The reliability and safety of power systems heavily depend on accurate forecasting of new energy generation. However, the non-stationarity and randomness of new energy generation power increase forecasting difficulty. This paper aims to propose a short-term wind power forecasting method with strong characterization ability to accurately understand future new energy generation conditions so as to ensure power systems' reliability and safety. The required input variables for wind power forecasting are determined by the gray relational analysis method. An advanced marine predators algorithm is proposed by improving the marine predators algorithm to enhance convergence ability and probability of escaping local optimal solutions. The advanced marine predators algorithm optimizes support vector regression machine to address the issue of insufficient utilization of its forecasting performance due to the selection of parameter values based on personal experience in traditional methods. Finally, different wind power generation scenarios verify its effectiveness and universality. This study promotes the application of artificial intelligence technology for improving short-term wind power forecasting accuracy, thereby enhancing the reliability and safety level of power systems.
{"title":"Enhancing short-term wind power forecasting accuracy for reliable and safe integration into power systems: A gray relational analysis and optimized support vector regression machine approach","authors":"Yuwei Liu, Lingling Li, Jiaqi Liu","doi":"10.1063/5.0181395","DOIUrl":"https://doi.org/10.1063/5.0181395","url":null,"abstract":"The reliability and safety of power systems heavily depend on accurate forecasting of new energy generation. However, the non-stationarity and randomness of new energy generation power increase forecasting difficulty. This paper aims to propose a short-term wind power forecasting method with strong characterization ability to accurately understand future new energy generation conditions so as to ensure power systems' reliability and safety. The required input variables for wind power forecasting are determined by the gray relational analysis method. An advanced marine predators algorithm is proposed by improving the marine predators algorithm to enhance convergence ability and probability of escaping local optimal solutions. The advanced marine predators algorithm optimizes support vector regression machine to address the issue of insufficient utilization of its forecasting performance due to the selection of parameter values based on personal experience in traditional methods. Finally, different wind power generation scenarios verify its effectiveness and universality. This study promotes the application of artificial intelligence technology for improving short-term wind power forecasting accuracy, thereby enhancing the reliability and safety level of power systems.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140523889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Ma Lu, D. Yang, M. C. Anderson, S. Zainali, B. Stridh, A. Avelin, P. Campana
Photosynthetically active radiation is a key parameter for determining crop yield. Separating photosynthetically active radiation into direct and diffuse components is significant to agrivoltaic systems. The varying shading conditions caused by the solar panels produce a higher contribution of diffuse irradiance reaching the crops. This study introduces a new separation model capable of accurately estimating the diffuse component from the global photosynthetically active radiation and conveniently retrievable meteorological parameters. The model modifies one of the highest-performing separation models for broadband irradiance, namely, the Yang2 model. Four new predictors are added: atmospheric optical thickness, vapor pressure deficit, aerosol optical depth, and surface albedo. The proposed model has been calibrated, tested, and validated at three sites in Sweden with latitudes above 58 °N, outperforming four other models in all examined locations, with R2 values greater than 0.90. The applicability of the developed model is demonstrated using data retrieved from Sweden's first agrivoltaic system. A variety of data availability cases representative of current and future agrivoltaic systems is tested. If on-site measurements of diffuse photosynthetically active radiation are not available, the model calibrated based on nearby stations can be a suitable first approximation, obtaining an R2 of 0.89. Utilizing predictor values derived from satellite data is an alternative method, but the spatial resolution must be considered cautiously as the R2 dropped to 0.73.
