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Experiences of spatial microsimulation with ‘big’ and ‘little’ data: A comparison of models for (parts of) the United Kingdom and New Zealand

Experiences of spatial microsimulation with ‘big’ and ‘little’ data: A comparison of models for (parts of) the United Kingdom and New Zealand

Anderson, B. , Rushby, T., Bahaj, A. and James, P. (2019) Experiences of spatial microsimulation with ‘big’ and ‘little’ data: A comparison of models for (parts of) the United Kingdom and New Zealand. Paper presented at the 7th World Congress of the International Microsimulation Association, 19-21 June, 2019, Galway, Ireland

Ben Anderson

June 19, 2019
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  1. Experiences of spatial microsimulation
    with ‘big’ and ‘little’ data: A comparison
    of models for (parts of) the United
    Kingdom and New Zealand
    Ben Anderson, Tom Rushby, 'Bakr Bahaj & Patrick James
    [email protected] / [email protected]
    @dataknut

    View Slide

  2. @dataknut
    The menu
    § The problem
    • Local demand peaks
    § The solution
    • Local demand models
    § Initial results
    • Observation based
    • Time-Use based
    § What have we learnt?
    2
    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.
    Households with especially high or low consumption do not have particular
    behaviours that make them easy to identify. Instead they tend to have a
    cluster of very ordinary behaviours that happen to culminate in high or low
    gas use. There are, it seems, many different ways to be a high or low gas
    user. The behaviours in question can be clustered under three broad
    headings:
    • physical properties of the home – the particular physical environment
    in which people live
    • temperature management – how people manage the temperature in
    their homes and their awareness of the energy implications of their
    actions
    • people in the home – who is in the home, and when, and what they are
    doing. 60
    Physical properties of the home
    Many UK homes have been modified by extensions, conservatories,
    conversions and/or open plan spaces. These modifications have the
    potential to affect the thermal properties of a home. But, typically, these
    have not been included in existing quantitative modelling of domestic
    energy consumption.
    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

    View Slide

  3. @dataknut
    What’s the problem?
    3
    Total NZ electricity demand per half hour
    (Winter: June)
    Source: Electricity Authority
    GW (sum)
    UK: A de-carbonisation story…?
    Source: Staffell (2018)
    https://doi.org/10.1016/j.enpol.2016.1
    2.037

    View Slide

  4. @dataknut
    UK: Why is ‘peak’ a 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

    View Slide

  5. @dataknut
    Estimating the Technical Potential for Residential Demand Response in New Zealand
    Fig. 3 illustrates electricity generation by time of day on GWh per half-hour trading
    period. Times of peak electricity generation are characterised by a higher electricity
    supply and demand at certain times and occur in early morning and evening hours in
    winter 2017. The maximum power on an average day in winter 2017 was 6.2 GW (equi-
    valent to 3.1 GWh per half-hour) and 5 GW in summer. Times of electricity peaks change
    by season. In summer 2017, the evening peak was much flatter and occurred slightly
    earlier compared to winter of the same year. This change in the electricity supply pat-
    tern is caused by weather conditions in December that do not necessitate appliances
    such as electrical heating systems to be activated, coupled with daylight saving and also
    longer daylight hours for summer, a lower use of lighting technologies in the early even-
    ing.
    All figures and calculations in this report consider New Zealand daylight saving.
    Fig. 3| Daily average half-hour electricity generation profile in summer and winter 2017
    Source: Based on (Electricity Authority, 2018c)
    Increased demand during time intervals of high electricity demand are largely supplied
    by hydro electricity generation. Hydro electricity generation as depicted in Fig. 4 rep-
    resents a significant part of New Zealand’s electricity supply and necessitates active
    Page 17 of 113
    NZ: Why is ‘peak’ a problem?
    • ‘Dirty’ energy (?)
    Carbon problems:
    • Higher priced energy
    Cost problems:
    • PV & Wind
    Renewables mis-match
    • Inefficient use of resources;
    • ‘Local’ (LV network) overload;
    Infrastructure problems:
    5
    Filling the
    trough
    Peak load
    Depends on hydro levels in Feb – April
    Khan et al (2018)
    10.1016/j.jclepro.2018.02.309

