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A review of demand response capability assessment based on CPSS perspective
Kairui ZHANG, Hao MING, and Ciwei GAO

Date posted: 4-22-2024
DOI: https://doi.org/10.19781/j.issn.1673-9140.2024.01.003

The combination of cyber-physical-social systems and demand response assessment is reviewed. First, the history and framework of cyber-physical systems are analyzed. Then, summaries are provided for the existing research on demand response, including the significance, classification, and evaluation methods of demand response potential assessment, as well as the data sources for demand response capability assessment, including questionnaire surveys and operational data collection. In terms of the combination of cyber-physical-social systems and demand response, the physical domain, information domain and social domain foundation of demand response are analyzed respectively, and the corresponding modeling methods and research contents are introduced. Finally, prospects are provided for market assessment mechanisms, rapid simulation and modeling technologies, and demand response management under integrated energy systems.

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Active damping control based on virtual resistance for stand‑alone DC microgrid
Xin TANG, Shuai WANG, Zhen LI, and Xin HUANG

Date posted: 7-23-2025
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.03.021

Due to the presence of a large number of constant power loads (CPLs) in a stand‑alone DC microgrid, the damping of the DC microgrid is reduced, leading to oscillations and even significant drops in the DC bus voltage. To address this issue, an active damping method is proposed to improve system stability. The damping of the converter is increased to suppress the resonant peak by connecting a virtual resistance in parallel at the port of the energy storage converter without adding extra sensors. The method for designing the active damping parameters is provided, and the effect of the proposed parallel virtual resistance method on system stability is analyzed using the frequency stability criterion. The performance of this method is also compared with that of the conventional damping method. Experimental results verify that the parallel virtual resistance method can effectively improve the stability of a stand‑alone DC microgrid system in a wide frequency band.

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Research on photovoltaic power forecasting method based on time series large model TimeGPT
Wenyu SHI, Zhenyi ZHANG, and Dechang YANG

Date posted: 10-22-2025
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.04.014

Currently，various statistical and machine models have been widely applied to photovoltaic （PV） power forecasting，but low forecasting accuracy is common with scarce PV historical data.Therefore，a time generative pre-trained transformer （TimeGPT ） is introduced into the short-term forecasting of PV power.Firstly，a time series large model is constructed based on a large-scale and diverse time series dataset of 100 billion data points （such as finance，traffic，banking，network traffic，weather， energy，and healthcare ）.Then，TimeGPT is fine-tuned using a small amount of PV power historical data to adapt to the data distribution and characteristics related to PV power forecasting.TimeGPT is simulated in the PV dataset with user privacy and compared with existing statistical and machine models.By taking case 1 as an example，the mean absolute error （MAE） of TimeGPT is reduced compared with the comparison models when the forecasting step is 1 h.Finally，the conditions for TimeGPT application and the direction of improvement are summarized for its application in new electric power systems.

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Monitoring algorithm of non -intrusive industrial loads based on improved GAF -inception network
Hui LI, Jiajie GAO, Rongjun XI, Siying CHEN, Yiqun HUANG, and Zefan SHEN

Date posted: 10-22-2025
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.04.010

In view of the existing problems of low recognition accuracy and weak generalization ability in non-intrusive load monitoring using low-frequency industrial data，a non-intrusive industrial load monitoring algorithm based on the combination of Gramian angular field （GAF） and an improved Inception network structure is proposed.The one-dimensional time series information of power is converted into two-dimensional data with temporal characteristics based on GAF.An improved Inception network is constructed，which leverages its sparse connection characteristics to perform multi-scale extraction of multi-parameter load characteristics，thereby reducing model complexity，improving computational efficiency，and achieving high-accuracy identification of industrial loads across multiple scenarios.Finally，the proposed algorithm is validated using the industrial appliance identification dataset （IAID）.The research results show that the proposed algorithm can effectively improve monitoring accuracy up to 94.48% and enhance computational efficiency by more than 8% compared to the existing Inception network.

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Research review on microgrid of integrated photovoltaic‑energy storage‑charging station
Qin YAN and Guoxiang YU

Date posted: 4-22-2024
DOI: https://doi.org/10.19781/j.issn.1673-9140.2024.01.001

To address the challenges posed by the large-scale integration of electric vehicles and new energy sources on the stability of power system operations and the efficient utilization of new energy, the integrated photovoltaic-energy storage-charging model emerges. The synergistic interaction mechanisms and optimized control strategies among its individual units have also become key issues urgently needing resolution in smart grid development. Due to the characteristics of integrated generation, load, and storage, mutual complementarity of supply and demand, and flexible dispatch, the photovoltaic-energy storage-charging (PV-ESS-EV) integrated station micro-grid (ISM) mode, incorporating "PV- PV-ESS-EV + intelligent building" features, has become a focal point for energy conservation, carbon reduction, and energy transition in China. In consideration of the challenges faced by the operational mode of microgrids, such as the strong uncertainty of distributed energy sources and the unclear interaction mechanisms during islanded and grid-connected operation, various aspects of the PV-ESS-EV ISM are reviewed, including its unit modules, key technologies, and operational states. Additionally, the current research status of PV-ESS-EV is summarized while future development trends are discussed, and the challenges that need to be addressed are examined. The research findings have important theoretical and practical implications for exploring the regulatory potential of various demand-response resources under economic incentives, ensuring the reliability of power grid supply, and serving as valuable references for both theory and practice.

