MCMC and Affinity Propagation Clustering-Based Generation of Annual Wind Power Time Series

In the context of energy structure transformation, the large-scale integration of wind power introduces challenges for power system planning due to its randomness and intermittency. This study proposes an annual wind power time-series generation method combining Markov Chain Monte Carlo (MCMC) and Affinity Propagation (AP) clustering. The method involves data normalization, MCMC-based series generation, and AP clustering for selecting representative series. Evaluation results show that the generated series closely match the original data in statistical characteristics, temporal correlation, and multifractal properties, effectively capturing the dynamic features of wind power output. This approach enhances the reliability of wind power time-series modeling for power system planning and operation.