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|a There is a need to provide low cost electric power systems in a number of locations around the world. However, the size and cost of renewable energy generators and storage increase when they are used alone, due to the stochastic nature of sources. Reliability similar to the grid power supply can only be achieved by combining more complementary energy sources in the presence of storage devices; thus creating a hybrid renewable or distributed energy system. The optimal sizing of a hybrid renewable energy system (HRES) is important in order to keep the system reliable with low investment cost and with adequate or full use of resources. In this work, the stochastic nature of renewable generation and demand and non-linear system characteristics are explored in the process of optimizing the size of a HRES. In achieving this, a hybrid optimization methodology was developed for the intention of matching the renewable generation with the demand of a site. The results showed that the demand profile dictates the size of a HRES and is as important as the optimization method. In previous studies, average hourly demand profiles for a day, total monthly load, seasonal daily load profile, average hourly demand profile repeated throughout the year have been examined, however, this work demonstrates that these demand profiles fail to represent real life electrical demand. As such, this work extends our understanding of the influence of demand, using multiple "real world" demand profiles, on sizing a HRES that result in varying temporal position of loads and the peak energy demand. It shows that the total daily demand of a site can vary significantly due to socio-demographic factors and that in sizing a HRES, the variation of the annual demand profile due to these factors must be considered. Moreover, it furthers this by exploring the random day to day variations of peak demand both in magnitude and temporal position throughout the year that occur due to the varying weather conditions and habits of the user. It was determined that the effect of this random variation of electrical load on the optimal size of a HRES was significant and an advanced method for sizing a HRES under these conditions was developed and demonstrated. Finally, a series of demand side management (DSM) options were proposed for the HRES and incorporated with the sizing method. It was shown that the investment costs could be significantly reduced with the introduction of each of the DSM options, in particular, by utilizing excess energy generated by the HRES to heat a thermal storage system.
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