IET Renewable Power Generation最新文献

{"title":"Federated learning assisted distributed energy optimization","authors":"Yuhan Du,&nbsp;Nuno Mendes,&nbsp;Simin Rasouli,&nbsp;Javad Mohammadi,&nbsp;Pedro Moura","doi":"10.1049/rpg2.13101","DOIUrl":"https://doi.org/10.1049/rpg2.13101","url":null,"abstract":"<p>The increased penetration of distributed energy resources and the adoption of sensing and control technologies are driving the transition from our current centralized electric grid to a distributed system controlled by multiple entities (agents). The transactive energy community serves as an established example of this transition. Distributed energy management approaches can effectively address the evolving grid's scalability, resilience, and privacy requirements. In this context, the accuracy of agents' estimations becomes crucial for the performance of distributed and multi-agent decision-making paradigms. This paper specifically focuses on integrating federated learning (FL) with the multi-agent energy management procedure. FL is utilized to forecast agents' local energy generation and demand, aiming to accelerate the convergence of the distributed decision-making process. To enhance energy aggregation in transactive energy communities, we propose an FL-assisted distributed consensus + innovations approach. The results demonstrate that employing FL significantly reduces errors in predicting net power demand. The improved forecast accuracy, in turn, introduces less error in the distributed optimization process, thereby enhancing its convergence behaviour.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 14","pages":"2524-2538"},"PeriodicalIF":2.6,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540869","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":"Optimal sitting, sizing and control of battery energy storage to enhance dynamic stability of low-inertia grids","authors":"Mohammad Rasol Jannesar,&nbsp;Sajad Sadr,&nbsp;Mehdi Savaghebi","doi":"10.1049/rpg2.13079","DOIUrl":"https://doi.org/10.1049/rpg2.13079","url":null,"abstract":"<p>As inverter-based resources like wind turbines increase, grid inertia and stability decrease. Optimal placement and control of energy storage systems can stablise low-inertia grids. This paper investigates how optimal battery energy storage systems (BESS) enhance stability in low-inertia grids after sudden generation loss. The sitting, sizing and control of BESS are determined simultaneously in each genetic algorithm (GA) population, then voltage and frequency stability is evaluated based on the network simulation. This continues until the optimal solution is found. A network based on Kundur's four-machine system is modelled for the first study and two of the four synchronous generators (SGs) have been replaced with wind farms. Then, the production of the third SG has been decreased by 13%. According to the results, addition of wind farms causes the frequency drop below 49.6 Hz for more than 5 min, indicating instability. It is also demonstrated that with optimal control parameters and placement, a 60 MW BESS can alleviate the voltage and frequency fluctuations, leading to enhanced stability. This method has also been tested on the IEEE 39-bus network, where the installation of a BESS with a capacity of 9 MVA could restore the frequency stability.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 15","pages":"2925-2941"},"PeriodicalIF":2.6,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142674145","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":"Unit commitment of power systems considering system inertia constraints and uncertainties","authors":"Yuxin Weng,&nbsp;Guangchao Geng,&nbsp;Quanyuan Jiang","doi":"10.1049/rpg2.13095","DOIUrl":"https://doi.org/10.1049/rpg2.13095","url":null,"abstract":"<p>Large-scale integration of renewable energy into the power grid results in a lack of system inertia, posing challenges to the optimal operation and scheduling of systems considering frequency stability. This article proposes a unit commitment model that considers both inertia constraints and the uncertainty of load and renewable energy. First, the time-domain expression of the system frequency response is calculated based on the aggregated System Frequency Response (SFR) model, considering the system's maximum frequency deviation and the maximum Rate of Change of Frequency (RoCoF) limit. This calculation determines the minimum inertia requirement for the system. Furthermore, inertia constraints suitable for mixed-integer programming model are derived to address the nonlinearity of conventional frequency constraints. Second, considering the uncertainties of load and wind energy from renewable sources, a unit commitment model with inertia constraints is constructed, and a robust method is used to solve the uncertainties. Finally, the accuracy of the proposed inertia constraints and unit commitment model is validated using case study of IEEE standard test cases and a provincial power grid in China.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 14","pages":"2512-2523"},"PeriodicalIF":2.