Renewable Energy最新文献

{"title":"Dragonfly-wing-inspired corrugated splitter plates for enhancing flow-induced vibration piezoelectric energy harvesting","authors":"Chenxu Guo,&nbsp;Xianzong Meng,&nbsp;Junlei Wang","doi":"10.1016/j.renene.2025.124009","DOIUrl":"10.1016/j.renene.2025.124009","url":null,"abstract":"<div><div>This study introduces a novel biomimetic strategy to enhance flow-induced vibration (FIV) energy harvesting by incorporating corrugated splitter plates inspired by the microstructural features of dragonfly wings. While conventional splitter plates have been proven effective in improving the performance of piezoelectric energy harvesters, the aerodynamic benefits of bio-inspired surface morphologies have received limited attention. Four innovative bionic splitter plate configurations are designed and integrated into a piezoelectric cylinder system. The effects of these corrugated designs are systematically investigated under three installation angles (30°, 60°, and 90°). The experimental results demonstrate that the bionic splitter plates significantly outperform smooth plates at all three angles, with maximum increases of 23.7 %, 41.6 %, and 20.5 %, respectively. Numerical simulations reveal that the corrugated structures affect vortex shedding patterns, vortex structures, and local aerodynamic characteristics on splitter plates. While the shedding mode remains unchanged at 30° and 90°, a clear transition is observed at 60°, accompanied by the formation of secondary vortices and intensified wake instability. Furthermore, the corrugated structures induce sudden changes in the average pressure distribution, resulting in a larger pressure differential across the splitter plate, which amplifies bluff body oscillations and boosts energy output.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"256 ","pages":"Article 124009"},"PeriodicalIF":9.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Layout optimization of mountain PV involving hydro-PV hybrid system and site selection","authors":"Dong Qi ,&nbsp;Jia Kang ,&nbsp;Jinshi Liu ,&nbsp;Shijun Chen ,&nbsp;Wenlong Chen ,&nbsp;Guo Xie ,&nbsp;Wenquan Wang","doi":"10.1016/j.renene.2025.124018","DOIUrl":"10.1016/j.renene.2025.124018","url":null,"abstract":"<div><div>Mountain PV technology associated with hydro-PV hybrid systems plays an important role in the future electricity market. This study presented a modified model for the mountain PV module layout, whereby optimal PV construction sites are determined and output performance are evaluated. A case study in Lianghekou Hydropower Station in China was conducted. The Geographic Information System-Analytic Hierarchy Process (GIS-AHP) technique was employed to preliminarily identify six candidate PV sites (Site I ∼ VI) in the study area. Six independent selections were executed based on suitability score distribution and raster image processing, then six specific regions for PV layout were selected. Accumulative area of the six selected regions was approximately 24.6 % of the six-candidate area. The annual power generation per unit area, the Levelized Cost of Energy (LCOE) and Payback Period (PBP) of optimal candidate site (Site II) were respectively 221.1 kWh/m², 0.024 USD/kWh and 9.28 year. Compared with conventional layout of large-scale PV modules, proposed layout from the model could achieve 7.7 % increase in power generation, 14 % reduction in LCOE and 18.4 % shortening in PBP. This study provided direct evidence for mountain PV feasibility in hydro-PV hybrid system, and paved the way on PV module layout method in complex mountain environment.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"256 ","pages":"Article 124018"},"PeriodicalIF":9.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Peer-to-peer power trading and pricing for rental energy storage shared community microgrid: A coordinated Stackelberg and cooperative game","authors":"Ping Li ,&nbsp;Jiajia Chen ,&nbsp;Hui Yang ,&nbsp;Zhenjia Lin","doi":"10.1016/j.renene.2025.123963","DOIUrl":"10.1016/j.renene.2025.123963","url":null,"abstract":"<div><div>The configuration of photovoltaic (PV) and energy storage (ES) systems for prosumers can significantly reduce their electricity bills. However, the high initial investment cost and prolonged recovery cycle of ES systems hinder their large-scale adoption. This paper proposes an ES renting and sharing business model to operate a community microgrid with multiple PV prosumers. The model derives a coordinated Stackelberg and cooperative game to thoroughly investigate peer-to-peer (P2P) power trading and pricing among prosumers, shared rental ES, and distribution system operator (DSO). Firstly, a Stackelberg game-based bi-level iterative optimization is proposed to capture the interaction between the DSO and multiple