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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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25%-Efficiency flexible perovskite solar cells via controllable growth of SnO_(2)

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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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A Strengthened Primal-Dual Decomposition Algorithm for Solving Electricity Market Pricing With Revenue-Adequacy and FFR Constraints

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第3期2卷 379-391页

作者： Goudarzi, Hamed Hesamzadeh, Mohammad Reza Bunn, Derek Fotuhi-Firuzabad, Mahmud Shahidehpour, Mohammad Sharif University of Technology Center of Excellence in Power System Management and Control Tehran11365-11155 Iran Kth Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm10044 Sweden London Business School Department of Management Science and Operations LondonNW1 4SA United Kingdom Illinois Institute of Technology Electrical and Computer Engineering Department ChicagoIL60616 United States

This paper develops a new decomposition algorithm for solving Electricity Market Pricing (EMP) problem, taking into account both revenue-adequacy and Fast Frequency Reserve (FFR) constraints. Due to revenue-adequacy constraint, a bilevel model of the EMP problem is introduced (BL-EMP). The upper level of the BL-EMP model represents the non-convex unit commitment (UC) decisions as well as the revenue-adequacy constraints of the market participants (generators, loads, and battery-storage owner). The lower level is a convex economic dispatch model with FFR constraint. To tackle the computational complexity of the considered BL-EMP model, this paper develops, tests, and proposes a Strengthened Primal-Dual Decomposition (SPDD) algorithm, which takes benefits from both Benders-like and Lagrange Dual-like algorithms. The new SPDD algorithm has a series of interesting computational properties, which are theoretically discussed in the paper. The SPDD algorithm has better computational performance than standard Benders decomposition algorithm and it also does not need tuning of the Big-M (or disjunctive) parameters for solving the proposed BL-EMP problem. Results from the modified IEEE 24-bus, the IEEE 118-bus, and the IEEE 300-bus system show the superiority of proposed SPDD algorithm over the classic Benders algorithm. © 2023 IEEE.

关键词： power markets

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Data envelopment analysis to determine reliability targets for power distribution systems

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Applied Energy 2025年 394卷

作者： Moghimy Fam, Amin Safdarian, Amir Fotuhi-Firuzabad, Mahmud Lehtonen, Matti Pourakbari-Kasmaei, Mahdi Department of Electrical Engineering and Automation Aalto University Maarintie 8 Uusimaa Espoo02150 Finland Centre of Excellence in Power System Control and Management Electrical Engineering Department Sharif University of Technology Tehran Iran

Distribution system (DS) reliability has the greatest impact on customer service availability. Enhancing the reliability level of a DS is a costly task, thus, determining an efficient and economical target levels for reliability indices is of high importance. To achieve this goal, this paper proposes a comprehensive Data Envelopment Analysis (DEA) based method (DBM). DBM integrates the effect of technical factors in the target determination process for the level of reliability indices in addition to including the conventional average method as one of its steps. By introducing a reliability–cost curve, DBM becomes capable of setting reliability targets based on the practical effect of investment in the system. Furthermore, this feature enables the estimation of the additional investment required to meet the targets. The results indicate the superiority of the proposed method in comparison with the conventional average method from the economic aspect and show the value of integrating the technical factors into it. The determined reliability targets are at least 2.85 % more cost-efficient and technically sounder for the feeders with a high value of reliability. The proposed method, due to its general features, can be applied in the benchmarking of other engineering systems where similar units are being operated and a relation between the unit's inputs and outputs can be established. © 2025 The Authors

关键词： Data envelopment analysis

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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 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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Data-Driven Adequacy and Security Assessment: Machine Learning Trends for Reliable Smart Energy Networks

Data-Driven Adequacy and Security Assessment: Machine Learni...

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Smart Grid Conference (SGC)

作者： Mohammad Mohsen Hayati Hassan Majidi Hossein Shahinzadeh Mehdi Abapour Mahmud Fotuhi-Firuzabad Gevork B. Gharehpetian Faculty of Electrical and Computer Engineering Energy Systems Research Institute (ESRI) University of Tabriz Tabriz Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Center of Excellence in Power System Management and Control (CEPSMC) Sharif University of Technology Tehran Iran

ISBN: (数字)9798331511333

ISBN: (纸本)9798331511340

Smart energy networks (SENs) face numerous challenges related to security, reliability, operational uncertainties, and increasing penetration of renewable energies (REs), necessitating advanced methodologies for adequacy and security assessment. This study delves into the utilization of machine learning-driven approaches (MLDAs) to quantify reliability and gauge the dependability of future sustainable SENs. By autonomously acquiring knowledge from datasets, MLDAs offer swift and reliable evaluations, surpassing traditional methodologies. The integration of artificial intelligence (AI) in enhancing power system reliability is highlighted, emphasizing the importance of extensive datasets and evolving AI algorithms. Data-driven approaches empower energy network operators to proactively identify and mitigate security risks, enhancing the reliability of critical infrastructure systems. The research underscores the significance of constructing reliable databases, addressing challenges in real-time operations, and ensuring the performance and trustworthiness of machine learning (ML) algorithms. Overall, the study showcases the potential of ML techniques in revolutionizing reliability management and risk assessment in smart energy networks, paving the way for sustainable and reliable power systems.

关键词： Uncertainty Machine learning algorithms Databases Machine learning power system reliability Smart grids management Reliability Security Risk management

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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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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

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： Adaptive robust optimization energy storage minimax regret planning studies transmission expansion uncertainties

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Multi-Objective DER Planning Framework as a TEP Alternative: An Augmented Epsilon-Constraint Approach

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IEEE Transactions on Industry Applications 2025年

作者： Saber, Hossein Ranjbar, Hossein Mazaheri, Hesam Sharif University of Technology Center of Excellence in Power System Control and Management Electrical Engineering Department Tehran 11365-11155 Iran University of Adelaide School of Electrical and Mechanical Engineering Adelaide Australia Texas A&M University Department of Electrical and Computer Engineering College Station TX United States

Recently, the construction of new transmission lines has faced extreme challenges due to ever-increasing environmental concerns and construction costs. Investing in distributed energy resources (DERs) offers a viable alternative to reduce reliance on new transmission lines. This study introduces a multi-objective bi-level DER planning model as an alternative to transmission network reinforcement. The upper-level problem aims to minimize the total cost of DERs, mitigate the congestion cost of the transmission network, and improve the system reliability while ensuring the profitability of the DER investment. In the lower-level problem, the wholesale electricity market is cleared to determine the optimal locational marginal prices and the operational status of the bulk power system. A scenario-based approach is employed to account for uncertainties in future loads, renewable generation, and generator bid prices. The bi-level problem is reformulated as a single-level optimization using the strong duality condition, with non-linear terms linearized through the big-M method and complementary slackness conditions. The augmented epsilon-constraint method is then employed to solve the multi-objective optimization problem. Finally, the effectiveness and applicability of the proposed DER planning model are evaluated using the IEEE 24-bus test system, offering insights from both system operators and DER investors. © 1972-2012 IEEE.

关键词： Augmented epsilon-constraint method distributed energy resources multi-objective optimization planning studies private investment

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