UTILIZATION OF MONTE CARLO SIMULATION IN GENERATING CAPACITY PLANNING

This thesis presents a formalized and practical approach using Monte Carlo simulation to assess the adequacy of generating systems. A Monte Carlo simulation method is developed to model base load generating units and utilized to calculate the state probabilities in both Markovian and non-Markovian systems. The results obtained from the simulation method are compared with those calculated by analytical methods to determine the effectiveness of simulation. A range of studies for selected unit models is conducted to examine the effect of various distributions on the system state probabilities. The basic simulation model is extended to include the duty cycle of peaking units and to examine the effect of distributional assumptions on the conditional probabilities of failure. The generating capacity models which were developed are then combined with the load model to produce generating capacity adequacy indices. These models provide a method of analysis which relaxes many of the traditional assumptions incorporated in the analytical methods used to calculate reliability indices. The models which were developed are applied to the IEEE Reliability Test System (RTS) and a test system (RBS) developed at the University of Saskatchewan. The potential application of Monte Carlo simulation to calculate an estimate of reliability worth from the individual load loss event data is also illustrated in this thesis. The models developed in this thesis are capable of explicitly recognizing many unit and system operating considerations which influence system reliability. These models can be utilized to study a wide range of alternatives which differ in terms of unit function and system operation.

Name thesis:degree_name

Master of Science (M.Sc.)

Level thesis:degree_level

Masters

Grantor

University of Saskatchewan

Year dc:date.issued

1986

Author dc:creator

• Ghajar, Raymond

Advisor dc:contributor.advisor

• Billington, Roy

Handle dc:identifier.uri

https://hdl.handle.net/10388/15625

OAI identifier oai:identifier

oai:harvest.usask.ca:10388/15625