    View Slide

  6. @dataknut
    Estimating the Technical Potential for Residential Demand Response in New Zealand
    Fig. 3 illustrates electricity generation by time of day on GWh per half-hour trading
    period. Times of peak electricity generation are characterised by a higher electricity
    supply and demand at certain times and occur in early morning and evening hours in
    winter 2017. The maximum power on an average day in winter 2017 was 6.2 GW (equi-
    valent to 3.1 GWh per half-hour) and 5 GW in summer. Times of electricity peaks change
    by season. In summer 2017, the evening peak was much flatter and occurred slightly
    earlier compared to winter of the same year. This change in the electricity supply pat-
    tern is caused by weather conditions in December that do not necessitate appliances
    such as electrical heating systems to be activated, coupled with daylight saving and also
    longer daylight hours for summer, a lower use of lighting technologies in the early even-
    ing.
    All figures and calculations in this report consider New Zealand daylight saving.
    Fig. 3| Daily average half-hour electricity generation profile in summer and winter 2017
    Source: Based on (Electricity Authority, 2018c)
    Increased demand during time intervals of high electricity demand are largely supplied
    by hydro electricity generation. Hydro electricity generation as depicted in Fig. 4 rep-
    resents a significant part of New Zealand’s electricity supply and necessitates active
    Page 17 of 113
    What makes
    up peak
    demand?
    What might be
    reduced?
    Who might
    respond?
    And what are
    the local
    network
    consequences?
    What to do?
    Storage
    •Just reducing it per se
    Demand Reduction
    •Shifting it somewhere
    else in time (or space
    and time)
    Demand Response
    6

