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Integrated energy microgrid bi-level game scheduling optimization taking into account electricity-heat-hydrogen enriched compressed natural gas coupling and shared energy storage

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International Journal of Electrical power and energy systems 2025年 169卷

作者： Wang, Changgang Sun, Xiaoning Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China

Hydrogen-enriched compressed natural gas (HCNG) is a promising technology capable of significantly reducing carbon emissions in conventional gas units. Its integration into microgrids enhances the coupling and complementarity of diverse energy sources. Shared energy storage systems, which play a crucial role in improving energy utilization, have garnered increasing attention in recent research. However, the deployment of HCNG technology involves multiple stakeholders, posing challenges in balancing their respective benefits. To address this, we propose a novel scheduling method for Integrated energy Management that accounts for electricity-heat-HCNG coupling and shared energy storage services. First, we model the proton exchange membrane electrolyzer and HCNG energy coupling device, incorporating HCNG loads on the demand side to enhance hydrogen production and utilization efficiency. Additionally, tiered carbon trading mechanisms, integrated demand response strategies, and shared energy storage are introduced to optimize carbon reduction on both the demand and storage sides while effectively constraining energy emissions. The strategic interactions between integrated energy microgrid operators and load aggregators are modeled using a Stackelberg game approach, and the existence and uniqueness of equilibrium solutions are rigorously proven. Finally, the proposed model is solved using the Black-winged Kite algorithm in conjunction with the CPLEX *** studies demonstrate the effectiveness of the proposed method, showing revenue increases of 9.15% for integrated energy microgrid operators and 8.29% for load aggregators, alongside a reduction in carbon emissions by 8.76%. © 2025 The Author(s)

关键词： energy utilization

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A framework of day-ahead wind supply power forecasting by risk scenario perception

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IEEE Transactions on Sustainable energy 2025年

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

Wind power forecasting (WPF) systems are essential to maintain the safe and stable operation of the power system in case of large-scale grid-connected wind farms. However, the current forecasting has the problem of disunity between statistical value and application value, that is, it only pays attention to its forecasting accuracy and ignores the risks caused by it in the power system. In order to solve the above problems, this study proposes a framework of wind supply power forecasting (WSPF) for wind farm cluster, which takes into account the risk scenario perception. First of all, aiming at the predicted risk phenomenon in WPF, TimesNet combined with the fluctuation information of Numerical Weather Prediction (NWP) wind speed is used to identify the corresponding risk scenarios. Secondly, the effective consumption area and power supply risk area evaluation index, as well as the accuracy of WSPF are defined, and the optimal forecasting curve correction scheme is fitted according to the index. Thirdly, taking into account the correction scheme and identification results, a variety of predictors are used to verify the WSPF according to the above framework. Finally, the proposed method is applied to a wind farm cluster in Inner Mongolia Autonomous region of China, the average accuracy of WSPF has increased by 37%, which verifies the effectiveness and universality of this method. © 2010-2012 IEEE.

关键词： Wind forecasting

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Aggregation Modeling of system Frequency Response Under Inertia control of Grid-following Direct-drive Wind Turbine

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Dianli Xitong Zidonghua/Automation of Electric power systems 2025年第10期49卷 39-48页

作者： Jiang, Chao Yang, Dongfeng Guo, Shujia Liu, Xiaojun Zhou, Shuyu Cao, Zhichong Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China School of Automation and Electrical Engineering Zhejiang University of Science and Technology Hangzhou310023 China State Grid Jilin Electric Power Research Institute Changchun130000 China

The inertia control of grid-following direct-drive wind turbines significantly influences the system frequency response pattern afrer disturbances. However, current research has not clearly identified the dominant response link of direct-drive wind turbines in frequency regulation, while the modeling is too complicated to be suitable for frequency characteristic analysis. Therefore, the linearized models under inertia control are derived for two typical types of direct-drive wind turbines according to the aerodynamic equations, generator models, power control link, and control circuit structure. The fast physical process of current control-boost circuit/stator voltage dynamic loop is clearly understood and reasonably ignored. It is revealed that the two types of direct-drive wind turbines have a common frequency response mechanism which can be described as the cascade effect of the speed regulator-wind rotor motion equation. Furthermore, combined with the frequency response model of synchronous unit, the aggregation model of system frequency response under the inertia control of grid-following direct-drive wind turbines is constructed, and the aggregation model is reduced to a second-order model by the balance truncation method for analytical calculation of frequency extremes. The accuracy and applicability of the aggregation model and its reduced-order model are verified by MATLAB/Simulink simulation, and the influence of control parameters and system states on frequency dynamic characteristics and stability margin is analyzed. © 2025 Automation of Electric power systems Press. All rights reserved.

关键词： Electric power system control

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Adaptive Adjustment Method for Grid-forming energy Storage Parameters Based on Buffer Functions and Damping Switching

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Dianli Xitong Zidonghua/Automation of Electric power systems 2025年第10期49卷 77-86页

作者： Cui, Yang Ma, Boyu Dai, Hanyang Ma, Xin Zhao, Yuting Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China China Electric Power Research Institute Beijing100192 China Electric Power Research Institute of State Grid Ningxia Electric Power Ltd. Co. Yinchuan750002 China

In the development tendency of high penetration of renewable energy and power electronic devices in new power systems, the grid-forming control technology for renewable energy integration mitigates the system stability issues caused by insufficient inertia and damping. However, it also reduces the dynamic regulation performance of the system. This paper proposes an adaptive adjustment method for grid-forming energy storage parameters based on buffer functions and steady-state/transient-state damping switching, to improve the dynamic regulation performance of the system. Firstly, the buffer functions are used in inertia adjustment to enhance the regulation smoothness of the system. Secondly, a steady-state/transient-state damping switching link is introduced in the damping adjustment to reduce the steady-state error of active power output during the frequency reduction process. Thirdly, the fuzzy control is employed to introduce correction terms for inertia and damping, and the stability of the proposed method is analyzed. Finally, simulations validate the effectiveness of the proposed method in enhancing small-signal stability and power response dynamic performance of the system. © 2025 Automation of Electric power systems Press. All rights reserved.

关键词： Electric power transmission networks

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Extraction and application of intrinsic predictable component in day-ahead power prediction for wind farm cluster

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energy 2025年 328卷

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

With the continuous updating and iteration of artificial intelligence algorithms, prediction models emerge one after another, but the research and utilization of the predictability of wind power is still less. Therefore, this paper proposed a day-ahead power prediction method for Wind Farm Cluster (WFC) based on intrinsic predictable component extraction. Firstly, based on the difference of power distribution between wind farm and WFC, a method of Wind power Curve (WPC) modeling for WFC is proposed, which provides the basis for establishing the Final Set of Wind power Curve (FSWPC). Secondly, the Intrinsic Predictable Component (IPC) of wind power is extracted based on the FSWPC, and the Interference Component (IC) of wind power is separated to eliminate the influence of IC on IPC in the process of prediction. Thirdly, the historical similarity matching method with large threshold is used to predict the IC to make up for the numerical deficiency of the IPC. Finally, the proposed method was applied to a WFC in China to verify its effectiveness. Compared with the traditional prediction strategy, the NRMSE, NMAE and MAPE are reduced by 1.59 %, 1.15 % and 6.42 %, respectively. © 2025 Elsevier Ltd

关键词： Iterative methods

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

arXiv

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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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