Energy Economics 29 (2007) 249 – 258 www.elsevier.com/locate/eneco The real-time price elasticity of electricity Mark G. Lijesen ⁎ CPB, Netherlands Bureau for Economic Policy Analysis and Vrije Universiteit, PO Box 80510, 2508 GM Den Haag, The Netherlands Received 17 August 2006; accepted 17 August 2006 Available online 20 September 2006 Abstract The real-time price elasticity of electricity contains important information on the demand response of consumers to the volatility of peak prices. Despite the importance, empirical estimates of the real-time elasticity are hardly available. This paper provides a quantification of the real-time relationship between total peak demand and spot market prices. We find a low value for the real-time price elasticity, which may partly be explained from the fact that not all users observe the spot market price. If we correct for this phenomenon, we find the elasticity to be fairly low for consumers currently active in the spot market. If this conclusion applies to all users, this would imply a limited scope for government intervention in supply security issues. © 2006 Elsevier B.V. All rights reserved. JEL classification: D12; Q41 Keywords: Electricity; Price elasticity; Empirical demand analysis 1. Introduction Following several major black-outs around the Western world, security of electricity supply has gained an enormous amount of attention, both from academics and policy makers. For decades, the provision of electricity was characterized by centralized systems with vast overcapacity. Following the liberalization of electricity markets, producers rationalize their capacity, leading to a decrease in overcapacity. Although the reduction in overcapacity may be desirable from a cost efficiency point of view, it also increases the risk of excess demand, ultimately resulting in black-outs. ⁎ Tel.: +31 70 338 33 22; fax: +31 70 338 33 50. E-mail address: mgl@cpb.nl. 0140-9883/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.eneco.2006.08.008 250 M.G. Lijesen / Energy Economics 29 (2007) 249–258 Policy makers wishing to counteract black-outs often aim their focus on re-establishing the traditional situation of overcapacity, which is why the focus in many of the discussions is on measures on the supply side. Recent focus in the literature is on capacity markets (e.g. Hobs et al., 2001) and capacity payments (e.g. Ford, 1999; Oren, 2000). Rather than increasing capacity, excess demand may also be counteracted by increasing the price responsiveness of demand. Most end-users do not observe real-time prices and hence cannot react to them. Policies aimed at providing price information as well as incentives to react may be much more cost effective than retaining large amounts of spare capacity. Mixed systems, combining spare capacity and demand response (e.g. Doorman, 2003), are currently being considered. Insight into the order of magnitude of demand reactions is useful to assess the effects of policy options aimed at demand response. This paper focuses on the demand response in the electricity market, trying to empirically estimate the real-time elasticity of electricity. We define the real-time elasticity as the price elasticity of demand on an hour-to-hour basis.1 The current literature regarding price elasticities focuses on quarterly (e.g. Beenstock et al., 1999), or annual data (e.g. Urga and Walters, 2003; Al Faris, 2002), or on elasticities of time of use pricing (e.g. Aigner et al., 1994; Filippini, 1995). Literature on real-time elasticities is very scarce however, Patrick and Wolack (1997) being the notable exception. The remainder of this paper is organized as follows. The following section looks more closely into the role of demand elasticity in demand response. Section 3 provides a brief overview of recent empirical results regarding price elasticities of electricity, followed by a discussion of our framework. Data and empirical results are reported in Sections 5 and 6 concludes, presenting the implications and conclusions. 2. Demand response and price elasticity The non-storability of electricity implies that electricity supply is capped in the short run. The combination of this feature with time-varying demand is often held responsible for the vulnerability of supply security in electricity markets. These characteristics of electricity markets do not constitute a novelty, the only difference is that the cap on supply was on a much higher level in the recent past, so that it was hardly ever binding. This combination of characteristics is not unique to electricity markets. All services are nonstorable and many of them have fluctuating demand over time. Let us turn to the very similar example of road transport. Virtually every urban area in the world suffers from highway congestion during rush hours, a problem very similar to that of the electricity market, as supply is fixed in the short run by road capacity and demand fluctuates strongly over time. Congestion, both on highways and on electricity networks, may be viewed as an externality. Adding one unit of demand above a certain threshold level has a negative impact on the quality of the good for all users. The marginal customer is not charged for all the costs he incurs. In the case of electricity, an increase in demand beyond available capacity levels increases the probability of a black-out, thus imposing (expected) outage costs on all users. Comparing policy proposals for security of supply with those for solving traffic congestion reveals that the implicit value of black-outs is much higher than that of traffic jams. 1 Note that electricity is often traded at day ahead markets, so that the trade reaction is real-time, but the actual effect on load is not. This implies that these reactions cannot take into account unexpected events, such as power plant outages. 