Probabilistic Assessment of Static Load Model Parameters in Renewable-rich Power Systems

Abstract

Load model parameters are affected due to uncertainties in power system parameters caused by higher renewable distributed generation (DG) and new types of loads. The inherent uncertainties associated with the renewable DGs and system loads are neglected in the deterministic approach of quantifying load model parameters, and hence the probabilistic approach is important to adopt with the load modeling in modern power systems. In this paper, a probabilistic approach has been presented to assess the impact of renewable DGs and system loads uncertainties on static load model parameter, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K_{p}$</tex> . In total, eight probability density functions (pdf) are adopted, and the most suitable probability distribution is determined with respect to various levels of solar PV and wind penetration, and multiple load compositions. The IEEE 33 bus network is simulated in DIgSILENT PowerFactory software. The results identified that the normal, lognormal and generalized extreme value (GEV) represent the most suitable pdf for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K_{p}$</tex> distribution with 20%, 40%, and 60% solar PV penetration, respectively, while GEV represents the most suitable pdf for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K_{p}$</tex> with 20%, 40%, and 60% for both wind penetration and dynamic load in the load composition.