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Business activity levels vary with season, day-of-the-week, and time-of -the-day [42]. For instance, in 2016, 6 out of the 9 major South African industries (at the 1-digit level of SA standard industrial classification (SIC) [75]) generated their highest quarterly gross value added (GVA) in the 4^th quarter (October – December) [76]. The impact of electricity interruptions on economic activities during these periods will be more significant than in other time windows with lower activity levels.

Also, weather which varies with time and season influences electricity dependence level and usage. During winter, there is an increase in the number of heating degree days especially for northerly countries like Sweden, Finland, and Canada. This increases the need of electricity for water and space heating. This implies that electricity interruptions (especially those of long durations) during winter can adversely impact comfort levels of electricity customers compared to similar interruptions in autumn and spring. The same discussion holds sway for summer season, where space cooling and refrigeration are predominant electricity dependent needs.

Thus, in studying the impact of electricity interruptions on business customers, it is important to also account for the effect of time-of-day, day-of-week and season on impact levels. The risks of extreme (high or low) values of business customers’ interruption cost can be can significantly underestimated when temporal factors are ignored. In several studies [28, 70, 77, 78], a worst-case electricity interruption scenario is chosen for CIC assessment, and CICs for other scenarios are derived by applying time-weight factors to the CIC in the reference scenario. However, Herman and Gaunt [79] proposed that using time-element matrices is a more robust approach to capture the effect of the variation of time and season on CIC.

26 Spatial/geographic factors

Risk susceptibility and electricity customer demographics (e.g. population, ethnic diversity, income, housing characteristics, density, settlement types, etc.) vary across different geographical locations within a country or region. The resulting distribution of political and socio-economic activities across regions is inconsistent. This can lead to significant regional variation in interruption costs as shown in [35, 65, 72-74, 79]. Thus, CIC assessment on a large regional footprint (e.g. country-wide) needs to consider spatial variations.

Extrapolating the results of a CIC study for a case study region has to be done using sound and justifiable assumptions, because the results for the case study region might not be representative of other regions. The drawback of an aggregate country-wide analysis disregarding spatial variations is that it suppresses meaningful information such as knowing the regions within a country for which electricity interruption is most significant. Information on the regional distribution of impacts can allow for evaluating equity considerations and communicating risk to stakeholders, thus facilitating their input in relevant policy processes. This way, affected parties can see what stake they have in dealing with electricity interruptions [49]. Regions for which electricity interruptions is most significant represent significant contributors to economic viability of a country and should be primarily considered in grid resiliency and reliability improvement programs.

A recent study [35] accentuates the importance of including the spatial factor in CIC assessment, although this adds an extra level of complexity in the assessment. A starting point will be identifying unique regions based on a chosen criterion. The following criteria may be explored:

• Susceptibility to risk [35];

• Census-based geographical regions [65, 72-74];

• Settlement type – rural or urban;

• Utility or municipality service territory.

27 Electricity customer characteristics

Electricity customers connected to a power system network may be classified into homogenous groups based on their economic activity or size – which may be electrical, economic, or physical.

Different customer segmentation methods may be derived by combining segmentation criteria (Table 2.3). These segmentation methods are mainly applied in CIC studies that are based on customer surveys (section 2.3). The choice of customer segmentation method can cause significant difference in CIC estimates [72].

Table 2.3: Customer segmentation methods

Customer segmentation method Segmentation criteria One-dimensional (1-D) Economic activity i.e. SIC⁴

Two-dimensional (2-D) Economic activity and one size parameter (could be electrical e.g.

maximum demand, energy consumption, voltage level, or economic e.g.

turn-over)

Multi-dimensional (Multi-D) Economic activity and more than one size parameter e.g. Energy consumption and turn-over as electrical and economic size parameters respectively.

The 1-D customer segmentation method has been adopted in many CIC researches [46, 80-82].

This allows for grouping electricity customers of similar economic activities together and has the advantage that CIC estimates can be obtained for each customer segment down to the last digit of the SIC. However, there is a disparity in this method. For instance, in the segmentation of business customers, large business customers may be grouped with smaller ones. Thus, in the CIC data, the CIC estimate for some business customers will appear as outliers. Some other authors have adopted the 2-D customer segmentation. These combine economic activity with a size parameter such as energy consumption [83], voltage level [84], or turnover [73].

Dzobo et al [85] identified the following drawbacks with the 1-D and 2-D customer segmentation methods for segmenting business customers: the high cost of extensive survey to survey all the

4The South African SIC includes 99 individual economic activity categories aggregated into 21 sections, and further into 9 sections; with each section containing categories with semblance in their economic activity.

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customer segments formed under the 1-D, and significant variation in electricity intensities of various business customers in the segments formed under the 1 and 2-D segmentation methods.

Consequently, a multi-D segmentation based on business customers’ economic activity, electricity consumption and turnover was proposed as a more effective customer segmentation method that yields CIC estimates with lower uncertainty. The multi-D customer segmentation involves the aggregation of customer segments via hierarchical clustering. The application of this method requires that ancillary data on the economic activity and size parameters of the business customers to be surveyed are available beforehand to allow for the adoption of a stratified random sampling of prospective respondents. However, such data might not be publicly available to researchers due to restrictions on electric utilities and public enterprises (like chambers of commerce) by the consumer protection act (CPA) to protect consumer privacy. Thus, the application of this method is limited to instances when collaborations can be made with researchers, electric utilities or public enterprises who might be willing to divulge such data.