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Summary of research on electricity theft behavior detection methods
Yu XIAO, Zhi YE, Rui HUANG, Mouhai LIU, Rui XIA, and Yunpeng GAO

Date posted: 11-6-2023
DOI: https://doi.org/10.19781/j.issn.1673-9140.2023.04.001

The non‑technical losses caused by electricity theft in the power system have always been a pressing issue for power grid companies to urgently address. With the deployment of a large number of smart meters in the power grid, the use of user‑side data collected by the power metering automation system to accurately detect electricity theft has attracted widespread attention from researchers and power grid companies. Firstly, the basic classification of users' electricity stealing behavior, evaluation indicators and existing electricity theft detection data sets are introduced. Then, from the four aspects of grid state analysis, machine learning, game theory and hardware, the existing detection methods of electricity theft behavior are comprehensively sorted, analyzed and compared, and the basic ideas, advantages and disadvantages of each method are summarized. Finally, the current challenges in the field of electricity theft behavior detection are deeply analyzed, and a prospective outlook on the focus of future research work is provided.

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Equivalent inertia estimati on of electric power system considering virtual inertia of wind power
Pin gping LUO and Jie CHEN

Date posted: 10-22-2025
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.04.001

Large-scale wind power i ntegration weakens the inertia level of the electric power system and brings challenges to the frequency stability of the electric power system.It is urgent to accurately estimate the inertia level of the system to ensure its safe operation.Therefore，an equivalent inertia estimation method of an electric power system considering the virtual inertia of wind power is proposed.Firstly，the inertia response of the synchronous generator and wind turbine is analyzed，and the principle of system equivalent inertia estimation is explained.Secondly，the Box-Jenkins model is used to dynamically model the power generation device.Based on the active-frequency disturbance data at the connecting bus of the unit，the bias compensation recursive least squares algorithm with forgetting factors is used to identify the parameters in the model.On this basis，the inertia constants contained in the model parameters are extracted，and the equivalent inertia of the system is calculated.Finally，a simulation analysis is conducted using examples to verify the effectiveness of the proposed method.The simulation results show that the proposed method can accurately estimate the equivalent inertia of the system under different disturbance types and different wind power penetration rates.

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Research on optimization of electric vehicle charging scheduling in rainy season based on peak valley difference electricity price model
Zhikun LUO, Wenhao YE, Yaohong CHEN, Qin YAN, Shusheng WU, and Jinxin WANG

Date posted: 10-22-2025
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.04.011

To address the issues of increased energy consumption of electric vehicles，grid load，and peak valley difference caused by high humidity and temperature in the rainy season in Southern China，a charging price model based on peak valley difference is proposed to stimulate electric vehicles to participate in demand response，so as to stabilize the peak valley difference of grid load and improve the economic benefits of electric vehicle users.Firstly，the Monte Carlo simulation is used to analyze and predict the disordered charging load of electric vehicles，considering factors such as high temperature and humidity during the rainy season in Southern China.Then，a peak-valley differential charging price model is built to minimize the peak valley difference of the power grid and the user charging cost.Lastly，simulation analysis is conducted on the model using the MATLAB application.The research shows that the proposed model considering the high humidity and temperature of the rainy season in Southern China is more accurate.The model can reduce the peak valley difference rate，improve the economic benefits of users，and realize the smoothing and optimization of grid load.The research provides a reference for a new electric vehicle charging scheduling.

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Robust control of output voltage of DAB converter by super -twisted sliding mode method
Jianlin LI, Laixin GUO, and Penghui HAN

Date posted: 10-22-2025
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.04.021

The traditional sliding mode control （SMC） used for the isolated dual active bridge （DAB） converter is robust and can improve the dynamic performance of nonlinear systems.To address the disadvantages of buffeting and decreasing tracking performance，a super-twisted sliding mode control （ST-SMC） method is proposed to enhance the output voltage regulation ability and robust control ability of the converter.The ST-SMC method is adopted，and the technical advantages of the sliding mode controller are combined to effectively eliminate the buffeting problem.During the design of the sliding mode controller，the average mathematical model of the DAB converter under single-phase shift control is fully considered，and the design process is effectively simplified.The stability and robustness of the direct current （DC） output voltage of the DAB converter are improved effectively，and accurate reference voltage tracking is realized.In addition，the method provides robust control over parametric uncertainties，and the steady-state output voltage of the DC converter contains fewer ripple components than traditional SMC and PI controllers.Finally，based on the TMS 320F28335 digital controller，a 1kW DAB converter prototype is installed to verify the effectiveness of this new control method.

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Traveling wave fault location method of transmission line based on CPO‑VMD
Jun ZHOU, Zhenguo QU, and Ziang ZHAN

Date posted: 6-5-2025
DOI: https://doi.org/10.19781/j.issn.1673-9140.2025.02.004

To address the low detection accuracy of traveling wave heads for long-distance transmission line faults as well as the location deviation caused by the clock asynchronization of fault detection devices and the uncertainty of traveling wave velocity, this paper proposes a new fault location method for transmission lines based on improved variational mode decomposition (VMD) combined with Teager-Kaiser energy operator (TKEO) to detect the fault traveling wave head and the double-ended traveling wave location formula independent of timing and wave velocity. Firstly, the crested porcupine optimizer (CPO) is used to optimize VMD parameters. Then the line mode and zero mode components of the fault traveling wave are decomposed by VMD, and the initial wave heads of the components are detected by TKEO. Finally, according to the difference in time to reach the measuring point between the initial wave heads of the line mode and zero mode components, the proportional relationship between the fault distance and the difference distance is written, and the double-ended traveling wave location formula independent of timing and wave velocity is obtained. A 220 kV transmission line fault simulation model is built using Matlab/Simulink. The simulation results show that the method has good applicability under noise, different ground resistances, and different fault types, and the accuracy of the location results is high.