Template-Type: ReDIF-Article 1.0
Author-Name: Hao Huo
Author-X-Name-First: Hao
Author-X-Name-Last: Huo
Author-Name: Chao Kang
Author-X-Name-First: Chao
Author-X-Name-Last: Kang
Author-Name: Ningrui Li
Author-X-Name-First: Ningrui
Author-X-Name-Last: Li
Author-Name: Bingbing Liu
Author-X-Name-First: Bingbing
Author-X-Name-Last: Liu
Author-Name: Huifeng Zhang
Author-X-Name-First: Huifeng
Author-X-Name-Last: Zhang
Title: Online identification method of power grid load sensitivity based on adaptive Kalman filter
Abstract:
In order to overcome the problems of large covariance, high deviation, and low sensitivity in traditional load sensitivity identification methods for power grids, this 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.
Journal: Int. J. of Energy Technology and Policy
Pages: 110-124
Issue: 1/2
Volume: 20
Year: 2025
Keywords: adaptive Kalman filter; grid load; sensitivity; online identification.
File-URL: http://www.inderscience.com/link.php?id=144300
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Handle: RePEc:ids:ijetpo:v:20:y:2025:i:1/2:p:110-124

Template-Type: ReDIF-Article 1.0
Author-Name: Yurong Pan
Author-X-Name-First: Yurong
Author-X-Name-Last: Pan
Author-Name: Chaoyong Jia
Author-X-Name-First: Chaoyong
Author-X-Name-Last: Jia
Title: A time series-based method for predicting electricity demand in industrial parks
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.
Journal: Int. J. of Energy Technology and Policy
Pages: 95-109
Issue: 1/2
Volume: 20
Year: 2025
Keywords: time series; industrial parks; electricity demand forecasting; seasonal ARIMA model; trend elimination method.
File-URL: http://www.inderscience.com/link.php?id=144301
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Handle: RePEc:ids:ijetpo:v:20:y:2025:i:1/2:p:95-109

Template-Type: ReDIF-Article 1.0
Author-Name: Yiying Zhu
Author-X-Name-First: Yiying
Author-X-Name-Last: Zhu
Author-Name: Ruiqian Su
Author-X-Name-First: Ruiqian
Author-X-Name-Last: Su
Author-Name: Junyue Wu
Author-X-Name-First: Junyue
Author-X-Name-Last: Wu
Author-Name: Qian Lv
Author-X-Name-First: Qian
Author-X-Name-Last: Lv
Author-Name: Huapeng Shan
Author-X-Name-First: Huapeng
Author-X-Name-Last: Shan
Title: Multiple fault diagnosis method for regional power grid based on DTS simulation system
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.
Journal: Int. J. of Energy Technology and Policy
Pages: 89-94
Issue: 1/2
Volume: 20
Year: 2025
Keywords: DTS simulation system; regional power grid; multiple faults; fault diagnosis; two phase short circuit; three phase short circuit.
File-URL: http://www.inderscience.com/link.php?id=144302
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Handle: RePEc:ids:ijetpo:v:20:y:2025:i:1/2:p:89-94

Template-Type: ReDIF-Article 1.0
Author-Name: Xiongfeng Ye
Author-X-Name-First: Xiongfeng
Author-X-Name-Last: Ye
Author-Name: Zhiguo Zhou
Author-X-Name-First: Zhiguo
Author-X-Name-Last: Zhou
Author-Name: Yizhi Cheng
Author-X-Name-First: Yizhi
Author-X-Name-Last: Cheng
Title: A detection method for electricity theft behaviour in low-voltage power stations: multi-source data fusion
Abstract:
In order to improve the accuracy of electricity theft detection in low-voltage substations, a method of electricity 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 electricity theft behaviour is used as input to design the electricity 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.
Journal: Int. J. of Energy Technology and Policy
Pages: 36-50
Issue: 1/2
Volume: 20
Year: 2025
Keywords: multi-source data fusion; low voltage power substation area; stealing electricity; behavioural detection.
File-URL: http://www.inderscience.com/link.php?id=144303
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Handle: RePEc:ids:ijetpo:v:20:y:2025:i:1/2:p:36-50

