A clustering scheme for performance benchmarking in the regulation of electric distribution utilities in Iran

Abstract

In electric distribution company (DisCos) performance, the effect of imposed features such as network size, economic progress, and geographical conditions is considerable. In this research, K-means clustering is used to model the impact of imposed characteristics in the benchmarking analysis. Commonly, benchmarking studies are focused on DisCos at the outcome stage. However, the efficiency of working processes is as crucial as the outcomes in every organization. This paper proposes a novel combinatorial method integrating quantitative and qualitative measures to benchmark DisCos in both outcome and process stages. In the quantitative section, DisCos’ outcomes are benchmarked through key performance indicators (KPIs). In the qualitative section, utilizing balanced scorecards (BSCs) for measuring the efficiency of DisCos at the process stage is suggested. Ranking under study DisCos, a scoring model based on the normalized summation of obtained scores from KPIs and BSCs, is formulated. Monitoring DisCos’ performance specifically under each of the performance fields in addition to the consequent efficiency scores is attended for analyzing benchmarking results. Eventually, a knowledge management scheme is suggested. Under this framework, each DisCo’s true performance instance is incorporated to gain insights from the accomplishments of similar utilities. This study is useful in regulatory programs for identifying the weaknesses and strengths of utilities or even regulators toward strategic targets, presenting a performance basis, and reducing the performance gaps between utilities.