assessing the potential of home automation in...

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Assessing the Potential of HomeAutomation in Norway

A report commissioned by NVE

VaasaETT 342017

2

Published by:

Editor:

Authors:

Printing: Circulation:Cover: ISBN

Summary:

Keywords:

Norwegian water resources and energy directorate (NVE)

Middelthunsgate 29 Postboks 5091 Majorstua 0301 OSLO

Telephone: 22 95 95 95 Web: www.nve.no

Assessing the Potential of Home Automation in Norway

Norwegian Water Resources and Energy Directorate

Cathrine Åsegg Hagen

VaasaETT

NVE

30

VaasaETT

978-82-410-1587-8ISSN 1501-2832

This report explores the potential of home automation technology toreduce electricity consumption and manage peak demand in Norway,both at household level and aggregated at national level. In order toreap the full benefits of home automation, VaasaETTs findings indicatethat home automation offerings should go hand in hand with dynamictariffs, consumption feedback and consumer education.

Consumption feedback, demand response, dynamic pricing, energyefficiency, home automation, smart meters.

April 2017

Preface This report is commissioned by NVE as part of the R&D-project “Smart meters, smarter consumers”. The project aims to generate knowledge about measures in the retail electricity market that can help consumers utilize the opportunities following the roll-out of smart meters in Norway. Every Norwegian household will have a smart meter installed by January 1 2019.

The report explores the potential of home automation technology to reduce electricity consumption and manage peak consumption in Norway, both at household level and aggregated at national level. In order to reap the full benefits of home automation, VaasaETTs findings indicate that home automation offerings should go hand in hand with dynamic tariffs, consumption feedback and consumer education.

In order to know the actual effects of home automation in Norway, different home automation solutions needs to be tested in a large scale amongst Norwegian household costumers. This report can hopefully help in preparations for future large-scale pilot projects.

The content and recommendations contained within this report are those of the consultant, and have neither been accepted nor rejected by NVE.

Oslo, April 2017

Ove Flataker Director

Guro Grøtterud Head of Section

AreportcommissionedbytheNorwegianWaterResourcesandEnergyDirectorate(NVE)

LeadWriters:ChristopheDromacqueThomasN.Mikkelsen

RafailaGrigoriou

AdditionalContributors:HannaLaunonenPhilipE.Lewis

Delivery:21February2017

Assessing the Potential of Home Automation in Norway

i

Table of Contents

ListofAcronyms...........................................................................................................................................iii

ExecutiveSummary.......................................................................................................................................1

Introduction..................................................................................................................................................11

Adefinitionofhomeautomation...........................................................................................................13

1 Successfactorsinhomeautomationprojects..........................................................................14

1.1 Communicationtechnologies............................................................................................................................14

1.2 Choiceoftechnology..............................................................................................................................................15

1.3 Tariffsasbarrierandenabler...........................................................................................................................18

1.4 Educationandengagement...............................................................................................................................20

1.5 MarketpotentialforhomeautomationtechnologyinNorway.........................................................25

2 PotentialofhomeautomationforEEandDR...........................................................................27

2.1 Methodology..............................................................................................................................................................27

2.2 Overallpilotresults................................................................................................................................................28

2.3 Effectivenessofdynamictariffschemes.......................................................................................................29

2.4 Automatingtheusageofhomeappliances.................................................................................................34

2.5 Educationandfeedbackinhomeautomationpilots..............................................................................38

3 ImpactofhomeautomationonelectricityconsumptioninNorway................................41

3.1 CharacteristicsofhouseholdelectricityconsumptioninNorway....................................................41

3.2 Methodologyandassumptions.........................................................................................................................43

3.3 Impactsonannualelectricityconsumption................................................................................................45

3.4 Impactsonthehighestelectricityconsumptionmonth........................................................................47

3.5 Impactsonthehighestconsumptionday....................................................................................................50

4 State‐of‐the‐arthomeautomationprojects..............................................................................52

4.1 NewEquipment.......................................................................................................................................................52

4.2 Nikola–EVsforFlexibilityandGreenEnergy...........................................................................................54

4.3 Elsa–EnergyLocalStorageAdvancedsystem..........................................................................................56

4.4 'CaronToon'–IntegratingHomeandCar.................................................................................................56

4.5 WEREL–BringingitTogether.........................................................................................................................57

5 References...........................................................................................................................................58

ii

Table of Figures

FIGURE1:THECONNECTEDHOMEIN2025.....................................................................................................................16FIGURE2:AMAZONECHO–AMAINCONTENDERTOBECOMETHEHEARTOFTHECONNECTEDHOME...................17FIGURE3ANYWAREANDGREENENERGYOPTIONS–HOMEAUTOMATIONSOLUTIONS..........................................17FIGURE4:DYNAMICPRICINGPILOTSANDEDUCATION...................................................................................................20FIGURE5:BE‐AWAREPROJECTDISCOVERY(2011).......................................................................................................21FIGURE6:BENEFITSOFDRPROGRAMSFORDIFFERENTSTAKEHOLDERS....................................................................22FIGURE7:THREEPILLARSOFTHECUSTOMERENGAGEMENTPROCESS.........................................................................24FIGURE8:SMARTHOMESHADITSOWNPAVILIONINEUROPEANUTILITYWEEK2010INVIENNA......................25FIGURE9:MARKETPENETRATIONOFHOMEAUTOMATIONTECHNOLOGYINNORWAY2015–2021...................26FIGURE10:IMPACTOFHOMEAUTOMATIONONELECTRICITYCONSUMPTION.............................................................28FIGURE11:CONSUMPTIONREDUCTIONATPEAKTIMES..................................................................................................30FIGURE12:IMPACTOFHOMEAUTOMATIONINCPPPILOTS...........................................................................................31FIGURE13:IMPACTOFHOMEAUTOMATIONINCPRPILOTS...........................................................................................32FIGURE14:IMPACTOFHOMEAUTOMATIONINTOUPILOTS..........................................................................................33FIGURE15:IMPACTOFHOMEAUTOMATIONINRTPPILOTS..........................................................................................34FIGURE16:APPLIANCECONSUMPTIONINNORWAY,KWHPERYEAR...........................................................................35FIGURE17:IMPACTOFAUTOMATEDELECTRICHEATING.................................................................................................36FIGURE18:IMPACTOFAUTOMATEDWATERBOILERS......................................................................................................37FIGURE19:IMPACTOFAUTOMATEDWHITEGOODS.........................................................................................................38FIGURE20:IMPACTOFFEEDBACKONHOMEAUTOMATIONPILOTS...............................................................................39FIGURE21:ELECTRICITYCONSUMPTIONBYTYPEOFHOUSING,2012.........................................................................41FIGURE22:HOUSEHOLD’SENERGYUSEINNORWAY.......................................................................................................42FIGURE23:SEASONALVARIATIONSINELECTRICITYCONSUMPTION,WEEKDAYSANDWEEKENDS..........................42FIGURE24:ESTIMATEDYEARLYAVERAGEDEMANDDURINGWORKDAYSSEGMENTEDINTOMAINEND‐USEGROUPS

.........................................................................................................................................................................................43FIGURE25:IMPACTASSUMPTIONSOFTARIFFSCHEME,HOMEAUTOMATIONANDCONSUMPTIONFEEDBACK.......44FIGURE26:IMPACTOFFEEDBACKANDAUTOMATEDDRONANNUALELECTRICITYCONSUMPTION.......................45FIGURE27:IMPACTOFFEEDBACKANDAUTOMATEDDRONANNUALPEAKELECTRICITYCONSUMPTION.............46FIGURE28:IMPACTONNORWAY’SOVERALLANDPEAKELECTRICITYCONSUMPTION...............................................47FIGURE29:IMPACTOFFEEDBACKANDAUTOMATEDDRONHIGHESTCONSUMPTIONMONTHELECTRICITY

CONSUMPTION...............................................................................................................................................................48FIGURE30:IMPACTOFFEEDBACKANDAUTOMATEDDRONHIGHESTCONSUMPTIONMONTHPEAKELECTRICITY

CONSUMPTION...............................................................................................................................................................48FIGURE31:IMPACTONNORWAY’SHIGHESTCONSUMPTIONMONTHOF2015...........................................................49FIGURE32:IMPACTOFFEEDBACKANDAUTOMATEDDRONHIGHESTCONSUMPTIONDAYPEAKELECTRICITY

CONSUMPTION...............................................................................................................................................................50FIGURE33:IMPACTONHIGHESTCONSUMPTIONDAYOF2015......................................................................................51FIGURE34:IMPACTOFAUTOMATEDHEATING‐HIGHESTCONSUMPTIONDAY2015.................................................51FIGURE35:THEVERAGEO‐FENCINGHOMEAUTOMATIONAPP....................................................................................52FIGURE36:TESLASOLARROOFTOPDESIGNS..................................................................................................................53FIGURE37:ENECO’SCARONTOONINTEGRATEDSOLUTION..........................................................................................56

iii

List of Acronyms

CAPEX:CapitalExpenditure

CPP:CriticalPeakPricing

CPR:CriticalPeakRebate

DR:DemandResponse

DSM:DemandSideManagement

DSO:DistributionSystemOperator

EE:EnergyEfficiency

EU:EuropeanUnion

EV:ElectricVehicle

HAN:HomeAreaNetwork

IHD:In‐HouseDisplay

OPEX:OperatingExpense

PV:Photovoltaic

RTP:RealTimePricing

TOU:TimeofUsetariff

TSO:TransmissionSystemOperator

1

Executive Summary

Thisreportexploresthepotentialofhomeautomationtechnologytoreduceelectricity

consumption andmanage peak consumption inNorway under different scenarios for

market adoption both at household level and aggregated at national level. It also

highlights critical success factors of home automation projects that, when combined,

ensurebetterconsumerengagementandultimatelygreater impactsof the technology

and, finally, presents several examples ofwhat the future of home automationmight

looklike.

Theabilitytobothincreaseandreduceenergydemandisoftenseenasanimportantand

relatively inexpensiveelement inprovidinggrid flexibilitywhilst integratinga greater

proportionofintermittentenergyandpreparingthegridfortheincreasingpenetration

of new electricity intensive appliances such as electric cars and heat pumps (both

expanding rapidly in Norway). There are however limits to the speed with which

consumerscanmanuallyreact–whentheycanreactatall‐topriceorvolumesignals.

By automating the usage of certain appliances, household electricity consumption can

instantlydropwheneverpricesarehighornetworkscongestedbenefitingconsumers,

networkoperatorsandthebroadercommunity.

However, the size of loads being switched is usually limited and the cost of the

technology required is often seen as a deterrent. Consequently, the scale of rewards

comparedtotheeffortandcostofinstallingahomeautomationsystemmaybeseenas

inadequate.

This is to ignorethatresidentialdemand isoftenasignificantportionof totalnational

consumption and an even higher portion of national peak consumption. In addition,

residential peak profile is different to that of industry and therefore the two provide

complementary flexibility resources. Multiple trials have demonstrated that home

automation delivers not only substantial consumption reductions and cost‐savings to

energy consumers, they can also bring high levels of satisfaction and loyalty, an

improved perception of the industry and allow for other services that provide added

valueandconveniencetotheconsumeraswellasnewbusinessmodelsfortheenergy

industry.

Intuitively,Norwayrepresentsoneofthemostsuitablemarketsintheworldforhome

automationandtheservicesthatfollowonfromthem:highresidentialconsumptionand

2

2015 2016 2017 2018 2019 2020 2021

Home Automation Technology Market Penetration Rate

MarketpenetrationofhomeautomationtechnologyinNorway2015–

2021(Figure9)

energybills(despitesomeofthelowestelectricitypricesinEurope),ahighproportion

ofelectricheating(controllableload),anincreasingpenetrationofelectriccarsandheat

pumps (also controllable loads), technologically savvypopulation, significant levels of

retail market competition, ongoing smart meter deployment and high income levels

(consumers with higher incomes are better able to invest in Energy Efficiency (EE)

solutions).

