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iEnergy 2024年第1期3卷 39-45页

作者： Ningyu Ren Liguo Tan Minghao Li Junjie Zhou Yiran Ye Boxin Jiao Liming Ding Chenyi Yi State Key Laboratory of Power System Operation and Control Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Center for Excellence in Nanoscience(CAS) Key Laboratory of Nanosystem and Hierarchical Fabrication(CAS)National Center for Nanoscience and TechnologyBeijing 100190China

High power conversion efficiency(PCE)flexible perovskite solar cells(FPSCs)are highly desired power sources for aerospace crafts and flexible ***,their PCEs still lag far behind their rigid ***,we report a high PCE FPSC by controllable growth of a SnO_(2)electron transport layer through constant pH chemical bath deposition(CBD).The application of SnSO_(4)as tin source enables us to perform CBD without strong acid,which in turn makes it applicable to acid-sensitive flexible indium tin ***,a mild and controllable growth environment leads to uniform particle growth and dense SnO_(2)deposition with full coverage and reproducibility,resulting in a record PCE of up to 25.09%(certified 24.90%)for FPSCs to *** as-fabricated FPSCs exhibited high durability,maintaining over 90% of their initial PCE after 10000 bending cycles.

关键词： Flexible perovskite solar cells chemical bath deposition SnO_(2) electron transport layer

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Minimax Regret Robust Co-Planning of Transmission and Energy Storage systems With Mixed Integer Recourse

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IEEE Transactions on Sustainable Energy 2025年第3期16卷 2144-2156页

作者： Barkom, Ehsan Saber, Hossein Moeini-Aghtaie, Moein Ehsan, Mehdi Shahidehpour, Mohammad Sharif University of Technology Center of Excellence in Power System Control and Management Department of Electrical Engineering Tehran11365-11155 Iran Sharif University of Technology Department of Energy Engineering Tehran Iran Illinois Institute of Technology Robert W. Galvin Center for Electricity Innovation ChicagoIL60616 United States King Abdulaziz University Jeddah Saudi Arabia

The growing penetration of renewable energy sources, with intermittent and uncertain nature, brings new challenges to the secure and efficient operation of power systems. Expanding transmission networks and utilizing energy storage (ES) have been introduced as effective solutions to address these challenges. This paper presents a minimax regret robust co-planning model with mixed integer recourse for transmission and ES systems, designed from the perspective of a central planner. The model considers a polyhedral uncertainty set for future peak load growth, while uncertainties in wind farm expansion are addressed through internal scenario analysis. This approach will guarantee the robustness of investment decisions and provide the central planner with a clear picture of the maximum regret among all possible scenarios. Furthermore, the proposed minimax regret framework facilitates strategic planning for ES installation after the resolution of long-term uncertainties. In this paper, we reformulate the model into a standard min-max-min problem, in which the maximization level is only over uncertainties. Subsequently, a five-level solution strategy based on a modified nested column and constraint generation decomposition technique is represented to deal with the intractability and complexity of the problem caused by binary variables of transmission lines and ES blocks. The model is finally evaluated through comprehensive simulation studies to verify its tractability, practicality, and effectiveness. © 2010-2012 IEEE.

关键词： Uncertainty Planning Load modeling power transmission lines Costs Investment Renewable energy sources Optimization Stochastic processes Programming

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Vulnerability of EV Charging Stations to Cyber Attacks Manipulating Prices

Vulnerability of EV Charging Stations to Cyber Attacks Manip...

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2023 International Conference on Future Energy Solutions, FES 2023

作者： Akbarian, Amirhossein Bahrami, Mahdi Vakilian, Mehdi Lehtonen, Matti Sharif University of Technology Center of Excellence in Power System Management and Control Electrical Engineering Department Tehran Iran Aalto University Department of Electrical Engineering and Automation Espoo Finland

