1

Howtojustifythe

implementationofsmartgrids

foranewneighborhood?

2

3

TABLEOFCONTENTS

LISTOFFIGURES..............................................................................................................................4

LISTOFTABLES................................................................................................................................5

LISTOFABBREVIATIONS..................................................................................................................6

ABSTRACT.......................................................................................................................................7

SAMMANFATTNING........................................................................................................................7

KEYWORDS.....................................................................................................................................7

1. INTRODUCTION...................................................................................................................8

2. CONTEXT.............................................................................................................................9

3. ACKNOWLEDGEMENTS.....................................................................................................10

4. RESEARCHMETHODOLOGY...............................................................................................10

4.1. RESEARCHSTRATEGY..................................................................................................104.2. SCOPEOFTHERESEARCH.............................................................................................104.3. RESEARCHPROCESS...................................................................................................114.4. RESEARCHQUESTIONS................................................................................................124.5. TIMELINEFORTHETHESIS............................................................................................124.6. CONTENTUSED.........................................................................................................13

5. SMARTGRIDSATTHENEIGHBORHOODSCALE:DEFINITION,GOALS,ACTORSANDRELATEDISSUES 15

5.1. SMARTGRIDSDEFINITION...........................................................................................155.2. MAINGOALSOFSMARTGRIDSCONSIDERED....................................................................165.3. STAKEHOLDERSINVOLVED...........................................................................................185.4. BARRIERSTOTHEIMPLEMENTATIONOFSMARTGRIDS.......................................................235.4.1. RT2012-FRENCHTHERMALREGULATIONFORNEWBUILDINGS..........................................235.4.2. REGULATEDPRICEOFELECTRICITY................................................................................245.4.3. SELF-CONSUMPTIONOFENERGYPRODUCEDBYPV-PANELS...............................................255.5. LESSONSLEARNEDFROMFIRSTBUILTECO-NEIGHBORHOODS...............................................26

6. REVIEWOFTHEMAININVESTMENTCOSTS(ENERGYWISE)FORANEWNEIGHBORHOOD29

6.1. ESTIMATIONOFINFRASTRUCTUREANDMAINTENANCECOSTSOFTHEMAINENERGYEQUIPMENT296.2. BILLSANDSUBSCRIPTION............................................................................................256.2.1. THECOSTOFELECTRICITY...........................................................................................256.2.2. THECOSTOFDISTRICTHEATINGANDDISTRICTCOOLING....................................................286.2.3. PVPANELS:FEED-INTARIFF........................................................................................286.3. SMARTGRIDSICTINFRASTRUCTUREANDSOFTWAREDEVELOPMENT....................................29

7. TOOLSBROUGHTBYSMARTGRIDSANDRELATEDIMPROVEMENTS.................................33

7.1. OVERVIEWOFTHENEWTOOLS.....................................................................................337.1.1. DATACOLLECTION....................................................................................................337.1.2. MONITORING..........................................................................................................35

4

7.1.3. ANALYSISOFFAILURECAUSES......................................................................................367.1.4. CONTROLOFENERGYSTREAMS....................................................................................377.1.5. THEVALUEOFDATA-FROMDATATOKNOWLEDGE.........................................................387.2. CONSEQUENCESANDIMPROVEMENTSOFTHEIMPLEMENTATIONOFASMARTGRIDFORANEIGHBORHOOD....................................................................................................................397.2.1. ECONOMICIMPROVEMENTS........................................................................................397.2.2. ENVIRONMENTALIMPROVEMENT:................................................................................407.2.3. SOCIALIMPROVEMENTS.............................................................................................41

8. ENERGYPERFORMANCECONTRACT..................................................................................42

8.1. CONTEXT.................................................................................................................428.2. DEFINITION..............................................................................................................428.3. ANEWACTOR:THEPERFORMANCESUPERVISOR..............................................................438.4. BUSINESSMODELCANVASOFTHEPERFORMANCESUPERVISOR...........................................43

9. STUDYCASES....................................................................................................................45

9.1. SIMULATIONTOOLS...................................................................................................459.2. “BORDEAUXAMÉDÉE”THERMALINFRASTRUCTURE..........................................................469.2.1. CONTEXTANDISSUES................................................................................................469.2.2. OBJECTIVESOFTHESIMULATION..................................................................................489.2.3. DETAILEDASSUMPTIONSMADEFORTHETWOSIMULATIONS..............................................499.2.4. RESULTSOFTHESIMULATIONS.....................................................................................519.3. “LARÉUNION”,ANISLANDTERRITORY..........................................................................559.3.1. CONTEXT:SPECIFICITIESOFANISLAND..........................................................................559.3.2. OBJECTIVEOFTHESIMULATIONS..................................................................................579.3.3. DETAILEDASSUMPTIONSMADEFORTHETHREESCENARIOS................................................619.3.4. RESULTSOFTHESIMULATIONS.....................................................................................64

10. DISCUSSIONS....................................................................................................................65

11. CONCLUSION....................................................................................................................66

12. ANNEXES...........................................................................................................................67

12.1. STUDYCASE1:BORDEAUXAMÉDÉE..............................................................................6712.1.1. SCENARIO1...........................................................................................................6712.1.2. SCENARIO2...........................................................................................................6912.2. STUDYCASE2:LAREUNION.......................................................................................7312.2.1. SCENARIO1...........................................................................................................7312.2.2. SCENARIO1BIS......................................................................................................7512.2.3. SCENARIO2...........................................................................................................7712.2.4. SCENARIO2BIS......................................................................................................7912.2.5. SCENARIO3...........................................................................................................81

REFERENCES..................................................................................................................................83

5

LISTOFFIGURES

Figure1:Researchprocess.....................................................................................................11

Figure2:Expectedtimeline....................................................................................................13

Figure3:Eco-systemofactorsarondanewneighborhooddevelopmentproject................23

Figure4:GeographicdistributionofFrenchThermalRegulation..........................................24

Figure5:Listofprojectsstudied............................................................................................26

Figure6:Energyperformancetargetedandreachedbythefirsteco-neighborhoods..........26

Figure7:Illustration“Linky”andLeGrandProducts�EMBIX�2016�...............................29

Figure8:Illustration-B&Rautomation.................................................................................29

Figure9:Overviewofthemainpossiblearchitecturesforresidentialbuildings�EMBIX�

2016�.....................................................................................................................................31

Figure10:Matchbetweenpeak-hoursandHighGHGemissions�EMBIX�2016�...........41

Figure11:Floorplanoftheneighborhood�EMBIX�2016�............................................46

Figure12:Dailyheatandhotwaterloadcurvesimulatedwiththeload-curvessimulator..47

Figure13:Dailywinterelectricloadcurvesimulatedwiththeload-curvessimulator..........47

Figure14:StudyCase1,Scenario1,FreeCashFlow..............................................................53

Figure15:StudyCase1,Scenario2,FreeCashFlow..............................................................54

Figure16:TransportnetworkoftheIslandof“LaRéunion”..................................................55

Figure17:Costofelectricityontheisland�EMBIX�2016��EDFSEI�2015��CRE�

2014�.....................................................................................................................................56

Figure18:GlobalSolarIrradiationmapoftheIsland;source:�ARER�2010�................56

Figure19:Typicalload-curveforadayinJanuary.................................................................57

Figure20:ComparisonofPvproductionandConsumptionoftheneighborhood................59

Figure21:IllustrationoftheenergysupplyoftheneighborhoodforatypicaldayinJanuary

.................................................................................................................................................59

6

LISTOFTABLES

Table1:Energyperformanceofthefirsteco-neighborhoods,(Blanchard,2015)................27

Table2:Malfunctionsidentifiedofthefirsteco-neighborhoods..........................................27

Table3:CAPEXandOPEXofthemainenergyequipmentforaneighborhood....................30

Table4:Priceofelectricityforresidentialconsumers,uniqueprice.....................................26

Table5:Priceforelectricityofresidentialconsumers,off-peakhourstariff........................26

Table6:Priceofelectricityfornon-residentialconsumers�EDF�2016�........................27

Table7:Priceofelectricityforlargeconsumers�EDF�2016�........................................27

Table8:Priceofelectricityforpubliclightning�ADEME�2012�....................................28

Table9:Priceofheatand,off-peakhours’tariff...................................................................28

Table10:PV-panelsfeed-intariff�EDF�2016�.................................................................28

Table11:Relevantenergydatatometerforaneighborhood�EMBIX�2016�...............34

Table12:Influencefactors�ACORBA�2012��EMBIX�2016�...................................35

Table13:Benefitsofmonitoring............................................................................................36

Table14:ExamplesofB2BandB2Cservicesforasmartcity................................................41

Table15:BusinessModelCanvasofthePerformancesupervisor.........................................44

Table16:StudyCase1,Scenario1,Detailedassumptions....................................................49

Table17:StudyCase1,Scenario2,Detailedassumptions...................................................50

Table18:Rateofreturnoninvestment.................................................................................51

Table19:Timeofreturnoninvestment.................................................................................51

Table20:Carbonfootprintoftheneighborhoodoverayear................................................52

Table21:Studycase2,detailledassumptions.......................................................................63

Table22:Resultsofthesimulation........................................................................................64

Table23:Differentscenariosofdata-usagescollectedbysmartmeters�CNIL�2014�..65

7

LISTOFABBREVIATIONS

GHG:GreenHouseGases

DSM:DemandSideManagement

TSO:TransportSystemOperator

DSO:DistributionSystemOperator

ROI:Returnoninvestment

NPV:NetPresentValue

FCF:FreeCashFlows

CAPEX:CApitalEXpenditure

OPEX:OPerationalEXpenditure

EU:EuropeanCommission

ICT:InformaticandCommunicationTechnologies

RT2012:FrenchThermalRegulationforbuildings

8

ABSTRACT

SmartGridstechnologiescoverawiderangeofapplications,fromenergyefficiencytoload

adjustments. However, they still rise interrogations and doubts around their economic

benefitsandenvironmentalimprovements.

Therefore, this thesis aims to understand and highlight the benefits of smart grids

technologiesappliedtoanurbandevelopmentprojectofnewneighborhood.

For suchaperimeter, it is firstnecessary to clearlydefine the smartgrids considered, the

stakeholdersinvolvedandthenewtoolsthatsmartgridstechnologiesbring.

Therefore,thisthesishighlightshow,foranurbandevelopmentproject,theeconomicvalue

of smart grids mainly lies in the reduction of infrastructure and tools to monitor and

maintainenergyperformanceofaneighborhoodinitsoperationalphase.

Furthermore, the introduction of intermittent and decentralized production, facilitated by

smart grids technologies, aims to extent the considered scale to tackle energy questions,

fromthebuildingscaletothedistrictscale.Therefore,newactorsandgovernancepractices

mayneedtoemergeinordertosupportSmartGridstechnologies.

