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

作者： Nan Wu Mao Yang Chenglian Ma Yunpeng Jia Guozhong Fang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China Jilin Power Supply Company of State Grid Jilin Electric Power Co. Ltd Jilin China State Gird Northeast China Branch Jilin China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

With the gradual acceleration of the world's energy transformation, the construction of new energy is becoming more and more large-scale. At the same time, the proportion of new energy in the power grid is increasing, and the high volatility of wind power poses certain challenges to the stability and reliability of the power system. Therefore, accurate large-scale wind power prediction is very necessary. This paper presents a multi-task learning predicting method for wind power clusters. Firstly, the wind power cluster is divided into subgroups with similar output characteristics, and then the different subgroup power is used to predict the multi-task targets. Meanwhile, the Kepler optimization algorithm (KOA) is used to optimize the model parameters. When the method is applied to a wind power cluster in China, RMSE and MAE are reduced by 2.07% and 1.92% compared with the traditional multi-task learning method, which proves the effectiveness of the proposed method.

关键词： Learning systems Clustering algorithms system integration Wind power generation power system stability Multitasking Prediction algorithms Stability analysis Reliability Optimization

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Analysis of the influence of wind farms in full operating conditions on the torsional vibration of the shaft system of synchronous generators

Analysis of the influence of wind farms in full operating co...

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International Conference on power system technology (powerCON)

作者： Qi Jia Wenchao Zhai Qiang Zhang Feng Sun Mohan Shen State Grid Liaoning Electric Power Research Institute Shenyang China Ministry of Education Key Laboratory of Modern Power System Simulation Control and Green Power Technology Northeast Electric Power University Jilin China

ISBN: (纸本)9781665407380

The power oscillation induced by the large-scale integration of wind power into the power grid has become one of the important issues affecting the safe and stable operation of the power grid. Aiming at the dynamic interaction between wind farms and synchronous generators, based on the additional excitation signal injection method, the influence of DFIG and PMSG wind farms on the damping characteristics of each torsional vibration mode of synchronous generators under full operating conditions is analyzed. By applying an AC voltage signal at both ends of the field winding to stimulate rotational speed oscillation, observe the difference in the free response characteristics of rotational speed disturbances, identify the electromechanical coupling characteristics between the wind farm and the synchronous generator set, and reveal the influence of the wind farm type and operating conditions on the shaft system of the synchronous generator set. The mechanism of torsional vibration. Finally, the validity of the theoretical analysis is verified based on time-domain simulation.

关键词： Vibrations Damping Shafts Fans Wind speed power system dynamics Wind farms

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Scheduling of Fully renewable energy systems with CSP Plants and Pumped Storage

Scheduling of Fully Renewable Energy Systems with CSP Plants...

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China International Electrical and energy Conference (CIEEC)

作者： Yang Cui Siyu Huang Huiquan Zhang Hongbo Li Key Laboratory of Modern Power Sysntem Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Ji Lin China State Grid Jilin Power Co. Ltd. Training Center Ji Lin China

ISBN: (数字)9781728116754

ISBN: (纸本)9781728116761

energy crisis and environmental pollution are becoming increasingly serious. Increasing the penetration rate of renewable energy in the power system can effectively solve this problem. Wind power and photovoltaic power generation are fluctuating power sources, and their large-scale interconnection will increase the difficulty of grid scheduling. Therefore, based on the regulation characteristics of concentrating solar power station, this paper proposes a green power dispatching strategy for power system with full renewable energy. The purpose of this strategy is to promote the consumption of renewable energy. The cost of power generation of each unit of the system and the security operation constraints of the grid are considered comprehensively. Finally, CPLEX optimization software was used to solve the model. The results verify the feasibility and validity of the proposed model.