stakeholders, aiming to derive optimal P2P trading and pricing under PV uncertainty. Additionally, an asymmetric cooperative game is presented to ensure coalition cost minimization and fair profit distribution between prosumers and shared rental ES. To address the privacy concerns of prosumers and enhance computational efficiency, an adaptive direction multiplier method (A-ADMM) is utilized to solve the cooperative game. Numerical simulations demonstrate the effectiveness of the proposed model in reducing the operational costs of the community microgrid while enhancing the level of PV power sharing among prosumers. The model also achieves a fair distribution of cooperative profits among the multiple prosumers and the shared rental ES alliance.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"256 ","pages":"Article 123963"},"PeriodicalIF":9.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-dimensional interpretable wind speed forecasting model with two-stage feature exploring","authors":"Binrong Wu ,&nbsp;Jiacheng Lin ,&nbsp;Rui Liu ,&nbsp;Lin Wang","doi":"10.1016/j.renene.2025.124028","DOIUrl":"10.1016/j.renene.2025.124028","url":null,"abstract":"<div><div>Accurate wind speed forecasting is critical for optimizing wind energy utilization, yet its inherent stochasticity, nonlinearity, and regional variability pose significant challenges. Existing models also lack multidimensional interpretability, limiting their practical utility. To address these issues, we introduce a new short-term wind speed prediction model with an hourly time span that combines state-of-the-art techniques: a two-stage feature selection for meteorological data, snow ablation optimizer (SAO), and temporal fusion transformer (TFT). Meteorological features are processed arithmetically and nonlinearly and combined with statistical feature extraction to form a comprehensive feature set that captures the unique fluctuating features in the wind speed series. A two-stage feature selection strategy ensures valid feature information and controls input quality. Finally, the TFT results and tree SHAP construct a multidimensional interpretable analytical framework for the input and forecasting process. The proposed method shows good prediction performance on all four wind speed datasets of the Williams Wind Farm, with MAPEs of 6.7 %, 6.29 %, 16.22 %, and 12.72 % in spring, summer, fall, and winter, respectively. It also provides decision makers with a clear analysis from feature engineering to predictive modeling, which contributes to orderly energy planning and strategic placement.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"256 ","pages":"Article 124028"},"PeriodicalIF":9.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A numerical study of horizonal axis wind turbine blade Contamination: Aerodynamic and sustainable impacts","authors":"Pratham Chauhan ,&nbsp;Mohammad Ja'fari ,&nbsp;Artur J. Jaworski","doi":"10.1016/j.renene.2025.124033","DOIUrl":"10.1016/j.renene.2025.124033","url":null,"abstract":"<div><div>Wind turbine blade contamination, particularly on the suction side, can significantly degrade aerodynamic performance and reduce output power, making it essential to understand its effects for efficient wind energy generation. This study investigates the influence of varying contamination extents on the aerodynamic performance of horizontal-axis wind turbine blades and quantifies the resulting environmental and economic impacts. To analyse these effects, computational fluid dynamics simulations were conducted at two Reynolds numbers under both clean and contaminated blade conditions. This was followed by a blade element momentum analysis to assess annual energy production. The results show that contamination increases drag, reduces lift, and shifts the transition onset upstream, thereby decreasing aerodynamic efficiency. The blade element momentum analysis confirms that greater contamination extent up to 10 % reduces annual energy production, contributing to higher carbon emissions and economic losses exceeding 7 % relative to the minimum contamination extent.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"256 ","pages":"Article 124033"},"PeriodicalIF":9.