Driven by concerns involving, among others, capacity (and investment) constraints, environmental issues, and the need to balance intermittent renewable generation, there has been a renewed policy interest across the world in the efficient pricing of electricity. Given the specificities of the electricity market, the wholesale price of electricity varies substantially over the day; nonetheless, consumers have long been charged a fixed retail price. There is a long literature in economics arguing that the use of a price that better reflects the true cost of producing electricity on a more dynamic basis (e.g. an hourly price) will in theory give rise to substantial efficiency gains, (1) and a variety of "dynamic" or "real time" pricing (RTP) schemes have been proposed (but rarely implemented). These efficiency gains arise largely from a more efficient allocation of consumption, leading to a reduction in the need for costly peak capacity. Further, price-driven demand flexibility also has the potential to balance the variability of increased intermittent production, most notably wind and solar power, and to reduce congestion of transmission networks in Sweden.
Empirical evidence for the practicability of RTP schemes, and in particular the possibilities and incentives for households to respond to such pricing by shifting load from "expensive" to "cheap" hours, is, however, rather scarce. Indeed, as documented in Faruqui and Sergici (2010), the evidence is relatively mixed and depends upon a variety of factors. In general, the lack of relatively large samples of households on RTP schemes hampers evaluation of the demand shift actually realized. Given that data on household behavior under RTP is scarce, one way of empirically exploring the efficacy of RTP would be to compare the timing of current within-day electricity consumption (by households on non-dynamic-price contracts) with possible restrictions on substitutability, such as working hours and temperature variation.
These explorations are most useful with data at the appliance or end-use level, since substitutability will vary between appliances and end-uses; to illustrate, it might be easier to shift laundry within a day than to shift lighting, since the use of lighting is to a large extent determined by available daylight. Further, estimates of current within-day consumption are necessary as a baseline for computing the cost savings of re-allocating load. Unfortunately, the data required for such analyses are rarely available and only a few studies (e.g., Bartels and Fiebig (1990, 2000); Larsen and Nesbakken (2004); Allcott (2011)) in the very large literature in economics on electricity demand have been able to illustrate how residential electricity end-use consumption varies within a day and how this information could be used for policy analysis, e.g. to understand substitution possibilities. Our analysis adds to this sparse literature, and considers the case of Sweden. Sweden introduced a law allowing consumers to have access to RTP, in the sense that consumers can choose to have hourly price contracts without having to pay for the necessary metering equipment (source: [section]3 Chap 11 in the the electricity act). (2) Sweden is, to our knowledge, among the few countries to have had such an explicit and country-wide possibility for RTP.
Our study contributes to the existing literature in two ways. First, using a unique, appliance-level direct metering data set, we are able to provide estimates of conditional load curves and understand the implications for substitutability of load across hours, a maintained assumption in the theoretical (and many empirical) studies estimating the benefits from RTP. Second, using the estimated load curves and wholesale price data, we are able to provide (an approximation to) estimates of the benefits to the consumers from such load shifts. Our analysis, thus, is able to shed light on the extent to which various demand management strategies, particularly RTP, are likely to be effective. We note that, to our knowledge, ours is the first study providing such estimates for Sweden, and the results of our study should be of considerable relevance for Swedish energy policy, given the Swedish government's strong thrust on RTP.
In more detail, our analysis uses data from a study commissioned by the Swedish Energy Agency, which metered household electricity consumption at the appliance-level at ten-minute intervals for 389 households, none of which were on RTP contracts. Data at this level of detail have rarely been available for most countries. The appliance-specific nature of the metered data we use provides a unique opportunity to obtain better understanding of appliance- and end-use-specific consumption patterns. Prior approaches to estimating appliance- (or end-use-) specific load curves used the method of Conditional Demand Analysis (CDA), which suffered from many limitations, or a combination of metered data and CDA approaches. Aggregating the ten-minute consumption data to hourly, we estimate end-use specific load curves (conditional on household characteristics) and analyze how these correlate to possible restrictions on substitutability of load within the day. That is, we do not explicitly explore substitutability of load, but rather analyze possible restrictions on substitutability. These restrictions, including working hours, outside temperature and (lack of) daylight might--independently of the type of contract the household has chosen--impose significant limitations on any short-run attempt to shift load from "expensive" to "cheap" hours.