{"title":"Photosynthetically active radiation separation model for high-latitude regions in agrivoltaic systems modeling","authors":"S. Ma Lu, D. Yang, M. C. Anderson, S. Zainali, B. Stridh, A. Avelin, P. Campana","doi":"10.1063/5.0181311","DOIUrl":"https://doi.org/10.1063/5.0181311","url":null,"abstract":"Photosynthetically active radiation is a key parameter for determining crop yield. Separating photosynthetically active radiation into direct and diffuse components is significant to agrivoltaic systems. The varying shading conditions caused by the solar panels produce a higher contribution of diffuse irradiance reaching the crops. This study introduces a new separation model capable of accurately estimating the diffuse component from the global photosynthetically active radiation and conveniently retrievable meteorological parameters. The model modifies one of the highest-performing separation models for broadband irradiance, namely, the Yang2 model. Four new predictors are added: atmospheric optical thickness, vapor pressure deficit, aerosol optical depth, and surface albedo. The proposed model has been calibrated, tested, and validated at three sites in Sweden with latitudes above 58 °N, outperforming four other models in all examined locations, with R2 values greater than 0.90. The applicability of the developed model is demonstrated using data retrieved from Sweden's first agrivoltaic system. A variety of data availability cases representative of current and future agrivoltaic systems is tested. If on-site measurements of diffuse photosynthetically active radiation are not available, the model calibrated based on nearby stations can be a suitable first approximation, obtaining an R2 of 0.89. Utilizing predictor values derived from satellite data is an alternative method, but the spatial resolution must be considered cautiously as the R2 dropped to 0.73.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140522459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Non-intrusive load monitoring (NILM) is a technique that efficiently monitors appliances' operational status and energy consumption by utilizing voltage and current data, without intrusive measurements. In NILM, designing efficient classification models and building distinctive load features are crucial. However, due to its continuously variable load characteristics, multi-state load identification remains the most challenging problem in NILM. In this paper, we improve the encoding of the color V–I trajectory by incorporating instantaneous power, thereby enhancing the uniqueness of V–I trajectory features. Furthermore, we investigate a NILM method based on deep learning methods and propose a densely connected convolutional network with squeeze-and-excitation network (SE-DenseNet) architecture to solve the multi-state load identification problem. Initially, the architecture leverages DenseNet's dense connectivity property to generate a multitude of feature maps from the V–I trajectory. Then, SENet's channel attention mechanism is employed to enhance the utilization of effective features, which is more effective for multi-state load identification. Experimental results on the NILM public datasets PLAID and WHITED show that the recognition accuracy of the proposed method reaches 98.60% and 98.88%, respectively, which outperforms most existing methods.
{"title":"Improving multi-state appliance classification by SE-DenseNet based on color encoding in non-intrusive load monitoring","authors":"Yinghua Han, Zhiwei Dou, Yu Zhao, Qiang Zhao","doi":"10.1063/5.0180804","DOIUrl":"https://doi.org/10.1063/5.0180804","url":null,"abstract":"Non-intrusive load monitoring (NILM) is a technique that efficiently monitors appliances' operational status and energy consumption by utilizing voltage and current data, without intrusive measurements. In NILM, designing efficient classification models and building distinctive load features are crucial. However, due to its continuously variable load characteristics, multi-state load identification remains the most challenging problem in NILM. In this paper, we improve the encoding of the color V–I trajectory by incorporating instantaneous power, thereby enhancing the uniqueness of V–I trajectory features. Furthermore, we investigate a NILM method based on deep learning methods and propose a densely connected convolutional network with squeeze-and-excitation network (SE-DenseNet) architecture to solve the multi-state load identification problem. Initially, the architecture leverages DenseNet's dense connectivity property to generate a multitude of feature maps from the V–I trajectory. Then, SENet's channel attention mechanism is employed to enhance the utilization of effective features, which is more effective for multi-state load identification. Experimental results on the NILM public datasets PLAID and WHITED show that the recognition accuracy of the proposed method reaches 98.60% and 98.88%, respectively, which outperforms most existing methods.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140522776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
David Alejandro Gómez-González, Luis Alejandro Méndez-Duran, Harvey Andrés Milquez-Sanabria
Some agro-industrial wastes are currently untreated, resulting in an increase in greenhouse gas emissions. Therefore, in relation to the pollution generated by fossil fuels, the study of the obtained fuels from agro-industrial and forestry residues has been promoted. Rice is a basic product for several families in the world, and its residue is a component that has enormous potential in Colombia due to its consumption. The objective of the present study is to conduct an exergoeconomic evaluation of the production of fuel from rice husks as agro-industrial waste by means of the slow and fast pyrolysis process. Using simulators like Aspen Plus, the simulation of the two processes was carried up, implementing a rigorous kinetic model. The yield values were validated with data from the literature, obtaining values of 42.3% and 41.4% for slow and fast pyrolysis, respectively, for pyrolytic oil. The total investment cost of the process is 2146.45 kUSD. According to the thermodynamic parameters of the simulator, an exergy analysis was conducted for the two processes. Overall exergy percentages of 73.84% and 78.19% were obtained for the slow and fast pyrolysis, respectively. The economic and exergy analysis was coupled to implement a specific exergy costing. The exergoeconomics factors obtained values of 72.21% and 76.78%, for the slow and fast pyrolysis reactors, respectively. The contribution of the present research is related to the rigorous kinetic model, in addition to its implementation in slow pyrolysis, involved in the exergoeconomic study of biomass pyrolysis processes.
