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Learning-Based Model Predictive Secondary Frequency control of Pv-Ess-Ev Microgrid

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SSRN 2024年

作者： Zhong, Cheng Zhao, Hailong Liu, Yudong Liu, Chuang Key Laboratory of Modern Power System Simulation and Control &Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin Province Jilin132012 China Northeast Electric Power University College of Electrical Engineering Jilin Province Jilin132012 China

Microgrids comprising photovoltaic (PV), energy-storage systems and electric vehicles have received increasing attention. However, the increase in renewable energy sources, such as PVs, will introduce uncertainty factors to the frequency response, resulting in frequency fluctuations that can reduce power quality. Hence, we propose an outstanding learning-based control approach that combines a model of physical principles with a Gaussian Process (GP) learning approach. The learning model corrects the physical principles model and estimates its uncertainty. simulations are performed by comparing the proposed controller with conventional model-predictive controllers. The results indicate that the learning-based controller proposed can adjust the frequency with less overshooting and a lower root mean square error compared to the conventional MPC control method. © 2024, The Authors. All rights reserved.

关键词： Model predictive control

An Optimal Scheduling Method for Shared energy Storage Based on Wind power Uncertainty

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An Optimal Scheduling Method for Shared Energy Storage Based...

power Science and technology (ICPST), IEEE International Conference on

作者： Baoju Li Zezhong Liu Jiyue Fu Junhui Li Xiaobiao Fu Xingxu Zhu Yao Wang Cuiping Li Power Dispatching and Control Center State Grid Jilin Electric Power Co. Ltd Chang Chun Ji Lin China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education(Northeast Electric Power University) Ji Lin Ji Lin China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

In order to improve the utilization of energy storage, to explore the value of energy storage utilization, and to study the utilization of energy storage. In this paper, a shared energy storage optimization scheduling method based on wind power uncertainty is proposed. Firstly, the scenario generation method of Latin hypercubic sampling and Cholesky decomposition is used to quantify the wind power output, and the uncertainty output is converted into deterministic output; then the economic operation is taken as the optimal scheduling objective, and environmental benefits such as carbon capture and grid constraints are considered to carry out the optimized scheduling of shared energy storage; finally, the validity of the method is verified through the scheduling of the 6-node arithmetic example system, and the utilization of the energy storage is analyzed. Finally, the effectiveness of the method is verified by scheduling in a 6-node arithmetic system, and energy storage utilization is analyzed.

关键词： Uncertainty Thermal decomposition Optimal scheduling Wind power generation Hypercubes Thermal analysis Scenario generation

Zoning Voltage Coordination control Method for High Proportion Distributed Photovoltaic power in Distribution Network

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Zoning Voltage Coordination Control Method for High Proporti...

energy Engineering and power systems (EEPS), International Conference on

作者： Bing Xue Yanfeng Ge Chuang Li Niankang Guo Chenglian Ma Dawei Huang State Grid Yingkou Power Supply Company State Grid Liaoning Electric Power Co. LTD Yingkou Liaoning China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China

ISBN: (数字)9798350375138

ISBN: (纸本)9798350375145

The high penetration of distributed photovoltaic generation into distribution networks has become a major development trend in the future. However, the intermittency and random fluctuations of distributed photovoltaic output have exacerbated voltage violation and fluctuation issues in distribution networks. To address this problem, a distribution network zoning voltage coordination control strategy is proposed. In terms of zoning, voltage sensitivity index information obtained from daily power flow calculations is utilized to analyse and express the impact variation of photovoltaic generation on the voltage of each node. Based on the threshold requirements of voltage variation indices, the zoning of nodes is determined. In terms of zoning voltage coordination control, a strategy of voltage sensitivity control within the partition is adopted, pairing nodes within each partition and utilizing the active and reactive regulation capabilities of photovoltaic generation to control all over-limit voltage within normal range. Furthermore, through the interval distributed voltage coordination control strategy, the interval state variables are equivalenced, and based on the SADMM algorithm, the dynamic reactive power compensation amount between adjacent partitions is optimized to achieve the optimization and suppression of network-wide voltage fluctuations. Taking the IEEE 33-node system as an example, a case study was conducted to verify the effectiveness and feasibility of the proposed strategy in practical applications.

