Renewable Energy最新文献

{"title":"On the choice of the parameter identification procedure in quasi-dynamic testing of low-temperature solar collectors","authors":"J.M. Rodríguez-Muñoz ,&nbsp;I. Bove ,&nbsp;R. Alonso-Suárez ,&nbsp;P.A. Galione","doi":"10.1016/j.renene.2025.122931","DOIUrl":"10.1016/j.renene.2025.122931","url":null,"abstract":"<div><div>The ISO 9806:2017 standard is widely used to characterize the thermal performance of solar collectors. It permits two test methods: Steady State Testing (SST) and Quasi-Dynamic Testing (QDT). While SST requires high stability and clear sky conditions, which limit its application, QDT offers more flexibility in sky conditions. In contrast, the QDT method adds complexity due to the handling of transient phenomena during data processing. There are two approaches to parameter identification in QDT: multilinear regression (MLR) and dynamic parameter identification (DPI). MLR, the most common tool, faces challenges with certain collector types and its results depend on the data averaging time. DPI, while more complex, has the potential to overcome MLR’s shortcomings. Which of these two methods is most suitable for testing low-temperature solar collectors in a broad sense is an issue that has not yet been addressed. This work provides evidence that the DPI procedure is more convenient than the MLR procedure, especially for evacuated tube collectors with heat pipes. Specifically, it is shown that DPI produces more reliable test results and provides more accurate estimates of useful power, and it exhibits less variability with respect to data averaging time, demonstrating its improved robustness.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"247 ","pages":"Article 122931"},"PeriodicalIF":9.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768808","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":"Day ahead solar forecast using long short term memory network augmented with Fast Fourier transform-assisted decomposition technique","authors":"Abhijeet Rathore ,&nbsp;Priya Gupta ,&nbsp;Raksha Sharma ,&nbsp;Rhythm Singh","doi":"10.1016/j.renene.2025.123021","DOIUrl":"10.1016/j.renene.2025.123021","url":null,"abstract":"<div><div>This work aims to develop a hybrid model for multistep PV power forecasting. The model comprises of decomposition (Noise Assisted Multivariate Empirical Mode Decomposition: NA-MEMD), dimensionality reduction (Fast Fourier Transform: FFT), and advanced deep learning (Attention mechanism-based Long short-term memory: AM-LSTM) methods. NA-MEMD addresses the non-stationary and nonlinear characteristics of complex multivariate time series data by splitting them into a number of subseries known as Intrinsic Mode Functions (IMFs). A large pool of IMFs is reduced to five sets of subseries using the Fast Fourier Transform (FFT). Finally, the model incorporates the advanced AM-LSTM technique, where the attention mechanism focuses on essential features while disregarding the irrelevant ones. The proposed N-FFT-AM-LSTM model demonstrates superior performance across multiple locations, with an average RMSE (W/m²) | nRMSE (%) | R-value of 62.97 | 6.33 | 0.9721. The proposed model surpasses both the AM-LSTM and N-AM-LSTM models, showcasing % mean RMSE (nRMSE) reduction of 36.86 % (35.25 %) and 12.98 % (11.56 %), respectively. These findings highlight the effectiveness of our approach, that is the N-FFT-AM-LSTM model, in accurately predicting solar irradiance levels across varied geographical regions.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"247 ","pages":"Article 123021"},"PeriodicalIF":9.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783558","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":"How do trade patterns of renewable energy products affect sustainable development goals? Evidence from Belt and Road countries","authors":"Xiaoli Hao ,&nbsp;Ke Li ,&nbsp;Yuhong Li ,&nbsp;Haitao Wu","doi":"10.1016/j.renene.2025.123023","DOIUrl":"10.1016/j.renene.2025.123023","url":null,"abstract":"<div><div>The trade pattern of renewable energy products is continuously evolving, offering a novel pathway for countries to achieve the harmonization of green energy supply, environmental protection, carbon reduction, pollution control, and economic sustainability. Based on the 2007–2021 data of 54 countries along the Belt and Road, this study constructs the Renewable Energy Products Trade Network (BRREPTN) through social network analysis. To examine how trade intensity and centrality affect the Sustainable Development Goal Index (SDGI). The following conclusions can be drawn: (1) Overall, BRREPTN exhibits a tightly integrated and concentrated structure. There is notable \"hub countries\" within the network. (2) Empirical results indicate that an increase in trade intensity and centrality of countries positively impacts SDGI. (3) Mechanism analysis reveals that import and export trade facilitate the development of renewable energy infrastructure, with import intensity and centrality contributing to enhanced electricity access levels. However, renewable energy technology exerts a negative mediation effect. (4) Heterogeneity tests demonstrate that import trade and centrality assist low- and middle-income countries, as well as those with low resource dependence, in achieving sustainable development. Conversely, other countries exhibit advantages in export trade intensity. Based on these findings, this paper recommends that countries develop import and export strategies for renewable energy products based on their respective developmental conditions. Specific policy suggestions include establishing technology transfer platforms, integrating import trade with the cultivation of local industrial chains, among others.