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Accurate assessment of customer interruption costs is essential for making informed decisions in power system planning and operation. This paper presents a multi-dimensional customer segmentation model for reliability-worth analysis of power systems. The proposed model uses a hierarchical clustering technique to cluster electricity customers into customer segments of similar cost characteristics. Three customer parameters - economic size, economic activity and energy consumption are used in the proposed model. The proposed model is examined on two case studies from South Africa and Sweden, and results are compared to the conventional customer segmentation models. The effectiveness of the proposed model is determined based on the coefficient of variation of the final CIC estimates for different duration and time of occurrence of power interruptions. The results show a reduction in the dispersion of the final CIC estimates and thus allow CICs to be estimated from smaller survey samples. [ABSTRACT FROM AUTHOR]

In the re-regulated electricity market, there is a growing interest in performance-based regulation accompanied by quality regulation for electric distribution networks. This study develops a new risk-based method for reliability investment decisions when the distribution system operator (DSO) is exposed to financial risks defined by a quality regulation. As quality regulation design becomes more complex, more detailed risk management methods are needed in order to adequately capture the financial risk the DSO is exposed to. The proposed method applies the Monte-Carlo simulation technique in order to assess the risks of the considered reinvestment projects. By using the proposed method, the impacts that different risk strategies (risk-neutral/risk-averse) and risk models (non-time-varying/time-varying) have on which reinvestment project is selected is investigated in a case study. This is investigated for two different quality regulation designs. The results show that primarily not only the quality regulation design but also the risk model formulation and risk strategy have a major impact on which reinvestment project is selected. [ABSTRACT FROM AUTHOR]