This article proposes a framework for stochastic eigenvalue analysis of electric power systems with a high penetration of inertialess renewable generation, focusing on the influential factors that affect the eigenvalue movement resulting from the inertia reduction. We analytically investigate the influence of the inertia and the variation in renewable generation on small-signal stability using stochastic Monte-Carlo based eigenvalue and modal controllability analysis. With the increasing penetration of renewable generation, power system behavior depends on meteorological conditions more, which results in the reduction of power system inertia due to the decommitment of generators and a consequent deterioration of power system stability. Against this backdrop, stochastic eigenvalue analysis is carried out to examine the movement of eigenvalues resulting from the variable operating conditions. The contribution of the generators to the oscillatory modes is theoretically proved using modal controllability in a power system with reduced inertia. For the verification of the research, the simulation is carried out using DIgSILENT/PowerFactory.
|Number of pages||11|
|Journal||IEEE Transactions on Power Systems|
|Publication status||Published - 2020 Nov|
Bibliographical noteFunding Information:
Manuscript received November 12, 2019; revised April 16, 2020; accepted May 30, 2020. Date of publication June 8, 2020; date of current version November 4, 2020. This work was supported under the framework of International Cooperation Program Managed in part by the National Research Foundation of Korea under Grant 2017K1A4A3013579, in part by the National Research Foundation of Korea (NRF) Grant funded in part by the Korea government (MSIT) under Grant 2018R1C1B5084745. Paper no. TPWRS-01708-2019. (Corresponding author: Jae Woong Shim.) Jae Woong Shim is with the Department of Energy Engineering, Inje University, Gimhae 50834, South Korea (e-mail: email@example.com).
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All Science Journal Classification (ASJC) codes
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering