In China, systemic techno-economic analysis for solar tracker has been absent. To fill the blank, by taking the economic analysis of solar tracker application as the research object and using the LCOE method widely us...In China, systemic techno-economic analysis for solar tracker has been absent. To fill the blank, by taking the economic analysis of solar tracker application as the research object and using the LCOE method widely used internationally, the techno-economic analysis model of solar tracker was established according to conditions in China. Influence factors on LCOE were analyzed by using the established model, and the relationship between each cost factor and the cost component of energy leveling of tracker was further studied. In addition, the calculation method of investment payback period based on energy leveling analysis was established, and the influence of various factors on investment payback period was revealed through an example calculation. The research results will help to measure the economy of tracker application more accurately and comprehensively, and promote the popularization and application of solar tracker. The economic analysis model of solar tracker application was established by using LCOE method. The influence factors and cost component of LCOE were analyzed with the model. The payback period of solar tracker investment was also analyzed based on LCOE method.展开更多
This paper presents a study aimed at evaluating and comparing the performance of six different tracking systems for photovoltaic (PV) with diesel-battery hybrid system in arid climate of Kingdom of Saudi Arabia (KSA)....This paper presents a study aimed at evaluating and comparing the performance of six different tracking systems for photovoltaic (PV) with diesel-battery hybrid system in arid climate of Kingdom of Saudi Arabia (KSA). The study considered various technical and economic factors including system net present cost (NPC), levelized cost of energy (LCOE), and PV power generation using energy analysis and microgrid design software “HOMER”. It also presents an overview of the current electricity production and demand in the Kingdom. The weather data used in this study have been collected from the new solar atlas launched by King Abdullah City for Atomic and Renewable Energy (KACARE). The selected solar resource monitoring station for this study is located near to Riyadh city and has an annual average daily total irradiation of 6300 W/m2/day. The study shows that, for stand-alone PV system in the vicinity of Riyadh city, tracking system is economically better than fixed angle system. Among the considered tracking systems, VCA system is the most preferable as it has low NPC and LCOE values with a high return on investment (ROI) as well as low carbon dioxide (CO2) emissions due to a high renewable energy penetration.展开更多
This article presents a new two-axis solar tracker based on an online optimization algorithm so as to track the position of the sun without using its movement model.In this research,four well-known optimization algori...This article presents a new two-axis solar tracker based on an online optimization algorithm so as to track the position of the sun without using its movement model.In this research,four well-known optimization algorithms are employed to find the two unknown parameters named azimuth and zenith angles,which determine the position of the sun.The magnitude of the sunray is considered as the cost function of all algorithms.Then,several experiments are carried out to find the best optimization algorithm with optimal population size,number of iterations,and also the best initialization method.Uniform initialization leads to faster convergence compared to random initialization.The results clearly show that the particle swarm optimization algorithm with a population size of 15 and 7 iterations using uniform initialization method has better performance than the other algorithms,with a convergence time of less than 40 s.The average fitness value or voltage received by the tracker is 2.4 Volts in this method,which is higher than other methods.TLBO also performs well with a population size of 15 and 7 iterations.Afterward,the artificial neural network with one hidden layer and 20 neurons is employed to predict these two parameters in each day and moment in a year in Shiraz city according to the experimental data extracted from PSO.Number of the day from January and the time are inputs and zenith and azimuth angles are considered the output of neural network modeling.The performance of the proposed ANN model is evaluated using regression plots,demonstrating a strong correlation between predicted and target outputs.Finally,the outcomes reveal the feasibility of using online optimization algorithms and neural network modeling in an effort to bypass the complex mathematical model of mechatronic systems and predict the movement of the sun automatically.展开更多
文摘In China, systemic techno-economic analysis for solar tracker has been absent. To fill the blank, by taking the economic analysis of solar tracker application as the research object and using the LCOE method widely used internationally, the techno-economic analysis model of solar tracker was established according to conditions in China. Influence factors on LCOE were analyzed by using the established model, and the relationship between each cost factor and the cost component of energy leveling of tracker was further studied. In addition, the calculation method of investment payback period based on energy leveling analysis was established, and the influence of various factors on investment payback period was revealed through an example calculation. The research results will help to measure the economy of tracker application more accurately and comprehensively, and promote the popularization and application of solar tracker. The economic analysis model of solar tracker application was established by using LCOE method. The influence factors and cost component of LCOE were analyzed with the model. The payback period of solar tracker investment was also analyzed based on LCOE method.
文摘This paper presents a study aimed at evaluating and comparing the performance of six different tracking systems for photovoltaic (PV) with diesel-battery hybrid system in arid climate of Kingdom of Saudi Arabia (KSA). The study considered various technical and economic factors including system net present cost (NPC), levelized cost of energy (LCOE), and PV power generation using energy analysis and microgrid design software “HOMER”. It also presents an overview of the current electricity production and demand in the Kingdom. The weather data used in this study have been collected from the new solar atlas launched by King Abdullah City for Atomic and Renewable Energy (KACARE). The selected solar resource monitoring station for this study is located near to Riyadh city and has an annual average daily total irradiation of 6300 W/m2/day. The study shows that, for stand-alone PV system in the vicinity of Riyadh city, tracking system is economically better than fixed angle system. Among the considered tracking systems, VCA system is the most preferable as it has low NPC and LCOE values with a high return on investment (ROI) as well as low carbon dioxide (CO2) emissions due to a high renewable energy penetration.
文摘This article presents a new two-axis solar tracker based on an online optimization algorithm so as to track the position of the sun without using its movement model.In this research,four well-known optimization algorithms are employed to find the two unknown parameters named azimuth and zenith angles,which determine the position of the sun.The magnitude of the sunray is considered as the cost function of all algorithms.Then,several experiments are carried out to find the best optimization algorithm with optimal population size,number of iterations,and also the best initialization method.Uniform initialization leads to faster convergence compared to random initialization.The results clearly show that the particle swarm optimization algorithm with a population size of 15 and 7 iterations using uniform initialization method has better performance than the other algorithms,with a convergence time of less than 40 s.The average fitness value or voltage received by the tracker is 2.4 Volts in this method,which is higher than other methods.TLBO also performs well with a population size of 15 and 7 iterations.Afterward,the artificial neural network with one hidden layer and 20 neurons is employed to predict these two parameters in each day and moment in a year in Shiraz city according to the experimental data extracted from PSO.Number of the day from January and the time are inputs and zenith and azimuth angles are considered the output of neural network modeling.The performance of the proposed ANN model is evaluated using regression plots,demonstrating a strong correlation between predicted and target outputs.Finally,the outcomes reveal the feasibility of using online optimization algorithms and neural network modeling in an effort to bypass the complex mathematical model of mechatronic systems and predict the movement of the sun automatically.
