Speaker
Majed Alotaibi
Title
Renewable Resources Modeling in Distribution System Planning
Abstract
In last decades, the attention of placing renewable resources in conventional power system has increased because of its ability to reduce the fossil fuel consumption which leads to the preservation of the environment. However, solid models that are able to handle the uncertainty in generation level are required. In this thesis, renewable resources (wind and PV based DGs) are optimally allocated and sized using a probabilistic optimization model. Genetic algorithm is used in order to minimize the annual energy losses of a distribution system. This work also proposes an iterative algorithm to determine the minimum states that can represent the behavior of wind and PV output power in operational planning problems. The proposed methodology utilized the probabilistic optimization model to testify the robustness of this method. The variability from the load side and the uncertainty from the feeding side are considered. The accuracy of results is evaluated by using two significant variables which are annual energy losses and total DGs penetration. The proposed method reveals that considerable savings in execution (computational) time are obtained with accurate results. The reduced state method could be used in reliability studies to reduce system states with renewable resources and in short period operation problems that require fast processing time.