Development of a software application for statistical analysis of photovoltaic plant performance

Fast, Sven (2015-12)

Thesis (MSc)--Stellenbosch University, 2015.

Thesis

ENGLISH 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.

AFRIKAANSE OPSOMMING: Ekonomiese en omgewingskwessies, tesame met toenemende fossiel brandstof pryse gee aanleiding tot die inlywing van verhoogde bedrae van hernubare energie bronne in die kragnetwerk. Internasionale beleide soos die Kyoto Protokol en regering onderskryfde finansiële steun meganismes bied aansienlik hulp in die vordering van hierdie rigting. Onder die talle hernubare energie tegnologie tot ons biskikking, lok sonkrag 'n groot deel van die aandag, want dit is 'n onuitputbaar en nie- besoedelende bron van energie. Sonkrag het egter die nadele van gebieds afhanklikheid en hortend in natuur te wees. Om hierdie rede, benodig energie diensverskaffers en onafhanklike energie produsente akkurate stelsels om die kraglewering van sonkrag aanlegte te voorspel. Tyd van gebruik gebaseerde kragopwekking statistieke en voorspelling modelle, dws met betrekking tot die tyd wanneer energie gegenereer of verbruik word, is belangrik in die konteks van 'n klein sonkragte aanleg in samewerking met plaaslike laste. Gegenereerde energie voorspellings en statistieke is veral nuttig in die bepaling van die opbrengs op belegging van sonkrag aanlegte en die uitvoer van 'n finansiële ontleding op in - voer tariewe en tyd van gebruik tarief strukture. Hierdie projek fokus op die ontwikkeling en sagteware implementering van 'n lang termyn vooruitskatting metode vir die energie-uitset van 'n sonkrag aanleg. Voorspellingsmodelle is afgelei deur 'n statistiese benadering wat gebaseer is op historiese data en vind plaas in die tyd van gebruik konteks. Die doel van die projek is om te bepaal of dit moontlik is om die energie-uitset van 'n sonkrag stasie te modelleer in die tyd van gebruik konteks , met waarskynlikheidsverdelings wat gebruik word om sonstraling te modelleer. Die implementering van die vooruitskatting metode sluit in die ontwikkeling van 'n relasionele databasis struktuur tesame met 'n vooruitskatting sagteware program. Die relasionele databasis bied aanhoudende stoorplek vir beide historiese data en tyd van gebruik struktuur data, terwyl die sagteware program statistiese teorie implementer om langtermyn voorspelling modelle af te lei. Laastens word 'n gevallestudie gedoen vir 'n operasionele sonkrag aanleg om die vooruitskatting metode en sagteware program te toets en evalueer. Die gevallestudie is uitgevoer met betrekking tot tyd van gebruik strukture vir seisoenale en maandelikse datastelle. Dit is bevind dat die energie-uitset van sonkrag aanlegte kan suksesvol gemodelleer en voorspel word in die tyd van gebruik konteks met bettrekking tot maandelikse datastelle. Verder word gegenereerde energie statistieke gebruik om 'n finansiële ontleding van hernubare energie in-voer tariewe uit te voer en om die jaarlikse monetêre besparing van gegenereerde energie vir die sonkrag aanleg te bepaal.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/97867
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