IET Renewable Power Generation最新文献

{"title":"A Comprehensive Review on Peer-to-Peer Energy Trading: Market Structure, Operational Layers, Energy Cooperatives and Multi-energy Systems","authors":"Zeynep Tanis,&nbsp;Ali Durusu,&nbsp;Nihan Altintas","doi":"10.1049/rpg2.70075","DOIUrl":"https://doi.org/10.1049/rpg2.70075","url":null,"abstract":"<p>The energy sector is experiencing a significant transformation due to the rising focus on renewable resources, smart grid technology advancements, energy management improvements, and operational efficiency. To successfully manage this transformation, all actors in the sector need to reassess their roles, develop innovative business models, and formulate effective strategies and policies. Peer-to-peer energy trading significantly contributes to this transformation by allowing individuals to trade energy with one another directly. While it offers advantages such as reducing peak demand, lowering backup needs, and minimizing network losses, its deployment in electricity grids increases the complexity of transactions at both virtual and physical layers. This paper provides an in-depth analysis of the latest advancements in peer-to-peer energy trading, highlighting its main features and the potential advantages for both the grid and prosumers. By organizing existing research, examining the market structure, and detailing common technical approaches, this paper offers a thorough overview of the future of peer-to-peer energy trading and the energy transition. This study highlights the necessity for stakeholders in the energy sector to embrace innovative strategies to adapt to the evolving market dynamics and achieve sustainable energy objectives.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244701","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":"Artificial-Intelligence-Based Reduced Sensor Voltage Control Strategy for DC Microgrid Applications","authors":"Hussain Sarwar Khan,&nbsp;Kimmo Kauhaniemi","doi":"10.1049/rpg2.70072","DOIUrl":"https://doi.org/10.1049/rpg2.70072","url":null,"abstract":"<p>The expeditious advancement in renewable energy technologies enables the concept of microgrids to boost the incorporation of renewable energy into power systems. In this context, distributed generation (DG)-based DC microgrids (MGs) are favoured because of their higher efficiency, greater reliability, and simpler development and control compared to their AC counterparts. This paper presents an artificial neural network (ANN) voltage control for a DC-DC step-up converter to reduce the number of sensors in the DC microgrids. The proposed approach offered cost-effective and better voltage regulation in multi-bus DC MG. The proposed methodology employs quasi-stationary line (QSL) modeling to account for DC MG uncertainties and disturbances, while simultaneously developing and implementing a model predictive voltage control (MPVC) strategy to generate the comprehensive dataset. The converter's voltage error and switching signals, extracted from the generated dataset, serve as input features for offline training of an artificial neural network (ANN). Once trained, the ANN is deployed online to regulate distributed generators (DGs) within a multi-bus DC MG. Real-time hardware-in-the-loop simulations using OPAL-RT 4510 demonstrate that the proposed controller effectively regulates voltage with reduced sensors, ensuring improved reliability and efficiency.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244699","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 Novel Approach to Flooding Fault Detection and Risk Assessment in PEM Fuel Cells Using Data-Driven Models","authors":"Meltem Yavuz Çelikdemir","doi":"10.1049/rpg2.70074","DOIUrl":"https://doi.org/10.1049/rpg2.70074","url":null,"abstract":"<p>The reliable and efficient operation of polymer electrolyte membrane fuel cells (PEMFCs) necessitates the implementation of preventive strategies and maintenance protocols to minimize the likelihood of failures. To address this, the study identifies effective diagnostic techniques for detecting faults in PEMFCs. A data-driven approach leveraging machine learning methods is proposed to enhance the detection of flooding faults under varying operational conditions. This approach enables the automatic extraction of fault-related features directly from raw data. Experimental data obtained from an 80 W PEMFC, widely used in the literature for comparability, was utilized in the study. Various machine learning classification algorithms were applied, and their performance metrics were analysed. Among these, the random subspace k-nearest neighbor algorithm demonstrated superior accuracy and the shortest training time, leading to the development of a novel model. The study evaluated 22 variables associated with PEMFCs, performed fault diagnosis, and assessed fault severity. Furthermore, a risk analysis was conducted using the proposed model, enabling the prediction of both the risk level and the probability of fault occurrence as percentages. Key performance metrics, including accuracy, sensitivity, precision, and specificity, were calculated as 99.97%, 99.98%, 99.90%, and 99.98%, respectively, during model validation. During testing, these metrics were recorded as 99.45%, 100%, 98.42%, and 99.16%, respectively.