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An Age-Dependent Battery Energy Storage Degradation Model for Power System Operations

IEEE TRANSACTIONS ON POWER SYSTEMS 2025年第1期40卷 1188-1191页

作者： Yong, Pei Guo, Fei Yang, Zhifang Chongqing Univ State Key Lab Power Transmiss Equipment Technol Chongqing 400044 Peoples R China

Power system operations need to consider the degradation characteristics of battery energy storage (BES) in the modeling and optimization. Existing methods commonly bridge the mapping from charging and/or discharging behaviors to the BES degradation cost with fixed parameters. However, BES degradation characteristics constantly change during the aging process, so the fixed-parameter models cannot be precise throughout the lifespan. This letter introduces an age-dependent BES degradation model that captures the changes in characteristics. Based on the Arrhenius battery degradation equation, we deduce an analytical expression of the degradation that uses the operation variables of BES in the power system perspective as inputs. Then, we convexify the expression to enable the embedding of age-dependent degradation characteristics into power system optimization models. The corresponding interaction framework between the proposed degradation model and power system optimization models is also established. Case studies show the proposed model can limit the error within three percent in the lifespan.

关键词： Degradation Power systems optimization optimization models Aging Batteries Analytical models Time-domain analysis Convex functions Benchmark testing Aging characteristics battery energy storage degradation model power system optimization

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Application of decision-dependent uncertainty in power system planning and operation analyses: A state-of-the-art review

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 233卷

作者： Wang, Bangyan Wang, Xiuli Zhang, Xiao-Ping Huang, Jing Song, Zhenzi Zhang, Likai Li, Yafei Xfan Jiaotong Univ Sch Elect Engn Shaanxi Key Lab Smart Grid Xian 710049 Shaanxi Peoples R China Univ Birmingham Dept Elect Elect & Syst Engn Birmingham B15 2TT England State Grid Suzhou Power Supply Bur Suzhou 215000 Jiangsu Peoples R China

Decision-dependent uncertainty appears when decisions can affect the random factors in the problem, which is endogenous. In recent years, it has gained considerable attention from scholars primarily due to its promising applications in the field of power systems. However, the emerging research is single and fragmented, which is not capable of showing the difference and complexity of this concept. This survey aims to provide a comprehensive and up -to -date overview of the various dimensions of decision-dependent uncertainty within power systems. First, it defines four types of modeling approaches in general forms. Subsequently, the survey identifies the primary sources of decision-dependent uncertainty from diverse perspectives within power systems. Furthermore, the paper lists the specific application fields where this uncertainty plays a crucial role. To tackle the challenges introduced by decision-dependent uncertainty, the survey delves into strategies that have been employed in previous studies. This is followed by a discussion of algorithms used in prior research. Lastly, the survey presents an outlook, highlighting its promising future. This research introduces key knowledge about the application of such uncertainty in power systems, attempts to make essential classifications, and contributes a summary perspective to both decision-dependent uncertainty and generalized uncertainty research.

关键词： Decision-dependent uncertainty Power systems analyses optimization models Power system planning and operation Power system resilience Renewable generations

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FADA-SMOTE-Ms: Fuzzy Adaptative Smote-Based Methods

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IEEE ACCESS 2024年 12卷 158742-158765页

作者： Mohammed, Roudani Karim, El Moutaouakil Univ Sidi Mohamed Ben Abdellah Multidisciplinary Fac Taza Math & Data Sci Lab MDSL Fes 28810 Morocco

The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well-known methods to solve the unequal class distribution problem in imbalanced datasets. However, it has three shortcomings: (1) it may cause the over-generalization problem due to oversampling of noisy samples, (2) over-sampling of uninformative samples, and (3) increasing the overlaps between different classes around the class boundaries. Different approaches SMOTE based have been proposed to handle these problems, but most of them implement hyperparameters and tend to generate noise because the synthetic sample is generated, randomly, in the area delimited by current random minority data. In this research, an improved SMOTE-based method, namely Fuzzy-ADAptative-SMOTE-Based-Methods (FADA-SOMTE-Ms), which targets all three problems at the same time, is introduced. In this regard, the alpha -SMOTE is chosen in such a way that the synthetic data is as far as possible from the two closest majority data. More precisely, this method processes into six steps: (a) clustering minority class into k groups (b) selecting a safe region (c) selecting random two minority data, (d) finding the M closest majority data to these minority data using original membership functions based on Fuzzy mean and flirting results, (e) finding the alpha -SMOTE producing a synthetic data as close as possible to the minority class and as far as possible from the M majority data by solving a very simple multi-objective mathematical optimization model, and (f) using SMOTE to generate synthetic samples using optimal alpha -SMOTE. FADA-SOMTE-Ms is evaluated using 5 classifiers, 21 unbalanced datasets, and it's compared to 8 oversampling methods using three performance measures. FADA-SOMTE-Ms consistently outperforms other popular oversampling methods.

