Accurate minute solar forecasts play an increasingly crucial role in achieving optimal intra-day power grid dispatch. However, continuous changes in cloud distribution and coverage pose a challenge to solar forecasting. This study presents a convolutional neural network-long short-term memory (CNN-LSTM) model to predict the future 10-min global horizontal irradiance (GHI) integrating all-sky image (ASI) and GHI sequences as input. The CNN is used to extract the sky features from ASI and a fully connected layer is used to extract historical GHI information. The resulting temporary information outputs are then merged and forwarded to the LSTM for forecasting the GHI values for the next 10 min. Compared to CNN solar radiation forecasting models, incorporating GHI into the forecasting process leads to an improvement of 18% in the accuracy of forecasting GHI values for the next 10 min. This improvement can be attributed to the inclusion of historical GHI sequences and regression via LSTM. The historical GHI contains valuable meteorological information such as aerosol optical thickness. In addition, the sensitivity analysis shows that the 1-lagged input length of the GHI and ASI sequence yields the most accurate forecasts. The advantages of CNN-LSTM facilitate power system stability and economic operation. Codes of the CNN-LSTM model in the public domain are available online on the GitHub repository https://github.com/zoey0919/CNN-LSTM-for-GHI-forecasting.
{"title":"Ultra-short-term forecasting of global horizontal irradiance (GHI) integrating all-sky images and historical sequences","authors":"Hui-Min Zuo, Jun Qiu, Fang-Fang Li","doi":"10.1063/5.0163759","DOIUrl":"https://doi.org/10.1063/5.0163759","url":null,"abstract":"Accurate minute solar forecasts play an increasingly crucial role in achieving optimal intra-day power grid dispatch. However, continuous changes in cloud distribution and coverage pose a challenge to solar forecasting. This study presents a convolutional neural network-long short-term memory (CNN-LSTM) model to predict the future 10-min global horizontal irradiance (GHI) integrating all-sky image (ASI) and GHI sequences as input. The CNN is used to extract the sky features from ASI and a fully connected layer is used to extract historical GHI information. The resulting temporary information outputs are then merged and forwarded to the LSTM for forecasting the GHI values for the next 10 min. Compared to CNN solar radiation forecasting models, incorporating GHI into the forecasting process leads to an improvement of 18% in the accuracy of forecasting GHI values for the next 10 min. This improvement can be attributed to the inclusion of historical GHI sequences and regression via LSTM. The historical GHI contains valuable meteorological information such as aerosol optical thickness. In addition, the sensitivity analysis shows that the 1-lagged input length of the GHI and ASI sequence yields the most accurate forecasts. The advantages of CNN-LSTM facilitate power system stability and economic operation. Codes of the CNN-LSTM model in the public domain are available online on the GitHub repository https://github.com/zoey0919/CNN-LSTM-for-GHI-forecasting.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"25 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":"135249086","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}
Wind turbine layout design has an important impact on the energy production and economic benefits of wind farms. The wind resource grid data include the realistic wind distributions of the wind farm. Combined with the Jensen wake model, it can be used to calculate the net production considering the wake effect of turbines. Based on the wind resource grid data and taking net energy production as the objective function, this paper proposes a random search algorithm for wind turbine layout optimization. The algorithm couples the random function with multiple optimization parameters and optimizes the wind turbine layout by considering restriction conditions of area and minimum turbine spacings. According to the results of the case study in an actual wind farm, the optimization processes using the proposed algorithm have high calculation efficiency and stability. The sensitivity analysis of parameters indicates that the effect of optimization calculation can be effectively improved by appropriately increasing the turbine coordinate searching range or the number of random operations within one single search.
