Abstract
Power system interruptions have financial impacts for the utility, the customer and the community. A consistent approach to financial impact assessment for both planning and operations starts with fault data. Faults can be categorized and correlated with season and time of day in a time-element matrix. Statistical data can be modeled with Beta probability density functions. The paper shows how the failure probability is used to perform a time-dependent probabilistic analysis of system reliability that is both accurate and converges more quickly than conventional simulation methods. The approach can be applied to system operations in six-hour windows. Customer interruption costs (CIC), derived from surveys using clustering techniques to reduce sample sizes, characterize the reliability as a probabilistic $@risk value. The paper shows how to use this information to derive the risk-dependent financial impact of possible interruptions as an improvement on conventional contingency analysis.
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