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16th IET International Conference on AC and DC power Transmission, ACDC 2020

作者： Jiang, Zhicheng Yu, Qingguang Li, Ang Xiong, Yufeng Long, Gaoxiang Liu, Yuming Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Key Laboratory of Power Systems Department of Electrical Engineering Tsinghua University Beijing China

ISBN: (纸本)9781839533914

With the fast development of renewable energy generation, the randomness and volatility seriously threaten the stability of the power system. In order to improve the consumption rate of renewable energy and the economic benefit, the source-network-load collaborative optimization is put forward and widely used in the power system. And the active power distribution network which consists of the energy storage devices and flexible load is the best carrier to realize it. Based on the active power distribution network planning, this paper firstly designs an economic index covering construction cost and operation cost as the optimization objective. Then with the physical constraints consisting of the physical features of generation units, energy storage devices and flexible load, the source-network-load collaborative optimization model of active power distribution network is established. Finally, a case of city distribution network is presented to verify the effect of source-network-load collaborative optimization and the efficiency of the optimization model. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Optimization

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Frequency-Domain Adaptive Parametric Model Order Reduction Method for Oscillatory Stability Analysis on Multi-Converter-Fed systems

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Journal of modern power systems and Clean energy 2024年第3期13卷 802-814页

作者： Xun Jiang Meiqin Mao Liuchen Chang Bao Xie Haijiao Wang Nikos D. Hatziargyriou Research Center for Photovoltaic System Engineering of Ministry of Education Hefei University of Technology Hefei China University of New Brunswick Fredericton New Brunswick Canada State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems (China Electric Power Research Institute) Beijing China National Technical University of Athens Athens Greece

The oscillatory stability analysis of multi-converter-fed systems (MCFSs) with modest computational resources needs a precise parametric reduced-order impedance model (PROIM). However, the traditional Krylov subspace based parametric model order reduction (KS-PMOR) method has difficulty in building precise PROIM for MCFSs. This is because the factors related to the errors of PROIM are complicated and coupled. To fill this gap, the factors associated with the accuracy of the KS-PMOR method are estimated by defining three indicators: the convergence error, cumulative error, and rank of projection matrix. Using the three indicators, a frequency-domain adaptive parametric model order reduction (FDA-PMOR) method is developed to form the precise PROIM of MCFSs for the accurate and fast oscillatory stability analysis. The accuracy of the obtained PROIM using the proposed FDA-PMOR method and its efficiency in actual oscillatory stability analysis are validated by three MCFSs with different scales, i. e., a small-scale MCFS with four paralleled converter-based renewable energy generators (CREGs), a real-time simulation-based MCFS with eighteen paralleled CREGs, and a larger MCFS with ninety paralleled CREGs.

关键词： Impedance power system stability Interpolation Computational modeling Reduced order systems Parametric statistics Accuracy Frequency-domain analysis Adaptation models Vectors

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Fuzzy optimal scheduling of hydrogen-integrated energy systems with uncertainties of renewable generation considering hydrogen equipment under multiple conditions

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Applied energy 2025年 393卷

作者： Song, Jianzhao Wang, Na Zhang, Zhong Wu, Hao Ding, Yi Pan, Qingze Pan, Xingzuo Shui, Siyuan Chen, Haipeng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Department of Electrical Engineering Northeast Electric Power University Jilin132012 China Gansu Branch of Luneng New Energy Group Co. Ltd. Lanzhou730000 China Lanzhou Power Supply Company State Grid Gansu Electric Power Co. Ltd. Lanzhou730000 China TBEA Shenyang Transformer Group Co. Ltd. Shenyang110144 China Northeast Electric Power University Jilin132012 China

Developing hydrogen-integrated energy systems (HIES) represents a cutting-edge strategy for harnessing renewable energy (RE). However, the inherent unpredictability and variability of RE significantly increase the operational uncertainty of HIES, which leads to severe energy curtailment issues when HIES integrates large-scale RE, necessitating greater operational flexibility in the system. To address the challenges, this paper proposes a fuzzy optimal scheduling approach for HIES that considers the hydrogen equipment under multiple operating conditions. The analysis begins by examining the operational mechanisms of hydrogen equipment. Subsequently, a multi-conditions model is developed for electrolyzers, while a reserve model is established for hydrogen fuel cells. Furthermore, the fuzzy chance constraint (FCC) is employed to quantify the uncertainty of RE generation. An integrated demand response mechanism is implemented, incorporating human thermal comfort and building thermal inertia. Finally, a fuzzy optimization scheduling model for HIES is constructed to minimize the total operating costs. The crisp equivalent of FCC is derived to solve this model, thereby transforming the scheduling model based on fuzzy chance-constrained programming into a solvable mixed-integer programming model. The simulation results indicate that the proposed scheduling method can reduce the overall costs of the HIES by 17.98 % and increase the RE accommodation rate by 19.67 %, validating the effectiveness of the method in enhancing the operational flexibility of the HIES. In addition, this study achieves a balance between economic efficiency and reliability, offering a better economy, lower energy curtailment rates, and faster decision-making times compared to robust optimization, scenario analysis, and other common fuzzy optimization methods. © 2025

关键词： Risk perception

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Dual-layer economic dispatch method for electric fused magnesia loads participating in peak shaving guided by price compensation

Dual-layer economic dispatch method for electric fused magne...

