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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Li, Yang Zhang, Xing He, Guoqing Chen, Danyang State Key Laboratory of Control and Operation of Renewable Energy and Storage Systems Beijing China State Grid Shanxi Electric Power Research Institute Shanxi Taiyuan China

ISBN: (纸本)9798350346671

The math model of power system with large scale new energy including a large number of power electronic switch devices model, and semi physical real time simulation research of new energy integration control algorithm often uses a relatively larger simulation step size, thus this situation greatly reduces the simulation accuracy and stability of model with a large number of power electronic devices. This paper proposes a real time simulation compensation algorithm for fixed step to solve the problem. This algorithm compensates PWM waveform sampling error because of the fixed step real time simulation to improve the simulation accuracy, at the same time, it avoids the problem that the traditional algorithm increases the amount of computation with the increase of switching devices, laying the foundation for power system real-time simulation with large scale new energy. © 2023 IEEE.

关键词： Transients

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Capacity Allocation of Hybrid power system with Hot Dry Rock Geothermal Energy,Thermal Storage,and PV Based on Game Approaches

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Journal of Modern power systems and Clean Energy 2023年第1期11卷 290-301页

作者： Yang Si Laijun Chen Xuelin Zhang Xiaotao Chen Shengwei Mei Qinghai Key Lab of Efficient Utilization of Clean Energy(Tus-Institute for Renewable Energy) University of QinghaiXining 810016China State Key Laboratory of Control and Simulation of Power System and Power Generation Equipment(Electric Machinery Department Tsinghua University)Beijing 100084China Technical Institute of Physics and Chemistry Beijing 100190China IEEE

This study utilizes hot dry rock(HDR)geothermal energy,which is not affected by climate,to address the capacity allocation of photovoltaic(PV)-storage hybrid power systems(HPSs)in frigid plateau *** study replaces the conventional electrochemical energy storage system with a stable HDR plant assisted by a flexible thermal storage(TS)*** HPS consisting of an HDR plant,a TS plant,and a PV plant is *** approaches are introduced to establish the game pattern model of the proposed HPS as the *** annualized income of each player is used as the payoff ***,non-cooperative game and cooperative game approaches for capacity allocation are proposed according to the interests of each player in the proposed ***,the proposed model and approaches are validated by performing calculations for an HPS in the Gonghe Basin,Qinghai,China as a case *** results show that in the proposed non-cooperative game approach,the players focus only on the individual payoff and neglect the overall system *** proposed cooperative game approach for capacity allocation improves the flexibility of the HPS as well as the payoff of each game ***,the HPS can better satisfy the power fluctuation rate requirements of the grid and increase the equivalent firm capacity(EFC)of PV plants,which in turn indirectly guarantees the reliability of grid operation.

关键词： Capacity allocation equivalent firm capacity game theory hot dry rock hybrid power system

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A fast power control method based on high-speed communication for the continuous co-phase traction power system

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iEnergy 2022年第4期1卷 395-399页

作者： Mingrui Li Yingdong Wei Yunzhi Lin Xiaoqian Li Chao Lu Changle Wang Zhanhe Li State Key Laboratory of Power System Operation and Control Department of Electrical EngineeringTsinghua UniversityBeijing 100084China China Railway Electrification Engineering Group Co. Ltd.Beijing 100036China

Continuous co-phase traction power system is an effective method to eliminate neutral sections and provide high quality power for both the public grid and the *** substations have the ability to provide cooperative support to each other to reduce capacity and improve system reliability.A fast power control method for substations is needed due to rapid load changes and low overload capability of the *** paper proposes a fast power control method based on high-speed communication between substations,with additional transient power control to significantly improve the dynamic response of the system.

关键词： Continuous co-phase traction power system cooperative power control dynamic response high-speed communication

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Distributed power Restoration Method Based on Joint control of DGs

Distributed Power Restoration Method Based on Joint Control ...

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2023 International Conference on power system Technology, powerCon 2023

作者： Wang, Ruimei Cong, Wei Tang, Yi Wang, Anning Zhang, Yongjie Gu, Qianshuo Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Jinan China State Grid Shandong Electric Power Company Jinan China

ISBN: (纸本)9798350300222

As the scale of power system continues to expand, the proportion of distributed generators (DGs) in the smart distribution network has been increasing year by year. To address the problem of power restoration after DGs access, distributed power restoration method based on joint control of photovoltaic unit and pv/battery unit is proposed. Analyzing the control modes of photovoltaic unit and pv/battery unit during distributed power restoration, available capacity and adjustment range are determined based on control principles and operation constraints. Based on this method, the network is gradually converted into Thevenin equivalent circuit, and power restoration calculations are performed while considering the operating characteristics of photovoltaic unit and pv/battery unit. At the same time, power output adjustment is carried out based on voltage and frequency constraints to avoid overvoltage or frequency problems during the power restoration process. Using IEEE 33-node system as an example, then the feasibility of this method is verified. © 2023 IEEE.

