Microgrid Optimal Scheduling Considering Impact of High Penetration Wind Generation

The objective of this thesis is to study the impact of high penetration wind energy in economic and reliable operation of microgrids. Wind power is variable, i.e., constantly changing, and nondispatchable, i.e., cannot be controlled by the microgrid controller. Thus an accurate forecasting of wind power is an essential task in order to study its impacts in microgrid operation. Two commonly used forecasting methods including Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) have been used in this thesis to improve the wind power forecasting. The forecasting error is calculated using a Mean Absolute Percentage Error (MAPE) and is improved using the ANN. The wind forecast is further used in the microgrid optimal scheduling problem. The microgrid optimal scheduling is performed by developing a viable model for security-constrained unit commitment (SCUC) based on mixed-integer linear programing (MILP) method. The proposed SCUC is solved for various wind penetration levels and the relationship between the total cost and the wind power penetration is found. In order to reduce microgrid power transfer fluctuations, an additional constraint is proposed and added to the SCUC formulation. The new constraint would control the time-based fluctuations. The impact of the constraint on microgrid SCUC results is tested and validated with numerical analysis. Finally, the applicability of proposed models is demonstrated through numerical simulations. iii Acknowledgements It has been one year now since started my work with wonderful advisor Dr. Amin Khodaei. It was an amazing experience in my life. He is a very supportive and helpful professor. A lot of challenges and obstacles were overcome by his guidance. Here it is the time to thank him for all of his assistance, which really helped me in my research and really improved my knowledge in my research area. Also, it is my honor to present my thesis in front of this grateful committee members, Dr. Mohammad Matin and Dr. Jun Zhang, and listen to their valuable observations, feedback and comments on my thesis. Moreover, I want to send my best regards to the external committee member, Dr. Caroline Li, for giving me some of her time and attending to my thesis defense. Last but not least, I should not forget to thank the Electrical and Computer Engineering Department faculties in the University of Denver since they have provided me with all information which assisted me in my Master’s degree either for course work or my research. Finally, I dedicate this work for my parents, my wife and my kids as they assisted me to finish my degree and overcame all challenges.

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