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Title: Reliability Assessment of Power Systems with Wind Power Generation
Authors: Wang, Shu
Advisors: Subhashish Bhattacharya, Committee Member
Mesut E Baran, Committee Chair
Alex Q. Huang, Committee Member
Keywords: auto-regressive moving average
capacity value
Monte Carlo simulation
unit commitment
wind power generation
power system reliability
Issue Date: 24-Nov-2008
Degree: MS
Discipline: Electrical Engineering
Abstract: Wind power generation, the most promising renewable energy, is increasingly attractive to power industry and the whole society and becomes more significant in the portfolio of generation systems. However, because of the unfavorable features of wind power, it affects all aspects of traditional processes of power system planning and operation. Power systems primarily planned for providing reliable and economic electric power to their customers. Therefore, it is critical to assess and understand the impacts of wind power on power system reliability. This thesis focuses on reliability assessment of power systems with wind power generation. Based on the investigation of reliability evaluation methodology and power system operations, a Monte Carlo based production cost simulation model is proposed and has been developed in the thesis. The model closely simulates actual system operation processes and takes system random behaviors into account. A simplified unit commitment method is created to fit the simulation for reliability evaluation purpose. The effects of wind forecast error is addressed in the model by applying forecasted value for day-ahead unit commitment and actual value for real-time operation. A process of Auto-Regressive Moving Average is designed to automatically perform day-ahead hourly wind generation forecasting through the whole simulation period. Methods for evaluating capacity value of wind power generation are also investigated. A realistic case study shows the proposed Monte Carlo based production cost simulation model can be used to assess reliability of power systems with wind power generation.
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