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144237316","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 Unified Control Design of Three Phase Inverters Suitable for Both Grid-Forming and Following Modes of Operation","authors":"Aravind Ingalalli,&nbsp;Ali Ihsan Aygun,&nbsp;Sukumar Kamalasadan","doi":"10.1049/rpg2.70043","DOIUrl":"https://doi.org/10.1049/rpg2.70043","url":null,"abstract":"<p>The primary cascaded control loops and the phase-locked loop (PLL) can enable voltage source inverter operation in grid-forming and grid-following mode. This article proposes a unified control for such inverters with current control, voltage control, and power control loops, including the PLL impact on <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>a</mi>\u0000 <mi>b</mi>\u0000 <mi>c</mi>\u0000 </mrow>\u0000 <annotation>$abc$</annotation>\u0000 </semantics></math>-<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>d</mi>\u0000 <mi>q</mi>\u0000 </mrow>\u0000 <annotation>$dq$</annotation>\u0000 </semantics></math> transformations as the building blocks. Small-signal-based linearization techniques are adopted to achieve the resultant linear time-invariant model. Moreover, a systematic definition of the unified controller is proposed to ensure the easy portability of the controller code in a model-driven development set-up. Model verification and experimental results of a 5 kW inverter set-up approve the efficacy of the proposed design approach.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244660","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":"The Diagnosis of Wind Turbine Blade Imbalance Using Dual-Input Signals in Parallel CNN-Transformer","authors":"Shu Cheng,&nbsp;Jingming Li,&nbsp;Chaoqun Xiang,&nbsp;Xizhuo Yu,&nbsp;Hongwen Liu,&nbsp;Ruirui Zhou","doi":"10.1049/rpg2.70071","DOIUrl":"https://doi.org/10.1049/rpg2.70071","url":null,"abstract":"<p>Prolonged exposure of wind turbine blades to wind forces can lead to blade twisting and structural loosening. These defects result in uneven mass distribution, causing severe vibrations in wind turbines, which reduce energy efficiency and increase operational costs. To address the challenges of weak vibration signal feature extraction and poor diagnostic model performance caused by blade mass imbalance, this paper proposes a dual-signal parallel CNN-transformer model based on Fast Fourier Transform (FFT) and Variational Mode Decomposition (VMD). A convolutional neural network (CNN) is employed to extract spatial features from the fused time-frequency domain signals, while the time-domain signals are input into a transformer encoder to capture long-term temporal dependencies. A cross-attention mechanism integrates temporal and spatial features by computing attention weights, allowing the model to focus on critical features while reducing computational complexity. Experiments using the vibration data of the wind turbine nacelle collected through the SCADA system show that when the stacked time-frequency signals are used as input, the accuracy of the model is increased by 36.04%, 7.34% and 5.41% compared with the original signal, FFT-processed signal and VMD-processed signal, respectively. The proposed method achieves a diagnostic accuracy of 97.5% under full-sample conditions and 95% under low-sample conditions.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206388","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":"Weak Coupling Effects in the PLL Modes of Grid-Following PMSG Wind Power Systems","authors":"Xumeng Cui,&nbsp;Lei Chen,&nbsp;Kaiyuan Hou,&nbsp;Yong Min,&nbsp;Kefei Wang,&nbsp;Fei Xu","doi":"10.1049/rpg2.70073","DOIUrl":"https://doi.org/10.1049/rpg2.70073","url":null,"abstract":"<p>Small-signal synchronisation stability is a critical issue of grid-connected permanent magnet synchronous generator (PMSG) wind power systems employing grid-following voltage source converters (VSCs). While most studies simplify the system by modelling multiple VSCs as a single unit to analyse oscillations caused by VSC-grid interactions, few have examined interactions among VSCs. This paper proposes the cross-synchronising coefficient to quantify these interactions and applies it to analyse