This master thesis aims at understanding the impact of wind on day-ahead-market (DAM) electricity prices using an integrated engineering-economic model. A realistic case study has been implemented, which is Electric Reliability Council of Texas (ERCOT) in Texas, U.S. in the month of August, 2014. To address the main research question of wind ?s influence on the level of DAM prices and its volatility, three main sub-questions to be answered are the followings: What is the connection between the theoretical marginal cost and the real electricity prices? How does changing wind load profiles influence electricity prices estimated in this study? And is there a better way of predicting the electricity prices to handle potential future changes more efficiently? The methodology used for solving these research questions consists of two main parts. First, DAM electricity price forecasting weekday-models are designed through two stages: building a unit commitment (UC) model to find the theoretical marginal cost and constructing an econometric analysis to derive the relationship between this value and the real price. It should be noted that the UC model is built upon the most state-of-art formulation, which does not only overcome several drawbacks of the conventional energy-block UC scheduling but also includes a more realistic modeling of thermal generators, i.e., startups and shutdowns, different types of operating reserves. All models are tested under different time frames, which are created by swapping the order of different weeks in the studied month. Amongst all, the most robust one is selected. After completing task one, the first and third research sub-question could be answered. In the second task of the methodology, different wind loads are fed into the best obtained model from the first task and their impacts on predicted prices are examined to respond to the second research sub- question. Finally, upon completion of the thesis, the main research question will be fully answered. i ? The main conclusions of this master thesis are the followings. First, the hybrid model combining an engineering UC approach and an econometric regression model has been proved to outperform both pure engineering and time series models. Importantly, this hybrid model could be used to predict the future electricity price one day in advance with high accuracy, thus, it could provide a significant contribution to the current electricity price forecasting tools. Second, wind generation generally has a negative impact on both electricity prices level and its deviation, i.e., increased wind generation drops the level and deviation of electricity prices and vice versa. The increased or decreased cost is coming from the changes in operating cost of thermal units, e.g., startups and shutdowns and fuel costs, and thus depends on the generation mix of the system. For instance, the average price deduction in the system with threefold increase of wind generation varies from around 3% to nearly 9% in the system with a fourfold increase of wind output. And the changes in price deviation are explained by an increase or decrease in price gaps between the off-peak and peak periods. The thesis starts with a brief introduction and literature review about impact of wind on electricity prices and price predicting techniques. The mathematical formulation and input data are presented afterwards. Finally, numerical results along with discussions and comparisons are shown at the last chapter of the thesis, followed with the appendix with detailed calculation and model ?s numerical outputs as evidential support.