Composite system reliability evaluation using sequential Monte Carlo simulation
Monte Carlo simulation methods can be effectively used to assess the adequacy of composite power system networks. The sequential simulation approach is the most fundamental technique available and can be used to provide a wide range of indices. It can also be used to provide estimates which can serve as benchmarks against which other approximate techniques can be compared. The focus of this research work is on the reliability evaluation of composite generation and transmission systems with special reference to frequency and duration related indices and estimated power interruption costs at each load bus. One of the main objectives is to use the sequential simulation method to create a comprehensive technique for composite system adequacy evaluation. This thesis recognizes the need for an accurate representation of the load model at the load buses which depends on the mix of customer sectors at each bus. Chronological hourly load curves are developed in this thesis, recognizing the individual load profiles of the customers at each load bus. Reliability worth considerations are playing an ever increasing role in power system planning and operation. Different methods for bus outage cost evaluation are proposed in this thesis. It may not be computationally feasible to use the sequential simulation method with time varying loads at each bus in large electric power system networks. Time varying load data may also not be available at each bus. This research work uses the sequential methodology as a fundamental technique to calibrate other non sequential methods such as the state sampling and state transition sampling techniques. Variance reduction techniques that improve the efficiency of the sequential simulation procedure are investigated as a part of this research work. Pertinent features that influence reliability worth assessment are also incorporated. All the proposed methods in this thesis are illustrated by application to two reliability test systems. In addition to the basic studies, a number of sensitivity analyses are conducted to show the impact of selected modeling assumptions.