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Renewable Scenario Generation Using Controllable Generative Adversarial Networks with Transparent Latent Space

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CSEE Journal of Power and Energy Systems 2021年第1期7卷 66-77页

作者： Ji Qiao Tianjiao PU Xinying Wang the China Electric Power Research Institute Haidian DistrictBeijing 100192China

With the growing penetration of renewable energysources in power systems, it becomes increasingly important tocharacterize their inherent variability and uncertainty. Scenariogeneration is a key approach to provide a series of possible powerscenarios in the future for the system planner and operator tomake decisions. In this paper, a data-driven method is presentedfor renewable scenario generation using stable and controllablegenerative adversarial networks with transparent latent space(ctrl-GANs). The machine learning based algorithm can capturethe nonlinear and dynamic renewable patterns without the needfor modeling assumptions and complicated sampling techniques.The orthogonal regularization and spectral normalization areadopted to improve the training stabilization of the GAN model.To control the generation process, a relationship is built betweenfeatures of the generated scenarios and latent vectors on themanifold. Moreover, several new metrics for GANs are used toevaluate the quality of the scenarios. The proposed approachis applied to generate realistic time series data of wind andphotovoltaic power. The results demonstrate that our methodhas a better performance on numerical stabilization and is ableto control the generation process with latent space.

关键词： Renewable scenario generation deep learning generative adversarial networks neural network transparent latent spacer

Successive MISOCP Algorithm for Islanded Distribution Networks with Soft Open Points

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 209-220页

作者： Tao Zhang Yunfei Mu Hongjie Jia Xinying Wang Tianjiao PU Key Laboratory of Smart Grid of Ministry of Education Key Laboratory of Smart Energy&Information Technology of Tianjin MunicipalityTianjin UniversityTianjin 300072China e China Electric Power Research Institute Beijing 100192China

An optimal operation scheme is of great significance in islanded distribution networks to restore critical loads and has recently attracted considerable attention.In this paper,an optimal power flow(OPF)model for islanded distribution networks equipped with soft open points(SOPs)is proposed.Unlike in the grid-connected mode,the adequacy of local power generation in distribution networks is critical for islanded systems.The proposed approach utilizes the power output of local distributed generations(DGs)and the benefits of reactive power compensation provided by SOPs to allow maximum loadability.To exploit the available resources,an optimal secondary droop control strategy is introduced for the islanded distribution networks,thereby minimizing load shedding.The formulated OPF problem is essentially a mixed-integer nonlinear programming(MINLP)model.To guarantee the computation efficiency and accuracy.A successive mixed-integer second-order cone programming(SMISOCP)algorithm is proposed for handling the nonlinear islanded power flow formulations.Two case studies,incorporating a modified IEEE 33-bus system and IEEE 123-bus system,are performed to test the effectiveness of the proposed approach.

关键词： Distribution network islanded operation second-order cone programming soft open points successive approximation

Spatio-temporal Convolutional Network Based Power Forecasting of Multiple Wind Farms

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Journal of Modern Power Systems and Clean Energy 2022年第2期10卷 388-398页

作者： Xiaochong Dong Yingyun Sun Ye Li Xinying Wang Tianjiao PU the School of Electrical and Electronic Engineering North China Electric Power UniversityBeijing 102206China he China Electric Power Research Institute Beijing 100192China

The rapidly increasing wind power penetration presents new challenges to the operation of power systems.Improving the accuracy of wind power forecasting is a possible solution under this circumstance.In the power forecasting of mul-tiple wind farms,determining the spatio-temporal correlation of multiple wind farms is critical for improving the forecasting accuracy.This paper proposes a spatio-temporal convolutional network(STCN)that utilizes a directed graph convolutional structure.A temporal convolutional network is also adopted to characterize the temporal features of wind power.Historical data from 15 wind farms in Australia are used in the case study.The forecasting results show that the proposed model has higher accuracy than the existing methods.Based on the structure of the STCN,asymmetric spatial correlation at different temporal scales can be observed,which shows the effectiveness of the proposed model.

关键词： Deep learning spatio-temporal correlation wind power forecasting graph conventional network(GCN).