Though slow to develop until recently, home energy management systems are being

increasingly commercialised.Revenues in thehomeautomationsegment areexpected

togrowatanannualrate

of 20.6% between now

and 2021; resulting in a

market volume of

US$78m or US$161 per

active Norwegian

household.By2021,21%

ofNorwegian households

are expected to have

home energymanagement systemsup from5% today.By extrapolating these figures,

marketpenetrationratesof48%and78%areexpectedin2030and2040.

Added value of home automation

Appliance automation

proves very effective

at shifting

consumption away

from peak hours 1 .

Pilots with home

automation managed

to reduce peak

consumption by 23%

Vs. 9% for pilots

1Peakconsumptionreferstohighconsumptionhoursatnationallevel.Reductioninpeakhour

consumptionreferstothereductioninconsumptionoverthedurationofthepeak.

‐0,07%

23,42%

1,77%2,48%

8,92%

‐1,65%‐5%

0%

5%

10%

15%

20%

25%

OVERALLREDUCTION

PEAKREDUCTION FOLLOWINGPEAKHOURS

Reduction(%)

ImpactofHomeAutomation

Automation No Automation

Impactofhomeautomationonelectricityconsumption(Figure10)

3

2,68%

23,42%

‐1,48%

0,41%

21,73%

‐3,77%

OVERALLREDUCTION


Reduction(%)

ImpactofFeedbackinAutomationPilots

Feedback No feedback

Impactoffeedbackonautomationpilots(Figure20)

without automation (manual response to dynamic pricing and/or consumption

feedback). There are several reasons for it. Even though consumers should always be

allowed to overrun the program, automation enables fast reactions as well as

controllable levels of reduction and has the advantage of being available during

unplanned system emergencies for instance. In addition, critical situations do not

alwaysoccurwhenresidentialconsumersareabletotakeaction(whentheyareaway

or asleep for instance). Another important consideration for grid operators is that

without automation they risk seeing millions of appliances come back on line at the

same time right afterhigh‐price‐hours end.Automation canhelpmitigate this risk by

switchingappliancesbackonincycles.

However, home automation alone often leads to increased levels of electricity

consumption. Pilots with home automation led to a slight increase in overall

consumption(‐0.07%)whilstpilotswithouthomeautomationtechnologyledtosizeable

reductions inoverall energyconsumption (2.48%).While someargue that there isno

point trying to engage and educate customers who have automated appliances, pilot

results(andbehaviouralscience)showthatwhenefficiencyimprovementscomesolely

from the technological side, people remain passive actors, leading to low levels of

awareness, continued inefficienthabitsandbehavioursandwelldocumentedrebound

effects. In summary, while pilot results indicate that automation drives peak

consumption reductions, it is essential to introduce other mechanism to develop

sustainableenergysavinghabits.

Automation, consumption feedback and consumer education

This point is supported by our findings. Pilots combining home automation with

consumption feedbackandconsumereducation (inotherwordsmakeuseof thedata

generated by the home

automation system to help

consumers reduce overall

energy consumption) are

more effective at reducing

bothpeak(23%vs22%)and

overall consumption (2.7%

vs. 0.41%). In real‐life

however, home automation

4

hasoftenbeenintroducedfollowinganinvertedevolutionwherebytechnologyhasbeen

atthefore‐front,withconsumereducationandfeedbackbeingintroducedasanext‐step

or a reaction to negative publicity. Consumer education and consumption feedback is

not about giving access to graphs, it is about establishing a continuous and dynamic

dialogue with the customer, based on collecting data and adapted to behaviour or

consumptionpatternsona regularbasisand throughmultipleplatforms.Lastbutnot

least, a positive business case and an appealing payback time are other fundamental

reasonswhyeducationandfeedbackshouldbepartofanyhomeautomationpackage.A

2017reportbyVaasaETTand JouleAssets lookingat thebusinesscase forresidential

demand side flexibility in 4 EU countries found that between 77% and 87% of end‐

consumers’financialbenefitscomefromoverallconsumptionreductions(therestfrom

peak consumption reduction). This can be easily understood if one considers the fact

that critical peaks take place for only about 30 hours a year whilst benefits from

loweringoverallelectricityconsumptiontakeplacecontinuously.

Automation and dynamic tariffs

Dynamicpricinginvolvessubstantiallyincreasedretailelectricitypricesduringtimesof

either heightened consumption (for example on abnormally cold winter days in

Norway)orwhenthestabilityofthesystemisthreatenedandblack‐outsmayoccur.In

this respect, the different tariffs in the report are not specifiedas eithera regulated

networktarifforacompetitiveretailprice. Thetariffscould in theory bemanagedby a

thirdentity“theDemandSideManagement(DSM)authority”bridgingtheeconomicsof

thewholesalemarketand thebalancingactivitiesof thegridcompanies.Thedynamic

tariffsarethusmakingupforthefactthatconsumers’decisionsdonotaccountforthe

costofproducingandtransportingelectricityinthedifferenttimeperiods–whichfrom

an economist’s point of view has been one of electricitymarket’smajor failures. The

durationandfrequencyofthehighpricedhoursdifferdependingonthepricingscheme

as explained in Chapter 2.3 and detailed in Figure 25. It is important to note that

whether thedynamicpartof the tariff is linked to the regulatednetwork tariff or the

competitive retail price ‐ or both ‐ does not influence the consumer’s (or the

technology’s) response. As far as the consumer (or the technology) is concerned,

he/she/itisreceivingpricesignalstoshiftconsumptiontocheaperhours.

5

14%

34%

21%

11%

5%

17%

12%10%

TOU CPP CPR RTP

PeakRedution(%)

PeakConsumptionReductioninDynamicPricingPilots

Automation No automation

+172%

+93%

+75%

+10%

Consumptionreductionatpeaktimes(Figure11)

Pilotresultsshowthathomeautomationenhancestheimpactofdynamictariffsby75‐

172%(ignoringRTP).Thoughallthetestedtariffsschemeshaveprosandcons,dynamic

pricingcoupledwithhomeautomationhaveprovenoneof themosteffectiveways to

securedemandflexibilityin

the residential sector. It is

important to keep in mind

that though TOU and RTP

peak consumption

reductions are the lowest,

theyoccurdaily,whilstCPP

and CPR produce the

highestreductionsbutonly

for critical peak periods,

typically about 30 hours a

year. Pilot results would

indicate that rewards for peak clipping (CPR) are much less effective than penalties

(CPP).ItisimportanttokeepinmindthatCPRmightconstituteamoreacceptableform

ofdynamicpricing,thusachievinggreatermarketpenetrationandagreateraggregated

impact on national consumption. CPP alone may also be perceived negatively by

consumersand thushinder the introductionofotherproductsandservices related to

homeautomationwhichrequiresatisfactionandtrustinenergysupplier.

In order to reap the full benefits of home automation (i.e. peak clipping, managing

surroundingpeak time consumption and energy efficiency), our findings indicate that

homeautomationofferingsshouldgohand inhandwithdynamic tariffs, consumption

feedbackandconsumereducation.Variousmodellingexerciseswereconductedbased

onthisfinding.

6

ImpactoffeedbackandautomatedDRonannualelectricityconsumption(Figure26)

How would home automation impact Norwegian households’ power consumption?

‐ Impactonannualconsumption

Norwegianhouseholdscoulddecreaseelectricityconsumptionbyanamountequivalent

to about 7% of their annual usage thanks to home automation, dynamic pricing,

consumptionfeedbackandconsumereducation.Thisamountsto1,065–1,104kWhper

yearforanaveragehouseholddependingonthedynamictariffscheme.Asimilarimpact

onannualconsumptionisobservedacrosspricingschemes.Thiscanbeexplainedbythe

factthatoverallconsumptionismostlyinfluencedbyfeedbackandconsumereducation

ratherthanbydynamicpricing(whichtargetspeakconsumption).

‐ Impactonannualpeakconsumption

When investigating the effects of home automation, dynamic pricing, consumption

feedback and consumer education on annual peak consumption, the effectiveness of

TOU stands out. An averageNorwegian householdwith automatedTOU could reduce

consumptionby an amount equivalent to about 14%of its annual peak consumption.

ThisisduetothefactthatTOUimpactsconsumptiondailywhereasCPPandCPRimpact

consumptiononcriticalpeakdaysonly(typically12–15timesayear).Itisimportantto

keep in mind that TOU, due to its rigid structure, lacks the flexibility to deal with

extremeprices on thewholesalemarket outside of “usual” peak hours or unexpected

network constraints. TOU and CPP (or CPR) can however be combined to retain the

1415

1048

627

1073

1406

1042

623

1066

1405

1041

622

1065

1456

1079

645

1104

kWh

AnnualElectricitySavingsperhousetype

Automated TOU+Feedback

Automated CPP/CPR+Feedback

AutomatedTOU+CPP/CPR+Feedback

Automated RTP+Feedback

Average household

7

possibility to dealwith unexpected events (CPP / CPRprices are triggered on critical

days)whilsthavingadailyimpactonpeakusage(asTOUareinforceeveryotherday).

In fact, our results indicate that a combination of TOU and CPP/CPR works best at

reducingannualpeakconsumption(512kWhperyearforanaveragehousehold).

ImpactoffeedbackandautomatedDRonannualpeakelectricityconsumption(Figure27)

‐ Impacts on the peak consumption of the year’s highest consumption day

Although, the impact of CPP/CPR on annual consumption is limited, they prove very

powerfulatloweringcriticalpeakconsumptionwhencombinedwithhomeautomation.

AnaverageNorwegianhouseholdonCPP/CPRpricingcouldlowerpeakconsumptionby

2.37kWh(28%)onthehighestconsumptiondayoftheyearwhenCPPpeakpricesare

inforce.

8

ImpactoffeedbackandautomatedDRonhighestconsumptiondaypeakelectricityconsumption

(Figure32)

How would home automation impact Norway’s power consumption?

TheresultsshowthatNorwaycouldbenefitenormouslyfromagreaterpenetrationof

home automation technology, dynamic pricing, consumption feedback and consumer

education.

‐ ImpactonNorway’sannualconsumption

Norway’sannualoverallandpeakelectricityconsumptioncoulddecreaseby414GWh

(1.17%) and 85 GWh (2.31%) respectively by 2020 when 17% of households have

adopted a combination of home automation, dynamic pricing, consumption feedback

and consumer education. By 2030, with a market penetration of 48%, these figures

couldreach1,142GWh(3.22%)and234GWh(6.37%).

9

0,46

1,27

2,09

2,66

0,0

0,5

1,0

1,5

2,0

2,5

3,0

PENETRATIONRATE2020(17%)



PENETRATIONRATE100%

GWh

PeakConsumptionReduction‐ Year'shighestconsumptionday

Peak Savings

Impactonhighestconsumptiondayof2015(Figure33)

ImpactonNorway’soverallandpeakelectricityconsumption(Figure28)

‐ ImpactonNorway’speakconsumption

UsingJanuary2nd2015(year’shighestconsumptionday)asabaseline–whenCPP/CPR

pricesareinforceandconsumerswithRTPhavebeennotifiedofhighwholesaleprices

–ourmodellingshowsthathomeautomationtechnology,dynamictariffsandfeedback

could lowerNorway’selectricitypeakconsumptionby0.46GWh(2.81%)by2020.By

2030,thisfigurecouldreach1.27GWh(7.75%).

Norway is one of the

few countries where

electricity is

household’s main

heating source. Our

findingsrevealthaton

Norway’s highest

consumption day,

electric heating alone

couldprovide43%of

85 234384 491414

1142

1875

2396

0

500

1000

1500

2000

2500

3000




PENETRATIONRATE100%

GWh

AnnualOverallandPeakConsumptionReduction

Peak Savings Overall

10

the country’s potential for residential peak consumption reduction. Our analysis also

shows that electric water boilers and white goods represent a significant portion of

electricity consumption in Norwegian homes and could provide sizeable demand

flexibilityandenergyefficiencygains.

