One major issue of wind generation is its intermittence and uncertainty due to the highly volatile nature of wind resource, and it affects both the economy and the operation of the wind farms and the distribution networks. It is thus urgently needed to develop modeling methods for accurate and reliable forecasts on wind power generation. Meanwhile, along with the ongoing electricity market deregulation and liberalization, wind energy is expected to be directly auctioned in the wholesale market. This brings the wind generation companies another issue of particular importance, i.e., how to maximize the profits by optimizing the bids in the gradually deregulated electricity market based on the improved wind forecasts. As such, the main objective of this dissertation research is to investigate and develop reliable modeling methods for tackling the two issues. To reach the objective, three main research tasks are identified and accomplished. Task 1 is about testing forecasting models for wind speed and power. After a thorough investigation into currently available forecasting methods, several representative models including autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) are developed for short-term wind forecasting. The forecasting performances are evaluated and compared in terms of mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The results reveal that no single model can outperform others universally. This indicates the need of generating a single robust and reliable forecast by applying a post-processing method. As such, a reliable and adaptive model for short-term forecasting the wind power is developed via adaptive Bayesian model averaging algorithms in Task 2. Experiments are performed for both long-term wind assessment and short-term wind forecasting. The results show that the proposed BMA-based model can always provide adaptive, reliable, and iv comparatively accurate forecast results in terms of MAE, RMSE, and MAPE. It also provides a unified approach to tackle the challenging model selection issue in wind forecasting applications. Task 3 is about developing a modeling method for optimizing the wind power bidding process in the deregulated electricity wholesale market. The optimal bids on wind power must take into account the uncertainty in wind forecasts and wind power generation. This research investigates the application of combining improved wind forecasts with agent-based models to optimize the bid and maximize the net earnings. The WSCC 9-bus 3-machine power system network and the IEEE 30-bus 9-GenCo power system network are adopted. Both single-sided and double-sided auctions are considered. The results demonstrate that improving wind forecasting accuracy helps increase the net earnings of wind generation companies, and that the implementation of agent learning algorithms further improves the earnings. The results also verify that agent-based simulation is a viable modeling tool for providing realistic insights about the complex interactions among different market participants and various market factors.