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.

中文翻译：

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结合大气过程分解的嵌套MCMC方法用于光伏功率仿真

大规模光伏（PV）发电的不确定性会严重影响电力系统的规划和运营。因此，迫切需要一种能够准确捕获此类不确定性的随机PV功率模拟方法，以为进一步研究具有光伏电站的电力系统提供基础。本文提出了一种结合大气过程分解（APD）的嵌套马尔可夫链蒙特卡罗（MCMC）方法进行光伏发电仿真。首先，设计一个与当地实际晴空大气匹配的虚拟晴空模型，以将PV功率转换为衰减系数（AC）。其次，提出了一种基于APD的嵌套AC马尔可夫链（MC），以在考虑它们之间的耦合关系的情况下区分宏AC和中微AC。第三，开发了一种改进的MCMC方法来模拟该MC' 以嵌套的方式随机地合成交流时间序列的每一层；由于采用了最佳状态数决策模型来确保MCMC模型的质量，并采用3D转换概率矩阵来捕获状态持续时间相关的动态变化，因此该方法可以提高合成精度。最后，将合成交流时间序列转换为光伏功率时间序列。结果证实了该方法在再现光伏功率特性方面的准确性优于先前方法。的质量和3D过渡概率矩阵，以捕获相对于状态持续时间的过渡概率的动态变化。最后，将合成交流时间序列转换为光伏功率时间序列。结果证实了该方法在再现光伏功率特性方面的准确性优于先前方法。的质量和3D过渡概率矩阵，以捕获相对于状态持续时间的过渡概率的动态变化。最后，将合成交流时间序列转换为光伏功率时间序列。结果证实了该方法在再现光伏功率特性方面的准确性优于先前方法。