Technologies – for now…

Even though there is no clear winner around home automation technologies, it does

seemtobecomingtogether.Thereportdiscussesfourscenarioswhichallareprobable

outcomesof thepolitical decisions andbusiness investments in thenext two to three

years.Thedependenciesarestillrelatedto findingcommoncommunicationstandards

ofHomeAreaNetwork(HAN),andtheinvestmentsbeingmadeinthisarealatelyshows,

that the industry is nowmaking their final decisions onwhom they thinkwill be the

winners.

…and the future

Finally,thereportintroducessomeofthemostinnovativeautomation‐focusedservices

and technologies currently being tested or commercialised around the world.

Interestingly, home automation technology is not only rapidly becoming far more

developed,butitisincreasinglyfocusedonmorethanjustoneorjustafewappliances.

Thetrendistocreatecomprehensiveandinclusivesolutionsthroughtheintegrationof

multipleservices,technologiesandconsumers.Thisevolutionisbuiltontheinternetof

things, communities and facilitating platforms, creating synergies of ever increasing

benefitsfortheconsumer.Putsimply,themoreelementsthatareintegrated,thegreater

thereturns forcustomers, themoreattractive thebusinesscase, themoresustainable

thebusinessmodel.

Smartmeters,homeenergymanagementsystems,smarthomes,distributedgeneration,

storage,electricvehiclesandmoreareallbeingbroughttogetherintoharmonisedeco‐

systems. Automation ensures that the integration takes place discretely in the

background in the way that we would want it to happen, with minimal need for

customereffort.What’smore,thegreaterthenumberandvarietyofelementsthatare

integrated,themoreimportanttheroleofautomationbecomes.

11

Introduction

Theabilitytobothincreaseandreduceenergydemandisoftenseenasanimportantand

relatively inexpensiveelement inprovidinggrid flexibilitywhilst integratinga greater

proportionofintermittentenergyandpreparingthegridfortheincreasingpenetration

of new electricity intensive appliances such as electric cars and heat pumps (both

expanding rapidly in Norway). There are however limits to the speed with which

consumerscanmanuallyreact–whentheycanreactatall‐topriceorvolumesignals.

Thismeansthatwithoutautomationautilitycanonlyaskresidentialconsumerstoshift

load if they know thiswill be necessarywell in advance (typically a day). This is not

alwaysthecaseasunforeseenemergencysituationscanoccurwhichdecreasesthevalue

oftheDemandResponse(DR)program.Byautomatingtheusageofcertainappliances,

householdelectricityconsumptioncaninstantlydropwhenpricesarehigh,networkare

congested or increasewhen green energy is available benefiting consumers, network

operatorsandthebroadercommunity.

However,intheresidentialsector,thesizeofloadsbeingswitchedisusuallylimitedand

thecostofthetechnologyrequiredisoftenseenasadeterrent.Consequently,thescale

ofrewardscomparedtotheeffortandcostofinstallingahomeautomationsystemmay

beseenasinadequate.

This is to ignorethatresidentialdemand isoftenasignificantportionof totalnational

demand and an even higher portion of national peak demand. In 2015 in the UK the

residential sector made up 30% of the total electricity power demanded (35% in

Norway)and60%ofpeakconsumption.Inaddition,residentialpeakprofileisdifferent

to thatof industryand therefore the twoprovidecomplementary flexibility resources.

Thechallengeisthustofindwaysofenablingittobeaccessed.Multipletrialshavebeen

conductedtoexplorethepotentialofhomeautomation.Whilethesetrialsgenerallyhave

demonstratedthatwhenusedeffectively,homeautomationdeliversnotonlysubstantial

consumptionreductionsandcost‐savingstoenergyconsumers,theycanalsobringhigh

levelsofsatisfactionandloyalty,animprovedperceptionoftheindustryandallowfor

otherservicesthatprovideaddedvalueandconveniencetotheconsumeraswellasnew

businessmodelsfortheenergyindustry.Thoughslowtodevelopuntilrecently,business

applicationsofthesetrialsarenowbeingincreasinglycommercialised.

12

ItisexpectedthatNorwayrepresentsoneofthemostsuitablemarketsintheworldfor

home automation and the services that follow on from them: high residential

consumptionandenergybills(despitesomeofthelowestelectricitypricesinEurope),a

high proportion of electric heating (controllable load), an increasing penetration of

electriccarsandheatpumps(alsocontrollableload),technologicallysavvypopulation,

significant levels of retail market competition, ongoing smart meter deployment and

high income levels (consumers with higher incomes are better able to invest in EE

solutions).Thisresearchthusexploresthepotentialofhomeautomationtechnologiesin

Norwayandidentifies:

1. Thecriticalsuccessfactorsofhomeautomationprojects:Mainlearnings

fromprojectsonhowtofacilitatetheadoptionbycustomersofsmarthome

technologiesandmaximisetheirimpacts;

2. The potential of home automation for EE and flexibility in Norway:

Assess the impact of home automation technology and services in the

Norwegiancontextbasedonresultsofseveralhundredpilots;

3. ThepotentialimpactofhomeautomationonNorway’sresidentialand

national power consumption: Output from the previous research

questions will constitute the building block of further analyses for

NorwegianhouseholdsandforNorway;

4. State‐of‐the‐arthomeautomationtechnologiesandprojects:Reviewof

the latest trends and most promising home automation technologies and

projects.

13

A definition of home automation

Inassessing thepotentialofhomeautomationonemay rapidly run into thequestion;

“Whatishomeautomation?”AsimplesearchofthetermonGooglereturnsnolessthan

21millionhitsandtherelated“SmartHome”morethantwicethisnumber.Accordingto

Collins English Dictionary home automation is: “thecontrolofdomesticappliancesby

electronically controlled systems”. However, this definition is not taking into

consideration the possible feedback and educative aspects also enabled by the

technology. Hence for this report we will broaden the term and describe home

automation as: “Buildingautomation inhouseholds,which involveselementsof control,

monitoring, feedbackandautomationofenergy consumingappliances suchas: lighting,

heating, ventilation, air conditioning, as well as white goods such as washing

machines/dryers,ovensor refrigerators/freezerswith theaimofdecreasingoveralland

peakenergyconsumption.”Homeautomation isoftenextended toalso includecomfort

and security related measures such as air quality and surveillance/alarms. These

features are not included in this report even though theymight be of importance for

developing a comprehensive value proposition to consumers and related business

models.

14

1 Success factors in home automation projects

Goingintodetailaboutallthebarrierstohomeautomationandhowtoovercomethem

wouldrequireaseparatestudyandshouldalsoincludeathoroughinvestigationofthe

specificNorwegianconditionstobeaddressed.Thischapterseeks insteadtohighlight

critical success factors from a high‐level perspective to be considered before

undertakinghomeautomationprojects.

1.1 Communication technologies

Oneofthemainbarrierswhichhaspreventedmany

people from buying existing home automation

systems is the lack of communication standards –

and in this respect the lack of confidence that the

productwill be able to upholdmaintenance supply

andcompatibilitywithnewproducts.Thisisoneofthemaincriticalfactorsinrelation

tohomeautomationbecomingasuccess.

The two leading technologies have so far been ZigBee and Z‐wave – two radio

technologies with different pros and cons – and different supporter groups in many

countries. Neither of these technologies however, has gained a significant advantage

overtheother–especiallybecausethetwotechnologiesaregoodfordifferentpurposes.

ZigBeeischeaperandeasierto incorporateintonewsolutions,as it isopensource.Z‐

wave tries to avoid interference problems by operating in a different frequency than

mostotherradiotransmitters.Italsotriestobecomethecustomer’schoicebykeepinga

strictpolicyfordevelopmentanduseoftheprotocol.

Many experts seem to believe that the end for Z‐

wave and ZigBee is soon to come partly because

none of the big market players has adopted them,

and more importantly because the Bluetooth

technology, which is already present in many

household devices, has recently announced a low

energy, MESH capable protocol called Bluetooth‐Low‐Energy or BLE; technology

recentlyacquiredbyQualcomm,oneof theverybigplayers in the field.Thisreport is

focusedonthecommunicationtechnologiestoenableasmarthometofunction,i.e.:the

communicationtechnologiesbetweensmartappliances,whichshouldbewirelesstoget

“ManyexpertsseemtobelievethattheendforZ‐waveandZigBeeissoontocome.”

15

customeracceptance.InaNorwegiancontext,itishowevergoodtoobservethatsome

thoughtonstandardshasalreadybeenenvisioned.AreportbyNEK(2015)concludes

that smart meters in a Norwegian context cannot function as a hub and that smart

appliances would need another central device to function, which could act as a

middlewarebetweenthesmartmeterandthesmarthome.

ThereporttouchesupontheconnectionbetweenthesmartmeterinitselfandtheHAN.

It is concluded that the connection needs to be wired and be based on the M‐Bus

standard.ThisisthesamestandardwhichispromotedbytheEUtoreadsmartmeters

remotely ‐ hence it doesmake sense to adopt these prescriptions also for residential

userstohaveaccesstorealtimedata.Therearehoweveralotofconsiderationstotake

inordertomakethetechnologyaccessibletotheend‐customer.Firstandforemost,the

wiringprocessmightcomplicatethecustomeracceptanceof In‐HouseDisplays(IHDs)

asmostofthesefunctionsareonlypossible if themeter isreadopticallyorfromwire

clamps.Itisnot100%clearwhetherthisisacceptedinaNorwegiancontext,asthereis

no real connection involved ‐ only an automated reading. Second, M‐Bus is not a

common standard inmost home automation equipment, since it requireswiring and

simply uses toomuch electricity to be performingwell (home automation equipment

often requires batteries to function). From this it should thus be noted, that home

automationunderthecurrentNorwegianlegislationwillneedapieceofmiddlewareto

ensuretheconnectionbetweenthesmartmeterandthehomeautomationsystem,asit

isunlikelythatthewholesystemwillworkfromtheM‐Busstandardalone.

1.2 Choice of technology

When it comes to home automation

systems,strategieshavebeenmanifold.

Thefirstsystemswereproprietary,not

connectedandfocusedononlyoneora

very few appliances in the household,

likeaheatpumporthelights.Thereis

still no satisfactory choice for a mass

marketdeploymentofanyhomeautomationsystemandthiscanbeseenasoneofthe

maincriticalsuccessfactorsinordertoreachscaleintheresidentialmarket.

“ThereisstillnosatisfactorychoiceforamassmarketdeploymentofanyHomeAutomationsystemandthiscanbeseenasoneofthemaincriticalsuccessfactorsinordertoreachscaleintheresidentialmarket.”

16

Therearedifferentanticipationstothefutureoftheconnectedhome.InMay2016PA

consultinghadasessionwithexpertsfromallovertheworldtoextractthemainviews

intooneconsolidatedmodel.

Basically,themodelpredictsfourdifferentscenarios,whichcouldallbearealityin2025

dependingondecisionsnowandinthecomingyears.Inthescenariosontheleft‐hand

side, there is a risk, that solutions are only fragmentarily adopted, if either no

communication standard emerges or if data securitymeasures are so strict that data

cannotbeexchangedrelativelyeasilybetweenserviceprovidersand thecustomer.As

discussed in Chapter 1.1 it seems that communications standardswill finally emerge.

For security, however it is still verymuch up to the national legislators to determine

howdatamustbehandled.