small-signal synchronisation stability in multi-PMSG wind power systems. When considering only PLL, the cross-synchronising coefficient is analytically derived and generally small, or even zero, indicating weak coupling between the synchronisation dynamics of different grid-side converters. Unlike strong synchronising interactions among nearby synchronous generators, the interaction between the PLL dynamics of PMSG converters remains weak even at short electrical distances. When outer control loops are included, the interaction increases but remains moderate. These findings suggest that the weak coupling of PLL modes in PMSG wind power systems allow for independent stability analysis and control design. Simulation results validate the theoretical analysis, offering insights into the small-signal synchronisation stability of PMSG wind power systems.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197362","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-Task Deep Reinforcement Learning with Scenario Clustering for Real-Time Scheduling of Wind-Solar-Hydro Complementary Generation Systems","authors":"Yuanyu Ge,&nbsp;Jun Xie,&nbsp;Shuo Feng,&nbsp;Jiaqi Chang,&nbsp;Zhangwei Wang","doi":"10.1049/rpg2.70070","DOIUrl":"https://doi.org/10.1049/rpg2.70070","url":null,"abstract":"<p>Real-time scheduling of wind-solar-hydro complementary power generation systems (WSHCPGS) is crucial for enhancing energy utilization efficiency and power supply quality. However, WSHCPGS encounter challenges stemming from the complexity of multi-energy coupling systems and the inherent uncertainty of renewable energy sources. Traditional scheduling methods struggle to quickly and accurately adapt to the dynamic environment. Therefore, this paper proposes a multi-task deep reinforcement learning (DRL) method with scenario clustering for real-time scheduling of WSHCPGS. Conventional single-task DRL methods suffer from low learning efficiency and insufficient generalization ability. Their scheduling strategies may not be robust enough when facing uncertain environments. To address these challenges, this paper divides typical scenarios using the t-distributed stochastic neighbor embedding (t-SNE) and density-based spatial clustering of applications with noise (DBSCAN) methods and identifies scenario categories based on the stacking ensemble learning (SEL) algorithm. Then, a multi-task soft actor–critic (MTSAC) algorithm is proposed for real-time scheduling. The proposed method enables targeted training for specific scenarios to ensure the optimality of scheduling strategies. Simulation results indicate that the multi-task method can handle uncertainty better and converge faster than conventional single-task DRL algorithms. Unlike single-task DRL methods, MTSAC with scenario clustering enhances adaptability and robustness. Furthermore, compared to traditional methods such as model predictive control (MPC) and particle swarm optimization (PSO), the proposed method achieves significant increases of 6.92% and 30.21% in reservoir energy storage, all while maintaining decision-making times below 0.1 s.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144179247","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":"Operational and Planning Strategy for Hydrogen Energy Storage in Distribution Networks Under Dynamic Transformer Capacity Expansion Scenarios","authors":"Jin Zhu,&nbsp;Jianfeng Zhao,&nbsp;Yuan Gao,&nbsp;Xiaodong Yuan,&nbsp;Lucheng Hong,&nbsp;Ai Du","doi":"10.1049/rpg2.70067","DOIUrl":"https://doi.org/10.1049/rpg2.70067","url":null,"abstract":"<p>The large-scale integration of distributed generation has significantly increased the complexity of distribution network operation optimization, leading to issues such as voltage violations and reverse power flows. To address these challenges, this paper proposes an operational and planning strategy for hydrogen energy storage in distribution networks under dynamic transformer capacity expansion scenarios. First, the impact of reverse power flow on transformer losses in distribution networks with high penetration of renewable energy is analyzed, clarifying the advantages of hydrogen energy storage in conjunction with dynamic transformer capacity expansion scenarios. Second, a collaborative optimization strategy for the operation of the distribution network that integrates PV and hydrogen energy is proposed for scenarios with dynamic transformer capacity expansion. Next, the two-level planning strategy for hydrogen energy storage in distribution networks under dynamic transformer capacity expansion scenarios is established. Meanwhile, an improved generative adversarial network is used to account for the uncertainty in renewable energy output, and a heuristic algorithm is applied