IET Renewable Power Generation最新文献

{"title":"RETRACTION: Probability Modelling of Storage Based-Smart Energy Hub Considering Electric Vehicles Charging Stations Performance and Demand Side Management","authors":"","doi":"10.1049/rpg2.70024","DOIUrl":"https://doi.org/10.1049/rpg2.70024","url":null,"abstract":"<p><b>RETRACTION</b>: S. S. M. Javid, G. Derakhshan, S. M. Hakimi: Probability modelling of storage based-smart energy hub considering electric vehicles charging stations performance and demand side management. <i>IET Renewable Power Generation</i> 17, 3221–3239 (2023). https://doi.org/10.1049/rpg2.12839</p><p>The above article, published online on 29^th August 2023 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editor-in-Chief; David Infield; the Institution of Engineering and Technology; John Wiley &amp; Sons Ltd.; and the authors.</p><p>Since publication, the authors have alerted the journal about substantial text overlap between the article and an unpublished source of a different research group. Following an investigation, the IET and the journal have verified a substantial text overlap. Accordingly, the article is retracted. The authors apologise for the oversight.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Cyber Insurance and Defense for Multi-Energy Systems Under False Data Injections","authors":"Alexis Pengfei Zhao,&nbsp;Chenghong Gu,&nbsp;Zhaoyao Bao,&nbsp;Xi Cheng,&nbsp;Mohannad Alhazmi","doi":"10.1049/rpg2.70011","DOIUrl":"https://doi.org/10.1049/rpg2.70011","url":null,"abstract":"<p>This article introduces a novel cyber insurance planning model specifically designed to enhance the resilience of information and communication technology (ICT)-integrated multi-energy systems (MES) against cyber threats, particularly false data injection (FDI) attacks. The proposed hierarchical cyber insurance planning model (HCIPM) offers an integrated approach to managing the dual challenges of financial risk and operational disruptions caused by sophisticated cyber-attacks. The model is built upon a two-stage hierarchical optimization framework: the first stage determines the optimal allocation of cyber insurance to minimize costs while ensuring adequate risk coverage, and the second stage focuses on real-time operational defense strategies, such as load shedding and resource management, to mitigate the impact of cyber incidents. A key innovation of the HCIPM is its incorporation of a distributionally robust optimization (DRO) methodology, combined with Conditional Value at Risk (CVaR), to effectively handle the uncertainties inherent in FDI attack scenarios. By representing extreme events and their probabilities, this framework ensures robust decision-making under high uncertainty. Extensive simulations conducted on a 33-20 node distribution system demonstrate the efficacy of the proposed model. Results indicate that the HCIPM achieves a 35% reduction in load shedding costs and a 28% improvement in resilience metrics, such as system recovery time and operational continuity, compared to traditional approaches. Additionally, the model demonstrates a significant decrease in financial losses attributable to cyber-attacks, with a 40% reduction in economic damages across high-risk scenarios. The findings underline the model's capability to not only reduce operational costs but also enhance system stability and resilience under diverse attack scenarios. By integrating financial mechanisms such as cyber insurance with technical defenses, the HCIPM represents a comprehensive solution for managing cyber risks in critical infrastructure. This research bridges the gap between operational resilience and financial protection, offering a pioneering framework for future applications of cyber insurance in power systems and other critical infrastructures. The proposed model provides a scalable and adaptable strategy, making it an invaluable tool for utilities and policymakers in their efforts to safeguard modern energy systems against evolving cyber threats.