The inherent uncertainty of wind power always hampers difficulties in the development of wind energy and the smooth operation of power systems. Therefore, reliable ultra-short-term wind power prediction is crucial for the development of wind energy. In this research, a two-stage nonlinear ensemble paradigm based on double-layer decomposition technology, feature reconstruction, intelligent optimization algorithm, and deep learning is suggested to increase the prediction accuracy of ultra-short-term wind power. First, using two different signal decomposition techniques for processing can further filter out noise in the original signal and fully capture different features within it. Second, the multiple components obtained through double decomposition are reconstructed using sample entropy theory and reassembled into several feature subsequences with similar complexity to simplify the input variables of the prediction model. Finally, based on the idea of a two-stage prediction strategy, the cuckoo search algorithm and the attention mechanism optimized long- and short-term memory model are applied to the prediction of feature subsequences and nonlinear integration, respectively, to obtain the final prediction results. Two sets of data from wind farms in Liaoning Province, China are used for simulation experiments. The final empirical findings indicate that, in comparison to other models, the suggested wind power prediction model has a greater prediction accuracy.
{"title":"An attention mechanism based deep nonlinear ensemble paradigm of strengthened feature extraction method for wind power prediction","authors":"Jujie Wang, Yafen Liu","doi":"10.1063/5.0165151","DOIUrl":"https://doi.org/10.1063/5.0165151","url":null,"abstract":"The inherent uncertainty of wind power always hampers difficulties in the development of wind energy and the smooth operation of power systems. Therefore, reliable ultra-short-term wind power prediction is crucial for the development of wind energy. In this research, a two-stage nonlinear ensemble paradigm based on double-layer decomposition technology, feature reconstruction, intelligent optimization algorithm, and deep learning is suggested to increase the prediction accuracy of ultra-short-term wind power. First, using two different signal decomposition techniques for processing can further filter out noise in the original signal and fully capture different features within it. Second, the multiple components obtained through double decomposition are reconstructed using sample entropy theory and reassembled into several feature subsequences with similar complexity to simplify the input variables of the prediction model. Finally, based on the idea of a two-stage prediction strategy, the cuckoo search algorithm and the attention mechanism optimized long- and short-term memory model are applied to the prediction of feature subsequences and nonlinear integration, respectively, to obtain the final prediction results. Two sets of data from wind farms in Liaoning Province, China are used for simulation experiments. The final empirical findings indicate that, in comparison to other models, the suggested wind power prediction model has a greater prediction accuracy.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"32 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139299680","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}
Oscar Eraso, Daniela Bolaños, Nikolas Echeverri, Carolina Orozco Donneys, T. Ameri, Jose Dario Perea
Computer science implements algorithms and techniques to automate problem-solving solutions. Due to the chemical versatility of organic building blocks, many organic semiconductors have been utilized for organic solar cells (OSCs). The computational methods can potentially drive experimentalists to discover and design high-performance materials. OSCs' objective is the performance of their energy conversion efficiency and stability. One idea that has improved efficiency and stability is that of ternary systems, known as ternary organic solar cells (TOSCs). The photoactive layer in TOSCs is formed by mixing three distinct components together. This review is about the employment of computational approaches for investigating TOSCs. Here, we outlined the basics of computational methods and standard application procedures. This article offers a concise overview of various computational algorithms, relevant software, and tools. Additionally, it examines the present state of research regarding computations in TOSCs. The challenges associated with TOSCs, including intricacy metrics, diverse chemical structures, and programming skills, are discussed. Furthermore, we suggest some ways to improve the utility of computation in TOSCs research enterprises.