Figure1:TheConnectedHomein2025(Source:PAConsulting2016)

17

Figure2:AmazonEcho–Amaincontendertobecometheheartoftheconnectedhome

Forahomeautomationscenario to function, the twoscenarioson theright‐handside

are however the most suitable, since they are the only ones with mass adoption of

technology.Thedifferencebetweenthetwoscenariosarealsoaclearindicationofthe

battlewhich isbeing foughtrightnowasApple,Microsoft,GoogleandAmazonareall

tryingtosecuretheirroleasthemainenablersofconnectedhomesofthefuture.

Figure 3: Anyware (right picture) and Green Energy Options (left picture) – Home Automation

Solutions

Itisbynomeanssurehowever,thattheleadingtechnologyprovidersoftodaywillalso

lead the future. One critical success factor has always been the ability to incorporate

new ideas, somethingwhichproprietary solutions have alwaysmissed out on. In this

respect the announcement of IBM to join theEnOceanAlliancewas a clear indication

that supporters of open standards had to stand up against the proprietary brands.

EnOceanalreadycounts suchprominentmembersasYamaha, SiemensandSchneider

Electric but is based on a protocol developed by one of the leading providers of

technology for theconnectedhomewhichhasbeenprovidingsolutions formore than

15years.Itisalsoworthmentioning,thatnew2ndgenerationtechnologyisnowbeing

developed and brought to market by smaller companies. One such solution is the

18

integrated storage battery by thewell acclaimedBritishmanufacturer of IHDs; Green

EnergyOptions.AnotheristheKickstarterfoundedgeo‐fencesolutionAnyware,which

integrateswithexistinglampsocketstoprovideadditionalservicesincombinationwith

otherwirelesstechnology.

1.3 Tariffs as barrier and enabler

Thefactthatconsumers’consumptiondecisionsdonotaccountforthecostofproducing

and transporting electricity in thedifferent timeperiods is one of electricitymarket’s

majorfailuresfromaneconomist’spointofview.Indeed,althoughthecostofsupplying

powertoconsumerscanvarybyanorderofmagnitudewithinthesameday,theprice

paid by most end‐users remains flat all year round in many countries leading to a

numberofinefficienciesandavoidableexternalities2.Mitigatingtheeffectofthismarket

failurebypassingonsomeormostof thepricevolatilityon toconsumers isarguably

dynamic pricing’s major objective. Dynamic pricing involves substantially increased

retail electricity prices during times of heightened wholesale prices caused by

heightenedconsumption(forexampleonabnormallycoldwinterdaysinNorway)and

or when the stability of the system is threatened and black‐outs may occur3. In this

respect, thedifferenttariffs inthereportarenotspecifiedaseitheraregulatednetwork

tariff ora competitive retailprice. Thetariffscould in theory be managed by a third

entity “the DSM authority” bridging the economics of the wholesale market and the

balancing activities of the grid companies. It is important to note that whether the

dynamic part of the tariff is linked to the regulated network tariff or the competitive

retailprice‐orboth‐doesnotinfluencetheconsumer’s(orthetechnology’s)response.

As far as the consumer (or the technology) is concerned, he/she/it is receiving price

signalstoshiftconsumptiontocheaperhours.HighpricesmayoccurdailyaswithTOU,

oratcriticaltimesforthesecurityoftheelectricitynetworkaswithCPPandCPR.There

is no doubt that dynamic pricing has proven its value in securing consumption

reductionsatpeaktimes.Therearehoweverimportantandcriticalfactorswhichshould

always be taken into account when assessing whether or not to introduce dynamic

tariffs.

2Needforbuildingseldomusedpeakcapacity,dirtieron‐peakgeneration,andcurtailmentofgreen

generationtociteonlyafew.3DynamicpricingschemesaredescribedinChapter2.3.

19

First and foremost, it is very

clear that dynamic tariffs work

because people try to avoid

paying high prices during peak

hours. However, the impact of

peak pricing is not similar

across all segments of

consumers.TheCaliforniaState‐widePricingPilot,alargepilotassessingtheimpactsof

dynamicpricingonpeak consumption and energybillswhich tookplace in 2003and

2004, showed that high‐use customers respond significantly more than do low‐use

customers,whilelow‐usecustomerssavesignificantlymoreontheirannualbillthando

high‐use customers. This illustrates just one of many elements in the complexity of

introducing a tariff like CPP.Whereas flexibility is secured by targeting only high‐use

customers, thebenefitandeconomicvalueofparticipating isworthmore for the low‐

usegroups.Followingthepilotresults,CPPtariffshavebeen introducedasopt‐outby

manyCalifornianutilities toavoidblack‐outsduring the summer. In a contextof very

high needs – andwhere people can see the immediate benefit (for example as in the

Californian case) introducing this kind of tariffs should be considered. However, the

other sideof the coin is thatCPP tariffs alonecanbe regardedas just anotherwayof

increasing the energy bill and thus ruining a utility’s opportunity to introduce other

productsandservices(e.g.homeautomation)whichrequirecustomersatisfactionand

engagement.

Itisimportanttoconsidercarefullywhatthemaingoalofintroducingdynamictariffsis

and what they can achieve. Dynamic tariffs work very well for peak clipping and to

address exceptional stress on the grid. However, if the target is also to secure

sustainablereductionsinoverallconsumption,dynamicpricingaloneisnottheanswer.

Education and customer engagementmust be part of the equation. The figure below

illustratesthispoint.Dynamicpricingpilots inwhichparticipantswereeducatedasto

howtotakeadvantageofthetariffsachievedmuchbetterresultsineveryaspect.

“Dynamictariffsareeffectiveatmanagingpeakdemandbutcanberegardedasjustanotherwayofincreasingincomeandthusruiningautility’sopportunitytointroduceotherproductsandserviceswhichrequirecustomersatisfactionandengagement.”

20

Nº of participants (Nº of samples) Education No Education

Overall reduction 82 330 (122) 380 758 (109)

Peak reduction 133 676 (241) 404 351 (149)

Following peak hours 46 628 (71) 114 363 (111)

Figure4:DynamicPricingPilotsandEducation(Source:VaasaETTDatabase2016)

1.4 Education and engagement

Whilesomearguethatthereisnopointtryingtoengageandeducateconsumerssince

homeautomationtechnologyisready,behaviouralscienceandpilotresultspointtothe

contrary. Behavioural experts state

that when efficiency improvements

come solely from the technological

side, people remain passive actors,

leading to low levels of awareness,

continued inefficient habits and

behaviours and well documented

reboundeffects.Ineffecthomeautomationonitsownwillalmostcertainlyleadtopeak

clipping but it will not lead to any significant long lasting reductions in overall

consumption.WewilldiscussthisinmoredetailsinChapter2.5.

Theremaybetwoadditionalreasonsrelatedtoeducationandengagementthatexplain,

whymarketadoptionofhomeautomationhasbeenslow.First,homeautomationhas

oftenbeenintroducedfollowinganinvertedevolutionwherebytechnologyhasbeenat

thefore‐front,withconsumereducationandfeedbackbeingintroducedasanext‐stepor

2,85%

14,11%

‐0,77%

1,39%

7,40%

‐2,12%‐4%‐2%0%2%4%6%8%10%12%14%16%

OVERALLREDUCTION PEAKREDUCTION FOLLOWINGPEAKHOURS

Reduction(%)

ImpactofEducationinDynamicPricingpilots

Education No Education

“Step‐by‐stepdiscoveryandconsumereducationarecriticaltocreateamassmarketforhomeautomation.”

21

reactiontonegativepublicity.Pilotsandconsumerresearchhaveshownthat itcanbe

usefulandimportant formanyconsumerstounderstand, for instancethroughmanual

involvement at first, the relationship between their consumption behaviour and their

billsandhowtheycouldbenefitfromhomeautomationtechnology.

AnotherreasonmaybethatDRseemsto

conflictwithtraditionalbusinessmodels

in the industry whereby retailers sell

kWhandDistributionSystemOperators

(DSO)/Transmission System Operators

“itisacriticalsuccessfactorforautomationprogrammesthatallstakeholdersseethebenefits.Educationandengagementarenotlimitedtoend‐customers.”

Launched in 2010 in Italy, Finland and Sweden, the project introduced

applications that enabled consumers to monitor their energy use and

compete with other participants through quizzes and serious games.

Consumers were able to check on the consumption of individual

appliancespresentedascardsinacarouselbytappingonthecardoftheir

choicetorevealfurtherdetails.Participantswereonlypresentedwiththe

amountofinformationtheyrequired,whentheyrequestedit.

Figure5:Be‐AwareProjectDiscovery(2011)

Users reported increased awareness and knowledge about energy

consumptionandahighsatisfactionwiththetestedsolution.

STEP‐BY‐STEPDISCOVERY:BE‐AWAREPROJECT

22

(TSO) are paid to build networks that meet peak demand. It is thus important to

understandthedriversandbarriersofthedifferentpartiesinthevaluechain.Education

and engagement are in this respect not limited to end‐consumers. Benefits from DR

programs are summarised in the following graph and analysed afterwards focusing

separately on the perspectives of the different stakeholders: DSOs, TSOs and

intermediaries(i.e.suppliers,aggregators).

Figure6:BenefitsofDRprogramsfordifferentstakeholders(Source:FP7projectAdvanced2013)

1.4.1 DSOs and TSOs

Increasing load on distribution substations has been noticed in periods with cold

temperaturesduringwintertimeinNorway.WithanincreasingnumberofEVsandthe

endofoilandkeroseneassourcesforheatingthistendencyisboundtocontinue.Both

TSOsandDSOsmustdesign theirnetworks in aneconomicandefficientway tomeet

peakdemand.TheflexibilityprovidedbyDRprogramscanhelpmanagepeaks,working

asacost‐effectivealternativetogridinvestments.

A well‐designed demand

flexibility program can help

balance the electricity

system,increasetheloading

capabilities of the

transformers and relieve

voltage‐constrained power

“InordertoensuretheeffectivenessofDRprogramsforDSOsandTSOs,itisimportanttotakeunderconsiderationthelengthoftheregulatoryperiodandthelocationoftheparticipant‐installations;DSOscanhighlybenefitfromDRprogramsthattargetcustomersatspecificlocations.”

23

transfer problems. Some countries are already investigating such benefits, bymoving

towardsTOU4distributionnetworktariffsasafirststepofgettingpeopletoadoptRTP

ata later stage.ByusingDRasapossible substitute fornetwork investments in their

networkplanningstrategies,DSOswouldbeexpected to reduce investmentsand thus

reducetheirCAPEX(i.e.depreciationandinterest)intheshort‐term.Atthesametime,it

could involve an increasedOPEX depending on how the DR program is implemented

andremunerated. If theCAPEXis includedinthecapalready,andthe increasedOPEX

doesnotsurpassthisreduction,theDSOcouldmakeadditionalbenefits.Thiswouldlast

untiltheendoftheregulatoryperiod,whenthefinancialeffectsofinvestinglessshould

bepassedthroughtoconsumersbymeansoftheupdateoftheallowedrevenueandthe

networktariffs.Therefore,boththeincentivetocarryoutDRinvestmentsbyDSOsand

the transfer of these benefits to DSOs would be dependent on the length of the

regulatory period and on the exact costs that were recognised as efficient costs to

determine the allowed revenue for that regulatory period. Thus, to ensure the

effectiveness of DR programs for DSOs and TSOs, it is important to take under

consideration the length of the regulatory period and the location of the participant‐

installations; DSOs can highly benefit from DR programs that target customers at

specificlocations.ThevalueofcontrollingtransmissionnetworkthroughDRprograms

also depends on the level of existing transmission capacity and generation fuel cost

differentials.

1.4.2 Intermediaries: Retailers / Aggregators

Inmost cases, DR service providers are retailers or third party aggregators as those

companies can use the flexibility as a tool to manage their customers' energy

consumptionmore effectively and be financially rewarded by other actors (e.g. DSOs,

TSOs)forit.Atthesametime,theycan

optimisesourcingcostsastheywillbe

able to purchase energy more

effectively on the wholesale market.

