Renewable Energy最新文献

英文中文

Predictive modeling of photovoltaic system cleaning schedules using machine learning techniques

IF 9 1区工程技术 Q1 ENERGY & FUELS

Renewable Energy

Pub Date : 2025-02-01 DOI: 10.1016/j.renene.2024.122149

Haneen Abuzaid, Mahmoud Awad, Abdulrahim Shamayleh, Hussam Alshraideh

Photovoltaic (PV) solar systems are a key contributor to sustainable energy generation, but their performance is significantly reduced by dust accumulation, highlighting the need for proper cleaning. This study develops predictive models to optimize cleaning schedules by forecasting the Performance Ratio (PR), a standardized metric essential to performance-guaranteed contracts. The first model uses time-series approaches (LSTM, ARIMA, SARIMAX) to predict PR, while the second uses a threshold-based ensemble voting classifier (RF, Logistic Regression, GBM) to predict cleaning needs. Two large datasets from case studies in the UAE and Jordan were used for validation. Results show SARIMAX outperforming other models, with R² values of 93.36 % and 91.74 %. The cleaning classification model achieved accuracies of 91 % and 88 % in the respective case studies. The PR prediction models outperformed the cleaning classification models in terms of accuracy. The study also identified location-specific factors influencing PV system performance, emphasizing the need for geographically tailored maintenance strategies. This research provides valuable insights for improving the efficiency and sustainability of PV systems.

{"title":"Predictive modeling of photovoltaic system cleaning schedules using machine learning techniques","authors":"Haneen Abuzaid, Mahmoud Awad, Abdulrahim Shamayleh, Hussam Alshraideh","doi":"10.1016/j.renene.2024.122149","DOIUrl":"10.1016/j.renene.2024.122149","url":null,"abstract":"<div><div>Photovoltaic (PV) solar systems are a key contributor to sustainable energy generation, but their performance is significantly reduced by dust accumulation, highlighting the need for proper cleaning. This study develops predictive models to optimize cleaning schedules by forecasting the Performance Ratio (PR), a standardized metric essential to performance-guaranteed contracts. The first model uses time-series approaches (LSTM, ARIMA, SARIMAX) to predict PR, while the second uses a threshold-based ensemble voting classifier (RF, Logistic Regression, GBM) to predict cleaning needs. Two large datasets from case studies in the UAE and Jordan were used for validation. Results show SARIMAX outperforming other models, with R<sup>2</sup> values of 93.36 % and 91.74 %. The cleaning classification model achieved accuracies of 91 % and 88 % in the respective case studies. The PR prediction models outperformed the cleaning classification models in terms of accuracy. The study also identified location-specific factors influencing PV system performance, emphasizing the need for geographically tailored maintenance strategies. This research provides valuable insights for improving the efficiency and sustainability of PV systems.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"239 ","pages":"Article 122149"},"PeriodicalIF":9.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143714","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}

引用次数: 0

Investigation of fully coupled wind field simulations in complex terrain wind farms considering automatic upwind control of turbines

IF 9 1区工程技术 Q1 ENERGY & FUELS

Renewable Energy

Pub Date : 2025-02-01 DOI: 10.1016/j.renene.2024.122146

Shuanglong Fan , Zhenqing Liu

The investigation of the wake characteristics of wind turbines in complex topography is essential for optimizing wind energy utilization. This paper presents a fully-coupled simulation method, the Actuator Disk Model with Dynamic Rotation (ADM-DR), for simulating wake flow in wind farms with real terrain. This method integrates automatic upwind and speed control of turbines and utilizes a multi-level grid encryption mode. Its application in wind farms with real terrain is studied in detail and compared with the traditional model, ADM-R (Actuator Disk Model with Rotation). It was observed that for a single wind turbine, the results of the two models regarding wind speed distribution in the wake zone exhibited negligible differences. However, in clustered wind turbine arrangements, the fully-coupled model demonstrated superior applicability compared to the traditional model. It provided more accurate predictions of wake characteristics and the power output of turbines in the rear row. Furthermore, the ADM-DR model's power forecast results were about 15.6 % greater compared to those of the ADM-R model. This underscores the crucial role of accounting for the automatic upwind alignment of wind turbines to accurately evaluate energy production when assessing wind resources for prospective wind farms.

