Valuing blackouts and lost leisure: Estimating electricity interruption costs for households across the European Union

Energy Research & Social Science 34 (2017) 39–48

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Original research article

Valuing blackouts and lost leisure: Estimating electricity interruption costs for households across the European Union

MARK

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Abhishek Shivakumara, , Manuel Welschb, Constantinos Taliotisa, Dražen Jakšićc, Tomislav Baričevićc, Mark Howellsa, Sunay Guptaa, Holger Rognerd a

KTH Royal Institute of Technology, Stockholm, Sweden International Atomic Energy Agency, Vienna, Austria c Energy Institute Hrvoje Požar, Zagreb, Croatia d International Institute of Applied Systems Analysis, Laxenburg, Austria b

A R T I C L E I N F O

A B S T R A C T

Keywords: Outage costs Electricity interruptions Household electricity consumption Value of lost load

Security of power supply is a crucial element of energy system planning and policy. However, the value that society places on it is not clearly known. Several previous studies estimate the cost of electricity interruptions for individual European Union (EU) Member States – as the Value of Lost Load (VoLL). In this paper, we use a production-function approach to estimate the average annual VoLL for households in all twenty-eight EU Member States. This is the first time that a unified approach has been applied for a single year across the EU. VoLL is further presented on an hourly basis to better understand the impact of the time at which the interruption occurs. Finally, we analyse the impact of ‘substitutability factor’ – the proportion of household activities that are electricity-dependent – on the VoLL. Results from this study show that the differences in VoLL between EU Member States is significantly large, ranging from 3.2 €/kWh in Bulgaria to 15.8 €/kWh in the Netherlands. The annual average VoLL for the EU was calculated to be 8.7 €/kWh. Results from this study can be used to inform key areas of European energy policy and market design.

1. Introduction Reliable and affordable electricity supply is critical for any economy to function efficiently. While Europe has enjoyed a high degree of supply security during the last few decades, the utility industry has identified ‘liberalization and privatization’ (which largely took place in the 1990s) and ‘renewable capacity expansion’ (which forms an essential option for sustainable energy systems) as the two major trends that increase the risk of power outages [1,2]. Increased shares of renewable energy sources (RES) affects energy security in several ways. From a long-term perspective, increased shares of RE positively affect energy security due to decreased reliance on depletable, and often imported, fossil fuels. In the short-term, however, the variability and temporal mismatch between demand and supply poses a considerable challenge to the integration of RES [3,4]. The EU, which pursues a policy of increasing the share of RES in national and regional generation mixes, must make additional efforts in order to maintain current levels of supply reliability. These may include grid adaptations as in the case of Germany [5] or Performance-Based Regulation (PBR) for Distribution System Operators (DSOs) in the UK [6]. As part of its Trans-

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European Energy Network planning, the European Commission foresee the application of a neutral pan-European transmission cost–benefit analysis (CBA) to facilitate the optimal expansion of the electricity transmission system [7]. Such a strategy would require information on the monetary value of supply of security in order to arrive at an ‘optimal’ solution from both a technical and a socio-economic perspective. End-users in an electricity system expect a reliable supply, available on demand. RES intermittency is already significantly changing the design of electricity markets, where it is leading to a paradoxical situation: back-up capacity is needed for a secure electricity supply but the right market incentives to ensure such capacity is absent [8]. Flexibility is also needed in the power system to respond to the increasingly sharp short-term variations in the market. Most grid expansion options involve significant investment expenditures, which must be justified by the potential adverse impact of compromising power supply security. Since nearly every economically productive activity is dependent on a reliable supply of electricity, power outages can have potentially far-reaching consequences for the entire socioeconomic system. This study aims to provide information that can be useful to determine financial incentives to improve electricity supply

Corresponding author at: KTH Royal Institute of Technology, Brinellvägen 68, K514, KTH-dESA, 100 44 Stockholm, Sweden. E-mail address: [email protected] (A. Shivakumar).

http://dx.doi.org/10.1016/j.erss.2017.05.010 Received 5 September 2016; Received in revised form 8 May 2017; Accepted 8 May 2017 2214-6296/ © 2017 Elsevier Ltd. All rights reserved.