Template-Type: ReDIF-Article 1.0
Author-Name: Tao Han
Author-X-Name-First: Tao
Author-X-Name-Last: Han
Author-Name: Juan Li
Author-X-Name-First: Juan
Author-X-Name-Last: Li
Author-Name: Rujie Liu
Author-X-Name-First: Rujie
Author-X-Name-Last: Liu
Author-Name: Yu Ruan
Author-X-Name-First: Yu
Author-X-Name-Last: Ruan
Title: New energy charging pile installation layout method based on terminal load demand fusion processing
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.
Journal: Int. J. of Energy Technology and Policy
Pages: 51-65
Issue: 1/2
Volume: 20
Year: 2025
Keywords: new energy vehicles; charging pile; charging power; retrofitting demand; charging queue time; charging distance; sparrow search algorithm.
File-URL: http://www.inderscience.com/link.php?id=144304
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Handle: RePEc:ids:ijetpo:v:20:y:2025:i:1/2:p:51-65

Template-Type: ReDIF-Article 1.0
Author-Name: Yang Yang
Author-X-Name-First: Yang
Author-X-Name-Last: Yang
Author-Name: Meng Li
Author-X-Name-First: Meng
Author-X-Name-Last: Li
Author-Name: Hongxia Wang
Author-X-Name-First: Hongxia
Author-X-Name-Last: Wang
Author-Name: Minguan Zhao
Author-X-Name-First: Minguan
Author-X-Name-Last: Zhao
Author-Name: Shuyang Ma
Author-X-Name-First: Shuyang
Author-X-Name-Last: Ma
Title: A method for monitoring and early warning of meteorological disasters in cross regional large power grid based on Doppler radar data
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, various meteorological disasters based on the characteristics of Doppler radar data for different meteorological disasters are classified and identified. Finally, based on the monitored meteorological disaster results, the level of meteorological disasters in cross regional large power grid, and accurately generate meteorological disaster warning results for cross regional large power grid is analysed. 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.
Journal: Int. J. of Energy Technology and Policy
Pages: 1-19
Issue: 1/2
Volume: 20
Year: 2025
Keywords: Doppler radar data; cross regional large power grid; meteorological disasters; monitoring and warning.
File-URL: http://www.inderscience.com/link.php?id=144305
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Handle: RePEc:ids:ijetpo:v:20:y:2025:i:1/2:p:1-19

Template-Type: ReDIF-Article 1.0
Author-Name: Lide Zhou
Author-X-Name-First: Lide
Author-X-Name-Last: Zhou
Author-Name: Zheng Liu
Author-X-Name-First: Zheng
Author-X-Name-Last: Liu
Author-Name: Siyan Pang
Author-X-Name-First: Siyan
Author-X-Name-Last: Pang
Author-Name: Jingyi Wei
Author-X-Name-First: Jingyi
Author-X-Name-Last: Wei
Title: A multi-objective optimisation configuration method for photovoltaic access microgrid energy storage capacity based on improved genetic algorithm
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.
Journal: Int. J. of Energy Technology and Policy
Pages: 66-79
Issue: 1/2
Volume: 20
Year: 2025
Keywords: genetic operators; extreme learning machine; ELM; genetic operators;  configuration constraints; multi-objective optimisation.
File-URL: http://www.inderscience.com/link.php?id=144306
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Handle: RePEc:ids:ijetpo:v:20:y:2025:i:1/2:p:66-79

Template-Type: ReDIF-Article 1.0
Author-Name: Chenghao Xu
Author-X-Name-First: Chenghao
Author-X-Name-Last: Xu
Author-Name: Baichong Pan
Author-X-Name-First: Baichong
Author-X-Name-Last: Pan
Author-Name: Weixian Che
Author-X-Name-First: Weixian
Author-X-Name-Last: Che
Title: Hierarchical classification of dynamic carbon emission factors based on improved support vector machine
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. Firstly, 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%, the highest factor comprehensiveness can reach 95%, and the practical application effect is good.
Journal: Int. J. of Energy Technology and Policy
Pages: 163-181
Issue: 1/2
Volume: 20
Year: 2025
Keywords: improving support vector machine; dynamic carbon emission factors; hierarchical classification; OC-SVM algorithm; LMDI decomposition method; Kernel function.
File-URL: http://www.inderscience.com/link.php?id=144307
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Handle: RePEc:ids:ijetpo:v:20:y:2025:i:1/2:p:163-181

