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Energy Management and Optimization of Vehicle-to-grid systems for Wind power Integration

CSEE Journal of power and Energy systems 2021年第1期7卷 172-180页

作者： Wei Wang Liu Liu Jizhen Liu Zhe Chen the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China the School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China with the Department of Energy Technology Aalborg University9220 AalborgDenmark

An approach to smoothing the fluctuations of largescale wind power is investigated using vehicle-to-grid(V2G)***,an energy management and optimization system is designed and *** using the wavelet packet decomposition method,the target grid-connected wind power,the required electric vehicle(EV)power,and supercapacitor power are *** energy management model for EVs is then developed by introducing a knapsack problem that can evaluate the needs of an EV ***,an optimized dispatch strategy for EVs and wind power is developed by using a dynamic programming method.A case study demonstrates that the energy management and optimization method for V2G systems achieves noticeable performance improvements over benchmark techniques.

关键词： Dynamic programming electric vehicle knapsack problem wavelet packet decomposition wind power integration

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

SSRN

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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

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Info-Hkelm Wind power Short-Term Prediction Based on Wind power Low Output Event Identification and Multi-Scenario Error Compensation

SSRN

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SSRN 2023年

作者： Yang, Mao Guo, Yunfeng Huang, Yutong Gu, Dake Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin Province Jilin132012 China

The penetration of a high percentage of renewable energy poses a challenge to the safe and stable operation of the power system, frequent wind power low output events have seriously affected power dispatch and power supply reliability, it becomes necessary to anticipate the occurrence of wind power low output events in advance and to develop effective supply assurance measures. In this paper, we propose a method for identifying and accurately predicting wind power low output events, firstly, we define wind power low output events and identify low output event types based on CNN-BiGRU. Secondly, A weighted clustering comprehensive assessment indicator is proposed to evaluate the effect of clustering of low output events, hybrid kernel extreme learning machine based on weighted mean of vectors algorithm optimization (INFO-HKELM) is used for preliminary prediction of low output events in different scenarios, and a multi-scenario error compensation mechanism based on similar fragment matching is proposed for further correction of power prediction results. The proposed method was applied to a wind farm in Inner Mongolia Autonomous Region for validation. Compared with the rest of the methods, the NRMSE of the method in this paper decreases by 1.56%~1.7%, NMAE decreases by 0.6%~1.11%, and R2 increases by 2.50%~2.92%. The multi-scenario error compensation mechanism reduces the NRMSE and NMAE of the preliminary power prediction results by 1.3% and 1.4%, respectively, which further improves the prediction accuracy and proves the effectiveness of the proposed method in this paper. © 2023, The Authors. All rights reserved.

关键词： Wind power

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Optimal Operation of an Integrated Electricity-heat Energy system Considering Flexible Resources Dispatch for Renewable Integration

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Journal of Modern power systems and Clean Energy 2021年第4期9卷 699-710页

作者： Wei Wang Shuhao Huang Guangming Zhang Jizhen Liu Zhe Chen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Department of Energy Technology Aalborg University9220 AalborgDenmark

Large fluctuations may occur on the energy supply and the load sides when large-scale renewable energies are integrated,leading to great challenges in power *** renewable power curtailment is especially numerous in the integrated electricity-heat energy system(IEHES)on account of electricity-heat *** flexible resources(FRs)on both the energy supply and load sides are introduced into the optimal dispatch of the IEHES and further modeled to alleviate the renewable fluctuations in this *** the energy supply side,three kinds of FRs based on electricity-heat coordination are modeled and *** the load side,the shiftable electricity demand resource is *** this basis,the solution for FRs participating in IEHES dispatch is given,with goals of maximizing the renewable penetration ratio and lowering operation *** scenarios are performed,and the results indicate that the proposed optimal dispatch strategy can effectively reduce the renewable energy curtailment and improve the flexibility of the *** contribution degrees of different FRs for renewable integration are also explored.

关键词： Integrated electricity-heat energy system(IEHES) renewable penetration flexible resource(FR) electricity-heat coordination shiftable demand load

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A Missing Data Imputation Method for Electricity Consumption Data Based on TCN-Attention with Mask Tokens

A Missing Data Imputation Method for Electricity Consumption...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Yifeng Mao Ming Yang Peng Li Zhujian Ou Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China State Grid Nantong Power Supply Company State Grid Jiangsu Electric Power CO. LTD Nantong China

With the popularization of smart meters, power companies can collect massive amounts of data from users for non-invasive load detection, electricity theft detection, etc. However, due to faults in smart meters and abnormal communication, the dataset often has missing values, making it difficult for data-driven methods to be widely applied. In this paper, we propose an unsupervised learning method based on TCN-Attention to fill in customers' electricity consumption data. Firstly, we extract electricity consumption features through TCN. In order to improve the feature extraction capability, we introduce a channel attention mechanism to TCN. The attention mechanism makes TCN focus on the effective features in the data by assigning weights. Meanwhile, we use mask tokens to cover a certain proportion of data, forcing the proposed model to learn potential feature representations of the data during the training process. Finally, the features are restored to data through feedforward neural networks. An example is given to verify the effectiveness of the proposed method.

关键词：

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Optimal Scheduling Strategy for Interconnected Multi-region Peaking Resources Considering Network Currents

Optimal Scheduling Strategy for Interconnected Multi-region ...

