Dynamic Security Analysis Framework for Future Large Grids With High Renewable Penetrations

Abstract

The 100% renewable energy targets from the policymakers for the future grids have drawn a significant amount of interest. The high renewable penetration made future large interconnected grids (LIGs) more volatile and harder to understand using historical observations. This led to the need to study future LIGs using a wide range of future-year operating conditions and contingencies. However, existing planning tools are not sufficient for the dynamic security assessment (DSA) of these future LIGs due to a lack of detailed modeling capabilities and computational limitations when processing a wide range of scenarios. This paper addresses these two challenges by proposing; 1) an efficient modeling framework that can generate large grid's dynamic data with cascade behaviors for a wide range of scenarios. This data is generated by respecting the constraints of production cost models and their respective AC power flow dynamic simulation models at an hourly resolution; 2) an unsupervised machine learning(ML)-based approach for fast scanning the DSA data. The proposed approach uses feature engineering techniques and fast Fourier transforms to transform the time series signals into visually distinguishable frequency domain signals for DSA. The proposed simulation framework is used to generate 1.485 terabytes of dynamic simulation data for the 2028 WECC system containing 4455 scenarios. The proposed ML framework used the 2028 WECC system to demonstrate its effectiveness and speed in identifying critical scenarios.INDEX TERMS Dynamic security assessment, unsupervised learning, feature engineering, large grids.

Review of Data-Driven Techniques for On-Line Static and Dynamic Security Assessment of Modern Power Systems

“…Specifically, if the stability criteria are challenging to violate, there will be few instances where the algorithm can learn an insecure state within the training set. This situation is well-documented in the literature as classification in the presence of imbalanced data sets [152].…”

“…Therefore, accurately assessing system security in the presence of high shares of renewable energies is crucial. However, few studies address the challenges posed by a large share of renewable power generators, such as [152], which attempts to generate and cluster a high number of scenarios rapidly due to the variability of non-programmable renewable power profiles for DSA, and [117], which incorporates wind power data to consider wind power uncertainty in frequency stability assessment. Time-series analysis is essential for understanding the temporal variation of wind power generation over multiple time scales to analyze system stability, as demonstrated by [153] in proposing a fast-scanning method for the current stability analysis of the system.…”

The role of data-driven methods in power system security assessment from aggregated grid data

“…On the same thinking line (Guddanti et al, 2023) deals with synthetic data generation and proposes a methodology that employs PCA for data dimensionality reduction and unsupervised algorithms (e.g., based on k-means) for swift scanning.…”