关键词： Noise measurement optimization models Synthetic data Linear programming Ensemble learning Costs Training Switches Data mining Classification algorithms Big Data Classification oversampling SMOTE unbalanced data big data

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Suppression to Commutation Failure of UHVDC in Hierarchical Connection Mode With Synchronous Condenser by Enhancing Post-Fault System Strength

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IEEE TRANSACTIONS ON POWER DELIVERY 2025年第2期40卷 864-877页

作者： Li, Shenghu Li, Yikai Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Peoples R China

The reactive power of synchronous condenser (SC) at the inverter of the ultra HVDC (UHVDC) in the hierarchical connection mode affects the system strength and the suppression effect on the commutation failure (CF). In this paper, a dynamic short-circuit ratio (DSCR) including the reactive power response of the SC is proposed to quantify the post-fault system strength. A coordinated control against the CF with the DSCR is proposed. The novelties are, (1) The analytical expression of the DSCR with the impedances of the Thevenin equivalent of both grids and the inter-layer coupling is newly derived. The recursive least-square method with the forgetting factor and the robust estimation is proposed to obtain the DSCR. (2) A multi-objective optimization model is proposed to tune the excitation parameters of SC and enhance the DSCR. (3) A coordinated control with the enhanced DSCR is proposed to adjust the dc current to suppress the CF. The simulation results verify the accuracy of the estimated DSCR and the effect of the coordinated control on enhancing the system strength and avoiding the CF under different fault conditions.

关键词： Reactive power Inverters Couplings Impedance Transformers Power system dynamics optimization Dynamic response optimization models HVDC transmission Commutation failure (CF) coordinated control dynamic short-circuit ratio (DSCR) hierarchical connection mode (HCM) parameter optimization recursive least square synchronous condenser (SC) UHVDC

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SAR-PAA: A Physically Adversarial Attack Approach Against SAR Intelligent Target Recognition

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 1377-1393页

作者： Ma, Yanjing Pei, Jifang Huo, Weibo Zhang, Yin Huang, Yulin Chen, Keyang Yang, Jianyu Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

Synthetic aperture radar (SAR) target recognition has entered a new era of intelligence due to the rapid development of deep learning. Naturally, an accompanying challenge arises in countering SAR intelligent target recognition technology and protecting the targets of interest from exposure risks. In this article, a novel adversarial attack approach against SAR intelligent target recognition is proposed, which is physically easy to implement in practice. Just with strategic deployment of simple scatterers near the target, such as corner reflectors, the SAR intelligent target recognition system will be effectively attacked. First, an adversarial attack framework against SAR intelligent target recognition is constructed based on the SAR imaging process and target recognition mechanism. Then, the corresponding adversarial attack optimization model is established considering the adversarial attack effectiveness, target and environment constraints. Finally, integrating electromagnetic computation and differential evolution algorithm, a solution method for the adversarial attack optimization model is designed to achieve the physical implementation of adversarial attacks against SAR target recognition systems. To comprehensively evaluate the performance of the proposed method, six typical SAR target recognition networks are attacked based on the MSTAR dataset, resulting in an average fooling rate of 81.83%. Extensive experiments demonstrate the effectiveness and superiority of the proposed method.

