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A Deep Reinforcement Learning Bidding Algorithm on Electricity Market

Journal of Thermal Science 2020年第5期29卷 1125-1134页

作者： JIA Shuai GAN Zhongxue XI Yugeng LI Dewei XUE Shibei WANG Limin Department of Automation Key Laboratory of System Control and Information ProcessingShanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Coal-based Low-carbon Energy ENN Science and Technology Development Co.Ltd.Langfang 065001China ENN Energy Power Technology(Shanghai)Co. Ltd.Shanghai 201306China

In this paper,we design a new bidding algorithm by employing a deep reinforcement learning *** use the proposed algorithm to estimate conjectural variation of the other firms and then employ this variable to generate the optimal bidding strategy so as to pursue maximal *** this algorithm,electricity generation firms can improve the accuracy of conjectural variations of competitors by dynamically learning in an electricity market with incomplete *** market will reach an equilibrium point when electricity firms adopt the proposed bidding algorithm for a repeated game of power *** simulation examples illustrate the overall energy efficiency of power network will increase by 9.90%as the market clearing price decreasing when all companies use the *** simulation examples also show that the power demand elasticity has a positive effect on the convergence of learning process.

关键词： electricity market reinforcement learning energy efficiency conjectural variation bidding strategy

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An Adaptive Restart Method for DC Transmission Line in LCC-HVDC system 8

An Adaptive Restart Method for DC Transmission Line in LCC-H...

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8th IEEE International Conference on Advanced power system Automation and Protection, APAP 2019

作者： Cai, Jing Dong, Xinzhou Tsinghua University State Key Laboratory of Control and Simulation of Power System and Generation Equipment Beijing China

ISBN: (纸本)9781728117225

DC-line fault recovery sequence (DFRS) is widely implemented in LCC-HVDC system to realize fast recovery in case of temporary fault. When permanent fault occurs, restart still happens several times as preset, resulting in repeated impacts and reducing the stability and reliability of the system. An adaptive restart for DC line fault is studied in this paper to deal with this problem. Firstly, the converter valves states and DC voltage characteristics under temporary fault and permanent fault are analyzed respectively. Then, a method based on DC voltage control is proposed to distinguish different fault states. Current DFRS strategy is improved by adding this process, through which the fault pole will only restart after the temporary fault disappearing, while directly blocked when a permanent fault is recognized. Fault handling time can be shortened and negative effects of DC line fault are minimized. The simulation results verify feasibility and effectiveness of proposed method. © 2019 IEEE.

关键词： HVDC power transmission

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Development of environmentally friendly high-capacity power cables

Nature Reviews Electrical Engineering

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Nature Reviews Electrical Engineering 2024年 565-566页

作者： Yao Zhou Jinliang He State Key Laboratory of Electrical Insulation and Power Equipment School of Electrical Engineering Xi’an Jiaotong University Xi’an China State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing China

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Traveling waves based fault line selection using sparse representation 8

Traveling waves based fault line selection using sparse repr...

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8th IEEE International Conference on Advanced power system Automation and Protection, APAP 2019

作者： Xu, Wen Shi, Shenxing Dong, Xinzhou Zhang, Guangmei Tsinghua University State Key Laboratory of Control and Simulation of Power System and Generation Equipment Beijing China

ISBN: (纸本)9781728117225

Traveling wave method has been proved to be effective in fault line selection of power distribution system with the neutral non-effectively grounded. However, it can be easily disturbed by non-faulty traveling wave when selecting the line. For this reason, this paper proposes a new traveling wave line selection method based on sparse representation, in which current traveling wave signals are measured for the selection task. The algorithm of selection is based on the idea that all non-zero terms in the sparse coefficients of the measured signal are found to be associated with training samples from a single class, and the test samples can be easily assigned to the class. This makes it possible to distinguish between the non-faulty traveling wave and the real fault traveling wave. © 2019 IEEE.

关键词： compressed sensing distribution system line selection sparse representation traveling wave

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Research on Lithium-ion Battery Safety Risk Assessment Based on Measured Information

Research on Lithium-ion Battery Safety Risk Assessment Based...