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basis, while Section 4.2 presents VoLL on an hourly basis to demonstrate the time-varying nature of outage costs. In Section 4.3, a sensitivity analysis of the influence of ‘substitutability factor’ on VoLL is discussed. Section 5 concludes with a discussion of the main outcomes of the study. Further, it provides policy recommendations and suggestions for future research.

reliability. Currently, reliability levels are often used to design the future power system. These may be quantified by defining an acceptable Loss of Load Probability (LOLP), which specifies the share of time when a generation shortfall may occur [9,10]. Other design criteria are redundancy measures to ensure the system can cope with an outage in essential supply infrastructure (e.g., N-1 rule) [11,12]. Both approaches have in common that they do not build on quantifications of the impacts of interruptions on individual consumers or consumer categories. Thus, both approaches will not result in a socially optimal level of interruptions. A clear understanding of the socio-economic costs of interruptions across the EU would be an important step to decide on such an optimal level. This study provides guidance on how to value the consequences of supply interruptions and thus determine the demand for security of electricity supply. From a socio-economic perspective, the most commonly used indicator to measure interruption costs is the value of lost load (VoLL1). VoLL is likely to play a significant part in informing a number of key areas of European energy policy and market design. In Capacity Markets, for instance, the amount of electricity generating capacity required in each EU Member State (MS) and that will be contracted through a Capacity Market is likely to be informed by the VoLL. For balancing markets, VoLL can represent the cost of electricity interruptions to end-users [13,14]. VoLL will therefore be used in a range of policy and market design decisions at both the EU level and Member State levels. Further, the household sector is often overlooked in discussions of supply security. However, the welfare losses of households (lost leisure) can be as important as the lost value addition of firms. Studies such as de Nooij et al. [15], in a study of the Netherlands, find that on a weekday in the evening the cost of a supply interruption is largest for households. Households, however, do not receive adequate attention in decisions on supply security. It is interesting to note that the number of publications on estimating VoLL has increased in recent years. Schröder and Kuckshinrichs [2] report that Germany has published six studies on the topic, all of them after 2011. As mentioned earlier, the increased penetration of RES – as is the case in Germany – is a strong justification to estimate the costs of electricity supply interruptions. At present, VoLL is completely lacking in international comparability. A uniform analytical framework is therefore urgently required [2]. In this paper, VoLL for households in all twenty-eight EU Member States is estimated based on a uniform methodology for the first time. The findings are envisaged to provide a basis for meaningful comparison between EU Member States. We use an established methodology, previously employed by several studies to estimate VoLL for households at the national level [16,17,19,20]. As the EU moves towards a common internal energy market, it is increasingly important to align the economic incentives of different EU Member States in ensuring reliable electricity supplies, both within and between Member States. Further, we study the time-varying nature of VoLL to identify both the time and location of highest potential electricity interruption costs for households. Finally, we analyse the impact of ‘substitutability factor’ – the fraction of leisure activities that are electricity-based – on the VoLL. This is also the first attempt to perform such a sensitivity analysis on VoLL for households. The rest of this study is structured as follows. Section 2 presents a literature review with common methods used to quantify electricity outage costs and compares the results obtained from studies using these different methods. Section 3 describes the methodology adopted in this study known as the production-function approach. In Section 4, the results obtained for all EU Member States is presented. Section 4.1 compares VoLL values between Member States on an annual average

2. Literature review Commonly used technical indicators of power system reliability include SAIFI, SAIDI, and CAIDI,2 which are statistical measures [19]. They are related to average power interruption frequency, duration, and intensity respectively. However, from a socio-economic perspective, VoLL is an important indicator that addresses the impact of electricity supply interruptions and the monetary valuation of a reliable, uninterrupted power supply. VoLL can, therefore, be understood as an economic indicator for electricity security. It is determined by relating the monetary damage arising from an electricity supply interruption – due to the loss of socio-economic activity – to the level of kWh that were not supplied during the interruption [20]. While a representation in monetary units/kWh is most commonly used, VoLL may also be measured in relation to time [21]. We have selected VoLL as an economic indicator of outage costs in this paper since it has a long-established history of usage in the field of power supply security [2,18,22,25,27,28]. Further, it is a more appropriate indicator from a socio-economic perspective as compared to the technical indices mentioned above [2]. Prior to estimating the costs of interruptions, however, it is useful to note that the consequences of supply interruptions are not created equal. There are different types of end-users in the electricity system. An interruption in a hospital has very different consequences than one in an industrial plant or household [6–13]. Another important aspect is the time of occurrence of the interruption [28]. The type of activity that is interrupted is dependent on the time of day, week and season. For instance, in the case of a household, an interruption at 20:00 may interfere with recreation (e.g. television, internet), while at 3:00 in the morning an interruption typically has much smaller effects. In addition to the time of occurrence, the duration of an interruption also significantly influences its impact. Certain types of damage, such as the loss of computer files, occur instantaneously. Others, such as the loss of working hours and the spoilage of food, are proportional to the length of the interruption and may only occur after a certain delay. It is important to distinguish the impacts of planned and unplanned outages as well. Advance notification of an impending electricity interruption also helps in mitigating its negative implications [15]. For example, if one is made aware of an imminent electricity interruption, then one may avoid using an elevator. Further, if electricity supply is interrupted on a regular basis, people may prepare for it even without advance notification. While this may reduce the cost per interruption, the overall impact of electricity supply interruptions will be larger (e.g., less confidence of industry in the reliability of the system). This relates as well to the “perceived reliability level”: the higher the perceived reliability in the affected area, the less firms and households are inclined to take precautionary measures (e.g., invest in backup facilities), and the greater the damage caused by an interruption (known as the ‘vulnerability conflict’) [29]. Since no market currently exists to trade electricity interruptions, it is not possible to ascertain a “market price” that shows the marginal cost per unit of time of a supply interruption. In this study, we use VoLL as the metric to measure outage costs. VoLL can be considered to be either marginal or semi-marginal, since it depends on the ‘discrete size’ of demand (in kWh or MWh) not served. For instance, if only a single