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power and Electrical Engineering (ACPEE), 5th Asia Conference on

作者： Junhui Li Yahui Pan Xuesong Qi Xiaozhuo Guan Tiebin Guo Yizhu Zhang Jia Sun Yixin Lu Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Northeast Electric Power University Jilin China Power dispatching and control center Jilin Power Supply Company Jilin China Science and Technology Section Jilin Power Supply Company Jilin China Science and Technology Innovation Department Jilin Electric Power Co Changchun China

ISBN: (数字)9798350309638

ISBN: (纸本)9798350309645

Under the background of the increasing proportion of new energy power generation, the mismatch of “source-load” occurs between regions. To effectively improve the utilization rate of peaking resources and increase the amount of “source-load” matching, this paper proposes the optimal scheduling of multiregional peaking resources considering network currents. The strategy takes into account the impact of wind power penetration rate on the demand for peaking resources and connects the regions with different peaking characteristics using contact lines. It evaluates and iterates the peaking resources of each region in each time period and finds the optimal amount of peaking resources and the optimal output of thermal power units in each region. Through simulation, the results show that the proposed strategy effectively improves wind power consumption and system operation economy.

关键词： Electrical engineering systems operation Asia Optimal scheduling Wind power generation

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

SSRN

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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

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Modeling and small-signal stability analysis of doubly-fed induction generator integrated system

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Global Energy Interconnection 2023年第4期6卷 438-449页

作者： Tianming Gu Puyu Wang Dingyuan Liu Ao Sun Dejian Yang Gangui Yan Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin132012P.R.China School of Automation Nanjing University of Science and TechnologyNanjing 210094Jiangsu ProvinceP.R.China

Owing to their stability,doubly-fed induction generator(DFIG)integrated systems have gained considerable interest and are the most widely implemented type of wind turbines and due to the increasing escalation of the wind generation penetration rate in power *** this study,we investigate a DFIG integrated system comprising four modules:(1)a wind turbine that considers the maximum power point tracking and pitch-angle control,(2)induction generator,(3)rotor/grid-side converter with the corresponding control strategy,and(4)AC power *** detailed small-signal modeling of the entire system is performed by linearizing the dynamic characteristic equation at the steady-state ***,a dichotomy method is proposed based on the maximum eigenvalue real part function to obtain the critical value of the ***-locus analysis is employed to analyze the impact of changes in the phase-locked loop,short-circuit ratio,and blade inertia on the system ***,the accuracy of the small-signal model and the real and imaginary parts of the calculated dominant poles in the theoretical analysis are verified using PSCAD/EMTDC.

关键词： Doubly-fed induction generator(DFIG) Maximum power point tracking Dichotomy Small-signal stability

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Low-Carbon Dispatch Strategy for Virtual power Plant Based on Bagging Ensemble Learning Forecasting Method

Low-Carbon Dispatch Strategy for Virtual Power Plant Based o...

引用

IEEE Student Conference on Electric Machines and systems

作者： Guang Li Xin Dong Li Li Yue Yu Changgang Li State Grid Jinan Power Supply Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350366945

ISBN: (纸本)9798350366952

Virtual power plants (VPPs) struggle with peak-shaving due to unreliable distributed generation (DG) data and the intermittent nature of wind and solar power, impacting carbon emissions. This paper proposes a low-carbon dispatch strategy for VPPs based on a Bagging ensemble forecasting model. Firstly, a VPP dispatch framework based on DG output and load forecasting is established, and a Bagging ensemble forecasting model with feature selection is proposed. Secondly, the control characteristics of flexible industrial, commercial, and residential loads are analyzed to establish a control cost model, which is then integrated with the balanced relationship between wind, photovoltaic, and energy storage, as well as the carbon emission mechanism, to formulate a low-carbon dispatch model. Finally, the simulation results show that the proposed strategy can improve the accuracy of DG output and load forecasting in complex feature environments while realizing the interaction between source-grid-load and reducing the generation and operation costs of the grid.

关键词： Analytical models Costs Carbon dioxide Predictive models Wind power generation Virtual power plants Forecasting Wind forecasting Bagging Load modeling

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Refined power modeling method of ultra-short-term wind power cluster based on power reverse deduction

Refined power modeling method of ultra-short-term wind power...

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

作者： Yupeng Sun Mao Yang Chenglian Ma Yunpeng Jia Guozhong Fang Key Laboratory of Modern Power System Simulation and Control & Renewable EnergyTechnology Ministry of Education Northeast Electric Power University Jilin China Northeast Electric Power University Jilin China Jilin Power Supply Company of State Grid Jilin Electric Power Co. Ltd. Jilin China State Grid Northeast China Branch Shenyang China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

The accurate prediction of wind power provides a guarantee for the safety and stability of the power system and the power market trading. The existing wind power prediction methods mainly focus on the depth of the model, and improve the accuracy of the prediction by constantly improving the model and deepening the model, but they ignore the limitations of the model itself. In this paper, a fine modeling method for wind power is proposed, and the prediction accuracy is improved by horizontal expansion of the model. Firstly, the power output class and its NWP characteristics are determined by power reverse deduction, and the power output class is divided by the degraded and simplified U-net model. Then, Non-stationary Transformer (NSTransformer) is introduced to model each output level separately to get the predicted power. Finally, the proposed method is applied to a wind farm cluster in China. The accuracy of the proposed method is higher than other methods in ultra-short-term prediction, which proves the reliability and effectiveness of the proposed method.

关键词： Accuracy system integration Predictive models Wind power generation power system stability Wind farms Transformers Stability analysis Safety Thermal stability

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