Day-Ahead Scenario Analysis of Wind Power Based on ICGAN and IDTW-Kmedoids

Aiming at the problem that current scenario analysis methods fail to fully capture complex time series correlations during scenario generation and do not consider time series similarities during scenario reduction, a wind power day-ahead scenario analysis method based on ICGAN and IDTW-Kmedoids is proposed. First, introducing a multi-time scale convolution layer into the CGAN scenario generation model(ICGAN) comprehensively extracts wind power time series correlation information, thereby improving scenario set generation quality. Secondly, the Kmedoids clustering algorithm (IDTW-Kmedoids) is used for scenario reduction. This algorithm uses an improved DTW algorithm to calculate the distance between clusters, which can better calculate the similarity of time series data and improve the effect of scenario reduction. The calculation results show that compared with traditional scenario analysis methods, this method can better capture the correlation and similarity of complex time series and can derive more representative typical scenarios.