{"title":"A present scenario of the computational approaches for ternary organic solar cells","authors":"Oscar Eraso, Daniela Bolaños, Nikolas Echeverri, Carolina Orozco Donneys, T. Ameri, Jose Dario Perea","doi":"10.1063/5.0172426","DOIUrl":"https://doi.org/10.1063/5.0172426","url":null,"abstract":"Computer science implements algorithms and techniques to automate problem-solving solutions. Due to the chemical versatility of organic building blocks, many organic semiconductors have been utilized for organic solar cells (OSCs). The computational methods can potentially drive experimentalists to discover and design high-performance materials. OSCs' objective is the performance of their energy conversion efficiency and stability. One idea that has improved efficiency and stability is that of ternary systems, known as ternary organic solar cells (TOSCs). The photoactive layer in TOSCs is formed by mixing three distinct components together. This review is about the employment of computational approaches for investigating TOSCs. Here, we outlined the basics of computational methods and standard application procedures. This article offers a concise overview of various computational algorithms, relevant software, and tools. Additionally, it examines the present state of research regarding computations in TOSCs. The challenges associated with TOSCs, including intricacy metrics, diverse chemical structures, and programming skills, are discussed. Furthermore, we suggest some ways to improve the utility of computation in TOSCs research enterprises.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"49 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139300692","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 Chinese national government and local governments have introduced multiple incentive measures to increase the market share of new energy vehicles (NEVs), such as dual credit policy, financial subsidies, and building new charging infrastructures. However, the government's budget to support the development of NEVs is limited. In this paper, we consider a duopolistic market consisting of a gasoline vehicle (GV) firm and an electric vehicle (EV) firm and develop a multi-level game-theoretic model based on the fact that the Chinese government seeks to achieve a given market share target with the minimum expenditure. A comparison of the equilibrium results in three incentive schemes differentiated by the financial subsidy is conducted to investigate the effectiveness of various incentive schemes. Furthermore, we consider a real situation in China that the government attempts to achieve a target for the total number of charging infrastructures through a reasonable policy design. The results in this study reveal that, with the EV market share target in mind, raising the requirements of dual credit policy has no effect on the EV firm's profit under EV purchase subsidy, is beneficial to the EV firm's profit under construction cost sharing subsidy, and is detrimental to the EV firm's profit under per-unit construction subsidy. It is worthwhile for the government to subsidize for infrastructure construction effort rather than consumers' purchase cost. Given a target for the total number of charging infrastructures, construction cost sharing subsidy can provide more motivation for the EV firm to build charging infrastructures than per-unit construction subsidy.
{"title":"Optimal incentive schemes to achieve a given market share target for new energy vehicles under China's dual credit policy","authors":"Xinming Zang, Xiangfeng Ji, Hui Zhao, Xue Liu","doi":"10.1063/5.0171148","DOIUrl":"https://doi.org/10.1063/5.0171148","url":null,"abstract":"The Chinese national government and local governments have introduced multiple incentive measures to increase the market share of new energy vehicles (NEVs), such as dual credit policy, financial subsidies, and building new charging infrastructures. However, the government's budget to support the development of NEVs is limited. In this paper, we consider a duopolistic market consisting of a gasoline vehicle (GV) firm and an electric vehicle (EV) firm and develop a multi-level game-theoretic model based on the fact that the Chinese government seeks to achieve a given market share target with the minimum expenditure. A comparison of the equilibrium results in three incentive schemes differentiated by the financial subsidy is conducted to investigate the effectiveness of various incentive schemes. Furthermore, we consider a real situation in China that the government attempts to achieve a target for the total number of charging infrastructures through a reasonable policy design. The results in this study reveal that, with the EV market share target in mind, raising the requirements of dual credit policy has no effect on the EV firm's profit under EV purchase subsidy, is beneficial to the EV firm's profit under construction cost sharing subsidy, and is detrimental to the EV firm's profit under per-unit construction subsidy. It is worthwhile for the government to subsidize for infrastructure construction effort rather than consumers' purchase cost. Given a target for the total number of charging infrastructures, construction cost sharing subsidy can provide more motivation for the EV firm to build charging infrastructures than per-unit construction subsidy.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"189 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139292265","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}
Enhancing production of methane from cellulose is of utmost importance to improve the fermentation efficiency of lignocellulosic biomass. Antibiotics have demonstrated their ability to stimulate anaerobic digestion (AD) by influencing micro-organism activity. However, there has been limited research on the specific effect of antibiotics on cellulose AD. In this study, we investigated the impact of three antibiotics—tetracycline (TC), cephalexin (CLX), and azithromycin (AZM)—on cellulose AD when inoculated with waste sewage sludge. The results revealed that the presence of AZM and TC led to significantly higher methane yields, with increases of 51.94% and 34.96%, respectively, during a 20-day AD period. In contrast, the presence of CLX resulted in a 23.95% lower methane yield compared to the control. Furthermore, detailed analyses indicated that AZM had a positive influence on cellulose AD at all stages, including methanogenesis, acidogenesis/acetogenesis, and hydrolysis. On the other hand, TC primarily promoted AD during the methanogenesis stage. These findings collectively offer valuable guidance for efficiently transforming the energy potential of lignocellulosic wastes.