{"title":"Research on a random search algorithm for wind turbine layout optimization","authors":"Huaiwu Peng, Wei Zhu, Haitao Ma, Huaxiang Li, Rikui Zhang, Kang Chen","doi":"10.1063/5.0159271","DOIUrl":"https://doi.org/10.1063/5.0159271","url":null,"abstract":"Wind turbine layout design has an important impact on the energy production and economic benefits of wind farms. The wind resource grid data include the realistic wind distributions of the wind farm. Combined with the Jensen wake model, it can be used to calculate the net production considering the wake effect of turbines. Based on the wind resource grid data and taking net energy production as the objective function, this paper proposes a random search algorithm for wind turbine layout optimization. The algorithm couples the random function with multiple optimization parameters and optimizes the wind turbine layout by considering restriction conditions of area and minimum turbine spacings. According to the results of the case study in an actual wind farm, the optimization processes using the proposed algorithm have high calculation efficiency and stability. The sensitivity analysis of parameters indicates that the effect of optimization calculation can be effectively improved by appropriately increasing the turbine coordinate searching range or the number of random operations within one single search.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"30 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":"135297907","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}
Due to the volatility and intermittency of renewable energy, injecting large amounts of renewable energy into the grid will have a tremendous impact on the stability and security of the network. In this paper, we propose the hierarchical energy optimization of flywheel energy storage array system (FESAS) applied to smooth the power output of wind farms to realize source-grid-storage intelligent dispatching. The energy dispatching problem of the FESAS is described as a Markov decision process by the actor-critic (AC) algorithm. In order to solve the problems of stability and low sampling efficiency of the AC algorithm, the soft actor-critic (SAC) algorithm, a deep reinforcement learning (DRL) algorithm based on the model-free off-policy method of the maximum entropy framework, is adopted. Furthermore, SAC and prioritized experience replay (PER) are utilized to greatly improve learning efficiency and sample utilization. The experimental results show that SAC-PER has better performance and stability in energy optimization of the FESAS.
{"title":"Hierarchical energy optimization of flywheel energy storage array systems for wind farms based on deep reinforcement learning","authors":"Zhanqiang Zhang, Keqilao Meng, Yu Li, Qing Liu, Huijuan Wu","doi":"10.1063/5.0141817","DOIUrl":"https://doi.org/10.1063/5.0141817","url":null,"abstract":"Due to the volatility and intermittency of renewable energy, injecting large amounts of renewable energy into the grid will have a tremendous impact on the stability and security of the network. In this paper, we propose the hierarchical energy optimization of flywheel energy storage array system (FESAS) applied to smooth the power output of wind farms to realize source-grid-storage intelligent dispatching. The energy dispatching problem of the FESAS is described as a Markov decision process by the actor-critic (AC) algorithm. In order to solve the problems of stability and low sampling efficiency of the AC algorithm, the soft actor-critic (SAC) algorithm, a deep reinforcement learning (DRL) algorithm based on the model-free off-policy method of the maximum entropy framework, is adopted. Furthermore, SAC and prioritized experience replay (PER) are utilized to greatly improve learning efficiency and sample utilization. The experimental results show that SAC-PER has better performance and stability in energy optimization of the FESAS.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47204486","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 response to the shortcomings of existing image detection algorithms in the early damage detection of wind turbine blades, such as insufficient applicability and unsatisfactory detection results, this paper proposes an improved DINO (DETR with improved denoizing anchor boxes for end-to-end object detection) model for wind turbine blade damage detection called WTB-DINO. The improvement strategy of the DINO model is obtained by collecting and analyzing unmanned aerial vehicle (UAV) daily inspection image data in wind farms. First, the lightweight design of DINO's feature extraction backbone is implemented to meet the requirement of fast and effective video inspection by drones. Based on this, the Focus down-sampling and enhanced channel attention mechanism are incorporated into the model to enhance the feature extraction ability of the Backbone for damaged areas according to the characteristics of wind turbine blade images. Second, a parallel encoder structure is built, and a multi-head attention mechanism is used to model the relationship between samples for each type of damage with uneven distribution in the dataset to improve the feature modeling effect of the model for less-sample damage categories. Experimental results show that the WTB-DINO model achieves a detection precision and recall rate of up to 93.2% and 93.6% for wind turbine blade damage, respectively, while maintaining a high frame rate of 27 frames per second. Therefore, the proposed WTB-DINO model can accurately and in real-time classify and locate damaged areas in wind turbine blade images obtained by UAVs.
