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Energy-based Directional Pilot Protection for Distribution Networks with IBDGs Considering Unmeasured Load Switching

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CSEE Journal of power and Energy systems 2024年第1期10卷 139-150页

作者： Chenghan Zhou Guibin Zou Shuo Zhang Xuhui Wen Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong UniversityJinan 250061China

Inverter-based distributed generations(IBDGs)are the main approach to utilizing clean energy in distribution networks(DNs).Compared with synchronous source,fault response of IBDG is very *** a result,in DNs with high penetration of IBDGs,legacy protection schemes will no longer be *** this paper,an energy-based directional pilot protection scheme suitabie for DNs with IBDGs is *** scheme considers the large range of phase angle caused by IBDG integration and uses improved energy polarity criterion to determine fault *** addition,magnitude of energy is used to distinguish between faults and load switching to overcome maloperation of directional pilot protection caused by internal unmeasured load *** proposed scheme first uses local measured information to determine fault direction sign and then exchanges the direction sign with the remote *** scheme does not require high-bandwidth communication and strict data synchronization,so it can be implemented at a low ***,simulation studies verify effectiveness of the proposed scheme.

关键词： Distribution network energy polarity inverterbased distributed generation pilot protection unmeasured load

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Secondary Frequency control Considering Optimized power Support From Virtual power Plant Containing Aluminum Smelter Loads Through VSC-HVDC Link

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Journal of Modern power systems and Clean Energy 2023年第1期11卷 355-367页

作者： Peng Bao Wen Zhang Yuxi Zhang Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong UniversityJinan 250061China

The growing number of renewable energy replacing conventional generators results in a loss of the reserve for frequency control in power systems,while many industrial power grids often have excess energy supply due to abundant wind and solar energy *** paper proposes a secondary frequency control(SFC)strategy that allows industrial power grids to provide emergency high-voltage direct current(HVDC)power support(EDCPS)for emergency to a system requiring power support through a voltage source converter(VSC)HVDC *** architecture including multiple model predictive control(MPC)controllers with periodic communication is designed to simultaneously obtain optimized EDCPS capacity and minimize adverse effects on the providing power support(PPS)***,a model of a virtual power plant(VPP)containing aluminum smelter loads(ASLs)and a high penetration of wind power is established for the PPS *** flexibility and controllability of the VPP are improved by the demand response of the *** uncertainty associated with wind power is considered by chance *** effectiveness of the proposed strategy is verified by simulation results using the data of an actual industrial power grid in Inner Mongolia,*** DC voltage of the VSCs and the DC in the potlines of the ASLs are also investigated in the simulation.

关键词： Secondary frequency control power support voltage source converter(VSC) high-voltage direct current(HVDC) model predictive control(MPC) virtual power plant(VPP) demand response

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Continuous Feature Representation of Fault Location Based on Electrical Distance for Generalizable Data-Driven Dynamic Security Assessment

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IEEE Transactions on power systems 2025年第4期40卷 2930-2942页

作者： Qi, Hang Li, Changgang Liu, Yutian Sun, Runjia Yan, Jiongcheng Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Jinan250061 China

Pre-fault dynamic security assessment (DSA) of power systems needs to consider different fault locations. Existing data-driven DSA methods lack generalizable fault location features, failing to accurately assess unlearned fault locations, i.e., fault locations not in the training set. To address this issue, this paper proposes a continuous feature representation method of fault location based on electrical distance, aiming to construct a pre-fault DSA model generalizable to unlearned fault locations. Firstly, fault location is represented continuously as an electrical coordinate with the electrical distance to preselected reference nodes. Then, the representation ability of electrical coordinates to fault locations is optimized by reference node selection and metric learning. Finally, with electrical coordinates and steady-state features as input, a hybrid DSA model composed of convolutional and radial basis function networks is constructed to assess system angular and voltage dynamics with different fault locations. The generalization performance of the proposed DSA method on unlearned fault locations is verified on New England 39-bus system and a provincial system of China. © 1969-2012 IEEE.

关键词： Fault location power system dynamics Impedance Security Encoding Measurement Steady-state power grids Circuit faults Vectors

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system Strength Assessment Based on Multi-task Learning

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CSEE Journal of power and Energy systems 2024年第1期10卷 41-50页

作者： Baoluo Li Shiyun Xu Huadong Sun Zonghan Li Lin Yu Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong UniversityJinan 250014China China Electric Power Research Institute Beijing 100192China

Increase in permeability of renewable energy sources(RESs)leads to the prominent problem of voltage stability in power system,so it is urgent to have a system strength evaluation method with both accuracy and practicability to control its access scale within a reasonable ***,a hybrid intelligence enhancement method is proposed by combining the advantages of mechanism method and data driven ***,calculation of critical short circuit ratio(CSCR)is set as the direction of intelligent enhancement by taking the multiple renewable energy station short circuit ratio as the quantitative ***,the construction process of CSCR dataset is proposed,and a batch simulation program of samples is developed accordingly,which provides a data basis for subsequent ***,a multi-task learning model based on progressive layered extraction is used to simultaneously predict CSCR of each RESs connection point,which significantly reduces evaluation error caused by weak *** performance and anti-noise performance of the proposed method are verified on the CEPRI-FS-102 bus system,which provides strong technical support for real-time monitoring of system strength.