    View Slide

  7. @dataknut
    UK Housing Energy Fact File
    65
    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.
    Households with especially high or low consumption do not have particular
    behaviours that make them easy to identify. Instead they tend to have a
    cluster of very ordinary behaviours that happen to culminate in high or low
    gas use. There are, it seems, many different ways to be a high or low gas
    user. The behaviours in question can be clustered under three broad
    headings:
    • physical properties of the home – the particular physical environment
    in which people live
    • temperature management – how people manage the temperature in
    their homes and their awareness of the energy implications of their
    actions
    • people in the home – who is in the home, and when, and what they are
    doing. 60
    Physical properties of the home
    Many UK homes have been modified by extensions, conservatories,
    conversions and/or open plan spaces. These modifications have the
    potential to affect the thermal properties of a home. But, typically, these
    have not been included in existing quantitative modelling of domestic
    energy consumption.
    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
    The trickier problem
    7
    Source: maps.google.co.uk
    UK Housing Energy Fact File
    65
    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.
    Households with especially high or low consumption do not have particular
    behaviours that make them easy to identify. Instead they tend to have a
    cluster of very ordinary behaviours that happen to culminate in high or low
    gas use. There are, it seems, many different ways to be a high or low gas
    user. The behaviours in question can be clustered under three broad
    headings:
    • physical properties of the home – the particular physical environment
    in which people live
    • temperature management – how people manage the temperature in
    their homes and their awareness of the energy implications of their
    actions
    • people in the home – who is in the home, and when, and what they are
    doing. 60
    Physical properties of the home
    Many UK homes have been modified by extensions, conservatories,
    conversions and/or open plan spaces. These modifications have the
    potential to affect the thermal properties of a home. But, typically, these
    have not been included in existing quantitative modelling of domestic
    energy consumption.
    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
    UK Housing Energy Fact File
    65
    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.
    Households with especially high or low consumption do not have particular
    behaviours that make them easy to identify. Instead they tend to have a
    cluster of very ordinary behaviours that happen to culminate in high or low
    gas use. There are, it seems, many different ways to be a high or low gas
    user. The behaviours in question can be clustered under three broad
    headings:
    • physical properties of the home – the particular physical environment
    in which people live
    • temperature management – how people manage the temperature in
    their homes and their awareness of the energy implications of their
    actions
    • people in the home – who is in the home, and when, and what they are
    doing. 60
    Physical properties of the home
    Many UK homes have been modified by extensions, conservatories,
    conversions and/or open plan spaces. These modifications have the
    potential to affect the thermal properties of a home. But, typically, these
    have not been included in existing quantitative modelling of domestic
    energy consumption.
    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
    UK Housing Energy Fact File
    65
    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.
    Households with especially high or low consumption do not have particular
    behaviours that make them easy to identify. Instead they tend to have a
    cluster of very ordinary behaviours that happen to culminate in high or low
    gas use. There are, it seems, many different ways to be a high or low gas
    user. The behaviours in question can be clustered under three broad
    headings:
    • physical properties of the home – the particular physical environment
    in which people live
    • temperature management – how people manage the temperature in
    their homes and their awareness of the energy implications of their
    actions
    • people in the home – who is in the home, and when, and what they are
    doing. 60
    Physical properties of the home
    Many UK homes have been modified by extensions, conservatories,
    conversions and/or open plan spaces. These modifications have the
    potential to affect the thermal properties of a home. But, typically, these
    have not been included in existing quantitative modelling of domestic
    energy consumption.
    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
    UK Housing Energy Fact File
    65
    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.
    Households with especially high or low consumption do not have particular
    behaviours that make them easy to identify. Instead they tend to have a
    cluster of very ordinary behaviours that happen to culminate in high or low
    gas use. There are, it seems, many different ways to be a high or low gas
    user. The behaviours in question can be clustered under three broad
    headings:
    • physical properties of the home – the particular physical environment
    in which people live
    • temperature management – how people manage the temperature in
    their homes and their awareness of the energy implications of their
    actions
    • people in the home – who is in the home, and when, and what they are
    doing. 60
    Physical properties of the home
    Many UK homes have been modified by extensions, conservatories,
    conversions and/or open plan spaces. These modifications have the
    potential to affect the thermal properties of a home. But, typically, these
    have not been included in existing quantitative modelling of domestic
    energy consumption.
    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
    UK Housing Energy Fact File
    65
    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.
    Households with especially high or low consumption do not have particular
    behaviours that make them easy to identify. Instead they tend to have a
    cluster of very ordinary behaviours that happen to culminate in high or low
    gas use. There are, it seems, many different ways to be a high or low gas
    user. The behaviours in question can be clustered under three broad
    headings:
    • physical properties of the home – the particular physical environment
    in which people live
    • temperature management – how people manage the temperature in
    their homes and their awareness of the energy implications of their
    actions
    • people in the home – who is in the home, and when, and what they are
    doing. 60
    Physical properties of the home
    Many UK homes have been modified by extensions, conservatories,
    conversions and/or open plan spaces. These modifications have the
    potential to affect the thermal properties of a home. But, typically, these
    have not been included in existing quantitative modelling of domestic
    energy consumption.
    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
    UK Housing Energy Fact File
    65
    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.
    Households with especially high or low consumption do not have particular
    behaviours that make them easy to identify. Instead they tend to have a
    cluster of very ordinary behaviours that happen to culminate in high or low
    gas use. There are, it seems, many different ways to be a high or low gas
    user. The behaviours in question can be clustered under three broad
    headings:
    • physical properties of the home – the particular physical environment
    in which people live
    • temperature management – how people manage the temperature in
    their homes and their awareness of the energy implications of their
    actions
    • people in the home – who is in the home, and when, and what they are
    doing. 60
    Physical properties of the home
    Many UK homes have been modified by extensions, conservatories,
    conversions and/or open plan spaces. These modifications have the
    potential to affect the thermal properties of a home. But, typically, these
    have not been included in existing quantitative modelling of domestic
    energy consumption.
    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
    UK Housing Energy Fact File
    65
    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.
    Households with especially high or low consumption do not have particular
    behaviours that make them easy to identify. Instead they tend to have a
    cluster of very ordinary behaviours that happen to culminate in high or low
    gas use. There are, it seems, many different ways to be a high or low gas
    user. The behaviours in question can be clustered under three broad
    headings:
    • physical properties of the home – the particular physical environment
    in which people live
    • temperature management – how people manage the temperature in
    their homes and their awareness of the energy implications of their
    actions
    • people in the home – who is in the home, and when, and what they are
    doing. 60
    Physical properties of the home
    Many UK homes have been modified by extensions, conservatories,
    conversions and/or open plan spaces. These modifications have the
    potential to affect the thermal properties of a home. But, typically, these
    have not been included in existing quantitative modelling of domestic
    energy consumption.
    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
    UK Housing Energy Fact File
    65
    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.
    Households with especially high or low consumption do not have particular
    behaviours that make them easy to identify. Instead they tend to have a
    cluster of very ordinary behaviours that happen to culminate in high or low
    gas use. There are, it seems, many different ways to be a high or low gas
    user. The behaviours in question can be clustered under three broad
    headings:
    • physical properties of the home – the particular physical environment
    in which people live
    • temperature management – how people manage the temperature in
    their homes and their awareness of the energy implications of their
    actions
    • people in the home – who is in the home, and when, and what they are
    doing. 60
    Physical properties of the home
    Many UK homes have been modified by extensions, conservatories,
    conversions and/or open plan spaces. These modifications have the
    potential to affect the thermal properties of a home. But, typically, these
    have not been included in existing quantitative modelling of domestic
    energy consumption.
    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
    Areas with more electric heating
    Areas with more students
    Areas with more EVs?
    1. Targeted interventions
    2. Network investment decisions £££