THz Wave Detection of Gap Defects Based on a Convolutional Neural Network Improved by a Residual Shrinkage Network

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CSEE Journal of Power and Energy Systems 2023年第3期9卷 1078-1089页

作者： Zhonghao Zhang Guozheng Peng Yuanpeng Tan Tianjiao PU Liming Wang China Electric Power Research Institute Tsinghua Shenzhen International Gradual School Shenzhen 518055China

Internal air gap is a serious type of defect in the insulation equipment,which threatens the safe operation of the power grid.In order to diagnose the position and thickness of the internal air gap,this paper proposes a terahertz wave detection method based on wavelet analysis and a CNN(convolution neural network)model.According to the time-frequency characteristics of the wavelet cluster,the calculation method of air gap depth is proposed.To determine the thickness of the internal air gap,the performances of several classification methods,such as waveform feature analysis,Bayes,MLP(Multi-layer Perceptron),SVM(Support Vector Machine)and CNN are compared.The results show that the CNN modified by a residual shrinkage network and SVM(CNN-RSN-SVM)has the best performance.By adjusting the parameters,the classification accuracy of the CNN-RSN-SVM model can be elevated to 98.91%.Furthermore,the 3D imaging method of air gap defect based on wavelet analysis and CNNRSN-SVM classification model is formed.

关键词： Convolutional neural network defect detection insulation equipment terahertz wave

Short-term Forecasting of Individual Residential Load Based on Deep Learning and K-means Clustering

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CSEE Journal of Power and Energy Systems 2021年第2期7卷 261-269页

作者： Fujia Han Tianjiao PU Maozhen Li Gareth Taylor Artificial Intelligence Application Department China Electric Power Research InstituteBeijing 100192China Department of Electronic and Computer Engineering Brunei University LondonUxbridgeUKUB83PH Brunei Institute of Power Systems Brunei University LondonUxbridgeUKUB83PH

In order to currently motivate a wide range of various interactions between power network operators and electricity customers,residential load forecasting plays an increasingly important role in demand side response(DSR).Due to high volatility and uncertainty of residential load,it is significantly challenging to forecast it precisely.Thus,this paper presents a short-term individual residential load forecasting method based on a combination of deep learning and k-means clustering,which is capable of effectively extracting the similarity of residential load and performing residential load forecasting accurately at the individual level.It first makes full use of k-means clustering to extract similarity among residential load and then employs deep learning to extract complicated patterns of residential load.The presented method is tested and validated on a real-life Irish residential load dataset,and the experimental results suggest that it can achieve a much higher prediction accuracy,in comparison with a published benchmark method.

关键词： Deep learning demand side response(DSR) interactions k-means clustering residential load forecasting similarity

An Edge Visual Incremental Perception Framework Based on Deep Semi-supervised Learning for Monitoring Power Transmission Lines

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CSEE Journal of Power and Energy Systems 2023年第2期9卷 759-768页

作者： Jun Zhang Jiye Wang Rui Song Guozheng Peng Tianjiao PU Shuhua Zhang College of Electrical and Information Engineering Hunan UniversityChangsha 410082China China Electric Power Research Institute Beijing 100192China

In recent years,several efforts have been made to develop power transmission line abnormal target detection models based on edge devices.Typically,updates to these models rely on participation of the cloud,which means that network resource shortages can lead to update failures,followed by unsatisfactory recognition and detection performance in practical use.To address this problem,this article proposes an edge visual incremental perception framework,based on deep semisupervised learning,for monitoring power transmission lines.After generation of the initial model using a small amount of labeled data,models trained using this framework can update themselves based on unlabeled data.A teacher-student joint training strategy,a data augmentation strategy,and a model updating strategy are also designed and adopted to improve the performance of the models trained with this framework.The proposed framework is then examined with various transmission line datasets with 1%,2%,5%,and 10%labeled data.General performance enhancement is thus confirmed against traditional supervised learning strategies.With the 10%labeled data training set,the recognition accuracy of the model is improved to exceed 80%,meeting the practical needs of power system operation,and thus clearly validating the effectiveness of the framework.