关键词： Critical short circuit ratio hybrid intelligence enhancement multi-task learning system strength

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Robust Consensus Based Secondary control for Microgrids Considering Communication Delay and Noises: A Kalman Filter Based Approach

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IEEE Transactions on Smart Grid 2025年第4期16卷 2791-2802页

作者： Zhao, Qi Zhang, Wen Zhang, Tingting Chen, Jian Ye, Hua Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education School of Electrical Engineering Jinan250061 China Shandong University Shandong Research Institute of Industrial Technology Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education School of Electrical Engineering Jinan250061 China

To achieve the frequency and voltage regulation as well as active and reactive power sharing, the consensus-based secondary controls of microgrids rely on communication network to exchange signals between neighbor inverters, which may be affected by the communication delay and noises. Unlike the existing methods that focus solely on communication delay, this paper proposes a Kalman filter-based robust consensus control to address the communication delay and noises comprehensively. Based on the discrete differential equation of the signal transmitted via communication network, the multi-step prediction model of the delayed signal is firstly built by considering the prediction errors. The state space model is then constructed by taking the predicted signals at each step as the state variables while the delayed and noise-involved signal and its differential value are seen as the measurements. A Kalman filter is further designed to comprehensively handle the communication delay, noises and prediction errors to estimate the true signal generated from neighbor inverters. With the estimated signals of all neighbor inverters, the consensus control is performed to coordinate the grid-following and grid-forming inverters within the microgrid. The effectiveness of the proposed method is demonstrated with simulations and experiments performed on the modified IEEE-33 bus-based microgrid. © 2010-2012 IEEE.

关键词： Kalman filters

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Multi-stage Provincial power Expansion Planning and Multi-market Trading Equilibrium

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Journal of Modern power systems and Clean Energy 2024年第5期12卷 1652-1665页

作者： Guangsheng Pan Zhongfan Gu Yuanyuan Sun Kaiqi Sun Wei Gu the School of Electrical Engineering Southeast UniversityNanjingChina Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education School of Electrical EngineeringShandong UniversityJinanChina

Decarbonization in the power sector is one of the critical factors in achieving carbon neutrality,and the top-level design needs to be carried out from the perspective of power planning.A multi-stage provincial power expansion planning(PPEP)model is proposed to simulate the power expansion planning at different stages of the power systems rich in renewable energy *** model covers 16 types of power supply,considering macro-policy demands and micro-operation *** stand-alone capacity aggregation model for coal-based units within the PPEP model allows for accurate construction and retirement with different stand-alone ***,the soft dynamic time warping(soft-DTW)based K-medoids technique is adopted to generate typical scenarios for balancing the model accuracy and solution ***,a multi-market trading equilibrium(MMTE)mechanism is proposed to address the differences in the levelized cost of energy between the coal-based and renewable-based units by participating in energy and ancillary service *** the coalbased units take on the task of providing ancillary services from renewable-based units in the ancillary service market,the MMTE mechanism can effectively equalize the profits of both by having renewable-based units purchase ancillary services from coal-based units and pay for them,thus improving the motivation of coal-based units.A case study in Xinjiang province,China,verifies the effectiveness of the planning results of the PPEP model and the profit equilibrium realization of the MMTE mechanism.

关键词： power expansion planning market design ancillary service market renewable energy generation energy market

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Optimizing battery deployment: Aging trajectory prediction enabling homogenous performance grouping

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Journal of Energy Chemistry 2025年第1期100卷 565-577页

作者： Shuquan Wang Feng Gao Zhan Ma Hao Tian Yusen Zhang School of Control Science and Engineering Shandong UniversityJinan 250061ShandongChina Key Lab of Power System Intelligent Dispatch and Control of Ministry of Education Shandong UniversityJinan 250061ShandongChina

As battery deployments in electric vehicles and energy storage systems grow, ensuring homogeneous performance across units is crucial. We propose a multi-derivative imaging fusion(MDIF) model, employing advanced imaging and machine learning to predict battery aging trajectories from minimal initial data, thus facilitating effective performance grouping before deployment. Utilizing a derivative strategy and Gramian Angular Difference Field for dimensional enhancement, the MDIF model uncovers subtle predictive features from discharge curve data after only ten cycles. The architecture includes a parallel convolutional neural network with lateral connections to enhance feature integration and *** on a self-developed dataset, the model achieves an average root-mean-square error of 0.047 Ah and an average mean absolute percentage error of 1.60%, demonstrating high precision and *** robustness is further validated through transfer learning on two publicly available datasets, adapting with minimal retraining. This approach significantly reduces the testing cycles required, lowering both time and costs associated with battery testing. By enabling precise battery behavior predictions with limited data, the MDIF model optimizes battery utilization and deployment strategies, enhancing system efficiency and sustainability.