{"title":"Impact of antibiotics on methane produced from cellulose","authors":"Qili Zhu, Toshinari Maeda, Chenghan Chen, Yanwei Wang, Furong Tan, Guoquan Hu, Mingxiong He","doi":"10.1063/5.0175655","DOIUrl":"https://doi.org/10.1063/5.0175655","url":null,"abstract":"Enhancing production of methane from cellulose is of utmost importance to improve the fermentation efficiency of lignocellulosic biomass. Antibiotics have demonstrated their ability to stimulate anaerobic digestion (AD) by influencing micro-organism activity. However, there has been limited research on the specific effect of antibiotics on cellulose AD. In this study, we investigated the impact of three antibiotics—tetracycline (TC), cephalexin (CLX), and azithromycin (AZM)—on cellulose AD when inoculated with waste sewage sludge. The results revealed that the presence of AZM and TC led to significantly higher methane yields, with increases of 51.94% and 34.96%, respectively, during a 20-day AD period. In contrast, the presence of CLX resulted in a 23.95% lower methane yield compared to the control. Furthermore, detailed analyses indicated that AZM had a positive influence on cellulose AD at all stages, including methanogenesis, acidogenesis/acetogenesis, and hydrolysis. On the other hand, TC primarily promoted AD during the methanogenesis stage. These findings collectively offer valuable guidance for efficiently transforming the energy potential of lignocellulosic wastes.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"29 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139295334","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}
Low-fidelity wake models are used for wind farm design and control optimization. To generalize to a wind farm model, individually modeled wakes are commonly superimposed using approximate superposition models. Wake models parameterize atmospheric and wake turbulence, introducing unknown model parameters that historically are tuned with idealized simulation or experimental data and neglect uncertainty. We calibrate and estimate the uncertainty of the parameters in a Gaussian wake model using Markov chain Monte Carlo (MCMC) for various wake superposition methods. Posterior distributions of the uncertain parameters are generated using power production data from large eddy simulations and a utility-scale wake steering field experiment. The posteriors for the wake expansion coefficient are sensitive to the choice of superposition method, with relative differences in the means and standard deviations on the order of 100%. This sensitivity illustrates the role of superposition methods in wake modeling error. We compare these data-driven parameter estimates to estimates derived from a standard turbulence-intensity based model as a baseline. To assess predictive accuracy, we calibrate the data-driven parameter estimates with a training dataset for yaw-aligned operation. Using a Monte Carlo approach, we then generate predicted distributions of turbine power production and evaluate against a hold-out test dataset for yaw-misaligned operation. For the cases tested, the MCMC-calibrated parameters reduce the total error of the power predictions by roughly 50% compared to the deterministic empirical model predictions. An additional benefit of the data-driven parameter estimation is the quantification of uncertainty, which enables physically quantified confidence intervals of wake model predictions.