Template-Type: ReDIF-Article 1.0
Author-Name: Linghan Xu
Author-X-Name-First: Linghan
Author-X-Name-Last: Xu
Author-Name: Jiaqi Zhang
Author-X-Name-First: Jiaqi
Author-X-Name-Last: Zhang
Author-Name: Qiuhui Zhang
Author-X-Name-First: Qiuhui
Author-X-Name-Last: Zhang
Author-Name: Xinxing Zhou
Author-X-Name-First: Xinxing
Author-X-Name-Last: Zhou
Author-Name: Shanshan Yu
Author-X-Name-First: Shanshan
Author-X-Name-Last: Yu
Title: A peak carbon emission prediction method for enterprises based on IoT blockchain and grey neural network
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.
Journal: Int. J. of Energy Technology and Policy
Pages: 144-162
Issue: 1/2
Volume: 20
Year: 2025
Keywords: carbon trading model; grey prediction model; internet of things blockchain; BP neural network; cumulative reduction.
File-URL: http://www.inderscience.com/link.php?id=144308
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Handle: RePEc:ids:ijetpo:v:20:y:2025:i:1/2:p:144-162

Template-Type: ReDIF-Article 1.0
Author-Name: Lijuan Deng
Author-X-Name-First: Lijuan
Author-X-Name-Last: Deng
Author-Name: Qilin Wu
Author-X-Name-First: Qilin
Author-X-Name-Last: Wu
Author-Name: Yunfei Ao
Author-X-Name-First: Yunfei
Author-X-Name-Last: Ao
Author-Name: Yuanxiang Yu
Author-X-Name-First: Yuanxiang
Author-X-Name-Last: Yu
Title: Distributed generation planning method for active distribution network based on frog leaping algorithm
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.
Journal: Int. J. of Energy Technology and Policy
Pages: 125-143
Issue: 1/2
Volume: 20
Year: 2025
Keywords: active distribution network; distributed generation planning; source load side fluctuation characteristics; leapfrog algorithm.
File-URL: http://www.inderscience.com/link.php?id=144309
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Handle: RePEc:ids:ijetpo:v:20:y:2025:i:1/2:p:125-143

Template-Type: ReDIF-Article 1.0
Author-Name: Taofang Xia
Author-X-Name-First: Taofang
Author-X-Name-Last: Xia
Author-Name: Shian Zhan
Author-X-Name-First: Shian
Author-X-Name-Last: Zhan
Author-Name: Yajuan Zhou
Author-X-Name-First: Yajuan
Author-X-Name-Last: Zhou
Author-Name: Hua Lin
Author-X-Name-First: Hua
Author-X-Name-Last: Lin
Author-Name: Yueqian Lan
Author-X-Name-First: Yueqian
Author-X-Name-Last: Lan
Title: Capacity optimisation configuration of active distribution network under distributed photovoltaic access
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.
Journal: Int. J. of Energy Technology and Policy
Pages: 20-35
Issue: 1/2
Volume: 20
Year: 2025
Keywords: distributed photovoltaic access; active distribution network; capacity optimisation configuration; particle swarm optimisation.
File-URL: http://www.inderscience.com/link.php?id=144310
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Handle: RePEc:ids:ijetpo:v:20:y:2025:i:1/2:p:20-35

Template-Type: ReDIF-Article 1.0
Author-Name: N. Remya Mol
Author-X-Name-First: N. Remya
Author-X-Name-Last: Mol
Author-Name: E.K. Bindumol
Author-X-Name-First: E.K.
Author-X-Name-Last: Bindumol
Author-Name: Rajesh Kuttappan Achary
Author-X-Name-First: Rajesh Kuttappan
Author-X-Name-Last: Achary
Author-Name: V. Mini
Author-X-Name-First: V.
Author-X-Name-Last: Mini
Title: Optimally distributed generator placement and phasor measurement unit integration for enhanced distribution system performance: a particle swarm optimisation-based approach
Abstract:
An optimally sized and placed distributed generator (DG) plays a vital role in the distribution sector to provide voltage stability by injecting sufficient active power to the access point. The phasor measurement unit (PMU) has a major role in the microgrid to monitor and control the grid effectively. It assesses the voltage magnitude and phase angle at its location along with the current magnitude and phase angle of the adjacent branch. This paper focuses on the particle swarm optimisation (PSO) algorithm to determine the optimal position and size of DG and binary-based PSO (BPSO) to identify optimal locations for PMUs to guarantee full observability of the network. The proposed method was tested on an IEEE 33-bus radial system. Results showed that power loss is reduced by 48.7% and 57.6% with single and two DGs, respectively, with penetration levels of 67.74% and 43.97%. The number of PMUs reduced with a cost reduction of 7%.
Journal: Int. J. of Energy Technology and Policy
Pages: 182-207
Issue: 1/2
Volume: 20
Year: 2025
Keywords: microgrid; MG; distributed generator; DG; phasor measurement unit; PMU; particle swarm optimisation; PSO.
File-URL: http://www.inderscience.com/link.php?id=144314
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Handle: RePEc:ids:ijetpo:v:20:y:2025:i:1/2:p:182-207