{"title":"Early stage damage detection of wind turbine blades based on UAV images and deep learning","authors":"Ruxin Gao, Yongfei Ma, Teng Wang","doi":"10.1063/5.0157624","DOIUrl":"https://doi.org/10.1063/5.0157624","url":null,"abstract":"In response to the shortcomings of existing image detection algorithms in the early damage detection of wind turbine blades, such as insufficient applicability and unsatisfactory detection results, this paper proposes an improved DINO (DETR with improved denoizing anchor boxes for end-to-end object detection) model for wind turbine blade damage detection called WTB-DINO. The improvement strategy of the DINO model is obtained by collecting and analyzing unmanned aerial vehicle (UAV) daily inspection image data in wind farms. First, the lightweight design of DINO's feature extraction backbone is implemented to meet the requirement of fast and effective video inspection by drones. Based on this, the Focus down-sampling and enhanced channel attention mechanism are incorporated into the model to enhance the feature extraction ability of the Backbone for damaged areas according to the characteristics of wind turbine blade images. Second, a parallel encoder structure is built, and a multi-head attention mechanism is used to model the relationship between samples for each type of damage with uneven distribution in the dataset to improve the feature modeling effect of the model for less-sample damage categories. Experimental results show that the WTB-DINO model achieves a detection precision and recall rate of up to 93.2% and 93.6% for wind turbine blade damage, respectively, while maintaining a high frame rate of 27 frames per second. Therefore, the proposed WTB-DINO model can accurately and in real-time classify and locate damaged areas in wind turbine blade images obtained by UAVs.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45692001","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 this paper, a small scale biomass gasification based solid oxide fuel cell/gas turbine (SOFC/GT) combined heat and power (CHP) plant is investigated by means of both conventional and advanced exergy and exergoeconomic analysis. A one-dimensional model of an internal reforming planner SOFC is employed to account for the temperature gradient within the fuel cell solid structure, which is maintained at the maximum allowable temperature gradient (150 K) under different operating conditions. Two main parameters of the gasification process, namely, air-to-steam ratio and modified equivalence ratio, are investigated, and the key parameters of the cycle exergy and exergoeconomic study are analyzed. Moreover, a multi-objective optimization procedure is applied to determine the unavoidable gasifier conditions required for the advanced exergy analysis of the system. The results of the conventional exergy and exergoeconomic analysis reveal that the highest rate of exergy destruction occurs in the gasifier, followed by the afterburner (AB) with 41.87% and 21.98%, respectively. Also, the lowest exergoeconomic factor is related to AB by 5.34%, followed by heat recovery steam generator (HRSG), gasifier, air compressor, and SOFC, which implies that the priority is to improve these components to reduce the exergy destruction cost rate. The results obtained from the advanced exergy and exergoeconomic analysis indicate that the most of the total exergy destruction rate is unavoidably in the CHP plant. The AB shows the least improvement potential in terms of reduction of the exergy destruction by almost 2% avoidable part, followed by Heat Exchanger 3 (H.X.3), gasifier, and SOFC duo to their lowest avoidable exergy destruction parts of almost 5%, 10% and 13%f respectively. Furthermore, the unavoidable part of the investment cost rate for all the components of the cogeneration plant is larger than the avoidable part, which means that it is difficult to reduce the investment cost rate of the system components. Meanwhile, the endogenous/exogenous analysis shows that the exergy destruction is completely endogenous for all components of the integrated plant, except for HRSG, GT, and HX1. Compressors and turbines have the highest potential to reduce endogenous exergy destruction. This is due to their higher avoidable endogenous exergy destruction. Reducing the investment cost rate seems difficult, as the main investment cost rate was found to be an unavoidable endogenous part for all system components. Finally, some results obtained from the advanced analysis approach are the opposite to those of the conventional method. This fact emphasizes that the results of conventional exergy analysis alone are insufficient and unreliable. For example, based on the advanced analysis perspective, the gas turbine and H.X.2 by 8.9% and 8.46% modified exergoeconomic factor, respectively, should be considered for reducing investment cost rate, while the conventional method gives opposite
