Loss Of Load Expectation Of Alkhoms Generating Units


Mohamed Altaher Ben Mouhsen
Department of Electrical and Computer Engineering, Elmergib University, Libya
Ali A Tamtum
Department of Electrical and Computer Engineering, Elmergib University, Libya


AlKhoms generating power station is one of the largest stations in the Libyan generation system. It consists of eight units represent approximately 18% of the Libyan generation capacity. Hence, it is chosen to perform the reliability study presented in this paper. Generation system reliability is an important aspect in the planning for the future system capacity expansion since it provides a measurement to make sure that the total generation system capacity is sufficient to provide adequate electricity when needed. There are two approaches used for generating units reliability, deterministic and probabilistic approaches. The probabilistic approach branches into Monte Carlo simulation and analytical methods which include loss of load expectation (LOLE). The LOLE is the most widely used index in generation adequacy evaluation; it indicates the expected time for which the available generation will not be sufficient to meet the demand. In this paper, a reliability study is performed on Alkhoms generating units. Forced outage rates (FOR) is calculated , annual load data is analyzed , annual load duration curve is constructed and convolved with the generation model, and the (LOLE) is evaluated. The effect of load growth and FOR variation are also considered. A computer program is written in MATLAB as a tool for this purpose and used to construct the annual load duration curve and capacity outage probability table (COPT). the importance of this study comes from the fact that the system consists from non -identical units since the eight units has a different FOR which complicate the evaluation; as well as the fact that the binomial distribution cannot be applied directly while most published studies simplify the evaluation by grouping up identical units

November 30, 2018
Online ISSN