Theycanalsosave transmissioncosts

bybeingable touse locallygenerated

4DynamicpricingschemesaredescribedinChapter2.3.

“Peakclippingasthemainachievementofhomeautomationlowerstheneedtoinvestinpeakcapacitywhichatthesametimelowerstheneedtoconstructnewpowerplants.”

24

energytomatchlocalsupplyanddemand.Theflexibilityprovidedbyhomeautomation

canbalancetheunpredictabilityofrenewableenergypowerplants,helpingtointegrate

them into the grid. Additionally, peak clipping as the main achievement of home

automation lowers theneed to invest inpeakcapacitywhichat thesame time lowers

theneed toconstructnewpowerplants.Although thesizeof thebenefitsdependson

many uncertain factors, it is reasonable to expect that, as long as the number of

consumers they represent is large enough, the commercial role of the

retailer/aggregatorwouldmakeforapositivebusinesscase.

1.4.3 Pillars of customer engagement

Researchintocustomerengagementhasrevealedasetofclearstepsthatarerequired

aspartoftheengagementprocess.Thesethreepillarsofengagementaresummarisedin

Figure7.

Inall engagementprocesses the first step is tomakeclearwhatneeds tobeachieved

(goalsandsegments).Itisamongthemostcommonerrorsandmisconceptionstoskip

thisstepormakesomeveryhighlevelgoalswhichareimpossibletomeetormeasure.

“Growincomplexity”,thesecondcriticalsuccessfactorofconsumerengagementisalso

the hardest. To keep people engaged the communication needs to grow with the

consumer’sincreasedunderstandingandassuchdeliverakindofdialogue.

Thethirdcriticalsuccessfactorisrelatedtotheabilitytosustaintheresultsandcollect

bothconsumptiondataand feedback fromend‐consumers toensuretheyaresatisfied

and have the ability to improve the product based on first‐hand experience. One

Figure7:Threepillarsofthecustomerengagementprocess

25

importantaspectinrelationtothispointistheoftenoverlookedfactthatparticipating

consumersshouldbegivenregularfeedbackontheprogrammetheyareparticipatingin

–evenifnoeventhasoccurred.Regularcommunicationiskeytoestablishingtrust.

1.5 Market potential for home automation technology in Norway

Thereisnodoubtthatthepast10yearshaveseenarapiddevelopmentaroundhome

automationtechnology.Six‐sevenyearsago,homeautomationwasthetalkofthetown–

and everybody in the utility industry

believed the systems would pop up,

plugandplayready,verysoon–butit

still hasn’t happened. Even the largest

energy event of the year; European

Utility Week had its own pavilion for

SmartHomeswheresmallstart‐ups–aswellasthelargestcorporations–bloomedwith

promising technologyand innovative ideas.By2012/13thismovementexperienceda

suddenstop–and in theaftermath, itbecameclear, that therearea lotofbarriers to

overcomebeforehomeautomationbecomesarealityfortheB2Cmassmarket.

Figure8:SmartHomeshaditsownpavilioninEuropeanUtilityWeek2010inVienna

Itshouldbementionedhowever,thatthebusinessisstillseenashavingabrightfuture.

As an illustration, market research company Statista expects revenues in the energy

management segment of home automation technologies to grow at an annual rate of

20.6%betweennowand2021; resulting inamarket volumeofUS$78m(US$161per

activehousehold) forNorwayalone.Thegraphbelowshowsprojectionsmadeby the

same company regarding the market penetration of home automation technology in

Norwayuntil2021.21%ofNorwegianhouseholds (about0.5millionhouseholds)are

“Six‐sevenyearsago,homeautomationwasthetalkofthetown–andeverybodyintheutilityindustrybelievedthesystemswouldpopup,plugandplayready,verysoon–butitstillhasn’thappened.”

26

expected to have energymanagement home automation technology by 2021 up from

5%today.

Figure9:MarketpenetrationofhomeautomationtechnologyinNorway2015–2021(Source:Statista

2016)

2015 2016 2017 2018 2019 2020 2021

Home Automation Technology Market Penetration Rate

27

2 Potential of home automation for EE and DR

This chapter investigates the potential of home automation to manage and reduce

electricityconsumptionintheNorwegiancontext.

2.1 Methodology

VaasaETTkeepsanup‐to‐datedatabaseconsistingof,atthetimeofwriting,closeto140

EE and DR programs around the world, including 569 samples, and involving over

930,000residentialcustomers.Thedatabasecompilesthefindingsofbothfeedbackand

dynamic pricing programs with and without appliance automation. The VaasaETT

database is the largest of its kind. It is able to provide statistically robust quantified

answers toquestionsrelatedto thepotentialofhomeautomationprogramstoreduce

consumptionlevelsand/ortomanageconsumptionintime.

Variousscenariosbasedonthedataextractedfromthedatabasehavebeeninvestigated.

The relevance of the results was ensured by focusing a) on pilots that took place in

conditions comparable and relevant to Norway i.e. high residential consumption and

long heating season and b) on pilots that focused on the most electricity consuming

appliancesfoundinNorwegianhomes.Thescenarioscomprisedthreebroadfeatures–

allofwhichareimportantaspectsofhomeautomationprojects:

Dynamictariffs5 Hometechnology Behaviouralchange

TOU Electricheating/Heatpump Consumptionfeedback

CPP/CPR Electricwaterboiler Engagementandeducation

RTP Whitegoods

To answer the research question, the following information was extracted from the

database:

• Changeinoverallconsumption(%kWh);

• Changeinconsumptionduringpeakhours(%kWh)6;

• Changeinconsumptionfollowingpeakhours(%kWh).

5DynamicpricingschemesaredescribedinChapter2.3.6Wereportaverageconsumptionreductionoverthewholepeakperiodwhenpeakpricesareinforce.The

durationofthepeaksdiffersdependingonthepricingscheme.Peakpriceperiodstypicallylastbetween1

and3hoursforCPP,CPRandRTPpilotsandbetween3and12hoursforTOUpilots.

28

As can be imagined, not all pilots researched all scenarios and all parameters. This

meansthatthesamplesizesonwhichourresultsarebasedvary.Tohelpthereader,the

numberofhouseholdparticipantsandthenumberofsamplesareindicatedundereach

correspondinggraph.

2.2 Overall pilot results

Thegraphbelowshowstheimpactofhomeautomationtechnologyonthreekeyaspects

of EE and DR: overall consumption reduction, peak consumption reduction and

consumptionimmediatelyfollowingpeakhours.

Nº of participants (Nº of samples) Automation No Automation


Peak reduction 34 305 (73) 577 046 (324)


Figure10:Impactofhomeautomationonelectricityconsumption(Source:VaasaETTDatabase2016)

Interestingly, this graph shows that home automation alone often leads to increased

levelsofelectricityconsumption.Pilotswithhomeautomationledtoaslightincreasein

overall consumption (‐0.07%) whilst

pilots without home automation

technology(manualresponsetodynamic

pricing and or consumption feedback)

‐0,07%

23,42%

1,77%2,48%

8,92%

‐1,65%‐5%

0%

5%

10%

15%

20%

25%


Reduction(%)

ImpactofHomeAutomation


“Ifautomationstandsalone,thereisariskofdisengagingpeopleandactuallyincreasetheiroverallconsumption.”

29

ledtosizeablereductionsinoverallenergyconsumption(2.48%)7.

Pilotswithautomationareneverthelessclearlymoreeffectiveatshiftingconsumption

awayfrompeakhours.Thereareseveralreasonsforit.Eventhoughconsumersshould

alwaysbeallowedtooverruntheprogram,automationenablesfastreactionsaswellas

controllable levels of reduction and has the advantage of being available during

unplanned system emergencies for

instance.Inaddition,criticalsituations

donot alwaysoccurwhenresidential

consumers are able to take action

(when they are away or asleep for

instance). Another important

consideration for grid operators is

that without automation they risk

seeingmillionsofappliancescomebackonlineatthesametimerightafterhigh‐price‐

hours end.Automation canhelpmitigate this riskby switching appliancesbackon in

cycles. This is also supported by the graph as consumption surrounding peak hours

slightly decreases in pilots with automation whilst it increases in pilots without

automation.

2.3 Effectiveness of dynamic tariff schemes

Dynamicpricing isoneof themostprovenwaysof enablingdemand flexibility.When

lookingatdifferentdynamicpricingschemesandtheaddedvalueofhomeautomation,

the results are straightforward. As shown by Figure 11 above, ignoring RTP, home

automationimprovestheresultsofpilotsby75‐172%.

7Chapter2.5shedslightonwhythismightbe

“Homeautomationismoreeffectiveatshiftingpeakconsumptionandmanagingthepaceatwhichappliancesarebroughtbackonline.”

30


TOU 20 124 (28) 509 508 (210)

CPP 13 201 (33) 59 272 (86)

CPR 699 (8) 3 217 (21)

RTP 281 (4) 5 049 (7)

Figure11:Consumptionreductionatpeaktimes(Source:VaasaETTdatabase2016)

ItisimportanttokeepinmindthatTOUandRTPpeakconsumptionreductionsarethe

lowestbuttheyoccurdaily,whilstCPPandCPRproducethehighestreductionsbutonly

forcriticalpeakperiods,typically12‐15timesayear.Itishoweverpossibletocombine

TOUwithCPPorCPR.

2.3.1 CPP – Most effective for peak shifting

CPP involves substantially increased retail electricity prices typically triggered by

heightenedconsumptionorwhenthestabilityofthesystemisthreatened.Thenumber

and the length of critical peak periodswhich the utility is allowed to call are agreed

upon in advance, when they are to occur is not. Residential customers are usually

notified a day in advance if the next day will be a critical day, but if automation

technology isprovided, theseratescanalsobeactivatedon thesameday.CPP isvery

effectiveatcuttingpeaks–anditisclearthatautomatedsolutionsworkbetter.

14%

34%

21%

11%

5%

17%

12%10%

0%

5%

10%

15%

20%

25%

30%

35%

40%

TOU CPP CPR RTP

PeakRedution(%)

PeakConsumptionReductioninDynamicPricingPilots

Automation No automation

+172%

+93%

+75%

+10%

31



Peak reduction 13 201 (33) 59 272 (86)


2.3.2 CPR – More rewarding for consumers but less effective than CPP

CPRareinverseformsofCPPtariffs.Participantsarepaidinaccordancetotheamounts

that they reduce consumptionbelow theirpredicted levelsduring criticalpeakhours.

ParticipantstoCPRpilotsusuallyreceiveapaymentaftereachcriticalpeakperiodora

deductionontheirnextbill.Thisdirectpaymentordiscountisbelievedtopresentthe

advantageofmakingtherewardofparticipants’effortsmoreconcretethantheconcept

ofsavingswhichmightbelesseasilyperceived.AsforCPP,thenumberandthelengthof

critical peak periods which the utility is allowed to call is agreed upon in advance

althoughwhentheyaretooccurisnot.ThegraphbelowshowsthatCPRislesseffective

than CPP at shifting consumption away frompeak periods bothwith automation and

withoutautomation.However,asconsumersarerewardedfordecreasingconsumption,

rather than punished for consuming at certain time, CPR might constitute a more

acceptable formofdynamicpricing, thusachieveinggreatermarketpenetrationanda

greaterglobalimpact.

1,88%

33,66%

‐1,68%

3,09%

17,47%

‐1,44%‐5%0%5%10%15%20%25%30%35%40%


Reuction(%)

ImpactofAutomationinCPPpilots

Automation NoAutomation

Figure12:ImpactofhomeautomationinCPPpilots(Source:VaasaETTDatabase2016)

32


Overall reduction 333 (4) 1 201 (8)

Peak reduction 699 (8) 3 217 (21)

Following peak hours 300 (4) 1 109 (8)

Figure13:ImpactofhomeautomationinCPRpilots(Source:VaasaETTDatabase2016)

2.3.3 TOU – Effective but lacks flexibility

TOU tariffs induce people into using electricity at timeswhen consumption is lower.

