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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.

Ben Anderson

July 10, 2019
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  1. SAVE
    Ben Anderson
    [email protected]
    @dataknut
    Tom Rushby
    [email protected]
    @tom_rushby
    Experiences in applying best practice sample
    recruitment and randomised control trial designs
    to demand response studies

    View Slide

  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

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  3. What’s the UK problem?
    § A de-carbonisation story…?
    • Staffell (2018) https://doi.org/10.1016/j.enpol.2016.12.037
    3

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  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

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  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?

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  6. What do we know?
    6

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

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  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

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  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

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  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

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  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

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  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

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  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

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  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

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  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

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  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

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  17. Recruitment & attrition…
    17
    With boosts
    700 – 800 left in each group
    January 2019

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  18. Thank you
    § @dataknut
    § Next: sample data
    § www.energy.soton.ac.uk/tag/save/
    18
    pixabay.com

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