Finally, using the estimated load curves as the baseline, we examine the monetary incentives for households to shift load under an hourly pricing scheme based on average and maximum Nord pool spot prices, for average working days in February. The results presented here have important implications for Swedish energy policy, and in particular for the Swedish government's stated goal of implementing RTP. The success of this pricing scheme depends heavily on demand response which, our results indicate, are likely to be small, absent substantial investments in new technology and a focus on it from the retailers. Both consumers and retailers appear to have little to gain from a potential switch to RTP--at least in the short run--based upon our simple cost shifting experiments.
The rest of this paper is structured as follows. A review of the different strands of literature relevant for our analyses is provided in Section 2 followed, in Section 3, by a brief description of the Swedish electricity market. Section 4 provides a description of the data used in this paper, together with a few summary statistics on key variables used in the analysis. Section 5 details the estimation of the load curves, along with computations of the cost of servicing different end-uses and cost changes due to load shifts. Section 6 provides a discussion of the policy context of our analysis and concludes. Load curves for the month of June and details regarding the goodness-of-fit measures for the Seemingly Unrelated Regression (SUR) system used for our estimation are relegated to Appendices 6.1 and 6.2, respectively.
We turn now to a brief review of the literature on within-day and end-use-level electricity consumption. As emphasized earlier, a clear understanding of both price responsiveness and baseline consumption patterns are key inputs to any analysis of policies concerning dynamic pricing. In particular, the success of RTP depends upon consumers responding to hourly price variation by reallocating consumption within a given day. (3) However, as already noted, the literature on sub-annual appliance-level electricity demand--necessary for such analysis--is sparse, and especially rare are studies using hourly data. The CDA approach pioneered in Parti and Parti (1980) and refined in Bartels and Fiebig (1990, 2000); Fiebig et al. (1991) has been used as a way of overcoming the lack of appliance- (end-use-) level data. The idea in this approach is to combine data on total load with survey information on appliance holdings to estimate the contribution of each appliance to total load, exploiting heterogeneity in household appliance portfolio. The estimated coefficients, interpretable as the the mean contribution of each appliance to total load, are then used to produce daily load curves for selected appliances.
An obvious disadvantage of this method is an inability to estimate the load of appliances with high penetration rates such as TV, washing machine and lighting. Bartels and Fiebig (2000) partly overcome this drawback by combining survey data with real-time metering data, using a random coefficient model to allow for variation in appliance size and intensity of utilization between households. The mean response associated with each appliance is then estimated using data from both types of households, those that were, and those that were not, directly metered. See also Hsiao et al. (1995) for a bayesian approach on combining metering data with conditional demand analysis. Larsen and Nesbakken (2004) compare the CDA approach with an engineering model, ERAD, whose inputs include engineering knowledge regarding technical and other features of housing stock, enabling estimation of energy demand for space heating. They compare the numerical results from these two approaches and provide a few recommendations regarding choice of end-use and what questions to implement in household surveys designed to disaggregate electricity consumption.
Before elaborating on how understanding within-day electricity consumption can assist in evaluating the scope for dynamic pricing, we briefly review some of the relevant literature on efficiency gains from RTP. We note that this literature is directly relevant for our analysis since substitution pattern across hours--the key aspect of...
Residential End-use Electricity Demand: Implications for Real Time Pricing in Sweden.
To continue readingFREE SIGN UP
COPYRIGHT TV Trade Media, Inc.
COPYRIGHT GALE, Cengage Learning. All rights reserved.
COPYRIGHT GALE, Cengage Learning. All rights reserved.