A new evaluation method for customer outage costs using long-term outage data and Monte Carlo simulation

This paper introduces an effective evaluation method for customer outage costs and durations based on defining customer damage functions (CDFs) and processing the long-term interruption data. The proposed approach sorts and processes interruption data, utilizing the Monte Carlo technique to assess outage durations. Then, by weighting sector customer damage functions in each load point, composite customer damage functions (CCDF) are defined. The determined CCDFs are transformed into algebraic functions through curve fitting for any substation. Then, using the estimated outage duration and designed CCDF, the customer outage costs are calculated at any load center. The methodology introduced enhances the predicting accuracy of outage duration and cost by reducing the influence of rare lengthy outages and amplifying that of recurring events. Furthermore, this study defines fixed CCDFs independent of outage durations and could be employed for future time intervals of the proposed load points. Moreover, sorting data based on outage durations improves the accuracy of Monte Carlo outage estimation. The proposed method validation is evaluated on the sub-transmission system of Sistan and Baluchestan, Iran.