ISBN: (纸本)9798350332308

Electric vehicles (EVs) have experienced an unprecedented increase in their penetration;in well developed countries;due to the advancement of battery technology and the need to make transportation more environmentally friendly. As a result of this trend, EVs have become an integral part of those countries power grid ecosystem, where they may be used as both a source, and a consumer of energy. However, cybersecurity risks associated with large fleets of EVs within the power grids can significantly impact the normal operation of distribution systems. This paper considers the cyber-attack consequences, from attackers' perspective. The cyber attackers under study target the charging price (CP) by increasing the CP in off-peak hours and accordingly subsiding it in on-peak hours. A two-stage optimization framework is proposed to identify the most important ultra-fast charging stations (XFCs) under the cyber-attack, meanwhile, the EV users' response to the compromised CP is considered. Then, the proposed model is implemented on a modified IEEE 123-bus distribution test system, and the effectiveness of the model is proved through some case studies. In particular, they demonstrated that the amount of load shedding is increased by 8.198 MW when the EV user response is taken into account. © 2023 IEEE.

关键词： Crime

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Improving performance of robots using human-inspired approaches: a survey

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Science China(Information Sciences) 2022年第12期65卷 5-35页

作者： Hong QIAO Shanlin ZHONG Ziyu CHEN Hongze WANG State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences School of Artifcial Intelligence University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

Realizing high performance of ordinary robots is one of the core problems in robotic research. Improving the performance of ordinary robots usually relies on the collaborative development of multiple research fields, resulting in high costs and difficulty to complete some high-precision tasks. As a comparison,humans can realize extraordinary overall performance under the condition of limited computational-energy consumption and low absolute precision in sensing and controlling each body unit. Therefore, developing human-inspired robotic systems and algorithms is a promising avenue to improve the performance of robotic systems. In this review, the cutting-edge research work on human-inspired intelligent robots in decisionmaking, cognition, motion control, and system design is summarized from behavior-and neural-inspired aspects. This review aims to provide a significant insight into human-inspired intelligent robots, which may be beneficial for promoting the integration of neuroscience, machinery, and control, so as to develop a new generation of robotic systems.

关键词： human-inspired intelligent robots brain-inspired intelligence decision making visual cognition musculoskeletal robots

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RELIABILITY ANALYSIS OF MULTI-STATE system CONSIDERING INTERDEPENDENT PERFORMANCE CONVERSION 14

RELIABILITY ANALYSIS OF MULTI-STATE SYSTEM CONSIDERING INTER...

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14th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2024

作者： Lu, He Wang, Yan Zhou, Yu Jia, Heping School of Economics & Management North China Electric Power University Beijing China Beijing Kedong Electric Power Control System Co. Ltd. Beijing China Qinghai Electric Power Trading Center Co. Ltd. Qinghai China

ISBN: (纸本)9781837242672

Multi-state systems characterized by interdependent performance conversion represent a complex category where system components exist in multiple states, each with distinct performances that are interdependent. Significant research has focused on modeling and analyzing such systems, as their reliability analysis is vital for numerous practical applications. This paper introduces a system model for multi-state systems with interdependent performance conversion. Additionally, it expands the universal generating function (UGF) method to analyze the system reliability of the proposed model, encompassing both reliability probability and expected instantaneous performance deficiency (EIPD). To demonstrate the efficacy of the proposed model and method, two numerical examples are presented, one featuring linear interdependence and the other non-linear interdependence. © The Institution of Engineering & Technology 2024.

关键词： Reliability analysis

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Brain-inspired Intelligent Robotics:Theoretical Analysis and systematic Application

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Machine Intelligence Research 2023年第1期20卷 1-18页

作者： Hong Qiao Ya-Xiong Wu Shan-Lin Zhong Pei-Jie Yin Jia-Hao Chen The State Key Laboratory of Management and Control for Complex System Institute of AutomationChineseAcademyof SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Chinese Academy of Science Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031China School of Mechanical Engineering University of Science and Technology BeijingBeijing 100083China

Traditional joint-link robots have been widely used in production lines because of their high precision for single *** the development of the manufacturing and service industries,the requirement for the comprehensive performance of robotics is *** types of bio-inspired robotics have been investigated to realize human-like motion control and manipulation.A study route from inner mechanisms to external structures is proposed to imitate humans and animals *** this idea,a brain-inspired intelligent robotic system is constructed that contains visual cognition,decision-making,motion control,and musculoskeletal *** paper reviews cutting-edge research in brain-inspired visual cognition,decision-making,motion control,and musculoskeletal *** software systems and a corresponding hardware system are established,aiming at the verification and applications of next-generationbrain-inspired musculoskeletal robots.

