International Journal of Energy Technology and Policy (11 papers in press)

Regular Issues

• Online identification method of power grid load sensitivity based on adaptive Kalman filter  
  by Hao Huo, Chao Kang, Ningrui Li, Bingbing Liu, Huifeng Zhang 
  Abstract: In order to overcome the problems of large covariance, high deviation, and low sensitivity in traditional load sensitivity identification methods for power grids. The paper proposes an online identification method for power grid load sensitivity based on adaptive Kalman filtering. Firstly, construct a power grid load sensitivity identification architecture consisting of data layer, service layer, and application layer. Secondly, construct a discrete Kalman filter model, determine the time update formula, and design a Kalman filter. Then, the adaptive Kalman filter is used to verify the load node status of the power grid and identify the load data. Finally, based on the data identification results, the relay protection setting value is calculated and used for adaptive online identification of power grid load sensitivity. The experimental results show that the covariance of the method proposed in this paper is stable at 0.01, the sensor acquisition information error remains below 1%, the sensitivity index is high, and it has good robustness.
  Keywords: adaptive Kalman filter; grid load; sensitivity; online identification.
  DOI: 10.1504/IJETP.2025.10066453
   
  
• A time series-based method for predicting electricity demand in industrial parks  
  by Yurong Pan, Chaoyong Jia 
  Abstract: In order to accurately predict electricity demand and improve the economy and security of the power system, a time series based method for predicting electricity demand in industrial parks is proposed. Firstly, the missing values of electricity consumption data are estimated using a seasonal exponential smoothing model. Then, the missing values are supplemented and the time series is decomposed. For each decomposed part, a suitable model is selected for fitting. For long-term trends, use univariate linear regression prediction method. For seasonal changes, choose seasonal ARIMA model for modelling. For periodic changes, use Fourier analysis method for prediction. For irregular changes, combine univariate linear regression prediction method and binary linear regression prediction method for prediction. Finally, the GARCH model is introduced to test the error sequence. The experimental results show that the proposed method improves the accuracy of the prediction model and has practical application value.
  Keywords: time series; industrial parks; electricity demand forecasting; seasonal ARIMA model; trend elimination method.
  DOI: 10.1504/IJETP.2025.10066454
   
  
• Multiple fault diagnosis method for regional power grid based on DTS simulation system  
  by Yiying Zhu, Ruiqian Su, Junyue Wu, Qian Lv, Huapeng Shan 
  Abstract: In order to solve the problems of high false alarm rate and long diagnostic time in existing power grid fault diagnosis methods, this paper studies a regional power grid multiple fault diagnosis method based on DTS simulation system. Firstly, use the DTS simulation system to obtain regional power grid status data. Then, effectively classify multiple faults in the power grid. Finally, based on the collected regional power grid status data and fault classification results, an ELM model is used to diagnose multiple faults in the regional power grid. According to the test results of fault diagnosis effectiveness, the false alarm rate of the proposed method has never exceeded 20%, and the average diagnostic time is 7.34 seconds, which is always less than 10 seconds. This indicates that the proposed method has high accuracy, fast efficiency, and good diagnostic effect in diagnosing multiple faults in the power grid.
  Keywords: DTS simulation system; regional power grid; multiple faults; fault diagnosis; two phase short circuit; three phase short circuit.
  DOI: 10.1504/IJETP.2025.10066455
   
  
• A detection method for electricity theft behavior in low-voltage power stations: multi-source data fusion  
  by Xiongfeng Ye, Zhiguo Zhou, Yizhi Cheng 
  Abstract: In order to improve the accuracy of electric theft detection in low-voltage substation, a method of electric theft detection based on multi-source data fusion is proposed and designed. Firstly, a structure design of power load data collection is designed to obtain multi-source power stealing behaviour data in low-voltage power station area. Then, K-means algorithm is used to extract the features of multi-source power theft data, and feature superposition method is used to complete the feature fusion of multi-source power theft data in low-voltage power station area. Finally, the integrated characteristic vector of electric theft behaviour is used as input to design the electric theft detection based on improved support vector machine (SVM) algorithm. The experimental results show that the method proposed in this paper can greatly improve the detection accuracy, and is better than the comparison method.
  Keywords: multi-source data fusion; low voltage power substation area; stealing electricity; behavioural detection.
  DOI: 10.1504/IJETP.2025.10066456
   