{"title":"Data-driven wake model parameter estimation to analyze effects of wake superposition","authors":"M. J. LoCascio, C. Gorlé, M. F. Howland","doi":"10.1063/5.0163896","DOIUrl":"https://doi.org/10.1063/5.0163896","url":null,"abstract":"Low-fidelity wake models are used for wind farm design and control optimization. To generalize to a wind farm model, individually modeled wakes are commonly superimposed using approximate superposition models. Wake models parameterize atmospheric and wake turbulence, introducing unknown model parameters that historically are tuned with idealized simulation or experimental data and neglect uncertainty. We calibrate and estimate the uncertainty of the parameters in a Gaussian wake model using Markov chain Monte Carlo (MCMC) for various wake superposition methods. Posterior distributions of the uncertain parameters are generated using power production data from large eddy simulations and a utility-scale wake steering field experiment. The posteriors for the wake expansion coefficient are sensitive to the choice of superposition method, with relative differences in the means and standard deviations on the order of 100%. This sensitivity illustrates the role of superposition methods in wake modeling error. We compare these data-driven parameter estimates to estimates derived from a standard turbulence-intensity based model as a baseline. To assess predictive accuracy, we calibrate the data-driven parameter estimates with a training dataset for yaw-aligned operation. Using a Monte Carlo approach, we then generate predicted distributions of turbine power production and evaluate against a hold-out test dataset for yaw-misaligned operation. For the cases tested, the MCMC-calibrated parameters reduce the total error of the power predictions by roughly 50% compared to the deterministic empirical model predictions. An additional benefit of the data-driven parameter estimation is the quantification of uncertainty, which enables physically quantified confidence intervals of wake model predictions.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135715173","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}
Mónica Zamora Zapata, Kari Lappalainen, Adam Kankiewicz, Jan Kleissl
The input of a solar inverter depends on multiple factors: the solar resource, weather conditions, and control strategies. Traditional design calculations specify the maximum current either as 125% of the rated module current or as the maximum 3 h average current from hourly simulations over a typical year, neglecting extreme irradiance conditions: cloud enhancement events that usually last minutes. Inverter power-limiting control strategies usually prevent extreme events to cause strong currents at the inverter, but in some cases, they can fail, leading to high currents. In this study, we aim to report how frequent and strong these high currents could be. We use 10 years of 1 min data from seven stations across the United States to estimate the photovoltaic string output through modeling the short-circuit current Isc, and the maximum-power point current Imp, and compare them to traditional inverter design values. We consider different configurations: minutely to hourly resolution; 5 min to 3 h averaging time intervals; monofacial and bifacial modules (with a case of enhanced albedo); and 3 fixed-tilt angles and horizontal single-axis tracking. The bifacial modules with enhanced albedo lead to the highest currents for 1 min data, exceeding 3 h averages by 53% for Isc and 38% for Imp. The 3 h average maxima surpass the conservative 125% design rule for bifacial modules. Inverter ratings at either a 200% of the rated current or 1.55 times the 3 h maximum could withstand all events regardless of control strategies. In summary, for some locations it is prudent to compare current design rules to subhourly simulations to guarantee the fault-free operation of solar PV plants.
{"title":"Comparing solar inverter design rules to subhourly solar resource simulations","authors":"Mónica Zamora Zapata, Kari Lappalainen, Adam Kankiewicz, Jan Kleissl","doi":"10.1063/5.0151042","DOIUrl":"https://doi.org/10.1063/5.0151042","url":null,"abstract":"The input of a solar inverter depends on multiple factors: the solar resource, weather conditions, and control strategies. Traditional design calculations specify the maximum current either as 125% of the rated module current or as the maximum 3 h average current from hourly simulations over a typical year, neglecting extreme irradiance conditions: cloud enhancement events that usually last minutes. Inverter power-limiting control strategies usually prevent extreme events to cause strong currents at the inverter, but in some cases, they can fail, leading to high currents. In this study, we aim to report how frequent and strong these high currents could be. We use 10 years of 1 min data from seven stations across the United States to estimate the photovoltaic string output through modeling the short-circuit current Isc, and the maximum-power point current Imp, and compare them to traditional inverter design values. We consider different configurations: minutely to hourly resolution; 5 min to 3 h averaging time intervals; monofacial and bifacial modules (with a case of enhanced albedo); and 3 fixed-tilt angles and horizontal single-axis tracking. The bifacial modules with enhanced albedo lead to the highest currents for 1 min data, exceeding 3 h averages by 53% for Isc and 38% for Imp. The 3 h average maxima surpass the conservative 125% design rule for bifacial modules. Inverter ratings at either a 200% of the rated current or 1.55 times the 3 h maximum could withstand all events regardless of control strategies. In summary, for some locations it is prudent to compare current design rules to subhourly simulations to guarantee the fault-free operation of solar PV plants.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135433721","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}
