张柏林,李希德,魏博,汪芙平,邵冲,赵伟.基于改进的场景分类和去粗粒化MCMC的风电出力模拟方法[J].电测与仪表,2024,61(7):41-49. zhangbolin,lixide,weibo,wangfuping,shaochong,zhaowei.Wind power output simulation method based on improved scene classification algorithm and time series correlation[J].Electrical Measurement & Instrumentation,2024,61(7):41-49.
基于改进的场景分类和去粗粒化MCMC的风电出力模拟方法
Wind power output simulation method based on improved scene classification algorithm and time series correlation
In order to achieve high-performance simulation of wind power output time series, this paper proposes a wind power simulation method based on SAGA-KM algorithm to achieve typical wind power scene classification and Copula function for wind power daily process Markov process modeling. The SAGA-KM algorithm combines the traditional KM algorithm with genetic algorithm and annealing algorithm, which can significantly improve the effect of wind power scene classification; based on the Copula function, the Markov chain fine probability model is used to realize the Markov process Monte Carlo simulation, overcoming the probability distribution deviation caused by coarse-grained. The actual simulation of the data of a wind farm in Gansu Province shows that the statistical distribution characteristics, autocorrelation function characteristics and daily average power distribution characteristics of the simulation sequence generated by the method in this paper are very close to the measured data. This method can well retain the wind power sequence. The probability distribution characteristics and time-varying fluctuation characteristics have important engineering practical value.