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540808","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":"Enhancing PV receiver efficiency in laser wireless power transmission through square elliptic hyperboloid concentrator","authors":"Meng Xian-long,&nbsp;Zhang Pu,&nbsp;Hou Yi-chao,&nbsp;Liu Bei,&nbsp;Chen Ying-xue,&nbsp;Tapas K. Mallick","doi":"10.1049/rpg2.13022","DOIUrl":"https://doi.org/10.1049/rpg2.13022","url":null,"abstract":"<p>Laser wireless energy transmission is a widely utilized method, yet its efficiency is constrained by a variety of factors. In order to improve the conversion efficiency of the receivers of the laser wireless power transmission (LWPT) system, the square elliptic hyperboloid (SEH) concentrating module designed for LWPT system receivers is developed. By analysing the I–V characteristic curves from the results of the experiments and employing non-linear parameter regression, a corrected battery characteristic curve was derived within a specific laser irradiation range. On this basis, an optical–thermal–electric multi-field coupling characteristic model was developed. The finite element method is used to simulate the multi-field coupling characteristics and conversion efficiency of the receiving end under diverse working conditions (including different rotation angles and different divergence angles) of the concentrating photovoltaic module. Research shows: First, the larger the divergence half-angle <i>β</i> of the laser beam, the more obvious the improvement of the effective optical efficiency of the system by the SEH concentrator. Second, the short-circuit current and the maximum output power of the PV cell at the receiving end are significantly improved by the SEH concentrator, and the improvement effect is more obvious with the increase of the divergence angle and the rotation angle. Third, SEH concentrators did not significantly affect the fill factor of PV cells.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 13","pages":"2044-2064"},"PeriodicalIF":2.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359905","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":"Coordinated planning of thermal power, wind power, and photovoltaic generator units considering capacity electricity price","authors":"Ye Xu,&nbsp;Wenxia Liu,&nbsp;Benhao Yang","doi":"10.1049/rpg2.13102","DOIUrl":"https://doi.org/10.1049/rpg2.13102","url":null,"abstract":"<p>With the implementation of China's carbon reduction policies, the role of thermal power units will transition to a regulating power source. Hence, the electricity market fails to accurately reflect the capacity value of thermal power units, resulting in potential future losses for these units. Therefore, it is imperative to establish a rational capacity compensation mechanism that guarantees the revenue of thermal power units and offers effective investment and construction signals. Therefore, this paper proposes a capacity compensation mechanism for thermal power units based on effective capacity. To achieve this, a two-layer power source planning model is established. At the upper level of the model, the installed capacity and capacity price of various types of power sources are optimized, while minimizing the operating costs of the planning horizon year under constraints such as annual net profit of units. The lower level focuses on the operation of typical days, optimizing the output of various types of units. Through case analysis, it can be concluded that the proposed model can achieve coordinated planning of capacity and capacity prices for various types of units, effectively ensuring the economic efficiency of the system while safeguarding the revenue of each unit.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 14","pages":"2539-2559"},"PeriodicalIF":2.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540807","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 probabilistic approach for optimal integration of EVs and RES using artificial hummingbird algorithm in distribution network","authors":"Mohd Bilal,&nbsp;Saket Gupta,&nbsp;Pitshou N. Bokoro,&nbsp;Gulshan Sharma","doi":"10.1049/rpg2.13074","DOIUrl":"https://doi.org/10.1049/rpg2.13074","url":null,"abstract":"<p>The adoption of electric vehicles (EVs) is crucial for reducing pollution from traditional automobiles. Strategic placement of electric vehicle charging stations (EVCS) is needed to meet demand while minimizing impacts on the electrical grid. This article outlines a practical method to identify optimal EVCS locations within the IEEE 69 bus system. The transition to EVs affects the electrical distribution network, requiring consideration of voltage regulation, power loss, stability, reliability, and energy loss costs when deploying EVCS. To manage increased energy demands, the article recommends integrating solar distributed generation (SDG) units at strategic points in the network, creating a self-sustaining system. The study explores the resilience of the distribution system with EVCS and SDGs through eight case studies (CS), examining EVCS deployment scenarios with and without SDG integration. The impact of slow and fast EV charging on system objectives is also analysed. The artificial hummingbird algorithm is used to solve the allocation problem, with results compared to other optimization methods. Notably, active power loss decreased from 224.67 kW (CS1) to 53.35 kW (CS8), and reactive power loss was reduced by 71.4% in CS8 compared to CS1.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 14","pages":"2305-2325"},"PeriodicalIF":2.6,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540875","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":"Wind energy system fault classification using deep CNN and improved PSO-tuned extreme gradient boosting","authors":"Chun-Yao Lee,&nbsp;Edu Daryl C. Maceren","doi":"10.1049/rpg2.13091","DOIUrl":"https://doi.org/10.1049/rpg2.13091","url":null,"abstract":"<p>Intelligent fault diagnosis for wind energy systems requires identifying unique characteristics to differentiate various fault types effectively, even when data discrepancy occurs due to the unpredictable and dynamic nature of its environment. This article addresses some of the challenges of fault classification in wind energy systems by proposing an integrated approach that combines deep learning features with a resampled supervisory control and data acquisition (SCADA) dataset. The methodology involves resampling the imbalanced SCADA dataset using synthetic minority oversampling technique (SMOTE) and near-miss undersampling techniques, extracting deep learning features using deep convolutional neural network, and feeding them into an XGBoost (extreme gradient boosting) classifier with tuned parameters using adaptive elite-particle swarm optimization (AEPSO). The effectiveness of the proposed method is demonstrated through validation conducted on a different imbalanced dataset showing superior performance metrics in terms of accuracy. Additionally, the study contributes to methodological advancements in wind turbine fault diagnosis by providing a rigorous framework for fault classification. It is confirmed that utilizing the extracted deep learning features into the resampled data can significantly affect the classification performance metrics. Furthermore, the proposed integrated approach shows significance for fault diagnosis enhancement in wind energy systems and advancing the field towards more efficient and reliable operation.