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2017 IEEE Conference on energy Internet and energy system Integration, EI2 2017

作者： Wang, Jiazhen Shen, Xinwei Guo, Qinglai Xiong, Wen Tang, Lei Shenzhen Environmental Science and New Energy Technology Engineering Laboratory Tsinghua-Berkeley Shenzhen Insitute Tsinghua University Shenzhen China State Key Lab of Control and Simulation of Power System and Generation Equipments Department of Electrical Engineering Tsinghua University Beijing China Guangzhou Power Grid Company Guangzhou Lei Tang Beijing Qingda Gaoke System Control Company Beijing China Beijing Qingda Gaoke System Control Company Beijing China

ISBN: (纸本)9781538614273

Integrated energy system is beneficial for promoting the distributed renewable energy (DRE) consumption, thereby alleviating environmental pollution and energy shortage. In this paper, we propose a virtual power plant (VPP) structure considering the feature of heat and electric power system integration, which is defined as Multi-energy Virtual power Plant (MVPP). MVPP is allowed to participate in the electricity market;and price information from bulk power market can affect heating system through the coupling devices, thus optimizing the co-operation of electric and heat system. MVPP is also involved in the auxiliary service market, a penalty term is designed for the Utility to charge MVPP service fee so that power fluctuation on transmission line can be reduced. The case study proved the effectiveness of the penalty term and MVPP is able to provide reserve service for Utility in peak load hours by multi-energy flow coupling under the regulation of feed-in tariff. © 2017 IEEE.

关键词： Virtual power Plants

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Author Correction: Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic

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Nature communications 2020年第1期11卷 6292页

作者： Zhu Liu Philippe Ciais Zhu Deng Ruixue Lei Steven J Davis Sha Feng Bo Zheng Duo Cui Xinyu Dou Biqing Zhu Rui Guo Piyu Ke Taochun Sun Chenxi Lu Pan He Yuan Wang Xu Yue Yilong Wang Yadong Lei Hao Zhou Zhaonan Cai Yuhui Wu Runtao Guo Tingxuan Han Jinjun Xue Olivier Boucher Eulalie Boucher Frédéric Chevallier Katsumasa Tanaka Yiming Wei Haiwang Zhong Chongqing Kang Ning Zhang Bin Chen Fengming Xi Miaomiao Liu François-Marie Bréon Yonglong Lu Qiang Zhang Dabo Guan Peng Gong Daniel M Kammen Kebin He Hans Joachim Schellnhuber Department of Earth System Science Tsinghua University Beijing China. zhuliu@***. Laboratoire des Sciences du Climat et de l'Environnement LSCE CEA CNRS UVSQ Centre d'Etudes Orme de Merisiers Gif-sur-Yvette France. Department of Earth System Science Tsinghua University Beijing China. Department of Meteorology and Atmospheric Science The Pennsylvania State University University Park PA USA. Department of Earth System Science University of California Irvine 3232Croul Hall Irvine CA USA. Division of Geological and Planetary Sciences California Institute of Technology Pasadena CA USA. Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science and Technology Nanjing China. Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China. Climate Change Research Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. School of Environment Tsinghua University Beijing China. School of Mathematical School Tsinghua University Beijing China. Department of Mathematical Sciences Tsinghua University Beijing China. Center of Hubei Cooperative Innovation for Emissions Trading System Wuhan China. Faculty of Management and Economics Kunming University of Science and Technology 13 Kunming China. Economic Research Centre of Nagoya University Furo-cho Chikusa-ku Nagoya Japan. Institut Pierre-Simon Laplace Sorbonne Université/CNRS Paris France. Université Paris Dauphine Place du Maréchal de Lattre de Tassigny 75016 Paris France.

A Correction to this paper has been published: https://***/10.1038/s41467-020-20254-5.

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Rotation Speed Recovery Strategy Based On Particle Swarm Optimization for Variable power Curve Optimization

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IOP Conference Series: Materials Science and Engineering 2018年第3期439卷

作者： Rui Liu Peng Song Xiaosheng Wang Yang Cui Zhongwei Lin State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing 102206 PR China phone: *** State Grid Jibei Electric Power Research Institute North China Electric Power Research Institute Co. Ltd. Beijing 100045 China Grid-connected Operation Technology for Wind-Solar Storage Hybrid System State Grid Corporation Key Laboratory Beijng 100045 China

With the increase of wind power capacity incorporated into the grid, the permeability of wind power increases continuously, which leads to the reduction of the equivalent inertia of the system. Rotor speed recovery will cause the secondary frequency dips when DFIG uses rotor inertial control to participate in system frequency modulation. In order to optimize the secondary frequency dips, this paper uses PSO (Particle Swarm Optimization) to optimize the Parameters of Variable power Functions on the basis of the previously proposed variable power curve strategy. This article simulates this strategy through setting up a system by Simulink. Simulate the results of optimization, compared with the traditional recovery strategy, validity of the proposed strategy is demonstrated through simulation and analysis.