    View Slide

  8. @dataknut
    The menu
    § The problem
    • Local demand peaks
    § The solution
    • Local demand models
    § Initial results
    • Observation based
    • Time-Use based
    § Where have we learnt?
    8
    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.
    Households with especially high or low consumption do not have particular
    behaviours that make them easy to identify. Instead they tend to have a
    cluster of very ordinary behaviours that happen to culminate in high or low
    gas use. There are, it seems, many different ways to be a high or low gas
    user. The behaviours in question can be clustered under three broad
    headings:
    • physical properties of the home – the particular physical environment
    in which people live
    • temperature management – how people manage the temperature in
    their homes and their awareness of the energy implications of their
    actions
    • people in the home – who is in the home, and when, and what they are
    doing. 60
    Physical properties of the home
    Many UK homes have been modified by extensions, conservatories,
    conversions and/or open plan spaces. These modifications have the
    potential to affect the thermal properties of a home. But, typically, these
    have not been included in existing quantitative modelling of domestic
    energy consumption.
    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

    View Slide

  9. @dataknut
    Local demand models: Concept
    Synthetic
    Electricity
    Census
    Census
    data
    Household
    data
    (demand)
    9
    • UK examples:
    • Output Areas
    • ~ 100 households
    • Lower Layer Super
    Output Areas
    • ~ 900 households
    Source: http://datashine.org.uk
    • NZ examples:
    • Area Units
    • ~ 600 households
    • Meshblock areas
    • ~ 100 households

    View Slide

  10. @dataknut
    Synthetic
    Electricity
    Census
    Census
    data
    Household
    data
    (demand)
    Local demand models: Data
    10
    Source: http://datashine.org.uk
    Household
    attributes
    (area level)
    Bespoke kWh
    monitoring?
    Household
    attributes
    Trials: kWh demand
    response?
    Time Use Survey Data?
    (imputed kWh)
    Smart meter kWh?

    View Slide

  11. @dataknut
    Conceptually…
    11
    AU 2
    Survey households with ‘constraint’
    variables + kW
    AU 1
    Iterative Proportional Fitting
    Deming and Stephan 1940;
    Fienberg 1970; Wong 1992
    Birkin & Clarke, 1989; Ballas et al, 1999
    Ballas et al (2005)
    R package: ipfp Blocker (2016)
    Weights
    Census ‘constraint’ tables

    View Slide

  12. @dataknut
    § UK: Southampton
    – LSOA level (1000 households)
    – Data:
    • Time Use -> imputed power
    • Observed kWh
    § NZ: Taranaki
    – Area Unit level (600 households)
    – Data:
    • Time Use -> imputed power
    • Observed kWh
    Local demand models: Case studies
    12

    View Slide

  13. @dataknut
    UK: SAVE model v1.0
    13
    •http://doi.org/10.5255/UKDA-SN-4504-1
    •Sample of ~ 6,000 households
    •~ 600 in South East England
    UK Time-Use Data
    •~ 1000 households per LSOA
    •For Southampton
    UK LSOA level Census tables
    •IPF re-weighting of survey cases (Ballas et al, 2005)
    Spatial Microsimulation Method