{"title":"Investigation of fully coupled wind field simulations in complex terrain wind farms considering automatic upwind control of turbines","authors":"Shuanglong Fan , Zhenqing Liu","doi":"10.1016/j.renene.2024.122146","DOIUrl":"10.1016/j.renene.2024.122146","url":null,"abstract":"<div><div>The investigation of the wake characteristics of wind turbines in complex topography is essential for optimizing wind energy utilization. This paper presents a fully-coupled simulation method, the Actuator Disk Model with Dynamic Rotation (ADM-DR), for simulating wake flow in wind farms with real terrain. This method integrates automatic upwind and speed control of turbines and utilizes a multi-level grid encryption mode. Its application in wind farms with real terrain is studied in detail and compared with the traditional model, ADM-R (Actuator Disk Model with Rotation). It was observed that for a single wind turbine, the results of the two models regarding wind speed distribution in the wake zone exhibited negligible differences. However, in clustered wind turbine arrangements, the fully-coupled model demonstrated superior applicability compared to the traditional model. It provided more accurate predictions of wake characteristics and the power output of turbines in the rear row. Furthermore, the ADM-DR model's power forecast results were about 15.6 % greater compared to those of the ADM-R model. This underscores the crucial role of accounting for the automatic upwind alignment of wind turbines to accurately evaluate energy production when assessing wind resources for prospective wind farms.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"239 ","pages":"Article 122146"},"PeriodicalIF":9.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143718","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}

引用次数: 0

Land use, soiling impact and distance to electrical grid applied to determine the viability of Solar Photovoltaic Systems in the south of Spain

IF 9 1区工程技术 Q1 ENERGY & FUELS

Renewable Energy

Pub Date : 2025-02-01 DOI: 10.1016/j.renene.2024.122108

G. Reguera Bueno , N. Simal Pérez , M. Cortés-Carmona , J. Alonso-Montesinos

This study assesses the viability of solar photovoltaic (PV) installations in southern Spain by developing a GIS-based viability index. Given the region’s high solar potential and the need to reduce carbon emissions, PV systems present a promising solution. However, their deployment requires analyzing key environmental, logistical, and technical factors. Using Geographic Information Systems (GIS), this research introduces a composite viability indicator that accounts for solar potential, soiling impact, environmental sensitivity, and proximity to electrical grid nodes. Results reveal that nearly 50% of the study territory is suitable for PV projects, with several high-potential zones for efficient, sustainable energy generation. This viability index provides an essential tool for informed strategic planning, supporting the transition to renewable energy and enabling more targeted, environmentally sensitive development of PV systems in southern Spain.

引用次数: 0

Comparative study on a solar-assisted ground source heat pump with CPC solar collector and phase change heat storage

IF 9 1区工程技术 Q1 ENERGY & FUELS

Renewable Energy

Pub Date : 2025-02-01 DOI: 10.1016/j.renene.2024.122065

Haifei Chen , Xulei Li , Jian Gao , Jingyu Cao , Hao Dong , Wenjie Wang , Yawei Chen

To address the challenges of low solar energy utilization, soil temperature imbalance, and excessive energy consumption in traditional heating systems, a novel solar-assisted ground source heat pump (SAGSHP) with phase change heat storage is proposed. It integrates a compound parabolic concentrator (CPC) solar collector and a buried pipe heat exchanger for combined heating, using RT52, Na₂S₂O₃·5H₂O, and paraffin as phase change materials (PCM) to store excess heat. A TRNSYS model is developed to analyze its performance under different CPC concentration ratios and PCM configurations. Results show that the CPC collector can save approximately 33 % collection area compared to evacuated tube and flat plate collectors, with heat collection efficiencies of 75.5 % and 65.1 % at 2 times and 5 times of the concentration ratios, respectively. The SAGSHP reduces annual energy consumption by about 2000 kWh and improves the average soil temperature stability. The heating time corresponding to RT52, Na₂S₂O₃·5H₂O, and Paraffin is 123.75 h, 80.75 h and 75 h, respectively. The average coefficient of performance of SAGSHP with RT52 reaches 3.096, and the energy consumption is reduced by about 24.3 % after ten-year operation. Results confirms the SAGSHP's potential to enhance heating efficiency, optimize solar energy use, and save energy in long-term operation.