SAMMANFATTNING

Smartanättekniktäckerettbrettspektrumavtillämpningar,frånenergieffektivitetattladda

justeringar.Mendefortfarandestigaförhörochtvivelkringderasekonomiskafördelaroch

miljöförbättringar.

Därfördennaavhandlingsyftartillattförståochlyftaframfördelarnamedsmartanätteknik

inomettstadsutvecklingsprojektavnystadsdel.

För en sådanomkrets, är det först nödvändigt att tydligt definierade smarta elnät anses,

berördaaktörerochdenyaverktygsomsmartanätteknikmedför.

Därförbelyseravhandlingenhurenstadsbyggnadsprojekt, liggerdetekonomiskavärdetav

smarta nät främst i minskningen av infrastruktur och verktyg för att övervaka och

upprätthållaenergiprestandanienstadsdelisinoperativafas.

Dessutomharinförandetavintermittentochdecentraliseradproduktion,underlättasgenom

smartanät teknik, syftar till attutsträckninganses skala föratt ta itumedenergifrågorna,

frånbyggnadenskalatilldistriktsskala.Därförkannyaaktörerochförvaltningspraxismåste

dykauppförattstödjasmartanätteknik.

KEYWORDS

Smart Grids, Monitoring, Neighborhood, Urban Development Project, Energy Efficiency,

ThermalandElectricGrids,Energyinfrastructure.

9

1. INTRODUCTION

The smart grid technology is a very broad field that aims to addresses key energy issues,

bothontheproductionandtheconsumptionside.Moreprecisely,SmartGridstechnologies

areexpectedto:

ü Reducethecostofenergyproduction

ü Reducethecostoftransportanddistributionofenergy

ü Reduceandsecureend-users’energy-bills

ü Reducetheemissionsofgreenhouse-gases

ü Allow a higher share of intermittent and decentralized renewable energy

production

ü Improvetheenergydelivery,intermsofqualityandservices

ü Improvethemaintenanceandreliabilityofenergynetworks

Smart grids are indeed identified as one of the key technological tool by the European

EnergyRoadmap2050�EuropeanCommission�2012�tocopewiththeenergytransitionandtheEuropeanEnergyGoalsthathavebeensetfor2050.

Hence,throughoutthisthesiswewillanalyzethebenefitsofasmartgridintheperimeterofnewneighborhoods.

Thisaspect(thescaleoftheneighborhood,andthenewnessoftheneighborhood)willimply

strong hypothesis about what will be considered as a smart grid and the various

stakeholders involved. Therefore, we will in a first time clearly define these boundaries

withinthescopeofsmartgridsconsidered.

Secondly, we will evaluate as much as possible the main energy-related costs at the

considered scale. This includes the costs related to energy equipment (with a particular

attention to infrastructure costs) alongwith energy bills and energy subscriptions of end-

users.

Thirdly,wewill be able to analyze and understand the new tools brought by smart grids

technology. Doing so we will get a first glance at the economic improvements they

represent.Indeed,toassessthebenefitsofsmartgrids,economicaspectswillbeconsidered

first.However,astheeconomicequationisnotalwaysclearlynoticeable(lackofdata,lack

of maturity of the field, …) or is simply neither the only nor the most relevant driver,

environmental and social (standard of living, level of services, …) arguments will be

scrutinizedinordertogivestrengthtotheargumentation.

Fourthly,twostudycasesofrealprojectswillallowtosimulatetheeconomicimprovements

previouslyexposed. Indeed,cost-benefitsanalysiswillbeconductedoveraperiodof20to

25yearsthankstosimulatorsdevelopedforthethesis.Keyperformance indicatorswillbe

definedforeachstudycaseinordertotracktheperformanceofdifferentscenarios.

Thefirststudycasewillhighlightthebenefitsoftheimplementationofasmartgridforthe

neighborhoodof “BordeauxAmédée” in France,with aparticular look at the thermal grid

andthepossiblesavingsintermsofinfrastructure.

10

Thesecondstudycaseswilltrytohighlighttheissuesrelatedtoanislandterritoryinterms

of electricity generation and electricity supply. To do so, we will take the case of a

neighborhood on the island of “La Réunion” (a French overseas department). We will

conductacost-benefitsanalysisfordifferentscenariosofenergysupply,energyproduction

throughPVpanelsandelectricstorage.

2. CONTEXTThis thesis presents the results of a 6-month working experience as an intern for the

companyEMBIX.Throughout this internship,oneof thechallengeswas to transformdaily

workintoaresearchactivity.

Inordertogetabetterunderstandingofbothwhattheinternshipconsistedinandhowit

helpedmyresearchwork,itappearsimportanttopresentthecompanyEMBIX.

EMBIXisacompanycreatedin2011andlocatedatIssy-les-Moulineaux,nearParis,France.

EMBIXhas two jointactivitiesandworksmainly for realestateagencies, collectivitiesand

architects.

Fortheseclients,EMBIXhasdevelopedafirstactivity,whichconsistinanexpertiseonsmart

grids and smart city services, through a consulting activity, that gives added value for

constructionprojectsofnewneighborhoods.

Intermsofsmartgrids,theconsultingactivityconsistinhelpingthecollectivitytodefinethe

energyneedsandenergymixofaneighborhoodprojectanddesigntheICTarchitecturethat

willcollectandgatherenergydata.Thisactivityisoftendoneincoordinationwithanenergy

consulting firm that providedynamic simulation and thermalmodeling of building. EMBIX

bringsasmartgridapproach (ability tomonitorandcontrolequipment, suitablesizing,…)

andway of thinking that aims at challenging this previous analysis to improve the global

performanceoftheenergystreams.

Intermsofsmartcity,theconsultingactivitycoversalltheaspectsofasmartcity(energy,

mobility, waste and water treatment, services to end-users, ...) and helps the customers

define various services that respond to the particular needs of their territory, taking into

accounttheregulatoryframework(handlingofprivatedata)anddefiningaviablebusiness

model.

Then,forthesesameclients,EMBIXhasalsocreatedasoftwaresolutiontomonitorenergy

streams (electricity, districtheatingand cooling) and control equipment (battery, charging

stations,public lighting,…)thatenablesacollectivitytotracktheenergyperformanceofa

neighborhoodandthecomplianceofthevariousstakeholders(realestateagencies,network

operators,…)withtheircommitments.Thesoftwaresolutionisthenalsoacommunication

tool for the territory and a way, through performance tracking, to secure economic

efficiencyofaneighborhooddevelopmentoperationandsecuretheenergybillofend-users.

EMBIXhastheambitiontobecometheoperatorofsuchaplatformduringtheoperational

phaseofaneighborhoodproject.

11

Ihadthechancetoget involved inboththeconsultingandsoftwaredevelopmentactivity

andthusgetabetter insightontheneedsand issuesofboththe ICT fieldandtheenergy

fieldappliedtoasmartgridatthescaleofaneighborhood.

3. ACKNOWLEDGEMENTS

I wish to acknowledgemy colleagues at EMBIX that have always answeredmy questions

(though they have been numerous) and placed their trust in my work from the very

beginning. I had the chance to get involved on various subjects too. Marisa, Alexandre,

Marie,Emilien,Mathieu,Eric, Jérôme,FabriceandCarole: ithasbeenapleasuretodomy

finaldegreeprojectatyoursides.

IalsowouldliketothanksOmarwhohavebeenveryhelpfultoputmeontherighttrackfor

thisthesis.Hehasalwaysgivenmeanenlightenedviewonhowtoconductmythesisand

whichaspectswerecrucialtodealwith.

4. RESEARCHMETHODOLOGY

4.1. RESEARCHSTRATEGY

My research strategy relies on a strongworking experience that I had acquired at EMBIX

alongthesixmonthoftheinternship.TheprojectsIhavebeenworkingongavemethetools

toanalyzeandanswertheresearchquestionsIchosetoaddress.

Tocomplement thisworkingexperience, I searched foradditionaldataand information in

publishedpapers.

I also attended showrooms and conferences to gather the points of views of various

stakeholdersandgetfeedbacksfromall-readybuiltsmartcitiesthroughoutEurope.

4.2. SCOPEOFTHERESEARCH

Thefieldofsmartgrid isverybroadandtherearemanywaysto lookat it.Therefore, it is

necessarytodefineascopefortheresearch.

Geographicalboundaries

Thefirstsetofboundariesaregeographicalones.

ThescopeofmyresearchislimitedtoFranceandthus,theanalysisisdoneinthelightofthe

Frenchregulation(intermsofdataandenergymainly).However,Ihavepunctuallyanalyzed

some feedbacks from smart cities outside France (as there are still few already achieved

projects) in order to better understand the issues faced by the first early eco-district

projects.

The scale of smart grid considered is also typical. It is the scale of the neighborhood,

meaning that the building stock of the neighborhoods, representing the energy usage, is

composedofhousingandshopsalongwithoffices,restaurants,hostels,etc…

Theneighborhoodsconsideredarenewones.Thishypothesisaffectsmainly thecostsand

benefitsanalysis.Thestudiesconductedthroughoutthisthesismightnotbeaccuratefora

renovationoperation.

12

Technicalboundaries

Froma technical perspective, the issueswill be analyzedwith anenergy approachat first

hand.However,astheICTfieldisstronglyatstakeforasmartgrid,someissueswillalsobe

treatedfromanICTpointofview.

Moreover, as an economic analysis will be done, economic tools will be used such as

calculationsofReturnonInvestment(ROI),TimeofReturnonInvestmentandNetPresent

Value(NPV).

4.3. RESEARCHPROCESS

Theresearchprocessishighlightedinthefollowingfigure.

FIGURE1:RESEARCHPROCESS

*Alongwiththesefoursteps,aglobalandcontinuousprocessofreportwritinghasbeen

done.

**Furthermore,thescopeofthestudyhasbeenadaptedalongtheresearchtotakefully

advantageoftheworkexperienceIhadatthecompany.

13

4.4. RESEARCHQUESTIONS

Overallresearchquestion:Howtojustifytheimplementationofasmartgridforanew

neighborhood?

Smartgrid,general

Consideringthescaleofaneighborhood,

ü What isconsideredassmartgrids?Why is it interestingthinkenergy

needsandsupplyattheneighborhoodscale?

ü Whicharetheexpectationsandreluctanceofthevariousstakeholders

towardsmartgrids?

ü HowmatureisthefieldofsmartgridsinFrance?

Theenergyrelatedcostsofinfrastructureforasmartgrid(bothelectricgridandthermalequipment)

ü Whicharetheenergyequipmentofkeyconcern?

ü Whataretheirrelativecost(investmentandmaintenance)?

ü How can the infrastructure cost of a new district can be reduced

thankstoasmartgrid?

TheData

Whichdataisrelevant?Andatwhichtimescale?