{"title":"Exergoeconomic evaluation of fuel production from rice husk residue through the pyrolysis process","authors":"David Alejandro Gómez-González, Luis Alejandro Méndez-Duran, Harvey Andrés Milquez-Sanabria","doi":"10.1063/5.0173767","DOIUrl":"https://doi.org/10.1063/5.0173767","url":null,"abstract":"Some agro-industrial wastes are currently untreated, resulting in an increase in greenhouse gas emissions. Therefore, in relation to the pollution generated by fossil fuels, the study of the obtained fuels from agro-industrial and forestry residues has been promoted. Rice is a basic product for several families in the world, and its residue is a component that has enormous potential in Colombia due to its consumption. The objective of the present study is to conduct an exergoeconomic evaluation of the production of fuel from rice husks as agro-industrial waste by means of the slow and fast pyrolysis process. Using simulators like Aspen Plus, the simulation of the two processes was carried up, implementing a rigorous kinetic model. The yield values were validated with data from the literature, obtaining values of 42.3% and 41.4% for slow and fast pyrolysis, respectively, for pyrolytic oil. The total investment cost of the process is 2146.45 kUSD. According to the thermodynamic parameters of the simulator, an exergy analysis was conducted for the two processes. Overall exergy percentages of 73.84% and 78.19% were obtained for the slow and fast pyrolysis, respectively. The economic and exergy analysis was coupled to implement a specific exergy costing. The exergoeconomics factors obtained values of 72.21% and 76.78%, for the slow and fast pyrolysis reactors, respectively. The contribution of the present research is related to the rigorous kinetic model, in addition to its implementation in slow pyrolysis, involved in the exergoeconomic study of biomass pyrolysis processes.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140520298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In reality, wind power data are often accompanied by data losses, which can affect the accurate prediction of wind power and subsequently impact the real-time scheduling of the power system. Existing methods for recovering missing data primarily consider the environmental conditions of individual wind farms, thereby overlooking the spatiotemporal correlations between neighboring wind farms, which significantly compromise their recovery effectiveness. In this paper, a joint missing data recovery model based on power data from adjacent wind farms is proposed. At first, a spatial–temporal module (STM) is designed using a combination of graph convolution network and recurrent neural networks to learn spatiotemporal dependencies and similarities. Subsequently, to provide a solid computational foundation for the STM, a Euclidean-directed graph based on Granger causality is constructed to reflect the hidden spatiotemporal information in the data. Finally, comprehensive tests on data recovery for both missing completely at random and short-term continuous missing are conducted on a real-world dataset. The results demonstrate that the proposed model exhibits a significant advantage in missing data recovery compared to baseline models.