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"247 ","pages":"Article 123023"},"PeriodicalIF":9.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783557","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":"Hybrid Surrogate Input Load Estimation Model in Offshore Wind Turbines using Transfer Learning and Multitask Learning","authors":"Azin Mehrjoo ,&nbsp;Eleonora M. Tronci ,&nbsp;Babak Moaveni ,&nbsp;Eric Hines","doi":"10.1016/j.renene.2025.123011","DOIUrl":"10.1016/j.renene.2025.123011","url":null,"abstract":"<div><div>Accurate input load estimation is useful for proactive maintenance, design optimization, and digital twining of offshore wind turbines. Traditional physics-based approaches for input load estimation, while accurate, face challenges due to high computational demands, limited availability of experimental data, and challenges with direct measurement of input loads. This paper introduces a novel hybrid surrogate model for real-time input load estimation that integrates data-driven and physics-based methodologies to address these limitations. The proposed framework focuses on training a neural network-based regression model that can predict the input load time history using the dynamic structural response (accelerometers and strain measurements) as input. The model is optimally designed to overcome the lack of experimental data by following a transfer learning strategy: the model is initially trained on synthetic data generated from a physics-based model and then fine-tuned using experimental data. Additionally, a multitasking approach is implemented to extend the applicability of the model to different regions of operation. Finally, a dropout Montecarlo strategy is proposed to quantify the uncertainty of the regression model. This hybrid surrogate model demonstrates superior computational efficiency and accuracy in input load estimation compared to conventional physics-based models, and it offers several practical benefits. It is scalable, does not necessitate continual recalibration, and exhibits robustness across diverse datasets and operational conditions. The model's potential for real-time applications, such as digital twin technology, underscores its contribution to advancing maintenance strategies and design improvements in offshore wind turbine engineering.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"247 ","pages":"Article 123011"},"PeriodicalIF":9.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783556","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":"Modelling and development of utility-scale energy system for Malaysia with advanced energy balance analysis: Supply, demand and trade","authors":"Malcolm Isaac Fernandez ,&nbsp;Yun Ii Go ,&nbsp;M.L. Dennis Wong ,&nbsp;Wolf-Gerrit Früh","doi":"10.1016/j.renene.2025.123013","DOIUrl":"10.1016/j.renene.2025.123013","url":null,"abstract":"<div><div>This article aims to develop national energy system models for Malaysia for a baseline scenario (2020) and future scenarios towards 2050 based on high temporal resolution data (8784 h) from IEA via the Malaysian Grid System Operator (GSO) and specifications of the future energy mix based on existing energy transition roadmaps. A literature review was conducted on modelling energy systems using various methods to achieve higher renewable energy (RE) integration towards meeting the net zero target in future years. This research demonstrates how the scaling up of variable renewable energy (VRE) sources particularly solar affects the national energy system in terms of the hourly generation patterns of central power plants and hydropower to match the hourly demand. The gradual scaling up of solar and phasing out of coal from 2020 to 2050 result in challenges that may be faced with the projected rise in fuel and electricity demands. There will be challenges in matching the growing demand as solar PV has much lower capacity factors of generation compared to coal. Therefore, five scenarios were analysed to study if the installed capacities can match the growing demand for energy by integrating energy storage systems (ESS) with upgrading transmission line capacity from 2035 onwards as specified in the Malaysian Renewable Energy Roadmap (MyRER) and the National Energy Transition Roadmap (NETR). Annual hourly electricity balance analysis was carried out for each scenario to understand the generation pattern of central power plants and their relationship with (VRE) to meet the respective electricity demands. Based on the 2050 models, the total ESS was increased in capacity from 500 MW (2.5 GWh) in scenario 3–22000 MW (110 GWh) in scenario 4 to curb the critical access energy production (CEEP) resulting from high penetration of peak solar PV power and the transmission line capacity was upscaled to 22000 MW to enable external imports to supply electricity to meet the demands during low periods of solar PV penetration. Scenario 5 was developed to investigate the implementation of energy efficiency (22 % electricity demand reduction) on scenario 4 to reduce ESS sizing, natural gas power plant generation, and imports in 2050. The CEEP was curbed to 0 for all hours in a year with the 22000 MW transmission line for import/export and a downsized ESS system of 750 MW (3.75 GWh). The annual total CO2 emissions from the electricity sectors in scenarios 1, 2, 3, 4, and 5 were 75.96 Mt, 85.04 Mt, 51.42 Mt, 51.42 Mt, and 40.17 Mt respectively. This demonstrated the potential for CO2 emission reduction from the electricity generation sector of Malaysia in transitioning towards a low-carbon power mix.