WhichITinfrastructuresarepossible?(advantages&inconvenient)

4.5. TIMELINEFORTHETHESIS

IstartedworkingonmythesisinJuly2016.Duringthefirstmonths,Itriedtodefinetoscope

of the thesis and themethodology Iwould adopt. Then Iwas able todig further into the

subjectandstart looking fromrelevant informationbothat thecompanyand in literature

papers.Duringthe lasttwomonthofthethesis, I intensifiedreportwritingandconducted

the analysis of the two study cases. Finally, I tried to take a step back and concludemy

thesis.

14

FIGURE2:EXPECTEDTIMELINE

4.6. CONTENTUSED

Forthethesis,Iusedavariousrangeofcontent

Contentavailableatthecompany

ü Real data (load curves) collected from real buildings through the software

developedbythecompany

ü Variousdeliverablesofthecompanyforsmartgridsandsmartcityprojects

ü Studiesmadebypartnercompaniessuchasenergyconsultingfirms

Personalresearchesinliterature

ü Regulationpapers:

o FrenchThermalRegulationfornewbuildings(RT2012)

o Cost of access to the distribution network: TURPE 4 and TURPE 5

deliberations

ü Costofequipmentstudies,forexample

o EGISstudy

o ENEDISreports

15

Showroomsandconferences

ü “Smartcity+Smartgrid”:Cityandterritoryshowroomforsmartcities

ü “SIMI”:Realestate’sagenciesshowroom

ü “PollutechLyon”:Environment,wastes,waterandenergyshowroomheld in

Lyon

16

5. SMARTGRIDSATTHENEIGHBORHOODSCALE:DEFINITION,GOALS,ACTORS

ANDRELATEDISSUES

5.1. SMARTGRIDSDEFINITION

TheEuropeanCommissiondefinessmartgridsas“networksthatcanautomaticallymonitorenergyflowsandadjusttochangesinenergysupplyanddemandaccordingly.”�European

Commission�

However, throughout this thesis, the scale consideredwill be theoneof a neighborhood.

Hence, it is important to define what this scale means in order to find a more relevant

definition.

First,let’stakealooktothisparticularscale.Thescaleoftheneighborhoodcanbedefined

asthesmallesturbanareathatcontainsall(ormost)theenergyusageofanurbanarea:

ü Consumptionfromavarietyofbuildings:residentialbuildings,offices,stores,

restaurants,hotels,sportfacilities,….

ü Publiclightningandurbaninfrastructure

ü Electricalvehiclesandchargingstations

Thisparticularconfigurationimpliesdirectconsequencesandadvantagesintermsofenergy

thatsmartgridsaimsattakingadvantageof:

ü Thevarietyofbuildingsimpliesavarietyofusageandcomplementaritiesofenergy consumption. For example, the consumption of residential buildings is

expectedtodecreasewhiletheconsumptionofofficesincreasesinthemorning.The

variety and density of buildings also implies a greater possibility to mutualize the

resourcesofthesebuildings.

ü The area considered is large enough and dense enough to make localproductionandlocalconsumptionparticularlyrelevant:

o Electricitycanbeproducedon-site(mainlywithPV-panels)

o Heat and cold can be produced on-site through geothermal plants,

heatpumpsorsolarthermalpanels

o The area and the density of energy usagemake it often relevant to

builddedicateddistrictheatinganddistrictcoolingnetworks.

Therefore, from the European Commission definition and in the light of the particular

boundarieschosen,wedefinewhatwillbeconsideredassmartgridsinthisthesisas

“Electricitynetworkandthermalnetwork(bothheatingandcooling)ofaneighborhoodthathavebeencoupledwithanICTnetwork(meters,actuatorsandcommunicationnetwork)to

automaticallymonitorenergyflows,andcontrolenergyequipment”

Indeed,wewillnotonlyconsidertheelectricalneeds,butalsothethermalneeds,often

suppliedthroughdistrictheatinganddistrictcoolingnetwork,whichpresentslotsof

flexibilities.

17

Thisdefinitionimpliesavarietyofgoalsandpurposesotherthanadjustingtochangesin

energysupplyanddemandaccordinglyinordertosecurethebalanceofthegrid.

Wewillnowhighlightthesegoals.

5.2. MAINGOALSOFSMARTGRIDSCONSIDERED

The2020climate&energypackage�Europeancommission�,whichwassetin2007byEU

leaders,quotesthreemainstargets:

ü 20%cutingreenhousegasemissions(from1990levels)

ü 20% of EU energy from renewables (The law “Grenelle 2”�Ministère de

l'environnement�2010�inFrancesetthisgoalupto23%forthecountry)

ü 20%improvementinenergyefficiency

Smartgridtechnologiesareexpectedtohelpcomplywiththesegoals.SmartGridshaveboth

directandindirectimpactsonthesegoals.

The impacts of smart grids technologies could be defined as “secondary impacts” in the

sense that they improve old systems (or old-ways to design a system) through the

implementation of a new layer, the ICT networks but they are not the core solutions.

However,SmartGridsalsohave“direct impacts”, since theyallownewcomplexusages to

existsanddevelop,suchasdecentralizedandintermittentrenewableproduction.

Tobetterunderstand theseaspects,wewill nowanalyzehow smart grids canhelp in the

particularscopeofnewneighborhoods.Atthisscale,wecanidentifyseveralobjectives.

The first objective is to consume less�CCI Nice Côte d'Azur�2012�. In the case of aneighborhood,thismeanstoimplement:

ü Passive actions of energy efficiency to improve to performance of both the

passivesystems (solarchimney, ...)and thebuildings (walls, roof,windows,…).For

example,leversforchangecanbetheorientationofthebuilding,theimprovement

ofinsulation,thematerialsused,etc...

ü Active actions of energy efficiency, that results in an improved control of

energysystemsandstreams(boththermalandelectricityrelated).Automaticactive

actionswillrequireanICTinfrastructuretocontrolequipment.

Passive and active actions are often closely dependant. For example, a highly-insulated

buildingwillbeabletoretainitsheatloadforlongerperiodsthanapoorlyisolatedbuilding.

Thesecondobjectiveistoconsumebetter�CCINiceCôted'Azur�2012�.Inthecaseofaneighborhood,itmeans:

ü Promote local and renewable energy production. In case of intermittent

production,thismeansenhancethematchbetweenproductionandconsumption

18

ü Reducethepeakpowerconsumptionoftheneighborhood

ð Thisaimsatreducingthemaximumpowerdemandfrom

a given neighborhood, which has a direct consequence on

theDSOnetworkand the related costof connection to the

DSOnetwork.

Thesetwoobjectiveswillleadtoreducethegreenhousegasesemissions,whichisthefinalgoaloftheenergypoliciessetbytheEU.

The thirdobjective is toenable thedevelopmentofnewusagesandsystems�CCINice

Côted'Azur�2012�. At the scaleof a neighborhood, thesenew systemsandusages are

precisely:

ü Electrical vehicles and charging stations. The issues come from both the

rangeofpowerusedtofillupelectricalvehicles(3kW,7kW,…,22kW)andthe

period they are to beused (if not controlled): the evening. Then, although still

marginal,weseethatthisnewusageputsadditionaltensionontheelectricalgrid

during the evening peak. Hence, its development requires adaptations of the

electricalgrid,bothphysicaladaptationandwaystocontroltheloadofcharging

stations inorder tooptimize thechargingwhilecontrolling theelectricalpower

demand.

However,paradoxically,withefficientcontrolsystem,electricalvehiclesmightcontributetothereductionoftheeveningpeakiftheenergyleftinthebatteryattheendofthedaywasthenusedtoinjectpowerintothegridandthechargingtimepostponed.

ü Thedecentralizedproductionofelectricity.Thisimplychangesinthewaythe

electrical grid has to be sized to enable to transport the part of energy not

consumed locally, and adapt to the often-intermittent characteristics of this

production.

ü The production of intermittent energy sources (mainly solar panels), as

exposedinthepreviouspoint, increasesthechallengestobalancetheelectrical

grid. The grid has to adapt not only to the production but also to the

consumption.

ü Thestorageofenergy(bothelectricityandheatorcold).Atthecrossroadofproduction and consumption, the storage of energy tends to improve the

managementof theenergyat thescaleofaneighborhood. In termsof thermal

energy storage, the solutions that can be implemented are insulated storage

tanks.Electricityishoweverhardertostore.Atthescaleofaneighborhood,itwill

take the form of Li-ion batteries or similar technologies. The cost of this

technology,althoughdecreasing,stillslowsdownitsdevelopment.

Wehaveseenthevariouspracticesthatsmartgridswishtosupportinordertokeepupwith

anincreasedperformancecomparedtoconventionalprojects.

19

However,ifwetakeastepback,wecanidentifyadditionalandparallelgoalsandobjectivesof the implementation of a smart grid at the scale of a neighborhood. Indeed, the

implementationofsmartgridsforaneighborhoodwillimplythecollectionofamultitudeof

dataaroundenergyconsumptionandproduction,alongwithperformanceofequipmentand

buildings.Thereisthenarealissueandgoaltomakethisdataasusefulaspossibleinorderto:

ü realize performance follow-up through feedbacks and means of

actionstorectifymalfunctions

ü inthelongterm,increasetheunderstandingofthebehavior,throughtime,ofbuildings,energynetworksandend-user’sbehavior

ü developnewservicesforoperatorsandend-users

5.3. STAKEHOLDERSINVOLVED

Thereisaverybroadrangeofstakeholdersinvolvedintheconception,constructionandlife

ofanewneighborhoodproject.Wewillhereseewhotheyare,whichroledotheyplay,in

thelightoftheexpectationsandreluctancestheymighthavetowardssmartgrids.

Thesestakeholdersplayaroleatdifferentphasesofaconstructionproject.

Let’sfirsthavealookatstakeholdersinvolvedintheconceptionandconstructionphaseof

theproject,butnotintheoperationalphase

Therealestatedeveloper

The real estate developer buy a piece of land (often from a collectivity), invest in the

construction and outsources part of the conception to consulting firms. It then makes a

profitsellingthebuildingsconstructed.

Thus, for the realestatedeveloper, implementingasmartgridon itneighborhoodproject

represents:

ü Uptonow,awaytodifferentiate fromotherrealestateagencies.Hence, it

helpsthemwinaprojectfromacollectivityanditrepresentsasalepoint.

ü Awaytoincreaseitsexposure,inordertowinmoreprojectsinthefuture.

ü Anargumenttosellitsbuildingsatahigherpricepersquaremeter.

Though, up to now, we can identify some key issues for the real estate developer to

implementsmartgrids:

ü the deployment of meters and an ICT infrastructure to monitor the

consumptionofitsbuildingwillbringtolightanydefaultsofconceptionandenable

tocheckwhetherornotithascompliedwithitsobjectives,promisesandobligations

ü themetersandICTnetworkrepresentsanewcostthatitmightnotwantto

support

20

Around the real estate developer, there is a multitude of consulting firms that conducts

specific studies for all the aspects of a constructionproject (fromwater treatment,waste

disposal,civilengineeringuptoenergyandsmartgridconsultingsuchasEMBIX).