Pricesare thereforesethigherduring thehigherconsumptionperiodsof theday,and

lowerduringtherestofthedayandonweekends.Theycanhavetwo(peakandoff‐peak

prices)orthree(peak,partialpeakandoff‐peakprices) levelsofpricesperdaywhich

are always the same. This lack of

flexibility makes them rather unfit

going forward with an ever higher

penetration of intermittent

generation unless they are coupled

with CPP or CPR prices. As is seen from the results below, the impact on peak

consumptionisthelowestofthetariffschemesanalysed;however,unlikeCPPandCPR

theseeffectstakeplacedaily.

‐0,93%

21,23%

‐3,78%

1,85%

12,15%

‐1,03%

‐10%

‐5%

0%

5%

10%

15%

20%

25%


Reduction(%)

ImpactofAutomationinCPRpilots


“TOUcanbecoupledwithCPPorCPRtoachievedailypeakclippingwhilemaintainingtheabilitytodealwithunforeseenstressonthegrid”

33



Peak reduction 20 124 (28) 509 508 (210)


Figure14:ImpactofhomeautomationinTOUpilots(Source:VaasaETTDatabase2016)

2.3.4 RTP – a tariff scheme for the future?

Withtheintroductionofsmartmeters,moreadvancedtariffschemeshavebeentested.

One such tariff is RTP. Price development on thewholesalemarket are passed on to

consumers–normallybythehour.Inordertofurtherencouragereductionsduringhigh

price periods and reduce risk of high bills, participants are warned when wholesale

pricesreachacertainthresholddecideduponinadvance.Unfortunately,onlyfewRTP

pilotshavebeenconducted,hencethe

results below should be taken with

caution. They are nonetheless very

important as a vast majority (70%8)

ofhouseholdcustomers inNorwayarealreadyonspot‐tiedcontracts (abasic formof

RTP) and itwould thusmake sense to assume these householdswill adoptRTPonce

smartmetersaredeployed.

6StatisticsNorway(2016).

‐0,19%

13,72%

6,74%

2,41%

5,05%

‐1,75%‐4%‐2%0%2%4%6%8%10%12%14%16%

OVERALLREDUCTION


Reduction(%)

ImpactofAutomationinTOUpilots


“Astariffsbecomemoredynamic,customerinvolvementbecomesmorenecessarytosecuresuccess.”

34


Overall consumption reduction 25 (1) 9 270 (6)

Peak reduction 281 (4) 5 049 (7)

Figure15:ImpactofhomeautomationinRTPpilots(Source:VaasaETTDatabase2016)

2.4 Automating the usage of home appliances

ThetablebelowshowsthebreakdownofelectricityconsumptioninNorwegianhomes

by appliance. Based on this information, we investigated the potential of automated

electricheaters/heatpumps,electricwaterboilersandwhitegoods(e.g.freezer,fridge,

etc.)forDR(peakconsumptionreduction)andEE(overallconsumptionreduction).

0,50%

11,25%

1,80%

10,19%

0%

2%

4%

6%

8%

10%

12%

OVERALLREDUCTION PEAKREDUCTION

Reduction(%)

ImpactofAutomationinRTPpilots

Automation NoAutomation

35

Appliances Electricityconsumption

(kWh/year)

Electricheaters 2,387

Electricfloorheating 1,268

Individualcentralelectricheater(possiblyalsooil,wood) 3,304

Electricwaterheater 2,955

Lighting,numberofspots>20 1,289

Refrigerator 1,076

Fridge‐freezer 1,093

Freezer 1,509

Tumbledryer 890

Washingmachine 1,575

PC 1,626

Swimmingpooletc. 5,967

Variouselectricalequipment 3,028

Figure16:ApplianceConsumptioninNorway,kWhperyear(Source:DalenandLarsen2013)

2.4.1 Electric heating and heat pumps

Norway is one of the few countries where electricity is the main heating source for

households. According to Statistics Norway, electricity accounts for about 73% of

household heating – and air heat pumps alone about 21%. Norway has seen a huge

growthinthenumberofheatpumpswhichmightbeduetothefactthatconventional

electrical heating panels are the most used source for heating. In 2012 conventional

heatingpanelsweretheprimaryheatingsourceinalmosthalfofNorwegianhouseholds.

Atthesametime27%ofallhouseholdshadaheatpump(+9%since2009).Especially

detachedhousesandfarmhousesinvestinheatpumps.

As heat pumps are driven by electricity, there is rarely a decrease in electricity

consumptionifahouseholdchangesheatingsourcetoaheatpumpfromoilorgas,but

householdswithaheatpumpuseproportionallylessenergythansimilarhomeswithout

aheatpump.AccordingtoStatisticsNorwayhouseholdslivingin150m2consume3,900

kWhlesselectricitywhenheatedbyaheatpumpthanwithotherheatingsources.For

36

villasbetween100and150m2thedifferenceisaround1,900kWh.Withelectricityas

theprimaryheatingsource,peakconsumptioniscloselyrelatedtotheweather,itthus

makessensetoconsiderelectricalheatingandheatpumpsasasourceofflexibility.

The figure below shows the impact of automated heating on electricity consumption.

Pilot results indicate that overall electricity consumptionwas reduced by 1.9%, peak

consumptionby23%andsurroundingpeakconsumptionincreasedbyabout0.8%.

Nº of participants (Nº of samples) Automated heating

Consumption reduction 13 481 (14)

Peak reduction 35 193 (30)

Following peak reduction 11 712 (8)

Figure17:Impactofautomatedelectricheating.(Source:VaasaETTDatabase2016)

2.4.2 Electric water boilers

Electric water boilers are among the most electricity consuming appliances in

Norwegianhomes.Theyarealsointuitivelyverysuitableforflexibilityaswatercanbe

heatedduringoff‐peakhoursandremainwarmastheboilerisswitchedoffduringpeak

hoursthusmakingthelossofcomfortalmostimperceptibletoconsumers.Thisisnota

newtechnologybutonethathasseldombeenputtouse.AnexceptionisFrancewhere

the load of 10 million water heaters (representing 3 GW) is currently automatically

shiftedaway frompeakhours tonight time (off‐peakhours).The figurebelowshows

the impact of automatedwater boilers on electricity consumption. Pilot results show

that overall electricity consumptionwas reducedby3.2%,peak consumptionby24%

andsurroundingpeakconsumptionincreasedonlyabout0.6%.

1,92%

23,24%

‐0,82%

‐5%

0%

5%

10%

15%

20%

25%

OVERALLREDUCTION


Reduction(%)

ImpactofAutomatedHeating

37

Nº of participants (Nº of samples) Automated water boilers




Figure18:Impactofautomatedwaterboilers(Source:VaasaETTDatabase2016)

2.4.3 White goods

As shown in Figure 16, a number of smaller appliances that can be grouped in the

categoryofwhitegoods(i.e.refrigerator,freezer,tumbledryer,washingmachine,dish

washer) represent a significant proportion of electricity consumption in Norwegian

homes‐especiallyinruralareaswherepeopledon’thaveaccesstosharedfacilities.The

figure below shows the impact of automatedwhite goods on electricity consumption;

overallelectricityconsumptionincreasedby1.7%,peakconsumptiondecreasedby26%

andsurroundingpeakhourconsumptionincreasedbyabout0.6%.

3,18%

24,43%

‐0,61%

‐5%

0%

5%

10%

15%

20%

25%

30%

OVERALLREDUCTION


Reduction(%)

ImpactofAutomatedWaterBoilers

38

Nº of participants (Nº of samples) Automated white goods




Figure19:Impactofautomatedwhitegoods(Source:VaasaETTDatabase2016)

Theresultsexhibitoneofthefundamental

differences between for instance an

automated electric heater and an

automated washing machine. While the

former’s consumption canbeboth shifted

(turn on or off) and optimised (turn up or down), the latter’s can only be shifted

(laundry needs done at some other time)whichmay bewhy automatedwhite goods

seemwellsuitedfordemandflexibilitybutnotforEE.

2.5 Education and feedback in home automation pilots

Figure10pointstoacrucialfinding:homeautomationalonetendstohaveanadverse

effectonhouseholds’overallenergyconsumption.Pilotswithhomeautomationsawa

slight increase in overall energy consumption (+0.07%) whilst pilots without home

automation(manualresponsetodynamicpricingand/orfeedbackprogrammes)ledto

significantreductions(‐2.48%).Althoughitmightappearcounter‐intuitive,thisfinding

isinfactconsistentwithbehaviouralscience.Indeed,whilesomewouldarguethatthere

is no point trying to engage and educate customerswho have automated appliances,

pilot results show that when efficiency improvements come solely from the

technological side, people remain passive actors, leading to low levels of awareness,

‐1,75%

25,85%

‐0,61%‐5%

0%

5%

10%

15%

20%

25%

30%

OVERALLREDUCTION


Reduction(%)

ImpactofAutomatedWhiteGoods

“Automatingwhitegoodsiswellsuitedforpeakclipping,notforenergyefficiency.”

39

continued inefficient habits and behaviours andwell documented rebound effects. In

other words, home automation is well suited to shift consumption away from peak

hours but not to create reductions in overall consumption. Similar findings were

reportedfordynamicpricingpilots.(c.f.Figure4.)

Thegraphbelowquantifiestheeffectofeducationandfeedback(inotherwordsmake

use of the data generated by the home automation system to reduce overall energy

consumption) on pilot results. Pilots

combining home automation and

education/feedback are more effective at

reducing both peak (23% vs 22%) and

overallconsumption(2.7%vs.0.41%).Feedbackandeducationarethereforecrucialto

reduce the quantity of energy consumed in the home and reaping the full benefit of

homeautomationtechnology.

Nº of participants (Nº of samples) Feedback No feedback

Overall reduction 25 495 (30) 228 (3)

Peak reduction 22 906 (49) 7 278 (17)


Figure20:Impactoffeedbackonhomeautomationpilots(Source:VaasaETTDatabase2016)

Apositivebusinesscaseandanappealingpaybacktimeareotherfundamentalreasons

whyeducationandfeedbackshouldbepartofanyhomeautomationpackage.VaasaETT

and Joule Assets, in an upcoming public report, looked at the business case for

residential demand side flexibility in 4 EU countries (France, UK, Italy and Germany)

andfoundthatbetween77%and87%ofend‐consumers’financialbenefitscomefrom

overall consumption reductions (the rest from peak clipping). This can be easily

2,68%

23,42%

‐1,48%

0,41%

21,73%

‐3,77%‐10%

‐5%

0%

5%

10%

15%

20%

25%


Reduction(%)

ImpactofFeedbackinAutomationPilots

Feedback No feedback

“Educationandfeedbackhelpreapthefullbenefitsofhomeautomationtechnology.”

40

understood if one considers the fact that critical peaks take place for only about 30

hoursayearwhilstbenefitsfromlowering

overall electricity consumption take place

daily. Offerings with both DR and

education/feedback are the best way to

secure financial returns for both grid

operatorsandconsumers.

MostNorwegianretailersalreadyofferconsumersaccesstogranularconsumptiondata

intheformsofenhancedbills,IHDs,mobileappsanddedicatedwebportalsasawayto

differentiate theirofferings,movecompetitionaway frompricesandestablisha trust‐

basedrelationshipwithcustomers9.

9SmartmetersareseenasnecessarytoprovidefeedbackandenableDR.Massrolloutiscurrentlyongoing

inNorwayandwillbecompletedbyJanuary1st2019.

“Couplinghomeautomationwithfeedbackisthebestwaytosecuresizeablefinancialbenefitsforthegridandconsumersboth.”