{"title":"Conventional and advanced exergy and exergoeconomic analysis of a biomass gasification based SOFC/GT cogeneration system","authors":"Reza Najar, A. Kazemi, M. Borji, M. Nikian","doi":"10.1063/5.0159977","DOIUrl":"https://doi.org/10.1063/5.0159977","url":null,"abstract":"In this paper, a small scale biomass gasification based solid oxide fuel cell/gas turbine (SOFC/GT) combined heat and power (CHP) plant is investigated by means of both conventional and advanced exergy and exergoeconomic analysis. A one-dimensional model of an internal reforming planner SOFC is employed to account for the temperature gradient within the fuel cell solid structure, which is maintained at the maximum allowable temperature gradient (150 K) under different operating conditions. Two main parameters of the gasification process, namely, air-to-steam ratio and modified equivalence ratio, are investigated, and the key parameters of the cycle exergy and exergoeconomic study are analyzed. Moreover, a multi-objective optimization procedure is applied to determine the unavoidable gasifier conditions required for the advanced exergy analysis of the system. The results of the conventional exergy and exergoeconomic analysis reveal that the highest rate of exergy destruction occurs in the gasifier, followed by the afterburner (AB) with 41.87% and 21.98%, respectively. Also, the lowest exergoeconomic factor is related to AB by 5.34%, followed by heat recovery steam generator (HRSG), gasifier, air compressor, and SOFC, which implies that the priority is to improve these components to reduce the exergy destruction cost rate. The results obtained from the advanced exergy and exergoeconomic analysis indicate that the most of the total exergy destruction rate is unavoidably in the CHP plant. The AB shows the least improvement potential in terms of reduction of the exergy destruction by almost 2% avoidable part, followed by Heat Exchanger 3 (H.X.3), gasifier, and SOFC duo to their lowest avoidable exergy destruction parts of almost 5%, 10% and 13%f respectively. Furthermore, the unavoidable part of the investment cost rate for all the components of the cogeneration plant is larger than the avoidable part, which means that it is difficult to reduce the investment cost rate of the system components. Meanwhile, the endogenous/exogenous analysis shows that the exergy destruction is completely endogenous for all components of the integrated plant, except for HRSG, GT, and HX1. Compressors and turbines have the highest potential to reduce endogenous exergy destruction. This is due to their higher avoidable endogenous exergy destruction. Reducing the investment cost rate seems difficult, as the main investment cost rate was found to be an unavoidable endogenous part for all system components. Finally, some results obtained from the advanced analysis approach are the opposite to those of the conventional method. This fact emphasizes that the results of conventional exergy analysis alone are insufficient and unreliable. For example, based on the advanced analysis perspective, the gas turbine and H.X.2 by 8.9% and 8.46% modified exergoeconomic factor, respectively, should be considered for reducing investment cost rate, while the conventional method gives opposite","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45408109","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 challenge of harmonizing the integration of renewable energy in market-driven transactions and assured accommodations presents a predicament in the development of China's electricity spot market. Moreover, as renewable energy penetration escalates, the issue of reserve undeliverability due to transmission congestion diminishes the power system's capacity to utilize renewable energy resources. To address this concern, this study introduces a secondary clearing mechanism for the electricity spot market, taking into account the proportion of renewable energy consumption quotas. Based on the first clearing, when renewable curtailment occurs, the bid pricing of abandoned power units undergoes flexible adjustment through the optimization of the price correction coefficient, followed by the execution of a secondary clearing utilizing the revised bidding information to fulfill the stipulations of the renewable energy consumption quota ratio. Drawing on the outcomes of the two-stage clearing, an incentive-compatible settlement compensation mechanism is proposed to preserve the impartiality of the market operator. The spot market clearing model accounts for the transmission safety margin, effectively mitigating the likelihood of transmission congestion, reserve inaccessibility, and renewable energy curtailment issues in real-time dispatching. Finally, a modified IEEE 30-bus system serves to substantiate the efficacy of the proposed market mechanism.
