| Summary: | Degree: Master of Science Abstract: Scientific and technology advances in electrical engineering and increasing demand for electrical energy have led to extensive research in power industry and formation of new markets for electrical energy. These developments have brought about interest and demand for power. In response to the increase in demand, the research focus is on upgrading the current infrastructure to higher capacity power transmission networks. Nevertheless, building new generating stations and transmission facilities is precluded by some environmental, social, and economic regulations. Therefore, the only feasible solution to deal with the energy requirement is to increase and optimize the capacity of existing power generation and transmission equipment. The current thesis proposes an optimization method based on the Mixed Integer Linear Programming technique to maximize the usage of the capacity of power transmission facilities. The method employs weather-based dynamic thermal ratings, costs of power generation, and power generation constraints such as cost of start-up/shut-down and generation ramp up/down limits. The method's accuracy is increased by incorporating a spatially resolved, high-resolution thermal model of the transmission system. By utilizing this extension, the thermal limits and temperature-dependent losses of the system are identified and dynamically used in calculations. Load forecasting is used in dispatch centers to improve generator scheduling and minimize the costs of the entire system. The multi-snapshot characteristic of the model, which is proposed in this thesis, provides the model with the ability to consider the load and meteorological data's forecasts, further improving the model's accuracy. The performance of the model is tested by simulating a year of data from Newfoundland and Labrador Hydro power generation and transmission system, and for the weather conditions the North American Regional Reanalysis (NARR) historical dataset is used. The simulation results show that ...
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