关键词： Target recognition Radar polarimetry Perturbation methods Jamming optimization models Interference Electromagnetics Adversarial attack intelligent target recognition optimization model synthetic aperture radar (SAR)

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Optimal Operation Strategy for Reconfigurable EV Chargers Under Component Failures

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IEEE TRANSACTIONS ON SMART GRID 2025年第2期16卷 1510-1522页

作者： Song, Bingkun Madawala, Udaya K. Univ Auckland Dept Elect Comp & Software Engn Auckland 1010 New Zealand

Recently, reconfigurable electric vehicle chargers (REVCs) have been commercialized due to their benefits, especially concurrent charging of multiple EVs. However, the operation of REVCs introduces new challenges that were not previously encountered with conventional EV chargers. Therefore, this paper proposes an optimal operation strategy and an optimization model, enabling the effective and economical operation of REVC charging stations. Initially, the strategy considers the optimal allocation of charging spaces based on EV arrival and departure times at the charging station, to maximize operational efficiency and avoid charging congestion. Additionally, to mitigate potential overloading on the grid and REVCs due to EV charging, the strategy leverages the energy storage capability of EVs with long parking durations to supply energy to EVs with short parking durations. Consequently, the proposed optimization model addresses the optimal scheduling of EV charging and discharging to meet the charging demands of all EVs. Furthermore, the strategy accounts for scenarios where some charger components may experience failures, incorporating optimal operation schemes for REVCs with different failures. This differs from existing operation strategies, which did not consider failures. A comprehensive case study with 115 EVs is conducted to validate the effectiveness of the proposed operation strategy and optimization model, under both normal and failure conditions. Simulation results demonstrate that the proposed operation strategy and optimization model effectively achieve efficient and appropriate operation of REVCs.

关键词： Electric vehicle charging Charging stations optimization models Costs Vehicle-to-grid Transportation Space charge Schedules Resource management Pricing Electric vehicle (EV) reconfigurable electric vehicle chargers (REVCs) discharging charging space allocation failures operation

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Distributionally Robust Bilevel optimization Model for Distribution Network With Demand Response Under Uncertain Renewables Using Wasserstein Metrics

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2025年第2期16卷 1165-1176页

作者： Yin, Can Dong, Jin Zhang, Yiling Univ Minnesota Dept Ind & Syst Engn Minneapolis MN 55455 USA Oak Ridge Natl Lab Electrificat & Energy Infrastruct Div Oak Ridge TN 37831 USA

We consider a distribution network integrating demand response (DR) participants in the presence of uncertain renewable suppliers and outdoor temperatures. A bilevel optimization model is proposed to capture the intricate dynamics between price-incentivized DR participants and distribution system operations, including energy procurement and active/reactive power flows. The model is formulated as a distributional robust bilevel optimization using Wasserstein metrics. We show favorable data-driven properties including out-of-sample guarantee and asymptotic consistency. Furthermore, we present a tractable mixed-integer linear programming reformulation and characterize the worst-case distribution. Computational experiments are conducted on a modified 33-bus system. Our findings underscore the efficacy of the pricing strategies derived from the proposed bilevel optimization model. These strategies not only effectively manage DR participants' behavior but also bring equity considerations among households with various characteristics to light. The results contribute to a deeper understanding of the interplay between distribution system operators and DR participants.

关键词： HVAC Pricing Uncertainty Renewable energy sources optimization optimization models Electricity Costs Buildings Real-time systems Bilevel decision-making distributionally robust optimization HVAC aggregator residential demand flexibility uncertain renewables

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Selection and Resource Allocation Strategies for Chatbot Technologies in Higher Education: An optimization Model Approach

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IEEE ACCESS 2025年 13卷 13156-13174页

作者： Wu, Suhan Luo, Min Nanjing Polytech Inst Sch Econ & Management Nanjing 210048 Peoples R China Shenzhen Inst Informat Technol Sch Management Shenzhen 518172 Peoples R China

Chatbot technologies are increasingly integral to higher education, offering significant potential to enhance teaching effectiveness and student engagement. This study explores the strategic integration of chatbot technologies, focusing on optimal selection and resource allocation to maximize educational efficacy. We developed a mathematical optimization model and introduced the "Relative Net Utility Differential" (RNUD) as a pivotal metric to facilitate the decision-making process for chatbot technology selection and resource distribution. Our findings reveal that when RNUD values for all chatbots fall within a specified range, the model efficiently determines the optimal resource allocations, providing clear analytical solutions for each technology. Additionally, we devised an algorithm for scenarios where RNUD values vary significantly, effectively isolating optimal solutions by excluding suboptimal choices. Numerical analysis confirms that this algorithm maintains robust performance and high computational efficiency, particularly when the chatbot count does not exceed 20. This efficiency persists across various distributions of chatbots' technical attributes, and increasing the number of candidate chatbots generally enhances overall educational utility. This suggests that a diverse technological environment can lead to more tailored and effective educational interventions. Moreover, our study highlights a competitive interaction among chatbots: those with superior technical attributes tend to receive more resources. However, when the technical attributes of one chatbot improve, the resources allocated to others decrease, and vice versa. These insights underscore the importance of not only providing more sophisticated chatbots but also carefully managing their interactions to maximize overall educational utility.