1 Other terms that are used synonymously with VoLL include “electricity outage costs”, “cost of unserved energy”, or “customer service reliability”.

2 SAIFI: System Average Interruption Frequency Index; SAIDI: System Average Interruption Duration Index; CAIDI: Customer Average Interruption Duration Index.

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MW were turned off for an hour, then the least cost among a ‘stack’ of consumers, and their VoLL per MWh, would be the VoLL to use. However, if an outage takes place over some time period, and it is not possible to identify the consumers who will receive the blackout, then the VoLL cannot be considered to be exactly the marginal cost of the interruption [15,28]. VoLL, therefore, addresses both the level of damage caused by a power interruption, and the resulting value of supply security [20]. Several methods are available to quantitatively evaluate the effects of a electricity supply interruption, as compiled in [20,21]. The methods are described and compared below.

during an outage can be estimated directly and then aggregated to a macro-economic total. Interactions between sectors can also be evaluated through input – output tables [36]. 3. Case studies: There are two principal approaches to using case studies. In one method, the effects of an actual supply interruption are collected and represented in monetary terms. Another approach is to undertake direct surveys after an interruption. 4. Market behaviour (revealed preferences): Another method to estimate how the industrial, commercial and household sectors value supply interruptions is through information on their expenditures on backup facilities, interruptible contracts and interruption insurances. The level of expenditure on backup facilities indicates how much businesses, industry and households are willing to pay for a higher level of supply security [37]. Past studies advocating this method were applied to cases with an average of 10 h of interruptions per year [38]. For most of the EU, this value is larger than the cumulative duration of interruptions in a year (see Fig. 1 [39]). In an EU context, backup generators would only have to be used for short periods of time. This would mean that the cost of capital per minute of operation is often too high for businesses or households to invest in backup technology (exceptions include hospitals and banks). This is in contrast to the US, where 22% of the peak demand equalling 170 GW is available in the form of consumer backup generators. This includes generators of up to 60 MW, but 98% of them are smaller than 100 kW [40]. In the case of Europe, the market for diesel generators has shown signs of steady growth in the past few years [41]. However, a figure of total backup generator capacity currently present in the EU could not be identified.

1. Surveys/interviews (stated preferences): In general, the costs of interruptions may be measured by estimating the value of lost load (VoLL). One method to determine this value is to ask people how much damage they have suffered due to supply interruptions, how much they are willing to pay (WTP) for a given reduction in interruptions, the minimum amount of money they are Willing-to-Accept (WTA) as compensation for an increase in interruptions, or which combination of electricity price and number, duration and timing of interruptions they prefer (conjoint analysis) [22,23]. The latter may also be referred to as choice experiment. When carried out in Sweden, such a study showed that marginal WTP of households to reduce power outages increases with duration, and is higher during weekends and winter months [28]. This is confirmed by a study in Austria, which found that the WTP is 33% higher in winter than in summer [24]. On a yearly average, values ranged from €1.4 to avoid a 1-h power cut to €17.3 to avoid a 24-h interruption. 2. Production-function approach: This approach estimates the welfare costs of supply interruptions across different sectors, durations and times of occurrence in a week (weekday during the day, weekday evenings, and weekends) and includes studies based on macroeconomic indicators. This can be quantified through lost production for the commercial sector and lost convenience (or leisure time) for households. Within the production-function approach, quantitative statistical information is used to determine the costs in relation to a given supply interruption [35]. The lost production in each sector

Each of the above methods has its own advantages and disadvantages. An advantage of case studies is that an actual rather than a hypothetical interruption is studied. On the other hand, revealed preferences (market behaviour) may provide a more objective basis than subjective valuation (surveys) for estimating the cost of power outages, as it reflects “what people do rather than what they say”. In this study, we use the production-function approach. The production-function

Fig. 1. Total duration of interruptions in the EU (minutes/year) [39].

