CO2 heat pump air conditioning (HPAC) systems for electric vehicles (EVs) have received widespread attention for their excellent low-temperature heating capabilities. However, the range of EVs is limited by the battery energy storage, which makes the energy demand of the heating system affect the energy use efficiency of the drive battery. In order to measure the thermal economy of the air conditioning (AC) system in terms of heating, the index of coefficient of performance (COP) is often used. Accurate COP prediction can help optimize the performance of heat HPAC systems for EVs to avoid energy wastage and thus improve the range of the vehicle. In this study, we use a backpropagation (BP) neural network combined with the particle swarm optimization (PSO) algorithm to predict and optimize the COP of the CO2 HPAC system for EVs. First, a COP prediction model of the CO2 HPAC system for EVs was established, which can consider a variety of influencing factors, and the key parameters affecting the COP of the AC system were obtained through experiments. Second, a BP neural network is used to predict the COP of the CO2 HPAC system, and in order to overcome the shortcomings of the BP neural network, which is slow and prone to fall into the minimum value, the particle swarm algorithm PSO is introduced to optimize the weights and biases of the BP neural network, so as to improve the accuracy and stability of the prediction. Through this study, we combine the BP neural network with the PSO algorithm to achieve accurate prediction and optimization of the COP of the HPAC system of an EV, which provides a strong support for the improvement of energy use efficiency. Second, we considered a variety of influencing factors, such as outdoor temperature, compressor speed, and EV status, which made the prediction model more accurate and applicable. Finally, the method proposed in this study is validated on a real dataset, and the optimization of the BP neural network using the particle swarm algorithm PSO can improve the accuracy of COP prediction for HPAC systems by 65.8%.
用于电动汽车（EV）的二氧化碳热泵空调（HPAC）系统因其出色的低温加热能力而受到广泛关注。然而，电动汽车的续航能力受到电池储能的限制，这使得加热系统的能量需求会影响驱动电池的能量利用效率。为了衡量空调（AC）系统在制热方面的热经济性，通常采用性能系数（COP）指标。准确的 COP 预测有助于优化电动汽车热 HPAC 系统的性能，避免能源浪费，从而提高车辆的续航里程。在本研究中，我们使用反向传播（BP）神经网络结合粒子群优化（PSO）算法来预测和优化电动汽车 CO2 HPAC 系统的 COP。首先，建立了电动汽车 CO2 HPAC 系统的 COP 预测模型，该模型可考虑多种影响因素，并通过实验获得了影响交流系统 COP 的关键参数。其次，利用 BP 神经网络预测 CO2 HPAC 系统的 COP，为了克服 BP 神经网络速度慢、容易陷入最小值的缺点，引入粒子群算法 PSO 来优化 BP 神经网络的权值和偏置，从而提高预测的准确性和稳定性。通过本研究，我们将 BP 神经网络与 PSO 算法相结合，实现了对电动汽车 HPAC 系统 COP 的精确预测和优化，为提高能源利用效率提供了有力支持。其次，我们考虑了室外温度、压缩机转速、电动汽车状态等多种影响因素，使预测模型更加准确和适用。最后，本研究提出的方法在真实数据集上得到了验证，利用粒子群算法 PSO 对 BP 神经网络进行优化，可将 HPAC 系统 COP 预测的准确率提高 65.8%。
{"title":"Prediction of heating performance of carbon dioxide heat pump air conditioning system for electric vehicles based on PSO-BP optimization","authors":"Yan Zhang, Yu Zhao, Fuwu Yan, Liang He, Donggang Zhao, Jianglu Huang","doi":"10.1063/5.0174811","DOIUrl":"https://doi.org/10.1063/5.0174811","url":null,"abstract":"CO2 heat pump air conditioning (HPAC) systems for electric vehicles (EVs) have received widespread attention for their excellent low-temperature heating capabilities. However, the range of EVs is limited by the battery energy storage, which makes the energy demand of the heating system affect the energy use efficiency of the drive battery. In order to measure the thermal economy of the air conditioning (AC) system in terms of heating, the index of coefficient of performance (COP) is often used. Accurate COP prediction can help optimize the performance of heat HPAC systems for EVs to avoid energy wastage and thus improve the range of the vehicle. In this study, we use a backpropagation (BP) neural network combined with the particle swarm optimization (PSO) algorithm to predict and optimize