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2020 IEEE Sustainable power and Energy Conference, iSPEC 2020

作者： Zhou, Xuyan Cheng, Lin Wan, Yuxiang Qi, Ning Tian, Liting You, Feng State Key Laboratory of Control and Simulation of Power Systems and Generation Equipments Tsinghua University Beijing China Energy Storage System Business Unite Tianjin Lishen Battery Joint-Stock Co. Ltd. Tianjin China

ISBN: (纸本)9781728191645

Lithium-ion batteries have the advantages of high energy density, fast power response, recyclability, and convenient to movement, which are unsurpassed by other energy storage systems. However, safety issues such as thermal runaway of lithium-ion batteries have become the main bottlenecks restricting the development of their extensive applications. In practical applications, the demand for battery energy storage scale and specific energy continues to increase, and the contradiction between battery high safety and battery safety has become increasingly prominent. This paper proposes a lithium-ion battery safety risk assessment method based on online information. Effective predictions are essiential to avoid irreversible damage to the battery and ensure the safe operation of the battery energy storage system before a failure occurs. This paper is expected to provide novel risk assessment method and research idea for the development and design of high-safety battery systems. © 2020 IEEE.

关键词： Lithium-ion batteries

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From distributed energy resources to virtual power plants: A cyber-physical system solution for integrating demand-side in smart grid 4

From distributed energy resources to virtual power plants: A...

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4th IEEE Conference on Energy Internet and Energy system Integration, EI2 2020

作者： Tian, Liting Cheng, Lin Wan, Yuxiang Yuan, Kai Liu, Rui Wu, Kuihua Tsinghua University State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Beijing China State Grid Economic Technology Research Institute Beijing China Institute State Grid Shandong Electric Power Company Economic Technology Research Jinan China

ISBN: (纸本)9781728196060

Distributed Energy Resources(DERs) will be an important part of the future power system under the spread of distributed renewable energy generation, electric vehicles, demand response and other technologies. Cyber-physical system (CPS) is one of the development directions for the future smart grid to integrate the widespread DERs. First, this paper presents the basic architecture and operation process of virtual power plant (VPP). Then the solution of communication structure and software platform are proposed. It is pointed out that virtual power plants have good applicability in information and communication infrastructure, power market and DER integration. Finally, a demonstration program in China is introduced. The research and application of virtual power plant is currently in its embryonic form. It provides a solution for the transmission from centralized power production to the distributed energy utilization, and is an adequate way to integrate geographical dispersion, heterogeneous DERs. © 2020 IEEE.

关键词： Virtual power Plants

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Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach

arXiv

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arXiv 2022年

作者： Li, Yang Bu, Fanjin Li, Yuanzheng Long, Chao Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China School of Artifcial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Water Energy and Environment Cranfield University CranfieldMK43 0AL United Kingdom

Multi-uncertainties from power sources and loads have brought significant challenges to the stable demand supply of various resources at islands. To address these challenges, a comprehensive scheduling framework is proposed by introducing a model-free deep reinforcement learning (DRL) approach based on modeling an island integrated energy system (IES). In response to the shortage of freshwater on islands, in addition to the introduction of seawater desalination systems, a transmission structure of "hydrothermal simultaneous transmission" (HST) is proposed. The essence of the IES scheduling problem is the optimal combination of each unit’s output, which is a typical timing control problem and conforms to the Markov decision-making solution framework of deep reinforcement learning. Deep reinforcement learning adapts to various changes and timely adjusts strategies through the interaction of agents and the environment, avoiding complicated modeling and prediction of multi-uncertainties. The simulation results show that the proposed scheduling framework properly handles multi-uncertainties from power sources and loads, achieves a stable demand supply for various resources, and has better performance than other real-time scheduling methods, especially in terms of computational efficiency. In addition, the HST model constitutes an active exploration to improve the utilization efficiency of island freshwater. © 2022, CC BY-NC-ND.

关键词： Desalination

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Ultra-short-term bus load forecasting method based on multi-source data and hybrid neural network 4

Ultra-short-term bus load forecasting method based on multi-...