    View Slide

  14. @dataknut
    Time Use Surveys: When do people do what?
    % of respondents reporting a selection of energy-demanding activities
    Source: Author’s calculations using UK Time Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504], weighted)
    14
    Winter (November 2000 - February 2001)
    0%
    10%
    20%
    30%
    40%
    50%
    60%
    0:00
    1:30
    3:00
    4:30
    6:00
    7:30
    9:00
    10:30
    12:00
    13:30
    15:00
    16:30
    18:00
    19:30
    21:00
    22:30
    % respondents
    Audio
    TV
    Reading
    Computer
    Ironing
    Laundry
    Cleaning
    Dish washing
    Cooking
    Wash/dress self
    Aged 25-64 who are in work
    0%
    10%
    20%
    30%
    40%
    50%
    60%
    0:00
    1:30
    3:00
    4:30
    6:00
    7:30
    9:00
    10:30
    12:00
    13:30
    15:00
    16:30
    18:00
    19:30
    21:00
    22:30
    % respondents
    Audio
    TV
    Reading
    Computer
    Ironing
    Laundry
    Cleaning
    Dish washing
    Cooking
    Wash/dress self
    Aged 65+
    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.
    Households with especially high or low consumption do not have particular
    behaviours that make them easy to identify. Instead they tend to have a
    cluster of very ordinary behaviours that happen to culminate in high or low
    gas use. There are, it seems, many different ways to be a high or low gas
    user. The behaviours in question can be clustered under three broad
    headings:
    • physical properties of the home – the particular physical environment
    in which people live
    • temperature management – how people manage the temperature in
    their homes and their awareness of the energy implications of their
    actions
    • people in the home – who is in the home, and when, and what they are
    doing. 60
    Physical properties of the home
    Many UK homes have been modified by extensions, conservatories,
    conversions and/or open plan spaces. These modifications have the
    potential to affect the thermal properties of a home. But, typically, these
    have not been included in existing quantitative modelling of domestic
    energy consumption.
    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

    View Slide

  15. @dataknut
    Imputing Demand
    15
    § Imputation at individual level
    – For each primary & secondary activity in each 10 minute
    time slot
    § Then aggregated to household level
    – Assume 100W for lighting if at home
    – Max: Cooking, Dish Washing, Laundry
    – Sum: everything else
    § Problems:
    – Wash/dress might just be ‘dress’
    – Hot water might be gas heated
    – TVs might be watched ‘together’
    – Not all food preparation = cooking and might be gas
    – People have MANY more lights on!
    – Several appliances may be ‘on’ but not recorded (Durand-
    Daubin, 2013)
    – No heating
    § => a very simplistic ‘all electricity non-heat’ model!
    J Widén et al., 2009
    doi:10.1016/j.enbuild.2009.02.013
    Assumes ‘shared’ use
    Assumes ‘separate’ use

    View Slide

  16. @dataknut
    Conceptually…
    16
    LSOA 2)
    Survey households with ‘constraint’
    variables + modelled kWh
    LSOA 1
    Iterative Proportional Fitting
    Deming and Stephan 1940;
    Fienberg 1970; Wong 1992
    Birkin & Clarke, 1989; Ballas et al, 1999
    Ballas et al (2005)
    Weights
    Census ‘constraint’ tables
    • Winter weekdays
    • SE England
    • All constraint data
    • => n = 162!
    But:

    View Slide

  17. @dataknut
    Results (Model 1.0)
    17
    Source: Author’s calculations using UK Time Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504],
    weighted), UKL Census 2001 small area tables and Model 1 power assumptions
    0
    0.2
    0.4
    0.6
    0.8
    1
    1.2
    1.4
    1.6
    0:00
    1:30
    3:00
    4:30
    6:00
    7:30
    9:00
    10:30
    12:00
    13:30
    15:00
    16:30
    18:00
    19:30
    21:00
    22:30
    MW
    3+ earners
    2 earners
    1 earner
    0 earners
    “all electricity,
    non-heat’ model’
    Mean power (all LSOAs) Total power (1 LSOA)

    View Slide

  18. @dataknut
    UK: SAVE model v2.0
    18
    • Survey sample of ~ 4,000 households
    SAVE kWh data
    • ~ 1000 households per LSOA
    • For Southampton
    UK LSOA level Census tables
    • IPF re-weighting of survey cases (Ballas et al, 2005)
    Spatial Microsimulation Method
    http://www.energy.soton.ac.uk/save-data-sources/

    View Slide

  19. @dataknut
    Conceptually…
    19
    LSOA 2)
    Survey households with ‘constraint’
    variables + observed kWh
    LSOA 1
    Iterative Proportional Fitting
    Deming and Stephan 1940;
    Fienberg 1970; Wong 1992
    Birkin & Clarke, 1989; Ballas et al, 1999
    Ballas et al (2005)
    Weights
    Census ‘constraint’ tables