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical energy profile characterization of electric vehicle charging stations integrated with photovoltaic systems based on clustering techniques","authors":"Antonio Bracale,&nbsp;Pierluigi Caramia,&nbsp;Pasquale De Falco,&nbsp;Luigi Pio Di Noia,&nbsp;Renato Rizzo","doi":"10.1049/rpg2.70006","DOIUrl":"https://doi.org/10.1049/rpg2.70006","url":null,"abstract":"<p>Secondary and primary substations of networks with electric vehicle (EV) chargers and photovoltaics (PVs) experience net loads characterized by uncertainty. Accurate characterization of EV, PV and net load energy profiles is necessary to plan new installations and to develop forecasting methodologies. This paper provides a novel contribution to the energy profile characterization of EVs and PVs, exploiting clustering techniques in a hierarchical framework to eventually characterize the overall net load profiles. In the proposal, the lower levels of the hierarchy identify clusters of EV load and PV generation profiles at individual installations, alternatively using one clustering technique among DBSCAN, Gaussian mixture models (GMMs), K-means algorithm (KMA), and spectral clustering (SC). The intermediate levels of the hierarchy reconstruct the overall EV load and PV generation profiles through a proposed frequentist combination of the lower-level profiles. The upper level of the hierarchy characterizes the overall net load through a novel approach based on the quantile convolution of the intermediate-level EV and PV profiles. Real EV load and PV generation data are used to evaluate the performance of the presented hierarchical methodology, with relative fitting improvements between 1% and 8% (compared to a two-level hierarchical benchmark) and between 16% and 29% (compared to a direct, non-hierarchical benchmark).</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143513667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-Layer Optimal Electricity Trading Method for Distribution Networks with Multi-MG","authors":"Chen Zhe,&nbsp;Lin Da,&nbsp;Li Zhihao,&nbsp;Wang Xiangjin,&nbsp;Tang Yajie","doi":"10.1049/rpg2.70012","DOIUrl":"https://doi.org/10.1049/rpg2.70012","url":null,"abstract":"<p>This paper proposes a distributed trading strategy for microgrids based on a Stackelberg game to enhance the operational benefits of distribution networks (DNs) and microgrids (MGs) while reducing microgrid energy costs. First, we establish a multi-objective two-layer optimization trading model for DN-MG interaction based on the Stackelberg game. In this model, the upper layer focuses on maximizing agent profits through a DN trading model, while the lower layer aims to minimize operator costs through an MG energy optimization model. Next, the Karush–Kuhn–Tucker conditions are applied to reformulate the two-layer model into a single-layer model. Subsequently, linear transformations are used to convert the model into a mixed-integer linear programming model. The transformations aim to address the nonlinear issues arising from multivariable coupling between the upper and lower-layers trading models. Simulation results indicate that the proposed trading strategy significantly increases the profits of DN agents while lowering the operational costs of MGs. This approach offers valuable insights for decision-making in electricity markets that involve microgrids.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143481631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic reactive power reserve assessment method for hybrid AC/DC networks based on a reduced equivalent model of an LCC-HVDC converter process","authors":"Sidun Fang,&nbsp;Yuli Lei,&nbsp;Tao Niu,&nbsp;Guanhong Chen,&nbsp;Lin Xue,&nbsp;Wenguo Wu,&nbsp;Nan Feng,&nbsp;Yuyao Feng","doi":"10.1049/rpg2.70010","DOIUrl":"https://doi.org/10.1049/rpg2.70010","url":null,"abstract":"<p>LCC-HVDC technology has been widely applied. Commutation failure is a common disturbance in converter stations. In the absence of an adequate and rapid dynamic reactive power supply, the system is prone to trigger DC blocking. Therefore, it is of paramount importance to assess the dynamic reactive power reserve in the nearby area of converter stations with both speed and accuracy. However, traditional methods mainly apply electromechanical transient differential equations or quasi-steady-state equations to describe the dynamic process of the converter, which cannot accurately capture the fast dynamics of reactive power/voltage levels on a timescale of hundreds of milliseconds during commutation failure. To address this issue, this paper proposes a reduced equivalent model of the dynamic process of an inverter converter, which approximates the accurate reactive power voltage dynamic process of the converter's electromagnetic transient time scale into the core reactive power dynamic process of the electromechanical transient time scale. Based on this reduced equivalent model, a dynamic reactive power reserve assessment method suitable for the nearby area of inverter converter stations is proposed, which considers both calculation speed and accuracy. Finally, the proposed method is validated using an improved IEEE 39 test system, demonstrating its effectiveness in practical applications.