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检索条件"机构=State Key Laboratory of Control and Simulation of Power System and Power"

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Uniform Modeling of Hybrid AC/DC Transmission Network for Path-Aware Inter-Provincial Electricity Trading

Uniform Modeling of Hybrid AC/DC Transmission Network for Pa...

引用

Sustainable power and Energy Conference (iSPEC)

作者： Shiyuan Tao Zhenfei Tan Zheng Yan Hui Deng Ziqing Zhou Key Laboratory of Control of Power Transmission and Conversion Ministry of Education hanghai Non-Carbon Energy Conversion and Utilization Institute Shanghai Jiao Tong University Shanghai China Electric Power Research Institute (Power Market Simulation Laboratory) State Grid Zhejiang Electric Power CO. LTD.

To support the path-aware trading clearing of the inter-provincial electricity market in China, a uniform modeling method for the hybrid AC/DC transmission network is proposed in this paper. This method uses a radial equivalent network to replace the AC network for trading path identification. The depth-first search method is then leveraged to enumerate all trading paths in the unified AC/DC network. To demonstrate the effectiveness of the proposed method in the inter-provincial market clearing, the path-aware market clearing model and the high-low matchmaking market clearing model are established. Case studies based on a 6-province test system verify the effectiveness of the proposed method and compare the differences between the two market clearing mechanisms.

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Analysis on the Physical and Chemical Properties of Decay-like Fracture Composite Insulator Shed 3

Analysis on the Physical and Chemical Properties of Decay-li...

引用

3rd International Conference on Electrical Materials and power Equipment, ICEMPE 2021

作者： Dong, Kai Zhang, Zhijin Lu, Ming Gao, Chao Jiang, Xingliang State Key Laboratory of Power Transmission Equipment System Security and New Technology Chongqing University Chongqing China Henan Key Laboratory of Power Transmission Line Galloping Prevention and Control Technology State Grid Henan Electric Power Research Institute Zhengzhou450052 China

ISBN: (纸本)9781665418409

In recent years, decay-like fracture has become the main fracture form of composite insulators, and the faults caused by it seriously threaten the safety and stability of the power system. In this paper, decay-like fracture composite insulators string on the 500kV are used as samples to determine the physical and chemical properties of their shed. Its physical properties are detected by appearance inspection, hardness, pollution test and hydrophobicity;its chemical properties are detected by nuclear magnetic resonance and Fourier transform infrared spectroscopy. The physical and chemical characteristics of the decay-like fracture shed are obtained, it is found that decay is closely related to aging and partial discharge is also an important factor leading to the decay of composite insulators. © 2021 IEEE.

关键词： Fracture

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Wind and PV power Ramp Events Prediction Based on Long Short-Term Memory Network

Wind and PV Power Ramp Events Prediction Based on Long Short...

引用

Sustainable power and Energy Conference (iSPEC)

作者： Zengwei Wang Zhendong Li Yutian Liu Huan Ma Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China. State Grid Shandong Electric Power Research Institute Jinan China.

With the increasing penetration rate of renewable generation, the challenges arising from its randomness and intermittent nature are becoming more pronounced. In particular, wind and photovoltaic power ramp events pose a serious threat to the secure and stable operation of power systems, and even lead to frequency instability and load shedding. Based on the long short-term memory neural networks (LSTM), this paper proposes a novel renewable power ramp events (RPREs) forecasting approach. First, filter the key tie-lines. Second, the LSTM network was driven by the known time series of wind, PV, load, and tie-line power to forecast the tie-line power for the next hour, thereby determining the adjustable capacity of the tie-line. Finally, the regulatory capability of the grid is considered to judge the occurrence of a power ramping event. Case study demonstrates that the proposed method can effectively predict power ramping events and meets the requirements of online assessment.

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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...

引用

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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Energy Storage Configuration Method for Distribution Network Resilience Enhancement Using Two-Stage Game Optimization

Energy Storage Configuration Method for Distribution Network...

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IEEE International Conference on power and power and Renewable Energy (ICPRE)

作者： Shuai Liu Yuwen Li Heng Zhou Kuan Cao Yutian Liu Development Planning Department State Grid Weihai Power Supply Company Weihai China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

In order to cope with the impact of extreme weather on distribution network, a two-stage multi-objective game optimization-based method of energy storage configuration for distribution network resilience enhancement is proposed in this paper. First, typical scenarios are generated based on N-k contingency as well as distributed photovoltaic (DPV) output characteristics, and evaluation indexes of resilience and economy are established. Then a two-stage multi-objective optimal configuration model is proposed, considering the operating state and resilience demand of distribution network before and after the power supply transfer. Finally, the model is solved by multi-objective particle swarm (MOPSO) and Nash game. simulation results on the modified IEEE33-node model demonstrate that the proposed method can achieve good equilibrium solution between resilience and economy.

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A Distributionally Robust Optimal Allocation Method of Energy Storage for Resilience Enhancement of Distribution Network

A Distributionally Robust Optimal Allocation Method of Energ...

引用

Energy Technologies for Future Grids (ETFG), IEEE International Conference on

作者： Kuan Cao Yutian Liu Chunyi Wang Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China Development Planning Department State Grid Shandong Electric Power Company Jinan China

To solve the problem of active power imbalance in distribution network with high penetrations of photovoltaic (PV) under extreme weather, the paper proposes a distributionally robust optimal allocation method of energy storage considering extreme scenarios. Firstly, considering that the effect of energy storage on enhancing the resilience of distribution network is strongly related to its location, resilience indexes based on node evaluation are established to select the installation sites of energy storage. Secondly, in view of the correlation and randomness of PV output and load, the typical scenarios with the empirical probability distribution are generated based on Frank Copula joint probability distribution function. Then, Kullback-Leibler divergence is utilized to characterize the probability distribution ambiguity set of the typical scenarios. Based on the ambiguity set, a two-stage distributionally robust optimization model for energy storage capacity is established to minimize the cost of energy storage investment and distribution network operation. For further improving the resilience, an operational resilience index as a chance constraint considering extreme scenarios is embedded into the model. Finally, the case study shows that the proposed method can effectively improve PV accommodation capacity of distribution network and reduce load loss in extreme scenarios.

