Markov chain-based wind power time series modelling method considering the influence of the state duration on the state transition probability

Due to the inherent uncertainties of wind power, its large-scale integration strongly impacts the planning and operation of power systems. To investigate these impacts, a stochastic model is required to more accurately capture the wind power's characteristics. This study proposes an improved Markov chain (MC)-based time series (TS) modelling method for the stochastic generation of synthetic wind power TS. First, a self-adaptive state division strategy is proposed to objectively classify historical data into several typical states. This strategy combines a state optimisation clustering model with a random-variable-modelling-oriented filter parameter optimisation method. Then, a three-dimensional state transition probability matrix (STPM) is proposed and constructed to generate synthetic wind power state TS. In contrast to the previous STPMs, the proposed STPM can capture the changing pattern of the transition probability against the state duration. Finally, the fluctuation quantity and noise are separately and sequentially added to the generated state TS, as an improvement over previous fluctuation characteristic addition methods, to obtain the final synthetic wind power TS. The results show that the proposed method outperforms previous MC-based TS modelling methods in reproducing historical characteristics, such as the transition and fluctuation characteristics, and does not increase the STPM construction algorithm's time complexity.