Modeling Electricity Demand in Time and Space

7bbeac78b5e6700946b5b6fd8aa1a58a?s=47 Ben Anderson
December 02, 2014

Modeling Electricity Demand in Time and Space

Paper/presentation given at the Australia New Zealand Regional Science Association International Conference 2014, Christchurch, New Zealand.

Work supported by: http://www.energy.soton.ac.uk/tag/save/

Citation: Anderson, B (2014) Modeling Electricity Demand in Time and Space, Paper presented at ANZRSAI 2014, Christchurch, New Zealand, 2/12/2014

7bbeac78b5e6700946b5b6fd8aa1a58a?s=128

Ben Anderson

December 02, 2014
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  1. Modeling Electricity Demand in Time and Space Ben Anderson b.anderson@soton.ac.uk

    (@dataknut) Sustainable Energy Research Group Faculty of Engineering & Environment
  2. The menu § What’s the problem? § What can we do? § How

    might we do it? § Did it work? § What do we need to do next? 2 @dataknut ANZSRAI 2014, Christchurch, New Zealand
  3. What’s the problem? §  Domestic electricity demand is ‘peaky’ § 

    Carbon problems: §  Peak load can demand ‘dirty’ generation §  Cost problems: §  Peak generation is higher priced energy §  Infrastructure problems: §  Local/national network ‘import’ overload on weekday evenings; §  Local network ‘export’ overload at mid-day on weekdays due to under-used PV generation; §  Inefficient use of resources (night- time trough) 3 @dataknut ANZSRAI 2014, Christchurch, New Zealand 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
  4. What to do? §  Storage §  Demand Reduction –  Just

    reducing it per se §  Demand Response –  Shifting it somewhere else in time (or space and time) 4 §  What makes up peak demand? §  What might be reduced? §  Who might respond? §  And what are the local network consequences? Key Questions 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 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
  5. What do we need? § Model: –  When do people do

    what at home? –  What energy demand does this generate? –  Scenarios for change •  Appliance efficiency •  Mode of provision •  Changing practices –  What affect might this have for local areas? 5
  6. How might this be done? §  When do people do

    what at home? •  Time Use Diaries §  What energy demand does this generate? •  Imputed electricity demand for each household §  A microsimulation model of change •  Ideally based on experimental/trial evidence •  Or presumed appliance efficiency gains •  Or ‘what if?’ scenarios of behaviour change §  A way of estimating effects for local areas •  Spatial microsimulation 6 UK ONS 2001 Time Use Survey J Widén et al., 2009 doi:10.1016/ j.enbuild. 2009.02.013 Using UK Census 2001
  7. How might this be done? § When do people do what

    at home? •  Time Use Diaries 7 UK ONS 2001 Time Use Survey
  8. 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) 8 Winter (November 2000 - February 2001) 0% 10% 20% 30% 40% 50% 60% 00:00 01:30 03:00 04:30 06:00 07:30 09:00 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% 00:00 01:30 03:00 04:30 06:00 07:30 09:00 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+
  9. Seasonal differences (25-64 in work) Summer vs winter: respondents aged

    25-64 who are in work Source: Author’s calculations using UK Time Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504], weighted) 9 Winter (November 2000 - February 2001) Summer (June/July/August 2000 & June/July/August 2001) -4% -2% 0% 2% 4% 6% 8% 10% 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 % point difference summer <-> winter (+ve = winter higher) Wash/dress self Cooking Dish washing Cleaning Laundry Ironing Computer Reading TV Audio
  10. -4% -2% 0% 2% 4% 6% 8% 10% 00:00 01:00

    02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 % point difference summer <-> winter (+ve = winter higher) Wash/dress self Cooking Dish washing Cleaning Laundry Ironing Computer Reading TV Audio Seasonal differences 65+ Summer vs winter: respondents aged 65+ Source: Author’s calculations using UK Time Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504], weighted) 10
  11. How might this be done? § When do people do what

    at home? •  Time Use Diaries § What energy demand does this generate? •  Imputed electricity demand for each household 11 UK ONS 2001 Time Use Survey J Widén et al., 2009 doi:10.1016/ j.enbuild. 2009.02.013
  12. Imputing electricity consumption 12 §  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
  13. Imputing electricity consumption 13 §  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 0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 0 10 20 30 40 50 60 70 80 90 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 % of recorded laundry Mean wa@s per half hour 'washing/drying' June ('work days', n = 76) June ('holidays', n = 74) summer laundry (ONS TU Survey 2005)
  14. Results: Mean consumption I 14 Mean power consumption per half

