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Identifying and estimating the effects of a mandatory billing demand charge
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Industrial Engineering & Management.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.ORCID iD: 0000-0002-1260-7737
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Industrial Engineering & Management.ORCID iD: 0000-0002-8223-9634
2019 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 237, p. 885-895Article in journal (Refereed) Published
Abstract [en]

As peak demand for electricity continues to rise, distributors have begun charging small and medium-sized users for their short term demand rather than just their energy use. This is not only to meet the political aspirations for increased demand-side flexibility that now exist in many corners of the world, but to make sure that users are charged for the costs they incur. As it is only until recently that this type of users have come to face demand charges, there are however very few studies on what the actual effects of such pricing policies are, and those studies that do exist suffer from different methodological shortcomings that reduce their validity as a basis for real-world policy evaluations. This study provides the first state-of-the-art causal analysis of the demand response effects of a billing demand charge involuntarily introduced to small and medium sized users (35–63 A), using novel two-level time series models on retrospective observational consumption and survey data. Our analyses suggest that the tariff has induced an average response of −0.32 kWh/day per user over a two year long posttreatment period in comparison to a matched control group, equal to 7.4% of their daily average use during the pretreatment period. The response seems to have increased over time and to be greater during wintertime: around −0.70 kWh/day or 16.2% of the treated users’ average daily use during the pretreatment period. Comparing the individual users’ response to the size of their financial incentive to respond given the new tariff as well as their self-reported perception of the relative importance of electricity expenditures, we did not find any support for the common assumption that users with a higher financial incentive to respond do so to a greater extent. This might suggest that small and medium-sized commercial users, just as residential users, may exhibit non-financial drivers and barriers for engaging in demand response that may be vital to understand as policy makers and industry continue to seek increased demand-side flexibility.

Place, publisher, year, edition, pages
2019. Vol. 237, p. 885-895
Keywords [en]
Demand response; Demand-based; Capacity charge; Capacity-based; Causal inference
National Category
Other Social Sciences Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:uu:diva-374770DOI: 10.1016/j.apenergy.2019.01.028ISI: 000459845100066OAI: oai:DiVA.org:uu-374770DiVA, id: diva2:1282116
Projects
Marknadsstyrd effekttariff inom eldistributionen
Funder
StandUp
Note

Projektet finansierades även av Energiforsk (EV32254).

Available from: 2019-01-24 Created: 2019-01-24 Last updated: 2019-09-27Bibliographically approved
In thesis
1. Causal Inference in Observational Studies and Experiments: Theory and Applications
Open this publication in new window or tab >>Causal Inference in Observational Studies and Experiments: Theory and Applications
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis consists of six papers that study the design of observational studies and experiments.

Paper I proposes strategies to consistently estimate the average treatment effect of the treated using information derived from a large number of pre-treatment measurements of the outcome. The key to this strategy is to use two-level time-series model estimates to summarize the inter-unit heterogeneity in the sample. It is illustrated how this approach is in line with the conventional identifying assumptions, and how sensitivity analyses of several key assumptions can be performed.

Paper II contains an empirical application of the identification strategy proposed in Paper I. This study provides the first causal analysis of the demand response effects of a billing demand charge involuntarily introduced to small and medium sized electricity users.

Paper III proposes strategies for rerandomization. First, we propose a two-stage allocation sample scheme for randomization inference to the units in balanced experiments that guarantees that the difference-in-means estimator is an unbiased estimator of the sample average treatment effect for any experiment, conserves the exactness of randomization inference, and halves the time consumption of the rerandomization design. Second, we propose a rank-based covariate-balance measure which can take into account the estimated relative weight of each covariate.

Paper IV discusses the concept of optimal rerandomization. It is shown that depending on whether inference is to be drawn to the units of the sample or the population, the notion of optimal differs. We show that it is often advisable to aim for a design that is optimal for inference to the units of the sample, as such a design is often near-optimal also for inference to the units of the population.

Paper V summarizes the current knowledge on asymptotic inference for rerandomization designs and proposes some simplifications for practical applications. Drawing on previous work, we show that the non-normal sampling distribution of the difference-in-means test statistic approaches normal as the rerandomization criterion approaches zero. Furthermore, the difference between the correct non-normal distribution and the proposed approximation based on a normal distribution is in many situations negligible even for near optimal rerandomization criteria.

Paper VI investigates and clarifies the relation between the traditional blocked designs and rerandomization. We show that blocking and rerandomization is very similar, and in some special cases identical. Moreover, it is shown that combining blocking and rerandomization is always at least as efficient as using only rerandomization, but the difference is in many cases small.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 36
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Social Sciences, ISSN 1652-9030 ; 172
Keywords
experimental design, identification, observational studies, rerandomization
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:uu:diva-393810 (URN)978-91-513-0762-6 (ISBN)
Public defence
2019-12-06, Hörsal 2, Ekonomikum, Kyrkogårdsgatan 10, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2019-11-05 Created: 2019-09-27 Last updated: 2019-11-27

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Öhrlund, IsakSchultzberg, MårtenBartusch, Cajsa

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