251 M.G. Lijesen / Energy Economics 29 (2007) 249–258 Table 1 Long and short run elasticity estimates Source Type of model Type of data Long term Short term Al Faris (2002) Error correction model Error correction model Annual time series, 1970–1997 Quarterly time series, 1973–1994 −0.82/−3.39 − 0.04/− 0.18 Households: − 0.124 Industry: − 0.123 Loglinear, fixed effects Bottom-up Panel, 1983–1996 Households: −0.579 Industry: −0.311 – Households: −0.09/−0.13 – – Industry: −0.77 −0.697 Industry: − 0.51 − 0.147 – Households: − 0.16/ − 0.39 −0.14/− 0.49 Households: − 0.15 Beenstock et al. (1999) Bjørner and Jensen (2002) Boonekamp (2007) Brännlund et al. (in press) AID-model Caloghirou et al. (1997) Elkhafif (1992) Translog Loglinear Filippini and Pachuari (2002) Hesse and Tarkka (1986) Holtedahl and Loutz (2004) Loglinear Ilmakunnas and Törmä (1989) Jones (1995) Roy et al. (in press) Taheri (1994), Urga and Walters (2003) Woodland (1993) Zachariadis and Pashourtidou (2007) a b Translog Long term: loglinear Short term: error correction model Generalized Leontief Loglinear Translog Translog Translog Loglinear Translog Translog Error correction model Annual time series, 1990–2000 Quarterly time series, 1980–1997 Panel, 1980–1991 Annual time series, 1963–1990 Monthly household panel, 1993–1994 Panel, 1973–1980 Annual time series, 1955–1996 Annual time series, 1960–1981 Annual time series, 1960–1992 Pooled country panel, 1980–1993 Panel, 1974–1981 Annual time series, 1960–1992b Panel, 1977–1985 Annual time series, 1960–2004 – Households: −0.16 – − 0.479 Households: − 0.24 − 0.73 −0.207 − 0.05 −0.201 − 0.276 Industrial: − 0.8/−1.76 – −0.845 −0.2609 −0.1042 – −0.3/−0.4 − 0.888a − 0.071 − 0.101 − 1.113 –a Not significantly different from zero. Same data as Jones (1995). Are these implicit valuations inaccurate, or may these differences be explained by a different starting point in looking for solutions? With traffic congestion, pricing policies become more and more popular, which raises the question whether pricing is also an option for securing electricity supply. If all externalities are internal to the market at stake (i.e. users impose costs on each other, as is often the case with congestion), either tradable permits or peak load pricing will bring about the optimal outcome.2 Many electricity markets already have a combination of peak load pricing and tradable permits in place, through spot markets and sometimes also through unbalance pricing mechanisms. One of the problems here is that many consumers do not observe real-time prices and hence cannot react to them. 2 See e.g. Boiteux (1960) for formal proof and further discussion. 252 M.G. Lijesen / Energy Economics 29 (2007) 249–258 Increasing demand reactions to price fluctuations may be a more efficient way to increase supply security than simply retaining large amounts of unused spare generation capacity. In order to assess the costs and benefits of possible options aimed at increasing demand responsiveness, one first needs to have insight into the current level of demand response. The level of demand response is also important in assessing the efficient level of capacity related security of supply measures.3 3. Price elasticities of electricity When looking at empirical estimates of elasticities of electricity, one can distinguish between long term elasticities, short term elasticities (i.e. 1 year or less) and elasticities from time-of-use studies. Long term elasticities often follow from the same study, simply by adding up lagged price effects of short term elasticities. Several authors have recently published empirical estimations of electricity elasticities. Table 1 summarizes the results of recent studies. 