    hour in winter (November 2000 - February 2001, all households) Source: Author’s calculations using UK Time Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504], weighted) and Model 1 power assumptions Age of household response person 0 200 400 600 800 1000 1200 1400 1600 1800 2000 00:00 01:30 03:00 04:30 06:00 07:30 09:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 Mean consumpCon per half houir (Wa@s) 0 1 2 3+ 0 200 400 600 800 1000 1200 1400 1600 1800 2000 00:00 01:30 03:00 04:30 06:00 07:30 09:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 Mean consumpCon per half houir (Wa@s) 25-64 65+ Number of earners
  15. Results: Mean consumption II 15 0 200 400 600 800

    1000 1200 1400 1600 1800 2000 00:00 01:30 03:00 04:30 06:00 07:30 09:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 Mean consumpCon per half houir (Wa@s) None One Two or more 0 200 400 600 800 1000 1200 1400 1600 1800 2000 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Mean consumpCon per half houir (Wa@s) married/partnered single parent single person other Mean power consumption per half hour in winter (November 2000 - February 2001, all households) Source: Author’s calculations using UK Time Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504], weighted) and Model 1 power assumptions Number of children present Household composition
  16. But this is what the network sees… 16 Sum of

    power consumption per half hour in winter (November 2000 - February 2001, all households) Source: Author’s calculations using UK Time Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504], weighted) and Model 1 power assumptions Number of earners 0 200000 400000 600000 800000 1000000 1200000 1400000 1600000 00:00 01:30 03:00 04:30 06:00 07:30 09:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 Sum of wa@s per half hour 3+ earners 2 earners 1 earner 0 earners 0 200000 400000 600000 800000 1000000 1200000 1400000 1600000 00:00 01:30 03:00 04:30 06:00 07:30 09:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 Sum of wa@s per half hour HRP: 75+ HRP: 65-74 HRP: 55-64 HRP: 45-54 HRP: 35-44 HRP: 25-34 HRP: 16-24 Age of household response person
  17. But this is what the network sees… 17 Sum of

    power consumption per half hour in winter (November 2000 - February 2001, all households) Source: Author’s calculations using UK Time Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504], weighted) and Model 1 power assumptions Number of earners 0 200000 400000 600000 800000 1000000 1200000 1400000 1600000 00:00 01:30 03:00 04:30 06:00 07:30 09:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 Sum of wa@s per half hour 3+ earners 2 earners 1 earner 0 earners 0 200000 400000 600000 800000 1000000 1200000 1400000 1600000 00:00 01:30 03:00 04:30 06:00 07:30 09:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 Sum of wa@s per half hour HRP: 75+ HRP: 65-74 HRP: 55-64 HRP: 45-54 HRP: 35-44 HRP: 25-34 HRP: 16-24 Age of household response person Morning spike too spiky!
  18. How might this be done? § When do people do what

    at home? •  Time Use Diaries § What energy demand does this generate? •  Imputed electricity demand for each household § A microsimulation model of change •  Ideally based on experimental/trial evidence •  Or presumed appliance efficiency gains •  Or ‘what if?’ scenarios of behaviour change 18 UK ONS 2001 Time Use Survey J Widén et al., 2009 doi:10.1016/ j.enbuild. 2009.02.013
  19. Microsimulation: But what if…? 19 § We change the washing assumption?

    § => an “all electricity non-wash, non-heat’ model!
  20. 0 200000 400000 600000 800000 1000000 1200000 1400000 1600000 00:00

    01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Sum of wa@s per half hour 3+ 2 1 0 0 200000 400000 600000 800000 1000000 1200000 1400000 1600000 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Sum of wa@s per half hour 3+ earners 2 earners 1 earner 0 earners Now the network sees.. 20 Source: Author’s calculations using UK Time Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504], weighted) and Model 2 power assumptions Sum of power consumption per half hour in winter by number of earners (November 2000 - February 2001, all households)
  21. But that’s the big picture 21 §  We need a

    way to estimate these totals –  At small area level §  Solution: –  Spatial microsimulation •  IPF re-weighting of survey cases –  Using UK Census 2001 •  To match time use survey –  At UK Lower Layer Super Output Area level •  c. 800-900 households •  For Southampton (146 LSOAs)
  22. Conceptually… 22 LSOA 2.1 (Region2) Survey data cases with ‘constraint’

    variables LSOA 1.1 (Region1) Iterative Proportional Fitting Ballas et al (2005) If Region = 2 Weights LSOA census ‘constraint’ tables If Region = 1
  23. ‘Iterative Proportional Fitting’ 23 §  Well known! §  Deming and