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Table 1 Comparison of VoLL estimation studies. Reference

Country

Consumer Segment

VoLL (EUR/ kWh3)

Methodology

Oakley Greenwood prepared for the Australian Energy Market Operator [44] Reichl et al. [22] Bliem [43] Zachariadis and Poullikkas [17] Küfeoğlu and Lehtonen [45] Growitsch et al. [18] Piaszeck et al. [42] Praktiknjo [46] Blass et al. [47] Bertazzi et al. [48] Baarsma and Hop [27] de Nooij et al. [15] Kjolle et al. [49] Leahy and Tol [16]

Australia

Consumption weighted average

34.74

Survey

Austria Austria Cyprus Finland Germany Germany Germany Israel Italy Netherlands Netherlands Norway Ireland

Households Households Households Households Households Households Households Households Households Households Households Households Households

1.24 5.89 9.79 65.88 12.06 13.44 10.53 8.92 12.89 9.71 16.38 0.82 14.57

Linares and Rey [25] Carlsson and Martinsson [28] London Economics [14] Lawton et al. [23]

Spain Sweden UK USA

Weighted average Households N/A Households

6.56 13.26 1.97 9.62

Survey Survey Production-function Survey Production-function Survey Survey Survey (elicited choice probability) Survey Survey Production-function Survey Production-function (with some survey data) Production-function Survey Survey Survey

common, well-established methodology allows for inter-comparison between studies in an international context. However, there are inevitable limitations related to adopting the production-function approach. For instance, the duration of interruption is difficult to account for. Further, we are unable to account for annoyance caused in households due to electricity supply interruption [16].

approach provides a top-down estimate of total costs without needing to study each user individually, as is the case for bottom-up methods such as 1 and 4. ‘Market behavior (revealed preferences)’ does not seem relevant for the case of the EU, where the electricity systems of Member States is characterized by high levels of reliability. Further, the production-function approach has been used in several previous nationallevel studies estimating VoLL for households, including de Nooij et al. [15], Leahy and Tol [16], Zachariadis and Poullikkas [17], and Growitsch et al. [18] for the Netherlands, Republic of Ireland, Cyprus, and Germany respectively. Overall, approximately 50% of recently published studies apply the production-function approach [2]. Table 1 summarises and compares the VoLL for households from past studies in different countries. The studies are distinguished based on the method used to estimate VoLL for households, base year of analysis, and the country analysed. All of the studies were published between 1995 and 2015. It is immediately apparent all previous studies to determine VoLL have been performed at a national level for individual countries. Therefore, while estimating VoLL has been considered and explored in an international context, the heterogeneity of methods used and base year analysed precludes a meaningful intercomparison of studies. In order to cope with data limitations, most studies employ simple input-output relationships and functional forms [42]. For instance, studies using the production-function approach assume a simple proportional relationship between output (or utility from leisure) and electricity consumption at an annual level. Studies that use this framework to calculate outage costs include de Nooij et al. [15], Bliem [43], Leahy and Tol [16], Zachariadis and Poullikkas [17], Piaszeck et al. [42] for national studies of the Netherlands, Austria, Republic of Ireland, Cyprus, and Germany. In this paper, the outage costs (VoLL) for households was estimated using a production-function approach. It purports to capture both direct and indirect costs of electricity supply interruptions. The productionfunction approach leads to substantial higher welfare costs than the simulations of demand models [17] but are however in line with current literature for other European countries cited above. We apply the methodology using data from the EU for the year 2013. Using a

3. Method In order to estimate the outage costs for the household sector, the value of the output generated by them needs to be considered. The primary effect of outages in households is the interruption of leisure activities, to which an economic value needs to be assigned. While people ascribe value to leisure activities, the relationship between electricity usage and leisure activities is not clear; some activities are directly and completely dependent on electricity (e.g. watching television), other activities are not (e.g. playing an outdoor sport). Therefore, any estimation of outage costs relies on the amount of hours spent on leisure activities and the proportion of these leisure activities that are dependent on electricity. In order to represent this substitutability between electricity-based leisure activities and non-electricity-based leisure, we utilize the approach propose by several previous studies including de Nooij et al. [15], Leahy and Tol [16], Zachariadis and Poullikkas [17], Growitsch et al. [18] and Bliem [43] and assume a substitutability factor of 0.5. This corresponds to a 50% reduction in the utility that households gain from leisure activities when an electricity interruption occurs. This simplifying assumption is in line with several well-established VoLL estimation studies [12–15,40,41]. However, the authors note that it is important to further investigate the impact of this assumption. Since different countries are likely to have varying ‘substitutability factor’, we conduct a sensitivity analysis to estimate its impact on the range of VoLL by country. Results of this analysis are presented later in this section. Becker [50] provides a framework to estimate an economic value for time spent on leisure activities. He proposes that households gain utility from consuming goods and time spent on leisure activities. The money required to purchase these goods to consume is gained by working. Moreover, the marginal utility of both consuming goods and leisure activities decreases with each additional unit leading to an optimal ratio between working and non-working hours. At this optimal point, a person or household is indifferent between an additional hour of work compared to an additional hour of leisure. Therefore, an

3 Values of VoLL for different years, countries and regions are represented as €2015, adjusted using inflation rates from Eurostat: Consumer prices – inflation and comparative price levels data [prc_hicp_aind] and historic currency exchange rates from http://www. xe.com/.

