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

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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Master-Slave Game Strategy for Electric Heating Load Group Considering Differences of Heterogeneous Household Types and Aggregation Structure

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Dianli Xitong Zidonghua/Automation of Electric power systems 2025年第10期49卷 58-66页

作者： Yang, Mao Wang, Jinxin Fang, Guozhong Zhu, Yidan Wang, Yuxin Su, Xin Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Northeast Branch of State Grid Corporation of China Shenyang110179 China

The regulation capability of the new power system dominated by renewable energy at the source side is gradually deteriorating. Exploiting the load-side regulation potential to participate in demand response has become an important approach to enhance grid regulation capacity. Firstly, taking distributed residential electric heating clusters as the research object, a dynamic model of temperature-varying power is established for electric heating rooms based on the building thermodynamics theory. Secondly, the impacts of household type differences and aggregated household proportions on the regulation capability of heating load clusters are investigated. A refined aggregation model for heating load clusters considering the differences of heterogeneous household types and aggregation structure is established, with adjustable electricity quantified as an indicator to measure the regulation potential of heating load clusters. Then, a master-slave game model is constructed with the load aggregator (LA) as the leader and cluster users as followers. The quantified adjustable electricity of load clusters is incorporated into the game model as flexible resources to participate in demand response. The upper-level game model establishes an LA pricing model to formulate electricity scheduling plans and optimal heating electricity prices within the system, while the lower-level develops a user payment cost model where users determine their heating schedules based on the electricity prices. Finally, case studies verify that the proposed game model can effectively stimulate the demand response potential of users with the differences of heterogeneous household type, achieving economic benefits for both LA and users. © 2025 Automation of Electric power systems Press. All rights reserved.

关键词： Cooling

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Analysis of Frequency Dynamic Distribution Characteristics in Chain-structured power system with renewable energy Integration

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Dianli Xitong Zidonghua/Automation of Electric power systems 2025年第10期49卷 49-57页

作者： An, Jun Shi, Yan Cao, Shuai Zhou, Yibo Li, Kexin Zhang, Wanxiang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Northeast Branch of State Grid Corporation of China Shenyang110179 China

The integration of renewable energy generation, represented by wind power, into the power system has a significant impact on frequency security, and the characteristics of frequency distribution have gradually become a focal point of attention. To study the influence of wind power integration into multi-machine systems on frequency distribution characteristics, based on the frequency response characteristics of the"grid, source, and load"sides, this paper constructs an analytical model of the frequency response distribution characteristics of a multi-machine system with wind power, and analyzes the impact mechanism of wind power integration on the frequency distribution characteristics. Then a multi-dimensional quantification index system is developed to describe the relative dispersion and absolute differences in frequency response. Next, the validity of the mechanism model is verified using a simple chain-structured power system with"generation-transmission-reception", and the influence rules of electrical distance, wind power integration location, and disturbance location on the frequency distribution characteristic indices are analyzed. Finally, the influence rules of distribution characteristics are analyzed and verified in an actual large-scale power system with a high proportion of renewable energy. © 2025 Automation of Electric power systems Press. All rights reserved.

关键词： Wind power integration

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Optimized Strategies for Peak Shaving and BESS Efficiency Enhancement through Cycle-Based control and Cluster-Level power Allocation

arXiv

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arXiv 2025年

作者： Gan, Guo Junhui, Li Gang, Mu Gangui, Yan Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

Battery energy Storage systems (BESS) are essential for peak shaving, balancing power supply and demand while enhancing grid efficiency. This study proposes a cycle-based control strategy for charging and discharging, which optimizes capture rate (CR), release rate (RR), and capacity utilization rate (CUR), improving BESS performance. Compared to traditional day-ahead methods, the cycle-based approach enhances operational accuracy and reduces capacity waste, achieving a CUR increase from 75.1% to 79.9%. An innovative cluster-level power allocation method, leveraging an improved Particle Swarm Optimization (PSO) algorithm, is introduced. This strategy reduces daily energy loss by 174.21 kWh (3.7%) and increases BESS efficiency by 0.4%. Transient and steady-state energy loss components are analyzed, revealing that transient loss proportion decreases significantly as power depth increases, from 27.2% at 1 MW to 1.3% at 10 MW. simulations based on a detailed Simulink/Simscape model validate these methods, demonstrating enhanced peak shaving effectiveness and prolonged BESS lifespan by reducing equivalent cycles. The study provides a robust framework for optimizing BESS performance and efficiency in real-world applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Charge storage

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Bi-level Optimization Strategy of Grid-side Multiple energy Storage Stations Considering Available Capacity and Frequency Regulation Cost

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Dianli Xitong Zidonghua/Automation of Electric power systems 2025年第10期49卷 101-111页

作者： Li, Junhui Song, Qinglong Guo, Qi Yan, Jun Li, Cuiping Hao, Qianpeng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Co. Ltd. Hohhot010020 China School of Future Technology Tianjin University Tianjin300350 China

Aiming at the problem that some energy storage stations in the region lose charging/discharging capability in advance due to uneven distribution of frequency regulation instructions, resulting in insufficient response ability of subsequent frequency regulation demand, a bi-level optimization strategy of grid-side multiple energy storage stations considering available capacity and frequency regulation cost is proposed. The proposed strategy includes the frequency regulation power distribution layers of energy storage stations and energy storage units. The upper layer introduces the available capacity state coefficient and the adaptive coefficient about the adjustment ability of energy storage resources, and designs the optimal distribution model of frequency regulation power partition of energy storage station to realize the balanced distribution of frequency regulation power in each energy storage station. The lower layer takes into account the state of health (SOH), the resistance coefficient, and the balanced recovery of the state of charge (SOC) for the energy storage unit to construct the objective function, so as to prioritize the recovery of SOC for energy storage units with high SOH. The simulation results show that the proposed strategy can improve the response ability of the energy storage resources in the region and realize the maximum utilization of energy storage resources while ensuring a certain economy. © 2025 Automation of Electric power systems Press. All rights reserved.

关键词： Electric power 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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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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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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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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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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