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4th energy Conversion and Economics Annual Forum (ECE Forum 2024)

作者： Junhui Li Tianzhen Fan Jing Xu Wei Zheng Chupeng Xiao Chaoyang Xu Cuiping Li Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin People's Republic of China State Grid Electric Power Research Institute Co. State Grid Electric Power Research Institute Wuhan Efficiency Evaluation Co. Ltd. Hubei People's Republic of China State Grid Liaoning Electric Power Supply Co. State Grid Fushun Electric Power Supply Co. Ltd. Liaoning People's Republic of China

ISBN: (数字)9781837242924

To address the severe challenge of increasing fluctuations in supply and demand due to the expansion of new energy installations, this paper proposes a hierarchical optimization control strategy for electric fused magnesia loads participating in peak shav ing, guided by a price compensation mechanism. In the upper-layer system control, a price mechanism is designed to enhance the flexibility of electric fused magnesia loads in participating in peak shaving, considering the overall benefit of the system. In the lower-layer enterprise net profit optimization model, the product revenue, demand response income, operation and maintenance costs, and carbon emission costs of the electric fused magnesia enterprise are balanced to ensure the maximization of the enterprise’s net profit. Finally, numerical examples are used to verify the effectiveness of this method in improving the system's peak shaving capability.

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DC fault ride-through for wind power integration via MMC-HVDC based on precise control of energy storage configured inside wind farm

DC fault ride-through for wind power integration via MMC-HVD...

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renewable power Generation and Future power systems Conference 2023 (RPG 2023 UK)

作者： Y. Sun Y. Guo F. Fan J. Fan G. Li Z. Wang X. Wu Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin People's Republic of China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin People's Republic of China Department of Electronic and Electrical Engineering University of Strathclyde Glasgow UK State Grid Smart Grid Research Institute Beijing People's Republic of China

The modular multilevel converter-based high voltage direct current (MMC-HVDC) overhead lines transmission scheme is the preferred option for wind power integration, though it is susceptible to DC faults. To ensure the connection of a wind farm (WF) to the grid during DC faults, this paper proposes a coordinated control strategy for DC fault ride-through (FRT) based on the energy storage system configured inside the WF for wind power integration through a bipolar MMC-HVDC. In the case of an instantaneous DC fault, the non-fault pole is regulated to operate at full power state to maximise wind power transmission. The exceedance of available wind power outputs over the transmission capacity of the non-fault pole is then accurately allocated to the internal energy storage units (ESUs) of wind generators (WGs) in order to ensure the power balance of the system and reduce the state of charge variance of ESUs as soon as possible. In order to reduce the energy capacities of ESUs required to store the excessive wind power in the event of a permanent DC fault, the method of step-by-step removing WGs is adopted to ensure the system power remaining balanced when the internal ESUs of the disconnected WGs are out of operation. The model is developed and carried out on the PSCAD/EMTDC simulation platform to verify the effectiveness of the proposed control strategies.

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Dynamic Security Evaluation Method for power systems Considering Cascading Failures

Dynamic Security Evaluation Method for Power Systems Conside...

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IEEE Conference on energy Internet and energy system Integration (EI2)

作者： Ren Liu Xingye Xu Wei Du Kaishun Xiahou Yihua Zhu Dongxu Chang Hengdao Guo CSG Key Laboratory for Power System Simulation Electric Power Research Institute of China Southern Power Grid Guangzhou China School of Electric power engineering South China University of Technology Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute of China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of China Southern Power Grid Intelligent Operation and Control for New Energy Power System Guangzhou China

With the increasing level of informatization and interconnection in the electric power system, the risk of external attacks on power systems is increasing. In response to the drawbacks of traditional static security assessment methods, this paper establishes two security metrics, which are voltage and power flow amplitude deviation security metric and power flow entropy security metric. Based on these security metrics, a dynamic security evaluation model that considers cascading failure is proposed. simulation results demonstrate that the proposed security metrics can effectively reflect the security of cascading failure propagation, thereby providing valuable insights for decision-making by power system operators.

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A Hybrid Model for Building-level Day Ahead power Load Forecasting Based on Gated Recurrent Unit Networks

A Hybrid Model for Building-level Day Ahead Power Load Forec...

