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SAVE: Experiences in applying best practice sample recruitment and randomised control trial designs to demand response studies

SAVE: Experiences in applying best practice sample recruitment and randomised control trial designs to demand response studies

Paper presented at the International Conference on Energy and Cities (ICEC2019), Southampton: University of Southampton, July 10th 2019.

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Ben Anderson

July 10, 2019
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Transcript

  1. SAVE Ben Anderson b.anderson@soton.ac.uk @dataknut Tom Rushby t.w.rushby@soton.ac.uk @tom_rushby Experiences

    in applying best practice sample recruitment and randomised control trial designs to demand response studies
  2. The menu § Flexibility: – What’s the problem? § Flexibility:

    – What do we (not) know? § The SAVE study design – Finding out what we don’t know § What we did § Where next? 2
  3. What’s the UK problem? § A de-carbonisation story…? • Staffell

    (2018) https://doi.org/10.1016/j.enpol.2016.12.037 3
  4. What’s the ‘peak’ problem? • ‘Dirty’ energy Carbon problems: •

    Higher priced energy Cost problems: • Inefficient use of resources; • ‘Local’ overload; Infrastructure problems: 4 UK Housing Energy Fact File Graph 7a: HES average 24-hour electricity use profile for owner-occupied homes, England 2010-11 Gas consumption The amount of gas consumed in the UK varies dramatically between households. The top 10% of households consume at least four times as much gas as the bottom 10%.60 Modelling  to  predict  households’  energy   consumption – based on the property, household income and tenure – has so far been able to explain less than 40% of this variation. Gas use varies enormously from household to household, and the variation has more to do with behaviour than how dwellings are built. 0 100 200 300 400 500 600 700 800 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Heating Water heating Electric showers Washing/drying Cooking Lighting Cold appliances ICT Audiovisual Other Unknown Watts Filling the trough Peak load
  5. What to do? Storage •Just reducing it per se Demand

    Reduction •Shifting it somewhere else in time (or space and time) Demand Response 5 What makes up peak demand? What might be reduced? Who might respond? And what are the local network consequences?
  6. What do we know? 6

  7. (How do we know) What we know? 7 DOI: 10.1016/j.erss.2016.08.020

  8. § There have been quite a lot of ‘demand response’

    trials § We reviewed over 30 major (published) studies How does the literature stack up? 8 “a representative random sample of households with random allocation to control and intervention groups of sufficient size to robustly detect the effect observed was achieved only by the Irish Smart Meter trial.” @tom_rushby
  9. What do we know? 9 “a representative random sample of

    households with random allocation to control and intervention groups of sufficient size to robustly detect the effect observed was achieved only by the Irish Smart Meter trial.” @tom_rushby Not a lot. Well, OK we do know a few things but they are mostly neither statistically robust nor generalisable
  10. What do we not know? § How people ‘flex’ –

    Hints: Higginson, Sarah, Murray Thomson, and Tracy Bhamra. 2013. ‘“For the Times They Are a-Changin”: The Impact of Shifting Energy-Use Practices in Time and Space’. Local Environment, June, 1–19. doi:10.1080/13549839.2013.802459. § Which kinds of people ‘flex’ – Hints: Nicholls, Larissa, and Yolande Strengers. 2015. ‘Peak Demand and the “family peak” period in Australia: Understanding Practice (in) Flexibility in Households with Children’. Energy Research & Social Science 9: 116–24. § What ‘normal’ people do? 10 https://upload.wikimedia.org/wikipedia/commons/f/fa/La undry_room_%28tv%C3%A4ttstuga%29.JPG
  11. The menu § Flexibility: – What’s the problem? § Flexibility:

    – What do we (not) know? § The SAVE study design – Finding out what we don’t know § What we did § Where next? 11
  12. SAVE Objectives § Test ‘Demand Response’ interventions: 12 Households 1.

    Data informed engagement Other trials suggest reductions of around 6% 2. Data informed engagement + price signals Other trials suggest reductions of around 6- 7% 3. LED lighting trials Lighting is responsible for 19% of evening peak demand
  13. SAVE Design Criteria 13 • => Random sample • =>

    Large enough sample Statistically robust: •=> Representative sample Generalisable: •=> Randomly allocated trial & control groups Controlled Image source: pixabay.com
  14. Large ‘enough’? 14 0 2 4 6 8 10 12

    14 200 400 600 800 1000 1200 1400 Detectable % effect (p = 0.05) Trial Group Size Required Designed effect size Required trial group size Source: UoS analysis of Irish CER Domestic Demand Response pre-trial consumption data Mean kWh 16:00 – 20:00 (“Evening peak”) p = 0.05, P = 0.8 Statistical Pow er Analysis => Each trial group > 1000
  15. Recruitment process •Hampshire, Isle of Wight, Southampton, Portsmouth Select study

    area •Stratify census areas by deprivation quintile •Randomly select n census areas within deprivation quintiles •Randomly select 50 address per census area from PAF Select Addresses •Letter sent by research agency Contact •Field visit: research agency staff Survey & install kit 15 4,318 households 32,000 letters
  16. SAVE: Study Design Trial Period 3 Trial Period 2 Trial

    Period 1 Trial Groups Survey Representative Random Sample N > 4000 Group 1: Control Group 2: (LEDs) Group 3: (Engagement) Group 4: (Engagement + £) 16 Update surveys & Time Use Diaries Update surveys & Time Use Diaries Update surveys & Time Use Diaries Random allocation
  17. Recruitment & attrition… 17 With boosts 700 – 800 left

    in each group January 2019
  18. Thank you § @dataknut § Next: sample data § www.energy.soton.ac.uk/tag/save/

    18 pixabay.com