Thelocalplanningauthority

The mission of any local planning authority is to develop the territory it is in charge of

throughamultitudeofurbanprojects.

Throughoutthisactivity,thelocalplanningauthorityhastoenforcethepoliciesdecidedby

thestateandrealizetheoperationalendofit,appliedtotheterritoryitisinchargeof.This

meansthatthelocalplanningauthoritywilldefinetheorientationofaprojectanditsglobal

goals.

This results in organizing calls for tenders to select the companies that will conduct the

operationaloperationsoftheproject(realestateagencies,networkoperators,…).

In order to respect the fairness of an operation, it is likely to be willing to ensure the

complianceofthevariousstakeholderstotheircommitments.

Therefore,regardingthesmartgridstechnology,thelocalplanningauthoritycanexpectthis

technologyto:

ü helpthemfollow-upkeyperformanceindicatorsofaproject

ü improvetheperformanceofitsterritoryinordertoreachthegoalstheyhave

set(bothintermofsustainabilityandeconomicefficiency)

ü givemoreexposuretoitsterritory

However, if it has to support any additional investment costs for the implementation of

smartgrids,itcanrepresentabarrierforthelocalplanningauthorityaslongasthebenefits

(economic,environmentalandsocial)ofthisadditionalinvestmentarenotobvious.

ConstructioncompaniesandHardwareproviderfirms

Theyareplayingakeyroleenergy-wisebecausetheyareresponsiblefor:

ü the quality of the equipment provided (insulatingmaterials, heat pump, PV

panelsandinverters,actuators,smartmeters,…)

ü thecorrectinstallationoftheseequipment

Hence,theyarecrucialforthewell-functioningofsmartgridsandthegoodcompliancewith

theobjectiveofagivenproject.

Moreover, their profession tends to evolve, along with the new equipment and new

practices(commissioning,….)thatemergeandcomplexitytheirwork.Transversalskillsmore

andmorerequired(electricity,ICT,…).

21

Let’snowhavealookatstakeholdersinvolvedinallthephasesofaproject

TheFrenchDistributionSystemOperator(DSO)-ENEDIS

ThemissionoftheFrenchDSO(ENEDIS)istoensure:

ü theaccesstoelectricityforeverybodywithoutanydiscriminations

ü Thecontinuityoftheservicetoallthesubscribersofitsnetwork

ü thequalityoftheelectricity(rangeoffrequencyandvoltage,…)

FortheDSO,smartgridsrepresentvariousadvantages:

ü itoffersthemnewtoolstobalancethegridsuchasmoreaccuratedataand

realtimedata,alongwithtoolstoactivatedemandresponse

ü it provides them tools and new perspectives to evaluate and reduce its

investmentininfrastructureandmaintenancecosts.Moreprecisely,limitationofthe

peakpowerconsumptionoftheneighborhoodshouldenablethemtomakeafairer

sizing (during the conception phase) of the needed infrastructure (transformer

stations)toconnecttheneighborhoodtothepublicdistributionnetwork.

However,theimplementationofasmartgridrepresentsforthemsomechangesintheway

toprocesstheirfunction:

ü theywillhavetohandlebi-directionalstreamsduetolocalenergyproducers

sellingtheirelectricity.

ü The more a neighborhood self-consumes its local production, the less

electricity goes through its distribution network, while he has made the same

investment to connect it to their network. In the extreme case where a

neighborhoodovercomeitselftoitsenergyneeds,thedistributionnetworkwouldbe

usedonly incaseofemergency (forpeakhours typically),which totallychanges its

businessmodelaspartof itsremunerationisproportionaltotheamountofenergy

hedistributes.

SocialLandlords

A social housing is characterized by a rent fixed by the state and lower than the actual

marketpricetoguaranteeanaccesstohousingforeveryone.

Thelaw“DuflotI”�LoiDuflotI�hassetin2013theminimumshareofsocialhousingfrom

20%to25%foreachcityofmorethan3500inhabitants.Manyofthenewneighborhoodsdo

thenplanashareofsocialhousingamongtheirhousingstock.

In a construction operation, social landlords play a similar role as real estate agencies,

though,theyaretheonesthattakecareofthesocialhousingconstruction.Theyareinfacta

bitdifferentthanrealestateagenciessincetheywillstayduringtheoperationalphaseofthe

building,playingtheroleofapropertymanagerforthesocialhousingstock.

They have then different expectations and issues than real estate agencies. Indeed, their

maindriveristoprovidehousingatanaffordableprice.Hence,theyareinterestedinany

22

tools(suchassmartgrids)thatwillbringtheenergybillsdownandstable.Though,theyare

not likely towish to supportany fancyorunnecessarymetersand ICTnetwork thatcould

composesmartgridsinfrastructure.

Themunicipality

Municipalitiesplayakeyrolefortheimplementationofasmartgrid.Theyhavethepowerto

influencedecisions(eitherboostorslow)ontheirterritory.

Therefore, according to their orientation and sensibilities they can either act in favor of

smartgridsorindisfavor.

Thebenefitstheycangetfromsmartgridsare:

ü To increase the exposure of their territorywith state of the art projects in

termsofsustainabilityandresilience

ü to improve the offer of services of their territory, thus increasing the

attractivenessoftheircity

ü tosolverecurrentissuesoftheirterritorythroughsmartgridsandsmartcity

technologiessuchasmobility,pollution,...

On the contrary, some states related issues can slow their will to develop smart grid

projects:

ü First,forsocialpurposes,collectivitiesmightwanttosecurethepriceofland

fortheircitizens,whichtendstosoaralongwithinnovation

ü Politicalorientationscanslowtheirwilltodevelopsuchsolutions.

Let’snowhavealookatstakeholdersinvolvedmainlyintheoperationalphaseoftheproject

Localenergyproducers

Local energy producers manage local (renewable) production (e.g.: geothermal heat, PV

panels,…).

They are key actors to cope with goals of renewable energy in the energy mix of a

neighborhood.

They should benefit from the implementationof smart grids. Indeed, itwill provide them

withmoredataandunderstandingonboththeirproductionandtheconsumptionof their

clients.

Energyproviders

Foraneighborhood,energyproviderscanbeeitherlocalornot.

They are the ones that supply energy to end-users and charge them for the electricity or

heat they consume. Hence, they could have a role to play towards the behavior of end-

consumers(throughpeakandoff-peakpricingforexample)

23

Smartservicesprovider(data-users)

Basically,weconsiderhereall theactors thatwillmakeuseofdatacollected tobuildand

offernewservices,bothforcustomersandforfirms.

They are data-users and actors of the neighborhoods. A smart grid is for them a great

opportunity,ifnotarequiredconditiontodeveloptheiractivity.

Buildingmanagers:Facilitymanagersandpropertymanager(unionofowners)

Oncetheneighborhoodhasbeenbuiltandthattheend-useractuallyusesthebuildings,new

actorsemerge:buildingmanagers.

Fortertiarybuildings,wemostlytalkof facilitymanagers.Forhousing,wetalkofproperty

managers(orsociallandlords).Atthescaleofaneighborhood,propertymanagersareoften

gatheredasunionofowners.

The role of these actors is to ensure the proper functioning of the buildings they are in

chargeof.

In termofenergy thismeans takingcareof themaintenanceofenergyequipmentand/or

choosetheoperatorsoftheenergyequipmentundertheirperimeter.

Theywishtoensurethatthechargesforthe inhabitantsarekeptstableanddealwiththe

dailyfailures,reparations,andrenovationstokeeptheirbuildingfunctional.

Theend-consumer

End-consumersarethelastlinkinthechainofenergy.Theyaretheoneswhoneedenergy

andthusconsumeelectricity,heatandcold.

Themaininterestsofthesmartgridsareforthem:

ü Lower and secure the amount of their energy bills and their power

subscription

ü Live in a neighborhoodwith a high level of services (“smart services”) that

takeadvantageoftheICTinfrastructuretodevelop.Intermofenergy,thistypically

meananenlightenedaccesstoitsconsumption,inrealtime,andwarningincaseof

malfunctioning of appliances.Moreover, these smart services tend to tackle other

fieldsthanenergysuchasmobility,health,andeverydaylifeservices.Thisrepresent

thenasocialimprovementandadvantagecomparedtootherneighborhoods

ü Theabilitytobecomeaproducerofenergy(throughPVpanelsinstallation)

ü Thefreedomofmindtoliveinamoresustainableplace,whichrepresentsan

environmentalinterestforapartofthepopulation.Asmallonethough.

On the other hand, smart grids represent for end-consumer the disadvantage of a higher

priceoflandcomparedtoneighboringdistrict,duetotheavailabilityofsmartservicesand

lowerenergyconsumption.

24

As we have seen, there is a very broad range of stakeholders involved in a new

neighborhooddevelopmentproject. This eco-systemof actors is summed-up in the figure

below:

FIGURE3:ECO-SYSTEMOFACTORSARONDANEWNEIGHBORHOODDEVELOPMENTPROJECT

Sources:�EMBIX�2016�;�CCINiceCôted'Azur�2012�

5.4. BARRIERSTOTHEIMPLEMENTATIONOFSMARTGRIDS

5.4.1. RT2012-FRENCHTHERMALREGULATIONFORNEWBUILDINGS

Themainregulationfora landdevelopmentproject inFranceisthethermalregulationfor

newbuilding,called‘RT2012’�RT2012�.Thisregulationsetsthresholdsofconsumption

fornewbuildingsaccordingtoseveralcriteria:

ü Geographicallocation

ü Typeofbuilding(office,residential,…)

ü Typeofheatingandcoolingsystem

ThethresholdvalueisatargetexpressedinkWh/m2/yrthatcovers5energyusages:

ü Heating

ü Cooling

ü Hotwater

ü Lighting

ü Auxiliaries(ventilation,pumps,…)

Facility managersand property manager

25

Specific uses (which includes electrical appliances such as dish washer, fridge, oven, TV,

printer,computers,…)arenotincludednorconcernedwiththisobligation.

Hence, the thermal regulation does not regulate the consumption of such appliances. As

muchasthethermalregulationlowersitsauthorizedlevels,specificuseswilltakeagreater

partof theremainedenergyconsumedbyahousehold.Thisseemsto indicate, thatother

measuresshouldbeimplementedtofurtherreduceenergyconsumptionsandactonspecific

uses, such as raising awareness of end-users, or further regulate authorized energy

consumptionofelectricappliancesforhardwareproviderfirmsforexample.

The average threshold value is 50 kWh/m2/yr. Though,we can see below on thismap of

Francehowthevalueisgeographicallydistributed:

FIGURE4:GEOGRAPHICDISTRIBUTIONOFFRENCHTHERMALREGULATION

Thisregulationisoneofthemainconstraintforrealestateagencieswhentheybuiltanew

building.

The thermal regulation if periodically reviewed in order to copewith technologyprogress

andsustainabilitygoals.In2018,anewthermalregulationisexpectedtoemergewithlower

thresholds.

