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Stackelberg Game for Bilateral Transactions between Energy Storage and Wind Farms Considering the Day-Ahead Electricity Market

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Energy Engineering 2025年第5期122卷 1645-1668页

作者： Xingxu Zhu Guiqing Zhao Gangui Yan JunhuiLi Hongda Dong Chenggang Li Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin132012China Electric Power Research Institute of State Grid Jilin Electric Power Co. Ltd.Jilin130021China

The participation of wind farms in the former energymarket faces challenges such as power fluctuations and energy storage construction *** this end,this paper proposes a joint energy storage operation scheme for multiple wind farms based on a leasingmodel,which assistswind farms in bidding for participation in the former energy market through leasing services,thereby enhancing energy storage efficiency and maximizing economic *** this paper,based on theWeibull probability distribution to portray the uncertainty of wind power,and considering the lifetime capacity loss caused by charging and discharging of energy storage,we construct a bilateral transaction model aiming at maximizing the multi-objective revenue of wind farms and shared energy *** trading strategy is designed based on the Stackelberg game framework and solved jointly by the improved genetic algorithm and interior *** exploring the effects of different lease price intervals on the overall systemperformance,and analyzing the systemstate undermultiple charging and discharging *** results show that a reasonable lease price range can significantly improve the energy storage system utilization and wind *** new ideas to enhance the economic benefits of wind farms and promote the application of shared energy storage,and promotes the wide application of shared energy storage systems.

关键词： Energy storage wind power uncertainty energy market stackelberg game leasing price

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A Short-Term power Prediction Method for Wind Farm Cluster Based on Time Dynamic Graph Network and Improved Transformer Model

A Short-Term Power Prediction Method for Wind Farm Cluster B...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Chao Han Zihang Chen Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China Jiangmen Power Supply Bureau of Guangdong Power Grid Co. Ltd Jiangmen China

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

In recent years, wind power has gradually replaced thermal power as the main source of new energy generation. Wind power prediction in advance can help formulate scheduling strategies and improve the stability of the power system. With the advent of the Industry 4.0 and the widespread application of artificial intelligence technology, artificial algorithms have been extensively used in the field of wind power prediction. To further explore the spatiotemporal correlation of every wind farm in the wind farm cluster, a temporal dynamic graph network is used to divide the wind farm farm into several sub-clusters. Then, the KAN-BiLSTM-Transformer algorithm is used to predict wind power of each wind sub-cluster. Finally, the wind power of each sub-cluster is added to attain the final predicted power of the wind farm cluster. After verification, the RMSE and MAE of the proposed method is 0.06849 and 0.05805 respectively, with an accuracy of 93.15%, which is superior to the compared algorithms. Proposed method can provide more accurate prediction results.

关键词： Accuracy Heuristic algorithms Clustering algorithms Wind power generation Wind farms Predictive models Prediction algorithms Transformers Spatiotemporal phenomena Wind forecasting

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Holomorphic Embedding Searching Method for Voltage Stability Region Boundary of power systems

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Dianli Xitong Zidonghua/Automation of Electric power systems 2025年第10期49卷 123-134页

作者： Li, Xue Zhi, Menglei Jiang, Tao Li, Guoqing Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

Static voltage stability region (SVSR) studies power system voltage stability from the perspective of"region", effectively addressing voltage stability issues caused by strong fluctuations and randomness of renewable energy output. To accurately and rapidly construct SVSR, a SVSR boundary (SVSRB) searching method is proposed based on the holomorphic embedding method (HEM). Firstly, a voltage amplitude-active power sensitivity holomorphic embedding function criterion for searching voltage saddle node bifurcation (SNB) points is proposed based on the HEM theory. Secondly, this criterion is combined with a variable step-size control strategy to achieve fast and accurate search of individual SNB point. Then, according to the topological characteristics of SVSRB and utilizing known SNB point information, adjacent SNB points are efficiently solved through fast and flexible holomorphic embedding, thereby realizing efficient multi-dimensional SVSRB search. Finally, cases on test systems such as WECC 9-bus and IEEE 118-bus systems verify that the proposed method enables rapid and accurate construction of SVSRB. © 2025 Automation of Electric power systems Press. All rights reserved.