the COP of the CO2 HPAC system for EVs. First, a COP prediction model of the CO2 HPAC system for EVs was established, which can consider a variety of influencing factors, and the key parameters affecting the COP of the AC system were obtained through experiments. Second, a BP neural network is used to predict the COP of the CO2 HPAC system, and in order to overcome the shortcomings of the BP neural network, which is slow and prone to fall into the minimum value, the particle swarm algorithm PSO is introduced to optimize the weights and biases of the BP neural network, so as to improve the accuracy and stability of the prediction. Through this study, we combine the BP neural network with the PSO algorithm to achieve accurate prediction and optimization of the COP of the HPAC system of an EV, which provides a strong support for the improvement of energy use efficiency. Second, we considered a variety of influencing factors, such as outdoor temperature, compressor speed, and EV status, which made the prediction model more accurate and applicable. Finally, the method proposed in this study is validated on a real dataset, and the optimization of the BP neural network using the particle swarm algorithm PSO can improve the accuracy of COP prediction for HPAC systems by 65.8%.","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":"139292735","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}
Icing degrades turbine performance by altering the geometry of blade airfoils, reducing turbine power output, and increasing structural loads. In this study, the impacts of atmospheric icing on the full performance and behavior of a controlled large-scale wind turbine are thoroughly investigated. Using the Mustafa Sahin bladed wind turbine simulation model, the National Renewable Energy Laboratory 5 MW turbine is simulated with and without iced blades. The turbine blades are considered fully covered by light icing at the leading edge, which causes a reduction of up to 9.27% in Cl and an increase of up to 48% in Cd data of blade airfoils. Turbine static performance and behavior are examined at different uniform winds between cut-in and cut-out wind speeds, while the dynamic performance and behavior are estimated under turbulent winds at below (region II) and above (region III) rated regions. Simulation results are presented in terms of various turbine parameters, such as rotor power, thrust, their coefficients, blade pitch angle, rotor speed, etc. Results show that such light icing alters the turbine's aerodynamic characteristics and dynamics, increasing the turbine's cut-in and rated wind speeds, and reducing the thrust and maximum power coefficients by 5.5% and 13.35%, respectively. Under the same uniform winds, due to icing, turbine static performance and behavior are drastically disrupted in below rated region, resulting in reduced rotor speed, turbine efficiency, thrust, and power output by up to 4.77%, 39.7%, 7.63%, and 40%, respectively. In region III, however, thrust increases by up to 15% although the power output, rotor speed, and turbine efficiency do not change considerably. When the dynamic responses are examined under turbulent wind with a mean of 7.9 m/s in region II, mean power and fluctuations reduce by 14.17% and 10.88%, respectively. The mean thrust decreases by 6.86%, while its fluctuations reduce by 11.33%. The mean rotor speed reduces by 3.83%, and its fluctuations decrease by 12.84%. Under turbulent wind with a mean of 15.7 m/s in region III, the mean power and fluctuations decrease by 0.053% and 1.95%, respectively. The mean thrust increases by 11.99% and its fluctuations drop by 0.84%. The mean rotor speed does not change much, but its fluctuations increase by 0.132%. The mean blade pitch angle reduces by 9.39%, while its fluctuations increase by 7.39%.
