Combining analytical power system reliability assessment methods with Monte Carlo simulation

This master thesis examines the field of power system reliability assessment through two parts, a literature study on the state of the art within power system reliability analysis methods, and an implementation of two hybrid power system reliability analysis approaches tested on two reliability test systems. The reliability analysis methods can be divided into two groups, analytical methods and Monte Carlo simulation. The advantages and disadvantages of both types of methods are evaluated. After the characteristics of each method has been established, a discussion regarding combination of re- liability methods into hybrid approaches ensues. The idea is to create hybrid methods that enhance the advantages of both methods while minimizing the disadvantages of both methods. Two possible hybrid reliability method configurations are proposed: 1) A combination of the OPAL methodology based on contingency enumeration and state sampling Monte Carlo simulation, 2) A combination of the OPAL methodology based on contingency enumeration and pseudo-sequential Monte Carlo simulation. Both methods are tested on two test systems, RBTS and The Four-Area Test Network. Simulation results are obtained for both the delivery points of the system, as well as the system as a whole. The hybrid methods are able to calculate the reliability indices, which measures the reliability of a power system. As a benchmark for comparison of the simulation results from the hy- brid methods, the OPAL method is used. The total system index for Expected Energy Not Supplied (EENS) is calculated to be at a maximum difference of 2.8% between the hybrid methods and the OPAL Benchmark test. Both test systems have over 95% of the contribution to the EENS index from one single branch outage, due to the islanding of a bus. The reliability indices calculated for the two hybrid methods and the benchmark on the RBTS network differs more than for The Four-Area Network, which is likely due to the fact that The Four-Area Network is much more reliable than RBTS. The hybrid methods have higher computational cost compared to the OPAL method. Many im- provements can be made on both hybrid implementations to reduce computational cost, including: optimizing of code, combine with intelligent techniques (variance reduction, particle swarm opti- mization, genetic algorithms, machine learning algorithms etc.). Both proposed hybrid methods can be used for reliability assessment of power systems, but further testing and improvement is required in order possibly be established as a more accurate method than the analytical reliability analysis method OPAL, or faster, and sufficiently accurate, than the Monte Carlo simulation methods.