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

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