{"title":"Comparative study on a solar-assisted ground source heat pump with CPC solar collector and phase change heat storage","authors":"Haifei Chen , Xulei Li , Jian Gao , Jingyu Cao , Hao Dong , Wenjie Wang , Yawei Chen","doi":"10.1016/j.renene.2024.122065","DOIUrl":"10.1016/j.renene.2024.122065","url":null,"abstract":"<div><div>To address the challenges of low solar energy utilization, soil temperature imbalance, and excessive energy consumption in traditional heating systems, a novel solar-assisted ground source heat pump (SAGSHP) with phase change heat storage is proposed. It integrates a compound parabolic concentrator (CPC) solar collector and a buried pipe heat exchanger for combined heating, using RT52, Na₂S₂O₃·5H₂O, and paraffin as phase change materials (PCM) to store excess heat. A TRNSYS model is developed to analyze its performance under different CPC concentration ratios and PCM configurations. Results show that the CPC collector can save approximately 33 % collection area compared to evacuated tube and flat plate collectors, with heat collection efficiencies of 75.5 % and 65.1 % at 2 times and 5 times of the concentration ratios, respectively. The SAGSHP reduces annual energy consumption by about 2000 kWh and improves the average soil temperature stability. The heating time corresponding to RT52, Na<sub>2</sub>S<sub>2</sub>O<sub>3</sub>·5H<sub>2</sub>O, and Paraffin is 123.75 h, 80.75 h and 75 h, respectively. The average coefficient of performance of SAGSHP with RT52 reaches 3.096, and the energy consumption is reduced by about 24.3 % after ten-year operation. Results confirms the SAGSHP's potential to enhance heating efficiency, optimize solar energy use, and save energy in long-term operation.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"239 ","pages":"Article 122065"},"PeriodicalIF":9.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143725","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}

引用次数: 0

Assessment of the criteria importance for determining solar panel site potential via machine learning algorithms, a case study Central Anatolia region, Turkey

IF 9 1区工程技术 Q1 ENERGY & FUELS

Renewable Energy

Pub Date : 2025-02-01 DOI: 10.1016/j.renene.2024.122145

Fatih Sari , Selmin Ener Rusen

In this study, 16 criteria influencing solar energy potential were identified, and interactions with 1311 existing solar power plants were examined using MaxEnt and Logistic Regression methods. Unlike traditional site suitability studies in the literature, this study determined criterion weights solely based on natural intersections of criteria with locations of existing solar power plants, without artificial weight assignment. Thus, correlations demonstrated by 1311 solar power plants across the 16 criteria were used to create solar energy potential maps for the entire study area. The MaxEnt analysis yielded an AUC value of 0.760, while the LR method calculated an R² value of 0.7904, indicating high correlation between all points and specific criterion values, with approximately 80 % of the study area's solar energy potential being determined by these criteria. In MaxEnt, criteria such as distance from land use, highways, and power transmission lines were highlighted, while LR showed that temperature-related criteria also significantly influenced potential determination. The study found that 6.21 % of the study area had the highest potential using MaxEnt, and 8.71 % using LR, with Aksaray, Karaman, Ereğli, and Karatay identified as districts with the highest potential. The correlation value between the results of both methods has been calculated as 0.756.

{"title":"Assessment of the criteria importance for determining solar panel site potential via machine learning algorithms, a case study Central Anatolia region, Turkey","authors":"Fatih Sari , Selmin Ener Rusen","doi":"10.1016/j.renene.2024.122145","DOIUrl":"10.1016/j.renene.2024.122145","url":null,"abstract":"<div><div>In this study, 16 criteria influencing solar energy potential were identified, and interactions with 1311 existing solar power plants were examined using MaxEnt and Logistic Regression methods. Unlike traditional site suitability studies in the literature, this study determined criterion weights solely based on natural intersections of criteria with locations of existing solar power plants, without artificial weight assignment. Thus, correlations demonstrated by 1311 solar power plants across the 16 criteria were used to create solar energy potential maps for the entire study area. The MaxEnt analysis yielded an AUC value of 0.760, while the LR method calculated an R<sup>2</sup> value of 0.7904, indicating high correlation between all points and specific criterion values, with approximately 80 % of the study area's solar energy potential being determined by these criteria. In MaxEnt, criteria such as distance from land use, highways, and power transmission lines were highlighted, while LR showed that temperature-related criteria also significantly influenced potential determination. The study found that 6.21 % of the study area had the highest potential using MaxEnt, and 8.71 % using LR, with Aksaray, Karaman, Ereğli, and Karatay identified as districts with the highest potential. The correlation value between the results of both methods has been calculated as 0.756.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"239 ","pages":"Article 122145"},"PeriodicalIF":9.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144306","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}