结冰会改变叶片机翼的几何形状，降低涡轮机的功率输出，增加结构载荷，从而降低涡轮机的性能。本研究深入探讨了大气结冰对受控大型风力涡轮机的全部性能和行为的影响。利用 Mustafa Sahin 叶片风力涡轮机仿真模型，对国家可再生能源实验室 5 兆瓦涡轮机在叶片结冰和不结冰的情况下进行了仿真。涡轮机叶片的前缘被轻微结冰完全覆盖，这导致叶片翼面的 Cl 值降低达 9.27%，Cd 值增加达 48%。在切入风速和切出风速之间的不同匀风条件下，对涡轮机的静态性能和行为进行了检验，而在低于（II 区）和高于（III 区）额定区域的湍流风条件下，对涡轮机的动态性能和行为进行了估算。模拟结果以各种涡轮机参数的形式呈现，如转子功率、推力、其系数、叶片俯仰角、转子速度等。结果表明，这种轻度结冰会改变涡轮机的空气动力特性和动力学特性，提高涡轮机的切入风速和额定风速，并使推力系数和最大功率系数分别降低 5.5% 和 13.35%。在相同的匀风条件下，由于结冰，涡轮机的静态性能和行为在额定以下区域受到严重破坏，导致转子速度、涡轮机效率、推力和功率输出分别降低了 4.77%、39.7%、7.63% 和 40%。然而，在区域 III 中，虽然功率输出、转子速度和涡轮效率变化不大，但推力却增加了 15%。当在区域 II 中平均速度为 7.9 m/s 的湍流风下考察动态响应时，平均功率和波动分别降低了 14.17% 和 10.88%。平均推力降低了 6.86%，波动降低了 11.33%。平均转子速度降低了 3.83%，波动降低了 12.84%。在区域 III 平均风速为 15.7 m/s 的湍流风下，平均功率和波动分别降低了 0.053% 和 1.95%。平均推力增加了 11.99%，波动下降了 0.84%。平均转速变化不大，但波动增加了 0.132%。平均叶片俯仰角减小了 9.39%，波动则增加了 7.39%。
{"title":"The impacts of atmospheric icing on performance and behavior of a controlled large-scale wind turbine","authors":"Mustafa Sahin, T. Farsadi","doi":"10.1063/5.0161724","DOIUrl":"https://doi.org/10.1063/5.0161724","url":null,"abstract":"Icing degrades turbine performance by altering the geometry of blade airfoils, reducing turbine power output, and increasing structural loads. In this study, the impacts of atmospheric icing on the full performance and behavior of a controlled large-scale wind turbine are thoroughly investigated. Using the Mustafa Sahin bladed wind turbine simulation model, the National Renewable Energy Laboratory 5 MW turbine is simulated with and without iced blades. The turbine blades are considered fully covered by light icing at the leading edge, which causes a reduction of up to 9.27% in Cl and an increase of up to 48% in Cd data of blade airfoils. Turbine static performance and behavior are examined at different uniform winds between cut-in and cut-out wind speeds, while the dynamic performance and behavior are estimated under turbulent winds at below (region II) and above (region III) rated regions. Simulation results are presented in terms of various turbine parameters, such as rotor power, thrust, their coefficients, blade pitch angle, rotor speed, etc. Results show that such light icing alters the turbine's aerodynamic characteristics and dynamics, increasing the turbine's cut-in and rated wind speeds, and reducing the thrust and maximum power coefficients by 5.5% and 13.35%, respectively. Under the same uniform winds, due to icing, turbine static performance and behavior are drastically disrupted in below rated region, resulting in reduced rotor speed, turbine efficiency, thrust, and power output by up to 4.77%, 39.7%, 7.63%, and 40%, respectively. In region III, however, thrust increases by up to 15% although the power output, rotor speed, and turbine efficiency do not change considerably. When the dynamic responses are examined under turbulent wind with a mean of 7.9 m/s in region II, mean power and fluctuations reduce by 14.17% and 10.88%, respectively. The mean thrust decreases by 6.86%, while its fluctuations reduce by 11.33%. The mean rotor speed reduces by 3.83%, and its fluctuations decrease by 12.84%. Under turbulent wind with a mean of 15.7 m/s in region III, the mean power and fluctuations decrease by 0.053% and 1.95%, respectively. The mean thrust increases by 11.99% and its fluctuations drop by 0.84%. The mean rotor speed does not change much, but its fluctuations increase by 0.132%. The mean blade pitch angle reduces by 9.39%, while its fluctuations increase by 7.39%.","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":"139298303","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}
Compressed air energy storage (CAES) effectively reduces wind and solar power curtailment due to randomness. However, inaccurate daily data and improper storage capacity configuration impact CAES development. This study uses the Parzen window estimation method to extract features from historical data, obtaining distributions of typical weekly wind power, solar power, and load. These distributions are compared to Weibull and Beta distributions. The wind–solar energy storage system's capacity configuration is optimized using a genetic algorithm to maximize profit. Different methods are compared in island/grid-connected modes using evaluation metrics to verify the accuracy of the Parzen window estimation method. The results show that it surpasses parameter estimation for real-time series-based configuration. Under grid-connected mode, rated power configurations are 1107 MW for wind, 346 MW for solar, and 290 MW for CAES. The CAES system has a rated capacity of 2320 MW·h, meeting average hourly power demand of 699.26 MW. It saves $6.55 million per week in electricity costs, with a maximum weekly profit of $0.61 million. Payback period for system investment is 5.6 years, excluding penalty costs.