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 14","pages":"2496-2511"},"PeriodicalIF":2.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540919","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":"Front Cover: Optimized DBN-based control scheme for power quality enhancement in a microgrid cluster connected with renewable energy system","authors":"Narendiran Sivakumar,&nbsp;Jaisiva Selvaraj,&nbsp;Karthika Jayaprakash,&nbsp;Kinde Anlay Fante","doi":"10.1049/rpg2.13098","DOIUrl":"https://doi.org/10.1049/rpg2.13098","url":null,"abstract":"<p>The cover image is based on the article <i>Optimized DBN-based control scheme for power quality enhancement in a microgrid cluster connected with renewable energy system</i> by Narendiran Sivakumar et al., https://doi.org/10.1049/rpg2.13058.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 12","pages":"i"},"PeriodicalIF":2.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13098","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137792","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":"Comparative analysis and optimal allocation of virtual inertia from grid-forming and grid-following controlled ESSs","authors":"Naixuan Zhu,&nbsp;Pengfei Hu,&nbsp;Chongxi Jiang,&nbsp;Yanxue Yu,&nbsp;Daozhuo Jiang","doi":"10.1049/rpg2.13085","DOIUrl":"https://doi.org/10.1049/rpg2.13085","url":null,"abstract":"<p>A broad consensus of neutralizing the carbon dioxide emissions facilitates the transition to the renewable energy power system. Meanwhile, the concerns about the volatility of renewable energies are growing as the rotational inertia of power system becomes inadequate. To maintain the frequency stability of power system, some studies for configuring inertia energy storage systems (ESSs) are carried out, mainly focusing on the allocation of virtual inertia from grid-forming ESS. In contrast, the allocation of virtual inertia from grid-following ESS has not been well elaborated and the differences in virtual inertia provided by these two modes are yet to be revealed. Based on <span></span><math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>H</mi>\u0000 <mn>2</mn>\u0000 </msub>\u0000 <annotation>$mathcal {H}_2$</annotation>\u0000 </semantics></math>-norm and Kron reduction, firstly, the state-space model of post-disturbance system is established, together with the transient performance evaluation. Then the inertia characteristics of both grid-forming and grid-following devices are formulated, followed by the unified gradient descent optimization method for allocating virtual inertia. A modified IEEE 39-bus system and its time-domain simulations help in the verification of the contribution of this paper. Through the comparative analysis of corresponding optimal results, the conclusions from two aspects are drawn: in terms of transient frequency support, the grid-forming devices can provide no less than <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>26</mn>\u0000 <mo>%</mo>\u0000 </mrow>\u0000 <annotation>$26%$</annotation>\u0000 </semantics></math> better inertia support; with the higher power capacity and similar energy capacity, the grid-forming devices can relieve the response pressure of other generators by approximately <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>77.1</mn>\u0000 <mo>%</mo>\u0000 </mrow>\u0000 <annotation>$77.1%$</annotation>\u0000 </semantics></math>.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 14","pages":"2416-2429"},"PeriodicalIF":2.6,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540893","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":"Multi-data classification detection in smart grid under false data injection attack based on Inception network","authors":"H. Pan,&nbsp;H. Yang,&nbsp;C. N. Na,&nbsp;J. Y. Jin","doi":"10.1049/rpg2.13086","DOIUrl":"https://doi.org/10.1049/rpg2.13086","url":null,"abstract":"<p>During operation, the smart grid is subject to different false data injection attacks (FDIA). If the different kinds of FDIAs and typical failures have been detected, the system operator can develop various defenses to protect the smart grid in multiple categories. Therefore, this article aims to propose a multi-data classification detection model to differentiate the data of regular operation, faults, and FDIAs when the smart grid suffers FDIAs. Due to the unbalanced number of different kinds of samples in the dataset, Affinitive Borderlinen SMOTE is used to pre-process the data by oversampling to improve the training accuracy. A multi-data detection model based on the Inception network is established, and the overall structure of the network and the individual Inception modules are given. A small power system is an example of simulating a smart grid suffering from FDIAs. The designed classification detection model is simulated, validated, and compared with two-dimensional convolutional neural networks and existing research results. The qualitative analysis of the evaluation metrics can show that the Inception network model has high accuracy and real-time performance for detecting different data.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 14","pages":"2430-2439"},"PeriodicalIF":2.6,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540819","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}