Template-Type: ReDIF-Article 1.0
Author-Name: Shan Gao
Author-X-Name-First: Shan
Author-X-Name-Last: Gao
Author-Name: Xinran Zhang
Author-X-Name-First: Xinran
Author-X-Name-Last: Zhang
Author-Name: Lihong Gao
Author-X-Name-First: Lihong
Author-X-Name-Last: Gao
Author-Name: Yancong Zhou
Author-X-Name-First: Yancong
Author-X-Name-Last: Zhou
Title: A novel residential electricity load prediction algorithm based on hybrid seasonal decomposition and deep learning models
Abstract:
Residential electricity load prediction is of great significance for power system planning. With the increasing complexity and uncertainty of the power grid, traditional prediction models still have insufficient accuracy and neglect seasonal changes. In this paper, a data-driven multi-scale hybrid prediction model for residential electricity load is proposed, which integrates a convolutional neural network (CNN), long short-term memory (LSTM), and attention mechanism. The seasonal decomposition was applied to extract seasonal patterns of the electricity consumption data. The hybrid model integrates the parallel processing capability of CNN and the long time-series modelling capability of LSTM to capture the spatial-temporal characteristics of electricity load accurately. The attention mechanism is employed to calculate the critical weight to enhance the prediction accuracy dynamically. Finally, detailed comparison experiments show that the proposed hybrid model outperformed state-of-the-art algorithms. The MAPE of the hourly and daily prediction results of the proposed model are 2.36% and 0.76%, respectively.
Journal: Int. J. of Energy Technology and Policy
Pages: 1-23
Issue: 5
Volume: 20
Year: 2025
Keywords: electricity consumption prediction; deep learning; convolutional neural network; CNN; long short-term memory; LSTM; attention mechanism.
File-URL: http://www.inderscience.com/link.php?id=146888
File-Format: text/html
File-Restriction: Open Access
Handle: RePEc:ids:ijetpo:v:20:y:2025:i:5:p:1-23

Template-Type: ReDIF-Article 1.0
Author-Name: Long Li
Author-X-Name-First: Long
Author-X-Name-Last: Li
Author-Name: Mingyue Liu
Author-X-Name-First: Mingyue
Author-X-Name-Last: Liu
Author-Name: Qingtao Wu
Author-X-Name-First: Qingtao
Author-X-Name-Last: Wu
Author-Name: Xinpeng Zhang
Author-X-Name-First: Xinpeng
Author-X-Name-Last: Zhang
Author-Name: Zhankun Liu
Author-X-Name-First: Zhankun
Author-X-Name-Last: Liu
Author-Name: Yulun Zhang
Author-X-Name-First: Yulun
Author-X-Name-Last: Zhang
Title: Optical fibre distributed sensing system based on high-power ultra-narrow linewidth laser
Abstract:
This study aims to explore the fibre distributed sensing system based on high-power ultra-narrow linewidth single-mode fibre lasers to achieve monitoring and protection of important areas such as borders, military restricted areas, power plants, nuclear power plants, prisons, etc. The study uses ultra-narrow linewidth single-mode fibre lasers as light sources, and uses the OTDR interference mechanism to interfere with the Rayleigh scattered light of each part of the optical cable to achieve sensing. The test results show that the maximum deviation between the transmission length and the centre length of the light source in each band does not exceed 0.06 nm, and the centre length offset of the two measurements is less than 0.01 nm. The system has good safety performance, high sensitivity, a wide range of applications and low cost advantages, and can complete precise measurements.
Journal: Int. J. of Energy Technology and Policy
Pages: 17-32
Issue: 6
Volume: 20
Year: 2025
Keywords: optical fibre distributed sensing system; fibre laser; high power; ultra-narrow linewidth.
File-URL: http://www.inderscience.com/link.php?id=149449
File-Format: text/html
File-Restriction: Open Access
Handle: RePEc:ids:ijetpo:v:20:y:2025:i:6:p:17-32