{"title":"Optimization of wind and solar energy storage system capacity configuration based on the Parzen window estimation method","authors":"Qihui Yu, Shengyu Gao, Guoxin Sun, Ripeng Qin","doi":"10.1063/5.0172720","DOIUrl":"https://doi.org/10.1063/5.0172720","url":null,"abstract":"Compressed air energy storage (CAES) effectively reduces wind and solar power curtailment due to randomness. However, inaccurate daily data and improper storage capacity configuration impact CAES development. This study uses the Parzen window estimation method to extract features from historical data, obtaining distributions of typical weekly wind power, solar power, and load. These distributions are compared to Weibull and Beta distributions. The wind–solar energy storage system's capacity configuration is optimized using a genetic algorithm to maximize profit. Different methods are compared in island/grid-connected modes using evaluation metrics to verify the accuracy of the Parzen window estimation method. The results show that it surpasses parameter estimation for real-time series-based configuration. Under grid-connected mode, rated power configurations are 1107 MW for wind, 346 MW for solar, and 290 MW for CAES. The CAES system has a rated capacity of 2320 MW·h, meeting average hourly power demand of 699.26 MW. It saves $6.55 million per week in electricity costs, with a maximum weekly profit of $0.61 million. Payback period for system investment is 5.6 years, excluding penalty costs.","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":"139299072","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}
{"title":"On the responsibility of energy journals in mitigating climate change impacts: Looking back at 5 years of editorship with the Journal of Renewable and Sustainable Energy","authors":"Carlos F. M. Coimbra","doi":"10.1063/5.0189213","DOIUrl":"https://doi.org/10.1063/5.0189213","url":null,"abstract":"","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":"139305616","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}
Xiu Li Wang, Ru Qing Xu, Jian Hong Zhang, Fu Shuan Wen, Chang Qing Liu
During the global energy crisis, a significant influx of renewable energy sources was connected to the power grid, resulting in adverse fluctuations. To address this challenge and simultaneously reduce environmental pollution, a hybrid energy storage system containing hydrogen energy storage (HES) and compressed air energy storage (CAES) are proposed. The system aims to reconfigure the energy storage devices by an economical means and effectively alleviate the volatility challenges by the large amount of renewable energy accessing. First, according to the behavioral characteristics of wind, photovoltaics, and the energy storage, the hybrid energy storage capacity optimization allocation model is established, and its economy is nearly 17% and 4.7% better than that of single HES and single CAES, respectively. Then, considering the difficulty of solving the complexity dimension, a carnivorous plant algorithm (CPA) is adopted to solve the model and accurately obtain the strategy of hybrid energy storage configuration in this paper. The running time of a CPA algorithm is 33.6%, 36%, and 55% shorter than particle swarm optimization, whale optimization algorithm, and firefly algorithm, respectively. Finally, the simulation analysis is performed by IEEE 33 node arithmetic. The results show that the network loss with hybrid energy storage is reduced by about 40% compared with that without hybrid energy storage. However, improving voltage stability and the economy is optimal by using configured hybrid energy storage.
