Browsing by Author "De Bruyn, Johannes"

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    Investigating probabilistic techniques for calculating the system capacity in the South African transmission network
    (Stellenbosch : Stellenbosch University, 2024-03) De Bruyn, Johannes; Bekker, Bernard; Dalton, Amaris; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
    ENGLISH ABSTRACT: he large-scale introduction of variable renewable energy (VRE) generators to existing power systems, which were predominantly designed for centralised and dispatchable generation, comes with capacity and stability planning challenges. Academic literature suggests that these challenges are not sufficiently addressed using traditional power system analysis and modelling methods. Many research papers therefore call for the adoption of more statistically sophisticated methodologies that can more adequately describe the uncertainties inherent to variable renewable energy resources. Probabilistic load flow techniques especially have been found to provide a more nuanced representation of the capacity of a power system to host additional generation. This study sets out to prove the hypothesis that using a simplified probabilistic load flow methodology for calculating system capacity would more comprehensively clarify the constraints associated with hosting VRE in the South African transmission network. It does this by developing and applying such a methodology to the Northern Cape transmission network based on similar methodologies in literature. The probabilistic methodology is compared against results gained from a deterministic system capacity analysis also applied to the same portion of the transmission network. Two significant concerns regarding probabilistic analyses are the relatively long solution times and extensive data requirements. Literature suggests that simplifying network representations with equivalent circuits could reduce both while maintaining an acceptable level of accuracy. This study included a brief analysis of these claims by reducing the transmission network model to only the Northern Cape using Ward equivalents for the rest of the system and comparing the power flows in the full and simplified system under various conditions. The results showed that equivalent circuits can reduce solution times considerably without introducing significant errors when care is taken in setting up the internal and external networks. This study further showed that deterministic scenario analyses do not consistently, when seasonal variations are introduced, predict the extreme behaviour of power systems unless a large number of scenarios are considered. Probabilistic system capacity analyses on the other hand were consistent in providing the likely and extreme loading states of the Northern Cape. Recommendations for future studies are the expansion of the methodology presented to include calculating the hosting capacity of the South African system probabilistically to determine whether current deterministic methods are overly conservative in their estimations. Another study would be identifying edge-case scenarios for deterministic analyses in the current South African power system.