Abstract

Increasing productivity without jeopardizing the network’s operation, consumers’ experience, and the safety and integrity of procedures, is a major goal for all utilities. Scheduling of work has a direct impact on productivity, especially in the case of utilities that cover a wide geographical area using a limited number of employees. In the case of power utilities, however, scheduling has to consider, apart from the location of issues, the type and technical characteristics of each issue as well as its priority has to be considered in order to produce an optimum schedule. This paper focuses on the impact on productivity of a geospatial ticket management system considering the experience from such a system applied on network studies performed by HEDNO, the Greek Distribution Network Operator, in Patras Area. The term "network studies" is used to describe the study of all expansion and alteration works, ranging from a single Low Voltage (LV) pole installation to major Medium Voltage (MV) network rearrangements/expansion, and includes visits and measurements on the actual location as well as in-door calculations. In Patras Area, the local HEDNO division implemented in 2021-2022 a geospatial ticket managing system, based on available network data and custom Google Maps, aiming to increase its productivity by optimizing the scheduling process. Initial results published in February 2022, showed a significant productivity increase (up to 42%). However, the initial results considered a time span of only one month and thus could easily be misleading. This paper revisits the issue considering a larger time span (more than two years) that should provide more trustworthy results. It also briefly presents the latest updates and improvements made to the system. Results show that the increase in the number of studies and their predicted costs are similar to the initial results, with the increase in productivity being around 41%. In September 2022, HEDNO set the very ambitious goal of significantly increasing the overall production of network studies (more than double in terms of predicted costs) and the use of such a system can provide valuable help towards achieving this goal.

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