电测与仪表2024，Vol.61Issue(7)：41-49,122,10.DOI:10.19753/j.issn1001-1390.2024.07.007
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 coarse-grained MCMC
摘要
Abstract
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 distri-bution characteristics,autocorrelation function characteristics and daily average power distribution characteristics of the simulation sequence generated by the method proposed in this paper are very close to the measured data.This method can well retain the probability distribution characteristics and time-varying fluctuation characteristics of wind power sequence,which has important engineering practical value.关键词
风电出力模拟/典型日/出力特征/聚类算法/蒙特卡洛Key words
wind power output simulation/typical day/output characteristics/clustering algorithm/Monte Carlo分类
信息技术与安全科学引用本文复制引用
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):41-49,122,10.基金项目
国家自然科学基金资助项目(52077112) （52077112）
国家电网有限公司科技项目(SGGSKY00WYJS2000129) （SGGSKY00WYJS2000129）