关键词： Bifurcation (mathematics)

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Location of Subsynchronous Oscillation Source in power systems Using Multivariate Synchrosqueezing Generalized S-Transform

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Dianli Xitong Zidonghua/Automation of Electric power systems 2025年第9期49卷 135-145页

作者： Jiang, Tao Zhang, Peng Li, Xue Liu, Bohan Li, Guoqing Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

Fast and accurate location of subsynchronous oscillation source is of great significance to the secure and stable operation of power system. The existing location methods for subsynchronous oscillation source are difficult to be applied to multi-mode time-varying subsynchronous oscillation scenarios, and the computational efficiency needs to be improved. Therefore, a location method for subsynchronous oscillation source in power systems using multivariate synchrosqueezing generalized S-transform (MSSGST) is proposed. First, the energy ratio coefficient is calculated in the adaptive sliding time window to detect the subsynchronous oscillation phenomenon in real time. For the detected subsynchronous oscillation phenomenon, the joint measurement information matrix is constructed with nodes as the unit, and the joint time-frequency matrix is obtained by synchronous decomposition of the joint measurement information matrix using MSSGST. Then, the MSSGST coefficient matrix, which can characterize the subsynchronous oscillation mode of the system, is screened and reassembled by ridge extraction technique. On this basis, the time-frequency domain transient energy flow calculation model based on MSSGST is derived, and the location index of oscillation source is constructed according to the energy characteristics in time-frequency domain during subsynchronous oscillation. Finally, the accuracy and effectiveness of the proposed method are verified by simulation data and measured data of subsynchronous oscillation of the actual power grid. © 2025 Automation of Electric power systems Press. All rights reserved.

关键词： Electric power transmission networks

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PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for Full-Waveform Inversion Without Dataset Dependency

arXiv

引用

arXiv 2025年

作者： Dong, Xintong Yuan, Zhengyi Lin, Jun Dong, Shiqi Tong, Xunqian Li, Yue College of Instrumentation and Electrical Engineering Jilin University Changchun130026 China Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Northeast Electric Power University Jilin132012 China College of Communication Engineering Jilin University Changchun130012 China

Full-waveform inversion (FWI) is a method that utilizes seismic data to invert the physical parameters of subsurface media by minimizing the difference between simulated and observed waveforms. Due to its ill-posed nature, FWI is susceptible to getting trapped in local minima. Consequently, various research efforts have attempted to combine neural networks with FWI to stabilize the inversion process. This study presents a simple yet effective training framework that is independent of dataset reliance and requires only moderate pre-training on a simple initial model to stabilize network outputs. During the transfer learning phase, the conventional FWI gradients will simultaneously update both the neural network and the proposed adaptive residual learning module, which learns the residual mapping of large-scale distribution features in the network’s output, rather than directly fitting the target mapping. Through this synergistic training paradigm, the proposed algorithm effectively infers the physically-informed prior knowledge into a global representation of stratigraphic distribution, as well as capturing subtle variations in inter-layer velocities within local details, thereby escaping local optima. Evaluating the method on two benchmark models under various conditions, including absent low-frequency data, noise interference, and differing initial models, along with corresponding ablation experiments, consistently demonstrates the superiority of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Stratigraphy

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The Day-ahead Scenario Generation Method for New Energy Based on an Improved Conditional Generative Diffusion Model

arXiv

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arXiv 2025年

作者： Wang, Changgang Liu, Wei Cao, Yu Liang, Dong Li, Yang Mo, Jingshan Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin Province Jilin132012 China School of Electrical Engineering Northeast Electric Power University Jilin Province Jilin132012 China Jilin Power Supply Company Substation Secondary Maintenance Center Jilin Province Jilin132012 China

In the context of the rising share of new energy generation, accurately generating new energy output scenarios is crucial for day-ahead power system scheduling. Deep learning-based scenario generation methods can address this need, but their black-box nature raises concerns about interpretability. To tackle this issue, this paper introduces a method for day-ahead new energy scenario generation based on an improved conditional generative diffusion model. This method is built on the theoretical framework of Markov chains and variational inference. It first transforms historical data into pure noise through a diffusion process, then uses conditional information to guide the denoising process, ultimately generating scenarios that satisfy the conditional distribution. Additionally, the noise table is improved to a cosine form, enhancing the quality of the generated scenarios. When applied to actual wind and solar output data, the results demonstrate that this method effectively generates new energy output scenarios with good adaptability. © 2025, CC BY-NC-ND.

关键词： Markov chains

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An Optimal Competitive Bidding and Pricing Strategy for Electric Vehicle Aggregator Considering the Bounded Rationality of Users

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IEEE Transactions on Industry Applications 2025年

作者： Wang, Qi Huang, Chunyi Wang, Chengmin Xie, Ning Li, Kangping Qiu, Peng Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Jilin132012 China Shanghai Jiao Tong University College of Smart Energy Shanghai200240 China State Grid Zhejiang Electric Power Company Electric Power Research Institute Hangzhou310014 China