Louiza Ait Mouloud, Aissa Kheldoun, Abdelhakim Deboucha, Saad Mekhilef
Accurate prediction of solar irradiance is essential for the successful integration of solar power plants into electrical systems. Despite recent advancements in deep learning technology yielding impressive results in solar forecasting, their lack of interpretability has hindered their widespread adoption. In this paper, we propose a novel approach that integrates a Temporal Fusion Transformer (TFT) with a McClear model to achieve accurate and interpretable forecasting performance. The TFT is a deep learning model that provides transparency in its predictions through the use of interpretable self-attention layers for long-term dependencies, recurrent layers for local processing, specialized components for feature selection, and gating layers to suppress extraneous components. The model is capable of learning temporal associations between continuous time-series variables, namely, historical global horizontal irradiance (GHI) and clear sky GHI, accounting for cloud cover variability and clear sky conditions that are often ignored by most machine learning solar forecasters. Additionally, it minimizes a quantile loss during training to produce accurate probabilistic forecasts. In this study, we evaluate the performance of hourly GHI forecasts on eight diverse datasets with varying climates: temperate, cold, arid, and equatorial, for multiple temporal horizons of 2, 3, 6, 12, and 24 h. The model is benchmarked against both climatological persistence for deterministic forecasting and Complete History Persistence Ensemble for probabilistic forecasting. To prove that our model is not location locked, it has been blind tested on four completely different datasets. The results demonstrate that the proposed model outperforms its counterparts across all forecast horizons.
{"title":"Explainable forecasting of global horizontal irradiance over multiple time steps using temporal fusion transformer","authors":"Louiza Ait Mouloud, Aissa Kheldoun, Abdelhakim Deboucha, Saad Mekhilef","doi":"10.1063/5.0159899","DOIUrl":"https://doi.org/10.1063/5.0159899","url":null,"abstract":"Accurate prediction of solar irradiance is essential for the successful integration of solar power plants into electrical systems. Despite recent advancements in deep learning technology yielding impressive results in solar forecasting, their lack of interpretability has hindered their widespread adoption. In this paper, we propose a novel approach that integrates a Temporal Fusion Transformer (TFT) with a McClear model to achieve accurate and interpretable forecasting performance. The TFT is a deep learning model that provides transparency in its predictions through the use of interpretable self-attention layers for long-term dependencies, recurrent layers for local processing, specialized components for feature selection, and gating layers to suppress extraneous components. The model is capable of learning temporal associations between continuous time-series variables, namely, historical global horizontal irradiance (GHI) and clear sky GHI, accounting for cloud cover variability and clear sky conditions that are often ignored by most machine learning solar forecasters. Additionally, it minimizes a quantile loss during training to produce accurate probabilistic forecasts. In this study, we evaluate the performance of hourly GHI forecasts on eight diverse datasets with varying climates: temperate, cold, arid, and equatorial, for multiple temporal horizons of 2, 3, 6, 12, and 24 h. The model is benchmarked against both climatological persistence for deterministic forecasting and Complete History Persistence Ensemble for probabilistic forecasting. To prove that our model is not location locked, it has been blind tested on four completely different datasets. The results demonstrate that the proposed model outperforms its counterparts across all forecast horizons.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135688549","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}
Qahtan A. Abed, Dhafer M. Hachim, Adrian Ciocănea, Viorel Badescu
The air is heated in an unglazed transpired collector (UTC) from three different regions of the perforated plate: from the front of the plate, from the back of the plate, and from the inner surface of the holes. The paper focuses on the relative contribution of each of these three regions, denoted r1, r2, and r3, respectively, to the total increase in the air temperature. A hybrid approach is used: it combines experimental results with results obtained by computational fluid dynamics simulations. Under no-wind conditions, the largest part of the heat received by the air comes from the front of the plate (r1 is about 60%). The second largest part of the heat received comes from the back of the plate (r2 ranges between 25% and 30%). The inner part of the holes contributes to the heat received by the air with a fraction r3 ranging between 10% and 15%. These percentages are rather constant during the day. r1 slightly decreases, while r2 slightly increases along the UTC. The influence of the wind direction on the values of r1, r2, and r3 is not significant. However, the influence of the wind speed magnitude is significant. When the wind speed increases from 0 to 1 m/s, r1 increases from 60% to about 75%, while r2 decreases from 25%–30% to about 15%. For a wind speed of 1 m/s, the values of r1 and r2 are quite the same along the UTC.