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4th IEEE Conference on Energy Internet and Energy system Integration, EI2 2020

作者： Fan, Shixiong Liu, Xingwei Ma, Weiwei Zhang, Wei China Electric Power Research Institute Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology Beijing China Global Energy Interconnection Research Institute Co. Ltd. State Key Laboratory of Advanced Power Transmission Technology Beijing China National Power Dispatching and Control Center State Grid Corporation of China Beijing China

ISBN: (纸本)9781728196060

Bus load forecasting plays an important role in terms of the safety of power grid dispatching and operation and the accuracy of online analysis and decision-making. In order to improve accuracy of bus load forecasting, this paper proposes an ultra-short-term load forecasting method based on multi-type data and hybrid model. Under the background of power big data, the multi-type data such as historical bus load data, date information and weather information are firstly taken as the input features of the prediction model, which is established based on the fusion of back propagation (BP-ANN) neural network and convolutional neural network(CNN). BP-ANN is used to extract the feature vectors of numeric type and category type data, which is concatenated with feature vectors extracted by CNN from image type data. The combined features are fed to a fully connected multilayer Neural Network to predict the bus load. In this paper, the historical data of active power load at the high voltage side of a 220kV substation in a certain area of China and the corresponding weather information are used for example analysis. The analysis of experimental results shows that the proposed method can make full use of the characteristics of multi-source data to conduct ultra-short-term bus load prediction, and it has higher load prediction accuracy compared to the single model prediction of BP-ANN and CNN. © 2020 IEEE.

关键词： Convolutional neural networks

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Computing Optimal Resilience-Oriented Operation of Distribution systems Considering Heterogenous Consumer-Side Microgrids

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IEEE Transactions on Consumer Electronics 2024年

作者： Bai, Ziyi Xu, Da Zhou, Bin Yang, Xiaodong Yang, Hanyu Liu, Ju Hubei University of Technology Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Wuhan430068 China Hubei University of Technology School of Electrical and Electronic Engineering Wuhan430068 China University of Macau State Key Laboratory of Internet of Things for Smart City Macao999078 China China University of Geosciences School of Automation Wuhan430074 China Hunan University College of Electrical and Information Engineering Changsha410082 China Hefei University of Technology State Key Laboratory of High Efficiency and High Quality Electric Energy Conversion Hefei230009 China Nanjing Tech University College of Electrical Engineering and Control Science Nanjing211816 China SGCC Jingmen Power Supply Company Jingmen448000 China

Mobile battery energy storage systems (MBESs), also called power-to-mobility, have been field-validated as backup generation resources. However, the role of multi-energy coordination towards resilient distribution systems (DSs) against large area disasters is still not yet involved. To cope with such challenges, this paper proposes a computing optimal resilient operation model for post-disaster microgrid-penetrated DS recovery based on the energy-to-mobility approach. In this model, mobile multi-energy storages (MMESs) are invested and routed based on their different transportation mobility, while DS with consumer-side biogas-solar-wind multi-energy microgrids are topologized by network reconfiguration. The coordinated resilient operation of different recovery actions of DS, MMESs, and consumer-side microgrids is a difficult optimization problem as results of its inherent nonconvexities, temporal-spatial mobility and strong multi-energy couplings. The original mixed-integer non-linear programming (MINLP) problem is thus equivalently linearized and reformulated into a mixed-integer linear programming (MILP) for computation performance improvement. The proposed methodology is benchmarked and performed on a modified radial DS with three consumer-side microgrids. Numerical studies demonstrate the effectiveness and superiority of the energy-to-mobility scheme over MBESs on system resilient level and economy. © 1975-2011 IEEE.

关键词： Mixed-integer linear programming

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The existence verification of barrier function with application to synchronous safety and stability

The existence verification of barrier function with applicat...

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第32届中国过程控制会议(CPCC2021)

作者： Zheren Zhu Yi Chai Zhihuan Song Xinmin Zhang IEEE State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University State Key Laboratory of Power Transmission Equipment and System Security and New Technology and School of AutomationChongqing University

This paper mainly discusses the existence of barrier function which can guarantee a dynamic system synchronously safe and ***,scholars will use the terms "if there exists a barrier function" for most related *** this paper,we use a particular case to verify the existence of barrier function and whether it can realize stabilization with guaranteed safety,such as synchronously realizing safety and asymptotic stability,safety and uniform asymptotic stability,safety and exponential stability,and safety and finite-time *** think this paper can help to know the safety under the definitions of barrier functions and realize the safety-and-stability control.

关键词： Existence of barrier function synchronous safety and stability pre-compact forward invariant set and dynamic nonlinear system

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