关键词： Edge intelligence semi-supervised learning transmission lines monitoring visual incremental perception

Review of Learning-assisted Power System Optimization

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CSEE Journal of Power and Energy Systems 2021年第2期7卷 221-231页

作者： Guangchun Ruan Haiwang Zhong Guanglun Zhang Yiliu He Xuan Wang Tianjiao PU Department of Electrical Engineering Tsinghua UniversityBeijing 100084China China Electric Power Research Institute Beijing 100192China

With dramatic breakthroughs in recent years,machine learning is showing great potential to upgrade the toolbox for power system optimization.Understanding the strength and limitation of machine learning approaches is crucial to decide when and how to deploy them to boost the optimization performance.This paper pays special attention to the coordination between machine learning approaches and optimization models,and carefully evaluates how such data-driven analysis may improve the rule-based optimization.The typical references are selected and categorized into four groups:the boundary parameter improvement,the optimization option selection,the surrogate model,and the hybrid model.This taxonomy provides a novel perspective to elaborate the latest research progress and development.We further compare the design patterns of different categories,and discuss several key challenges and opportunities as well.Deep integration between machine learning approaches and optimization models is expected to become the most promising technical trend.

关键词： Artificial intelligence data-driven deep learning machine learning neural network smart grid

Analysis of users’ electricity consumption behavior based on ensemble clustering

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Global Energy Interconnection 2019年第6期2卷 479-489页

作者： Qi Zhao Haolin Li Xinying Wang Tianjiao PU Jiye Wang China Electric Power Research Institute Haidian DistrictBeijing100192P.R.China Northeastern University 360 Huntington AveBostonMA02115United States of America

Due to the increase in the number of smart meter devices,a power grid generates a large amount of data.Analyzing the data can help in understanding the users’electricity consumption behavior and demands;thus,enabling better service to be provided to them.Performing power load profile clustering is the basis for mining the users’electricity consumption behavior.By examining the complexity,randomness,and uncertainty of the users’electricity consumption behavior,this paper proposes an ensemble clustering method to analyze this behavior.First,principle component analysis(PCA)is used to reduce the dimensions of the data.Subsequently,the single clustering method is used,and the majority is selected for integrated clustering.As a result,the users’electricity consumption behavior is classified into different modes,and their characteristics are analyzed in detail.This paper examines the electricity power data of 19 real users in China for simulation purposes.This manuscript provides a thorough analysis along with suggestions for the users’weekly electricity consumption behavior.The results verify the effectiveness of the proposed method.

关键词： Users’electricity consumption Ensemble clustering Dimensionality reduction Cluster validity

Sharing Economy in Local Energy Markets

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Journal of Modern Power Systems and Clean Energy 2023年第3期11卷 714-726页

作者： Zhaoyuan Wu Jianxiao Wang Haiwang Zhong Feng Gao Tianjiao PU Chin-Woo Tan Xiupeng Chen Gengyin Li Huiru Zhao Ming Zhou Qing Xia School of Economics and Management North China Electric Power UniversityBeijing 102206China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijing 100871China State Key Lab of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Industrial Engineering and Management College of EngineeringPeking UniversityBeijing 100871China China Electric Power Research Institute Beijing 100192China Department of Civil and Environmental Engineering Stanford UniversityPalo AltoCA 94305USA Engineering and Technology Institute Groningen University of Groningen9742 AG Groningenthe Netherlands

With an increase in the electrification of end-use sectors,various resources on the demand side provide great flexibility potential for system operation,which also leads to problems such as the strong randomness of power consumption behavior,the low utilization rate of flexible resources,and difficulties in cost recovery.With the core idea of“access over ownership”,the concept of the sharing economy has gained substantial popularity in the local energy market in recent years.Thus,we provide an overview of the potential market design for the sharing economy in local energy markets(LEMs)and conduct a detailed review of research related to local energy sharing,enabling technologies,and potential practices.This paper can provide a useful reference and insights for the activation of demand-side flexibility potential.Hopefully,this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.

关键词： Energy sharing flexibility potential local energy market information and communication technology sharing economy