关键词： Brain-inspired intelligent robot software and hardware decision making muscle control cognitive intelligence.

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Vulnerability of EV Charging Stations to Cyber Attacks Manipulating Prices

Vulnerability of EV Charging Stations to Cyber Attacks Manip...

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Future Energy Solutions (FES), International Conference on

作者： Amirhossein Akbarian Mahdi Bahrami Mehdi Vakilian Matti Lehtonen Electrical Engineering Department Center of Excellence in Power System Management and Control Sharif University of Technology Tehran Iran Department of Electrical Engineering and Automation Aalto University Espoo Finland

Electric vehicles (EVs) have experienced an unprecedented increase in their penetration; in well developed countries; due to the advancement of battery technology and the need to make transportation more environmentally friendly. As a result of this trend, EVs have become an integral part of those countries power grid ecosystem, where they may be used as both a source, and a consumer of energy. However, cybersecurity risks associated with large fleets of EVs within the power grids can significantly impact the normal operation of distribution systems. This paper considers the cyber-attack consequences, from attackers’ perspective. The cyber attackers under study target the charging price (CP) by increasing the CP in off-peak hours and accordingly subsiding it in on-peak hours. A two-stage optimization framework is proposed to identify the most important ultra-fast charging stations (XFCs) under the cyber-attack, meanwhile, the EV users’ response to the compromised CP is considered. Then, the proposed model is implemented on a modified IEEE 123-bus distribution test system, and the effectiveness of the model is proved through some case studies. In particular, they demonstrated that the amount of load shedding is increased by 8.198 MW when the EV user response is taken into account.

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A Data Valuation Framework for the Electricity Market Data

A Data Valuation Framework for the Electricity Market Data

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IEEE International Conference on power and power and Renewable Energy (ICPRE)

作者： Shijie Ji Yuke Zhou Wen Xie Feng Gao Wanli Hu Beijing Power Exchange Center Beijing China Department of Industrial Engineering & Management Peking University Beijing China Beijing Kedong Electric Power Control System Co. Ltd. Beijing China

ISBN: (数字)9798350377460

ISBN: (纸本)9798350377477

In the context of the digital transformation of the electricity market, the electricity transaction data held by exchange centers have significant application potential. This paper proposes a comprehensive data valuation framework to assist power trading centers in extracting the value of electricity trading data. The framework integrates the cost and revenue approaches, evaluating electricity market data from the perspectives of data cost and relative value. Furthermore, accounting for the characteristics of electricity market data, our approach includes data quality, data significance, data application, and data risk within the relative value dimension. Using real electricity market data from the power exchange center, we validate that the proposed indicator system can comprehensively and dynamically reflect the value of electricity trading data.

关键词： Renewable energy sources Electric potential Costs Electricity Reinforcement learning Electricity supply industry power markets Software Indexes Cost accounting

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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A Risk-Aware and Socp-Based Wind Expansion Planning Model Considering Correlated Uncertainties Using Copula Method

SSRN

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SSRN 2023年

作者： Ranjbar, Hossein Saber, Hossein School of Electrical and Mechanical Engineering The University of Adelaide Adelaide Australia Centre of Excellence in Power System Control and Management Department of Electrical Engineering Sharif University of Technology Tehran Iran

Integration of large-scale renewable energy sources (RESs) into existing power grids is challenging for power system operators and planners due to the intermittent and uncertain characteristics of RESs and the limitation of transmission capacity. This paper presents a new risk-based framework to obtain the optimal placement and sizing of wind farms in the bulk power system considering the impact of mutual correlation between wind farms' power productions in different candidate buses. In this regard, a second-order cone programming (SOCP) optimization problem is developed to minimize the total expected social cost including the operation cost of conventional generators, wind curtailment cost, and the investment cost of newly installed wind farms. In the proposed model, the correlated uncertainties of wind power production are incorporated using the copula method. Also, the conditional value-at-risk (CVaR) measure is utilized to manage the risk of the model associated with uncertainties. The proposed planning model is then implemented into the 6-bus Garver and IEEE 118-bus test systems to demonstrate the effectiveness and applicability of the proposed wind power expansion planning model. © 2023, The Authors. All rights reserved.

关键词： Second-order cone programming

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