{"title":"A joint missing power data recovery method based on the spatiotemporal correlation of multiple wind farms","authors":"Haochen Li, Liqun Liu, Qiusheng He","doi":"10.1063/5.0176922","DOIUrl":"https://doi.org/10.1063/5.0176922","url":null,"abstract":"In reality, wind power data are often accompanied by data losses, which can affect the accurate prediction of wind power and subsequently impact the real-time scheduling of the power system. Existing methods for recovering missing data primarily consider the environmental conditions of individual wind farms, thereby overlooking the spatiotemporal correlations between neighboring wind farms, which significantly compromise their recovery effectiveness. In this paper, a joint missing data recovery model based on power data from adjacent wind farms is proposed. At first, a spatial–temporal module (STM) is designed using a combination of graph convolution network and recurrent neural networks to learn spatiotemporal dependencies and similarities. Subsequently, to provide a solid computational foundation for the STM, a Euclidean-directed graph based on Granger causality is constructed to reflect the hidden spatiotemporal information in the data. Finally, comprehensive tests on data recovery for both missing completely at random and short-term continuous missing are conducted on a real-world dataset. The results demonstrate that the proposed model exhibits a significant advantage in missing data recovery compared to baseline models.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140516267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qianyue Wang, Gangquan Si, Kai Qu, Zihan Shan, Jiahui Gong, Chen Yang
Multi-turbine wind power (WP) prediction contributes to wind turbine (WT) management and refined wind farm operations. However, the intricate and dynamic nature of the interrelationships among WTs hinders the full exploration of their potential in improving prediction. This paper proposes a novel spatio-positional series attention long short-term memory (SPSA-LSTM) method, which extracts the hidden correlations and temporal features from wind speed (WS) and WP historical data of different WTs for high-precision short-term prediction. Using embedding techniques, we incorporate crucial spatial location information of WTs into time series, enhancing the model's representative capability. Furthermore, we employ a self-attention mechanism with strong relational modeling capability to extract the correlation features among time series. This approach possesses remarkable learning abilities, enabling the thorough exploration of the complex interdependencies within inputs. Consequently, each WT is endowed with a comprehensive dataset comprising attention scores from all other WTs and its own WS and WP. The LSTM fuses these features and extracts temporal patterns, ultimately generating the WP prediction outputs. Experiments conducted on 20 WTs demonstrate that our method significantly surpasses other baselines. Ablation experiments provide further evidence to support the effectiveness of the approach in leveraging spatial embedding to optimize prediction performance.
{"title":"Integrating spatio-positional series attention to deep network for multi-turbine short-term wind power prediction","authors":"Qianyue Wang, Gangquan Si, Kai Qu, Zihan Shan, Jiahui Gong, Chen Yang","doi":"10.1063/5.0187227","DOIUrl":"https://doi.org/10.1063/5.0187227","url":null,"abstract":"Multi-turbine wind power (WP) prediction contributes to wind turbine (WT) management and refined wind farm operations. However, the intricate and dynamic nature of the interrelationships among WTs hinders the full exploration of their potential in improving prediction. This paper proposes a novel spatio-positional series attention long short-term memory (SPSA-LSTM) method, which extracts the hidden correlations and temporal features from wind speed (WS) and WP historical data of different WTs for high-precision short-term prediction. Using embedding techniques, we incorporate crucial spatial location information of WTs into time series, enhancing the model's representative capability. Furthermore, we employ a self-attention mechanism with strong relational modeling capability to extract the correlation features among time series. This approach possesses remarkable learning abilities, enabling the thorough exploration of the complex interdependencies within inputs. Consequently, each WT is endowed with a comprehensive dataset comprising attention scores from all other WTs and its own WS and WP. The LSTM fuses these features and extracts temporal patterns, ultimately generating the WP prediction outputs. Experiments conducted on 20 WTs demonstrate that our method significantly surpasses other baselines. Ablation experiments provide further evidence to support the effectiveness of the approach in leveraging spatial embedding to optimize prediction performance.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140524625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Masoumeh Gharaati, Nathaniel J. Wei, J. Dabiri, L. Martínez‐Tossas, Di Yang
Effects of helical-shaped blades on the flow characteristics and power production of finite-length wind farms composed of vertical-axis wind turbines (VAWTs) are studied numerically using large-eddy simulation (LES). Two helical-bladed VAWTs (with opposite blade twist angles) are studied against one straight-bladed VAWT in different array configurations with coarse, intermediate, and tight spacings. Statistical analysis of the LES data shows that the helical-bladed VAWTs can improve the mean power production in the fully developed region of the array by about 4.94%–7.33% compared with the corresponding straight-bladed VAWT cases. The helical-bladed VAWTs also cover the azimuth angle more smoothly during the rotation, resulting in about 47.6%–60.1% reduction in the temporal fluctuation of the VAWT power output. Using the helical-bladed VAWTs also reduces the fatigue load on the structure by significantly reducing the spanwise bending moment (relative to the bottom base), which may improve the longevity of the VAWT system to reduce the long-term maintenance cost.