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Remote Sensing Image Object Detection Based on Improved Sparse R-CNN

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1303-1320页

作者： Zhao, Li-Quan Chen, Chun-Lu Zhong, Tie Cui, Ying Jia, Yan-Fei Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China School of Electric Power Engineering Northeast Electric Power University Jilin 132012 China Zhuhai Power Supply Bureau Guangdong Electric Power Corporation Zhuhai 519000 China School of Electrical and Information Engineering Beihua University Jilin 132013 China

A more precise sparse region convolutional neural network algorithm is developed to increase the accuracy of remote sensing object recognition. Firstly, a new intersection over union method is proposed to solve the problem that the intersection over union used in sparse region convolutional neural network cannot well measure the offset between the ground-truth box and the proposal box. It introduces multiple geo-metric factors that are overlap area, center point distance, vertex distance between the ground-truth box and the proposal box, and diagonal distance of minimum enclosing of the ground-truth box and the proposal box and into the new intersection over union. The proposed intersection over union can measure the offset between the ground-truth box and the proposal box to optimal the proposal box. Secondly, to improve the detection precision of sparse region convolutional neural network, a dual branches dynamic instance interaction head is designed. The proposed dynamic interaction head consists of a fully connected branch and a convolution branch. The fully connected head is used for both bounding box regression and object classification. The convolution branch is only used for bounding box regression. Compared with original sparse region convolutional neural network, the average mAP and recall are 3.55% and 5.75% higher, and the average detection speed is only 0.85 FPS slower. The proposed method realizes balance between detection precision and detection speed. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Remote sensing

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Ultra-short-term power Prediction of Photovoltaic based on CNN-AM-BiLSTM

Ultra-short-term Power Prediction of Photovoltaic based on C...

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

作者： Junhui Li Mingxuan Du Haozheng Yu Jie Ma Youwen Zhang Jiatao Gan Xingxu Zhu Cuiping Li Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China AnYang Power Supply Company State Grid HeNan Electric Power Company AnYang China Economic and Technical Research Institute State Grid HeNan Electric Power Company ZhengZhou China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

The power of photovoltaic power generation is affected by a variety of factors such as weather, radiation and temperature, exhibit a high degree of randomness and uncertainty. To address this problem, this paper proposes a ultra-short-term photovoltaic power prediction model based on Convolutional Neural Networks (CNN) and attention mechanism and bidirectional long short-term memory (Bi-LSTM). Firstly, Pearson's correlation coefficient was used to filter out the meteorological factors which have significant influence on the historical power. Subsequently, in order to capture the spatial features of the input data, a convolutional neural network is utilized for feature extraction and an attention mechanism is introduced to strengthen the model's ability to discriminate key features. Meanwhile, the Bi-LSTM network is utilized to capture the temporal dependencies in the time series data, and finally the PV power prediction value is obtained. Finally, the validation is based on data from actual PV power plants, and the results show that the proposed Convolutional Neural Networks and attention mechanism (CA) and Bi-LSTM Ultra-short-term PV power prediction model outperforms the single model in terms of prediction accuracy, reflecting the effectiveness and superiority of the hybrid model.

关键词： Photovoltaic systems Accuracy Attention mechanisms Time series analysis Weather forecasting Predictive models Feature extraction Data models Hybrid power systems Convolutional neural networks

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Dc Fault Ride-Through control for Mmc-Hvdc Grid-Connected Wind Farm Based on energy Storage system

SSRN

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SSRN 2023年

作者： Sun, Yinfeng Guo, Yuhang Lu, Yuze Lu, Mingzhi Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Shijiazhuang Gaocheng District Electric Supply Company No. 76 Changsheng South Street Gaocheng District Shijiazhuang052160 China

Large wind farms connected to the grid through MMC-HVDC systems can cause overloading of the non-faulty converter station after a DC fault. This short communication proposes a system fault ride-through method by coordinating and controlling the energy storage system, the non-faulty converter station and the wind farm to jointly consume the unbalanced power during the fault. The model is created in the PSCAD simulation platform, and the correctness and effectiveness of the method are verified by simulation. © 2023, The Authors. All rights reserved.

关键词： energy storage

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Operational Optimization of Photovoltaic-Storage system Considering Demand Response

Operational Optimization of Photovoltaic-Storage System Cons...

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

作者： Xuefeng Gao Wenwen Qin He Wang Yiwen Yao Cuiping Li Hao Peng Xingxu Zhu Junhui Li State Grid Jilin Electric Power Company Limited Economic and Technical Research Institute Changchun China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University China Songyuan Power Supply Company State Grid Jilin Electric Power Company Limited Songyuan China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Aiming to address the stability challenges in power systems caused by the volatility and uncertainty of photovoltaic (PV) power generation, an optimization strategy for PV-storage systems incorporating demand response (DR) is proposed. Which is based on PV generation characteristics, Time-of-Use Pricing (TUP), and demand side flexibility. A multi-objective optimization model is established to balance the operational and environmental costs of PV-storage configuration. Building on dynamic adjustments at the demand side, a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm with dynamic inertia weighting is introduced to enhance global search capability for optimized energy storage (ES) configuration. In case study, two scenarios are designed to validate the effectiveness of the proposed model in reducing costs and improving PV utilization rates.