{"title":"Optimal capacity allocation and economic evaluation of hybrid energy storage in a wind–photovoltaic power system","authors":"Xiu Li Wang, Ru Qing Xu, Jian Hong Zhang, Fu Shuan Wen, Chang Qing Liu","doi":"10.1063/5.0165774","DOIUrl":"https://doi.org/10.1063/5.0165774","url":null,"abstract":"During the global energy crisis, a significant influx of renewable energy sources was connected to the power grid, resulting in adverse fluctuations. To address this challenge and simultaneously reduce environmental pollution, a hybrid energy storage system containing hydrogen energy storage (HES) and compressed air energy storage (CAES) are proposed. The system aims to reconfigure the energy storage devices by an economical means and effectively alleviate the volatility challenges by the large amount of renewable energy accessing. First, according to the behavioral characteristics of wind, photovoltaics, and the energy storage, the hybrid energy storage capacity optimization allocation model is established, and its economy is nearly 17% and 4.7% better than that of single HES and single CAES, respectively. Then, considering the difficulty of solving the complexity dimension, a carnivorous plant algorithm (CPA) is adopted to solve the model and accurately obtain the strategy of hybrid energy storage configuration in this paper. The running time of a CPA algorithm is 33.6%, 36%, and 55% shorter than particle swarm optimization, whale optimization algorithm, and firefly algorithm, respectively. Finally, the simulation analysis is performed by IEEE 33 node arithmetic. The results show that the network loss with hybrid energy storage is reduced by about 40% compared with that without hybrid energy storage. However, improving voltage stability and the economy is optimal by using configured hybrid energy storage.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135609814","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, L. Xiao, Z. Cheng, M. Liu, Y. Chen, K. Zhang
The sea area in China demands high requirements for water depth adaptability, stability, structural integrity, dynamic response characteristics, and economic performance of large-scale floating wind turbines (FWTs). The aim of the research is to propose the 10 megawatts (MW) SPIC concept (Semi-submersible platform with Partially Inclined Columns, SPIC for short) FWT in intermediate water depth, providing guidance for the concept design of large-scale FWT. The SPIC concept FWT incorporates partially tilted outward side columns, which effectively minimize the risk of bottom contact and significantly enhance the stability of the floating wind turbine. This is achieved by increasing the inertia moment of the waterplane without increasing the displaced water or water surface area. The 10 MW SPIC concept FWT exhibits superior performance in terms of smaller static heeling angle, motion amplitude response function, and wave force transfer function. It also features lower steel consumption and less displaced water, achieving good stability, hydrodynamic performance, and low cost. The rationality of the concept design and the accuracy of the numerical simulation process were validated in this study using experimental results. The study assessed the extreme responses of the 10 MW SPIC concept FWT in its six degrees of freedom (DOFs) under various scenarios, including power production, power production with faults, parked condition, and parked condition with faults, thus verifying the safety of the SPIC concept.