    Stephan 1940 –  Fienberg 1970; Wong 1992 –  Birkin & Clarke, 1989; Ballas et al, 1999 §  A way of iteratively adjusting statistical tables –  To give known margins (row/column totals) –  ‘Raking’ §  In this case –  Create weights for each case so LSOA totals ‘fit’ constraints –  Weighting ‘down’
  24. Key First Job: § Choose your constraints 24 Census data Survey

    data § How? –  Regression selection methods? –  Whatever is available!
  25. Constraints used 25 §  Age of household response person (HRP)

    §  Ethnicity of HRP §  Number of earners §  Number of children §  Number of persons §  Number of cars/vans §  Household composition (couples/singles) §  Presence of limiting long term illness §  Accommodation type §  Tenure
  26. Constraints used 26 §  Age of household response person (HRP)

    §  Ethnicity of HRP §  Number of earners §  Number of children §  Number of persons §  Number of cars/vans §  Household composition (couples/singles) §  Presence of limiting long term illness §  Accommodation type §  Tenure “Everything” !
  27. Constraints used 27 §  Age of household response person (HRP)

    §  Ethnicity of HRP §  Number of earners §  Number of children §  Number of persons §  Number of cars/vans §  Household composition (couples/singles) §  Presence of limiting long term illness §  Accommodation type §  Tenure “Everything” ! Why? No clear way to select or prioritise?
  28. Results (Model 1) 28 Sum of half hourly power consumption

    (winter 2000/1) By number of earners 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 20000 40000 60000 80000 100000 120000 140000 00:00 01:30 03:00 04:30 06:00 07:30 09:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 Sum of watts per half hour 3+ 2 1 0 0 20000 40000 60000 80000 100000 120000 140000 00:00 01:30 03:00 04:30 06:00 07:30 09:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 Sum of watts per half hour 3+ 2 1 0 LSOA E01017139: highest % of households with 0 earners in Southampton LSOA E01017180: lowest % of households with 0 earners in Southampton
  29. Results (Model 1) Example 29 Sum of half hourly power

    consumption (winter 2000/1) at 00:00 – 00:30 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. Map created in R (ggmap)
  30. Results (Model 2) 30 Sum of half hourly power consumption

    (winter 2000/1) By number of earners 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 2 power assumptions LSOA E01017139: highest % of households with 0 earners in Southampton LSOA E01017180: lowest % of households with 0 earners in Southampton 0 20000 40000 60000 80000 100000 120000 140000 00:00 01:30 03:00 04:30 06:00 07:30 09:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 Sum of watts per half hour 3+ 2 1 0 0 20000 40000 60000 80000 100000 120000 140000 00:00 01:30 03:00 04:30 06:00 07:30 09:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 Sum of watts per half hour 3+ 2 1 0
  31. Summary & Next Steps 31 §  It works! –  A

    temporal electricity demand spatial microsimulation –  But we don’t know how well §  The model is over-simple –  But we knew that! §  Constraint selection should be evidence based –  ? §  And we need to update to 2011!! –  But no UK time use data §  Next steps: –  “Solent Achieving Value through Efficiency” (SAVE) project •  Large n RCT tests of demand response interventions •  Linked time use & power monitoring •  (Some) substation monitoring •  => evidence base for model development! Validation against observed substation loads? Implement more complex model (Widen et al, 2010) or gather better data Separate ½ hour models?? Saved by SAVE!
  32. Thank you §  b.anderson@soton.ac.uk §  This work has been supported

    by the UK Low Carbon Network Fund (LCNF) Tier 2 Programme "Solent Achieving Value from Efficiency (SAVE)” project: –  http://www.energy.soton.ac.uk/save-solent-achieving-value-from- efficiency/ §  STATA code (not the IPF bit): –  https://github.com/dataknut/SAVE –  GPL: V2 - http://choosealicense.com/licenses/gpl-2.0/ applies 32 @dataknut ANZSRAI 2014, Christchurch, New Zealand