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143475743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Measurement-Based SSO Probability Assessment Approach Considering Multiple Uncertainties","authors":"Zhenyu Lei,&nbsp;Kaiyuan Hou,&nbsp;Lei Chen,&nbsp;Feng Guo,&nbsp;Qingchen Liu,&nbsp;Yong Min","doi":"10.1049/rpg2.70021","DOIUrl":"https://doi.org/10.1049/rpg2.70021","url":null,"abstract":"<p>Sub-synchronous oscillation (SSO) of wind power system has long plagued grid operators and researchers. It is affected by different uncertain factors such as wind speed and its probability assessment is crucial for SSO prevention and emergency control. The black box characteristics of wind turbines impede the precise modelling and stability analysis. Meanwhile, researchers concentrated on the uncertainty of wind speed and ignored the impact of grid strength variation. This paper proposed a measurement-based SSO probability assessment approach considering multiple uncertainties. First, a method is proposed to precisely estimate damping ratios of SSO mode based on measured impedance-frequency curves. Then, probabilistic collocation method (PCM) theory is introduced to construct an approximate function between the damping ratio and multiple uncertain inputs. In SSOs induced by interaction between full power convert of Permanent Magnet Synchronous Generator and weak grid, both wind speed and grid impedance are set as uncertain inputs, while only wind speed is considered in SSOs induced by Doubly Fed Induction Generator. For common truncated distribution in power grid, the significant advantages of PCM over other analytical methods are pointed out. Finally, the efficiency and accuracy of proposed approaches, compared with Monte Carlo and other approaches, are validated by simulation.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143466213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Meta-Learning for Enhanced False Data Injection Detection in Smart Grids: The ONF-ML Approach","authors":"Mohammadreza Pourshirazi,&nbsp;Mohsen Simab,&nbsp;Alireza Mirzaee,&nbsp;Bahador Fani","doi":"10.1049/rpg2.70016","DOIUrl":"https://doi.org/10.1049/rpg2.70016","url":null,"abstract":"<p>While digitalization promotes grid management efficiency, it also makes power systems more vulnerable to a variety of anomalies, especially false data injection (FDI) anomalies. FDI intrusions pose a serious threat to the security of smart grids. The existing approaches, like machine learning, have certain limitations, which can be addressed by proposing the optimized neuro-fuzzy meta-learning (ONF-ML) model. This model combines several machine learning classifiers serving as a two-step optimization process including hyperparameter optimization for individual classifiers and simulated annealing for tuning neuro-fuzzy parameters. Simulation results conducted on the IEEE 14-bus system using MATPOWER demonstrate the superior performance of ONF-ML in detecting FDI intrusions compared to baseline models, especially for subtle injections. In every bus, FDI intrusion has occurred and average performance metrics are considered. The results illustrate an average detection rate of 91.7% and 81.9% for intrusion samples and 99.9% and 99.8% for normal instances in cases of −3% and +3% occurrences, respectively. While baseline models illustrated critical performance degradation during robust analyses, this technique was remarkably stable, maintaining a detection rate of over 75%, outperforming the second-best technique by up to 45% in worst-case scenarios. By addressing real-world challenges such as sensitivity to noise, inflexibility and incompetence in detecting subtle intruders, the ONF-ML approach enables continuous learning from new data, ensuring adaptability to new threats. Taken together, these features make ONF-ML a practical and scalable solution to overcome the limitations of traditional FDI detection techniques and provide a path to improved smart grid security.