A Nested MCMC Method Incorporated With Atmospheric Process Decomposition for Photovoltaic Power Simulation

Abstract

Large-scale photovoltaic (PV) generation's uncertainties significantly affect power system planning and operations. Thus, a stochastic PV power simulation method, which can accurately capture such uncertainties, is urgently needed to provide a foundation for further uncertain studies on power systems with PV stations. This paper proposes a nested Markov chain Monte Carlo (MCMC) method incorporated with atmospheric process decomposition (APD) for PV power simulation. First, an imaginary clear-sky model matching the local actual clear-sky atmosphere is designed to convert PV power to an attenuation coefficient (AC). Second, a nested AC Markov chain (MC) is proposed based on APD to distinguish the macroscale and meso-microscale ACs while consider their coupling relationship. Third, an improved MCMC method is developed to simulate this MC's each layer in a nested manner for stochastically synthesizing AC time series; this method can improve synthesizing accuracy thanks to the adoption of an optimal state number decision-making model to ensure the MCMC model's quality and a 3D transition probability matrix to capture the dynamics of transition probabilities with respect to state duration. Finally, synthetic AC time series are reconverted to PV power time series. The results validate the proposed method's accuracy over previous ones in reproducing PV power characteristics.