  
• New energy charging pile installation layout method based on terminal load demand fusion processing  
  by Tao Han, Juan Li, Rujie Liu, Yu Ruan 
  Abstract: In order to shorten the charging queue time and average charging distance, the paper designs a new energy charging pile installation layout method based on terminal load demand fusion processing. First, combined with the number of new energy vehicles and battery parameters, the terminal load demand is integrated, and the additional installation demand is calculated according to the charging power of the charging pile. Then, with the goal of the shortest charging queue time, the shortest average charging distance and the lowest running cost, the installation layout model is constructed. Finally, the sparrow search algorithm is introduced to obtain the optimal location of individual sparrows, and the optimal installation layout scheme is obtained. After applying this method, the queuing time of the user for charging is less than 25 min, and the maximum average charging distance of the user to drive is only 0.86 km, indicating that the method is effective.
  Keywords: new energy vehicles; charging pile; charging power; retrofitting demand; charging queue time; charging distance; sparrow search algorithm.
  DOI: 10.1504/IJETP.2025.10066457
   
  
• A method for monitoring and early warning of meteorological disasters in cross regional large power grid based on Doppler radar data  
  by Yang Yang, Meng Li, Hongxia Wang, Minguan Zhao, Shuyang Ma 
  Abstract: In order to improve the accuracy of meteorological disaster warning and shorten the monitoring and warning time, a cross regional large power grid meteorological disaster monitoring and warning method based on Doppler radar data is proposed. Firstly, based on the characteristics and requirements of the cross regional large power grid, atmospheric data obtained from Doppler radar is utilised. Secondly, classify and identify various meteorological disasters based on the characteristics of Doppler radar data for different meteorological disasters. Finally, based on the monitored meteorological disaster results, analyse the level of meteorological disasters in cross regional large power grid, and accurately generate meteorological disaster warning results for cross regional large power grid. The experimental results show that the proposed method can accurately monitor and warn of meteorological disasters in cross regional large power grid, and effectively shorten the time for monitoring and warning of meteorological disasters.
  Keywords: Doppler radar data; cross regional large power grid; meteorological disasters; monitoring and warning.
  DOI: 10.1504/IJETP.2025.10066458
   
  
• A multi-objective optimisation configuration method for photovoltaic access microgrid energy storage capacity based on improved genetic algorithm  
  by Lide Zhou, Zheng Liu, Siyan Pang, Jingyi Wei 
  Abstract: This study proposes to improve the genetic algorithm, and based on the improved genetic algorithm to complete the optimisation method design of photovoltaic microgrid energy storage configuration. Considering the goal of jointly establishing a new type of energy microgrid, a mathematical model of energy storage is established. Then, determine the configuration constraints of multiple micro energy grid energy storage stations, including battery operation constraints and micro energy grid operation constraints. In the final stage, the genetic algorithm was enhanced using extreme learning machine (ELM), and this improved algorithm was then utilised to address the design model mentioned earlier. The design simulation experiment proves the advanced nature of the proposed method. The experimental results show that after applying the proposed method, the energy efficiency can reach 99.21%, the power balance is only 0.01, and the voltage stability is 0.95, which can guarantee the stability of photovoltaic micro-grid operation to the maximum extent and meet its energy storage needs.
  Keywords: genetic algorithm; extreme learning machine; ELM; genetic operators; configuration constraints; multi-objective optimisation.
  DOI: 10.1504/IJETP.2025.10066459
   