41

3 Impact of home automation on electricity

consumption in Norway

This chapter investigates the effects of home automation technology combined with

dynamic pricing and feedback onNorwegian household’s electricity consumption and

theiraggregatedimpactatnationallevel.Impactsweremodelledfordifferenthousehold

types,timeperiodsandunderdifferentscenariosformarketadoption.

3.1 Characteristics of household electricity consumption in Norway

According to Statistics Norway, the residential segment of themarket consists of 2.3

million households, consuming an average of 16,044 kWh per year (the highest in

Europe).Figure21showstheelectricityconsumptionbyhousetype.Detachedhouses

arethemostenergy‐intensivehousetype,withanaverageenergyconsumptionthatis

morethantwicethatforapartmentbuildings.Thisisduetoalargerdwellingarea,more

exteriorwallsand,often,morehouseholdmembersthaninanapartment.

Typeofhousing Annualconsumption2012

Detachedhouse 20211kWh

Rowhouse 14975kWh

Flat8953kWh

Averagehousehold 16044kWh

Figure21:Electricityconsumptionbytypeofhousing,2012(Source:StatisticsNorway)

Total net electricity consumption amounted to 120 TWh in 2015, a 2.6% increase

comparedto2014withhouseholdandagricultureroughlyaccounting forathirdof it.

ElectricityistheprimaryenergysourceforNorwegianhouseholdsasshownbyFigure

22.Italsoshowsthatwhilsthouseholdenergyconsumptionhasdecreasedsince2006

(mostly due to lower usage of oil and kerosene) electricity consumption has slightly

increased.

42

Figure23:SeasonalVariationsinelectricityconsumption,weekdaysand

weekends(Source:StatisticsNorway2008)

The high penetration of electric heating (c.f. Chapter 2.4.1)makes national electricity

consumption very temperature dependant and high peak consumption can occur on

abnormallycoldwinterdays.Thegraphsbelowshowthehourlyloadcurve(weekdays

upper graph andweekends lower graph)month bymonth. Logically, consumption is

highestduringwintermonths.

Figure22:Household’sEnergyUseinNorway(Source:StatisticsNorway2014)

43

Figure 24 shows the distribution of the annual electricity consumption of end‐use

appliances.As canbeexpected,heating is consistently the largest contributor todaily

consumption;45%ofelectricityconsumptionisduetospaceheating.Therestisdivided

intohotwater12%,lighting5%,otherappliances19%andresidual19%10,

Figure24:Estimatedyearlyaveragedemandduringworkdayssegmentedintomainend‐usegroups

(Source:ECEEESUMMERSTUDYproceedings2013)

3.2 Methodology and assumptions

The impact of home automation combined with dynamic pricing and feedback on

Norway’s households and national consumption were based on the following

assumptions:

Consumer feedbackandeducationarecrucial toreap the fullbenefitsofhome

automation.GiventhatsmartmeterswillbedeployedinNorwaywithin2years,

we assumed feedback and education to be part of home automation offerings

andthustookintoaccounttheirimpactsinouranalysis;

VaasaETT’sdatabaseoffeedbackpilots(132samples)showsanaverageoverall

electricity consumption reduction of 7%. This number is used tomeasure the

impactoffeedbackonoverallelectricityconsumption;

Market penetration rates of 17%, 48% and 78% were assumed for home

automation technology corresponding to the years 2020, 2030 and 2040

respectively. These figures were based on projections for the year 2020 (c.f.

10Non‐identifiedloadthatcouldnotbelinkedtoanyappliancebytheresearchers.

44

Figure9).Alinearregressionwasperformedtoextrapolatethesepredictionsto

lateryears;

Currentlyabout70%ofNorwegianhouseholdsareonspot‐pricetiedcontracts

(abasicformofRTPtariffs).Itthereforeseemssafetoassume70%ofhousehold

consumers will have RTP once smart meters are deployed. Thus, in our

modelling, the impactofautomatedRTPwereassumedfor70%ofhouseholds

with home automation. The combined impacts of TOU (daily) and CPP/CPR

(critical peak days only) were in turn assumed for the remaining 30% of

householdswithhomeautomation;

Peakconsumptionisdefinedashighconsumptionhoursatnationallevelduring

whichpeakpriceswouldbeinforce.Reductioninpeakhourconsumptionrefers

tothereductioninconsumptionoverthedurationofthepeak;

To remain conservative in our estimates, we assumed feedback does not

influence energy savings during critical peak days for RTP and CPP/CPR

schemes;

Impacts in % were applied to 2015 national consumption levels and 2012

consumptionlevelsforthedifferenthousetypes.

The table below summarises the impact assumptions for each combination of tariff

scheme,homeautomationandconsumptionfeedbackbasedonfindingsfromChapter2:

Peakconsumptionreduction

Overallconsumptionreduction

Occurrence

RTP 11.25% 0.50% Top100hours/year

TOU 13.72% ‐0.19% Daily

CPP/CPR 27.93% 0.20% Top30hours/year

TOU+CPP/CPR 13.72%/

27.93%

‐0.19%/

0.20%

Daily/Top30hours/year

Feedback 7% 7% EachdayforTOUandexceptcritical

peakdaysforRTP,CPP/CPR

Figure25:Impactassumptionsoftariffscheme,homeautomationandconsumptionfeedback.

45

3.3 Impacts on annual electricity consumption

Thissubsectionpresentstheresultsofthemodellingonhouseholdandnationalannual

electricityconsumption.

3.3.1 Householdannualconsumption

Norwegianhouseholdscoulddecreaseelectricityconsumptionbyanamountequivalent

to about 7% of their annual usage thanks to home automation, dynamic pricing,

consumptionfeedbackandconsumereducation.Thisamountsto1,065–1,104kWhper

year for an average household depending on the dynamic tariff scheme. As can be

anticipated, detached houses can save themost in absolute terms (kWh/year) due to

higherconsumptionlevels.

Figure26:ImpactoffeedbackandautomatedDRonannualelectricityconsumption

Asimilarimpactonannualconsumptioncanbeobservedacrosspricingschemes.This

canbeexplainedbythefactthatoverallconsumptionismostlyinfluencedbyfeedback

and consumer education rather than by dynamic pricing (which targets peak

consumption by passing on high wholesale prices or network constraints on to

consumers).

When investigating the effects of home automation, dynamic pricing, consumption

feedback and consumer education on annual peak consumption, the effectiveness of

TOU stands out. An averageNorwegian householdwith automatedTOU could reduce

consumptionby an amount equivalent to about 14%of its annual peak consumption.

46

ThisisduetothefactthatTOUimpactsconsumptiondailywhereasCPPandCPRimpact

consumptiononcriticalpeakdaysonly(12–15timesayear).Itisimportanttokeepin

mindthatTOU,duetoitsrigidstructure,lackstheflexibilitytodealwithextremeprices

on thewholesalemarket or unexpectednetwork constraints. (c.f. Chapter 2.3.3.) TOU

and CPP (or CPR) can however be combined to retain the possibility to deal with

unexpectedevents(CPP/CPRpricesaretriggeredoncriticaldays)whilsthavingadaily

impactonpeakusage(asTOUareinforceeveryotherday).Infact,ourresultsindicate

thatapricingschemecombiningTOUandCPP/CPRworksbestatreducingannualpeak

consumption(512kWhperyearforanaveragehousehold).

Figure27:ImpactoffeedbackandautomatedDRonannualpeakelectricityconsumption

3.3.2 National annual consumption

Basedon2015 consumption levels andassuming70%ofNorwegianhouseholdswith

home automation are on RTP and the remaining 30% on a combination of TOU and

CPP/CPR, our modelling shows that Norway’s annual electricity consumption could

decrease by 414GWh (1.17%) and annual peak consumptionby 85GWh (2.31%)by

2020 when 17% of households have adopted a combination of home automation

technology, dynamic tariffs and feedback. As market adoption of home automation

technology increases, these figures could reach 1,142 GWh (3.22%) and 234 GWh

(6.37%)by2030.

47

Figure28:ImpactonNorway’soverallandpeakelectricityconsumption

3.4 Impacts on the highest electricity consumption month

This subsection presents the results of the modelling on household and national

electricityconsumptionduringthehighestconsumption(alsocoldest)monthoftheyear

2015;January.Basedonnationalconsumptionlevels,thismonthalonewouldhaveseen

4 critical peak days (triggering CPP/CPR prices) and 33 out of the 100 peak hours

assumedforRTP.

3.4.1 Household highest consumption month

An average Norwegian households could decrease electricity consumption by an

amountequivalentto6.57–7.75%ofitsusageonthehighestconsumptionmonthofthe

year thanks to home automation, dynamic pricing and consumption feedback. This

amountsto134‐158kWhdependingonthedynamictariffscheme.Findingsarevery

similar to the findings detailed in the previous section.Detachedhouses can save the

most in absolute terms (kWh/year) due to higher consumption levels. Impacts are

similaracrosspricingschemes.

85 234384 491414

1142

1875

2396

0

500

1000

1500

2000

2500

3000




PENETRATIONRATE100%

GWh

AnnualOverallandPeakConsumptionReduction


48

Again,theeffectivenessofTOUstandsout.NorwegianhouseholdswithautomatedTOU

could reduce consumption by an amount equivalent to about 13.7% of peak

consumption during that month. TOU and CPP/CPR combined provides the most

flexibility(10–67kWhor14%foranaveragecustomer).

Figure29:ImpactoffeedbackandautomatedDRonhighestconsumptionmonthelectricity

consumption

Figure30:ImpactoffeedbackandautomatedDRonhighestconsumptionmonthpeakelectricity

consumption

49

An interesting finding relates to CPP and RTP. Whilst CPP led to peak consumption

reductionofonly0.59%onanannualbasis,itledto2.03%peakconsumptionreduction

duringthehighestconsumptionmonthoftheyear.Theeffectisevenmorepronounced

forRTP.Whilstitledtopeakconsumptionreductionof2.27%onanannualbasis,itled

to 8.39% peak consumption reduction during the highest consumptionmonth of the

year.Thisagainpointstowardsthefactthatmoreflexibledynamicpricingschemesare

moreeffectiveatdealingwithhouseholdpeakconsumption.Chapter3.5willmakethis

evenmoreexplicit.

3.4.2 National highest consumption month

OurmodellingshowsthatNorway’selectricityconsumptioncoulddecreaseby50GWh

(1.05%)andpeakconsumptionby20GWh(4.09%)by2020when17%ofhouseholds

have adopted a combination of home automation technology, dynamic tariffs and

feedback during the country’s highest consumption month of the year. As market

adoptionofhomeautomationtechnologyincreases,thesefigurescouldreach137GWh

(2.90%)and54GWh(11.27%)by2030.Savingsfromthehighestconsumptionmonth

alone represent 12% of the annual overall saving potential and 23% of annual peak

clipping potential. It would thus be worth considering a special focus and special

incentivesduringthistimeofyear,especiallyasNorwegiansarealreadyawarethrough

their spot‐price tied contracts that prices and consumption are higher duringwinter

months.

Figure31:ImpactonNorway’shighestconsumptionmonthof2015

20 5489

113

50

137

225

287

0

50

100

150

200

250

300

350




PENETRATIONRATE100%

GWh

OverallandPeakConsumptionReduction‐ Year'shighestconsumptionmonth


50

3.5 Impacts on the highest consumption day

This subsection presents the results of the modelling on household and national

electricityconsumptionduringthehighestconsumptiondayoftheyear2015‐January

2nd2015.Onthisday,CPPpeakpricesandCPRrewardswouldhavebeeninforceand

RTPcustomerswouldhavebeeninformedofhighpricesonthewholesalemarket.

3.5.1 Household highest consumption day

CPP involves substantially increased electricity prices during times of heightened

consumptionorwhenthestabilityofthesystemisthreatened.CPRareinverseformsof

CPP tariffs in which consumers are paid for the electricity they did not use during

critical peak times.By contract, such events can typically occur10‐15 timesper year.