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing probabilistic wind speed forecasting by integrating self-adaptive Bayesian wavelet denoising with deep Gaussian process regression under uncertainties","authors":"Huize Chen ,&nbsp;Xiaomo Jiang ,&nbsp;Huaiyu Hui ,&nbsp;Kexin Zhang ,&nbsp;Wenqing Meng ,&nbsp;Etienne Cheynet","doi":"10.1016/j.renene.2025.123966","DOIUrl":"10.1016/j.renene.2025.123966","url":null,"abstract":"<div><div>Wind speed forecasting is crucial for wind power prediction, wind farm operations, and power optimization scheduling. However, the inherent randomness and uncontrollability of wind resources make accurate forecasting a significant challenge. Traditional methods often struggle to effectively handle noise and uncertainty, limiting their practical applicability. This paper introduces a hybrid wind speed forecasting model that integrates self-adaptive Bayesian Wavelet Packet Thresholding (BDWPT) and a Deep Gaussian Process (DGP) to enhance prediction accuracy. BDWPT is utilized to adaptively reduce noise while preserving essential time series trends, thereby minimizing input uncertainties. The DGP model is then employed to capture the stochastic nature of wind speed fluctuations and generate probabilistic forecasts. Additionally, Monte Carlo simulation is applied to quantify output uncertainties. The proposed model was validated through a comparison study using real-world data from four wind farms operating under various conditions. Results demonstrate that the hybrid approach significantly outperforms traditional methods, achieving over 90% improvement in forecast accuracy. This method offers a reliable tool for wind power applications, enabling more informed decision-making and enhancing wind farm efficiency.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"256 ","pages":"Article 123966"},"PeriodicalIF":9.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modernisation of curricula on renewable energy technologies and energy efficiency for the built environment in Higher Education Institutions of Western Balkan countries","authors":"Luca Cioccolanti ,&nbsp;Matteo Moglie ,&nbsp;Juan J. Hernandez ,&nbsp;Amparo Pazo ,&nbsp;Natia R. Anastasi ,&nbsp;Flora Krasniqi ,&nbsp;Klodjan Xhexhi ,&nbsp;Jelisaveta Marjanovic ,&nbsp;Damir Gashi","doi":"10.1016/j.renene.2025.123932","DOIUrl":"10.1016/j.renene.2025.123932","url":null,"abstract":"<div><div>The building sector in Western Balkan countries not only represents one of the main energy consumers (&gt;40 %) but is also currently based on dated and low-efficient technologies far from EU standards. Therefore, implementing the energy transition in these countries requires a new type of professional with a stronger background in clean technologies and energy efficiency for achieving near zero energy buildings. Herein we present the efforts within the EU co-funded project ‘reZEB’ of a group of Higher Education Institutions (HEIs) towards modernising the current content of modules and teaching methods in four Universities in Albania and Kosovo and one Vocational Education Training for energy efficient solutions and renewable energy technologies (RETs) in buildings.</div><div>The selection of the modernised content and the design of the learning outcomes to be pursued was guided by an analysis of the labour market needs and the peculiarities of the existing study programs. More precisely, more than 80 organisations have been interviewed, showing great interest in professionals and engineers with enhanced knowledge on energy efficiency and RETs for the built environment, and 26 modules belonging to bachelor, master and professional study programs have been proposed as new or modernised (accounting for around 110 ECTS).</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"256 ","pages":"Article 123932"},"PeriodicalIF":9.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short-term multi-step wind speed forecasting with multi-feature inputs using Variational Mode Decomposition, a novel artificial intelligence network, and the Polar Lights Optimizer","authors":"Shibao Li,&nbsp;Liang Guo,&nbsp;Jinze Zhu,&nbsp;Menglong Liu,&nbsp;Jiaxin Chen,&nbsp;Zihan Meng","doi":"10.1016/j.renene.2025.123965","DOIUrl":"10.1016/j.renene.2025.123965","url":null,"abstract":"<div><div>Wind energy is vital to the energy industry, but wind speed fluctuations pose a challenge to stable grid integration. To improve forecasting accuracy, this paper integrates artificial intelligence and decomposition method to propose a novel forecasting framework; the Polar Lights Optimizer is applied for parameter tuning. It conducts 3–6 step multi-step forecasting on wind speed time series with a sampling frequency of 1 h. Variational Mode Decomposition is employed to decompose four feature types, thereby reducing the complexity of the time series and facilitating the construction of multi-feature inputs. In the neural network component of the artificial intelligence model, a Graph Convolutional Network is used to update and aggregate multi-feature inputs by treating time steps as nodes. Sequence Squeeze-and-Excitation is applied to add channel attention, capturing deep correlations among multiple features. Cascaded LSTM is employed for time series modeling, and Linear layers are used for output forecasting. Evaluated on the public BSG dataset, the framework proposed in this paper outperforms multiple advanced models or frameworks, achieving <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> scores of 0.9857, 0.9792, 0.9706, and 0.9614 in 3/4/5/6-step forecasting. Ablation and significance tests confirm the contribution of each component. The coefficient of variation is used to assess model stability across multiple runs.