energy grid using the utility function, and collect operational data of the new energy grid. Secondly, wavelet transform is used to analyse the sudden changes in voltage signals and identify the sources of voltage fluctuations in the medium voltage power grid. Finally, the Hilbert Huang transform is used to decompose the voltage fluctuation source signal, obtain the instantaneous fluctuation frequency of the power grid voltage and complete the tracking of voltage fluctuations. The results show that the voltage fluctuation tracking error of the proposed method is significantly reduced, with a maximum error of only 1.3 V.
  Keywords: double layer master slave game; high proportion; new energy grid; voltage fluctuation tracking; medium voltage distribution network.
  DOI: 10.1504/IJGEI.2024.10063322
   
  
• Optimisation method for economic dispatch of wind power connected to microgrid considering carbon emission  
  by Hui Li, Xin Wen, Zhengyang Peng, Jing Zhang, Shitao Chen 
  Abstract: In order to improve the economic dispatching effect of distribution network, the optimisation method of economic dispatching of wind power connected to microgrid considering carbon emissions is studied. Firstly, taking the minimum operating cost and environmental cost of wind power connected to microgrid as the design goal, and fully considering equality constraints and inequality constraints, an economic scheduling optimisation model of wind power connected to microgrid is constructed. Then, the improved particle swarm optimisation algorithm is used to solve the economic scheduling optimisation model of wind power connected to microgrid, and the economic scheduling optimisation is realised. Finally, the practicability of the proposed method is proved by experiments. The experimental results show that this method has strong calculation ability and good iterative performance in model solving, and the application of the proposed method can get more ideal economic dispatching effect and has high application value.
  Keywords: carbon emissions; wind power access; microgrid; economic dispatch optimisation; particle swarm optimisation; abandoned air volume.
  DOI: 10.1504/IJGEI.2024.10063321