  
• Hierarchical classification of dynamic carbon emission factors based on improved support vector machine  
  by Chenghao Xu, Baichong Pan, Weixian Che 
  Abstract: In order to solve the problems of low factor coverage and low factor comprehensiveness existing in the traditional hierarchical classification method of carbon emission factors, a hierarchical classification method of dynamic carbon emission factors based on improved support vector machine is proposed. Collect dynamic carbon emission data and pre-process the data, calculate the contribution of each factor to the change of total carbon emission according to LMDI decomposition method, and determine the weight of each dynamic carbon emission factor. Introduce kernel function into OC-SVM algorithm in improved support vector machine, map dynamic carbon emission factors to high-dimensional space, and update the optimal hyperplane position with disturbance factors to realise hierarchical classification of dynamic carbon emission factors. The experimental results show that the factor coverage rate of the proposed method is above 90%, and the highest factor comprehensiveness can reach 95%, and the practical application effect is good.
  Keywords: improving support vector machine; dynamic carbon emission factors; hierarchical classification; OC-SVM algorithm; LMDI decomposition method; Kernel function.
  DOI: 10.1504/IJETP.2025.10066467
   
  
• A peak carbon emission prediction method for enterprises based on IoT blockchain and grey neural network  
  by Linghan Xu, Jiaqi Zhang, Qiuhui Zhang, Xinxing Zhou, Shanshan Yu 
  Abstract: In order to solve the problems of low accuracy and poor carbon emission potential of traditional enterprise carbon emission peak prediction methods, this paper proposes an enterprise carbon emission peak prediction method based on a combination model of the internet of things (IoT), blockchain, and grey neural network. Firstly, use IoT technology to obtain carbon emission data of enterprises. Secondly, use the blockchain carbon trading model to analyse the factors affecting corporate carbon emissions. Then, with the help of a grey prediction model, the predicted carbon emissions of the enterprise are obtained through cumulative reduction. Finally, the grey neural network combination model is used to predict the peak carbon emissions of enterprises by taking cumulative emissions as input. The experimental results show that the accuracy of the carbon emission peak prediction method in this article can reach 99.9%, which can effectively improve the prediction effect of enterprise carbon emission peaks.
  Keywords: carbon trading model; grey prediction model; internet of things blockchain; BP neural network; cumulative reduction.
  DOI: 10.1504/IJETP.2025.10066468
   
  
• Distributed generation planning method for active distribution network based on frog leaping algorithm  
  by Lijuan Deng, Qilin Wu, Yunfei Ao, Yuanxiang Yu 
  Abstract: In order to improve the fault tolerance of the planned power grid and reduce the transmission loss rate, a frog leaping algorithm based distributed generation planning method for active distribution network is proposed. Considering the source load side fluctuation characteristics, a probability model is constructed to obtain the source load characteristics. Taking this as the input, probabilistic power flow calculation is carried out, and a bi-level programming model is constructed with opportunity constraints. Determine the upper power source location according to the probabilistic power flow calculation results. In order to improve the planning performance, the intermediate and acceleration factors are introduced to improve the frog leaping algorithm, so as to solve the lower level power configuration and realise the distributed generation planning of active distribution network. The results show that the proposed method has strong fault tolerance ability after planning, and the transmission loss rate is 5.8%. The longest planning time is 11.8 s.
  Keywords: active distribution network; distributed generation planning; source load side fluctuation characteristics; leapfrog algorithm.
  DOI: 10.1504/IJETP.2025.10066469
   
  
• Capacity optimisation configuration of active distribution network under distributed photovoltaic access  
  by Taofang Xia, Shian Zhan, Yajuan Zhou, Hua Lin, Yueqian Lan 
  Abstract: After the integration of distributed photovoltaics, the active distribution network is prone to significant voltage fluctuations and high failure rates. Therefore, a new method for optimising the capacity of the active distribution network is studied. Firstly, under distributed photovoltaic access, an active distribution network power model is constructed from three aspects: wind power model, photovoltaic model, and energy storage system model. Secondly, with the goal of minimising the annual cost and daily operating cost, construct an active distribution network capacity optimisation configuration objective function. Finally, the particle swarm optimisation algorithm is used to obtain the optimal solution of the objective function and complete the active distribution network capacity optimisation configuration. The analysis of experimental results shows that under the optimised configuration of the method proposed in this article, the fault rate of the active distribution network is significantly reduced, and the voltage stability is improved.
  Keywords: distributed photovoltaic access; active distribution network; capacity optimisation configuration; particle swarm optimisation.
  DOI: 10.1504/IJETP.2025.10066470