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additional hour of leisure can be valued as equal to the income from an additional hour of work (i.e. the hourly wage).4 Becker’s approach, however, may not be applicable to people that are non-employed, such as unemployed, pensioners, children, sick or disabled persons. This is due to the fact that the value of their leisure can no longer be equated to hourly wages, and would lead to an overestimation. However, the leisure time of people that are non-employed is, of course, still valuable. This study assumes that an hour of leisure for non-employed persons is valued as half the hourly wage of those that are employed, as suggested by Refs. [25,15]. After obtaining a value of leisure, the VoLL for households in each MS was calculated as

VoLLMS =

LVMS ELCMS

Table 2 VoLL by country using production-function approach.

(1)

where LVMS is the annualised leisure value (economic) of each MS, measured in €, and ELCMS is the annual household electricity consumption of each MS, measured in kWh. In order to obtain LVMS, labour market data on hourly wages, number of employed/non-employed persons, and the number of hours worked per employee annually was used. Labour market data was obtained from Eurostat5 and a detailed summary of this data is provided in Table 2. Following the approach used in [18], the average time spend on personal care such as sleeping, eating, washing, and dressing is around 11 h per day. Considering the information described above, the annual economic value of leisure for all persons in each Member State was calculated as LVMS, = ((hours per day − days per day × hours on personal care per day − hours workedMS) × hourly wageMS) × substitutability factor × (number of employed personsMS + 0.5 × number of unemployed personsMS) (2) where substitutability factor is the fraction of leisure activities that are electricity-based, as described earlier and where the value of leisure of non-employed persons is 50% of the hourly wage of employed persons. This assumption is line with current literature that calculates VoLL for households at national levels [15,16,18,22]. The VoLL obtained from the above equation, measured as €/kWh, represents an average annual value. However, leisure activities are clearly not distributed equally throughout the day or the year. In order to capture the time varying nature of electricity interruption costs, it is important to consider the absolute value of electricity not supplied. This is obtained by combining the average annual VoLL with the hourly household electricity consumption. The time-varying VoLL was calculated as

VoLLMS, t =

LVMS × ELCMS, t = LVMS × dfMS, t ELCMS

Country

Electricitybased leisure activity (hours/year)

Value of leisure (total) (Million €/year)

Electricity consumption (households) (GWh/year)

VoLL (PPPa adjusted (€/kWh)

Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom

1407.9 1405.3 1314.3 1337.7 1337.7 1319.5 1498.9 1363.7 1413.1 1397.5 1454.7 1280.5 1345.5 1452.1 1413.1 1363.7 1381.9 1407.9 1374.1 1592.5 1314.3 1350.7 1332.5 1314.3 1342.9 1384.5 1428.7 1423.5

176619 291517 16568 24986 8690 65621 161665 7926 117332 1490576 1768204 88706 46834 92828 1112956 8049 12168 12846 3391 431322 192110 85808 56618 30383 20081 596244 250300 914742

17641 19756 10510 6213 1434 14677 10280 1861 21460 167470 135649 17401 10553 7927 66810 1778 2584 875 607 25068 28369 12282 11866 4917 3220 72326 38105 113160

9.49 13.58 3.20 5.90 6.50 6.55 11.38 5.71 4.46 8.28 12.64 5.68 7.47 9.71 16.29 6.43 7.49 12.20 6.87 15.80 12.15 8.42 8.97 9.01 7.51 8.70 5.03 6.94

Purchasing power parity: Purchasing power parity is used to equalise the purchasing power of two currencies or countries. It is determined by the relative cost of living and inflation rates in different countries.