5.4.2. REGULATEDPRICEOFELECTRICITY

InFrance,thepriceofelectricityisregulatedbytheCRE(RegulationCommitteeofEnergy-

Frenchpublicorganismthatdeliberateenergyissues).

Indeed, the price of electricity is composed of three main parts that represents

approximatelyonethirdofthepriceeach:

ü Thecostofaccesstoelectricity,calledTURPE.�CRE�ThispartofthepriceisredistributedtoENEDIS(FrenchDSO)andRTE(FrenchTSO).Itissupposedtocover

their expenditure to build and maintain the public distribution and transport

networksthatconnecteachandeveryelectricityconsumerinFrance.

ü Taxesü Thepriceoftheelectronproduced.

26

5.4.3. SELF-CONSUMPTIONOFENERGYPRODUCEDBYPV-PANELS

Self-consumption�HESPUL�2015� of the electricity produced by PV-panels is totally

possible if theownerof thePVpanels is the sameoneas the consumerof theelectricity

produced.Though,itisstillhardtomakethisactivityprofitabledueto:

ü Thestillhighexistingfeed-intariffforelectricityproducedthroughPV-panels

(stillhigherthantheLCOEproducedwithPV-panels)

ü Theabilityforasingleconsumertoconsumemostoftheenergyitproduces

withoutunder-sizingitsinstallation.Indeed,withoutusingthefeed-intariff,tomake

self-consumptionprofitable,theend-consumerneedstoself-consumethemaximum

oftheenergyproducedlocally.Todoso,withoutanystoragecapacity,heneedsto

consumethemostpowerwhenproductionoccurs.Otherwise,everytimeproduction

exceeds consumption, some part of the electricity produced is lost. However, to

maximizehisconsumptionwhenaproductionoccurs,eitherheshiftshisuses,orhe

undersizehisPV-panelsinstallation,sothatit reduces thepower output. This tends

toundersizethePV-installationcomparedtoone’senergyneeds.

However, another type of self-consumption is “collective” self-consumption,whichmeans

that one or more local energy producers produce electricity that would be locally self-

consumedbytwoormorelocalenergyconsumers

Collectiveself-consumptionisanimportantissueatthescaleofaneighborhood.Indeed,the

closeness of the buildings, the density of energy usage and the variety of uses (offices,

housing, …) would make it possible to collectively self-consume most of the electricity

producedonsite.Hence,collectiveself-consumptioncouldbecomeprofitableandwouldbe

apowerfultooltoreduceenergybills.�HESPUL�2015�

However,severalbarriersstillexistforcollectiveself-consumptiontodevelop.

First,thefeed-intariff,whichisanartificialpricesetbythegovernment,isstillhighenough

tomakeitprofitabletosellalltheenergytoanenergyprovidercompany.

Second,technologicallyspeaking,theywouldbeaneedtotrackwhoconsumestheenergy

locallyproduced inorder toadjust theenergybills.Eitherpreciselywitheachelectron,or

decidebyacontractualruleinadvance.

Finally,thebiggestbarrier isduetotheregulation.Theframeworkwasuntil July2016not

definedintheenergyFrenchlaw�Ministredel'environnement,del'énergieetdelamer�

2016�.Nowthatadefinitionhasbeenadded,adecreeisexpectedtoclearlydefinealegal

basisforthisactivity.

Indeed, self-consumption is a big economic issue for the FrenchDSO ENEDIS. In the case

whereaneighborhoodwouldachievecollectiveself-consumptionatalargescale,theDESO

wouldstillhavetodothesameinvestmentstoconnectend-usersoftheneighborhood,but

would have fewer remuneration (through the TURPE) since lesswould transit through its

network to supply the neighborhood (most of the electricity needs would be provided

throughlocalPVpanelproductionandaprivatenetwork).Tomakeitfairandreasonablefor

everybody,thewaytheTURPEiscalculatedwillneedtochange.

27

5.5. LESSONSLEARNEDFROMFIRSTBUILTECO-NEIGHBORHOODS

The very first eco-neighborhoods have been great successes in the sense that they have

achievedhighenergyperformanceandtheyhaveintegratedagreatdealofnewservices.

To furtherunderstandthe lessons learnedof the firsteco-districtsbuilt,wehaveanalyzed

thesuccessandfailureoffiveofthemaroundEurope�Blanchard�2015�:

FIGURE5:LISTOFPROJECTSSTUDIED

The statedgoalsof theseneighborhoodswereanenergyperformanceof thebuildings, in

kWhperm2peryear.

Wecanseefromtheanalysisofthesefiveprojectsthat,althoughtheenergyperformanceof

these neighborhoods represents a great improvement compared to national levels, the

targetssetatthebeginningofeachprojectwerenotachieved:

FIGURE6:ENERGYPERFORMANCETARGETEDANDREACHEDBYTHEFIRSTECO-NEIGHBORHOODS

0

50

100

150

200

250

BedZED- England BO01- Malmö HammarbySjöstad Kronsberg CasernedeBonne

kWh/m2/yr

Lessons learnedfromfirsteco-neighborhoods

Target Achived Nationnallevel

28

Thevariationbetweenthetargetandtheachievedperformancediffersfrom20%upto

100%:

TABLE1:ENERGYPERFORMANCEOFTHEFIRSTECO-NEIGHBORHOODS,(BLANCHARD,2015)

Variousmalfunctionsexplaintheseperformances:

TABLE2:MALFUNCTIONSIDENTIFIEDOFTHEFIRSTECO-NEIGHBORHOODS

Somelessonslearnedhaveemerged:First, the actors involved have realized that consumption forecast over the lifetime of a

buildingisverycomplexandnotsufficient:

ü Thebehavioroftheinhabitantsisstillnotmastered

ü Variousimpactingfactorscanchallengetheperformanceofabuilding

“Itappearscrucialtoevaluateandconductanenvironmentalapproachtoreadjusttheinitialroadmapandensurecontinuousimprovements”�ARENE�2005�Furthermore,

ü The lack of consultation and clear definition of the specification leads to

failuresofconception

ü Itisnecessarytosupervisetheconstruction,oneofthemostcrucialphases.

29

***

Inthisfirstpart,wehavefirstdefinedthescopeofsmartgridsconsideredandthegoalsof

smartgrids:consumeless,consumebetterandincreasetheunderstandingofenergyusage.

Wehavethenlistedandpresentedthestakeholdersinvolved,theirrespectiverole,interests

andexpectations.

Therefore, we will now focus on the role that smart grids can play to enhance the

performance of a neighborhood, how that can be translated economically through the

reductionofinfrastructurecosts.

However, fromthe lessons learnedofalreadybuiltneighborhoods throughoutEurope,we

have seen that the energy performance of buildingswas hardlymet. Hence, after having

analyzedthenewtoolsbroughtbythesmartgridstechnology,wewilltakeastepbackand

describe the role that a contractual tool could play, an energy performance contract, to

secure the economic improvements unlocked by the opportunities of the smart grids

technology.

30

6. REVIEWOFTHEMAININVESTMENTCOSTS(ENERGYWISE)FORANEW

NEIGHBORHOOD

Inordertounderstandtheeconomicbenefitsofsmartgrids,wewillnowanalyzewhichare

the energy-related costs to cover when building a new district.We have classified these

costsinthreecategories:thecostsrelatedtoenergyequipment,thecostrelatedtoenergy

bills and energy subscriptions and finally the cost of smart grids in their-self in terms of

hardwareandsoftware.

Wehavetriedbelowtoestimateasmuchaspossiblethesecoststhankstotheliteratureand

real projects. However, these costs are only good indications and orders of magnitude.

Indeed,theactualpriceofenergyequipmentoftendiffersfromoneprojecttoanother,with

itsspecificities.

6.1. ESTIMATIONOFINFRASTRUCTUREANDMAINTENANCECOSTSOFTHEMAIN

ENERGYEQUIPMENT

Thecostsoftheequipmentanalyzedherearetheonesthathaveakeyroletoplayatthe

scaleofadistrict.Indeed,theycanbereducedthemostfromtheimplementationofsmart

gridsatthescaleofaneighborhood,mostlythankstomutualizationandglobalthinking in

conceptionphase.Thestudycases inpartVwillhelpusgetabetterunderstandingof the

benefitsofasmartgrid.

Untilthen,itisimportanttogetafirstinsightontherelativecostsoftheseequipment.

Theequipmentwewilltakeacloserlookatarethefollowing:

1. Thecostsrelatedtotheconnectiontotheelectricgrid

a. thetransformer

b. electricdistributionwiresandrelatedcivilengineeringcosts

2. Thecostoflocalproductionsystems

a. PVpanels

b. Geothermalpowerplantcoupledwithheatpumps(fordistrictheating)

c. Electricheaters

d. Gasheaters

e. Dry-coolers

3. Thecostofenergystorageequipment

a. Electricstorage(batteries)

b. Thermalstorage(heattank)

4. Thecostofurbaninsfrastructure

a. Publiclighting

b. Chargingstationsforelectricvehicles

31

TABLE3:CAPEXANDOPEXOFTHEMAINENERGYEQUIPMENTFORANEIGHBORHOOD

Sources:�ADEME�2012��AFE�2014��CRE�2014��EGIS��EMBIX�2016��ENEDIS�2016��SDEduCher�2013��VNF�2008�

Type Equipment CAPEX Unit OPEX Unit

Thecostofconnectiontothe

electricgrid

Transformer(400kVA) 30000,00 €/unit €/unit/yearElectricdistributionwiresandcivilengineering

120,00 €/ml €/ml/year

Thecostoflocalproductionsystems

PVpanels 1to1,68 €/Wc 0,03 €/Wc/yearGeothermalpowerplantcoupledwithheatpumps(fordistrictheating)

1203 €/kWinstalled 65,41 €/kWinstalled/year

Electricheaters 180,00 kW/apartments 3,60 kW/apartments/yearGasheaters 428 €/kWinstalled 38,04 /yearDry-Cooler 428 €/kWinstalled 38,04 /year

Thecostofenergystorage

equipment

Electricstorage(batteries) 500 €/kWh 2%ofCAPEX €/kWh/year

Thermalstorage(heattank) 250 €/kWh 2%ofCAPEX €/kWh/year

Thecostofurbaninfrastructure

Publiclighting 350 €/streetlight 20,00 €/streetlight/yearChargingstationsforelectricvehicles(22kW)

24000 €/chargingstation(22kW) 2400,00 €/chargingstation/year

Thesecostsareestimatedbasedonexistingprojectandliteratureresearch.Althoughthesecostsmustbeadaptedtoeachspecificproject,theygiveaninterestingorderofmagnitudeandtheabilitytoquicklyandefficientlyunderstandthecostsassociatedtodifferentsolutions.TheywillbeusedinpartVforthesimulationofdifferentscenarios.

25

6.2. BILLSANDSUBSCRIPTION

ThecostofenergyperkWhdependsonthemaximumpowersubscribed.Sodoestheannual

costofsubscriptiontoenergysupply.