{"title":"Study on the electricity spot market trading mechanism considering the proportion of renewable energy consumption quota","authors":"Yujian Yang, Yuewen Jiang","doi":"10.1063/5.0155007","DOIUrl":"https://doi.org/10.1063/5.0155007","url":null,"abstract":"The challenge of harmonizing the integration of renewable energy in market-driven transactions and assured accommodations presents a predicament in the development of China's electricity spot market. Moreover, as renewable energy penetration escalates, the issue of reserve undeliverability due to transmission congestion diminishes the power system's capacity to utilize renewable energy resources. To address this concern, this study introduces a secondary clearing mechanism for the electricity spot market, taking into account the proportion of renewable energy consumption quotas. Based on the first clearing, when renewable curtailment occurs, the bid pricing of abandoned power units undergoes flexible adjustment through the optimization of the price correction coefficient, followed by the execution of a secondary clearing utilizing the revised bidding information to fulfill the stipulations of the renewable energy consumption quota ratio. Drawing on the outcomes of the two-stage clearing, an incentive-compatible settlement compensation mechanism is proposed to preserve the impartiality of the market operator. The spot market clearing model accounts for the transmission safety margin, effectively mitigating the likelihood of transmission congestion, reserve inaccessibility, and renewable energy curtailment issues in real-time dispatching. Finally, a modified IEEE 30-bus system serves to substantiate the efficacy of the proposed market mechanism.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46479454","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}
Marcelo Pinho Almeida, Alex Renan Arrifano Manito, Gilberto Figueiredo Pinto Filho, R. Zilles
This paper presents a computational tool based on a genetic algorithm and artificial neural network for optimizing the operation of isolated diesel-photovoltaic-battery hybrid power systems using day-ahead power forecasts obtained with quantile random forests. The optimization tool was conceived to be flexible, i.e., it can be used to operate isolated power systems with multiple configurations of diesel generator sets (DGS), to work with a reduced number of input data, and to be as simple as possible to be used. The optimization relies on combining valley-filling and peak-shaving strategies using battery energy storage systems while considering the combined forecast of demand and photovoltaic (PV) generation. The tool also simulates the behavior of the DGS to define the optimum arrangement of diesel generators considering the variability of both demand and PV generation. The output consists of hourly values of energy storage power dispatch, DGS arrangement, and, if necessary, load shedding and/or PV curtailment. The algorithm that implements the optimization tool, which is currently in the phase of field-test in the isolated diesel-photovoltaic-battery hybrid power system of Fernando de Noronha, Brazil, demonstrated a good performance in computer simulations validated with real measured data.
本文提出了一种基于遗传算法和人工神经网络的计算工具，用于利用分位数随机森林获得的日前功率预测来优化孤立型柴油-光伏-电池混合动力系统的运行。优化工具被认为是灵活的，即它可以用于运行具有多种配置的柴油发电机组(DGS)的隔离电力系统，减少输入数据的数量，并且使用起来尽可能简单。在考虑需求和光伏发电组合预测的同时，利用电池储能系统将填谷和调峰策略结合起来进行优化。该工具还模拟了DGS的行为，以确定考虑需求和光伏发电的可变性的柴油发电机的最佳配置。输出包括储能功率调度、DGS安排以及必要时的减载和/或光伏弃风的小时值。实现优化工具的算法目前正在巴西Fernando de Noronha的孤立柴油-光伏-电池混合动力系统中进行现场测试，在计算机模拟中显示出良好的性能，并得到了实际测量数据的验证。
{"title":"Optimization tool for operating isolated diesel-photovoltaic-battery hybrid power systems using day-ahead power forecasts","authors":"Marcelo Pinho Almeida, Alex Renan Arrifano Manito, Gilberto Figueiredo Pinto Filho, R. Zilles","doi":"10.1063/5.0156371","DOIUrl":"https://doi.org/10.1063/5.0156371","url":null,"abstract":"This paper presents a computational tool based on a genetic algorithm and artificial neural network for optimizing the operation of isolated diesel-photovoltaic-battery hybrid power systems using day-ahead power forecasts obtained with quantile random forests. The optimization tool was conceived to be flexible, i.e., it can be used to operate isolated power systems with multiple configurations of diesel generator sets (DGS), to work with a reduced number of input data, and to be as simple as possible to be used. The optimization relies on combining valley-filling and peak-shaving strategies using battery energy storage systems while considering the combined forecast of demand and photovoltaic (PV) generation. The tool also simulates the behavior of the DGS to define the optimum arrangement of diesel generators considering the variability of both demand and PV generation. The output consists of hourly values of energy storage power dispatch, DGS arrangement, and, if necessary, load shedding and/or PV curtailment. The algorithm that implements the optimization tool, which is currently in the phase of field-test in the isolated diesel-photovoltaic-battery hybrid power system of Fernando de Noronha, Brazil, demonstrated a good performance in computer simulations validated with real measured data.