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.