引用次数: 0

Capacitive bio–electrocatalyst Mxene@CoMo–ZIF sulfide heterostructure for boosted biofilm electroactivity to enhance renewable energy conversion

IF 9 1区工程技术 Q1 ENERGY & FUELS

Renewable Energy

Pub Date : 2025-02-01 DOI: 10.1016/j.renene.2025.122545

Liuqingying Yang , Qing Wen , Ye Chen , Cunguo Lin , Haiping Gao , Zhenghui Qiu , Xu Pan

Microbial Fuel Cells (MFC), as a technology that utilizes microbial metabolic activity to convert organic matter into electrical energy, has the dual advantage of efficient use of organic matter and renewable energy potential. However, the underdeveloped extracellular electron transfer (EET) between biofilm and anode and its weaker colonization are the main factors limiting the power enhancement and energy conversion in microbial fuel cells (MFCs). Therefore, interfacial properties of catalysts loaded on electrodes are the key to rise these restrictions. In this work, a capacitive bio–electrocatalyst has been successfully prepared through ion exchange and in–situ etching methods to anchored Co₉S₈–MoS₂–CoMo₂S₄ (CMCS) on few–layered Mxene (MX). MX applied as substrate could effectively inhibit the stacking of CMCS particles and increase reactive sites, EET efficiency and redox reaction rates. Hence, the as–prepared powders were coated on carbon felt utilized as bio–electrocatalyst in MFCs. The MFC with MX@CMCS/CF achieved significant faster start–up time and maximum power density of 6.01 W m⁻³, higher than that of CMCS (5.34 W m⁻³), MX@CoMo–ZIF (5.11 W m⁻³) and CoMo–ZIF (2.74 W m⁻³). Biofilm community analysis on anode surface indicated that MX@CMC specifically selected the electrogenic bacteria, Desulfuromonas, denoting a more effective electricity production process. The high performance could be attributed to internal resistance reduction of MX@CMCS and promotion of flavin–related protein expression. This study validated the prospective potential of MX and sulfide heterostructure as capacitive bio–electrocatalyst materials for MFCs on power generation, energy regeneration and microbial community structure.

{"title":"Capacitive bio–electrocatalyst Mxene@CoMo–ZIF sulfide heterostructure for boosted biofilm electroactivity to enhance renewable energy conversion","authors":"Liuqingying Yang , Qing Wen , Ye Chen , Cunguo Lin , Haiping Gao , Zhenghui Qiu , Xu Pan","doi":"10.1016/j.renene.2025.122545","DOIUrl":"10.1016/j.renene.2025.122545","url":null,"abstract":"<div><div>Microbial Fuel Cells (MFC), as a technology that utilizes microbial metabolic activity to convert organic matter into electrical energy, has the dual advantage of efficient use of organic matter and renewable energy potential. However, the underdeveloped extracellular electron transfer (EET) between biofilm and anode and its weaker colonization are the main factors limiting the power enhancement and energy conversion in microbial fuel cells (MFCs). Therefore, interfacial properties of catalysts loaded on electrodes are the key to rise these restrictions. In this work, a capacitive bio–electrocatalyst has been successfully prepared through ion exchange and in–situ etching methods to anchored Co<sub>9</sub>S<sub>8</sub>–MoS<sub>2</sub>–CoMo<sub>2</sub>S<sub>4</sub> (CMCS) on few–layered Mxene (MX). MX applied as substrate could effectively inhibit the stacking of CMCS particles and increase reactive sites, EET efficiency and redox reaction rates. Hence, the as–prepared powders were coated on carbon felt utilized as bio–electrocatalyst in MFCs. The MFC with MX@CMCS/CF achieved significant faster start–up time and maximum power density of 6.01 W m<sup>−3</sup>, higher than that of CMCS (5.34 W m<sup>−3</sup>), MX@CoMo–ZIF (5.11 W m<sup>−3</sup>) and CoMo–ZIF (2.74 W m<sup>−3</sup>). Biofilm community analysis on anode surface indicated that MX@CMC specifically selected the electrogenic bacteria, <em>Desulfuromonas</em>, denoting a more effective electricity production process. The high performance could be attributed to internal resistance reduction of MX@CMCS and promotion of flavin–related protein expression. This study validated the prospective potential of MX and sulfide heterostructure as capacitive bio–electrocatalyst materials for MFCs on power generation, energy regeneration and microbial community structure.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"243 ","pages":"Article 122545"},"PeriodicalIF":9.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093289","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}