关键词： Photovoltaic systems Reactive power Fluctuations Voltage fluctuations Sensitivity Distribution networks Regulation Indexes Voltage control Optimization

Critical link identification of power system vulnerability based on modified graph attention network

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arXiv

arXiv 2024年

作者： Wang, Changgang Wang, Xianwei Cao, Yu Li, Yang Lü, Qi Zhang, Yaoxin Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Northeast Electric Power University Jilin132012 China State Grid Tianjin Electric Power Company Tianjin300010 China

As the power grid expands and the proportion of renewable energy increases, the uncertainty and randomness of the power grid have increased, posing a threat to the security of system operation. It becomes crucial to identify the crucial links in the power grid to ensure its reliability during operation. Addressing the issues of slow identification speed and difficulty in meeting the actual operational requirements of traditional methods for identifying crucial links in the power grid, this paper proposes a method based on the Modified Graph Attention Network (MGAT) algorithm for identifying crucial link in the power grid. Firstly, combining complex network theory with actual power grid operational data, an evaluation index set is established. Subsequently, the MGAT is utilized to explore the mapping relationships between various indicators during power grid operation and the crucial links, forming a model for crucial link identification. Considering the requirements of training accuracy and efficiency, the original data is preprocessed to optimize the model. Next, through simulation, the original dataset is obtained for training, validation, and testing of the identification model. Finally, the proposed model is applied to an improved IEEE 30-node system and a real power grid. The results indicate the feasibility of the proposed method, with accuracy and speed surpassing traditional methods, demonstrating its practical engineering value. © 2024, CC BY-NC-ND.

关键词： Smart power grids

Ultra-Short-Term Wind power Interval Prediction Based on Fluctuating Process Partitioning and Quantile Regression Forest

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FRONTIERS IN energy RESEARCH 2022年 10卷

作者： Sun, Yong Huang, Yutong Yang, Mao Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China State Grid Jilin Electric Power Company Ltd. Changchun China

As errors in point forecasts of wind power are unavoidable, interval forecasts can adequately describe the uncertainty in wind power and thus provide further guidance to dispatchers in their decision making. Current interval prediction methods are still incomplete in terms of tapping into the physical variability of wind power, especially for the specific time scale of the ultra-short term. This paper therefore proposes a new framework for interval forecasting of ultra-short-term wind power that incorporates the power fluctuation process. Firstly, a fluctuating process of wind power series is defined and a Kalman-SOM method for clustering the fluctuating processes of wind power is constructed. Secondly, a quantile regression forest interval prediction model is constructed for multiple fluctuation processes for ultra-short-term time scales. Finally, the effectiveness of the overall framework is validated at a wind farm in Jilin Province, China. Compared with the traditional interval prediction method. The interval bandwidth is reduced by 0.86% on average, and the interval coverage is increased by 1.4% on average. The results demonstrate the effectiveness and feasibility of the method in this paper.

关键词： wind power ultra-short-term prediction self-organizing map fluctuation quantile regression forest

Long-Foresight-Term power Prediction Considering the Evolution of Significant Weather Information for Wind Farm Cluster

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SSRN 2023年

作者： Yang, Mao Jiang, Renxian Zhang, Xiaobin Huang, Yutong Guo, Yunfeng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Gansu Electric Power Company Gansu730030 China

The current wind power prediction horizon extends up to 7 days, and the research on power prediction for the 8 to 15 day-ahead is still lacking. However, with the increasing proportion of renewable energy in novel power systems, breaking through the long-foresight-term power prediction barrier is crucial support for guaranteeing the secure and stable operation of the power system. To address the aforementioned issues, this study presents a power prediction method for wind farm cluster (WFC) in long-foresight-term, based on the poor availability of existing numerical weather prediction (NWP) in long-foresight-term. Firstly, a significant wind process identification method considering autocorrelation analysis is employed to predict future power trends, and an optimal method is explored for power generation (PG) prediction in the 8 to 15 day-ahead. Secondly, under the constraint of predicted PG, the power corresponding to the historical similar wind process is selected as the predicted value. Finally, the proposed methodology is applied to a WFC in Gansu Province, China. The results indicate that the proposed method reduces the average RMSE and MAE by 4.15% and 3.3%, respectively, effectively tracking the changing trends of wind power. Moreover, it achieves long-foresight-term prediction and enhances the reliability of the prediction. © 2023, The Authors. All rights reserved.

