Abstract:
This thesis presents a time sequential Monte Carlo simulation approach to distribution
reliability evaluation and distribution reliability worth, taking into account equipment failure
originated from substations. Substations, which transfer electrical energy between
subtransmission systems and distribution systems, can have significant impact on the overall
system reliability of customers. The Monte Carlo method represents the random behaviors of
the system components, such as time to failure, time to repair and switching time by randomly
sampling sequences of component states in chronological manner for several periods of time.
Each of the component state is superimposed to determine system states for reliability
evaluation.
Reliability worth is assessed by relating it to the costs or losses incurred by utility
customers due to electric supply interruption interpreted in terms of customer damage functions
(CDFs). The CDFs can then be determined for given customer types and aggregated to make
sector customer damage functions (SCDFs), which reflect economic consequences of supply
interruption as a function of cost in different groups of customers. With average disconnected
load, outage duration, average interruption cost, reliability worth at the load points can be
estimated.
A developed Monte Carlo Simulation programme is tested by the Roy Billinton test
system (RBTS) and a system of Provincial Electricity Authority (PEA) with different
substation configurations. The study results show that the simulation method can provide
reliability indices comparable with those calculated from an analytical method. Reliability
worth of load points in terms of expected cost and their probability distributions, and a useful
index known as the interrupted energy assessment rate (IEAR) that relates the reliability
indices and the momentary implications of customer supply interruptions, are also evaluated.