4 Al Faris (2002) estimates separate error correction models for Saudi Arabia, UAE, Kuwait, Oman, Bahrain and Qatar, finding short term demand elasticities ranging from − 0.04 to − 0.18. Estimates by Elkhafif (1992), Holtedahl and Loutz (2004), Jones (1995, loglinear specification), Urga and Walters (2003) and Beenstock et al. (1999) lie within this range, although the latter also find much lower values using the Engle and Granger method (Engle et al., 1989). These results are however hard to compare to the other outcomes presented here. Several studies find somewhat higher (Brännlund et al. (in press), Filippini and Pachuari (2002), Hesse and Tarkka (1986)) or much higher own price elasticities for electricity. The results in Table 1 suggest that panel data yield higher (absolute) results than aggregated time series. This may result from the causality problem in measuring demand reactions. If demand grows because of an exogenous reason, scarcity increases and prices rise, suggesting a positive relationship between the two. This positive relationship counteracts the negative relationship of price on demand and may dampen the price effect visible on an aggregated level. On a disaggregated level this problem does not arise, as demand growth of an individual consumer is unlikely to influence prices. In the past decade, several publications on time-of-use (TOU) pricing of electricity have been published. These publications study the demand effect of price differentials between peak and offpeak prices for end-users and may also give some clue on price elasticities of electricity. Table 2 lists elasticities from recent studies on TOU-pricing. Aigner et al. (1994) link price elasticities to the ratio of the peak price to the off-peak price for two- and three-price systems. For ease of comparison, we only show elasticities from the two-price system here. At higher peak prices, elasticities for peak and off-peak are closer to each other. Mountain and Lawson (1992) report fairly low values for time of use pricing elasticities, whereas the results found in Filippini (1995) are very high. The magnitude of the values found for own and cross price elasticities suggests that Fillipini's results may be influenced by misspecification. Ham et al. (1997) estimate elasticities for small commercial establishments, finding a range from − 0.038 to − 0.050 for off-peak own price elasticities and − 0.069 to − 0.091 for peak own price elasticities. They find that substitution between peak and off-peak does not differ significantly from zero for this group of users. 3 4 See Lijesen and Vollaard (2004). For older studies, see the review in Taylor (1975). 253 M.G. Lijesen / Energy Economics 29 (2007) 249–258 Table 2 Elasticity estimates from recent studies on TOU-pricing Source Type of model Own elasticity Cross elasticity Aigner et al. (1994) Generalized Leontief Not reported Boisvert et al. (2004) Filippini (1995) Ham et al. (1997) Generalized Leontief Partial equilibrium model, loglinear demand Loglinear Mountain and Lawson (1992) Loglinear Off-peak: − 0.013/− 0.049 Peak: − 0.054/− 0.158 Peak: − 0.05/− 0.0675 Off-peak: − 2.30/− 2.57 Peak: − 1.25/− 1.41 Off-peak: − 0.038/− 0.050 Peak: − 0.069/− 0.091 Off-peak: − 0.003/− 0.036 Peak: − 0.002/− 0.138 See main text 0.34/1.57 Not significantly different from zero 0.003/0.037 Boisvert et al. (2004) estimate substitution elasticities for peak–off-peak price differentials of 0.2 to 0.27, and calculate that 75% of the change in electricity usage is due to shifting load. This implies that the own price elasticity of electricity is about one quarter of the substitution elasticity. The only authors to address real-time elasticities explicitly are Patrick and Wolack (1997). They give a detailed empirical analysis of real-time demand response for 5 industrial sectors in the UK.5 They find fairly low price elasticities, ranging from virtually zero to − 0.05 for four of the five sectors. The water supply industry exhibits a wider range, with elasticities between zero and − 0.27. For three out of five sectors, Patrick and Wolack find the absolute value of the elasticity to be relatively high at peak hours, late in the afternoon. As prices are higher during peak hours, this finding may support a more or less linear demand relationship. Looking at hour-to-hour patterns of own and cross price elasticities, Patrick and Wolack (1997) conclude that “…most of the substitutability in electricity consumption within the day comes from substitution across adjacent load periods.”6 For continuously running production processes they find complementarity within production shifts. A demand reduction in a production shift decreases demand in that entire shift. 4. Framework We determine the relationship between total demand and its determining factors, among which the spot market price. Electricity demand is influenced by a great deal of factors. As electricity is used as an input to many production processes, demand is partly determined by the characteristics of these processes, such as time of day, banking and religious holidays and the summer holidays. Economic growth and technical change are other determinants of production processes influencing electricity demand. Weather also affects electricity demand, particularly through the use of air conditioning on hot days and lighting equipment in the winter months of the year. We estimate a function explaining total system load on an hourly basis from the spot market price and several other factors affecting energy demand. We limit our analysis to 5 Water supply (BIC 17000), steel tubes (BIC 22200), copper, brass and other copper alloys (BIC 22460), ceramic goods (BIC 24890) and Hand tools and finished metal goods (BIC 31600). 