{"title":"Research on the conceptual design and performance analysis of a 10 MW SPIC concept floating wind turbine foundation in intermediate water depth","authors":"Q. Cao, L. Xiao, Z. Cheng, M. Liu, Y. Chen, K. Zhang","doi":"10.1063/5.0161913","DOIUrl":"https://doi.org/10.1063/5.0161913","url":null,"abstract":"The sea area in China demands high requirements for water depth adaptability, stability, structural integrity, dynamic response characteristics, and economic performance of large-scale floating wind turbines (FWTs). The aim of the research is to propose the 10 megawatts (MW) SPIC concept (Semi-submersible platform with Partially Inclined Columns, SPIC for short) FWT in intermediate water depth, providing guidance for the concept design of large-scale FWT. The SPIC concept FWT incorporates partially tilted outward side columns, which effectively minimize the risk of bottom contact and significantly enhance the stability of the floating wind turbine. This is achieved by increasing the inertia moment of the waterplane without increasing the displaced water or water surface area. The 10 MW SPIC concept FWT exhibits superior performance in terms of smaller static heeling angle, motion amplitude response function, and wave force transfer function. It also features lower steel consumption and less displaced water, achieving good stability, hydrodynamic performance, and low cost. The rationality of the concept design and the accuracy of the numerical simulation process were validated in this study using experimental results. The study assessed the extreme responses of the 10 MW SPIC concept FWT in its six degrees of freedom (DOFs) under various scenarios, including power production, power production with faults, parked condition, and parked condition with faults, thus verifying the safety of the SPIC concept.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135610323","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}
Qiyue Xie, Yu Liu, Chao Liang, Qiang Fu, Xiaoli Wang
Addressing the issue of flow dead zones in molten salt heat exchangers in concentrating solar power generation systems, this study focuses on the conventional shell and tube heat exchanger using molten salt and heat transfer oil as the working medium. The flow dynamics of molten salt within the heat exchanger are analyzed. To quantify the volume fraction of the molten salt flow dead zones, the residence time distribution curve is employed. Four baffle salt flow configurations are comparatively assessed. Findings indicate that the four opening configurations effectively enhance the reduction of molten salt flow dead zones, with volume fraction reductions ranging from 57.8% to 68.21%. Notably, configuration 4 yields the most optimal results. Furthermore, molten salt flow states in varying regions were examined: the innermost flow dead zone exhibited the highest improvement, followed by the middle area, with the edge area showing the least enhancement. Additionally, the impact of the opening diameter on the flow dead zone was explored. The volume fraction of the molten salt flow dead zone diminishes as the opening diameter expands, with the rate of this change also decelerating. Given that molten salt at the opening manifests as a jet, enlarging the opening diameter lessens the pressure differential across the baffle, subsequently weakening the jet's intensity and its influence on the flow dead zone.
{"title":"Flow state analysis of molten salt in shell and tube heat exchanger with perforated baffles","authors":"Qiyue Xie, Yu Liu, Chao Liang, Qiang Fu, Xiaoli Wang","doi":"10.1063/5.0168689","DOIUrl":"https://doi.org/10.1063/5.0168689","url":null,"abstract":"Addressing the issue of flow dead zones in molten salt heat exchangers in concentrating solar power generation systems, this study focuses on the conventional shell and tube heat exchanger using molten salt and heat transfer oil as the working medium. The flow dynamics of molten salt within the heat exchanger are analyzed. To quantify the volume fraction of the molten salt flow dead zones, the residence time distribution curve is employed. Four baffle salt flow configurations are comparatively assessed. Findings indicate that the four opening configurations effectively enhance the reduction of molten salt flow dead zones, with volume fraction reductions ranging from 57.8% to 68.21%. Notably, configuration 4 yields the most optimal results. Furthermore, molten salt flow states in varying regions were examined: the innermost flow dead zone exhibited the highest improvement, followed by the middle area, with the edge area showing the least enhancement. Additionally, the impact of the opening diameter on the flow dead zone was explored. The volume fraction of the molten salt flow dead zone diminishes as the opening diameter expands, with the rate of this change also decelerating. Given that molten salt at the opening manifests as a jet, enlarging the opening diameter lessens the pressure differential across the baffle, subsequently weakening the jet's intensity and its influence on the flow dead zone.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135566492","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}
Integration of unmanned aerial vehicles (UAVs) and edge computing into the wind farm routine inspection provides a promising approach to enhancing inspection effectiveness and decreasing operation maintenance costs. In light of the finite battery power and computational capacity of UAVs, a dynamic queue-aware UAV-assisted edge computing inspection wind farm framework is investigated with the goal of minimizing the long-term energy consumption of UAVs. The Lyapunov optimization theory is utilized to decouple the long-term stochastic optimization problem into four short-term deterministic subproblems, including the task splitting, the UAV-side computing resource allocation, the task offloading, and the edge server-side computing resource allocation. Furthermore, a Lyapunov optimization-based dynamic queue-aware computation offloading algorithm (LODQCO) is presented to optimize task offloading and resource allocation jointly. The optimal UAV-side computing resource is determined by a closed form formula, and then the optimal task offloading decision is tackled by applying the classical interior point method. Finally, the edge server-side computing resource is addressed via a linear optimization CPLEX solver. Based on simulation results, LODQCO is superior to the benchmark algorithms with respect to the energy consumption, queue backlogs, and queuing delays.
