Wind and Photovoltaic Power Time Series Data Aggregation Method Based on an Ensemble Clustering and Markov Chain

Reducing the input wind and photovoltaic power time series data can improve the efficiency of time sequential simulations.In this paper,a wind and photovoltaic power time series data aggregation method based on an ensemble clustering and Markov chain(ECMC)is proposed.The ECMC method can effectively reduce redundant information in the data.First,the wind and photovoltaic power time series data were divided into scenarios,and ensemble clustering was used to cluster the divided scenarios.At the same time,the Davies-Bouldin Index(DBI)is adopted to select the optimal number of clusters.Then,according to the temporal correlation between wind and photovoltaic scenarios,the wind and photovoltaic clustering results are merged and reduced to form a set of combined typical day scenarios that can reflect the characteristics of historical data within the calculation period.Finally,based on the Markov Chain,the state transition probability matrix of various combined typical day scenarios is constructed,and the aggregation state sequence of random length is generated,and then,the combined typical day scenarios of wind and photovoltaic were sampled in a sequential one-way sequence according to the state sequence and then are built into a representative wind and photovoltaic power time series aggregation sequence.A provincial power grid was chosen as an example to compare the multiple evaluation indexes of different aggregation methods.The results show that the ECMC aggregation method improves the accuracy and efficiency of time sequential simulations.

Short-term wind power prediction based on extreme learning machine with error correction

Introduction:Large-scale integration of wind generation brings great challenges to the secure operation of the power systems due to the intermittence nature of wind.The fluctuation of the wind generation has a great impact on the unit commitment.Thus accurate wind power forecasting plays a key role in dealing with the challenges of power system operation under uncertainties in an economical and technical way.Methods:In this paper,a combined approach based on Extreme Learning Machine(ELM)and an error correction model is proposed to predict wind power in the short-term time scale.Firstly an ELM is utilized to forecast the short-term wind power.Then the ultra-short-term wind power forecasting is acquired based on processing the short-term forecasting error by persistence method.Results:For short-term forecasting,the Extreme Learning Machine(ELM)doesn’t perform well.The overall NRMSE(Normalized Root Mean Square Error)of forecasting results for 66 days is 21.09%.For the ultra-short term forecasting after error correction,most of forecasting errors lie in the interval of[-10 MW,10 MW].The error distribution is concentrated and almost unbiased.The overall NRMSE is 5.76%.Conclusion:The ultra-short-term wind power forecasting accuracy is further improved by using error correction in terms of normalized root mean squared error(NRMSE).