Abstract
Using a unique and highly detailed data set on energy consumption at the appliance-level for 200 households, seemingly unrelated regression (SUR)-based end-use specific load curves are estimated. The estimated load curves are then used to explore possible restrictions on load shifting (e.g. the office hours schedule) as well as the cost implications of different load shift patterns. The cost implications of shifting load from “expensive” to “cheap” hours, using the Nord pool spot prices as a proxy for a dynamic price, are computed to be very small; roughSwedishly 2-4% reduction in total daily cost from shifting load up to five hours ahead, indicating small incentives for households (and retailers) to adopt dynamic pricing of electricity.
Highlights
Driven by concerns involving, among others, capacity 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
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 will in theory give rise to substantial efficiency gains,1 and a variety of “dynamic” or “real time” pricing (RTP) schemes have been proposed
Aggregating the ten-minute consumption data to hourly, we estimate end-use specific load curves and analyze how these correlate to possible restrictions on substitutability of load within the day
Summary
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 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. 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. 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 7.1 and 7.2, respectively
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