4. Results 4.1. VoLL for households by country (EU-28) One of the dimensions of the European Commission’s Energy Union relates to a “fully integrated European energy market”. A significant focus of current EU energy policy is directed towards removing barriers to achieving such an integrated energy market. An important barrier is the reconciliation of objectives at the EU-level with those at the Member State-level. Among the factors that strongly affect national energy policy is the need for reliable electricity supply to all end-use sectors: industry, commerce, agriculture, transportation, and households. As mentioned in previous sections, it is important to quantify and compare electricity outage costs at the household level across all EU Member States. Using the methodology described in the previous section, an annual average VoLL was determined for each EU Member State. Fig. 2 shows a comparison of the annual average VoLL, by EU Member State on a map in order to highlight the differences between neighbouring Member States. These disparities are of particular importance in the context of achieving the “fully integrated European energy market” described above. Table 2 also provides a summary of annual average VoLL by EU Member State. The annual average VoLL for EU Member States ranges from 3.2 €/kWh for Bulgaria to 15.8 €/kWh for the Netherlands. The average VoLL for the EU was calculated to be 8.7 €/kWh. As detailed in the Methodology Section, the VoLL directly proportional to hourly wages and inversely to per capita electricity consumption. On the one hand, Bulgaria has the lowest hourly wage, but an above average per capita electricity consumption partially due to an inexpensive – although inefficient – generation mix [45,46]. On the other hand, the Netherlands has a relatively high hourly wage but a below average per capita

(3)

where dfMS,t represents the demand factor6 in hour t for each MS. The hourly household electricity consumption for each EU Member State was obtained from ENTSO-E.7 The following section presents the results of calculating VoLL for each EU-28 Member State using the methodology described above, first as annual average values and then on an hourly basis. This is supplemented by a sensitivity analysis on the impact of substitutability factor on VoLL. 4 This method to value leisure time is sometimes considered controversial, especially outside the field of economics. As de Nooij [15] reports, this idea is used regularly in economics. Economists often use a ‘reservation wage’ in labour economics; if people cannot earn more than this reservation wage, they will not accept a job because, to them, their leisure time is more valuable than the earnings from the job offered. 5 Eurostat: Unemployment rate by sex and age – monthly average, % [une_rt_m]. 6 Demand factor is the ratio of average electricity demand to peak electricity demand over a period of time (one year in this case). 7 ENTSO-E (European Network of Transmission System Operators for Electricity) electricity consumption data is available at https://www.entsoe.eu/data/data-portal/ consumption/Pages/default.aspx.

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Fig. 2. Comparison of VoLL in EU households by Member State.

electricity consumption. Other Member States with a low VoLL (below 6 €/kWh) are Croatia, Cyprus, Estonia, Finland, Greece, Hungary, Latvia, Lithuania, Malta, and Romania. Member States with a high VoLL (above 11 €/kWh) include Belgium, Denmark, Italy, Luxembourg, Poland and the Netherlands. The VoLL obtained in this study can also be compared to previous country-level estimates, as listed in Table 1. As described previously, VoLL strongly depends on the measurement method (survey, macroeconomic, production function etc.), and the year of measurement. The VoLL obtained in this study – which uses the production-function approach and data for the year 2013 – closely match the values obtained in previous studies that used a similar approach for country-level analysis. In particular, Growitsch et al. [18] and Piaszeck et al. [42] estimated the annual average VoLL for Germany to be 12.06 €/kWh and 13.44 €/kWh which closely matches the value of 12.64 €/kWh obtained in this paper.

be between 17:00–19:00 in the Winter months (November- February) for Denmark and the Netherlands, and between 10:00–16:00 in the Summer months (June- August) for Italy. Since the comparison in Fig. 3 is scaled over a wide range of values across the EU, the VoLL in Bulgaria, for instance, appears to be relatively low for the entire year. In addition, some general trends for all the Member States can be identified. VoLL is found to be the lowest between the hours of 23:00 and 6:00. In most Member States, the greatest hourly outage cost is found to between the hours of 17:00–21:00. The time of year that this peak VoLL occurs varies for each Member State based on their latitude; for Member States in Northern Europe – such as Denmark, Sweden and Finland- peak VoLL occurs in the Winter months (November-February) while for Member States in Southern Europe – such as Spain, Italy and Greece- peak VoLL occurs in the Summer months (June-August). In addition, VoLL in Central European Member states, such as the Netherlands, Germany and Austria, show less seasonal variation.

4.2. Time-varying outage costs

4.3. Sensitivity analysis of substitutability factor

As electricity-based leisure activities vary over time – both through the day and between seasons – so does the VoLL. The level of detail to which the time-varying nature of VoLL can be analysed depends on the resolution of electricity consumption data available. In this study, hourly electricity consumption data was used to determine the VoLL over 24 h of the day for 12 months of the year, in each EU Member State.8 These results are shown in Fig. 3. The highest VoLL is found to

As described in Section 3, substitutability factor – the proportion of leisure activities that are electricity-based – may have a significant impact on the VoLL. Moreover, substitutability factor is likely to vary between countries and have different impacts on the VoLL in each country. For example, playing outdoor sports is typically an activity with low dependence on electricity, while watching television is completely dependent on electricity. Therefore, a sensitivity analysis to better understand the impact of substitutability factor on the VoLL was performed. In this sensitivity analysis, we vary the substitutability factor from 0 (leisure activities are completely independent of electricity) to 1 (leisure activities are completely dependent on electricity).

