Neighbourhood Demand Profiles: Why, how and how far have we got?

Neighbourhood Demand Profiles Ben Anderson [email protected] / [email protected] @dataknut Why,
how and how far have we got?

@dataknut The menu § The problem • Local peaks §
The solution • Local demand models § Initial results • Time-Use based • Observation based § Where have we got to? § Where next? 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

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

@dataknut 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

@dataknut 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?

@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 6 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 £££

@dataknut Solution: Local demand models • Appliance efficiency • Mode
of provision • Changing practices When do people do what at home? What energy demand does this generate? Scenarios for change What affect might this have for local areas? 8 Local demand model

@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: • Meshblock areas • ~ 100 households

@dataknut Local demand models: Data Synthetic Electricity Census Census data
Household data (demand) 10 Bespoke kW monitoring? Household attributes Household attributes (area level) Trials: kW demand response? Time Use Survey Data? (imputed kW) Smart meter kW?

@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) 12 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 Au di o TV Rea di n g Co m p ut er I r o ni ng Laun dr y Cl eani ng Di sh w a shi n g Co oki n g Wash/d r ess sel f 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 Au di o TV Rea di n g Co m p ut er I r o ni ng Laun dr y Cl eani ng Di sh w a shi n g Co oki n g Wash/d r ess sel f 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

@dataknut ‘Time Use’ Power Demand Profiles 13 § Widen et
al 2009 - doi:10.1016/j.apenergy.2009.11.006 § McKenna et al 2017 - doi: 10.1007/s12053-017-9525-4 X kW X kW Time Use Survey Data X kW X kW

@dataknut Imputing Demand 14 § 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

@dataknut This is what the network sees… 15 Sum of
pow er consum ption per half hour in w inter (Novem ber 2000 - February 2001, all households, not scaled to UK population) Source: Author’s calculations using UK Tim e Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504], w eighted) and M odel 1 pow er assum ptions Number of earners 0 0. 2 0. 4 0. 6 0. 8 1 1. 2 1. 4 1. 6 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 M W 3+ ear ne r s 2 ear ner s 1 ear ner 0 ear ner s 0. 0 0. 2 0. 4 0. 6 0. 8 1. 0 1. 2 1. 4 1. 6 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 M W HR P: 75 + HR P: 65 - 7 4 HR P: 55 - 6 4 HR P: 45 - 5 4 HR P: 35 - 4 4 HR P: 25 - 3 4 HR P: 16 - 2 4 Age of household response person Morning spike too spiky!

@dataknut Model 2: Microsimulation 16 •We change the ‘washing’ assumption?
What if? “all electricity non-HW, non- heat’ model?

@dataknut 0 0. 2 0. 4 0. 6 0. 8
1 1. 2 1. 4 1. 6 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 M W 3+ 2 1 0 Model 2: Now the network sees.. 17 Source: Author’s calculations using UK Tim e Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504], w eighted) and M odel 2 pow er assum ptions Sum of power consumption per half hour in winter (November 2000 - February 2001, all households, not scaled to UK population) Number of earners “all electricity non-HW, non- heat’ model?

@dataknut The small area picture: 18 • To match 2001
time use survey Using UK Census 2001 • c. 800-900 households per LSOA • For the City of Southampton (146 LSOAs) At UK Lower Layer Super Output Area level • IPF re-weighting of survey cases (Ballas et al, 2005) Spatial Microsimulation Method

@dataknut Conceptually… 19 LSOA 2) Survey households with ‘constraint’ variables
+ kW 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

@dataknut Results (Model 1) 20 Sum of half hourly power
consumption (winter 2000/1) Source: Author’s calculations using UK Tim e Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504], w eighted), UKL Census 2001 sm all area tables and M odel 1 pow er assum ptions 0 0. 2 0. 4 0. 6 0. 8 1 1. 2 1. 4 1. 6 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 M W 3+ ear ne r s 2 ear ner s 1 ear ner 0 ear ner s

