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Minkowski Sum Based Flexibility Aggregating Method of Load Dispatching for Heat Pumps

Dianli Xitong Zidonghua/Automation of Electric power systems 2019年第5期43卷 14-21页

作者： Li, Zihao Li, Tie Wu, Wenchuan Zhang, Boming Jiang, Feng Cui, Dai Department of Electrical Engineering Tsinghua University Beijing100084 China State Key Laboratory of Control and Simulation of Power System and Generation Equipments Tsinghua University Beijing100084 China State Grid Liaoning Electric Power Supply Company Shenyang110006 China

With the implementation of the "coal to electricity" project, the heat pump load in the distribution network increases significantly. Standing in the point of heat pump users, cooperative game mode is used for heat pumps joining in day-ahead dispatching of distribution network. In order to accurately describe the polymerization flexibility of the heat pump load cluster, this paper uses the constraint space superposition method based on Minkowski Sum, and describes the heat pump cluster as a "virtual synchronous machine" or "virtual energy storage" model by looking for its maximal inner cube or rectangular pyramid simplification constraint. Furthermore, a method of income distribution among users based on approximated Shapley Value is proposed. The validity of the proposed method is verified by the case based on IEEE 33 mode system. © 2019 Automation of Electric power systems Press.

关键词： Game theory

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Autonomous Decentralized control of Distributed generation using Multi-Agent Reinforcement Learning 11

Autonomous Decentralized Control of Distributed Generation u...

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11th International Conference on Innovative Smart Grid Technologies - Asia, ISGT-Asia 2022

作者： Liul, Jian Xul, Weifeng Liul, Zhijun Ful, Guanhua Jiang, Yunpeng Wang, Jun Zhejiang Electric Power Co. Ltd. Hangzhou Power Supply Company of State Grid Hangzhou310016 China Wuxi Research Institute of Applied Technologies Tsinghua University China Tsinghua University State Key Lab of Control and Simulation of Power Systems and Generation Equipments Dept. of Electrical Engineering Beijing100084 China

ISBN: (纸本)9798350399660

With the large-scale integration of distributed generations (DGs), a centralized control approach is being challenged concerning communication efficiency, resiliency to communication failure, privacy, and scalability. An autonomous decentralized control for DGs in distribution networks is proposed to ensure privacy while also reducing the computational burden. Meanwhile,a multi-agent reinforcement learning method considering privacy protection constraints is developed to solve the autonomous decentralized control problem. The effectiveness of the proposed control method is verified by the numerical example of the modified 141-bus distribution system test-feeder. © 2022 IEEE.

关键词： Distributed power generation

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Open Architecture Design of Universal Terminal for Multiple Measurement and control Scenarios of Distribution Network 3

Open Architecture Design of Universal Terminal for Multiple ...

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3rd International Conference on Green Energy and Sustainable Development, GESD 2020

作者： Zhang, Ruifeng Xiao, Xiaobing Tian, Hao Cai, Yongxiang Fu, Yu He, Hongliu Electric Power Research Institute of Guizhou Power Grid Co. Ltd. Guiyang China State Key Lab of Control and Simulation of Power Systems and Generation Equipments Dept. of Electrical Engineering Tsinghua University Beijing China Binjiang College Nanjing University of Information Science and Technology Wuxi China

Introduce the IoT platform technology based on SDN (Software Defined Network), cloud-based, flexible management and configuration of smart terminals, and support access to millions of terminal devices and smart operation and maintenance management. The traditional monitoring system is integrated with the Internet of Things platform, and on the basis of realizing the general SCADA system monitoring function, through the side-cloud collaboration, the lean management of the distribution station area is realized. © Published under licence by IOP Publishing Ltd.

关键词： SCADA systems

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Low carbon transmission expansion planning considering demand side management

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Dianli Xitong Zidonghua/Automation of Electric power systems 2016年第23期40卷 61-69页

作者： Cheng, Yaohua Zhang, Ning Kang, Chongqing Meng, Junxia Yan, Huaguang Department of Electrical Engineering Tsinghua University Beijing100084 China State Key Laboratory of Control and Simulation of Power System and Generation Equipments Tsinghua University Beijing100084 China China Electric Power Research Institute Beijing100085 China

The implementation of transmission expansion planning towards low carbon development can effectively alleviate the "carbon lock" effect in the power industry and facilitate the transition to low carbon power system. The demand side management (DSM) is able to reduce the needs of power supply and transmission capacity and further bring down the generation coal consumption and transmission losses. It is therefore an important means of carbon emission reduction in power system. The paper analyzes the impact of DSM and low carbon development on transmission planning at first. By analyzing the performance of typical DSM measures in the constraints, a low carbon transmission expansion planning model is established considering DSM. Nonlinear constraints in the planning model are transformed into linear ones using inequality separation method, turning the planning model into a mixed integer linear programming model. The case study shows the effectiveness of the proposed model with IEEE RTS79 system. The results suggest that considering DSM in the transmission expansion planning can effectively reduce the transmission expansion investment and the total carbon emissions, which can improve overall benefits of the power system. © 2016 Automation of Electric power systems Press.