Accurate estimation of the charging demand of electric vehicle (EV) users has a profound impact on the profitability of the EV aggregator (EVA). Existing studies concerning the market strategy of EVA normally assume the EV users are equipped with complete rationality and simplify the real interactive process among EVAs and EV users, making the derived strategy unable to adapt to the practical situations. To address this bottleneck, a novel competitive bidding and pricing strategy for EVA considering both the bounded rationality of EV users and various game relationships between multiple agents is proposed. Firstly, the evolutionary game theory is utilized to simulate the bounded rational charging behaviors of EV users under differentiated charging prices, while their demand elasticity and uncertainty of driving pattern is also taken into account. Secondly, the complex interactions among EVAs and EV users are modeled, where the competition between EVAs is constructed by a non-cooperative game with incomplete information and the interplay between EVAs and EV users is built as a Stackelberg game. Finally, numerical results validate the effectiveness of the proposed method and demonstrate the pricing strategy preferences of EVAs based on the diverse price elastic coefficients of EV users. © 2025 IEEE. All rights reserved

关键词： Game theory

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Optimal Configuration Planning of Multi-energy Microgrid Based on Source-load-temperature Scenarios Deep Joint Generation

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CSEE Journal of power and Energy systems 2023年第3期9卷 1090-1102页

作者： Nantian Huang Wenting Wang Guowei Cai School of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology(Northeast Electric Power University)Jilin 132012China

An optimal configuration method of a multi-energy microgrid system based on the deep joint generation of sourceload-temperature scenarios is proposed to improve the multienergy complementation and the reliability of energy supply in extreme ***,based on the historical meteorological data,the typical meteorological clusters and extreme temperature types are ***,to reflect the uncertainty of energy consumption and renewable energy output in different weather types,a deep joint generation model using a radiation-electric load-temperature scenario based on a denoising variational autoencoder is established for each weather *** the same time,to cover the potential high energy consumption scenarios with extreme temperatures,the extreme scenarios with fewer data samples are ***,the scenarios are reduced by clustering *** normal days of different typical scenarios and extreme temperature scenarios are determined,and the cooling and heating loads are determined by ***,the optimal configuration of a multi-energy microgrid system is carried *** show that the optimal configuration based on the extreme scenarios and typical scenarios can improve the power supply reliability of the *** proposed method can accurately capture the complementary potential of energy *** the economy of the system configuration is improved by 14.56%.

关键词： Deep joint generation denoising variational autoencoders multi-energy microgrid system optimal configuration planning

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Coordinated Suppression Method of Fault Current for DC Grid with Novel Dissipative Resistors Topology

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CSEE Journal of power and Energy systems 2024年第6期10卷 2371-2383页

作者： Shouqi Jiang Huanyu Zhao Guoqing Li Yechun Xin Lixin Wang Weiru Wang Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin 132012China.

A DC grid based on half-bridge modular multilevel converters(HB-MMC)is a feasible means to realize the friendly grid connection of renewable *** solve problems such as the high cost and technical difficulty of DC circuit breakers(DCCB),a coordinated control method for fault current suppression of DC grid connecting wind power is *** key influencing factors of DC fault current are revealed by fault characteristics analysis,and an adaptive current-limiting control method for MMC is proposed,whose parameter selection principles are designed to ensure the safe operation of equipment while achieving effective suppression of fault *** addition,a novel configuration method of dissipative resistors with the current-limiting function is proposed,which can solve the problem of surplus power in the DC grid and reduce the current stress of converter *** on this,a coordination scheme of dissipative resistors,the adaptive current-limiting control method,and DCCBs are proposed to block fault current,effectively reducing the manufacturing difficulty and cost of ***,a four-terminal DC grid simulation model is built based on the RTLAB OP5600 real-time digital simulation platform,and the effectiveness and feasibility of the proposed methods are verified.

关键词： Adaptive current-limiting control coordinated suppression method dissipative resistor DC grid parameter selection principle

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Fault Location Detection of Transmission Lines in Noise Environments Based on Random Matrix Theory

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CSEE Journal of power and Energy systems 2022年第4期8卷 1233-1241页

作者： Jun An Zihan Deng Haipeng Chen Gang Mu Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012.Jilin ProvinceChina

Fault detection and location are critically significant applications of a supervisory control system in a smart *** methods,based on random matrix theory(RMT),have been practiced using measurements to detect short circuit faults occurring on transmission ***,the diagnostic accuracy is infuenced by the noise signal in the *** relationship between mean eigenvalue of a random matrix and noise is detected in this paper,and the defects of the Mean Spectral Radius(MSR),as an indicator to detect faults,are theoretically determined,along with a novel indicator of the shifting degree of maximum eigenvalue and its *** comparing the indicator and the threshold,the occurrence of a fault can be ***,an augmented matrix is constructed to locate the fault *** proposed method can effectively achieve fault detection via the RMT without any influence of noise,and also does not depend on system *** experiment results are based on the IEEE 39-bus ***,actual provincial grid data is applied to validate the effectiveness of the proposed method.

关键词： Fault detection maximum eigenvalue noise random matrix theory smart grid

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