Candidate
Abdulaziz Almutairi
Title
Generating Capacity Adequacy Evaluation Incorporating Wind Power Time Series Model Using MCMC
Supervisor
Abstract
In recent decades, there has been a dramatic increase in utilizing the renewable energy resources by many power system utilities around the world. The tendency toward using the renewable energy resources is mainly due to the environmental concerns and fuel cost escalation associated with the conventional resources. Among the renewable resources, wind energy is a proven energy source for the power generation that successfully contributes to global, social and economic environments. Nowadays, wind energy is a mature, abundant, and emission-free power energy technology, and a significant percentage of electrical power demand is supplied by wind generation. However, the intermittent nature of wind generation makes its operation and planning a complex problem and has made the integration of wind generating into the power systems a prime concern to power system planners and operators. One of the great problems of increasing the use of wind generation can be seen from the reliability assessment point of view. Indeed, there is a recognized need to study the contribution of wind generation to the overall system reliability and ensure the adequacy of generation capacity.
Wind power generation behaves unlike conventional generation (fossil based power) where wind power is variable, uncertain, and non-controllable, which can affect the power system reliability. Therefore, modeling the wind generation in the reliability assessment calls for reliable stochastic simulation techniques that can properly handle the uncertainty and precisely reflect the variable characteristics of the wind at a particular site. The research work focuses on developing a reliable and an appropriate model for the reliability assessment of power system generation including wind energy sources. Monte Carlo Markov Chain (MCMC) technique is used as regard to its ability to produce synthetic wind power time series data that sufficiently considers the randomness of the wind along with keeping the statistical and temporal characteristic of the measured data. Thereafter, the synthesized wind power time series based on MCMC is coupled with a probabilistic sequential methodology for the conventional generation in order to assess the adequacy of the overall generating systems.