{"title":"The useful heat flux provided by the perforated plate of unglazed transpired collectors under no-wind and windy conditions","authors":"Qahtan A. Abed, Dhafer M. Hachim, Adrian Ciocănea, Viorel Badescu","doi":"10.1063/5.0165313","DOIUrl":"https://doi.org/10.1063/5.0165313","url":null,"abstract":"The air is heated in an unglazed transpired collector (UTC) from three different regions of the perforated plate: from the front of the plate, from the back of the plate, and from the inner surface of the holes. The paper focuses on the relative contribution of each of these three regions, denoted r1, r2, and r3, respectively, to the total increase in the air temperature. A hybrid approach is used: it combines experimental results with results obtained by computational fluid dynamics simulations. Under no-wind conditions, the largest part of the heat received by the air comes from the front of the plate (r1 is about 60%). The second largest part of the heat received comes from the back of the plate (r2 ranges between 25% and 30%). The inner part of the holes contributes to the heat received by the air with a fraction r3 ranging between 10% and 15%. These percentages are rather constant during the day. r1 slightly decreases, while r2 slightly increases along the UTC. The influence of the wind direction on the values of r1, r2, and r3 is not significant. However, the influence of the wind speed magnitude is significant. When the wind speed increases from 0 to 1 m/s, r1 increases from 60% to about 75%, while r2 decreases from 25%–30% to about 15%. For a wind speed of 1 m/s, the values of r1 and r2 are quite the same along the UTC.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135686845","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}
Kun Ding, Changhai Yang, Zhuxiu Wang, Chunjuan Zhao
With the rapid development of renewable energy, the integration of multiple power sources into combined power generation systems has emerged as an efficient approach for the energy utilization. Pumped storage power stations, as large-capacity flexible energy storage equipment, play a crucial role in peak load shifting, valley filling, and the promotion of new energy consumption. This study focuses on the combined pumped storage-wind-photovoltaic-thermal generation system and addresses the challenges posed by fluctuating output of wind and photovoltaic sources. First, a K-means clustering analysis technology has been introduced to identify the typical daily scene output and load fluctuation patterns in an energy base in northwest China. Based on the operation constraints of each subsystem, aiming at the optimal comprehensive benefit, minimum generalized load fluctuation, and minimum carbon emission, an operation optimization scheduling model for the pumped storage-wind-photovoltaic-thermal combined power generation system has been established. When the optimization model has a configuration scale of 3000 MW for wind power and 2800 MW for photovoltaics, the pumped storage power station in the combined power generation system can achieve full pumping for 4 h and full generation for 5 h, which plays an obvious role in peak and valley regulation. Meanwhile, the combined system minimizes operating costs and carbon emissions, resulting in a minimum fluctuation of thermal power output by 6.6%. Furthermore, different capacity configurations demonstrate a non-linear relationship between the comprehensive benefits, carbon emissions, and the scene penetration rate. When prioritizing economic stability over carbon emissions, a thermal power capacity configuration of 7200 MW leads to the lowest total operating cost for the combined system, amounting to 26.38 million ¥. Results indicate that pumped storage effectively suppresses grid power fluctuations, promotes the consumption of renewable energy sources, and enhances the stability of thermal power output.