Although,theimpactonannualconsumptionarelimited,CPPandCPRareveryeffective

atloweringcriticalpeakconsumption.Thisisillustratedbythegraphbelow;anaverage

NorwegianhouseholdonCPP/CPRpricingcouldlowerpeakconsumptionby2.37kWh

(28%)onthehighestconsumptiondayoftheyear.

3.5.2 National highest consumption day

Ourmodelling shows that home automation technology, dynamic tariffs and feedback

could lower Norway’s electricity peak consumption by 0.46 GWh (10.65%) by 2020

Figure32:ImpactoffeedbackandautomatedDRonhighestconsumptiondaypeakelectricity

consumption

51

when17%ofhouseholdsareequippedduringthecountry’shighestconsumptiondayof

theyear.By2030,withamarketpenetrationof48%,peakconsumptioncoulddecrease

by1.27GWh(29.36%)onthatday.

Figure33:Impactonhighestconsumptiondayof2015

3.5.3 Impact of automated electric heating

The graph below stresses the importance of automated electric heating formanaging

Norwegianpeakconsumption.OnNorway’shighest consumptionday, electricheating

wouldaccountforover43%ofthecountry’spotentialforresidentialpeakconsumption

reduction.

Figure34:Impactofautomatedheating‐highestconsumptionday2015

0,46

1,27

2,09

2,66

0,0

0,5

1,0

1,5

2,0

2,5

3,0




PENETRATIONRATE100%

GWh

PeakConsumptionReduction‐ Year'shighestconsumptionday

Peak Savings

0,20

0,55

0,90

1,15

0,0

0,2

0,4

0,6

0,8

1,0

1,2

1,4

PENETRATION2020 PENETRATION2030 PENETRATION2040 PENETRATION100%

GWh

PeakConsumptionReductionduetoAutomatedHeating

‐ Year'shighestconsumptionday

Peak reduction

52

4 State‐of‐the‐art home automation projects

4.1 New Equipment

SometechnologiessuchasautomatedheatingandACarewellproven.Thepotentialof

these technologies is thoroughly investigated in Chapter 2. The industry has recently

seenan influxofwhatwecall the2ndgenerationofhomeautomationtechnologies.As

the1stgenerationwasfocusedononeortwoappliancesinthehome,the2ndgeneration

addresses many more appliances, which are more complex and have much deeper

impact on everyday life. Thus, in order to understand the full potential of home

automation going forward, we also need to understand the potential of technologies

whicharenotsowellprovenyet,butwhichareundergoingarapiddevelopment.

4.1.1 Geo‐fencing – eliminating all stand‐by

Geo‐fencing is a virtual barrier. It

uses different existing technologies

like GPS in a mobile phone or an

RFID tag in a small piece of

hardware to locate a person – or

morepreciselyadevice–according

topositioning it inrelation toageographical location.Programs that incorporategeo‐

fencing allow the user or an administrator to

set up predefined rules, so when a device

enters (or exits) the defined boundaries, an

action is triggered. Geo‐fencing has many

practical uses, with home automation it is

likelytoenabletheeliminationofallstand‐by

useandensurethatallunintendedenergyuse

iscutwhennobodyisathome.

Approximately 6,000 kWh or ¼ of electrical

consumptioninNorwegianhouseholdscanbe

attributed to lighting, PC’s and miscellaneous

other small electrical appliances (c.f. Figure

Figure35:TheVERAgeo‐fencinghome

automationapp

“Geo‐fencinghasmanypracticaluses,inrelationtohomeautomationitislikelytoenabletheeliminationofallstand‐byuseandensurethatallnotintendedenergyuseiscutwhennobodyisathome.”

53

19),which geo‐fencingwouldbe able to address. In this respect the technology could

provetohavethelargestimpactofallthetechnologies,sinceitcomprisesalotof“small”

usesintoonecomprehensibleload.

4.1.2 EVs, roof top solar and storage – the future is decentralised

With Tesla’s acquisition of SolarCity it became clear that solar, storage and Electric

Vehicles (EVs) are a very powerful combination. This is even more true in Norway

where hybrid and EVs have become very popular. Elon Musk’s “master plan”, which

aims to provide customers with full‐stack solutions for owning their own energy

production, storage and consumption has also recently been hyped with the

introductionofasolarrooftilewhich,forthefirsttime,bringsaestheticsanddesigninto

solarpower.

With regards to EVs; the latest sales figures for July 2016 show a 17% year‐on‐year

growthrate.EVsmadeup28%ofnewcarregistrationsinNorwayfortheyearthrough

July.(EVObsession.com)

4.1.3 Customer‐Led Network Revolution – multiple technologies

Figure36:TeslaSolarRoofTopdesigns

54

TheCustomer‐LedNetworkRevolutionprojectwasasmartgridprojectledbyNorthern

Powergrid in partnership with British Gas, Durham University, Newcastle University

and EA Technology. The project involvedmonitoring the electricity consumption and

generation profiles of around 13,000 domestic and SME customers, both with and

without certain technologies (heat pumps, solar Photovoltaics (PVs),micro‐combined

heatandpower,EVs).Thisisthelargestsampleofelectricitycustomers’usagetohave

beenundertakenintheUKandIrelandtodate.

The project introduced and tested smart

meters and different forms of dynamic

tariffs. TOU tariffs proved popular; the

majority of the customers taking part in

the trial saved money and used

approximately10%lesselectricityinpeak

periods than customers on a regular tariff. Household chores such as laundry and

dishwashing were the most commonly used to flex the times of electricity usage.

CustomerswithPVweresuccessfulatadjustingtheirelectricityusagetotakeadvantage

of their own generation and were arguably the most engaged customers of all. The

projectalsoconcluded,thatEVsorheatpumpscaneffectivelydoublethedomesticload,

sothereisastrongcaseforencouragingoff‐

peak EV charging behaviour at an early

stage. Last but not least the project also

concluded,thatiftheofferedsolutionistoo

simpleandlacksflexibilityfortheend‐user

itcanmissthetarget.

4.2 Nikola – EVs for Flexibility and Green Energy

ConsideringthegrowingnumberofEVsinNorway

Danish project Nikola launched in 2013 may

provideusefulfindings.Vehicle‐to‐Gridtechnology

– inwhichEVs communicatewith thegrid to sell

DRservicesbyeitherreturningelectricitytothegridorbythrottlingtheirchargingrate

‐wastestedtoinvestigateEVs’potentialinsupportingacost‐efficientandsecurepower

system with a high degree of renewable energy. EVs offer a very good flexibility

“EVsorheatpumpscaneffectivelydoublethedomesticload,sothereisastrongcaseforencouragingoff‐peakEVchargingbehaviouratanearlystage.”

“Ifthesolutionlacksflexibilityfortheend‐useritcanmissthetarget.”

55

potential as they are basically batteries offering high‐power, fast‐response and bi‐

directional capabilities. These properties can be used for flexibilitywhile at the same

timeloweringthecostsofowninganEV.Thepilotshowsthatatypicalnightlyplugin

lastsforaround13hoursofwhichonly4hoursareneededforcharging.Thisindicatesa

highdegreeofchargingflexibility.However,withouthavingtheownerconnectingitto

the power system for sufficiently long durations of times which are predictable and

recurringitishardtomakesubstantialuseofthecapacity.

The project concluded that EVs will only

realise their full value as an instrument for

flexibilityifownersareinvolvedandwilling

toaccepttheautomatedre‐andde‐charging.

Inotherwords, the communication and the

userinterfacesplayanimportantroleforthe

successofsuchservices.

WhatisalsointerestingisthatNikolaledto

some of the highest financial savings for

participants we have seen in a flexibility

pilot indicating thatDR servicesbasedona

highconsumption/highflexibilityasset(e.g.

EV)canbeveryrewardingforhouseholds.

• Theownerofthevehicleneedstobeabletooverrideanyautomatedsettings

• Theserviceshouldsuggestbestchargingtimesbasedonananalysisofuserbehaviour–butanysettingshouldalwaysbeacceptedbytheuser.

• Engagementcomesonlywitheducation.Allserviceofferingsneedthoroughexplanationsofprosandcons

THENIKOLAPROJECTCONCLUDEDTHAT:

“EVswillonlyrealisetheirfullvalueasanassetforflexibilityifownersareinvolvedandwillingtoaccepttheautomatedre‐andde‐charging.”

“DRservicesusinghighload/highconsumptionassets(e.g.EV)canbeveryrewardingfinanciallyforhouseholdconsumers.”

56

Figure37:Eneco’sCaronToonintegratedsolution

4.3 Elsa – Energy Local Storage Advanced system

ELSA(EnergyLocalStorageAdvancedsystem)isanenergystorageprojectaddressing

local/small scale storage. From 2015 to 2019, ELSA will develop distributed storage

solutionstomaturitybycombining2ndlifebatterieswithaninnovativelocalICT‐based

energymanagementsystem.

ELSAstoragesystemswillbeappliedinsixdemonstrationsites.Inthisrespectthepilot

doesn’thaveanyresultyet,butitismentionedhere,asitrepresentsacleartrendinthe

mostrecentprojectstofocusonstoragetechnologyaspartofhomeautomation.Hence,

theELSAprojectfocusesondevelopingtechnologythatisalreadyclosetomaturityand

will be applied with different application contexts, covering services such as: grid

congestionrelief,localgridbalancing,peakshaving,voltagesupportandregulation.

4.4 'Car on Toon' – Integrating Home and Car

Over a million smart thermostats have already been installed around Europe. These

thermostatsprovideanaturalhubfortheintegrationofhomeandEVs.Enecohasbeen

one of the most successful utilities in Europe in terms of the uptake of smart

thermostatsbyitscustomers.

57

Its service, Toon not only provides excellent consumption feedback own‐solar

generation and smoke alarm information to customers, but also controls smart plugs

and lamps and learns customers' preferences and habits to intelligently control the

heating of the home. Now it is evolving to incorporate EV, providing insight into

consumptionandcostandprovidingthefirststeptowardsahomeenergymanagement

system that extendsbeyond thehome. Integrationof carandhome into the feedback,

controlandautomationeco‐systemisimminent.

4.5 WEREL – Bringing it Together

Ultimatelythemoreelementsofthefuturearebroughttogether,thegreaterthebenefits

for the consumer. Home energy management, smart home, solar, storage, EV and

communitieswill ultimately all work together through the Internet of Things,mostly

discretely in the background through automation and ‐ increasingly ‐ artificial

intelligence. One of the first steps towards this integration can be seen in the newly

releasedWERELofferinginSweden.WERELprovideshomeswithafinancedsolarand

storage solution that

intelligently works

together with other

homes to buy energy

from and sell energy to

those other homes,

through the wholesale

market. The result is a

communityofdistributed

energy homes that can either use their energy for independence or sharing. The

supplier,WERELalsoactsasatraditionalretailer,providingthehomeswithenergythat

theycannoteitherproducethemselvesorbuyfromtheothersinthecommunity.Thisis

justthefirststepinabiggervisionofWEREL,butovertime,asthecommunitygrows,

solutionefficiencyevolves,thehomesbecomeevermoreautomated,andEVsandother

communityresourcesbecomeintegrated,theautomationofthecomponentswithinthis

excitingarchitecture could leada situationwhere communitiesno longerneed tobuy

theirenergyfromretailersatall.

58

5 References

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CharlesRiverAssociates(2005).ImpactEvaluationoftheCaliforniaStatewidePricingPilot‐FinalReport.

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Morch,A.,Saele,H.,Feilberg,N.,Lindberg,K.S.,(2013).Methodfordevelopmentandsegmentationofloadprofilesfordifferentfinalcustomersandappliances.

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Norwegian Water Resources and Energy Directorate

Middelthunsgate 29Postboks 5091 Majorstuen0301 Oslo

Telephone: 09575 Internet: www.nve.no

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