关键词： Demand side management

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Ultra-short-term wind power prediction based on variational mode decomposition and clustering method

Ultra-short-term wind power prediction based on variational ...

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第三十八届中国控制会议

作者： Liu Chenyu Zhang Xuemin Mei Shengwei Huang Shaowei State Key Lab of Control and Simulation of Power Systems and Generation Equipments (Dept. of Electrical Engineering Tsinghua University)

Wind power ultra-short-term prediction plays a key role in day scheduling of power systems and cross-provincial power trading. However, the randomness and non-stationary nature of wind power, and the mismatch between the training data and the predicted data have become the main obstacles for the improvement of prediction accuracy. This paper proposes a prediction algorithm based on variational mode decomposition(VMD) and clustering. Firstly, the original non-stationary sequence is transformed into several relatively stationary modes utilizing VMD. Secondly, key features can be conveniently selected for each mode, which has a central frequency. Then the k-means clustering method is used to cluster each mode into several typical patterns based on correlation coefficient based distance definition. Finally, the method of "offline training, online matching" is adopted to perform training and prediction process. The actual data of a wind farm in Jilin Province was used to verify the results. The results show that the proposed method can improve the accuracy of ultra-short-term in multi-step prediction of wind power.

关键词： wind power ultra-short-term prediction variational mode decomposition stationary

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ANALYSIS OF THE MULTI-MODE OSCILLATIONS OF OFFSHORE WIND FARMS 18

ANALYSIS OF THE MULTI-MODE OSCILLATIONS OF OFFSHORE WIND FAR...

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18th International Conference on AC and DC power Transmission, ACDC 2022

作者： Fan, Yupeng Dong, Wenkai Li, Peng Qin, Yi Jiang, Haojie Xie, Xiaorong Datang Guoxin Binhai Offshore Wind Power Generation Co. Ltd Jiangsu Province Yancheng224000 China State Key lab of Control and Simulation of Power Systems and Generation Equipment Dept. of Electric Engineering Tsinghua University Beijing100084 China China Datang Corporation Science and Technology General Research Institute Ltd Beijing100040 China

ISBN: (数字)9781839537615

ISBN: (纸本)9781839537615

Multi-mode oscillation (MMO) is more prone to happen in the offshore wind farm (OWF) than the onshore, which seriously impacts the safe operation of the system. In this paper, the mechanism for the MMO of the OWF is analytically studied. First, the impedance model (IM) of a grid-connected OWF is derived with the AC submarine cable modelled as a distributed parameter circuit (DPC). Second, since the DPC of N n sections can be approximately seen as composed of N identical capacitances in daisy-chain connection, based on the consistent control theory for homogeneous systems, the IM of the DPC is decoupled into N subsystems, each of which is composed of a x circuit. The IM of the DPC is the weighted sum of the IMs of the N subsystems. As each x circuit has one tip (jump between ±90°) in the amplitude(phase)-frequency characteristics (APFC), the IM of the cable will have N tips (jumps) in the APFC and is much more prone to intersect with the IM of the OWF. Thus, resonance is more likely to happen at multiple frequency points. Finally, effectiveness of the methods proposed and conclusions drawn is validated via study cases. © ACDC 2022. All rights reserved.

关键词： Offshore wind farms

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Ultra-short-term solar PV power forecasting based on cloud displacement vector using multi-channel satellite and NWP data

Ultra-short-term solar PV power forecasting based on cloud d...

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第40届中国控制会议

作者： Rui Gao Xuemin Zhang Zhao Zhen Shengwei Mei State Key Lab of Control and Simulation of Power Systems and Generation Equipments （Dept. of Electrical Engineering Tsinghua University）

Satellite cloud image data plays an important role in the ultra-short-term photovoltaic（PV） power forecasting. By calculating the cloud displacement vectors of two adjacent cloud images, it is possible to calculate the cloud cluster’s occlusion of the PV power plant for a period of time in the future, thereby forecasting the PV power. The satellite cloud image data provided by FY-4 A Satellite has large and unequal sampling time intervals, which brings difficulties to cloud displacement vector calculation. This paper proposes a method for calculating the displacement vector of the cloud edge block based on the cosine similarity and the adaptive adjustment of the matching spatial scale. The parameter equation is used to establish the relationship between the relative position of the cloud cluster and the PV plant over time, and to predict the future position. On this basis, this paper proposes an ultra-short-term forecasting method of PV power based on cloud displacement vector. The performance of the proposed model is verified through case analysis.