关键词： Chatbots Resource management Education Artificial intelligence Electronic learning Ethics Solid modeling optimization models Generative AI Decision making Chatbot technology technology selection resource allocation mathematical modeling higher education

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optimization of New Energy Vehicle Industry Structure Based on optimization Link Prediction Similarity Algorithm and CGE

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IEEE ACCESS 2025年 13卷 47722-47733页

作者： Xie, Zhenjun Chongqing Business Vocat Coll Sch E Commerce & Logist Chongqing 401331 Peoples R China

In light of the growing global concerns over energy and climate change, new energy vehicles are confronted with both opportunities and challenges posed by industrial structural transformation. To foster the optimization of the new energy vehicle industry structure while considering environmental impact, this paper first uses an improved link prediction algorithm to construct an industrial structure optimization model. Then, a computable general equilibrium model is used to explore the effect of a carbon tax on the new energy vehicle industry. The proposed model had the best area under the curve on the Karate network and FWFW network, which were 90.64% and 65.05%. In the Jazz network, the prediction accuracy was 92.65%. The prediction accuracy of the proposed model for the "Yangtze River Delta" and "Beijing Tianjin Hebei" regions was 86.37% and 85.62%. With the gradual rise of carbon tax rates and emission reductions, the demand for electricity, coal, and oil in the new energy vehicle manufacturing industry was gradually decreasing. The gross domestic product and total output of the manufacturing industry were gradually decreasing, and the total investment was gradually increasing. This result demonstrates the application effect of the proposed industrial structure optimization model and indicates that carbon tax collection will have an impact on carbon emissions and energy demand. This research will help promote the optimization of the new energy vehicle industry structure and promote the sustainable growth of the national economy.

关键词： Climate change Carbon tax Vehicles Energy management optimization models Manufacturing industries Computational modeling Accuracy Predictive models Carbon emissions Supply and demand Renewable energy sources Battery powered vehicles Modeling Market opportunities New energy vehicles industrial structure link prediction computable general equilibrium model carbon tax

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An Online Reinforcement Learning-Based Energy Management Strategy for Microgrids With Centralized Control

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第1期61卷 1501-1510页

作者： Meng, Qinglin Hussain, Sheharyar Luo, Fengzhang Wang, Zhongguan Jin, Xiaolong State Grid Tianjin Elect Power Co Comprehens Serv Ctr Tianjin 300010 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Tiangong Univ Sch Elect Engn Tianjin 300387 Peoples R China Minist Educ Key Lab Smart Grid Tianjin 300072 Peoples R China Key Lab Smart Energy & Informat Technol Tianjin Mu Tianjin 300072 Peoples R China Zhejiang Univ Inst Marine Elect & Intelligent Syst Ocean Coll Zhoushan 316000 Peoples R China

To address the issue of significant unpredictability and intermittent nature of renewable energy sources, particularly wind and solar power, this paper introduces a novel optimization model based on online reinforcement learning. Initially, an energy management optimization model is designed to achieve plan adherence and minimize energy storage (ES) operation costs, taking into account the inherent challenges of wind power-photovoltaic energy storage systems (WPESS). An online reinforcement learning framework is employed, which defines various state variables, action variables, and reward functions for the energy management optimization model. The state-action-reward-state-action (SARSA) algorithm is applied to learn the joint scheduling strategy for the microgrid system, utilizing its iterative exploration mechanisms and interaction with the environment. This strategy aims to accomplish the goals of effective power tracking and reduction of storage charging and discharging. The proposed method's effectiveness is validated using a residential community with electric vehicle (EV) charging loads as a test case. Numerical analyses demonstrate that the approach is not reliant on traditional mathematical models and adapts well to the uncertainties and complex constraints of the WPESS, maintaining a low-cost requirement while achieving computational efficiency significantly higher than that of the model predictive control (MPC) and deep Q-network (DQN) algorithm.

关键词： Reinforcement learning Renewable energy sources Microgrids Costs Monitoring Wind power generation optimization models Grid integration optimal energy management reinforcement learning SARSA algorithm wind-solar-storage microgrid

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