6 Op. cit., p. 40. 254 M.G. Lijesen / Energy Economics 29 (2007) 249–258 peak-load use only, as demand response during peak hours is relevant for security of supply. In order to capture the general daily demand pattern, we use time of day dummies as timespecific constants in the equation. We use a variable labeled ‘maximum day temperature’ to capture the influence of temperature on the energy use by air-conditioning equipment. We also constructed a cross term between maximum temperature and a ‘noon to 4 PM’ dummy to reflect the fact that air-conditioning units are used mainly during the hottest hours of the day. Apart from its influence on air-conditioning equipment, the weather also affects the demand for lighting. We proxy this effect using a daylight variable, constructed as the quadratic difference from the longest day (measured in days). Cross terms with early and late hours are constructed in order to correctly represent their effect on electricity use, being the extra use of lights during dark morning and evening hours. We constructed a trend variable, which counts days from January 1st, aiming to represent the combined effect of ongoing phenomena, such as economic growth (+) and technical progress (−) on electricity demand. Summer holidays also influence electricity use through reduced economic activity. In the Netherlands, summer holidays are differentiated over three regions; North, South and Centre. As the summer holidays in the Centre region overlaps with those in both other regions, no dummy is added here. A dummy for ‘week 53’ reflects that many companies are closed in the week between Christmas and New Year's day. In order to understand the pricing mechanisms in the electricity market, let us devote some attention to how electricity is traded. Many European countries have a so-called spot market for electricity. Buyers and sellers of electricity bid their offers 24 h ahead of delivery. Prices are set on an hourly basis. After the spot market has closed, trade volumes for the following day are known. Note that suppliers at the spot market are not necessarily producers of electricity. Large users who have contracted a fixed amount of electricity may want to sell their electricity on the spot market if the price is high enough. A similar feature may be found on the demand side of the spot market. Producers who have sold more electricity in advance than they actually produce may purchase the deficit at the spot market. The above implies that trade volumes on the spot market do not represent demand. The spot market often represents just a small part of all electricity trade. The lion's share of electricity is traded through bilateral contracts between users and suppliers. This is sometimes also referred to as the over-the-counter (OTC) market. The contents of bilateral contracts are in general undisclosed information. Some of those contracts have fixed prices, others may be linked to the spot market price, either real-time or based on averages over time. Small end-users are supplied by retailers, often on bilateral contracts with standardised terms and a fixed per unit price. They do not observe real-time prices and can therefore not respond to them. It makes sense to look at the elasticity of total demand to the spot price. This approach captures both the effects in the spot market itself as well as the effects on the OTC contracts with prices linked to the spot price. Furthermore, we avoid the measurement problems related to distinguishing between demand and supply on the spot market. One should keep in mind however that the price elasticity found depends on the amount of demand that is subject to the spot price. If the relative importance of the spot market as a pricing mechanism increases, the demand elasticity will increase as well. Estimating the relationship between prices and quantity constitutes a specific difficulty because demand fluctuates greatly under influences other than prices. This causes the demand curve to shift along the supply curve, causing correlation between the price variable and the error 255 M.G. Lijesen / Energy Economics 29 (2007) 249–258 Table 3 Descriptives Load (MW) Spot market price (€/MWh) Maximum temperature (°C) Daylight Mean S.D. Min Max 14.24 95.15 10.31 10,957.09 1.21 172.59 7.00 9851.552 9.75 13.00 − 7.80 1 17.87 2000.00 25.70 37,249 term. We solve this problem by estimating a two stage least square regression, using lagged price as an instrumental variable for price.7 5. Data and empirical results Our data relate to 2003 energy use and price figures from the Netherlands. We proxy demand by the load figures published at the website of the Tennet, the Dutch TSO.8 As stated before, we limit ourselves to peak load periods, defined here as working days from 9 AM to 6 PM. Load data are available on the basis of 15-min intervals, but we aggregate these figures to hourly data in order to use them in the same analysis as our other data. Spot market prices are published on an hourly basis at the website of the Amsterdam Power Exchange.9 The variable ‘maximum day temperature’ is retrieved