关键词： ultra-short-term prediction photovoltaic power generation cloud displacement vector multi-channel satellite data

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Comprehensive comparison and analysis of non-inverting buck boost and conventional buck boost converters

Comprehensive comparison and analysis of non-inverting buck ...

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作者： Weng, Xing Xiao, Xiang He, Weibin Zhou, Yongyan Shen, Yu Zhao, Wei Zhao, Zhengming State Key Lab of Control and Simulation Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100084 China Guangdong Electric Power Research Institute Guangdong510080 China Dongguan Power Supply Bureau Guangdong Power Grid Company Dongguan523000 China

The non-inverting buck boost (NIBB) converter has attracted significant attention in recent years, as it shares ground between input and output, and the voltage stress of switches is lower. In order to investigate the differences between NIBB and conventional buck boost converters, a comprehensive comparison and analysis of these two converters were conducted in terms of their operation principles, which includes multi-mode control strategy and dual-edge modulation here, and also the characteristics of switches and passive components in the two converters were analysed. The results show that NIBB is better than conventional buck boost circuit in these aspects of electrical stress, power loss, cost, passive component volume, and so on. Two prototypes for the two converters with 10 kW/20 kHz were designed and simulated, respectively, for verifying the results. Analytical and simulated results confirmed the conclusions. © 2019 Institution of Engineering and *** right reserved.

关键词： Buck-Boost converter

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Vulnerability Assessment Based on Operational Reliability Weighted and Preventive Planning

Vulnerability Assessment Based on Operational Reliability We...

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Sustainable power and Energy Conference (iSPEC)

作者： Ning Qi Lin Cheng Yi Jiang Jinshan Luo Ke Sun Dan Wang Weiyuan Wang Wentao Sun State Key Lab. of Control and Simulation of Power System and Generation Equipment Tsinghua University Beijing China State Grid Jiangsu Economic Research Institute Jiangsu China

Vulnerability assessment is the key to the security and stability analysis of power system. During the operation of power grid, the fault risk of electrical equipment is not fixed because of the influence of system operating conditions, operating environment and equipment itself, so the vulnerability of power system will also be changeable. Based on the existing research results of complex network theory, a vulnerability assessment method considering operational reliability is proposed in this paper. Firstly, the basic idea of reliability weighted vulnerability assessment is introduced. Then HITS algorithm and PageRank algorithm will be used to analyze the basic vulnerability of power grid topology. Moreover, the vulnerability index of power grid considering reliability weighted is calculated. Finally, numerical analysis focusing on the difference between basic and improved assessment method is tested in IEEE RTS-79 system, and the preventive planning suggestions will be given based on the vulnerability assessment results.

关键词：

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Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application

arXiv

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arXiv 2018年

作者： Han, Te Liu, Chao Yang, Wenguang Jiang, Dongxiang Department of Energy and Power Engineering Tsinghua University Beijing100084 China State Key Lab of Control and Simulation of Power System and Generation Equipment Tsinghua University Beijing100084 China

In recent years, an increasing popularity of deep learning model for intelligent condition monitoring and diagnosis as well as prognostics used for mechanical systems and structures has been observed. In the previous studies, however, a major assumption accepted by default, is that the training and testing data are taking from same feature distribution. Unfortunately, this assumption is mostly invalid in real application, resulting in a certain lack of applicability for the traditional diagnosis approaches. Inspired by the idea of transfer learning that leverages the knowledge learnt from rich labeled data in source domain to facilitate diagnosing a new but similar target task, a new intelligent fault diagnosis framework, i.e., deep transfer network (DTN), which generalizes deep learning model to domain adaptation scenario, is proposed in this paper. By extending the marginal distribution adaptation (MDA) to joint distribution adaptation (JDA), the proposed framework can exploit the discrimination structures associated with the labeled data in source domain to adapt the conditional distribution of unlabeled target data, and thus guarantee a more accurate distribution matching. Extensive empirical evaluations on three fault datasets validate the applicability and practicability of DTN, while achieving many state-of-the-art transfer results in terms of diverse operating conditions, fault severities and fault types. Copyright © 2018, The Authors. All rights reserved.

关键词： Fault detection

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