关键词： Restoration

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MCMC Wind power Sequence Modeling Method Considering Climbing Direction

MCMC Wind Power Sequence Modeling Method Considering Climbin...

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2023 IEEE International Conference on power Science and Technology, ICPST 2023

作者： Hao, Ling Xu, Fei Chen, Lei Chen, Qun Min, Yong Gu, Yi Chang, Yiming State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China Tsinghua University Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Department of Engineering Mechanics Beijing100084 China State Grid Liaoning Electric Power Co. Ltd Liaoning 110055 China

ISBN: (纸本)9798350311358

Since the traditional Markov Chain Monte Carlo (MCMC) method has the problem that the wind power is stuck in a certain state and it is difficult to jump, this paper proposes an improved MCMC method considering the climbing direction, first dividing the state by the size of the output and the climbing direction, and then determining the duration of each state by changing the state, and then sampling and generating the corresponding number of output samples according to the duration of each state, and sorting these samples according to the climbing direction, and the sorted output is the output timing of the state. This method was used to generate wind power sequences from a wind farm in the Northeast China Sea, and the characteristics of a wind power sequence were compared and analyzed with the original wind power sequence, and the results were better than the wind power sequences generated by the traditional MCMC method. © 2023 IEEE.

关键词： Wind power

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A Classification and Recognition Model of Distributed Resources Based on Feature Extraction

A Classification and Recognition Model of Distributed Resour...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Zhu, Yu Yang, Le Yang, Xizai Lu, Jiaxin Wang, Yuqing Wang, Fei State Grid Shaanxi Electric Power Co. Ltd. Information and Communication Company Information and Communication Technology R & D Center Xi'an710048 China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing100084 China

ISBN: (纸本)9781665471640

The extensive integration of distributed resources such as distributed photovoltaic system and electric vehicles enhances the uncertainty of the load side greatly. Corresponding demand response strategies can be developed for different distributed resource types and characteristics of households' load, which is of great significance for new energy consumption and stable operation of the power system. However, only the net load power of households can be obtained through smart meters instead of the distributed resource types of households. Based on this, a distributed resource classification and identification model based on feature extraction is proposed in this paper, and effectively solves the problems of low accuracy and strong data dependence of existing recognition methods. Firstly, a generalized weather class generation algorithm based on clustering and voting is established, which is used to determine the weather class of each day to provide a basis for the following feature extraction. Then, based on the typical net load profiles under different generalized weather classes, a two-stage feature extraction and a classification identification model based on integrated learning algorithms are established to identify whether a user contains DPVS and EV, respectively. Finally, based on the category labels of each user for each stage, the final classification of the category to which the user belongs is carried out. Simulation experiments show that the recognition model built using the typical features extracted in the paper has good recognition accuracy. © 2023 IEEE.

关键词： classification and recognition Distributed photovoltaics system electric vehicles ensemble learning feature extraction

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Distributed Photovoltaic power Forecasting Method Based on AP Clustering and Transfer Learning

Distributed Photovoltaic Power Forecasting Method Based on A...

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2024 IEEE IAS Industrial and Commercial power system Asia, I and CPS Asia 2024

作者： Yang, Baolong Yang, Ming Bu, Qiangsheng Wang, Chuanqi Zhang, Yuanhe Li, Menglin Luo, Fei Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China Research Institute State Grid Jiangsu Electric Power CO. LTD. Nanjing China

ISBN: (纸本)9798350352290

Highly accurate photovoltaic (PV) power forecast technology can provide a scientific basis for grid planning to ensure safe, economical, and efficient grid operation. The investment cost for a specialized power prediction system for each PV plant is high. Consequently, a distributed PV (DPV) power forecast method based on affinity propagation (AP) clustering and transfer learning is proposed in this paper. First, the dynamic time warping (DTW) algorithm determines the similarity of power curves between DPV plants;on this basis, multiple DPV power plants in the area to be forecasted are divided into station clusters by using AP clustering;and the PV power plants in each station cluster are divided into source and target domain by transfer learning, combined with long-short term memory (LSTM) network, the power forecasting model is established, and enabling transfer of power models from source to target domain. Finally, data from 20 DPV plants in China are used to demonstrate the effectiveness of the approach. Compared to separate modeling, the proposed method reduces the average training time by 58% while maintaining the forecasting accuracy. © 2024 IEEE.