6.2.1. THECOSTOFELECTRICITY

First, let’s take a look at the price of electricity in France. It depends on the type of

consumer.Wepresentherethepriceofelectricityfordifferentconsumers.Thebilldepends

bothofthepowersubscribed(“subscription”)andtheamountofenergyconsumed.

Residentialconsumers EDF 2016

Forresidentialconsumers,thepriceofelectricityisfixedbytheCRE.

Theelectricitybillhastwocomponents:

ü A fixedcomponent, the subscription, in€peryear, thatdependson themaximum

powersubscribedbytheclient

ü A variable component, which represents the price of energy, in € per MWh

consumed, that is proportional to the energy consumed, but also depends on the

maximumpowersubscribedbytheclient

In addition to that, residential consumers can choose between two tariff options:

ü Auniquepriceoption,wherethevariablecomponentisthesameforeachhoursofa

day

ü Anoff-peakhours’option,wherethevariablecomponentdiffersalongthehoursofa

day.Generally,ahighpricefrom7a.m.to22p.m.whileenergydemandishigh,anda

lower price at night when the demand is usually lower. This tariff option can be

profitableiftheend-consumerisabletoshifttheuseofsomeapplianceatnight.

Wepresentindetailthepricesofeachcomponentforthetwotariffoptionsinthetables

below:

Uniquepriceoption

Powersubscribed Subscription

€/year

Electricityprice

€/MWh

3kVA 56,07 156,4

26

6kVA 96,50 144,9

9kVA 111,35 146,2

12kVA 172,78 146,2

15kVA 199,59 146,2

TABLE4:PRICEOFELECTRICITYFORRESIDENTIALCONSUMERS,UNIQUEPRICE

Off-peakhoursoption

Powersubscribed

Subscription

€/year

off-peakhours

€/MWh

peakhours

€/MWh

6kVA 100,51 156 127

9kVA 117,50 156 127

12kVA 183,25 156 127

15kVA 212,05 156 127

18kVA 239,84 156 127

TABLE5:PRICEFORELECTRICITYOFRESIDENTIALCONSUMERS,OFF-PEAKHOURSTARIFF

Fornon-residentialconsumers,suchasshops,restaurants,orsmalloffices,theoff-peak

optionisthemostcommon.Theelectricitybill,asforresidentialconsumers,iscomposedof

afixedcomponent(thesubscription)andavariablecomponent(thepriceofenergy).

Inthetablebelow,wecanseetheamountofthesetwocomponentsinfunctionofthe

maximumpowersubscribed,inkVA:

27

PowersubscribedSubscription

€/year

off-peakhours

€/MWh

peakhours

€/MWh

6kVA 102,72 94,6 72,8

9kVA 114,36 94,6 72,8

12kVA 163,20 91,8 68,5

15kVA 184,44 91,8 68,5

18kVA 204,24 91,8 68,5

24kVA 397,20 83,9 68,8

30kVA 475,20 83,9 68,8

36kVA 533,80 83,9 68,8

TABLE6:PRICEOFELECTRICITYFORNON-RESIDENTIALCONSUMERS EDF 2016

Adifferenttariffappliesforlargeconsumers,suchaslargeofficesinthecaseofaneighborhood.Thistariffappliesforconsumersthatneedsamaximumpowerover36kVA,

andcanbeseeninthetablebelow:

powersubscribed Subscription

€/kVA

off-peakhours

€/MWh

peakhours

€/MWh

>36kVA 5,72 25,29 40,44

TABLE7:PRICEOFELECTRICITYFORLARGECONSUMERS EDF 2016

Forcollectivities,adifferenttariffalsoappliesforpubliclightninguses.Thepriceofeachcomponentcanbefoundinthetablebelow:

28

powersubscribed Subscription

€/kVA

Consumption

€/MWh

<99kVA 80,16 61,4

TABLE8:PRICEOFELECTRICITYFORPUBLICLIGHTNING ADEME 2012

6.2.2. THECOSTOFDISTRICTHEATINGANDDISTRICTCOOLING

Here,weonlygiveanaveragepriceofheatandcold.Indeed,eachdistrictheatingorcooling

networkisbydefinitionspecifictoitslocationandthepriceofheatmaydifferabit.

Subscription Consumption

27to33€/kW 50to60€/MWh

TABLE9:PRICEOFHEATAND,OFF-PEAKHOURS’TARIFF

AMORCE 2014 CEREMA CPCU 2016

6.2.3. PVPANELS:FEED-INTARIFF

In France, for any PV panels installation under 12MWof capacity installed, there exists a

feed-intariffsetbytheCRE(theCommitteeofRegulationofEnergy),andanobligationfor

EDF(Frenchpublicenergycompany)tobuytheenergyproducedbysuchinstallation.

Thefeed-intariffdependsonthekWcofPVpanelsinstalled:

TABLE10:PV-PANELSFEED-INTARIFF EDF 2016

Capacityinstalled Feed-intariff

0-36kWc 132,7€/MWh

36-100kWc 126,1€/MWh

29

Thefeed-intariffforPV-panelstendstodecreasequicklywithtimeandisexpectedtosoon

disappear.However,uptonow,itis,inmostcasesprofitabletoselltheelectricitydirectlyto

EDFinsteadofself-consumingit.

Although, once this feed-in tariff would have disappeared, it would be necessary to self-

consumetheenergyproducedtokeepPV-panelsprofitable

6.3. SMARTGRIDSICTINFRASTRUCTUREANDSOFTWAREDEVELOPMENT

Wewillherehavea lookat thecomplexityof the ICT infrastructureneededto implement

smart grids. It will give us clues to understand the order of magnitude of the related

investmentandmaintenancecosts.

Hence,wepresentnow themainpartsof this ICT infrastructure thatenablesenergydata

collection.

The first part of a smart grid infrastructure is composed of smart meters. These devicesmeterdifferentstreamsofenergy(differentusesofelectricity,heatconsumption,hotwater

consumption,waterflow,etc.…).Wecanseeonthefigure6belowexamplesofsuchsmart

meters:

FIGURE7:ILLUSTRATION“LINKY”ANDLEGRANDPRODUCTS EMBIX 2016

Then, to control energy equipment,actuators are used. Depending on the actuator, theyeitherexecutephysicalaction(motors)orsendsignalstootherequipment.Someexamples

ofactuatorscanbeseeninthefigurebelow.

FIGURE8:ILLUSTRATION-B&RAUTOMATION

30

Acommunication networkwill establish the linkbetween thepreviousequipment (smart

metersandactuators) andadatabaseor software that collect thisdataor sendorders to

actuators.

Thereareseveralwaystoestablishthislink.Eachsolutiondiffersintermsofcost,reliability

and perimeter of application (data metered, equipment controlled and type of building

considered (housing, offices, shops…). We present below an overview of the four main

possiblearchitecturesforresidentialbuildings:

ConfigurationA–PrivateIPNetwork:thesmartmetersinstalledinsidetheapartmentsare

connected through a private IP network (RJ45 cables) installed inside the building. This

private IP network is connected to an internet box in the basement in order to send the

collecteddataoutsideofthebuilding

ConfigurationB–Networkoftheinhabitant:thesmartmetersinstalledintheapartments

areconnecteddirectlytotheprivateinternetboxoftheinhabitantsthankstoanRJ45cable.

Eachboxoftheinhabitantsisusedtosendthecollecteddataoutsideofthebuilding.

Configuration C or D – GSM or wireless network: the smart meters installed inside each

apartment send the collected data through awireless network (either a GSMnetwork or

newwirelessnetworkprotocolsusedforIOTsuchasLoRaorSigfox).

Anillustrationofeacharchitecturecanbefoundinthefigurebelow:

31

FIGURE9:OVERVIEWOFTHEMAINPOSSIBLEARCHITECTURESFORRESIDENTIALBUILDINGS EMBIX 2016

32

Acurrentestimationofthepriceofthesmartgrids’hardwareICTinfrastructureis2,5€/m2

(smartmeters,actuatorsandcommunicationnetworkincluded).

In addition to the hardware infrastructure, software development is needed to collect,monitor and control data and equipment. It is still hard to estimate the cost of such

development at the scale of a neighborhood. In this thesis, we estimated this softwaredevelopmentcosttobearound2,5€/m2too. EMBIX 2016

33

7. TOOLSBROUGHTBYSMARTGRIDSANDRELATEDIMPROVEMENTS

7.1. OVERVIEWOFTHENEWTOOLS

7.1.1. DATACOLLECTION

The first toolbroughtbya smart grid is at the foundationof the ICT technology, it’sdata

collection.ThisisthemostbasicfunctionthattheICTnetworkbringstoenergy.

All data that can be gathered is not necessarily relevant. Hence, at the scale of a

neighborhood,wehavegatheredinthenexttablerelevantenergydatatometerandcollect.

Wehavemadeadistinctionbetween

● the consumption side: data metered inside a building (private and shared

areas)anddatameteredinurbanareas

● andtheproductionside

A relevant time-scale toacquire thedata forpost-treatmenthasalsobeenproposed. The

largestisthetime-scaletoacquirethedata,thelessitispossibletocapturepowerpeaks.

Thenexttablepresentsrelevantdatatogatherforaneighborhood

Energydata

Insideabuilding–privateareas

Data Unit Timescale

Electricconsumption

-Specificuses(electricaloutlets)

-Lighting

-Auxiliaries(ventilation,pumps)

kWh 1minto1hour

Hotwaterconsumption kWh 1minto1hour

Heatconsumption kWh 1minto1hour

Coldconsumption kWh 1minto1hour

Powerwithdrawnfrom

publicdistribution

network

kVA 1second

34

Waterflow m3 1minto1hour

Insideabuilding-sharedareas

Elevator kWh 1minto1hour

Sharedarealightning kWh 1minto1hour

Collectiveventilationfans kWh 1minto1hour

Urbanconsumption

Publiclighting(andurban

infrastructure)

kWh 1minto1hour

ChargingstationsforEVs kWh 1minto1hour

Localproduction

Localheatproduction

-Energyproduced

-Temperatureofdelivery

-Temperatureofreturn

-Flow

kWh

°C

kg/s

1minto1hour

Localelectricity

production

kWh 1minto1hour

TABLE11:RELEVANTENERGYDATATOMETERFORANEIGHBORHOOD EMBIX 2016

Along with these energy data streams, it is important to collect additional data from

identifiedinfluencefactors.

Thefactorsofinfluenceidentifiedareexposedinthenexttable:

35

Influencefactors

Data Unit Timescale

Weatherdata -outsidetemperature

-solarirradiation

°C

W/m2

1hour

Insidetemperature °C 1hour

Insidehumidity % 1hour

Workingday/Dayoff - 1day

Activetimeperiodofaplace hour 1hour

TABLE12:INFLUENCEFACTORS ACORBA 2012 EMBIX 2016

7.1.2. MONITORING

It is interesting and important to go further only collecting data. Thenext step is then to

monitorenergystreamsfromthedata-collectionpreviouslyrealized.

