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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? @dataknut 2 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) 8 0 0 7 0 0 6 0 0 5 0 0 4 0 0 3 0 0 2 0 0 1 0 0 Filling the trough Peak load Peak load @dataknut 3 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 nhouseholds’ e ergy 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 0 0 :0 0 0 2 : 0 0 0 4 :0 0 0 6 :0 0 0 8 :0 0 1 0 :0 0 1 2 :0 0 1 4 :0 0 1 6 :0 0 1 8 : 0 0 2 0 :0 0 2 2 :0 0 H e a t i n g W a t e r h e a t i n g E l e c t r i c s h o w e r s W a s h i n g / d r y i n g C o o k i n g L i g h t i n g C o l d a p p l i a n c e s I C T A u d i o v i s u a l O t h e r U n k n o w n W a t t s Filling the trough
• 4. What to do?  Storage  Demand Reduction – Just reducing it per se  Demand Response 8 0 0 7 0 0 6 0 0 5 0 0 4 0 0 3 0 0 2 0 0 1 0 0 – Shifting it somewhere else in time (or space and time)  What makes up peak demand?  What might be reduced?  Who might respond?  And what are the local network consequences? 4 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 nhouseholds’ e ergy 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 0 0 :0 0 0 2 : 0 0 0 4 :0 0 0 6 :0 0 0 8 : 0 0 1 0 :0 0 1 2 :0 0 1 4 : 0 0 1 6 :0 0 1 8 : 0 0 2 0 : 0 0 2 2 :0 0 H e a t i n g W a t e r h e a t i n g E l e c t r i c s h o w e r s W a s h i n g / d r y i n g C o o k i n g L i g h t i n g C o l d a p p l i a n c e s I C T A u d i o v i s u a l O t h e r U n k n o w n W a t t s
• 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.enbu ild.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? Aged 25-64 who are in work Aged 65+ Winter (November 2000 - February 2001) % 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
• 9. 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.enbu ild.2009.02.013
• 10. 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.2009.02.01 3 Assumes ‘shared’ use Assumes ‘separate’ use
• 11. 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.2009.02.01 3 Assumes ‘shared’ use Assumes ‘separate’ use
• 12. Results: Mean consumption I 14 Age of household response person Number of earners 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
• 13. Results: Mean consumption II 15 Number of children present Household composition 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
• 14. But this is what the network sees… 16 Age of household response person Number of earners 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
• 15. But this is what the network sees… 17 Age of household response person Number of earners 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
• 16. 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.enbu ild.2009.02.013
• 17. Microsimulation: But what if…?  We change the washing assumption?  => an “all electricity non-wash, non-heat’ model! 19
• 18. Now the network sees.. 20 Sum of power consumption per half hour in winter by number of earners (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 2 power assumptions
• 19. 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)
• 20. Conceptually… LSOA census ‘constraint’ tables 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 If Region = 1
• 21. ‘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’
• 22. Key First Job:  Choose your constraints 24  How? – Regression selection methods? – Whatever is available!
• 23. 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
• 24. 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
• 25. 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
• 26. Results (Model 1) LSOA E01017180: lowest % of households with 28 LSOA E01017139: highest % of households with 0 earners in Southampton 0 earners in Southampton 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
• 27. Results (Model 2) LSOA E01017180: lowest % of households with 29 LSOA E01017139: highest % of households with 0 earners in Southampton 0 earners in Southampton 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
• 28. Summary & Next Steps 30  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!
• 29. 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 @dataknut 31 ANZSRAI 2014, Christchurch, New Zealand