@dataknut Results (Model 2) 22 Sum of half hourly power
consumption (winter 2000/1) By number of earners Source: Author’s calculations using UK Tim e Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504], w eighted), UKL Census 2001 sm all area tables and M odel 2 pow er assum ptions LSOA E01017139: highest % of households with 0 earners in Southampton LSOA E01017180: lowest % of households with 0 earners in Southampton 0 0. 02 0. 04 0. 06 0. 08 0. 1 0. 12 0. 14 00 : 0 0 01 : 3 0 03 : 0 0 04 : 3 0 06 : 0 0 07 : 3 0 09 : 0 0 10 : 3 0 12 : 0 0 13 : 3 0 15 : 0 0 16 : 3 0 18 : 0 0 19 : 3 0 21 : 0 0 22 : 3 0 M W 3+ 2 1 0 0 0. 02 0. 04 0. 06 0. 08 0. 1 0. 12 0. 14 00 : 0 0 01 : 3 0 03 : 0 0 04 : 3 0 06 : 0 0 07 : 3 0 09 : 0 0 10 : 3 0 12 : 0 0 13 : 3 0 15 : 0 0 16 : 3 0 18 : 0 0 19 : 3 0 21 : 0 0 22 : 3 0 M W M illions 3+ 2 1 0 Input to network load monitoring & investment tool “all electricity non-HW, non- heat’ model’

@dataknut Local demand models: Data Synthetic Electricity Census Census data
Household data (demand) 23 Bespoke kW monitoring? Household attributes Household attributes (area level) Trials: kW demand response? Time Use Survey Data? (imputed kW) Smart meter kW?

@dataknut SAVE Objectives § Test ‘Demand Response’ interventions: 24 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

@dataknut SAVE Design Criteria 25 • => Random sample •
=> Large enough sample Statistically robust: • => Representative sample Generalisable: • => Randomly allocated trial & control groups Controlled Image source: pixabay.com

@dataknut Large ‘enough’? 26 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 Power Analysis => Each trial group > 1000

@dataknut 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 28 4,318 households 32,000 letters

@dataknut What was done § Whole House Meter Clamp –
15 minute Wh • ~ 414k records/day (130Mb/week) – 10 second W • ~ 37m records/day (11Gb/week) § 20 minute household survey – Deferred to telephone/web – ~ 80% response § Control Group – Yearly update surveys § Trial Groups – Yearly update surveys – Interventions 29 Navetas Loop AWS S3 UoS

@dataknut Recruitment & attrition… 30

@dataknut Illustrative results: daily profiles 32 Dwelling: Main heat source
Error bars: 95% CI (assuming normality) N = 120 N = 18 N = 155 N = 2,581

@dataknut Illustrative results: power 33 Christmas Day 2016 Not just
another Sunday Error bars: 95% CI (assuming normality) N = 18 N = 155

@dataknut Illustrative results: power 34 Royal Wedding Saturday May 19th
2018 Error bars: 95% CI (assuming normality) http://www.energy.soton.ac.uk/super-saturday-and-spikes-in-demand/

@dataknut SRDC 4 Evidence Report SSET206 SAVE Solent Achieving Value
from Efficiency Figure 16: Interior page of initial engagement booklet Over the next nine weeks, this booklet was followed up with one general knowledge postcard and five postcards with specific asks, such as: x Waiting until after 8pm to do the washing or running it only with full loads x Waiting until after 8pm to charge mobiles and tablets x Waiting until after 8pm to use the tumble dryer x Waiting until after 8pm to run the dishwasher or using its timer/delay function x Waiting until after 8pm to watch television or turn the television off in rooms that are not being used SRDC 4 Evidence Report SSET206 SAVE Solent Achieving Value from Efficiency Figure 17 Sample Postcard (Front and Back) All three treatment groups received some sort of consumer engagement messaging: x Group 2 received emails and web portal notifications x Group 3 (data informed engagement and price signals) received emails, web portal notifications and postal mailings x Group 4 (data informed engagement) received postal mailings Although the delivery mechanism differed, the content was identical across all platforms. 5.1.3 Price Signalling and Event Day Trial 1 4-8: Preliminary results 35 SRDC 4 Evidence Report SSET206 SAVE Solent Achieving Value from Efficiency Page 44 The price levels in TP1 were determined based upon analysis put together in the SAVE business case (Appendix N of full submission) and ensuring any level was deemed market competitive (this is important to consider for aggregator models of domestic DSR). Given the ‘event day’ structure of the trials present clear similarities to National Grid’s triads; commercial analysis was performed between average household demand and £/kW payment levels for triads, the outcome of which suggested a £10 incentive would require at least a 7% load-reduction from each household to be cost-competitive. Accounting behavioural economics in this equation it was determined that consumer responsiveness would benefit from a more relatable, less precise figure of load-reduction and hence this was rounded to 10% for £10. Below is an example of the email message group 2 received two days before the event day. Group 3 received a similar email but with a note about the incentive. Figure 18: Event day messaging 5.2 Trial Outcomes 5.2.1 LED Trial As described earlier, mailers directed the LED trial participants to http://saveled.co.uk, which was set up by RS Components. This website allowed participants to purchase discounted LEDs from a • Specific Day ◦ 15th March 2017 • 16:00 – 20:00 period ◦ Control Group • Nothing ◦ Group 2 • Messages ◦ Group 3 • Messages + • £ Incentive Source: pixabay.com