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Rotation Speed Recovery Strategy Based On Variable power Curve of Inertia control from DFIG Wind Turbine

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IOP Conference Series: Earth and Environmental Science 2018年第1期192卷

作者： R Liu P Song X S Wang Z W Lin State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing 102206 PR China State Grid Jibei Electric Power Research Institute North China Electric Power Research Institute Co. Ltd. Beijing 100045 China Grid-connected Operation Technology for Wind-Solar Storage Hybrid System State Grid Corporation Key Laboratory Beijng 100045 China

With the increase of wind power capacity incorporated into the grid, the permeability of wind power increases continuously, which leads to the reduction of the equivalent inertia of the system. A new strategy of recovering rotor speed that increases the output power by modifying the variable power curve is proposed in this paper, which decreases the drop of the active power. So as to reduce the secondary frequency dips caused by the sudden drop of the active power when the speed restores. The paper optimizes the secondary frequency dips by modifying the variable power curve continuously. This article simulates this strategy through setting up a system by Simulink. Compared with the traditional recovery strategy, validity of the proposed strategy is demonstrated through simulation and analysis.

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Improved minimum entropy control for two-input and two-output networked control systems

Improved minimum entropy control for two-input and two-outpu...

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UKACC International Conference on control (control)

作者： Jianhua Zhang Yamin Kuai Shuqing Zhou Guolian Hou Mifeng Ren State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China College of Information Engineering Taiyuan University of Technology Taiyuan China

In this paper, the problem of control algorithm design for a class of nonlinear two-input and two-output (TITO) networked control systems (NCSs) with non-Gaussian random time delays is investigated, where a general non-linear auto-regressive moving average with exogenous model is used to describe the plant. Due to the non-Gaussian random time delays involved in the systems, it is insufficient to obtain a satisfactory optimal control algorithm by only controlling the expected value of the tracking errors. The Renyi entropies of the tracking errors and control inputs are adopted to characterize the randomness of the closed-loop system. The formulations of the probability density functions (PDFs) of the tracking errors and control inputs are deduced. By minimizing the new performance index, a recursive optimal control algorithm is obtained. Furthermore, the local stability condition of the closed-loop systems is established. Finally, the simulation results are presented to illustrate the effectiveness of the proposed method.

关键词： Entropy Probability density function Performance analysis Delays Delay effects Networked control systems Stochastic processes

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Short-term prediction model of module temperature for photovoltaic power forcasting based on support vector machine

Short-term prediction model of module temperature for photov...

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International Conference on renewable power Generation, RPG 2015

作者： Sun, Yujing Wang, Fei Mi, Zengaiang Sun, Hongbin Liu, Chun Wang, Bo Lu, Jing Zhen, Zhao Li, Kangping State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Baoding Hebei Province071003 China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Haidian District Beijing100084 China China Electric Power Research Institute Haidian District Beijing100192 China

ISBN: (纸本)9781785610400

To improve the prediction accuracy of photovoltaic (PV) power generation, the temperature of PV modules and its prediction is very important. A short-term step-wise temperature prediction model for PV module based on Support Vector Machine (SVM) is proposed in this paper. Firstly, the primary impact factors of PV module temperature are determined in terms of the PV module physical characteristics and the correlation coefficient between each factor and module temperature. Secondly, two kinds of prediction models, direct and step-wise models, are built to predict module temperature respectively. For direct model, the prediction model is based on SVM using the historical data. For step-wise one, the prediction models of the primary impact factors are built at the first step and then the mapping models between module temperature and the impact factors are established. In the end, the deviations of two different kinds of prediction models are analyzed and discussed based on actual operation data. The results indicate that the stepwise prediction model has better accuracy than the direct prediction model if other things are equal.

关键词： Forecasting

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Photovoltaic plant Operating Statuses Identification model based on support vector machine using loss quantity of electricity feature parameters

Photovoltaic plant Operating Statuses Identification model b...

