Renewable Energy最新文献

{"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":"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}

{"title":"Evaluating bias correction methods for wind power estimation using numerical meteorological models","authors":"A. Maciel-Tiburcio ,&nbsp;O. Martínez-Alvarado ,&nbsp;O. Rodríguez-Hernández","doi":"10.1016/j.renene.2025.122927","DOIUrl":"10.1016/j.renene.2025.122927","url":null,"abstract":"<div><div>Enhancing our understanding of the meteorological factors influencing renewable energy is crucial in the energy transition, as inherent biases in widely used meteorological numerical models reduce their reliability in accurately simulating essential variables for electricity modeling. This study examines five bias correction methods for estimating wind power capacity factors, utilizing ERA5 reanalysis, Weather Research and Forecasting Model (WRF) simulations, and experimental data from multiple anemometric towers. Areas influenced by large-scale effects, such as the interaction between large-scale atmospheric circulation and orography, were accurately reproduced; however, regions with complex terrain exhibited larger errors. In some cases, the constraints imposed by large-scale features on near-surface winds are strong enough to make bias correction unnecessary. The Weibull quantile mapping and the quantile percentile method produced the lowest errors, however the latter preserved bi-modality. The mean state, linear scale, and quantile mapping Rayleigh methods produced the highest errors in 72% of the cases examined. Analysis of ERA5 revealed the dependence of its ability to reproduce the capacity factors on the conditions around the site. Bias correction alters the probability distribution’s shape, significantly impacting CF estimates through its interaction with the power curve.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"247 ","pages":"Article 122927"},"PeriodicalIF":9.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739404","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":"Active control method based on operation strategy for a novel combined cooling, heating and power system integrated with solar thermochemical process","authors":"Qiushi Wang ,&nbsp;Liqiang Duan ,&nbsp;Luyao Liu ,&nbsp;Xiaoqing Fan ,&nbsp;Hanfei Zhang ,&nbsp;Nan Zheng ,&nbsp;Chaoyun Yang ,&nbsp;Chu Wang","doi":"10.1016/j.renene.2025.123001","DOIUrl":"10.1016/j.renene.2025.123001","url":null,"abstract":"<div><div>Effectively solving the problem of mismatch between combined cooling, heating and power (CCHP) system outputs and user's load demands is one of the current research hotspots. An improved active control operation strategy (ACS) is proposed for a novel CCHP system integrating with solar thermochemical process, which gives priority to the use of flue gas waste heat for heating, and actively adjusts the generated electricity to supplement the cooling capacity with the electric chiller. And a new performance evaluation index named as valuable primary energy efficiency (VPEE) is proposed to reflect the valuable primary energy efficiency of CCHP system based on meeting user's energy demands. The fossil energy saving rate, carbon dioxide emission reduction rate, cost saving rate and comprehensive performance matching (CPM) are also taken into account. The results show that the proposed CCHP system can effectively meet the user's energy demands under different situations with ACS method. The comprehensive performance of the CCHP system with ACS method is superiority to those under reference strategies. The average VPEE of the CCHP system is the highest under ACS strategy, reaching 89.07 %. The average CPM values of the CCHP system in typical summer, transition and winter day are 58.90 %, 45.80 % and 47.87 % respectively, which are the highest values under the three operation strategies in the corresponding days. The comparison of application results in different cities shows that the CCHP system has better CPM when applied in cities with higher solar radiation under the ACS strategy, especially significantly improves the “outputs-demands” matching.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"247 ","pages":"Article 123001"},"PeriodicalIF":9.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746954","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":"Thermo-hydraulic assessment of energy tunnels under various thermal, hydrogeological and operational conditions","authors":"Seyed Raef Sheikhahmadi ,&nbsp;Mohammad Oliaei ,&nbsp;Amir Akbari Garakani ,&nbsp;Mohammad Amir Kiani Fordoei","doi":"10.1016/j.renene.2025.123005","DOIUrl":"10.1016/j.renene.2025.123005","url":null,"abstract":"<div><div>Energy tunnels, promising structures for shallow geothermal energy extraction, require deeper insights into their thermo-hydraulic behavior. This study investigates the output power from the lining of energy tunnels numerically through a finite element model. After calibrating the model with both numerical and experimental data, the effects of various parameters and groundwater flow (GWF) on output heat were examined. Results demonstrate that pipe diameter and flow velocity dominate output power variation (74 %), exceeding soil thermal properties (8 %). Non-isolated conditions enhance heat exchange compared to isolated setups. GWF significantly boosts output power, with exponential increases up to 436 % at 10⁴-fold velocity rises in isolated, perpendicular flows. GWF amplifies the impact of pipe layout by 0.8 %–10.8 %, and reduces power discrepancies between isolated/non-isolated conditions over time by restoring soil temperatures. Short-term output power surpasses long-term due to soil heat capacity depletion during prolonged operation. Inclined seepage, modeled as multidirectional flow, enhances thermal compensation by 10 %, with two-dimensional flows improving soil temperature recovery beyond one-dimensional flows. This study emphasizes GWF's crucial role in optimizing energy tunnels through GWF velocity and directionality, noting minimal soil influence. It offers quantitative guidelines on pipe configuration and hydrogeological conditions to maximize geothermal system efficiency.