Exploring the potentiality of future standard candles and standard sirens to detect cosmic opacity

In this work,we explore the potentiality of future gravitational wave(GW)and Type la supermovae(SNe la)measurements to detect cosmic opacity by comparing the opacity-free luminosity distance(LD)of GW events with the opacity-dependent LD of SNe la observations.The GW data are simulated from the future measurements of the ground-based Einstein Telescope(ET)and the space-borne Deci-Herz Interferometer Gravitational wave Obser-vatory(DECIGO).The SNe la data are simulated from the observations of the Wide Field Infrared Survey Tele-scope(WFIRST)that will be collected over the next few decades.A binning method is adopted to match the GW data with the SNe la data at the same redshift z with a sclection criterion|△z|<0.005,and most of the available data from the GW measurements is employed to detect cosmic opacity due to improvements in the distribution of the fu-ture SNe la observations.Results show that the uncertainties of the constraints on cosmic opacity can be reduced toσe~0.0041 and 0.0014 at the lσconfidence level(CL)for 1000 data points from the ET and DECIGO measure-ments,respectively.Compared with the allowable limits of intergalactic opacity obtained from quasar continum ob-servations,these future astronomical observations can be used to verify the cosmic opacity.In this way,GW and SNe la measurements can be used as important and effective tools to detect cosmic opacity in the future.

Adaptive Power Control Based on Double-layer Q-learning Algorithm for Multi-parallel Power Conversion Systems in Energy Storage Station

An energy storage station(ESS)usually includes multiple battery systems under parallel operation.In each battery system,a power conversion system(PCS)is used to connect the power system with the battery pack.When allocating the ESS power to multi-parallel PCSs in situations with fluctuating operation,the existing power control methods for parallel PCSs have difficulty in achieving the optimal efficiency during a long-term time period.In addition,existing Q-learning algorithms for adaptive power allocation suffer from the curse of dimensionality.To overcome these challenges,an adaptive power control method based on the double-layer Q-learning algorithm for n parallel PCSs of the ESS is proposed in this paper.First,a selection method for the power allocation coefficient is developed to avoid repeated actions.Then,the outer action space is divided into n+1 power allocation modes according to the power allocation characteristics of the optimal operation efficiency.The inner layer uses an actor neural network to determine the optimal action strategy of power allocations in the non-steady state.Compared with existing power control methods,the proposed method achieves better performance for both static and dynamic operation efficiency optimization.The proposed method optimizes the overall operation efficiency of PCSs effectively under the fluctuating power outputs of the ESS.