Generating wind power time series based on its persistence and variation characteristics

Generation of wind power time series is an important foundational task for assisting electric power system planning and mak- ing decision. By analyzing the characteristics of wind power persistence and variation, th!.s paper proposes an improved Mar- kov chain Monte Carlo （MCMC） method, identified as the PV-MC method, for the direct generation of a synthetic series of wind power output. On the basis of the MCMC method, duration time and variation features are concluded in PV-MC method, gaining a more comprehensive reflection of wind power characteristics in the generated wind power time series. First, the wind power state series is generated to meet the state transition matrix based on the definition of the wind power state. Then, the time duration of each state in the series is determined by its respective duration character. Finally, the variation characteristic is used to convert the state series to a wind power time series. A significant amount of simulations are performed based on the PV-MC and MCMC methods and are then compared for 25 wind farms at 6 different locations throughout the world. The sim- ulation results show that the PV-MC method offers an excellent fit for the time domain features （persistence and variation characteristic） while holding other statistic features （mean value, variance, autocorrelation coefficient （ACC） and probability density function （PDF）） close to the MCMC method.

新时代中国共产党党内集中教育的马克思主义哲学意蕴

开展党内集中教育是中国共产党自我革命的主要方式和重要途径,是马克思主义哲学在新时代发展过程中的现实彰显与时代表达。党内集中教育作为推动中国共产党自我革命的重要举措和接续前进的力量之基,其中内含着丰富的哲学意蕴,体现了人民至上的群众观、知行合一的实践观、长抓不懈的发展观、聚焦关键的矛盾观和统揽全局的整体观。