关键词： Numerical methods

Partial Discharge Fault Diagnosis in power Transformer Based on Symplectic Geometry Mode Decomposition and Optimized Convolutional Neural Networks

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SSRN 2023年

作者： Shang, Haikun Liu, Zhidong Huang, Tao Zhang, Ranzhe Li, Feng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Electric Power Research Institute Xinjiang830011 China

To address challenges in feature extraction, such as difficulty in accurately extracting valuable information and low recognition accuracy during pattern recognition, this paper proposes a novel method for power transformer PD fault diagnosis based on symplectic geometry mode decomposition (SGMD) and optimized Convolutional Neural Networks (CNN). First, three different types of power transformer PD fault signals are extracted, namely floating discharge (FD), corona discharge (CD), and bubble discharge (BD), in a laboratory environment. Second, PD signals are decomposed using SGMD to obtain different symplectic geometric components (SGCs). Afterwards, approximate entropy (ApEn) values of each SGC are calculated as PD feature parameters. Subsequently, the sparrow search algorithm (SSA) algorithm is utilized to optimize and configure the CNN parameters to enhance the diagnostic accuracy and stability. Finally, the obtained feature parameters are fed into the optimized CNN for fault diagnosis. simulation and experimental analysis verify the effectiveness of the proposed method. © 2023, The Authors. All rights reserved.

关键词： Mode decomposition

Exergic Perspective of Low Carbon Economy Dispatch of Carbon Capture power Plant and power to Gas Synergistic Operation

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SSRN 2023年

作者： Wang, Xiuyun Zhang, Wanyu Wang, Rutian Wen, Xiangyun Cui, Benwang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Tianjin Electric Power Company Tianjin300010 China

In order to improve the system economy and low carbon, the power to gas-carbon capture power plants joint operation system is introduced in the integrated energy system to reduce carbon emissions while increasing the consumption of renewable energy. From the perspective of exergy, both the quantity and quality of energy were taken into account, the calculation methods of different exergy values of energy flow were refined, and the sub-model of exergy flow calculation was established. To further achieve low carbon emission reduction, the costs of carbon emission trading, carbon capture energy consumption and carbon storage were included in the non-energy costs, and the exergoeconomic cost distribution model was established. Compared with the traditional pricing method, this paper considers the impact of carbon cost and energy grade difference on unit exergic cost of cooling, heating and electricity in the system, reflecting the principle of higher quality and higher price. Finally, the constructed model is solved with the total cost as the objective function. The simulation example shows that the proposed scheduling method can improve the low-carbon emission reduction capacity of the system and achieve reasonable cost distribution. © 2023, The Authors. All rights reserved.

关键词： Carbon capture

An Improved Virtual Impedance Droop control of Three-Port Converter Interfaced Hybrid energy Storage systems

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An Improved Virtual Impedance Droop Control of Three-Port Co...

International Conference on power Electronics systems and Applications (ICPESA)

作者： Sisi Liang Xiaolin Wang Yuanmao Ye Yao Ma School of Automation Guangdong University of Technology Guangzhou China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China Hainan Electric Power School Hainan Power Grid Co. Ltd. Haikou China

ISBN: (数字)9798350365801

ISBN: (纸本)9798350365818

The power allocation of hybrid energy storage system (HESS) is crucial for the development of hydrogen fuel cell vehicles (HFCVs). To improve the operational efficiency, a triple active bridge (TAB) converter is designed as the interface circuit, which can transfer power bi-directionally and isolate the voltage in a wide range of transient conditions. This paper proposes a droop control strategy based on adjustable virtual impedance for HESS. The fuel cell (FC) side converter adopts the virtual resistance and inductance droop (VRID) control, while the Lithium battery side converter uses the virtual capacitance droop (VCD) control. The Lithium battery can quickly absorb the high-frequency component of the system power fluctuation, which improves the dynamic response of the system. Moreover, the value of the virtual resistance is adjusted according to the operating state of the circuit, which helps to enhance the power quality and eliminate the voltage deviations caused by conventional droop control. The proposed control strategy and analytical results are validated by both simulations and experiments.

关键词： Resistance Analytical models Fluctuations Fuel cells Lithium batteries Hybrid power systems Impedance