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2021 IEEE Sustainable power and energy Conference, iSPEC 2021

作者： Zhang, Hualu Deng, Boya Shi, Jie Northeast Electric Power University Jilin Key Laboratory of Modern Power System Simulation Control and Green Power New Technology Ministry of Education Jilin China Nanjing Nanrui Information Communication Technology Co. Ltd. Nari Group Corporation State Grid Electric Power Research Institute Nanjing China

ISBN: (纸本)9781665414395

Accurate forecasting of day-ahead electrical load is a key to optimize the operation of the integrated energy system (IES) and a fundamental function of the cyber-physical system (CPS) of IES. Due to the characteristics of different features, data collected by CPS often have different sampling frequencies, resulting in mining data trends inadequately when making a load forecast. This paper proposes an GRU-based model to combine historical data with different sampling frequencies in forecasting day-ahead building-level load. First, day-level features are clustered by K-means. Hour-levels features are then partitioned according to clustering results and inputted separately into a gated recurrent unit (GRU) based neural network to capture relationships between external features and load fluctuations. The temporal dependencies of minute-level data are captured by the GRU network directly without any external features. Finally, the least square method is used to combine forecasting results at each hour of two models. The simulation results present that the proposed model performs better than a single model when applying to real-world data. © 2021 IEEE.

关键词： Forecasting

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Hybrid Impedance Reshaping Method of Grid-Connected Inverters in Weak Grid

Hybrid Impedance Reshaping Method of Grid-Connected Inverter...

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power Electronics and power system Conference (PEPSC)

作者： Weike Zheng Haijiao Wang Bao Xie Meiqin Mao State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing China Photovoltaic System Engineering Research Center of the Ministry of Education Hefei University of Technology Hefei China

Both the phase-locked loop (PLL) and the common coupling point (PCC) voltage feedforward may have impacts on the inverter system stability in weak grid. Therefore, a hybrid impedance reshaping control strategy is proposed in the paper. Firstly, the inverter impedance model is established. Next, the system stability is analyzed under weak grid, reflecting on the influence of PLL and the PCC voltage feedforward. Then, to improve the inverter system stability, the band-pass filter is added to the feedforward branch of PCC voltage, since it can attenuate the influence of unstable factors. Moreover, a new phase-locked loop structure containing a first-order decoupled complex filter is proposed, taking into account the fundamental positive voltage extraction characteristics of the first-order decoupled complex filter. The parameters of the hybrid impedance reshaping strategy are designed in detail. Finally, simulation is carried out to prove the theory analysis and hybrid impedance reshaping strategy in the paper.

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Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach

arXiv

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arXiv 2022年

作者： Li, Yang Bu, Fanjin Li, Yuanzheng Long, Chao Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China School of Artifcial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Water Energy and Environment Cranfield University CranfieldMK43 0AL United Kingdom

Multi-uncertainties from power sources and loads have brought significant challenges to the stable demand supply of various resources at islands. To address these challenges, a comprehensive scheduling framework is proposed by introducing a model-free deep reinforcement learning (DRL) approach based on modeling an island integrated energy system (IES). In response to the shortage of freshwater on islands, in addition to the introduction of seawater desalination systems, a transmission structure of "hydrothermal simultaneous transmission" (HST) is proposed. The essence of the IES scheduling problem is the optimal combination of each unit’s output, which is a typical timing control problem and conforms to the Markov decision-making solution framework of deep reinforcement learning. Deep reinforcement learning adapts to various changes and timely adjusts strategies through the interaction of agents and the environment, avoiding complicated modeling and prediction of multi-uncertainties. The simulation results show that the proposed scheduling framework properly handles multi-uncertainties from power sources and loads, achieves a stable demand supply for various resources, and has better performance than other real-time scheduling methods, especially in terms of computational efficiency. In addition, the HST model constitutes an active exploration to improve the utilization efficiency of island freshwater. © 2022, CC BY-NC-ND.

关键词： Desalination

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Deep Sequence to Sequence Attention Network Approach for Short-Term Electricity Price Forecasting

SSRN

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SSRN 2022年

作者： Chen, Haipeng Wu, Huayue Chen, Jian Kan, Tianyang Miao, Sen Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Department of Electrical Engineering Northeast Electric Power University Jilin132012 China Chengde Power Supply Company State Grid Jibei Electric Power Co. Ltd. Chengde067000 China

With increasing renewable energy penetration and the extension of the power system scale, accurate electricity price prediction has long become a challenging task. The complexity of electricity market information, insufficient model training, and the lack of model generalization ability limit the prediction accuracy of the existing electricity price forecasting methods. This paper proposes a Sequence-to-Sequence Attention algorithm based on the Double Deep Q Network optimization method for short-term electricity price prediction. This algorithm first conducts the maximum information coefficient correlation analysis to select the input dataset for the prediction model of electricity price. Secondly, the Sequence-to-Sequence Attention model is proposed for point forecasting and probabilistic forecasting of short-term electricity prices. Finally, the hyperparameters of the model are optimized by the Double Deep Q Network method, which improves the prediction accuracy and the generalization ability of the prediction model of electricity price. simulations are carried out on Pennsylvania–New Jersey–Maryland market data and the New South Wales electricity market. The results show that the proposed method has superior performance than the existing prediction methods, which is appliable for the short-term electricity price prediction and provides a powerful decision-making basis for market participants and regulators. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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