{"title":"Queue-aware computation offloading for UAV-assisted edge computing in wind farm routine inspection","authors":"Yinghua Han, Qinqin Xu, Qiang Zhao, Fangyuan Si","doi":"10.1063/5.0152767","DOIUrl":"https://doi.org/10.1063/5.0152767","url":null,"abstract":"Integration of unmanned aerial vehicles (UAVs) and edge computing into the wind farm routine inspection provides a promising approach to enhancing inspection effectiveness and decreasing operation maintenance costs. In light of the finite battery power and computational capacity of UAVs, a dynamic queue-aware UAV-assisted edge computing inspection wind farm framework is investigated with the goal of minimizing the long-term energy consumption of UAVs. The Lyapunov optimization theory is utilized to decouple the long-term stochastic optimization problem into four short-term deterministic subproblems, including the task splitting, the UAV-side computing resource allocation, the task offloading, and the edge server-side computing resource allocation. Furthermore, a Lyapunov optimization-based dynamic queue-aware computation offloading algorithm (LODQCO) is presented to optimize task offloading and resource allocation jointly. The optimal UAV-side computing resource is determined by a closed form formula, and then the optimal task offloading decision is tackled by applying the classical interior point method. Finally, the edge server-side computing resource is addressed via a linear optimization CPLEX solver. Based on simulation results, LODQCO is superior to the benchmark algorithms with respect to the energy consumption, queue backlogs, and queuing delays.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135456293","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}
Understanding the flow of embodied energy between provinces in China and the factors affecting the network has an important impact on reducing energy consumption in each province and promoting balanced regional development. This paper uses the multi-regional input–output model to construct the interprovincial embodied energy flow network in China and defines the energy flow between provinces under the trade of products and services. Then, it uses the ecological network model to carry out the ascendency and network environ analysis of the interprovincial embodied energy flow network in China and uses the Quadratic Assignment Procedure regression model to calculate the impact of various factors on the flow network. The main research conclusions are as follows: (1) The embodied energy flow in the middle reaches of the Yangtze River and the Yellow River is relatively high. The embodied energy flow value in Ningxia and Qinghai is low. Zhejiang, Guangdong, and Jiangsu often have the highest embodied energy outflow value, while Inner Mongolia, Shanxi, and other provinces have higher inflows. (2) The center of gravity of through flow in the flow system is biased to Jiangsu, Guangdong, and other places; Gansu, Ningxia, Qinghai, and Hainan are at the edge of the system. (3) The differences in the economic development level, population size, and energy structure have a positive impact on the embodied energy flow network between provinces in China; the geographic distance will have a negative impact on the flow network.