8 Time-varying VoLL was not calculated for Malta as hourly electricity consumption data was not available for it on the ENTSO-E data portal. Time-varying VoLL results for the remaining 27 EU Member States have been calculated and reported.

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Fig. 3. Time-varying VoLL by EU Member State.

31.46 (Denmark), and 29.52 (Belgium) €/kWh. The variation of VoLL is linked to the specific leisure activities that may be interrupted in each country, which can in turn be linked to

The result of this sensitivity analysis is shown in Fig. 4. The sensitivity analysis shows that VoLL varies significantly with substitutability factor. The VoLL varies from 0 to 34.42 (the Netherlands), 33.32 (Italy),

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Fig. 4. Impact of substitutability factor on VoLL.

may lead to an overestimation of outage costs. By using average net wages as a measure of marginal value of leisure may lead to overestimation or underestimation of its value. The latter since average tax rates are lower than marginal tax rates, hence marginal net wages may be lower. Underestimation may arise from the fact that the marginal value of leisure may be especially high on days when an outage occurs. Further, using half of net wages as the marginal value for leisure of nonworking people may lead to an overestimation in cases where their leisure time is mainly composed of, for instance, outdoor activities, which are not dependent on electricity supply. Similarly, outages lasting two hours and repeated daily may lead to higher than those costs indicated by average annual VoLL values since they would represent a quasi-permanent obstacle to regular activities. As people adapt their behavior to these altered circumstances, our estimation of loss of leisure may be overestimated. This further highlights the importance of differentiating planned and unplanned interruptions: in the former case, households may be able to re-schedule domestic activities without a substantial loss of leisure. The aforementioned caveats acknowledge that assessing actual costs of prolonged power outages may be a highly uncertain exercise. This is further evidenced by Zachariadis and Poullikkas [17] for the case of Cyprus. In this study, we estimate outage costs on an hourly basis, but do not take into account different durations of outages. This approach can provide indicative values of outage costs and highlight differences between Member States. We can therefore use this information to draw conclusions that are relevant for policy makers. For instance, Member State-level VoLL results can be used to determine an optimum distribution of remaining electricity to end-users in the case of a power outage. One may argue that capacity replacement and the magnitude of power outages can be sufficiently determined through engineering judgement. Nevertheless, a key policy issue is whether outages are implemented in an socio-economically optimal way. At present, concepts such as utility, and welfare losses may appear vague for noneconomists. Therefore, national accounts of governments, for instance, do not ascribe monetary values to these terms. The ‘value’ created by households is, therefore, not considered in decisions of supply security. Instead, the focus of these decisions remains on the supply side or demand sectors such as industries. This is in contrast with the economic analyses carried in recent VoLL estimation studies (de Nooij et al. [15], Leahy and Tol [16], Zachariadis and Poullikkas [17], and Growitsch et al. [18]) where the optimal response to an outage is to secure electricity supply to high VoLL-households. However, the authors

cultural factors. For instance, the OECD reports that there are significant differences in the leisure activities people undertake across OECD countries [53]. The OECD report groups leisure activities into five major categories: multimedia entertainment at home (TV or radio), other leisure activities (various hobbies, internet use, phone conversations, etc.), visiting and/or entertaining friends (both in private and public venues), participating in and/or attending social events (such as concerts, cinema, museums, etc.), and sports (actively participating in regular physical activities, whether individual or organised). Across OECD18 countries, watching TV or listening to the radio is the most popular leisure activity at nearly 40% of total leisure time on average. Globally, watching TV accounts for as high as 48% of time in Mexico and as low as 25% in New Zealand. “Other leisure activities” are the next most popular leisure activity. This popularity can be partly attributed to the fact that this is a broad category that includes hobbies, computer games, recreational internet use, telephone conversations, arts and crafts, walking pets, and so on. “Other leisure activities” account for as much as 48% of people’s leisure time in Italy, but only 25% in Turkey. “Visiting and entertaining friends”, reaches as high as 34% in Turkey and as low as 3% in Australia. This category is particularly variable between countries [54]. More “active” types of leisure activities such sports take up around 12% of leisure time on average in Spain and but only 5% in Belgium, Mexico, New Zealand and the United States. Today, the share of consumption of electrical appliances represents over 50% of the total electricity consumption in 23 out of 28 EU Member States [55]. At the same time, electronic appliance usage – such as computers, tablets and mobile phones – is increasingly represented as part of a household’s leisure time. For the UK, Torriti [56] reports that out of six household activities, namely, preparing food, washing, cleaning, washing clothes, watching TV and using a computer, washing is the most-time dependence and using computers is the least time-dependent practice. Therefore, in the case of a short interruption, activities such as computer and mobile phone usage – that comprise a growing portion of household leisure time – are likely to affected to a lesser extent than in the future. This will in turn inform the ‘substitutability factors’ to be used to improve future VoLL estimation studies. 5. Discussion In Section 2, we discussed the possibility that VoLL obtained using the production-function approach – the method applied in this study – 46