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"247 ","pages":"Article 123013"},"PeriodicalIF":9.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Experimental study of rectangular micro heat pipe PV/T series and parallel systems","authors":"Rui Li ,&nbsp;Zijiao Jia ,&nbsp;Xiaohua Sun ,&nbsp;Jinping Li ,&nbsp;Vojislav Novakovic","doi":"10.1016/j.renene.2025.123016","DOIUrl":"10.1016/j.renene.2025.123016","url":null,"abstract":"<div><div>Photovoltaic/thermal (PV/T) systems, due to the abundant solar resources and cold climate in China's northwest regions, have attracted significant attention and show great application potential. Current researches on improving the performance of PV/T systems mostly focused on modifying the structure of the PV/T panels and changing the inclination angle of the PV/T panels, with relatively little attention paid to the connection methods of the PV/T panels. Therefore, building on the team's previous rectangular micro heat pipe PV/T (R-M-HP-PV/T) foundation, both parallel and series connection methods were employed in the experiments to enhance system performance. In the experiment on January 9th, the R-M-HP-PV/T series system achieved an average thermal conversion efficiency (TCE) of 30.3 %, an average photovoltaic conversion efficiency (PCE) of 13.1 %. In the subsequent experiment on January 10th, the R-M-HP-PV/T parallel system exhibited an average TCE of 35.0 %, an average PCE of 11.9 %. In addition, numerical simulation was accurately validated. The solar cell temperature in the numerical simulation was lower in the parallel connection setup. The total efficiency of the parallel connection reached 51.5 %, surpassing the series connection by 5.8 %. The results indicate that the parallel connection exhibits superior overall performance compared to the series configuration.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"247 ","pages":"Article 123016"},"PeriodicalIF":9.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783559","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":"Future prediction on the remaining useful life of proton exchange membrane fuel using temporal fusion transformer model","authors":"Jia-Hong Chou,&nbsp;Fu-Kwun Wang","doi":"10.1016/j.renene.2025.123019","DOIUrl":"10.1016/j.renene.2025.123019","url":null,"abstract":"<div><div>Proton exchange membrane fuel cell (PEMFC) is regarded as one of the leading developmental directions of the green power source. Predicting the remaining useful life (RUL) of online PEMFCs is challenging, primarily due to factors such as complex nonlinear degradation patterns and unknown data. To enhance the performance of RUL prediction, we present a deep learning model-based temporal fusion transformer model to predict the future total voltage of a PEMFC stack. The performance of RUL predictions was evaluated using different starting RUL prediction points to compare them with the existing study. The experiments are conducted with different future prediction steps for future RUL prediction to determine the model's prediction step limit. The proposed method achieved relative errors of 0.0148 %, 0.0060 %, 0.0662 %, and 0.0486 % for training lengths 300, 400, 500, and 600, respectively, in the FC1 and 0.0326 % in the FC2 datasets compared to the existing study scenarios. The experimental analysis resulted in relative errors of 0.0738 % for the FC1 dataset, 1.0239 % and 1.6512 % for the two FC2 dataset scenarios.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"247 ","pages":"Article 123019"},"PeriodicalIF":9.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768804","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":"Integrating prospect theory with DEA for renewable energy investment evaluation in South America","authors":"Chia-Nan Wang ,&nbsp;Hoang-Kha Nguyen ,&nbsp;Nhat-Luong Nhieu","doi":"10.1016/j.renene.2025.123018","DOIUrl":"10.1016/j.renene.2025.123018","url":null,"abstract":"<div><div>The transition to renewable energy is critical to address climate change and energy security in South America, where abundant resources exist but investment efficiency varies across countries. Traditional evaluation methods often overlook behavioral biases and risk perceptions, resulting in incomplete assessments. This study proposes a Risk-Aware DEA (RA-DEA) model by integrating Prospect Theory with Data Envelopment Analysis to provide a comprehensive efficiency evaluation. By incorporating risk-adjusted measures, RA-DEA captures both economic performance and behavioral risk considerations, offering a more realistic view of investment decisions. Findings show Paraguay, Uruguay, and Chile exhibit higher efficiency due to supportive policies and infrastructure, while Bolivia and Venezuela face structural and institutional barriers. Including behavioral factors highlights the significance of risk perceptions in shaping investment attractiveness. Based on these insights, policy interventions that offer financial incentives, regulatory stability, and regional cooperation are recommended to reduce investment uncertainty and enhance efficiency. Strengthening institutional frameworks and risk-mitigation mechanisms can boost investor confidence and accelerate renewable energy adoption. This study significantly contributes to theory by advancing DEA methodology with Prospect Theory and to practice by providing a risk-sensitive decision-support tool. Future research could incorporate additional behavioral factors and expand the RA-DEA model to other regions.