    View Slide

  20. @dataknut
    Example results: Baseline
    20
    Source: http://datashine.org.uk
    • Mean kWh per halfhour: winter
    weekdays (January 2017)
    • 148 LSOAs

    View Slide

  21. @dataknut
    Comparing models
    21
    Sim: Observed kWh
    Sim: Modelled kWh (Time Use)
    • Mean kWh per halfhour: winter weekdays, 148 LSOAs

    View Slide

  22. @dataknut
    Comparing models
    22
    Pearson Spearman
    1. Morning
    Peak
    0.547 0.529
    2. Day time 0.144 0.180
    3. Evening
    Peak
    0.473 0.524
    4. All other
    times
    0.889 0.831
    “all electricity,
    non-heat’
    model’

    View Slide

  23. @dataknut
    NZ: GREENGrid area unit model (v0.01a)
    23
    • Sample of ~ 30 monitored households
    • Hawke’s Bay & Taranaki
    NZ GREENGrid Data
    • ~ 600 households per AU
    • For Hawke’s Bay & Taranaki
    NZ Area Unit level Census data
    • IPF re-weighting of survey cases (Ballas et al, 2005)
    Spatial Microsimulation Method
    Estimating the Technical Potential for Residential Demand Response in New Zealand
    Fig. 3 illustrates electricity generation by time of day on GWh per half-hour trading
    period. Times of peak electricity generation are characterised by a higher electricity
    supply and demand at certain times and occur in early morning and evening hours in
    winter 2017. The maximum power on an average day in winter 2017 was 6.2 GW (equi-
    valent to 3.1 GWh per half-hour) and 5 GW in summer. Times of electricity peaks change
    by season. In summer 2017, the evening peak was much flatter and occurred slightly
    earlier compared to winter of the same year. This change in the electricity supply pat-
    tern is caused by weather conditions in December that do not necessitate appliances
    such as electrical heating systems to be activated, coupled with daylight saving and also
    longer daylight hours for summer, a lower use of lighting technologies in the early even-
    ing.
    All figures and calculations in this report consider New Zealand daylight saving.
    Fig. 3| Daily average half-hour electricity generation profile in summer and winter 2017
    Source: Based on (Electricity Authority, 2018c)
    Increased demand during time intervals of high electricity demand are largely supplied
    by hydro electricity generation. Hydro electricity generation as depicted in Fig. 4 rep-
    resents a significant part of New Zealand’s electricity supply and necessitates active
    Page 17 of 113

    View Slide

  24. @dataknut
    Data: NZ GREENGrid
    24
    Get the data: https://dx.doi.org/10.5255/UKDA-SN-853334
    • Circuits measured:
    • Hot water
    • Lighting
    • Heat pumps
    • Kitchen
    • Bedrooms
    • etc
    • Data:
    • Household survey
    • Mean power (W) per minute

    View Slide

  25. @dataknut
    Data: NZ GREENGrid – Lighting
    25
    Get the data: https://dx.doi.org/10.5255/UKDA-SN-853334
    We want to estimate
    these for each unit
    area!
    VERY small n…

    View Slide

  26. @dataknut
    § Area Unit level
    • Hawke’s Bay
    • Taranaki
    § Variables used:
    • N rooms
    • N people
    • Presence children
    § Potential future variables:
    • Main heating fuel
    • Dwelling type
    • Income band
    • Age of adults/children
    Data: NZ Census
    26
    Matches GREENGrid sample
    ~ 90,000 households
    Some are not in GREENGrid data
    Because they correlate with demand

    View Slide

  27. @dataknut
    Remember how this works…
    27
    AU 2
    Survey households with ‘constraint’
    variables + kW
    AU 1
    Iterative Proportional Fitting
    Deming and Stephan 1940;
    Fienberg 1970; Wong 1992
    Birkin & Clarke, 1989; Ballas et al, 1999
    Ballas et al (2005)
    R package: ipfp Blocker (2016)
    Weights
    Census ‘constraint’ tables

    View Slide

  28. @dataknut
    Remember how this works…
    28
    AU 2
    Survey households with ‘constraint’
    variables + kW
    AU 1
    Iterative Proportional Fitting
    Deming and Stephan 1940;
    Fienberg 1970; Wong 1992
    Birkin & Clarke, 1989; Ballas et al, 1999
    Ballas et al (2005)
    R package: ipfp Blocker (2016)
    Weights
    Census ‘constraint’ tables
    GREEN Grid sample