关键词： Photovoltaic systems Costs Uncertainty Heuristic algorithms Optimization models power system dynamics power system stability Demand response Optimization Load modeling

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Dc Fault Ride-Through control for Mmc-Hvdc Grid-Connected Wind Farm Based on energy Storage system

SSRN

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SSRN 2023年

作者： Sun, Yinfeng Guo, Yuhang Lu, Yuze Lu, Mingzhi Key Laboratory of Modern Power System Simulation and Control& Renewable Energy Technology Ministry of Education [Northeast Electric Power University Jilin132012 China State GridShijiazhuang Gaocheng District Electric Supply Company No. 76Changsheng SouthStreet Gaocheng District Shijiazhuang052160 China

关键词： energy storage

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The impacts of interprovincial agricultural trade on water resources in China: from perspective of grey water footprint

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energy Procedia 2018年 152卷 253-258页

作者： Changyi Liao Saige Wang Jiake Fang Yiyi Zhang Guangxi Key Laboratory of Power System Optimization and Energy Technology Guangxi University Nanning Guangxi 530004 China Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Beijing Normal University Beijing 100875 P.R. China

As the largest water consumption sector, agriculture sector plays a significant role in water resources reallocating via economic trade. Different from previous studies focusing on the impacts of economic trade on water quantity, this paper concentrates on the impacts on the water quality. Grey water footprint (GWF) of agricultural sector is used for investigating the impacts of trade on water resources. In this paper, the regional virtual grey water of agricultural trade network was built based on the GWF and multiregional input–output analysis. The GWF for agricultural sector in each region was inventoried. Then GWF of agriculture embodied in monetary flows among regions were calculated using multiregional input–output analysis. The main results showed that Shandong, Henan, Heilongjiang, Inner Mongolia, and Hebei were the most critical regions for water resources. Besides, Zhejiang and Guangdong have saved a large amount of water by importing water resources through agricultural trade. While, Hebei, Heilongjiang, Anhui, Henan and Xinjiang are exporting a large amount of water resources. Moreover, the regions with developed economy and higher population are more dependent on agricultural imports. By identifying the critical node and pathway in virtual grey water trade, this paper aims to provide theoretical foundations for mitigating water pressure and reducing water pollution in economic trade activities.

关键词： Agricultural sector grey water footprint multiregional input-output analysis water polllution

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The electricity-water nexus in Chinese electric trade system

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energy Procedia 2018年 152卷 247-252页

作者： Jiake Fang Saige Wang Yiyi Zhang Bin Chen Guangxi Key Laboratory of Power System Optimization and Energy Technology Guangxi University Nanning Guangxi 530004 China Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Beijing Normal University Beijing 100875 P.R. China

A great amount of electricity generation exerts a tremendous pressure on China's water resources. The spatial mismatch between primary energy and water in China aggravates the pressure of water resources and results in virtual water transfers from drier to wetter regions through electricity trade. Firstly, we construct an electricity generation-consumption (EGC) model to model inter-provincial electricity trade in China and propose a hybrid virtual water intensity (HVWI) indicator to represent the provincial overall virtual water consumption factor. Then, we combine the inter-provincial electricity trade and the provincial HVWI to build China's virtual water trade network. The results show that total inter-provincial virtual water embodied in the electricity trade is 5637 GL. The top three largest electricity trade flows are Yunnan-to-Guangdong (111 billion kWh), Inner Mongolia-to-Beijing (32.7 billion kWh) and Inner Mongolia-to-Liaoning (31.1 billion kWh), and the top three largest virtual water flows are Yunnan-to-Guangdong (1527 GL), Sichuan-to-Chongqing (432.6 GL) and Sichuan-to-Shanghai (418.7 GL).

关键词： Electricity trade virtual water electricity-water nexus electricity management

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