Template-Type: ReDIF-Article 1.0
Author-Name: Chanjuan Li
Author-X-Name-First: Chanjuan
Author-X-Name-Last: Li
Title: Construction of a multiscale renewable energy economic evaluation system considering low-carbon economy and energy storage integration
Abstract:
The study collected data on RE and LCE in China from 2014 to 2020, selecting RE utilisation, ecological environment, economic development (ED), and residents' quality of life as primary indicators, further refined into eight evaluation sub-indicators. Using hierarchical analysis, the relative weights of these indicators were calculated, with carbon emission intensity receiving the highest weight of 0.18. Using the constructed evaluation system, the study analysed LCE development trends in China from 2019 to 2023, as well as regional differences between northern and southern China in 2021. Results indicated that while China's overall LCE development level improved from 2019 to 2023, the growth rate declined significantly, from 92% in 2010 to 17% in 2023. The proposed RE economic evaluation system effectively assesses LCE development and highlights the need for balanced regional development to reduce disparities between northern and southern China.
Journal: Int. J. of Energy Technology and Policy
Pages: 3-16
Issue: 6
Volume: 20
Year: 2025
Keywords: evaluation system; renewable energy; low-carbon economy; LCE; analytic hierarchy process; AHP; China's economic development; energy storage integration; multi-scale energy.
File-URL: http://www.inderscience.com/link.php?id=149451
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File-Restriction: Open Access
Handle: RePEc:ids:ijetpo:v:20:y:2025:i:6:p:3-16

Template-Type: ReDIF-Article 1.0
Author-Name: Yunlong Zhao
Author-X-Name-First: Yunlong
Author-X-Name-Last: Zhao
Author-Name: Man Hu
Author-X-Name-First: Man
Author-X-Name-Last: Hu
Title: Intelligent monitoring method for variable working conditions in intelligent manufacturing systems under digital twin
Abstract:
This study presents a digital twin (DT)-based intelligent monitoring method for intelligent manufacturing systems (IMS) under variable working conditions. A five-dimensional DT architecture is designed to enable real-time data acquisition, synchronisation, and virtual-physical mapping. Linear regression is applied for data processing, enhancing monitoring accuracy. By constructing a DT virtual model of the production line, the system achieves dynamic monitoring and early anomaly detection. Experimental comparisons with genetic algorithm-based monitoring show the DT approach improves average monitoring accuracy by 8.47%, demonstrating its superior reliability, real-time performance, and potential for enhancing production quality and safety in complex, changing environments.
Journal: Int. J. of Energy Technology and Policy
Pages: 51-73
Issue: 6
Volume: 20
Year: 2025
Keywords: intelligent monitoring; variable working condition; digital twin; intelligent manufacturing system; virtual model.
File-URL: http://www.inderscience.com/link.php?id=149452
File-Format: text/html
File-Restriction: Open Access
Handle: RePEc:ids:ijetpo:v:20:y:2025:i:6:p:51-73

Template-Type: ReDIF-Article 1.0
Author-Name: Shanshan Li
Author-X-Name-First: Shanshan
Author-X-Name-Last: Li
Author-Name: Yu Shen
Author-X-Name-First: Yu
Author-X-Name-Last: Shen
Title: Application of an infrared sensor based on edge computing in power electronics technology
Abstract:
This paper examines the application of infrared sensors based on edge computing in power electronics technology to improve the fault detection efficiency of power equipment. An infrared sensor is a type of sensor that uses the outside of the red line for data processing. It has high sensitivity and can control the operation of the driver. The communication network between the edge and the power server detection is established by applying edge operation, which realises the intelligent scheduling of power equipment, reduces the detection delay, and enhances the security of power electronics. This work lays a foundation for the development of intelligent power electronics. This study provides support for the application of infrared sensors in the detection of power electronic equipment. In the future, the development of intelligent power systems can be promoted by improving the response speed and accuracy and combining artificial intelligence and edge computing.
Journal: Int. J. of Energy Technology and Policy
Pages: 33-50
Issue: 6
Volume: 20
Year: 2025
Keywords: edge computing; infrared sensor; power electronic technology; power fault detection; power equipment.
File-URL: http://www.inderscience.com/link.php?id=149453
File-Format: text/html
File-Restriction: Open Access
Handle: RePEc:ids:ijetpo:v:20:y:2025:i:6:p:33-50