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"256 ","pages":"Article 123965"},"PeriodicalIF":9.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic coefficient of variation: Assessing the variability and forecastability of solar irradiance","authors":"Cyril Voyant ,&nbsp;Alan Julien ,&nbsp;Milan Despotovic ,&nbsp;Gilles Notton ,&nbsp;Luis Antonio Garcia-Gutierrez ,&nbsp;Claudio Francesco Nicolosi ,&nbsp;Philippe Blanc ,&nbsp;Jamie Bright","doi":"10.1016/j.renene.2025.123913","DOIUrl":"10.1016/j.renene.2025.123913","url":null,"abstract":"<div><div>This work presents a robust framework for quantifying solar irradiance variability and forecastability through the Stochastic Coefficient of Variation (<span><math><mstyle><mi>s</mi><mi>C</mi><mi>V</mi></mstyle></math></span>) and the Forecastability (<span><math><mstyle><mi>F</mi></mstyle></math></span>). Traditional metrics, such as the standard deviation, fail to isolate stochastic fluctuations from deterministic trends in solar irradiance. By considering clear-sky irradiance as a dynamic upper bound of measurement, <span><math><mstyle><mi>s</mi><mi>C</mi><mi>V</mi></mstyle></math></span> provides a normalized, dimensionless measure of variability that theoretically ranges from 0 to 1. <span><math><mstyle><mi>F</mi></mstyle></math></span> extends <span><math><mstyle><mi>s</mi><mi>C</mi><mi>V</mi></mstyle></math></span> by integrating temporal dependencies via maximum autocorrelation, thus linking <span><math><mstyle><mi>s</mi><mi>C</mi><mi>V</mi></mstyle></math></span> with <span><math><mstyle><mi>F</mi></mstyle></math></span>. The proposed methodology is validated using synthetic cyclostationary time series and experimental data from 68 meteorological stations (in Spain). Our comparative analyses demonstrate that <span><math><mstyle><mi>s</mi><mi>C</mi><mi>V</mi></mstyle></math></span> and <span><math><mstyle><mi>F</mi></mstyle></math></span> proficiently encapsulate multi-scale fluctuations, while addressing significant limitations inherent in traditional metrics. This comprehensive framework enables a refined quantification of solar forecast uncertainty, supporting improved decision-making in flexibility procurement and operational strategies. By assessing variability and forecastability across multiple time scales, it enhances real-time monitoring capabilities and informs adaptive energy management approaches, such as dynamic outage management and risk-adjusted capacity allocation.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"256 ","pages":"Article 123913"},"PeriodicalIF":9.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chance-constrained dynamic thermal line rating of power system for enhancing stochastic renewable penetration","authors":"Omid Sadeghian ,&nbsp;Behnam Mohammadi-Ivatloo","doi":"10.1016/j.renene.2025.124030","DOIUrl":"10.1016/j.renene.2025.124030","url":null,"abstract":"<div><div>The escalating electricity demand, accompanied by the growing prevalence of renewable energy sources—often situated in remote regions distant from load centers—is pushing power lines to function near their transfer capacity margins. This line congestion brings about suboptimal system operation, overloading risks, and renewable energy spillage. Nevertheless, the thermal capacity of power lines is inherently dynamic, denoted as dynamic thermal line rating (DTLR), and fluctuates with prevailing weather conditions. This study investigates the impact of DTLR, acting as remedial capacity, on bolstering renewable energy penetration, emphasizing its economic, environmental, and energy losses effects. By leveraging DTLR under real-time thermal balance equations, the untapped line loadability within safe operating thresholds is realized. A chance-constrained programming framework is applied to help cope with uncertainties and associated risks across varying confidence levels. The outcomes underscore the substantial benefits of DTLR for congestion relief, evidenced by a marked increase in renewable penetration from 30.7 % to 44.7 %. Additionally, operational expenses decline from $53,051 to $43,123 and emissions drop from 690.71 tCO₂ to 554.08 tCO₂ (a nearly 20 % reduction). This research provides critical insights for researchers owing to its thorough analysis and for grid operators striving to employ DTLR over conservative ratings.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"256 ","pages":"Article 124030"},"PeriodicalIF":9.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}