to solve the two-level configuration model in the hydrogen energy storage planning. Finally, the effectiveness of the hydrogen energy storage operational planning strategy is validated through the study of the IEEE 33-bus and IEEE 118-bus distribution network. In the IEEE 33-bus distribution network, the proposed strategy reduces the maximum voltage from 1.07 to 1.05 and decreases the maximum reverse power flow by 78.95%. After integrating hydrogen energy storage, the electricity purchase cost for the hydrogen production system is 1.9 × 10⁶ ¥, the annual total maintenance cost is 4.4 × 10⁴ ¥, and the hydrogen sales revenue reaches 6.6 × 10⁶ ¥, demonstrating significant economic benefits of hydrogen energy storage operation. Similar results are also observed in the 118-bus system, further validating the effectiveness of the proposed strategy.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171843","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":"Resilient Distributed Predefined Time Secondary Control for Cyber-Physical Microgrids","authors":"Junfeng Tan,&nbsp;Fan Zhang,&nbsp;Yanlu Huang,&nbsp;Shuai Zhao,&nbsp;Hongyu Su","doi":"10.1049/rpg2.70055","DOIUrl":"https://doi.org/10.1049/rpg2.70055","url":null,"abstract":"<p>This paper proposed a resilient distributed predefined-time sliding mode control for islanded AC microgrids with external disturbances caused by noisy circumstances or cyber-attacks. By utilizing the predefined-time convergence theory, the voltage regulation and frequency restoration as well as active power sharing can be achieved within a predefined time, which is directly equal to an adjustable parameter. Furthermore, based on the integral sliding mode control approach, the proposed method can completely compensate the external disturbance. Different from some voltage control methods based on complex second-order consensus, a novel secondary controller is designed by adopting the virtual control technique, such that the voltage regulation can be achieved under a first-order consensus with a corresponding tracking controller. In addition, the direct Lyapunov method is utilized to prove the stability of islanded AC microgrids under the proposed controller, and the analysis of predefined-time convergence is also given. Finally, case studies on a microgrid test system with four distributed generator is built in the MATLAB/SimPowerSystems software environment are conducted to demonstrate the effectiveness and superior performance of the proposed control scheme.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148576","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":"Enhanced Transient Stability in Hybrid DC/AC Microgrids: Robust Composite Control Strategy With Virtual Capacitors Integration Using ANFIS-Optimized Control Gain Parameters","authors":"Md. Saiful Islam,&nbsp;Israt Jahan Bushra,&nbsp;Tushar Kanti Roy,&nbsp;Amanullah Maung Than Oo","doi":"10.1049/rpg2.70066","DOIUrl":"https://doi.org/10.1049/rpg2.70066","url":null,"abstract":"<p>Fluctuations in renewable energy generation due to unpredictable weather pose major challenges to power balance in hybrid DC/AC microgrids (HDAMGs). The inclusion of bio-renewable energy sources further complicates operational stability. This paper proposes a robust composite control strategy integrating a non-singular integral terminal sliding mode controller with a nonlinear backstepping controller. The scheme is enhanced by an adaptive fractional-order reaching law, ensuring dynamic stability, chattering elimination, and finite-time convergence. To maximize renewable energy utilization, an artificial neural network-based global power point tracking algorithm optimizes energy extraction from solar PV and wind turbines. An adaptive neuro-fuzzy inference system further tunes control parameters in real time. A virtual capacitor is employed to enhance inertia, transient response, and power-sharing accuracy. System stability is validated through control Lyapunov functions and the complete strategy is implemented on the Simulink platform. Extensive simulations demonstrate that the proposed method eliminates overshoots and improves settling time by 72% compared to the same controller without the virtual capacitor. Compared to existing controllers, it achieves up to 86% overshoot reduction and 78% faster settling. Under <span></span><math>\u0000 <semantics>\u0000 <mo>±</mo>\u0000 <annotation>$pm$</annotation>\u0000 </semantics></math>15% parameter variation, it maintains robustness, delivering 58–76% improved settling time and 75–81% overshoot reduction, thereby ensuring reliable HDAMG performance under dynamic conditions.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144091725","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}