This article proposes a framework for stochastic eigenvalue analysis of electric power systems with a high penetration of inertialess renewable generation, focusing on the influential factors that affect the eigenvalue movement resulting from the inertia reduction. We analytically investigate the influence of the inertia and the variation in renewable generation on small-signal stability using stochastic Monte-Carlo based eigenvalue and modal controllability analysis. With the increasing penetration of renewable generation, power system behavior depends on meteorological conditions more, which results in the reduction of power system inertia due to the decommitment of generators and a consequent deterioration of power system stability. Against this backdrop, stochastic eigenvalue analysis is carried out to examine the movement of eigenvalues resulting from the variable operating conditions. The contribution of the generators to the oscillatory modes is theoretically proved using modal controllability in a power system with reduced inertia. For the verification of the research, the simulation is carried out using DIgSILENT/PowerFactory.

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具有高可再生渗透率的电力系统的随机特征分析：改变惯性对振荡模式的影响

本文提出了一种无惯性可再生能源发电渗透率高的电力系统的随机特征值分析框架，重点关注影响惯性减少引起的特征值运动的影响因素。我们使用基于随机蒙特卡罗的特征值和模态可控性分析来分析研究惯性和可再生能源发电的变化对小信号稳定性的影响。随着可再生能源发电的日益普及，电力系统行为更多地依赖于气象条件，这导致由于发电机退役导致电力系统惯性降低，电力系统稳定性随之恶化。在这样的背景下，执行随机特征值分析以检查由可变操作条件引起的特征值的移动。在具有减小惯性的电力系统中使用模态可控性从理论上证明了发电机对振荡模式的贡献。为了验证研究，使用 DIgSILENT/PowerFactory 进行模拟。