Research Information System
12th International Statistics Days Conference, İzmir, Turkey, 13 - 16 October 2022, pp.53
A scientific and reasonable power emergency supplies workforce management is essential to
speed up the restoration process after damage of a power system. In this study simulation
approach is proposed for annual planning of power restoration workforce related to an GDZ
electricity distribution company. Workforces are employed to restore power after
interruptions throughout the province on service point. According to the electricity
distribution network, locations and field sizes, service points are divided into 29 operation
centers, each to restorate power interruptions. The scale of the study is a discrete, stochastic
and multi-criteria problem due to the nature of the electricity distribution system therefore
determining the size of the team in each service point over the year is challenging. Given this
background, firstly, an event definition was made for the period from the occurrence of the
interruption notification to its completion. Time intervals in events have stochastic lengths
because they are affected by unpredictable factors. Using the outage data of the last two years,
monthly outage frequency estimations were made for each operation center. The probability
distributions of each event were determined and distributons are simulated. Monthly labor
demand is then forecasted based on an optimistic scenario where there is a large enough labor
resource at the time of the outage to keep the downtime level to a minimum for the operations
centers. Finally, for purpose of validation of workforce distribution multicriteria model are
developed. The model is based on a survey of relevant data, especially those related to the
need to perform tasks on the lines, as the number of consumers, voltage lines, transformers,
so on. Multicriteria model is grouped according to theoperation centers. Correlations between
model coefficients and estimates of operations center team size are examined. The presented
model is expected to increase efficiencies by focusing especially on peak hours, with optimum
design teams that ensure the distribution of workforce according to operation centers,
minimizing customer interruptions and personnel costs.
Keywords: decision making, mathematical programming, statistical analysis, power interruption,
workforce planning