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"247 ","pages":"Article 123005"},"PeriodicalIF":9.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746762","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":"Design and coupled analysis of a novel floating offshore wind turbine combined with a fish cage for deep water","authors":"Jiazhi Wang ,&nbsp;Zhaobo Song ,&nbsp;Wei Shi ,&nbsp;Shuaishuai Wang ,&nbsp;Wenhua Wang ,&nbsp;Rizwan Haider ,&nbsp;Xin Li","doi":"10.1016/j.renene.2025.123004","DOIUrl":"10.1016/j.renene.2025.123004","url":null,"abstract":"<div><div>As floating offshore wind projects expand into deeper waters, floating platforms have emerged as the most viable solution for wind turbine support. The integration of aquaculture within offshore wind farms has gained considerable research interest as an efficient marine spatial utilization strategy. To advance the synergistic development of offshore wind energy and marine ranching, this study introduces an innovative floating offshore wind turbine integrated with a fish cage structure. A 1:6-scaled demo system was developed, incorporating design optimizations derived from full-scale configurations. Frequency-domain hydrodynamic analysis and survivability assessment were conducted to evaluate the demo system's performance under various environmental conditions, including wind-wave-current interactions and tidal variations. A coupled time-domain model was established by quantifying equivalent wind turbine loads and accounting for foundation-mooring dynamic interactions. Results demonstrate that wave and current forces dominate the demo system's response under operational conditions. Platform motion amplitudes and mooring tensions escalate with decreasing tidal elevations. Importantly, safety factors remain compliant with regulatory thresholds even at minimal tidal levels across diverse wind-wave-current directional combinations, confirming Demo II's structural integrity under extreme marine environments.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"247 ","pages":"Article 123004"},"PeriodicalIF":9.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746952","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 method on mapping the distribution of photovoltaic power stations in complex terrain regions","authors":"Hongzhi Mao ,&nbsp;Weili Liu ,&nbsp;Xie Chen ,&nbsp;Zhiyong Tian ,&nbsp;Angelo Zarrella ,&nbsp;Yongqiang Luo ,&nbsp;Jianhua Fan ,&nbsp;Wentao Wu","doi":"10.1016/j.renene.2025.122964","DOIUrl":"10.1016/j.renene.2025.122964","url":null,"abstract":"<div><div>With the goal of achieving carbon neutrality, the number of new photovoltaic (PV) installations worldwide has surged in recent years. Accurate statistics on the area and distribution of centralized solar PV installations are essential. Due to land use constraints and poverty alleviation policies, an increasing number of PV stations are being installed in mountainous areas in China. The fragmented distribution of these PV stations presents challenges for remote sensing identification. This study proposes a method based on Object-Based Image Analysis (OBIA) and the Cascade Random Forest Classifier, utilizing Sentinel-1/2 imagery. The method is capable of extracting PV stations across diverse terrains, including mountains, plateaus, and plains. Specifically, five different scenarios with varying feature combinations (including spectral, index, geometric, and texture features from Sentinel-1 and Sentinel-2) were compared. A second-level random forest classifier, trained on false-positive samples, was employed to enhance the model's robustness. The results indicate that the spectral features of Sentinel-1, along with index and geometric features, positively influence identification accuracy, while texture features showed less impact. Independent test results demonstrated an overall classification accuracy of 99.9904 %, with a producer accuracy of 92.73 % and a user accuracy of 81.38 % for PV stations in a complete city region. Finally, a 2024 map of centralized PV installations was created for five cities west of Beijing, where the proportion of total PV area in mountainous regions is 45 %. This method represents the first approach capable of identifying PV stations in complete regions with complex terrain, including mountains, without the need for manual post-processing.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"247 ","pages":"Article 122964"},"PeriodicalIF":9.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739478","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":"Acoustic emission-based wind turbine blade icing monitoring using deep learning technology","authors":"Lei Jiang ,&nbsp;Shi Ping Zhang ,&nbsp;Guo Qing Shen ,&nbsp;Ling Zhou","doi":"10.1016/j.renene.2025.122980","DOIUrl":"10.1016/j.renene.2025.122980","url":null,"abstract":"<div><div>Blade icing on wind turbines can significantly affect the efficiency, safety, and sustainable operation of wind energy systems. However, the existing blade icing monitoring methods face certain limitations, such as delayed response, low accuracy, environmental interference, and compromised aerodynamic performance. Although acoustic emission (AE) technology has demonstrated its effectiveness in structural health monitoring of wind turbine blades such as crack and delamination, this study attempts to extend its application by introducing a novel real-time monitoring system for blade icing detection. Through theoretical analysis, this study reveals that acoustic emission signals are generated during blade icing process, particularly at the initial icing and final melting stages, due to stress variations caused by centrifugal force, aerodynamic load, and gravity during blade rotation. The acoustic impedance analysis further demonstrates that these AE signals can be effectively transmitted from ice layer to internal sensors owing to