Probabilistic renewable energy modelling in South Africa
type: CreativeWork
description: The variability of solar power creates problems in planning and managing power system operations. It is critical to forecast accurately in order to maintain the safety and stability of large-scale integration of solar power into the grid. Accurate forecasting is vital because it prevents transmission obstruction and maintains a power equilibrium. This thesis uses robust models to solve this problem by addressing four main issues. The first issue involves the construction of quantile regression models for forecasting extreme peak electricity demand and determining the optimal number of units to commit at minimal costs for each period using the forecasts obtained from the developed models. The bounded variable mixed-integer linear programming (MILP) model solves the unit commitment (UC) problem. This is based on priority constraints where demand is first met from renewable energy sources followed by energy from fossil fuels. Secondly, the thesis discusses the modelling and prediction of extremely high quantiles of solar power. The methods used are a semi-parametric extremal mixture (SPEM), generalised additive extreme value (GAEV) or quantile regression via asymmetric Laplace distribution (QR-ALD), additive quantile regression with covariate t (AQR-1), additive quantile regression with temperature variable (AQR-2) and penalised cubic regression smoothing spline (benchmark) models. The predictions from this study are valuable to power utility decision-makers and system operators in knowing the maximum possible solar power which can be generated. This helps them make high-risk decisions and regulatory frameworks requiring high-security levels. As far as we know, this is the first application to conduct a comparative analysis of the proposed robust models using South African solar irradiance data. The interaction between global horizontal irradiance (GHI) and temperature helps determine the maximum amount of solar power generated. As temperature increases, GHI increases …
abstract: The variability of solar power creates problems in planning and managing power system operations. It is critical to forecast accurately in order to maintain the safety and stability of large-scale integration of solar power into the grid. Accurate forecasting is vital because it prevents transmission obstruction and maintains a power equilibrium. This thesis uses robust models to solve this problem by addressing four main issues. The first issue involves the construction of quantile regression models for forecasting extreme peak electricity demand and determining the optimal number of units to commit at minimal costs for each period using the forecasts obtained from the developed models. The bounded variable mixed-integer linear programming (MILP) model solves the unit commitment (UC) problem. This is based on priority constraints where demand is first met from renewable energy sources followed by energy from fossil fuels. Secondly, the thesis discusses the modelling and prediction of extremely high quantiles of solar power. The methods used are a semi-parametric extremal mixture (SPEM), generalised additive extreme value (GAEV) or quantile regression via asymmetric Laplace distribution (QR-ALD), additive quantile regression with covariate t (AQR-1), additive quantile regression with temperature variable (AQR-2) and penalised cubic regression smoothing spline (benchmark) models. The predictions from this study are valuable to power utility decision-makers and system operators in knowing the maximum possible solar power which can be generated. This helps them make high-risk decisions and regulatory frameworks requiring high-security levels. As far as we know, this is the first application to conduct a comparative analysis of the proposed robust models using South African solar irradiance data. The interaction between global horizontal irradiance (GHI) and temperature helps determine the maximum amount of solar power generated. As temperature increases, GHI increases …
author: Ravele, Thakhani
language: en
keywords: Bivariate Dependence Modelling, External Mixture Model, Global Horizontal Irradiance Forecastingn, Multivariate Adaptive Regression Spline, Quantile repression, Unit commitment, UCTD
date published: 2024-05-05
date modified: 2026-07-27T21:57:10Z
provider: University of Venda
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Venda