Key performance indicators can be defined to efficiently monitor the performance of a

neighborhood,accordingtothegoalssetatthebeginningofaproject.

A wide range of KPI can be defined. They depend on the goals and ambitions set at the

beginningofaproject,someinterestingonesare:

ü Theshareofrenewableinthelocalenergymix

ü TheamountofGHGemitted

ü The level of consumption of a building per usage defined in the thermal

regulation

ü Theself-sufficiencyofneighborhood

ü Theaverageenergybillsoftheinhabitants

Continuousandefficientmonitoringofenergystreamsisatoolprovidedbythesmartgrids

technology.Thefirstconsequencesarelistedinthetablebelow:

36

Consequences Benefits

Theabilitytogetrealtimeoverviewofthe

energystreamsofaneighborhoodknowledgeofthesystem

Remotedetectionoffailure(whenaKPI

differsfromtheexpectedgoal)

increasemaintenanceefficiency&decrease

maintenancecosts

Giveexposuretotheterritorythroughreal

timeperformancevisualizationnewcommunicationtool,exposure

TABLE13:BENEFITSOFMONITORING

7.1.3. ANALYSISOFFAILURECAUSES

Asseenpreviously,theabilitytofollowinreal-timeaKPIallowsonetoseewhenthetracked

performancedeviatesfromitsexpectedvalue.However,ananalysisofcausesoffailurehas

tobefurtherconductedtounderstandtheoriginofthedeviation.

Atthescaleofaneighborhood,theoriginofadeviationcaneitherbedueto:

ü particularweatherconditions

ü thefailureofanequipmentand/orofanhypothesisofconception

ü astakeholderthatdoesnotcomplywithitscommitments

Therefore,toconducttheanalysisofcausesoffailure,thefactorsofinfluencecollectedare

usedinordertoadjusttheKPIcalculatedpreviouslyaccordingtothefactorsofinfluenceand

theirrelativeimpactonaparticularKPI.

Iftheadjustedvaluereachesthetarget,thedeviationisduetoparticularweatherconditions

(extremecoldwinter,…). If there is stillagapbetweentheadjustedvalueandthetarget,

thenoneormorepiecesofequipment is failingorastakeholderdoesnotcomplywith its

commitments. ACORBA 2012

The further analysis of the collected-data will enable operators to find the origin of the

failureandunderstandwhichequipmentorstakeholderisatfault.

The ability to send alarms in real-timewhen an equipment fails, represents a great time

savings,awaytolimittheaveragetimewithbadperformance,andanaddedguaranteeto

secureenergysupplyfortheend-users.

37

7.1.4. CONTROLOFENERGYSTREAMS

Throughactuators,smartgridstechnologyallowsonetocontrolenergystream,andtosend

orderstoenergyequipment.

First, let's see how to use control of equipment to optimize energy stream andwhy it is

relevant at our scale.Wewill analyze in a second-timewhich equipment can be and are

relevanttocontrolforaneighborhood.

The control of an equipment is done in response to several signals depending on the

assignedroleoftheequipment.Thesignalsrelevantintermsofenergyarethefollowing:

ü Energy demand: the intent is there to reduce the peak power of the

neighborhood(oratalargerscale:territory,country,…)

ü Thepriceofenergy(electricity,heatandcold):theintentistheretomatchas

muchaspossibleusageswithlowpriceofenergy

ü GHG emissions: the intent is there to consume when the share of GHG

emissionisthelowest

ü Localenergyproduction:theintentistheretomaximizetheself-consumption

ofalocalrenewableproduction

ü Apre-definedsignal,notreal-timedependent(e.g.atimewindowwhereto

turn-offthelights).

Thesesignalscanbecombined.

Theoptimizationofanequipmentisthendoneaccordingtorulesbetweenthesesignalsand

theprimaryfunctionoftheequipmentandenergy-streamrelated(e.g.:heaterthatheatsa

room).

AsseeninpartI,thescaleoftheneighborhoodisreallyrelevantherebecauseofthevariety

ofusage.Synergiesaremorelikelytobefoundthanatthescaleofasinglebuildingandthe

proximityoftheusageseliminatessomeconstraintsoftransportofelectricity.

Theequipmentinterestingtocontrolatthescaleofaneighborhoodarethefollowing:

Atthebuildingscale

- Theventilationsystem:acontroloffansinfunctionofairqualityispossibletoincreaseairqualityandreduceventilationneedstothecurrentusageofspace.

- Lighting:Acontrol canbe implemented to reduce intensity atnight, switch

offforgottenlightsordecideofatimewindowforlightning

Atthescaleofaneighborhood

- heaters, chillers, hot water production and heat storage: Thermal needs

representagreatpartoftheenergyconsumedbyend-usersandhavetheadvantage

ofbeingaproductionthatcanbequitedisconnectedfromthetimeofneedthanksto

thethermalenvelopeofbuildingsorhotwatertanks.

38

Controllingsuchequipmentenables to increase thecomfortofend-usersbut isalsoakey

issuetoreducepeakpowers.Thepossiblecontrol-actionsatthescaleofaneighborhoodare

toshifttheactivationofthedifferentheatersandhotwaterproductiontoadifferenttime-

period in order to smooth the load curve of the neighborhood and reduce morning or

eveningpeakpowers.

Furthermore,controlcanbeimplementedtomatchtheirconsumptionwithlocalproduction

oftheneighborhood(PVpanelsmainly),inordertomaximizelocalself-consumption.

- Charging stations: Charging stations are developing along with electricvehicles. Ifnumerous,theycanaddabigconstraintontheelectricgrid.Controlling

chargingstationisthusparticularlyrelevantforaneighborhood,inordertolimitthe

maximumpowerpeakofagroupofchargingstations.Thislimitationhastobedone

withtheconstraintthatend-usersshouldhavetheirEVchargedintime.Charging stations could also adjust their load to increase self-consumption of local

renewableenergyproduction.To furtherextent, theycouldbeusedasenergyproduction

sourcesifthegridneedsit,usingthebatteryfromtheEVs.

- Public lighting: Control of public lighting can be a great cost-saver formunicipalities.Atypicalcontrolofpublic lighting istoreducethe intensity(oreven

turn-off)atnightfordefinedtime-windows.

- Electric storage (battery mainly): electricity storage can be controlled forseveral purposes. First, to prevent the load curve of the neighborhood from

exceeding a peak power value, the system was not designed for. Then, electric

storage canbe controlled toachieve consumption cut-off in response toelectricity

priceonthespotmarket.Finally,itcanbecontrolledtostorePVpanels’production

surplus.

Batterypricesarestillabithightomakeoneofthisuseprofitablebyitself.However,with

decreasingpricesandthecombinationofthesevarioususages,itwillbecomeprofitable.sources EMBIX 2016

7.1.5. THEVALUEOFDATA-FROMDATATOKNOWLEDGE

Allthedatagatheredandstoredinthedatabaseisexpectedtoincreaseourunderstanding

ofbuildings,energyconsumptionandprediction.

Machinelearningwillsupporttheconversionofthisdataintoknowledge.

Furthermore,thedatagatheredopensthedoortonewB2CandB2Bservicesaroundenergy

usages.

***

39

Wehavejustseenthenewtoolsandopportunitiesbroughtbyasmartgridatthescaleof

theneighborhood.Wewillnowseehowthesetoolsandopportunitiescanbetranslatedin

termsofeconomic,environmentalandsocialimprovements

7.2. CONSEQUENCESANDIMPROVEMENTSOFTHEIMPLEMENTATIONOFASMART

GRIDFORANEIGHBORHOOD

7.2.1. ECONOMICIMPROVEMENTS

The economic improvement to the implementation of smart grids will derive from three

things.

First,theawarenessofthepossibilities,previouslyexposed,offeredbythistechnologytobetterdesigntheenergyinfrastructureforanewneighborhood.

Then,theabilitytomonitortheperformanceoftheenergystreamsoverthelife-timeofaproject.

Finally,theabilitytocontroltheequipmentexposedaboveinordertooptimizetheirusage

inresponsetoeconomicsignals.

Thesethreeconceptscorrespond in fact to twophasesofaproject: theconceptionphase

andtheoperationalphase.

Let’s take a closer look at these two phases and analyze how smart grids economically

improvetheequation:

Conceptionphase:Takeintoaccountthenewopportunitiesofthesmartgridtechnologytodesigntheenergyinfrastructureoftheneighborhood

In the lightof thenewtools (datacollection,monitoringandanalysisofcausesof failure),

andopportunitiesofferedbythesmartgridtechnology,intheconceptionphase,thesavings

willberealizedthroughafairersizingoftheenergyinfrastructure.

Thismeansboth:

ü lessequipmentonsite

ü lessoversizingoftheequipment

Thethermalproductionmix:

A cheaper and more efficient mix can be found through the implementation of energy

storageandcontrolofthermalstoragetanks;

The reduction of peak power (due to the reduction of thermal needs) also enables the

reductionofthesizingofthethermalgrid.

Thecostofconnectiontotheelectricpublicdistributionnetwork:

Thereductionofthepeakpowerneedoftheneighborhoodwillleadtothereductionofthe

numberoftransformers(ortransformerstations).

40

The way to secure this value is to take into account the possibilities of the smart grid

technologyduringengineeringdesignintheconceptionphase.

***

However, the lessons learned from the first eco-neighborhood have shown us that thegoalsandexpectedperformanceare seldomreached.These firsteco-districts revealover-

consumptionof around20 to100% from the initial target. Therefore, it isdifficult for the

stakeholderstosizetheenergyinfrastructureaccordingtotheinitialtargetsandthus,truly

doachieveafairersizing.Theyoftenconsiderittooriskytosizetheinfrastructureaccording

tothetargetsandgoalsofaprojectsincetheyarelikelynottobemet.

Anewtoolseemsneededtoavoidthesedeviationsandtobeabletofairlysizeenergygrids

at the scaleof theneighborhoodand secure theeconomic reduction that smart grids can

provide.Wewillpresentandanalyzewhichcouldbethisnewtool(TheEnergyPerformance

Contract)inthenextpart.

But first, let’s see theothereconomic improvements alongwithenvironmental and social

improvements

Operationalphase:

Controlofenergystreams-Demandresponse

Thecontrolofequipment inresponsetopricesignals(e.g.:activationofdemandresponse

accordingtoelectricitypricesonthespotmarket)willleadtoannualsavings.

Also, improvingthematchbetweenlocalproductionandconsumptioncanreducethebills

ofend-usersthroughself-consumption.

Furthermore,theabilitytotakeadvantageofoff-peakshourswillalsoleadtoannualsavings

Monitoring

The ability tomonitor the energy performance of a neighborhood gives the possibility to

detect deviation of performance in real time and act in consequence to maintain the

performanceofthesystem.