{"title":"Large-eddy simulations of turbulent flows in arrays of helical- and straight-bladed vertical-axis wind turbines","authors":"Masoumeh Gharaati, Nathaniel J. Wei, J. Dabiri, L. Martínez‐Tossas, Di Yang","doi":"10.1063/5.0172007","DOIUrl":"https://doi.org/10.1063/5.0172007","url":null,"abstract":"Effects of helical-shaped blades on the flow characteristics and power production of finite-length wind farms composed of vertical-axis wind turbines (VAWTs) are studied numerically using large-eddy simulation (LES). Two helical-bladed VAWTs (with opposite blade twist angles) are studied against one straight-bladed VAWT in different array configurations with coarse, intermediate, and tight spacings. Statistical analysis of the LES data shows that the helical-bladed VAWTs can improve the mean power production in the fully developed region of the array by about 4.94%–7.33% compared with the corresponding straight-bladed VAWT cases. The helical-bladed VAWTs also cover the azimuth angle more smoothly during the rotation, resulting in about 47.6%–60.1% reduction in the temporal fluctuation of the VAWT power output. Using the helical-bladed VAWTs also reduces the fatigue load on the structure by significantly reducing the spanwise bending moment (relative to the bottom base), which may improve the longevity of the VAWT system to reduce the long-term maintenance cost.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139291318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nico J. Dekker, L. Slooff, Mark J. Jansen, Gertjan de Graaff, Jaco Hovius, R. Jonkman, Jesper Zuurbier, Jan Pronk
The Dutch climate agreement anticipates the large-scale implementation of solar and wind energy systems on land and water. Combining solar and wind farms has the benefit of multiple surface area use, and it also has the advantage of energy generation from both solar and wind energy systems, which is rather complementary in time; thus, a better balance can be found between electricity generation and demand and the load on the electricity grid. In combined solar and wind farms (CSWFs), the turbines will cast shadows on the solar panels. This concerns the static shadow from the construction tower of the turbine as well as the dynamic shadow caused by the rotating blades. This paper reports on the results of millisecond data monitoring of the PV farm of a CSWF in the Netherlands on land. Static and dynamic shadow effects are discussed, as well as their dependency on farm design. It is observed that the dynamic shade of the wind turbine blade causes serious disturbances of the DC inputs of the inverter, resulting in deviation of the maximum power point tracking monitored. The shadow of the wind turbine results in a total energy loss of about 6% for the given period, park configuration, PV modules, inverter type, and setting.
