This article presents a framework for managing the uncertainty in power system dynamics through distributionally robust stability-constrained optimization. The key highlights and insights are:
The authors identify that the uncertainty associated with the system dynamic parameters, such as the impedances of synchronous generators and inverter-based resources, can significantly influence the system stability and operation. This uncertainty needs to be explicitly considered in the stability-constrained optimization.
To address this issue, the authors propose a two-step approach:
a. Quantify the uncertainty of the stability constraint coefficients by propagating the statistical moments of the uncertain system parameters through a nonlinear and implicit function composition.
b. Formulate a distributionally robust chance-constrained optimization problem to ensure system stability under the derived uncertainty of the constraint coefficients.
The authors derive the analytical expressions for the first and second-order derivatives of the stability index with respect to the uncertain system parameters, which are then used to estimate the statistical moments of the stability constraint coefficients.
The proposed distributionally robust stability-constrained optimization is demonstrated on a modified IEEE 39-bus system. The results show that the approach can effectively manage the system dynamic uncertainty and maintain stable system operation.
An alternative formulation based on distributionally robust learning is also discussed, which directly considers the uncertainty in the training process of the stability constraints. However, this formulation becomes computationally intractable due to the highly nonlinear relationship between the stability index and the uncertain system parameters.
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Önemli Bilgiler Şuradan Elde Edildi
by Zhongda Chu,... : arxiv.org 04-23-2024
https://arxiv.org/pdf/2309.03798.pdfDaha Derin Sorular