    View Slide

  29. @dataknut
    The consequence…
    29
    Source: Author’s calculations using NZ GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334],
    weighted), NZ Census 2013 small area tables [http://nzdotstat.stats.govt.nz/wbos/Index.aspx]
    We’re replicating a lot of households
    Each dot = 1 unit area
    so weird stuff can happen…

    View Slide

  30. @dataknut
    But even so…
    30
    Source: Author’s calculations using NZ GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334],
    weighted), NZ Census 2013 small area tables [http://nzdotstat.stats.govt.nz/wbos/Index.aspx]
    Simulated household counts work OK
    Each dot = 1 unit area

    View Slide

  31. @dataknut
    But even so…
    31
    Source: Author’s calculations using NZ GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334],
    weighted), NZ Census 2013 small area tables [http://nzdotstat.stats.govt.nz/wbos/Index.aspx]
    Simulated household counts in categories used as constraints work
    OK
    Each dot = 1 unit area

    View Slide

  32. @dataknut
    And quite surprisingly…
    32
    Source: Author’s calculations using NZ GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334],
    weighted), NZ Census 2013 small area tables [http://nzdotstat.stats.govt.nz/wbos/Index.aspx]
    Simulated household counts in categories NOT used as constraints
    work quite well (sometimes)
    Each dot = 1 unit area

    View Slide

  33. @dataknut
    Example: Lighting (spatial, seasonal)
    33
    Source: Author’s calculations using NZ GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334],
    weighted), NZ Census 2013 small area tables [http://archive.stats.govt.nz/Census/2013-census/data-
    tables/meshblock-dataset.aspx]
    Where
    might LEDs
    reduce
    demand?
    As modelled
    Each line = 1 area
    unit
    Highest
    lighting
    Lowest
    lighting

    View Slide

  34. @dataknut
    The menu
    § The problem
    • Local demand peaks
    § The solution
    • Local demand models
    § Initial results
    • Observation based
    • Time-Use based
    § What have we learnt?
    34
    period. Times of peak electricity generation are characterised by a higher electricity
    supply and demand at certain times and occur in early morning and evening hours in
    winter 2017. The maximum power on an average day in winter 2017 was 6.2 GW (equi-
    valent to 3.1 GWh per half-hour) and 5 GW in summer. Times of electricity peaks change
    by season. In summer 2017, the evening peak was much flatter and occurred slightly
    earlier compared to winter of the same year. This change in the electricity supply pat-
    tern is caused by weather conditions in December that do not necessitate appliances
    such as electrical heating systems to be activated, coupled with daylight saving and also
    longer daylight hours for summer, a lower use of lighting technologies in the early even-
    ing.
    All figures and calculations in this report consider New Zealand daylight saving.
    Fig. 3| Daily average half-hour electricity generation profile in summer and winter 2017
    Source: Based on (Electricity Authority, 2018c)
    Increased demand during time intervals of high electricity demand are largely supplied
    by hydro electricity generation. Hydro electricity generation as depicted in Fig. 4 rep-
    resents a significant part of New Zealand’s electricity supply and necessitates active
    Page 17 of 113

    View Slide

  35. @dataknut
    § We have shown:
    – The method works…
    § UK:
    – Time-Use model allows activity scenarios
    • But is mis-specified?
    • Some models are useful…
    – ‘Observed’ model inflates ‘outliers’
    – Both offer spurious precision
    § NZ:
    – GREENGrid data is insufficient
    – The results are probably garbage
    § We need to:
    – Gather better kW/h data
    – Represent uncertainty
    – Validate, validate, validate
    What have we learnt?
    35
    N * 100
    Representative sample

    View Slide

  36. @dataknut
    Questions?
    § [email protected]
    § [email protected]
    § @dataknut
    § www.energy.soton.ac.uk/tag/spatialec
    – 3 year EU Global Fellowship
    – 2017-2020
    § “This research was supported by a Marie Sklodowska-Curie
    Individual Global Fellowship within the H2020 European
    Framework Programme (2014 -2020) under grant
    agreement no. 700386.”
    36
    pixabay.com

    View Slide