随着新能源发电和电动汽车等技术的发展，电力系统中的不确定性因素显著增加，准确辨识影响系统输出的关键输入变量有利于提高系统运行控制水平，降低电力系统随机问题的求解规模。传统的局部灵敏度分析一般基于输出量对输入量的偏导数，无法考虑系统的非线性、输入变量的所有取值以及输入变量间的相关性，对此提出了面向新能源电力系统负荷裕度的全局灵敏度分析（Global Sensitivity Analysis，GSA）方法，旨在辨识影响负荷裕度的关键不确定性因素。首先，建立了新能源电力系统负荷裕度的概率计算模型；其次，介绍了针对独立输入变量的GSA理论；然后，基于条件方差理论和Nataf变换技术，提出了计及输入变量相关性的GSA方法，用于量化风光发电功率随机波动的影响；最后，通过算例仿真，计算了风光发电功率作为输入变量的一阶灵敏度和总灵敏度，有效辨识了关键输入变量，对比了强弱相关性下源端和荷端对负荷裕度的影响程度，验证了所提方法的有效性。

With advancements in new energy generation and electric vehicle technologies, uncertainties in power systems have significantly increased. Accurately identifying key input variables affecting system output helps enhance operation control and reduce the scale of solving stochastic problems. Traditional local sensitivity analysis, based on partial derivatives of output to input, fails to account for system nonlinearity, all input values, and variable correlations. A Global Sensitivity Analysis (GSA) method for new energy power systems' load margin has been proposed to identify key uncertainty factors. First, a probabilistic model for load margin was established. Second, GSA theory for independent input variables was introduced. Then, using conditional variance theory and Nataf transformation, a GSA method considering correlations was developed to quantify the impact of wind and solar power fluctuations. Finally, simulations calculated first-order and total sensitivity indices for wind and solar power, identifying key inputs and comparing source and load impacts under strong and weak correlations, validating the method's effectiveness.