{"title":"Analysis of the characteristics and influencing factors of China's embodied energy flow network","authors":"Guangyao Deng, Huihui Chen, Jiao Qian","doi":"10.1063/5.0164811","DOIUrl":"https://doi.org/10.1063/5.0164811","url":null,"abstract":"Understanding the flow of embodied energy between provinces in China and the factors affecting the network has an important impact on reducing energy consumption in each province and promoting balanced regional development. This paper uses the multi-regional input–output model to construct the interprovincial embodied energy flow network in China and defines the energy flow between provinces under the trade of products and services. Then, it uses the ecological network model to carry out the ascendency and network environ analysis of the interprovincial embodied energy flow network in China and uses the Quadratic Assignment Procedure regression model to calculate the impact of various factors on the flow network. The main research conclusions are as follows: (1) The embodied energy flow in the middle reaches of the Yangtze River and the Yellow River is relatively high. The embodied energy flow value in Ningxia and Qinghai is low. Zhejiang, Guangdong, and Jiangsu often have the highest embodied energy outflow value, while Inner Mongolia, Shanxi, and other provinces have higher inflows. (2) The center of gravity of through flow in the flow system is biased to Jiangsu, Guangdong, and other places; Gansu, Ningxia, Qinghai, and Hainan are at the edge of the system. (3) The differences in the economic development level, population size, and energy structure have a positive impact on the embodied energy flow network between provinces in China; the geographic distance will have a negative impact on the flow network.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135515257","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}
India has made an international commitment to generate about 50% of its total energy needs from renewable energy sources by 2030. Here, to meet such a commitment, this study developed high-resolution (1 × 1 km2) geospatial solar and wind geographical potential maps with an aim to calculate the theoretical and technical potentials with economic and environmental sustainability. These geographical potential maps are developed by taking into account 13 evaluation and restriction factors pertaining to technical, economic, and socio-environmental categories. The investigation identifies that Rajasthan, Karnataka, and Gujarat have a plurality of extremely favorable land areas for solar and wind energy sources. Furthermore, the results imply that appropriate planning for the installation of renewable projects at the identified optimum locations can fulfill India's commitments with regard to an optimal energy mix scenario, with energy available twice the available potential for consumption in 2030. Furthermore, with the least average levelized cost of energy of 38.8 $/MWh (2.83₹/kWh) and 42.3 $/MWh (3.09₹/kWh), solar and wind energy sources are potentially more appealing and affordable than conventional energy sources. The findings of this study will also significantly advance India's attempts to accept and develop renewable energy sources, helping to realize the government's objective for sustainable electricity production.
{"title":"A geospatial integrated multi-criteria approach for assessment of solar and wind energy potentials with economic and environmental analysis","authors":"S. Saraswat, A. Digalwar","doi":"10.1063/5.0177752","DOIUrl":"https://doi.org/10.1063/5.0177752","url":null,"abstract":"India has made an international commitment to generate about 50% of its total energy needs from renewable energy sources by 2030. Here, to meet such a commitment, this study developed high-resolution (1 × 1 km2) geospatial solar and wind geographical potential maps with an aim to calculate the theoretical and technical potentials with economic and environmental sustainability. These geographical potential maps are developed by taking into account 13 evaluation and restriction factors pertaining to technical, economic, and socio-environmental categories. The investigation identifies that Rajasthan, Karnataka, and Gujarat have a plurality of extremely favorable land areas for solar and wind energy sources. Furthermore, the results imply that appropriate planning for the installation of renewable projects at the identified optimum locations can fulfill India's commitments with regard to an optimal energy mix scenario, with energy available twice the available potential for consumption in 2030. Furthermore, with the least average levelized cost of energy of 38.8 $/MWh (2.83₹/kWh) and 42.3 $/MWh (3.09₹/kWh), solar and wind energy sources are potentially more appealing and affordable than conventional energy sources. The findings of this study will also significantly advance India's attempts to accept and develop renewable energy sources, helping to realize the government's objective for sustainable electricity production.","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":"139303864","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：