Unit commitment with wind generators fuzzy/mixed integer linear programming

Day-ahead unit commitment (UC) solution methods seek to determine the status and output of all available generators. In a world with an increasing integration of renewable energy sources such as wind electric generators (WEG), the UC solution process must model and include WEGs too in the decision making process. Power output of WEGs in a day-ahead decision making process are usually modeled using a short term probabilistic forecast.;Two strategies are proposed in this thesis to minimize costs and handle risks introduced due to WEGs. These formulations are posed as fuzzy optimization models and are solved using the MILP (Mixed Integer Linear Programming) technique. The proposed strategies are tested on a 26 generator system that has 3 wind generators. The results with two strategies of handling WEGs are discussed.;Inclusion of WEGs introduces uncertainty in the solution that may be quantified through an imbalance risk function. Thus a UC solution method should seek a solution to minimize cost and risk in schedule.