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International Conference on renewable power Generation, RPG 2015

作者： Li, Kangping Wang, Fei Zhen, Zhao Sun, Yujing Mi, Zengqiang Sun, Hongbin Liu, Chun Wang, Bo Lu, Jing State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Baoding Hebei Province071003 China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Haidian District Beijing100084 China China Electric Power Research Institute Haidian District Beijing100192 China

ISBN: (纸本)9781785610400

Operating Statuses Identification (OSI) can help operators to find fault timely and minimize the loss. So it is of great significance for optimal operation of photovoltaic (PV) plants. The loss quantity of electricity (LQOE) is defined as that should have been generated but actually not, which is caused by inverter fault, PV modules fault, dust stratification or combinations of them. Firstly, mathematical models of PV cells are proposed to figure out the LQOE of each PV module. Secondly, after analysing the relation between LQOE and operating statuses, an index set of LQOE describing the distinction of different operating statuses are defined including four statistical feature parameters and a user-defined index. Thirdly, Support Vector Classification models for OSI (OSI-SVC) are built with input features extracted from the index set. Lastly, simulations are carried out to verify the effectiveness and evaluate the performance of the OSI-SVC models. The results indicated that the operating statuses can be effectively recognized by the proposed model.

关键词： Support vector machines

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Cloud identification model for sky images based on Otsu

Cloud identification model for sky images based on Otsu

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International Conference on renewable power Generation, RPG 2015

作者： Li, Hongping Wang, Fei Ren, Hui Sun, Hongbin Liu, Chun Wang, Bo Lu, Jing Zhen, Zhao State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Baoding Hebei Province071003 China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Haidian District Beijing100084 China China Electric Power Research Institute Haidian District Beijing100192 China

ISBN: (纸本)9781785610400

Irradiance changes caused by clouds movement are the mam factors, which lead to mutation of photovoltaic power. In order to increase the accuracy of the ultra-short-term photovoltaic power prediction, based on of clouds images, the cloud identification for sky images is one of the primary aspects for improving photovoltaic power accuracy. A cloud identification model for sky images is established based on Otsu. Firstly, a series of sky images are collected by Total Sky Imager (TSI) with the regions of clouds and sky are distinguished. Secondly, the color features (based on RGB values) of pixels in sample images and the spectral and texture features of the whole image are extracted;Finally, we established a cloud identification model for sky images based on Otsu according to the clouds' and sky areas' division and the general characteristics featured by pixels and images. The simulated results show that the model in this paper can identify the clouds in a sky images effectively.

关键词： Pixels

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Evaluation method for node importance based on service in electric power communication network

Evaluation method for node importance based on service in el...

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作者： Zeng, Ying Li, Xingnan Deng, Boren Wu, Runze Zou, Yingjie Luo, Yun Power Dispatch and Control Center of Guangdong Grid GuangzhouGuangdong China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China Sichuan Enrising Information Technology Co. Ltd ChengduSichuan China

ISBN: (纸本)9781138028302

A new evaluation method for node importance is proposed with the combination of the node contraction method in complex networks and the functional analysis of the importance evaluation for power service in practical operation. The definite application of this algorithm features the particularity of the power industry. Firstly, we classify the power services, then relate the importance degree of power service to the edge weight which is used to calculate the node weight and its importance. Finally, the simulation example, we have testified corresponds ultimately to the practical situation. © 2015 Taylor & Francis Group, London.

关键词： Complex networks

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Short-term prediction model of module temperature for photovoltaic power forcasting based on support vector machine

Short-term prediction model of module temperature for photov...

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International Conference on renewable power Generation (RPG 2015)

作者： Yujing Sun Fei Wang Zengqiang Mi Hongbin Sun Chun Liu Bo Wang Jing Lu Zhao Zhen Kangping Li State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources North China Electric Power University Baoding Hebei China State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources Tsinghua University Beijing Haidian China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Tsinghua University Beijing Haidian China China Electric PowerResearch Institute Beijing Haidian China

ISBN: (纸本)9781510821415

To improve the prediction accuracy of photovoltaic (PV) power generation, the temperature of PV modules and its prediction is very important. A short-term step-wise temperature prediction model for PV module based on Support Vector Machine ( SVM) is proposed in this paper. Firstly, the primary impact factors of PV module temperature are determined in terms of the PV module physical characteristics and the correlation coefficient between each factor and module temperature. Secondly, two kinds of prediction models, direct and step-wise models, are built to predict module temperature respectively. For direct model, the prediction model is based on SVM using the historical data. For step-wise one, the prediction models of the primary impact factors are built at the first step and then the mapping models between module temperature and the impact factors are established. In the end, the deviations of two different kinds of prediction models are analy%ed and discussed based on actual operation data. The results indicate that the stepwise prediction model has better accuracy than the direct prediction model if other things are equal.

关键词： impact factor module temperature photovoltaic power forecasting step-wise prediction support vector machine

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