论文标题
概率负载 - 边缘 - 边缘评估，使用藤蔓和高斯工艺仿真
Probabilistic Load-Margin Assessment using Vine Copula and Gaussian Process Emulation
论文作者
论文摘要
可再生能源的渗透不断增加，以及负载的变化，使电力系统的大量不确定性在威胁到电力系统计划和操作的安全性。面对这些挑战，本文提出了一种具有成本效益的非参数方法，以量化不确定功率注射对负载边缘的影响。首先，我们建议通过新型的葡萄藤生成系统不确定的输入，因为它在模拟复杂的多元高度依赖模型输入方面的能力。此外，为了减少传统的蒙特卡洛方法中所需的刺激性计算时间，我们建议使用非参数，基于高斯 - 过程 - 启示剂的减少阶模型来替换原始复杂的延续电源模型。该模拟器使我们能够以可忽略的计算成本以采样值执行耗时的延续功率流求解器。在IEEE 57-BUS系统上进行的模拟附加了相关的可再生生成，揭示了该方法的出色性能。
The increasing penetration of renewable energy along with the variations of the loads bring large uncertainties in the power system states that are threatening the security of power system planning and operation. Facing these challenges, this paper proposes a cost-effective, nonparametric method to quantify the impact of uncertain power injections on the load margins. First, we propose to generate system uncertain inputs via a novel vine copula due to its capability in simulating complex multivariate highly dependent model inputs. Furthermore, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. The simulations conducted on the IEEE 57-bus system, to which correlated renewable generation are attached, reveal the excellent performance of the proposed method.
