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Deep Learning and Projection Neural Network With Finite-Time Convergence for energy Management of Multi-energy system

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IEEE Transactions on Smart Grid 2025年第3期16卷 2156-2168页

作者： Liu, Xueying He, Xing Li, Chaojie Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China North China Electric Power University State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources Beijing102206 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, an approach based on projection neural network (PNN), sliding mode control technique, and deep learning is proposed to solve the energy management problem of multi-energy systems (MES) containing dynamic parameters. First, the sliding mode technique is introduced in the PNN to design a finite-time PNN (FTPNN). The stability and finite-time convergence of the proposed FTPNN are proved by the Lyapunov method and the setting time bound is given. Then, the deep FTPNN (DFTPNN) is designed by combining deep learning with the proposed FTPNN. The dynamic parameters in the MES that change over time are used as input variables for the DFTPNN, allowing the trained DFTPNN to respond immediately to changes in the dynamic parameters and to predict the solutions of the FTPNN with different parameters directly. simulation experiments show that FTPNN has faster convergence compared to PNN. DFTPNN significantly reduces the computation time compared to FTPNN. DFTPNN provides predicted solutions to FTPNN. Since DFTPNN can respond immediately to changes in dynamic parameters and directly provide energy management strategies under different parameters, it can adapt to changing environments and promote the economic and stable operation of MES. © 2010-2012 IEEE.

关键词： Cogeneration Heuristic algorithms Convergence Deep learning Prediction algorithms Optimization Heating systems energy management Approximation algorithms power system stability

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Heterogeneous ensemble short-term integrated load forecasting considering differentiated cycle-trend properties

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Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Huang, Nantian Wang, Yaoyao Li, Bingling Cai, Guowei Zhang, Liang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology (Northeast Electric Power University) Ministry of Education Jilin Province Jilin 132012 China

Aiming at the Integrated energy system (IES), there are key problems such as the potential change pattern of seasonal differentiation of multiple loads, the lack of mining complex coupling characteristics, the limited generalization ability of forecasting models and the weak interpretability. A heterogeneous integrated short-term multiple load forecasting method considering differentiated cycle-trend characteristics is proposed. Firstly, Multiple Seasonal Trend decomposition using Loess (MSTL) is used to decompose the multivariate loads into multi-timescale cycle and trend series, and to construct multi-timescale cycle-trend feature sets, which breaks through the limitations of the traditional single-cycle decomposition in characterizing inter-seasonal coupling characteristics. Secondly, the evolutionary multi-objective optimization mechanism based on the dual indexes of diversity and accuracy is introduced into the Stacking integration framework for the first time, and the seasonal adaptive optimal model combination selection of electric/cooling/heating loads is realized. Finally, a two-layer interpretable analysis paradigm based on Shapley Additive Explanations (SHAP) values is established to improve the interpretability of the proposed multivariate load forecasting method by performing input feature attribution analysis from both global and individual dimensions. The experimental analysis is carried out on the example of Tempe Campus IES of Arizona State University in the U.S.A. The results show that the average percentage errors of electricity, cooling, and heating forecasts of the proposed method reach 1.783 %, 1.012 %, and 2.225 %, respectively, demonstrating superior forecasting accuracy compared to existing methods. © 2025 Elsevier Ltd

关键词： Evolutionary multi-objective optimization algorithm Heterogeneous ensemble Interpretability Multivariate load forecasting Stacking ensemble learning

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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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Resilient Cooperative control of Multiple DC Microgrids With Interconnection Networks Against Cyber Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 116-126页

作者： Xiao, Feng Liu, Shiyu Wei, Bo Fang, Fang Qin, Jiahu North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China North China Electric Power University School of Control and Computer Engineering Beijing102206 China North China Electric Power University New Energy Generation National Engineering Research Center Beijing102206 China University of Science and Technology of China Department of Automation Hefei230027 China

The two control objectives of voltage regulation and precise current sharing are conflicting in multi-bus DC microgrids. In this paper, a distributed control strategy is proposed, and it can achieve accurate current sharing and regulate the voltage of a critical node to the rated value. However, such a distributed control system is susceptible to potential cyber attacks that could deteriorate microgrids performances by corrupting communication links or actuators. To overcome these difficulties, we design the interconnected communication networks to establish a resilient cooperative system of multiple microgrids against unknown bounded injection attacks. The proposed resilient control strategy can achieve the control objectives of maintaining voltage regulation and current sharing within each microgrids under different operating conditions, and the interconnected system consisting of multiple microgrids cooperates to mitigate the adverse effect of cyber attacks. The dynamics and stability of the overall close-loop system of the DC microgrids are analyzed, in the present or absent of cyber attacks. simulation results validate the effectiveness of the proposed approach in two 4-bus DC microgrids. © 2023 IEEE.

