This paper investigates the impact of generator dispatch (dictated by cost) and locational aspects related to renewable generation connection and consequent disconnection of synchronous generation, on transient stability. As conventional synchronous generation (SG) is displaced by power electronic converter interfaced generation (PEC), the transient behaviour of the power system may change significantly due both to the changed power flows resulting from the different locations of renewables compared with thermal plant and the different dynamic characteristics of PEC. This paper uses an AC Optimal Power Flow (OPF) to determine credible, minimum cost dispatches of generation in a test network with increasing penetration of PEC in different locations. It is found that the critical locations of the system can vary significantly with respect to economic dispatch, location of disconnection of SG and location of PEC, highlighting the increasing temporal and spatial change in system dynamic behavior.
Increasing renewable generation can lead to significant spatial and temporal changes to the rotor angle stability boundary, such that critical contingencies may drastically change. Additionally, the inherent variability of renewables increases the number of operational scenarios that require stability assessment. This paper presents a methodology whereby a series of location-specific Decision Tree Regressors are trained, using power system variables to estimate the Critical Clearing Time (CCT) on a locational basis throughout a network. Permutation feature importance is used to reveal the most important power system variables for CCT estimation at each location (capturing aspects related to physical system characteristics, operational parameters as well as economic dispatch). Consequently, estimation of the duration and location of the critical fault can also be made -along with identification of important system variables that explicitly impact the critical fault. Results on the IEEE 39-bus network show accurate estimation of locational CCTs, with a mean absolute percentage error of 1.19% on average. Moreover, the mean absolute percentage error for the minimum CCT is 0.49%. An analysis of important power system variables is provided, demonstrating how the method can assist in the design of targeted locational interventions to improve the stability margin at specific locations.
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