To improve the operation efficiency of the photovoltaic power station complementary power generation system,an optimal allocation model of the photovoltaic power station complementary power generation capacity based o...To improve the operation efficiency of the photovoltaic power station complementary power generation system,an optimal allocation model of the photovoltaic power station complementary power generation capacity based on PSO-BP is proposed.Particle Swarm Optimization and BP neural network are used to establish the forecasting model,the Markov chain model is used to correct the forecasting error of the model,and the weighted fitting method is used to forecast the annual load curve,to complete the optimal allocation of complementary generating capacity of photovoltaic power stations.The experimental results show that thismethod reduces the average loss of photovoltaic output prediction,improves the prediction accuracy and recall rate of photovoltaic output prediction,and ensures the effective operation of the power system.展开更多
Since it was first proposed,the space solar power station(SSPS)has attracted great attention all over the world;it is a huge space system and provides energy for Earth.Although several schemes and abundant studies on ...Since it was first proposed,the space solar power station(SSPS)has attracted great attention all over the world;it is a huge space system and provides energy for Earth.Although several schemes and abundant studies on the SSPS have been proposed and conducted,it is still not realized.The reason why SSPS is still an idea is not only because it is a giant and complex project,but also due to the requirement for various excellent space materials.Among the diverse required materials,we believe energy materials are the most important.Herein,we review the space energy conversion materials for the SSPS.展开更多
文摘To improve the operation efficiency of the photovoltaic power station complementary power generation system,an optimal allocation model of the photovoltaic power station complementary power generation capacity based on PSO-BP is proposed.Particle Swarm Optimization and BP neural network are used to establish the forecasting model,the Markov chain model is used to correct the forecasting error of the model,and the weighted fitting method is used to forecast the annual load curve,to complete the optimal allocation of complementary generating capacity of photovoltaic power stations.The experimental results show that thismethod reduces the average loss of photovoltaic output prediction,improves the prediction accuracy and recall rate of photovoltaic output prediction,and ensures the effective operation of the power system.
基金Project supported by Fundamental Research Funds for the Central Universities(Grant No.FRF-TP-20-006A2).
文摘Since it was first proposed,the space solar power station(SSPS)has attracted great attention all over the world;it is a huge space system and provides energy for Earth.Although several schemes and abundant studies on the SSPS have been proposed and conducted,it is still not realized.The reason why SSPS is still an idea is not only because it is a giant and complex project,but also due to the requirement for various excellent space materials.Among the diverse required materials,we believe energy materials are the most important.Herein,we review the space energy conversion materials for the SSPS.