关键词： Cyber attacks

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FDCA-DSTGCN: A Wind Farm Cluster power Day-ahead Prediction Model Based on Frequency Domain Information Gain and Dynamic Trend Sensing

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IEEE Transactions on Sustainable energy 2025年

作者： Yang, Mao Niu, Jiajun Wang, Bo Huang, Dawei Su, Xin Ma, Chenglian Ministry of Education Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Jilin132012 China China Electric Power Research Institute State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems Beijing100912 China

Accurate wind farm cluster power prediction (WFCPP) is of vital significance for new power systems with large-scale wind power integration. The current WFCPP modeling method ignores the important role of wind direction and frequency domain information, resulting in insufficient use of spatial information and difficult to improve the prediction accuracy. In order to solve the above problems, a day-ahead power prediction model for wind farm cluster power is proposed based on information gain in frequency domain and dynamic trend sensing. Firstly, a cluster division method based on graph theory and progressive fusion of multiple information is proposed, and a virtual information node is set up. Secondly, a method to identify prevailing wind direction under time window is proposed for subsets, and a dynamic weighted directed graph structure is designed based on prevailing wind direction and wind speed. Thirdly, a dynamic spatio-temporal graph convolutional network model with frequency domain gain channel attention mechanism (FDCA-DSTGCN) is proposed to integrate the above information and complete the prediction. Finally, the proposed method is applied to a wind farm cluster in Inner Mongolia of China. Compared with the existing and traditional prediction methods, the normalized root mean square error of the proposed method is reduced by 1.13% -2.15%, which verifies the effectiveness of the proposed method. © 2010-2012 IEEE.

关键词： Mean square error

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Multi-Objective Coordination and Stability control of High Proportion Wind power system Based on K-Domain Tuning Damping 19th

Multi-Objective Coordination and Stability Control of High P...

引用

19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Zhang, Qingtao Gao, Diju Zhang, Dan Cai, Haiqing Gu, Haohan Chen, Wei Wu, Wencong Chen, Zhihao Institute of Logistics Engineering Shanghai Maritime University Shanghai China Institute of Logistic Science and Engineering Shanghai Maritime University Shanghai China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9789819608966

With the further development of "double high" characteristics of power system, the traditional stability problem dominated by synchronous generator has gradually evolved into a new stability problem dominated by power electronic equipment control. In addition, different types of failure problems can occur in wind power systems. In order to achieve multi-objective coordinated and stable operation of high proportion wind power system, a damping control method with parameters tuning is proposed, which includes the design of filtering link and proportional amplification link. Among them, the key parameters of the proportional link are adjusted. Finally, the feasibility of the proposed control method is verified by simulation tests. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Electric power system stability

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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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Distributionally robust electricity-carbon collaborative scheduling of integrated energy systems based on refined joint model of heating networks and buildings

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renewable energy 2025年 251卷

作者： Chen, Haipeng Song, Jianzhao Li, Zhiwei Shui, Siyuan Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China Department of Electrical Engineering Northeast Electric Power University Jilin 132012 China School of Automation and Engineering Northeast Electric Power University Jilin 132012 China Northeast Electric Power University Jilin 132012 China

The rapid increase in the grid integration demand of intermittent renewable energy (RE) has raised higher requirements for the flexible regulation and energy storage capacity of integrated energy systems (IES) with combined heat and power (CHP) units. To solve the issues of efficient accommodation of high proportion RE and low-carbon economic operation of the system, this study proposes an optimal scheduling method for IES based on a refined joint model of heating networks and buildings. Firstly, a refined thermal energy transmission model integrating heating networks and buildings is established to accurately depict the dynamic thermal energy transmission process within the system. Then, this paper proposes an electricity - carbon demand response considering the dynamic carbon emission factor, guiding users to use electricity in a low-carbon way. A distributionally robust optimization (DRO) scheduling model is established to quantify the impact of RE output uncertainty, which is efficiently solved using the column and constraint generation algorithm. The simulation results show that the proposed DRO model ensures the robustness of system operation while improving low-carbon economic benefits. Notably, the RE consumption rate is increased by 9.52 % compared with traditional scheduling methods, which provides an essential reference for promoting the sustainable development of IES. © 2025

关键词： Thermal energy

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

arXiv

引用

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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Evaluation of Regulating Capacity of power system Nodes and Transmission Lines

Evaluation of Regulating Capacity of Power System Nodes and ...

引用

Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Xiuyu Yang Zekun Fan Almasbek Shakenbek Baoyu Di Chongbi Li Yi Wang Key Laboratory of Modern Power System Simulation Control and New Green Energy Technology of Ministry of Education Northeasst Electric Power University Jilin China State Grid Xinjiang Electric Power Co. Ltd. State Grid Xinjiang Electric Power Co. Ltd. Electric Power Research Institute Urumqi China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

The large-scale access of renewable energy to the power grid leads to a sharp increase in the demand for power system regulation capacity. In order to solve this problem, this paper explores the mechanism of supply and demand balance of power system regulation capacity, analyzes the transmission characteristics of regulation capacity, and proposes the adequacy index of node regulation capacity and the adequacy index of network transmission regulation capacity. The Garver-6 node system is used to verify the effectiveness and practicability of the proposed method. The example shows that the proposed evaluation method can accurately identify the weak links of regulation ability and clarify the causes of insufficient regulation ability.

关键词： renewable energy sources Supply and demand power transmission lines Evaluation models Regulation Smart grids Planning Artificial intelligence Load flow Capacity planning

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