引用次数: 0

Thermal performance analysis of a deep coaxial borehole heat exchanger with a horizontal well based on a novel semi-analytical model

IF 9 1区工程技术 Q1 ENERGY & FUELS

Renewable Energy

Pub Date : 2025-02-01 DOI: 10.1016/j.renene.2024.122073

Ye Wang , Hengjian Liu , Qiqiang Zhang , Luyu Zhang

This study introduces a novel semi-analytical model for deep coaxial borehole heat exchanger with a horizontal well(H-DBHE), which incorporates the effects of formation stratification and transient heat conduction within the pipe walls. Initially, the model's validity was checked through a comparison with the results derived from the published numerical models. This semi-analytical approach significantly reduces computational time while ensuring calculation accuracy. Subsequently, the study quantitatively examined the influence of the operation parameters, structural characteristics and the thermal capacity within the borehole on the dynamic heat extraction of H-DBHE. The numerical results demonstrate that the thermal capacity within the borehole significantly influences the heat extraction performance during intermittent operations of H-DBHE. Based on this finding, the study further investigated the effects of intermittent operation and heating time on the heat recovery performance of the system. Additionally, the study compared the long-term operational performance and thermal attenuation between H-DBHE and deep coaxial borehole heat exchanger (DBHE) over a ten-year simulation period. This research introduces a novel semi-analytical model for H-DBHE, which is of significant theoretical guidance for accurately predicting the heat extraction performance of H-DBHE and scientifically designing geothermal utilization systems.

{"title":"Thermal performance analysis of a deep coaxial borehole heat exchanger with a horizontal well based on a novel semi-analytical model","authors":"Ye Wang , Hengjian Liu , Qiqiang Zhang , Luyu Zhang","doi":"10.1016/j.renene.2024.122073","DOIUrl":"10.1016/j.renene.2024.122073","url":null,"abstract":"<div><div>This study introduces a novel semi-analytical model for deep coaxial borehole heat exchanger with a horizontal well(H-DBHE), which incorporates the effects of formation stratification and transient heat conduction within the pipe walls. Initially, the model's validity was checked through a comparison with the results derived from the published numerical models. This semi-analytical approach significantly reduces computational time while ensuring calculation accuracy. Subsequently, the study quantitatively examined the influence of the operation parameters, structural characteristics and the thermal capacity within the borehole on the dynamic heat extraction of H-DBHE. The numerical results demonstrate that the thermal capacity within the borehole significantly influences the heat extraction performance during intermittent operations of H-DBHE. Based on this finding, the study further investigated the effects of intermittent operation and heating time on the heat recovery performance of the system. Additionally, the study compared the long-term operational performance and thermal attenuation between H-DBHE and deep coaxial borehole heat exchanger (DBHE) over a ten-year simulation period. This research introduces a novel semi-analytical model for H-DBHE, which is of significant theoretical guidance for accurately predicting the heat extraction performance of H-DBHE and scientifically designing geothermal utilization systems.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"239 ","pages":"Article 122073"},"PeriodicalIF":9.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143318","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}

引用次数: 0

Prediction of output temperature and fracture permeability of EGS with dynamic injection rate based on deep learning method

IF 9 1区工程技术 Q1 ENERGY & FUELS

Renewable Energy

Pub Date : 2025-02-01 DOI: 10.1016/j.renene.2024.122102

Chuan-Yong Zhu , Di Huang , Wen-Xian Lei , Zhi-Yang He , Xin-Yue Duan , Liang Gong