the similar acoustic impedance characteristics between ice and blade materials, validating the feasibility of internal wall sensor placement. A small-scale experimental system is designed to collect acoustic emission signals under normal operation, icing, and rainy conditions. The time-frequency domain analysis reveals distinct spectral characteristics - icing conditions show significant peaks in the 147 kHz–190 kHz band, whereas rain signals are concentrated in the low-frequency region of 6 kHz–9 kHz. The symplectic geometry mode decomposition (SGMD) is employed for signal processing, and an optimal reconstruction scheme combining the SGC1 and SGC2 components is designed, effectively preserving the key signal features (i.e., the correlation coefficient of more than 0.95, and the amplitude retention rate higher than 90 %) while reducing noise interference. Various deep learning models, including the convolutional neural network, long-short term model, and two versions of the Transformer network, are employed for state recognition. The results indicate that they can all achieve average recognition accuracy exceeding 99.5 % with a standard deviation below 0.001. The Transformer model demonstrates superior performance over the other models, with 99.89 % accuracy and a high computational efficiency of 0.09 <span><math><mrow><mi>s</mi><mo>·</mo><mi>e</mi><mi>p</mi><mi>o</mi><mi>c</mi><msup><mi>h</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span>. This method effectively addresses key limitations of existing monitoring approaches by enabling early detection, avoiding aerodynamic interference, and achieving reliable discrimination between icing and environmental interference, providing an innovative technical solution for enhancing wind farm operational safety and economic benefits.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"247 ","pages":"Article 122980"},"PeriodicalIF":9.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739402","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":"Pathways to environmental sustainability: Do fintech, natural resources, and environmental patents matter in E−7 nations?","authors":"Yu Bai ,&nbsp;Babatunde Sunday Eweade ,&nbsp;Sahar Aghazadeh ,&nbsp;Ruth Oluyemi Bamidele ,&nbsp;Yingshan Xu","doi":"10.1016/j.renene.2025.122987","DOIUrl":"10.1016/j.renene.2025.122987","url":null,"abstract":"<div><div>Growing concerns about climate change and environmental sustainability have heightened the global urgency to identify and implement effective strategies for mitigating environmental impacts. In this context, financial technology (Fintech), combined with sustainable natural resource management, offers significant opportunities to address environmental risks. This study examines the roles of Fintech, natural resource management, environmental patents, and economic growth in advancing environmental sustainability in E7 countries from 2000Q1 to 2020Q4, using the Method of Moments Quantile Regression (MMQR) for analysis. The findings reveal that natural resources have a positive and significant effect on the ecological footprint across all quantiles, with a slight reduction at mid-quantiles. Environmental patents are associated with a negative impact on the ecological footprint, suggesting that technological advancements contribute to reducing environmental degradation, particularly around the median quantile. The negative coefficients for GDP indicate that economic growth may contribute to reducing the ecological footprint. Fintech consistently exhibits negative coefficients across all quantiles, with a stronger impact at higher quantiles, underscoring its effectiveness in reducing the ecological footprint in E7 nations. The study recommends that policymakers incentivize Fintech companies to prioritize green financing solutions, such as carbon credit trading platforms and investments in renewable energy, to enhance environmental sustainability efforts.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"247 ","pages":"Article 122987"},"PeriodicalIF":9.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759262","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":"Design and performance evaluation of small-scale biogas digesters using locally available materials in rural Uganda","authors":"Afam Uzorka ,&nbsp;Moses Wonyanya","doi":"10.1016/j.renene.2025.122994","DOIUrl":"10.1016/j.renene.2025.122994","url":null,"abstract":"<div><div>This research explores the design, fabrication, and performance evaluation of small-scale biogas digesters constructed from locally available materials in rural Uganda. The study evaluates the impact of different operational conditions, specifically temperature and hydraulic retention time (HRT), on the efficiency of biogas production. A series of digesters were constructed and tested under varying temperatures (35 °C, 45 °C, and 55 °C) and HRTs (5, 10, 15, 20, and 25 days). Performance metrics, including daily and cumulative biogas yields, were recorded and analyzed. Results indicated that digesters operating at 45 °C with a 25-day HRT yielded the highest cumulative biogas production, highlighting this combination as optimal for the local context. In contrast, higher temperatures (55 °C) resulted in decreased efficiency due to thermal stress, while lower temperatures (35 °C) produced lower yields. The study also examined the durability and maintenance requirements of digesters made from locally sourced materials, highlighting cost reductions as key advantages. A cost-benefit analysis revealed that the initial investment cost of the digester is recoverable in less than one year, depending on fuel savings and maintenance expenses. However, scaling up biogas technology in rural settings faces challenges, including initial costs, durability concerns, and limited community awareness.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"246 ","pages":"Article 122994"},"PeriodicalIF":9.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725443","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}