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The findings are envisaged to provide a basis for meaningful comparison between EU Member States and to inform key areas of European energy policy such as the design of capacity and balancing markets. At present, VoLL is completely lacking in international comparability. A uniform analytical framework is therefore urgently required [2]. All previous studies to determine VoLL have been performed at a national level for individual countries. Therefore, while estimating VoLL has been considered and explored in an international context, the heterogeneity of methods used and base year analysed precludes the inter-comparison of studies. As mentioned earlier, this is the first study that uses a common, well-established methodology for a single year to estimate the VoLL for twenty-eight EU Member States simultaneously. The results of this study can be further supplemented by, for instance, combining it with national level survey data through WTP/WTA. Further developing the VoLL approach as an economic index would, thus, effectively complement other technical indices.

acknowledge that resolving this discrepancy is a non-trivial task for policy makers. Any policy action at a national or regional level will require a better representation of the different local contexts. Building on the work in this paper, an ad hoc methodology that includes multiple estimation methods such as surveys, conjoint analysis, and power system modelling can be used. 6. Conclusions In this paper, VoLL for households in all twenty-eight EU Member States is estimated based on a uniform methodology for the first time. Results from this study show that the differences in VoLL between EU Member States is significantly large, ranging from 3.2 €/kWh in Bulgaria to 15.8 €/kWh in the Netherlands. The annual average VoLL for the EU was calculated to be 8.7 €/kWh. Further, we analyse the time varying nature of outages in order to determine times of highest VoLL. These hourly results highlight the importance of considering the time of interruptions and that these timings may be different among Member States. Finally, we carry out a sensitivity analysis of ‘substitutability factor’ – the proportion of leisure activities that are electricity-dependent. This is the first time that such a sensitivity analysis has been carried out.

Acknowledgement This study was partly funded through INSIGHT_E (Grant Agreement no. 612743), a European Commission project under the Seventh Framework Programme (FP7).

Appendix A See Table A1.

Table A1 Data on labour market, leisure time, and population (2013). Country

Working time hours/ year

Leisure activity (hours/ year)

Electricity-based leisure activity (hours/ year)

Unemployment (%)

Population

Labour costs (net) (€/hour)

Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom

1929.2 1934.4 2116.4 2069.6 2069.6 2106.0 1747.2 2017.6 1918.8 1950.0 1835.6 2184.0 2054.0 1840.8 1918.8 2017.6 1981.2 1929.2 1996.8 1560.0 2116.4 2043.6 2080.0 2116.4 2059.2 1976.0 1887.6 1898.0

2815.8 2810.6 2628.6 2675.4 2675.4 2639.0 2997.8 2727.4 2826.2 2795.0 2909.4 2561.0 2691.0 2904.2 2826.2 2727.4 2763.8 2815.8 2748.2 3185.0 2628.6 2701.4 2665.0 2628.6 2685.8 2769.0 2857.4 2847.0

1407.9 1405.3 1314.3 1337.7 1337.7 1319.5 1498.9 1363.7 1413.1 1397.5 1454.7 1280.5 1345.5 1452.1 1413.1 1363.7 1381.9 1407.9 1374.1 1592.5 1314.3 1350.7 1332.5 1314.3 1342.9 1384.5 1428.7 1423.5

5.3% 8.4% 12.9% 17.4% 15.9% 7.0% 7.0% 8.6% 8.1% 10.3% 5.2% 27.5% 10.1% 13.1% 12.2% 11.9% 11.9% 5.8% 6.4% 7.3% 10.4% 16.4% 7.1% 14.2% 10.1% 26.1% 8.0% 7.6%

8451860 11161642 7284552 4262140 865878 10516125 5602628 1320174 5426674 65560721 80523746 11003615 9908798 4591087 59685227 2023825 2971905 537039 421364 16779575 38062535 10487289 20020074 5410836 2058821 46727890 9555893 63905297

15.3 19.4 1.9 4.8 8.2 4.9 20.0 4.6 16.0 17.2 15.5 7.3 3.7 14.9 14.1 3.1 3.2 17.5 6.1 16.8 4.1 6.6 2.2 4.6 7.7 10.6 19.1 10.5

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