In this thesis we construct and analyze a mean-variance utility maximization model for a risk-averse electric power generation company who wishes to determine the optimal levels of capacity and production from a single conventional fuel source and wind energy subject to the state Renewable Portfolio Standard (RPS). We assume the conventional fuel price and the federal wind power Production Tax Credit (PTC) level are random variables. This study is motivated by the highly stochastic nature of the PTC level and the existing competing claims for the impact of the RPS on the renewable energy development. Throughout our model we show how vastly different arguments and claims for the PTC and RPS policy can be accommodated within a single framework. We also analytically and numerically show how the RPS level, standard deviations of the fuel price and PTC level and their correlation coefficient would affect the generation company's decisions. Interesting and relevant managerial insights and economic implications are presented, as well as policy guidelines and recommendations for the PTC and RPS.