Lamprianidou, I. S.Papadopoulos, T. A.Kryonidis, G. C.Yetkin, E. FatihPippi, K. D.Chrysochos, A., I2023-10-192023-10-19202132352-4677https://doi.org/10.1016/j.segan.2021.100509https://hdl.handle.net/20.500.12469/5163Quasi-static analysis of power systems can be performed by means of timeseries-based and probability density function-based models. In this paper, the effect of different load and generation modelling approaches on the quasi-static analysis of distribution networks is investigated. Different simplified load and distributed renewable energy sources generation timeseries-based models are considered as well as probabilistic analysis. Moreover, a more sophisticated approach based on cluster analysis is introduced to identify harmonized sets of representative load and generation patterns. To determine the optimum number of clusters, a three-step methodology is proposed. The examined cases include the quasi-static analysis of distribution networks for different operational conditions to identify the simplified modelling approaches that can efficiently predict the network voltages and losses. Finally, the computational efficiency by using the simplified models is evaluated in temperature-dependent power flow analysis of distribution networks. (C) 2021 Elsevier Ltd. All rights reserved.eninfo:eu-repo/semantics/closedAccessPattern-RecognitionTime-SeriesPower-FlowReanalysisEnergyClusteringPattern-RecognitionDistributed generation modellingTime-SeriesLoad modellingPower-FlowLoad timeseriesReanalysisPhotovoltaic systemsEnergyWind turbinesArticle27WOS:00068744490000710.1016/j.segan.2021.1005092-s2.0-85111263267Q1Q1