The heat recovery of the enhanced geothermal system (EGS) declines noticeably as mining progresses. Therefore, it is extremely important for the regulation of the later stage thermal extraction process. The later stage thermal extraction process can be reasonably regulated if the heat recovery capacity of EGS and reservoir parameters in the late stage can be precisely predicted based on the primary production data from the same EGS. In the present work, we developed a deep learning model based on the Long Short-Term Memory (LSTM) neural network to predict the late-stage output temperature and fracture permeability of EGS with dynamic injection rate. This model could deal with time series problems. When developing this model, the numerical results for 80 years of EGS operating dynamic injection conditions are adopted as a database in which the last 20 % are set as prediction data and can be considered as later stage (the upcoming) production data. We thoroughly assess the output temperature and fracture permeability prediction performance of the LSTM network by comparing them with the numerical results. The comparisons reveal that the developed deep learning model could accurately predict the output temperature and fracture permeability of EGS under different dynamic injection rate, outperforming Gated Recurrent Unit (GRU) in prediction accuracy. This study demonstrates the potential of LSTM networks, in providing accurate, data-driven predictions for critical reservoir parameters, enabling more effective regulation of the thermal extraction process and optimizing long-term geothermal energy recovery.

{"title":"Prediction of output temperature and fracture permeability of EGS with dynamic injection rate based on deep learning method","authors":"Chuan-Yong Zhu , Di Huang , Wen-Xian Lei , Zhi-Yang He , Xin-Yue Duan , Liang Gong","doi":"10.1016/j.renene.2024.122102","DOIUrl":"10.1016/j.renene.2024.122102","url":null,"abstract":"<div><div>The heat recovery of the enhanced geothermal system (EGS) declines noticeably as mining progresses. Therefore, it is extremely important for the regulation of the later stage thermal extraction process. The later stage thermal extraction process can be reasonably regulated if the heat recovery capacity of EGS and reservoir parameters in the late stage can be precisely predicted based on the primary production data from the same EGS. In the present work, we developed a deep learning model based on the Long Short-Term Memory (LSTM) neural network to predict the late-stage output temperature and fracture permeability of EGS with dynamic injection rate. This model could deal with time series problems. When developing this model, the numerical results for 80 years of EGS operating dynamic injection conditions are adopted as a database in which the last 20 % are set as prediction data and can be considered as later stage (the upcoming) production data. We thoroughly assess the output temperature and fracture permeability prediction performance of the LSTM network by comparing them with the numerical results. The comparisons reveal that the developed deep learning model could accurately predict the output temperature and fracture permeability of EGS under different dynamic injection rate, outperforming Gated Recurrent Unit (GRU) in prediction accuracy. This study demonstrates the potential of LSTM networks, in providing accurate, data-driven predictions for critical reservoir parameters, enabling more effective regulation of the thermal extraction process and optimizing long-term geothermal energy recovery.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"239 ","pages":"Article 122102"},"PeriodicalIF":9.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143323","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}

引用次数: 0

In-situ blade strain measurements and fatigue analysis of a cross-flow turbine operating in a tidal flow

IF 9 1区工程技术 Q1 ENERGY & FUELS

Renewable Energy

Pub Date : 2025-02-01 DOI: 10.1016/j.renene.2024.121977

Mason Bichanich , Aidan Bharath , Patrick O’Byrne , Michael Monahan , Hannah Ross , Robert Raye , Casey Nichols , Charles Candon , Martin Wosnik

Cross-flow turbines (CFTs) are inherently unsteady devices with regards to operating principle and loading. By improving our understanding of the dynamic loading on these turbines, we hope to better inform CFT design, improve survivability, and reduce overall costs. The University of New Hampshire (UNH) and the National Renewable Energy Laboratory (NREL) collaborated on a project to instrument and test a four-bladed New Energy Corp. vertical axis cross-flow turbine in a real tidal flow. One blade from the 3.2 m diameter x 1.7 m height turbine was instrumented with eight full-bridge strain gauges along the span of the blade. The turbine was then deployed at the UNH-Atlantic Marine Energy Center (AMEC) Tidal Energy Test Site in Portsmouth, NH. Time-synchronized measurements of blade strain, inflow, thrust, rotational speed, and electrical output were obtained to characterize blade loading under various conditions. The blade strain was examined to assess the dynamic loading and conduct a fatigue analysis on the device.