关键词： AP clustering distributed photovoltaic power plant clusters LSTM transfer learning

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Design of Trading Adjustment Mechanism for Energy Storage in Electricity Market Based on Equivalent Net Load Fluctuation 6

Design of Trading Adjustment Mechanism for Energy Storage in...

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6th International Conference on Energy, Electrical and power Engineering, CEEPE 2023

作者： Song, Shihang Zhang, Li Qi, Zhiqiang Wang, Haibo Wang, Yuxi Su, Xinyan Chen, Guanting Wang, Fukun Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Jinan China State Grid Shandong Electric Power Company Jinan China

ISBN: (纸本)9798350348279

As the proportion of renewable energy connected to grid increases continuously, the volatility and uncertainty of its output affect the safe operation of the power system, so it is necessary to adjust the trading mechanism of electricity market. As a high-quality flexible resource, energy storage becomes an important means to deal with the challenge caused by renewable energy. How to improve the mechanism of electricity market to better play the role of energy storage is worthy of in-depth research. This paper proposes a trading adjustment mechanism for energy storage in electricity market based on the fluctuation degree of equivalent net load, and establishes a joint market model of energy market and frequency regulation market including storage power station. The example simulation shows that the trading adjustment mechanism proposed in this paper can effectively enhance the role of energy storage, and has a good effect on improving the fluctuation degree of net load and reducing system operating costs. © 2023 IEEE.

关键词： power markets

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Deep Reinforcement Learning-based Multi-Scenario Multi-Scale Economic Scheduling for Integrated Energy systems

Deep Reinforcement Learning-based Multi-Scenario Multi-Scale...

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Energy systems and Electrical power (ICESEP), International Conference on

作者： Guangxi Li Qingsheng Zhao Xiaoqing Han Tingjun Li Shanxi Key Laboratory of Power System Operation and Control Taiyuan University of Technology Taiyuan China

ISBN: (数字)9798350353563

ISBN: (纸本)9798350353570

The integrated energy system (IES) with multi-energy coupling and gradient utilization has become an important means to help realize the goal of “double carbon”. The optimal operation of IES is a nonlinear, nonconvex and complex problem. With the gradual increase of intermittent energy and load uncertainty, traditional scheduling methods are limited to fixed physical models and parameter settings, which make it difficult to quickly and accurately obtain optimal scheduling strategies. In this paper, we propose a distributed proximal policy optimization (DPPO) algorithm to train the IES optimal scheduling model offline and solve the optimization online, which transforms the physical scheduling problem into a reinforcement learning sequential decision-making problem, and adaptively learns the scheduling policy through continuous interaction between the agent and the integrated energy system environment, and the multi-threaded structure enhances the exploration ability of the reinforcement learning agent in the complex environment. Example simulations show that online applications do not need to rely on accurate modeling of the physical model of the IES to achieve fast scheduling decisions in multiple scenarios and scales.

关键词： Economics Adaptation models Uncertainty Biological system modeling Stochastic processes Optimal scheduling Reinforcement learning

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A data-driven topology identification method for low-voltage distribution network

A data-driven topology identification method for low-voltage...

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power systems and Electrical Technology (PSET), IEEE International Conference on

作者： Shuo Shi Xiaoqing Han Tingjun Li Hao Zhang Hongbo Yan Shanxi Key Laboratory of Power System Operation and Control Taiyuan University of Technology Taiyuan China

ISBN: (数字)9798350353204

ISBN: (纸本)9798350353211

Correctly identifying the topology of low-voltage distribution grid is the key to ensure the safe and stable operation of the power system, and it is also the basis for realizing the loss analysis and fault diagnosis of the lines in the station area. Aiming at the situation that the data collected by smart meters may be missing, this paper proposes a data-driven method based on the topology identification of low-voltage distribution grids. The method firstly fills in the missing values and normalizes them according to the voltage time series data collected by smart meters. Second, the K-means clustering algorithm is used to classify and determine the upstream and downstream relationship of each user in a class in the distribution grid based on the magnitude of voltage amplitude. Then the correlation between nodes is calculated using Pearson correlation coefficient to calibrate the correctness of the topology. Finally, the feasibility of the proposed method is verified by simulation analysis of the IEEE33 node arithmetic example.

关键词： Correlation coefficient Fault diagnosis Low voltage Network topology Time series analysis Clustering algorithms Energy measurement Distribution networks Smart meters Topology

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