{"title":"Wind turbine dynamic shading: The effects on combined solar and wind farms","authors":"Nico J. Dekker, L. Slooff, Mark J. Jansen, Gertjan de Graaff, Jaco Hovius, R. Jonkman, Jesper Zuurbier, Jan Pronk","doi":"10.1063/5.0176121","DOIUrl":"https://doi.org/10.1063/5.0176121","url":null,"abstract":"The Dutch climate agreement anticipates the large-scale implementation of solar and wind energy systems on land and water. Combining solar and wind farms has the benefit of multiple surface area use, and it also has the advantage of energy generation from both solar and wind energy systems, which is rather complementary in time; thus, a better balance can be found between electricity generation and demand and the load on the electricity grid. In combined solar and wind farms (CSWFs), the turbines will cast shadows on the solar panels. This concerns the static shadow from the construction tower of the turbine as well as the dynamic shadow caused by the rotating blades. This paper reports on the results of millisecond data monitoring of the PV farm of a CSWF in the Netherlands on land. Static and dynamic shadow effects are discussed, as well as their dependency on farm design. It is observed that the dynamic shade of the wind turbine blade causes serious disturbances of the DC inputs of the inverter, resulting in deviation of the maximum power point tracking monitored. The shadow of the wind turbine results in a total energy loss of about 6% for the given period, park configuration, PV modules, inverter type, and setting.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139303836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The abundance and replenishment nature of solid biomass prompt fuel substitution for gasification and thermal power plants. However, many challenges are encountered while utilizing raw biomass, such as seasonality, strong hydrophilicity, low bulk and energy density, excess oxygen content, less compositional homogeneity, and poor grindability. It is, therefore, indispensable to augment the thermo-chemical properties of the solid biomass by performing suitable pretreatment. Among the various pretreatment techniques, non-oxidative torrefaction effectively upgrades solid biomass to coal-like fuel altering its physico-chemical properties. Therefore, in this work, torrefaction of rice husk and sugarcane bagasse have been performed in a fixed bed reactor by varying temperatures from 210–330 °C and residence time from 30–60 min under a non-oxidative environment. The experimental investigation illustrates a decrease in mass and energy yield of the biomass with a rise in temperature and residence time. Conversely, the higher heating value of rice husk and sugarcane bagasse has improved by 119.4% and 128.9%, respectively. The hydrogen-to-carbon (H/C) and oxygen-to-carbon (O/C) ratio of the torrefied biomass has reduced to enriched fuel variety as indicated by the van Krevelen plot. The decomposition and structural modifications were assessed using Fourier transform infrared spectroscopy, x-ray diffraction, and morphology analysis. Based on the experimental observations, it has been found that torrefaction of rice husk at 290 °C and 30 min and sugarcane bagasse at 270 °C and 30 min would generate enriched syngas using a dual fluidized bed gasification system. Furthermore, water gas shift reactions will be promoted to enhance the percentage of hydrogen in the gas mixture.
固体生物质的丰富性和可再生性促使其成为气化和热电厂的替代燃料。然而，在利用原料生物质时会遇到许多挑战，如季节性、亲水性强、体积密度和能量密度低、含氧量过高、成分不均匀以及研磨性差等。因此，通过适当的预处理来增强固体生物质的热化学特性是必不可少的。在各种预处理技术中，非氧化预处理技术能有效地将固体生物质升级为煤燃料，并改变其物理化学特性。因此，本研究在非氧化环境下，通过改变 210-330 °C 的温度和 30-60 分钟的停留时间，在固定床反应器中对稻壳和甘蔗渣进行了热解。实验结果表明，随着温度和停留时间的增加，生物质的质量和能量产量都有所下降。相反，稻壳和甘蔗渣的较高热值分别提高了 119.4% 和 128.9%。从 van Krevelen 图中可以看出，焙烧生物质的氢碳比（H/C）和氧碳比（O/C）已经降低，成为富燃料品种。傅立叶变换红外光谱、X 射线衍射和形态分析评估了分解和结构改性情况。根据实验观察发现，在双流化床气化系统中，稻壳在 290 °C 和 30 分钟的温度下，甘蔗渣在 270 °C 和 30 分钟的温度下，都能产生富合成气。此外，还将促进水气变换反应，以提高气体混合物中氢的比例。
{"title":"Enrichment of fuel properties of biomass using non-oxidative torrefaction for gasification","authors":"Rabindra Kangsha Banik, Pankaj Kalita","doi":"10.1063/5.0168553","DOIUrl":"https://doi.org/10.1063/5.0168553","url":null,"abstract":"The abundance and replenishment nature of solid biomass prompt fuel substitution for gasification and thermal power plants. However, many challenges are encountered while utilizing raw biomass, such as seasonality, strong hydrophilicity, low bulk and energy density, excess oxygen content, less compositional homogeneity, and poor grindability. It is, therefore, indispensable to augment the thermo-chemical properties of the solid biomass by performing suitable pretreatment. Among the various pretreatment