The risks associated with rare events threatening the security of power system operation are of paramount importance to power system planners and operators. To analyze the risks caused by high-impact, low-frequency rare events, an immensely large number of samples are typically required for the Monte-Carlo (MC) method on the high-fidelity power system model to achieve a sufficient accuracy, thereby rendering this approach computationally prohibitive. To handle this problem efficiently, it is desirable to construct a surrogate model for the power system response. However, the straightforward MC sampling of the low-fidelity surrogate can lead to biased results in the low-probability tail regions that are vital to risk assessment. Moreover, a single surrogate is unable to handle the topology uncertainties caused by random branch outages. To overcome these issues, we propose a hybrid multi-surrogate (HMS) method based on the polynomial chaos expansion (PCE) with low-probability tail events reevaluated by the high-fidelity model through a probabilistic analysis. This method improves the computational efficiency of the MC method for rare-event risk assessment by leveraging multi-fidelity models while retaining the desired accuracy. Simulations conducted in three test systems verify the excellent performances of the HMS method.

中文翻译：

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混合多代理方法评估概率潮流中稀有事件的风险

对于电力系统规划人员和操作人员而言，与罕见事件相关的威胁到电力系统运行安全的风险至关重要。为了分析由高影响力的低频罕见事件引起的风险，高保真电力系统模型上的蒙特卡洛（MC）方法通常需要使用大量样本才能获得足够的精度，从而呈现出这种方法在计算上是禁止的。为了有效地解决该问题，期望构造用于电力系统响应的替代模型。但是，低保真替代品的直接MC采样可能会导致对风险评估至关重要的低概率尾部区域中的结果有偏差。而且，单个代理无法处理由随机分支中断引起的拓扑不确定性。为了克服这些问题，我们提出了一种基于多项式混沌扩展（PCE）的混合多代理（HMS）方法，该方法具有通过概率分析由高保真模型重新评估的低概率尾部事件。这种方法通过利用多保真度模型，同时保持所需的准确性，提高了MC方法用于罕见事件风险评估的计算效率。在三个测试系统中进行的仿真验证了HMS方法的出色性能。该方法通过利用多保真度模型，同时保持所需的准确性，提高了MC方法用于罕见事件风险评估的计算效率。在三个测试系统中进行的仿真验证了HMS方法的出色性能。这种方法通过利用多保真度模型，同时保持所需的准确性，提高了MC方法用于罕见事件风险评估的计算效率。在三个测试系统中进行的仿真验证了HMS方法的出色性能。