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47048140","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 need for decarbonization and diversification of energy resources has led to the development of integrated energy systems (IESs), where multiple resources supply more than one energy sector. One such IES with small modular nuclear reactors and renewables (wind and solar) as generating resources, catering to the demand of the electric grid while producing hydrogen for industries, is modeled in this paper. The physics-based component models are represented using the Modelica language and interconnected to form the IES. The control and coordination of the overall system are ensured by designing a suitable control architecture composed of individual subsystem-level controls and supervisory control. The dynamic performance and the load-following capability of the IES are evaluated, while satisfying the safe operational limits of the components. Different configurations and modes of IES operation are considered, where the adaptability of the control system in the presence of varying demands and renewable generations is validated. The simulation results indicate that hydrogen as a flexible load facilitates the supply of varying grid demand. Additionally, the renewables are also accommodated into the IES owing to the flexibility of the balance of plant associated with the nuclear reactors.
{"title":"Modeling and control of nuclear–renewable integrated energy systems: Dynamic system model for green electricity and hydrogen production","authors":"R. Jacob, J. Zhang","doi":"10.1063/5.0139875","DOIUrl":"https://doi.org/10.1063/5.0139875","url":null,"abstract":"The need for decarbonization and diversification of energy resources has led to the development of integrated energy systems (IESs), where multiple resources supply more than one energy sector. One such IES with small modular nuclear reactors and renewables (wind and solar) as generating resources, catering to the demand of the electric grid while producing hydrogen for industries, is modeled in this paper. The physics-based component models are represented using the Modelica language and interconnected to form the IES. The control and coordination of the overall system are ensured by designing a suitable control architecture composed of individual subsystem-level controls and supervisory control. The dynamic performance and the load-following capability of the IES are evaluated, while satisfying the safe operational limits of the components. Different configurations and modes of IES operation are considered, where the adaptability of the control system in the presence of varying demands and renewable generations is validated. The simulation results indicate that hydrogen as a flexible load facilitates the supply of varying grid demand. Additionally, the renewables are also accommodated into the IES owing to the flexibility of the balance of plant associated with the nuclear reactors.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47934518","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}
E. Emenike, K. Iwuozor, Kingsley Chidiebere Okwu, Adeyemi Hafees Qudus, Abel U. Egbemhenghe, A. Adeniyi
Soot is an aerosol formed by incomplete combustion of carbonaceous materials, and its formation in biomass gasification is inevitable. It is crucial to know the properties of the soot produced in the exhaust of gasification reactors in order to appreciate both its advantages and disadvantages. In this study, a variety of analytical techniques were used to examine the content and morphology of biomass soot produced by a top-lit updraft gasifier. The results of the experiment revealed that carbon and oxygen make up the majority of the soot, with minor amounts of other components. Both aromatic and aliphatic groups with significant oxygen concentrations can be seen in the soot based on the distribution of functional groups. The morphology revealed an uneven, stratified, amorphous sample. Meanwhile, the sample had a surface area of 193.8 m2/g and a pore diameter of 2.68 nm. These porous qualities point to a potential use of the soot sample as an adsorbent in water filtration after activation.