关键词： Lithium-ion battery Battery prognostics Fusion model Convolutional neural network Transfer learning

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Integrated dispatching Model for power system Incorporating Multiple Modules 5

Integrated Dispatching Model for Power System Incorporating ...

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5th International Conference on Advances in Electrical, Electronics and Computing Technology, EECT 2025

作者： Xu, Chang Wang, Mingqiang Wang, Zihan Zhang, Yizhe Wang, Jialiang Sun, Fengbin Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

ISBN: (纸本)9798331541545

The widespread integration of renewable energy presents significant challenges for power system scheduling. Traditional power system scheduling is mainly based on the concept of "coordinating on a multi-time-level and gradually refining at each level," using a sequential scheduling framework. However, with the growing integration of renewable energy, the uncertainty in power systems has significantly increased, leading to severe issues such as conflicts in the coordination between different modules. This paper first introduces the scheduling framework of the traditional sequential scheduling model. Then, addressing the problems of module coordination and insufficient flexibility, an integrated multi-time-scale scheduling model is proposed. This model combines day-ahead and intraday scheduling models, optimizing decision variables including unit start/shut states, operation points, and reserves simultaneously to maximize the collaborative potential between different scheduling modules. After linearization, the proposed model is converted into a mixed-integer linear programming (MILP) model, which can be solved directly using commercial optimization solvers. Finally, case analyses demonstrate the effectiveness and superiority of the proposed integrated model compared to the traditional sequential scheduling model. © 2025 IEEE.

关键词： Integer programming

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Optimal Scheduling Considering Multiple Reserves Based on Active power Curtailment Operation of Wind power 5

Optimal Scheduling Considering Multiple Reserves Based on Ac...

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5th International Conference on Advances in Electrical, Electronics and Computing Technology, EECT 2025

作者： Wang, Zihan Wang, Mingqiang Xu, Chang Wang, Jialiang Zhang, Yizhe Liang, Caishuai Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

ISBN: (纸本)9798331541545

Under the current general trend of the power industry's transformation towards clean energy, wind power generation, as an important form of renewable energy utilization, is steadily increasing its proportion in the power system. However, the inherent volatility and uncertainty of wind power bring numerous challenges to the stable operation of the power system. Firstly, based on the traditional unit commitment model, this paper comprehensively considers the capacity reserve and ramping reserve of the power system, and constructs a unit commitment model that considers the supply and demand constraints of both types of reserves simultaneously. Then, on the basis of this model, an in-depth and comprehensive discussion is carried out, focusing on the key operation strategy of the active power curtailment operation of wind power. This not only helps to improve the power system's ability to accommodate wind power, but also enables a better response to the challenges of volatility and uncertainty brought by wind power to the power system, thus ensuring the safe, stable and economic operation of the power system. Finally, the model is transformed into a mixed-integer linear programming model that can be directly called by the commercial solver CPLEX, and the effectiveness of the model is verified by a modified IEEE 26-node distribution network. © 2025 IEEE.

关键词： Integer programming

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Fluctuation Classification and Feature Factor Extraction to Forecast Very Short-Term Photovoltaic Output powers

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CSEE Journal of power and Energy systems 2025年第2期11卷 661-670页

作者： Mao Yang Xiaoxuan Shen Dawei Huang Xin Su Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin 132012China

In recent years, a spurt of photovoltaic power generation has brought certain impact on stability of the power system, which puts forward higher requirements on accuracy of photovoltaic power prediction. Therefore, this paper proposes a hybrid power prediction model based on fluctuation classification and feature factor extraction. First, based on fluctuation characteristics of photovoltaic power, fluctuation classification is applied to forecast power before the day, and weather is divided into complex fluctuation types and simple types. Then, parallel factor algorithm is used to reduce prediction model redundancy, which can reduce high-dimensional numerical weather prediction feature matrix to extract relevant features. Finally, the Long Short-Term Memory (LSTM) deep learning model is used to forecast very short-term photovoltaic power. The proposed hybrid model is compared with other methods, and photovoltaic data from several sites are selected for comparison and validation in this paper. Simulation results show that very short-term prediction method of photovoltaic power proposed in this paper can significantly improve prediction accuracy.

关键词： Deep neural networks fluctuation classification PARAFAC photovoltaic generation very short-term forecasting

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