@dataknut Trial 1 4-8 Event: Preliminary results Figure 5: Temporal
profiles of consumption around the event day (with 95% CI) The set of charts below in Figure 6 show the overall mean for the 16:00 - 20:00 periods of each day compared to the 4 hours before/after and as above, the 95% confidence intervals give an indication of the statistical significance of any numerical difference. Ben Anderson 5/7/2017 14:58 Deleted: 9 Ben Anderson 5/7/2017 14:58 Deleted: Figure 10 36 Day before Day of Day after

@dataknut Figure 6: Mean 15 minute Wh per period during
pre/event/post-event day The charts suggest that: • On the day preceding the event day: Group 3 appeared to use more than the other groups during the evening peak period which would be the case if consumption had been shifted to Ben Anderson 5/7/2017 14:58 Deleted: 10 Trial 1 4-8 Event: Preliminary results 37 Day before Day of Day after

@dataknut Modeling ‘local’ flexibility Synthetic Electricity Census UK Census 2011
SAVE survey & kWh data 40 6,136 Output Areas (c 100 households) Source: http://datashine.org.uk

@dataknut Example results: ‘Event day’ 42 To illustrate the output
from the small area estimation, two highly contrasting OAs are selected as the ‘target’ areas: x the OA with highest % of single person households: E00167003 x the OA with the lowest % of single person households: E00115898 The OAs have been selected in this way to provide test cases that tease out any limitations in the modelling technique. The household counts for these OAs are shown in Table 20 and the resulting weighted household counts are expected to match these. Table 20 Census counts and % single-person households for selected OAs OA Code Total household count Number of single- person households % single-person households E00115898 85 0 0 E00167003 200 182 91 The OA with the lowest percentage of single-person households (0 households, 0%) has 85 households in total, whilst the OA with the highest percentage (182 households, 91%) has rather more at 200. As each of the four illustrative models described in Section 5.1 above will draw upon the consumption data from a different pool of SAVE sample households, the weighting file generated by the IPF procedure for each separate model is applied to each of the two OAs in turn. The following sections describe briefly the results gained from each model. The results for each model include tables to illustrate that each of the different treatment groups produce different ‘pools’ of SAVE households, and that the weights resulting from the IPF process change according to their different characteristics. 5.6.1 Baseline model (all households) Having established that two quite different OAs have been selected, kWh profile data for the first (non-holiday) Sunday in January 2017 (8/1/2017) is attached as a ‘baseline’ test. Half-hourly (sum) kWh consumption data is merged to the households that were pushed through the IPF process.19 First, the weighted counts for each household size type (single, two person etc) are checked. Table 21 contains the number of households in the SAVE sample ‘pool’ (N unweighted column) for each household size in both test OAs, along with the mean, minimum and maximum weights that the IPF Source: http://datashine.org.uk

@dataknut Where have we got to? 44 UK NZ Time
Use Model Done (UK 2001 Survey) - Observed W model Done (SAVE) - Intervention W model Done (SAVE) - Scenario Model - - Local area model Done (TU/SAVE/UK 2011 Census) -

@dataknut Where next? 45 UK NZ Time Use Model Update
(UK 2014 Survey) To do: GREENGrid To do: NZ 2011 TU Survey Observed W model Done (SAVE) No data? Intervention W model Done (SAVE) No data? Scenario Model To do To do Local area model Done (TU/SAVE/UK 2011 Census) To do: NZ TU Survey & 2013/2018 Census 2018-2019 2019-2020 energy.soton.ac.uk/tag/spatialec What if: EV uptake was demographically clustered? What if: All lighting was LED? What if: Cooking shifted for retired people?

@dataknut Questions? § @dataknut § www.energy.soton.ac.uk/tag/save/ § www.energy.soton.ac.uk/tag/spatialec – 2
year EU Global Fellowship @Otago CfS 46 pixabay.com

Neighbourhood Demand Profiles: Why, how and how...

Neighbourhood Demand Profiles: Why, how and how far have we got?

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