techniques, non-oxidative torrefaction effectively upgrades solid biomass to coal-like fuel altering its physico-chemical properties. Therefore, in this work, torrefaction of rice husk and sugarcane bagasse have been performed in a fixed bed reactor by varying temperatures from 210–330 °C and residence time from 30–60 min under a non-oxidative environment. The experimental investigation illustrates a decrease in mass and energy yield of the biomass with a rise in temperature and residence time. Conversely, the higher heating value of rice husk and sugarcane bagasse has improved by 119.4% and 128.9%, respectively. The hydrogen-to-carbon (H/C) and oxygen-to-carbon (O/C) ratio of the torrefied biomass has reduced to enriched fuel variety as indicated by the van Krevelen plot. The decomposition and structural modifications were assessed using Fourier transform infrared spectroscopy, x-ray diffraction, and morphology analysis. Based on the experimental observations, it has been found that torrefaction of rice husk at 290 °C and 30 min and sugarcane bagasse at 270 °C and 30 min would generate enriched syngas using a dual fluidized bed gasification system. Furthermore, water gas shift reactions will be promoted to enhance the percentage of hydrogen in the gas mixture.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139305147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Q. Cao, Y. Chen, K. Zhang, X. Zhang, Z. Cheng, B. Wen
Rotor redesign approaches have been widely proposed to solve the thrust mismatch issue caused by scaling effects for basin model tests of horizontal axis floating wind turbines (FWTs). However, limited basin model tests utilized the thrust-matched rotor (TMR) to accurately evaluate the aerodynamic loads applying to the vertical axis FWTs. This paper described the detailed design approach of the TMR of floating straight-bladed vertical axis wind turbines (VAWTs) with a rated power of 5.3 MW. First, the AG455 airfoil was selected to replace the NACA0018 airfoil. AG455 airfoil can show a larger lift coefficient and a smaller drag coefficient at low Reynolds number. On this basis, the load distribution match algorithm was used to assign the blade pitch angle and chord length at each section of the blade. This method takes the spanwise load and load change rate of model-scaled blade and full-scaled blade as the constraint conditions. By adopting this method, the rotor thrust can be tailored to match the prototype values across a wide range of tip speed ratios. This design approach proves advantageous in assessing the aerodynamic performance of VAWTs under varying inflow wind speeds and unsteady wind conditions. The redesigned TMR model under low Reynolds number can meet Froude similarity criterion, which is helpful to improve the accuracy of vertical axis FWT model tests in the wave basin.
{"title":"Design approach of thrust-matched rotor for basin model tests of floating straight-bladed vertical axis wind turbines","authors":"Q. Cao, Y. Chen, K. Zhang, X. Zhang, Z. Cheng, B. Wen","doi":"10.1063/5.0176064","DOIUrl":"https://doi.org/10.1063/5.0176064","url":null,"abstract":"Rotor redesign approaches have been widely proposed to solve the thrust mismatch issue caused by scaling effects for basin model tests of horizontal axis floating wind turbines (FWTs). However, limited basin model tests utilized the thrust-matched rotor (TMR) to accurately evaluate the aerodynamic loads applying to the vertical axis FWTs. This paper described the detailed design approach of the TMR of floating straight-bladed vertical axis wind turbines (VAWTs) with a rated power of 5.3 MW. First, the AG455 airfoil was selected to replace the NACA0018 airfoil. AG455 airfoil can show a larger lift coefficient and a smaller drag coefficient at low Reynolds number. On this basis, the load distribution match algorithm was used to assign the blade pitch angle and chord length at each section of the blade. This method takes the spanwise load and load change rate of model-scaled blade and full-scaled blade as the constraint conditions. By adopting this method, the rotor thrust can be tailored to match the prototype values across a wide range of tip speed ratios. This design approach proves advantageous in assessing the aerodynamic performance of VAWTs under varying inflow wind speeds and unsteady wind conditions. The redesigned TMR model under low Reynolds number can meet Froude similarity criterion, which is helpful to improve the accuracy of vertical axis FWT model tests in the wave basin.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139300103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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