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Energy Storage Sizing for IES Considering Carbon Trading and Demand Response

Energy Storage Sizing for IES Considering Carbon Trading and...

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IEEE International Conference on power and Energy Applications (ICPEA)

作者： Lipeng Zhang Kuan Cao Yuwen Li Jie Gao Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China Development Planning Department State Grid Weihai Power Supply Company Weihai China

An optimal energy storage sizing method for integrated energy system (IES) considering carbon trading and demand response is presented in this paper. Firstly, to effectively limit carbon emissions, a reward-penalty laddered carbon trading model is designed to convert carbon emissions into system costs or revenues. Secondly, the integrated demand response (IDR) model for electrical-cold-heat loads is established based on time-of-use price to enhance the operational flexibility and economy. Then, based on these two sub-models, an optimal energy storage sizing model is built to minimize the total life cycle cost including investment, operation and carbon trading cost. Finally, the simulation results indicate that a successful balance between low-carbon and economy can be achieved by the proposed method.

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Spatial-Temporal Attention Mechanism and Graph Convolutional Networks Based Distributed PV Ultra-short-term power Forecasting

Spatial-Temporal Attention Mechanism and Graph Convolutional...

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Energy Technologies for Future Grids (ETFG), IEEE International Conference on

作者： Xinyu Liu Xu Xie Qin Fang Fei Xu Chen Huang Zhao Zhen Fei Wang State Grid Chongqing Electric Power Company Chongqing China State Grid Chongqing Electric Power Company Shinan Power Supply Branch Chongqing China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China Department of Electrical Engineering North China Electric Power University Baoding China

To ensure the efficient operation and healthy development of photovoltaic power generation systems, reliable distributed photovoltaic power prediction is significant. However, the lack of modeling ability of existing distributed photovoltaic power forecasting methods for dynamic spatiotemporal correlation of distributed photovoltaic power data limits the further improvement of prediction accuracy. Therefore, a distributed photovoltaic ultra-short-term power forecasting method based on spatial-temporal attention mechanism and graph convolutional networks is proposed in this paper. Firstly, distributed photovoltaic cluster is divided into sub-regions and graph structured data can be generated with each sub-region being a node on the graph. Then, spatial-temporal attention mechanism is used to adaptively capture spatiotemporal correlation information and dynamically update adjacency matrix. Finally, spatial-temporal convolution is used to extract deep spatiotemporal features of distributed photovoltaic power data and achieve regional power prediction. The validity and progressiveness of the model have been verified in real world data sets.

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Wind power Interval Prediction Based on CGAN and KELM under Extreme Weather Scenarios

Wind Power Interval Prediction Based on CGAN and KELM under ...

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IEEE/IAS Industrial and Commercial power system Asia (I&CPS Asia)

作者： Weisi Deng Zhongfu Dai Ruofan Chen Haohuai Wang Siyu Lu Chonghao Li Baorong Zhou Hydropower and New Energy Department Power Dispatch and Control Center of China Southern Power Grid Guangzhou China New Power System Research Department State Key Laboratory of HVDC Electric Power Research Institute of China Southern Power Grid Company Limited Guangzhou China

As the proportion of wind power in grid continues to increase, more and more newly built or expanded wind farms are facing problems such as lack of historical samples and insufficient training of prediction models. With the increasing frequency of extreme weather events, most wind farms also face problems such as scarce samples of extreme weather scenarios and large power forecasting errors. In response to the above-mentioned issues, this paper proposes an wind power interval prediction method based on conditional generative adversarial networks (CGAN) and kernel extreme learning machines (KELM). With the historical samples of extreme weather scenarios, a large number of new samples are generated through CGAN. Based on KELM, an interval prediction model under extreme weather is constructed. Compared to point prediction, this model can provide richer probability information, and compared to other interval prediction methods, the results show an effective improvement in prediction performance.

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A Spatiotemporal Interpolation Approach for Distributed PV power Based on WPT and DTW

A Spatiotemporal Interpolation Approach for Distributed PV P...

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IEEE/IAS Industrial and Commercial power system Asia (I&CPS Asia)

作者： Chuanqi Wang Ming Yang Yixiao Yu Menglin Li Xudong Zheng Qiangsheng Bu Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China State Grid Jiangsu Electric Power CO. LTD. Research Institute Nanjing China

The loss of electricity during transmission cannot be ignored, and a large number of power consumers are seeking flexible and affordable distributed generation methods to reduce electricity costs. However, a large number of distributed photovoltaic (PV) connections pose challenges to electric power dispatch and the security of power system. Accurate PV power forecast can solve these problems, which requires a large amount of accurate historical data to build forecast model. This article proposes a spatiotemporal interpolation method to complement historical data of PV sites. Wavelet packet transform (WPT) is applied to decompose original time sequence to stable sequence and fluctuant sequence. To eliminate the fluctuations caused by cloud movement, dynamic time warping (DTW) is used to regularize fluctuant sequences of adjacent PV sites. Finally, through spatial interpolation, a precise historical data can be obtained. The case study shows that under cloudy conditions, proposed interpolation method reduces the NRMSE by 0.51% compared to the direct interpolation algorithm.

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