{"title":"Optimal scheduling of combined pumped storage-wind-photovoltaic-thermal generation system considering the characteristics of source and load","authors":"Kun Ding, Changhai Yang, Zhuxiu Wang, Chunjuan Zhao","doi":"10.1063/5.0157303","DOIUrl":"https://doi.org/10.1063/5.0157303","url":null,"abstract":"With the rapid development of renewable energy, the integration of multiple power sources into combined power generation systems has emerged as an efficient approach for the energy utilization. Pumped storage power stations, as large-capacity flexible energy storage equipment, play a crucial role in peak load shifting, valley filling, and the promotion of new energy consumption. This study focuses on the combined pumped storage-wind-photovoltaic-thermal generation system and addresses the challenges posed by fluctuating output of wind and photovoltaic sources. First, a K-means clustering analysis technology has been introduced to identify the typical daily scene output and load fluctuation patterns in an energy base in northwest China. Based on the operation constraints of each subsystem, aiming at the optimal comprehensive benefit, minimum generalized load fluctuation, and minimum carbon emission, an operation optimization scheduling model for the pumped storage-wind-photovoltaic-thermal combined power generation system has been established. When the optimization model has a configuration scale of 3000 MW for wind power and 2800 MW for photovoltaics, the pumped storage power station in the combined power generation system can achieve full pumping for 4 h and full generation for 5 h, which plays an obvious role in peak and valley regulation. Meanwhile, the combined system minimizes operating costs and carbon emissions, resulting in a minimum fluctuation of thermal power output by 6.6%. Furthermore, different capacity configurations demonstrate a non-linear relationship between the comprehensive benefits, carbon emissions, and the scene penetration rate. When prioritizing economic stability over carbon emissions, a thermal power capacity configuration of 7200 MW leads to the lowest total operating cost for the combined system, amounting to 26.38 million ¥. Results indicate that pumped storage effectively suppresses grid power fluctuations, promotes the consumption of renewable energy sources, and enhances the stability of thermal power output.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135735570","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}
Thanh Thuy Trinh, Cam Phu Thi Nguyen, Chi-Hieu Nguyen, Ngo Thi Thanh Giang, Phuong T. K. Nguyen, Junsin Yi, Vinh-Ai Dao
Finding the optimal condition from a wide range of cell fabrication conditions and design parameters is typically a time-consuming and cumbersome task. In this study, the combination of the Taguchi approach and Grey relational analysis was employed for optimization of the conversion efficiency of hydrogenated amorphous silicon/crystalline silicon heterojunction (a-Si:H/c-Si HJ) solar cells. With the help of the Taguchi method via an orthogonal array, the reconstruction of the impact of input parameters on single performance characteristics is still ensured while reducing the number of simulations by 99.8%. The simulated results suggested that the density of interfacial defects (Dit) plays a key role in obtaining a high open-circuit voltage (Voc) and fill factor (FF), respectively. Meanwhile, the emitter thickness is the dominant factor in achieving a high short-circuit current density (Jsc). As a result, these two factors dominate the conversion efficiency. Furthermore, the overall optimal condition is also obtained by the Grey relational analysis. The simplified HJ cell configuration using this optimal condition displayed the highest conversion efficiency of 25.86%, yielding a 2.25% absolute increase in efficiency compared to the initial condition. The results highlight the effectiveness of our proposed approach in reducing the number of experiments needed for cell optimization.
{"title":"Optimal performances of a-Si:H/c-Si heterojunction silicon solar cells based on a statistical approach","authors":"Thanh Thuy Trinh, Cam Phu Thi Nguyen, Chi-Hieu Nguyen, Ngo Thi Thanh Giang, Phuong T. K. Nguyen, Junsin Yi, Vinh-Ai Dao","doi":"10.1063/5.0159362","DOIUrl":"https://doi.org/10.1063/5.0159362","url":null,"abstract":"Finding the optimal condition from a wide range of cell fabrication conditions and design parameters is typically a time-consuming and cumbersome task. In this study, the combination of the Taguchi approach and Grey relational analysis was employed for optimization of the conversion efficiency of hydrogenated amorphous silicon/crystalline silicon heterojunction (a-Si:H/c-Si HJ) solar cells. With the help of the Taguchi method via an orthogonal array, the reconstruction of the impact of input parameters on single performance characteristics is still ensured while reducing the number of simulations by 99.8%. The simulated results suggested that the density of interfacial defects (Dit) plays a key role in obtaining a high open-circuit voltage (Voc) and fill factor (FF), respectively. Meanwhile, the emitter thickness is the dominant factor in achieving a high short-circuit current density (Jsc). As a result, these two factors dominate the conversion efficiency. Furthermore, the overall optimal condition is also obtained by the Grey relational analysis. The simplified HJ cell configuration using this optimal condition displayed the highest conversion efficiency of 25.86%, yielding a 2.25% absolute increase in efficiency compared to the initial condition. The results highlight the effectiveness of our proposed approach in reducing the number of experiments needed for cell optimization.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135587925","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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