Browsing by Author "Fast, Sven"
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- ItemDevelopment of a software application for statistical analysis of photovoltaic plant performance(Stellenbosch : Stellenbosch University, 2015-12) Fast, Sven; Vermeulen, H. J.; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic EngineeringENGLISH ABSTRACT: Economic and environmental concerns together with increasing fossil fuel prices are giving rise to the incorporation of increased amounts of renewable energy sources into the power grid. Furthermore, international policies such as the Kyoto Protocol and government endorsed financial support mechanisms aid significantly in making headway in this direction. Amongst the numerous renewable energy technologies available, solar power is attracting a great deal of attention as it is a non-depletable and non-polluting source of energy. However, solar power has the drawbacks of being site dependant and intermittent in nature. For this reason, energy service providers and independent energy producers require accurate systems to forecast the power output of solar plants. Furthermore, time of use based energy generation statistics and forecasting models, i.e. with respect to the time when energy is being generated or consumed, are important in the context of small solar plants operating in conjunction with a local load. Generated energy forecasts and statistics are particularly useful in determining the return on investment of solar plants and conducting a financial analysis on feed-in tariffs and time of use tariff structures. This project focusses on the development and software implementation of a long term forecasting methodology for the energy output of a solar plant. Forecasting models are derived using a statistical approach based on measured historical generation data and takes place in the time of use context. The project aims at determining whether it is possible to model the energy output of a solar plant, in the time of use context, with probability distributions commonly used to model solar radiation. The implementation of the forecasting methodology includes the development of a relational database structure together with a forecasting software application. The relational database provides persistent storage for both historical generation data and time of use structure data, while the software application implements statistical theory to derive long term forecasting models. Finally, a case study is conducted for an operational solar plant to test and evaluate the implemented forecasting methodology and software application. The case study is conducted with respect to time of use structures for seasonal and monthly datasets. It is found that the energy output of the solar plant can be successfully modelled and forecasted in the time of use context using monthly datasets. Furthermore, generation statistics are used to conduct a financial analysis on renewable energy feed-in tariffs and to determine the annual monetary savings from generated energy for the solar plant.