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143456068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-mode control and switching control strategy of microgrid for power battery formation and grading testing system","authors":"Di Xie,&nbsp;Liangliang Wang,&nbsp;Tao Jiang,&nbsp;Longyun Kang,&nbsp;Shoumo Wang","doi":"10.1049/rpg2.70004","DOIUrl":"https://doi.org/10.1049/rpg2.70004","url":null,"abstract":"<p>To address the stability issues of the microgrid system for power battery formation and grading testing systems in scenarios involving multiple parallel converters, this paper proposes a hybrid dual-mode control strategy combining grid-following and grid-forming modes to ensure stable operation of the microgrid system. However, the conventional switching between the two control strategies during the operation of the converter can lead to transient power and current surges, which may even affect the stable operation of the converter. Therefore, this paper studies the characteristics of grid-following and grid-forming control strategies. Based on the fact that both have the same type of output variables, a seamless switching control strategy based on the method of controller output state following is proposed, the switching time can be shortened by more than 1 s to the greatest extent possible. Furthermore, a seamless switching control strategy for grid-connected and islanded operation modes of the microgrid system is introduced. Finally, the effectiveness of the proposed method is verified using the Simulink simulation platform and a hardware-in-the-loop experimental simulation platform. The experimental results demonstrate the validity of the proposed approach.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A risk uncertainty–based flexible dispatch method for pumped storage under extreme weather","authors":"Miaoyong Feng,&nbsp;Zhanhong Huang,&nbsp;Tao Yu,&nbsp;Zhenning Pan,&nbsp;Qianjin Liu,&nbsp;Ziyao Wang,&nbsp;Shuangquan Liu","doi":"10.1049/rpg2.70009","DOIUrl":"https://doi.org/10.1049/rpg2.70009","url":null,"abstract":"<p>The large-scale integration of renewable energy into new power systems presents significant challenges in terms of controllability and predictability due to its inherent randomness and volatility. The uncontrollability is further compounded by the sudden impacts of extreme weather events, making multi-scale power balance increasingly difficult. This paper proposes a two-stage flexible dispatch method (TFDM) for measuring uncertainty under extreme weather. The model constructs probabilistic disaster impact assessments for wind farms and transmission lines using fuzzy sets and captures the associated uncertainties through chance constraints and risk costs. It provides more flexible disaster risk reduction methods by integrating uncertainty prediction errors and unit commitment plans into the interaction between two-stage dispatch. An accelerated algorithm that combines historical scene learning with improved K-nearest neighbours (KNN) dispatch is proposed. It is employed to obtain initial solutions for binary unit commitment variables, accelerating the resolution of frequent unit combination switching problems. Tests on the IEEE 39-bus and IEEE 118-bus systems show that the proposed method can effectively utilize the regulating capacity of pumped storage units to significantly improve the resilience of the system to cope with extreme weather. The improved KNN algorithm has stronger convergence and efficiency for large-scale power grid scenarios.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized Coordination of Electric Vehicles, Distributed Compensation Devices, and Distributed Generation for Risk Mitigation in Radial Distribution Networks","authors":"Chen Zhongbo,&nbsp;Liang Liang,&nbsp;Lu Chao","doi":"10.1049/rpg2.70019","DOIUrl":"https://doi.org/10.1049/rpg2.70019","url":null,"abstract":"<p>The charging demand of electric vehicles (EVs), intelligent and flexible compensation units, and renewable energy generation are key components of the smart grid framework. This paper aims to evaluate the benefits of coordinating distributed compensation units while mitigating the risks associated with the uncertainties arising from the aggregation of distributed generation and EV charging loads within the smart grid. Linear models for electric spring (ES) were developed to facilitate their integration into collaborative operational strategies. Subsequently, probabilistic load flow (PLF) analysis was combined with branch power flow (BPF) equations to assess voltage fluctuations and the stochastic distribution of energy demand. The proposed methodology aims to optimize coordinated operations for risk mitigation. Finally, case studies were carried out to verify the effectiveness of the proposed optimization model and provide further insights into its practical applications.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143439142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}