Template-Type: ReDIF-Article 1.0
Author-Name: Liudong Zhang
Author-X-Name-First: Liudong
Author-X-Name-Last: Zhang
Author-Name: Bo Wang
Author-X-Name-First: Bo
Author-X-Name-Last: Wang
Author-Name: Yanliu Li
Author-X-Name-First: Yanliu
Author-X-Name-Last: Li
Author-Name: Xinying Zhu
Author-X-Name-First: Xinying
Author-X-Name-Last: Zhu
Author-Name: Zhizhu Qu
Author-X-Name-First: Zhizhu
Author-X-Name-Last: Qu
Author-Name: Haoyu Zhong
Author-X-Name-First: Haoyu
Author-X-Name-Last: Zhong
Title: Methods of realising grid frequency modulation by using adiabatic electromagnetic compressed-air energy storage
Abstract:
To address the issue of increased frequency fluctuations in the power grid following the integration of a high proportion of renewable energy sources, this paper develops a frequency regulation (FM) control model for an adiabatic electromagnetic compressed air energy storage system (CAES). Simulation results show that when the disturbance intensity is 0.02 p.u., for strategy 1, the frequency deviation (FD) of the proposed method is ±0.011 p.u., with a response time of only 0.477 seconds; for strategy 2, the frequency modulation (FM) accuracy of the proposed method is ±0.031 p.u., with a response time of 0.79 seconds. The research results show that an adiabatic electromagnetic compressed air energy storage system can effectively improve the frequency regulation accuracy and response speed of the power grid, providing a stable and efficient frequency regulation method for power grids with a high penetration of renewable energy sources.
Journal: Int. J. of Energy Technology and Policy
Pages: 90-118
Issue: 6
Volume: 20
Year: 2025
Keywords: grid frequency modulation; frequency control strategy; adiabatic electromagnetic compressed-air energy storage; frequency stability; energy storage and release optimisation; short-time Fourier transform; STFT.
File-URL: http://www.inderscience.com/link.php?id=149455
File-Format: text/html
File-Restriction: Open Access
Handle: RePEc:ids:ijetpo:v:20:y:2025:i:6:p:90-118

Template-Type: ReDIF-Article 1.0
Author-Name: Wei Hou
Author-X-Name-First: Wei
Author-X-Name-Last: Hou
Title: Construction of urban sewage treatment environment model based on energy and ecological restoration concept
Abstract:
This study aimed to apply a mathematical model of USTS based on the ecological restoration concept to optimise the urban sewage treatment process and improve treatment efficiency and environmental protection level. By analysing the structure and treatment process of USTS, a mathematical model of the sewage treatment system was established. The back propagation (BP) neural networks algorithm was used to train and optimise the model to improve its prediction and control capabilities. The effectiveness and reliability of the model were verified through practical application and validation. The experiment in this article showed that the reliability of the mathematical model of USTS based on ecological restoration concept was between 95%-98%, and the effectiveness of the mathematical model of USTS based on ecological restoration concept was between 93%-97%. The results show that the mathematical model of urban sewage treatment systems incorporating ecological restoration concepts can significantly reduce energy consumption and operating costs.
Journal: Int. J. of Energy Technology and Policy
Pages: 74-89
Issue: 6
Volume: 20
Year: 2025
Keywords: urban sewage treatment; ecological restoration; mathematical models; sustainable utilisation; water quality improvement; back propagation; BP.
File-URL: http://www.inderscience.com/link.php?id=149456
File-Format: text/html
File-Restriction: Open Access
Handle: RePEc:ids:ijetpo:v:20:y:2025:i:6:p:74-89