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"247 ","pages":"Article 123018"},"PeriodicalIF":9.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746763","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":"First-principles quantum insights into phonon dynamics and thermophysical potential of MgH2 and MgH2:Mo for enhanced thermoelectricity and hydrogen energy applications","authors":"Azmat Iqbal Bashir ,&nbsp;M.H. Sahafi ,&nbsp;Sanum Saeed","doi":"10.1016/j.renene.2025.123008","DOIUrl":"10.1016/j.renene.2025.123008","url":null,"abstract":"<div><div>Amidst the growing world population and depletion of conventional energy sources and their harmful impact, there is an urgent need for a sustainable energy-paradigm shift from fossil fuels to renewable and alternative clean energy sources. In this context, hydrogen and thermoelectric energy sources hold great promise and could be the best substitute for fossil fuels. However, hydrogen generation, storage, and transport under ambient conditions remain challenging due to high costs and safety concerns. The competitive relation between thermoelectric properties is a big hindrance in harnessing the thermoelectric materials of high thermal efficiency. This urges scientists to search for energy-efficient thermoelectric and hydrogen-storage solid materials, with enormous ability to convert thermal energy into electricity and store and release hydrogen on demand. Despite being a potential hydrogen storage material owing to high hydrogen storage density, poor thermodynamic kinetics of magnesium hydride (MgH₂) with high stability under normal conditions hinder its domestic and industrial-scale usage. The possible solutions are alloying, nanostructuring, clustering, and doping of MgH₂ with transition metals. This study reports on the investigation of a systematic analysis on phonon dynamics, thermodynamic, and thermoelectric properties of MgH2 and Mg <span><math><mrow><msub><mi>H</mi><mn>2</mn></msub></mrow></math></span>:Mo(10 wt% Mo), employing the density functional theory within the generalized gradient approximation. The Quasi-harmonic Debye-Gruneisen model is executed to compute thermodynamic properties, while the Boltzmann theory is employed to compute the key thermoelectric properties. Besides enhanced thermodynamic properties for (de)hydrogenation, the computed results yield a high thermoelectric performance with the figure of merit of order 1.2.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"247 ","pages":"Article 123008"},"PeriodicalIF":9.0,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768810","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":"Spatiotemporal dynamics and driving factors of the coupling coordination between solar photovoltaic efficiency and installed capacity in China (2015–2023)","authors":"Mengdi Li ,&nbsp;Chuanfeng Han ,&nbsp;Lingpeng Meng ,&nbsp;Pihui Liu ,&nbsp;Zhiguo Shao","doi":"10.1016/j.renene.2025.122991","DOIUrl":"10.1016/j.renene.2025.122991","url":null,"abstract":"<div><div>As the global energy transition accelerates, optimizing the relationship between solar photovoltaic (PV) production inputs and generation efficiency is becoming increasingly crucial. However, existing research often overlooks the spatiotemporal mismatches between these two components. This study uses the SBM-DDF model to assess PV efficiency across 31 Chinese provinces, analyzing its spatiotemporal evolution with the standard deviation ellipse model. The coupling coordination between PV efficiency and installed capacity is explored using an improved coupling coordination degree model. A spatiotemporal geographically weighted regression model identifies key factors influencing this coordination. Our results reveal that, by 2023, PV efficiency exhibited a “V”-shaped pattern, with higher efficiencies in the eastern and western regions, and lower efficiencies in the central area. Installed capacity saw a significant increase in the east, remained stable in the west, and stayed low in the center. The coupling coordination in the northwest was strong before 2017 but weakened thereafter, while the east experienced an improvement. These findings highlight significant spatiotemporal variations in the influence of market demand, resource input, policy environment, and technological capacity on PV efficiency and coordination. This study provides valuable insights for optimizing the spatial distribution of PV resources and improving regional coordination in China's energy transition.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"247 ","pages":"Article 122991"},"PeriodicalIF":9.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746953","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}