{"title":"In-situ blade strain measurements and fatigue analysis of a cross-flow turbine operating in a tidal flow","authors":"Mason Bichanich , Aidan Bharath , Patrick O’Byrne , Michael Monahan , Hannah Ross , Robert Raye , Casey Nichols , Charles Candon , Martin Wosnik","doi":"10.1016/j.renene.2024.121977","DOIUrl":"10.1016/j.renene.2024.121977","url":null,"abstract":"<div><div>Cross-flow turbines (CFTs) are inherently unsteady devices with regards to operating principle and loading. By improving our understanding of the dynamic loading on these turbines, we hope to better inform CFT design, improve survivability, and reduce overall costs. The University of New Hampshire (UNH) and the National Renewable Energy Laboratory (NREL) collaborated on a project to instrument and test a four-bladed New Energy Corp. vertical axis cross-flow turbine in a real tidal flow. One blade from the 3.2 m diameter x 1.7 m height turbine was instrumented with eight full-bridge strain gauges along the span of the blade. The turbine was then deployed at the UNH-Atlantic Marine Energy Center (AMEC) Tidal Energy Test Site in Portsmouth, NH. Time-synchronized measurements of blade strain, inflow, thrust, rotational speed, and electrical output were obtained to characterize blade loading under various conditions. The blade strain was examined to assess the dynamic loading and conduct a fatigue analysis on the device.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"239 ","pages":"Article 121977"},"PeriodicalIF":9.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143332","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}

引用次数: 0

Technoeconomic optimization of coaxial hydrokinetic turbines

IF 9 1区工程技术 Q1 ENERGY & FUELS

Renewable Energy

Pub Date : 2025-02-01 DOI: 10.1016/j.renene.2024.122041

Mehedi Hassan, Matthew Bryant, Andre Mazzoleni, Kenneth Granlund

Tethered coaxial dual-rotor turbine technology shows promise in the global shift towards renewable energy to address climate change. To assess its economic feasibility, a low-order technoeconomic optimization framework is proposed based on a levelized cost of energy (LCOE) model specially tailored for this device. This framework can predict the optimal size of the turbines and farms that minimize LCOE for given resource characteristics. To demonstrate the functionality and robustness of the proposed tool, the effects of varying device numbers and sizes on capital and operational expenditures, annual energy production, wake effects, and the convergence behavior of the optimization algorithm are investigated. Additionally, two case studies in the Gulf Stream off the coasts of North Carolina and Florida are performed to facilitate informed decision-making towards the economic viability of TCDT deployment in those locations. The results show that while the Florida coast demonstrates economic feasibility under current capital and operational costs, the North Carolina coast faces higher LCOE values, necessitating significant cost reductions, and government support to enhance feasibility and competitiveness against alternative energy sources. This tool offers a quick and easy method for preliminary assessments of the economic viability of this new technology at desired deployment sites.

{"title":"Technoeconomic optimization of coaxial hydrokinetic turbines","authors":"Mehedi Hassan, Matthew Bryant, Andre Mazzoleni, Kenneth Granlund","doi":"10.1016/j.renene.2024.122041","DOIUrl":"10.1016/j.renene.2024.122041","url":null,"abstract":"<div><div>Tethered coaxial dual-rotor turbine technology shows promise in the global shift towards renewable energy to address climate change. To assess its economic feasibility, a low-order technoeconomic optimization framework is proposed based on a levelized cost of energy (LCOE) model specially tailored for this device. This framework can predict the optimal size of the turbines and farms that minimize LCOE for given resource characteristics. To demonstrate the functionality and robustness of the proposed tool, the effects of varying device numbers and sizes on capital and operational expenditures, annual energy production, wake effects, and the convergence behavior of the optimization algorithm are investigated. Additionally, two case studies in the Gulf Stream off the coasts of North Carolina and Florida are performed to facilitate informed decision-making towards the economic viability of TCDT deployment in those locations. The results show that while the Florida coast demonstrates economic feasibility under current capital and operational costs, the North Carolina coast faces higher LCOE values, necessitating significant cost reductions, and government support to enhance feasibility and competitiveness against alternative energy sources. This tool offers a quick and easy method for preliminary assessments of the economic viability of this new technology at desired deployment sites.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"239 ","pages":"Article 122041"},"PeriodicalIF":9.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143334","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}

引用次数: 0

首页上一页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Renewable Energy

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