from the website of the Dutch meteorological institute, KNMI.10 Table 3 below lists the descriptive of the most important non-dummy variables. Table 4 presents the results from our empirical estimation, using both a linear and a loglinear approach. Both equations give a similar image of energy demand, though the influence of the spot market price is quite different. The linear specification implies a price elasticity of − 0.0014, whereas a price elasticity of − 0.0043 follows from the loglinear specification.11 In both cases the elasticity is low compared to the elasticities presented in Tables 1 and 2, and closer to the figures provided by Patrick and Wolack (1997), which is what was to be expected, as their results represent realtime elasticities, as do our results. 6. Implications and conclusions The empirical literature suggests that demand elasticities for electricity are generally low. The real-time elasticity found in our analysis is even lower. Note that we look at total demand versus the spot market price. Keeping in mind that the trade volume at the spot market is approximately 15% of total load, we may get a rough idea of the order of magnitude of the price elasticity in the spot market by dividing the elasticity from our result by 0.15. The resulting figure of − 0.029 (for the loglinear relationship) is still quite low, especially if we consider that users currently trading at the spot market are likely to be more price sensitive than users who voluntarily refrain from trading at the spot market. 7 8 9 10 11 For optimal efficiency, all other explanatory variables are used as instruments as well. www.tennet.nl. www.apx.nl. www.knmi.nl. Computed standard deviations for these parameters are 1.54 ⁎ 10− 3 and 1.59 ⁎ 10− 3 respectively. 256 M.G. Lijesen / Energy Economics 29 (2007) 249–258 Table 4 Estimation results for load Variable a Price (€/MWh) Trend a Time of day dummies, hour starting at: 9 AM 10 AM 11 AM Noon 1 PM 2 PM 3 PM 4 PM 5 PM 6 PM Day of week dummies Thursday Friday Month of year dummies January February April July September October Weather variables Maximum day temperature Maximum day temperature times noon–4 PM dummy a Daylight times 9 AM dummy a Daylight times 5 PM dummy Daylight times 6 PM dummy a Holiday dummies Summer holidays North (dummy) Summer holidays South (dummy) Week 53 (dummy) Adjusted R2 No. of observations a Linear specification Loglinear specification Coefficient t-Statistic Coefficient t-Statistic − 0.000218 0.0074 − 2.3 34.8 −0.0043 0.103 − 2.7 43.8 12.203 13.182 13.329 13.176 12.924 12.995 12.826 12.581 12.499 11.907 134.1 177.7 179.5 173.6 170.4 171.3 169.1 165.8 137.4 130.9 2.044 2.182 2.192 2.180 2.162 2.167 2.155 2.137 2.143 2.010 113.8 162.0 162.0 158.5 159.1 159.0 159.0 158.6 161.6 114.7 0.1877 0.1734 5.7 5.2 0.012 0.010 5.1 4.2 − 0.7889 − 0.4442 0.6621 − 0.9966 − 0.5165 − 0.4053 −11.0 − 6.6 12.5 −11.1 − 10.2 − 8.2 0.075 0.012 0.043 −0.079 −0.049 −0.037 11.2 2.5 11.3 − 12.5 − 13.7 − 10.3 3.4 5.7 5.3 3.4 13.3 – 0.0018 0.0102 – 0.0150 – 7.5 6.6 – 9.6 − 4.2 − 3.5 − 9.8 0.726 2500 −0.024 −0.023 −0.073 − 4.0 − 6.8 − 8.3 0.738 2500 0.01264 0.02374 0.00002 0.00002 0.00006 − 0.372 − 0.176 − 1.210 Log of variable in loglinear specification. If the result holds for the entire market, the following implications may be derived. First, a low (constant) elasticity implies that even small amounts of market power may lead to very high welfare transfers. On the other hand, welfare losses because of market power will be relatively small, as price increases have a limited real-time effect on electricity demand. Note that this applies to short temporary increases, implying that the transfers from market power come in the form of scarcity rents. With low real-time elasticities, peak prices and scarcity rents may reach very high levels. Apart from the welfare transfers they create, high scarcity rents also give rise to profitable investments in spare capacity. This implies that government intervention on the supply side may not be necessary, and may even lead to substantial free riding. M.G. Lijesen / Energy Economics 29 (2007) 249–258 257 If the real-time elasticity computed above applies to all users, it also implies that security of supply policies aimed at increasing the awareness of real-time prices (such as capacity subscriptions) may not be very effective, as customers may not be willing to react to real-time prices at all. We would like to emphasize however that these conclusions are conditional on the assumption that the elasticity found here applies to all consumers. The validity of this assumption does not follow from our analysis and should be tested empirically, for instance through controlled experiments. The main policy implication is that the real-time elasticity of targeted users should be assessed before expensive demand response measures are taken on a large scale. References Aigner, D.J., Newman, J., Tishler, A., 1994. The response of small and medium-size business customers to time-of-use (TOU) electricity rates in Israel. 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