{"title":"Composition and morphology of biomass-based soot from updraft gasifier system","authors":"E. Emenike, K. Iwuozor, Kingsley Chidiebere Okwu, Adeyemi Hafees Qudus, Abel U. Egbemhenghe, A. Adeniyi","doi":"10.1063/5.0154780","DOIUrl":"https://doi.org/10.1063/5.0154780","url":null,"abstract":"Soot is an aerosol formed by incomplete combustion of carbonaceous materials, and its formation in biomass gasification is inevitable. It is crucial to know the properties of the soot produced in the exhaust of gasification reactors in order to appreciate both its advantages and disadvantages. In this study, a variety of analytical techniques were used to examine the content and morphology of biomass soot produced by a top-lit updraft gasifier. The results of the experiment revealed that carbon and oxygen make up the majority of the soot, with minor amounts of other components. Both aromatic and aliphatic groups with significant oxygen concentrations can be seen in the soot based on the distribution of functional groups. The morphology revealed an uneven, stratified, amorphous sample. Meanwhile, the sample had a surface area of 193.8 m2/g and a pore diameter of 2.68 nm. These porous qualities point to a potential use of the soot sample as an adsorbent in water filtration after activation.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47708585","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 this paper, we propose to enhance the nested-loop extremum seeking control (NLESC)-based wind farm control strategy with the predictor-based delay compensation in order to improve its convergence characteristics under fluctuating wind. Earlier work has shown the effectiveness of NLESC for region-2 wind farm operation, i.e., maximizing the total power output of cascaded wind turbine array, while its convergence speed is highly limited by the delay of power output for downstream turbines due to wake propagation along the wind direction. By utilizing the delay compensated ESC proposed by Oliveira and Krstic, the delay compensated NLESC (DCNLESC) wind farm control is proposed, allowing the dither frequencies to be of similar magnitude as that in the single-turbine ESC. This can significantly improve the convergence speed of optimum tracking for real-time wind farm control. The wake propagation delay is estimated from turbine power outputs using cross correlation and proper filtering. Using the SimWindFarm platform, the proposed DCNLESC strategy is simulated with both a single-column three-turbine array and a 2 × 3 turbine array, under different wind speeds. The results show that the convergence speed toward the calibrated optimum is significantly improved over the NLESC operation. The convergence time for the upstream turbines' torque gain is reduced by 55%–14% in terms of integral time-weighted absolute error, while the impact on turbine fatigue loads is as low as no more than 3.5% increase on turbine tower and shaft.
{"title":"Optimal control of wind farm power output with delay compensated nested-loop extreme seeking control","authors":"Zhongyou Wu, Yaoyu Li","doi":"10.1063/5.0134878","DOIUrl":"https://doi.org/10.1063/5.0134878","url":null,"abstract":"In this paper, we propose to enhance the nested-loop extremum seeking control (NLESC)-based wind farm control strategy with the predictor-based delay compensation in order to improve its convergence characteristics under fluctuating wind. Earlier work has shown the effectiveness of NLESC for region-2 wind farm operation, i.e., maximizing the total power output of cascaded wind turbine array, while its convergence speed is highly limited by the delay of power output for downstream turbines due to wake propagation along the wind direction. By utilizing the delay compensated ESC proposed by Oliveira and Krstic, the delay compensated NLESC (DCNLESC) wind farm control is proposed, allowing the dither frequencies to be of similar magnitude as that in the single-turbine ESC. This can significantly improve the convergence speed of optimum tracking for real-time wind farm control. The wake propagation delay is estimated from turbine power outputs using cross correlation and proper filtering. Using the SimWindFarm platform, the proposed DCNLESC strategy is simulated with both a single-column three-turbine array and a 2 × 3 turbine array, under different wind speeds. The results show that the convergence speed toward the calibrated optimum is significantly improved over the NLESC operation. The convergence time for the upstream turbines' torque gain is reduced by 55%–14% in terms of integral time-weighted absolute error, while the impact on turbine fatigue loads is as low as no more than 3.5% increase on turbine tower and shaft.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44540073","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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