Furthermore,thereductionofmaintenancecostscanbeexpected.Itishardtoestimatebut

it is a direct consequence of monitoring and control of equipment (remote detection of

failureandremotecontrol).

7.2.2. ENVIRONMENTALIMPROVEMENT:

The implementationof smart grids representsanenvironmental improvement in termsof

reductionofGHGemissions.Thisisachievedthrough

ü thereductionoftheenergyconsumption

ü theabilitytoenableahighershareofrenewableintheenergymix

ü the reduction of peak power consumption that corresponds to higher GHG

emissionsintheelectricitymix

41

ü furthermore,theabilitytocontrolequipmentinordertofollowGHGemission

signalscanfurtherimprovethereductionofGHGemissions

Wecanobserveinthegraphbelow,whichrepresentinbluetheexpectedconsumptionofa

theneighborhood“EoleEvangile” EMBIX 2016 inParis,thematchbetweenpeak-power

consumptionthisneighborhoodandrelatedhighGHGemissionsoftheFrenchnationalgrid.

7.2.3. SOCIALIMPROVEMENTS

Smartgridsbringsocial improvementstakingtheformofnewservicesforthe inhabitants.

Indeed,thesesmartcityserviceswillbebasedoncollecteddata.

Smart city serviceswill developandwill benefit to address all theaspectsof a city. Some

examplesof thedomainsandassociated services thathaveemergedorwill thanks to the

newdataacquiredare:

TABLE14:EXAMPLESOFB2BANDB2CSERVICESFORASMARTCITY

THEME SERVICE THEME SERVICE

Energy Energy Performance Supervisor

Energy, water, waste

Follow-up of consumption, serious gaming, sensitizing

Energy

Control of energy equipment, Demand side management operatorControl of public lightning

Mobility Multimodal mobility platform

Energy, water, waste

Operation aid based on collected data Mobility Shared fleet of electrical

bikes, electrical vehicles

All theme Service portal of the neighborhood Mobility Local car-sharing platform

Mobility Shared car parkBusiness services Co-working centersServices to individuals

Remote control of smart home (heat, shutters, …)

Services to individuals Janitorial service

Services to individuals Connected health devices

B to C servicesB to B services

FIGURE10:MATCHBETWEENPEAK-HOURSANDHIGHGHGEMISSIONS EMBIX 2016

42

8. ENERGYPERFORMANCECONTRACT

8.1. CONTEXT

The lessons learned from the first eco-neighborhoods have shown us in Part I that the

objectivesofperformancedefinedatthebeginningofaprojectwereseldomreached.

Thedeviationsobservedfromtheexpectedperformancewerequitehigh(from20%upto

100%ofover-consumptioncomparedtoexpectedlevels).

Thefirstobservationthatwasmadefromtheselessonslearnedisthatthecausesoffailure

arediverseandtheirorigintakesplaceduringtheconceptionphaseasmuchasduringthe

operationalphaseoftheneighborhood.

The secondobservation is that a poorer energyperformance from theneighborhood falls

backonawiderangeofactors:

ü the inhabitants (or union of owners) thatwill pay a higher energy bill than

expected

ü networkoperators, local energyproducers, facilitymanagers. The reduction

of infrastructureexpected fromtheopportunitiesof smartgrids technologycannot

takesplace if thehighperformance targeted isnot reachedandmaintainedduring

the operational phase. A proper sizing of the energy infrastructure cannot beexpectedfromthisvariousrangeofactors ifthecommitmentsarenotreachednor

cancompensationhappenwhenastakeholderdoesnotrespectitscommitments.

Hence,anadditionaltoolappearstobeneededinordertosecuretheenergyperformance

ofaneighborhoodtofullytakeadvantageoftheimprovedperformance.

We here present the Energy Performance Contract at the scale of a neighborhood as apossiblesolution.

8.2. DEFINITION

AnEnergyPerformanceContractatthescaleofaneighborhoodisabindingtoolthat

involvesthemainstakeholdersofaproject:

ü Unionofowners

ü Facilitymanagers

ü Realestateagencies

ü Sociallandlords

ü LocalEnergyProducers

ü Networkoperators

Suchacontractdefines:

ü the set of assumption used in the conception phase to design the energy

infrastructureandcalculatetheexpectedperformance

ü the related indicators of performance to follow the behavior of the

neighborhoodinitsoperationalphase

ü theexpectedvalueoftheseKPIunderthesetofassumptiondefined

ü theroleofeachstakeholdertowardthisperformance,anditscommitments

43

ü thechainofresponsibilitybetweenthedifferentactors

Furthermore,asystemofbonusandmalushastobeimplement,inorderto

ü encouragethestakeholderstokeepupwiththeircommitmentsandoutscore

them(bonus)ü compensate the losses of any stakeholder that would suffer from a lower

performance than expected due to the non-compliance of one or several

stakeholderstotheircommitments(malus)

Such an energy performance contract has to be defined during the conception phase in

concertationwithallthestakeholdersinvolved. ADEME 2015

Therefore,theobjectivesofsuchacontractare:

ü toeducateeachactortowardsitscommitments

ü to reduce and secure the risk taken by the different actors to design the

infrastructureinharmonywiththeexpectedperformanceoftheneighborhood

ü toactuallyreachandactivelymaintaintheperformanceoftheneighborhood

duringtheoperationalphase,over-time

Thislastpointstandsonsmartgridstechnologytobeabletoberealizedandimpliesthata

new actor emerges in order to enforce the energy performance contract during the

operationalphaseoftheneighborhood

8.3. ANEWACTOR:THEPERFORMANCESUPERVISOR

This new actor, that can be called the performance supervisor, has, as explained, thefollowingactivitiestoconduct:

ü Tocollectawiderangeofdatafromenergynetworksandbuildings

ü Usethisdatatofollowthekeyperformanceindicatorsdefined

ü AdjusttheKPIvaluecalculatedaccordingtoinfluencefactors

ü Conductacausesoffailureanalysis

ü Warntheappropriateactorswhentheperformancedeviates

ü Make sure that each stakeholder respects its commitment defined in the

energyperformancecontract.Ifnot,ensurethatbonusandmalusareimplemented.

8.4. BUSINESSMODELCANVASOFTHEPERFORMANCESUPERVISOR

Inordertobetterunderstandthemissionsandadded-valuesbroughtbysuchanewactor,

wehaveproposedbelowthebusinessmodelcanvasforthisnewactor:

44

TABLE15:BUSINESSMODELCANVASOFTHEPERFORMANCESUPERVISOR

BusinessModelCanvas-PerformanceSupervisorKeypartnersAcquiredata

Building

Management

System

Network

operators

Hardware

providers

KeyactivitiesDevelopment

Webdevelopment

Personalized interfaceof the

neighborhood

DataAnalysis

Datacollection

KPI calculation and adjust the value

accordingtoinfluencefactors

Analysisofcausesoffailure

Identification of the chain of

responsibilities

Warningsinrealtime

Monthlyperformancereport

Contractenforcement

Management and enforcement of the

contract

Management and distribution of bonus

andmalusbetweenthestakeholder

ValuepropositionA-Sustaintheperformanceofaprojectinthelong

term

-achieveresilientandsustainableurban

development

B-Differentiatefromotherrealestateagencies

-Developtoolstopromoteoneselfforfurther

projects

C-Securelowandaffordableenergybills

-Reduceoperationalcosts

D-Reduceinfrastructurecostsofconnectionofthe

neighborhoodtothepublicdistributionnetworkon

thebaseofexpectedperformance

-Securethisreductionwhileavoidingtobe

penalizedifathirdpartydoesnotcomplywithits

commitments

E-Reduceinfrastructurecosts

-Reduceoperationalcostsofdistrict

heating/cooling

-Securethisreductionwhileavoidingtobe

penalizedifathirdpartydoesnotcomplywithits

commitments

F–Reduceinfrastructurecosts

-Reduceoperationalandmaintenancecosts

-Securethisreductionwhileavoidingtobe

penalizedifathirdpartydoesnotcomplywithits

commitments

Customerrelationship-Monthlyperformance

reviewandanalysisof

failure

-Monthlycommittee

-Webaccesstoplatform

tofollowuprealtime

performanceofthe

neighborhood

-Automaticwarningin

caseoffailure

CustomersegmentsA-Collectivities,

municipalitiesand/or

localplanning

authority

B-Realestate

agencies

C-Unionofowners

andfacilitymanagers

D-DSO(ENEDIS)

E-district

heating/cooling

operator(s)

F-Localenergy

producers

KeyresourcesHuman

ICTandsoftwaredevelopmentengineers

Energyandsmartgridsengineers

Legalsupport

Material

Datacollectionplatform

Intellectuals

Chainofresponsibilities

Factorsofinfluence

Regulation, juridical and contractual

inputs

DistributionChannels

-Callfortenders

-Commercial

prospection

-Proofofconcept

Coststructure-Softwaredevelopment(platform)

-Continuousoperationoftheplatforminoperationalphase

(wages)

Revenues-Annualrevenuesfromunionofownersandfacilitymanagers

-Subventionsfromcollectivitiesandlocalplanningauthorities

45

9. STUDYCASES

We have seen the new tools brought by smart grids. We will now put in practice the

economicbenefitsoftheimplementationofsmartgridswithtwostudycases.

9.1. SIMULATIONTOOLS

Theimprovementsexposedabovewillbesimulatedonthesetwostudycaseswiththehelp

ofthreesimulatorsdevelopedduringmyinternship:

1. A load curve simulator that simulates the expected consumption of a

neighborhood,overayear,withanhourtime-step.

Tosimulatessuchcurves,theinputsofthisfirstsimulatorarethefollowingones:

ü The types of buildings (offices, housing, restaurants, shops, …) that compose the

considered neighborhood, and the relative area constructed of each of these

buildings

ü Theexpectedperformanceoftheconsideredbuildings,inkWh/m2/yr

ü Realloadprofilesobtainedfromdatameteringonexistingbuildings

2. A PV-panel production simulator that simulates the expected PV panels

productionoverayear,withanhourtimestep

TheinputsofthePV-panelproductionsimulatorarethefollowingones:

ü First,inputsaboutthePV-panelsused:thetotalareaofPVpanelsinstalled,theirtilt

andelevationangle,andtheirefficiency

ü ThentheGPScoordinatesofthelocationtheyareinstalled

ü Finally, realdirectanddiffuse radiationat local location,overayear,withanhour

time-step.Thisdataisgivenbyweatherstations

3. An economic simulator, that simulates the revenues, maintenances and

investmentscosts,overa20to25timeperiod,relatedtochosenenergymixchosen

tosupplyenergy(bothheat,coldandelectricity)toaneighborhood.

Tosimulatessuchresults,thislastsimulatortakesintoaccounts:

ü Theloadcurvessimulatedbythetwoprevioussimulators

ü Infrastructurecostsandmaintenancecostsofmainenergyequipment

ü Theparticularequipmentchoosetobeinstalledonsitetosupplytheneighborhood,

andtheirrelatedcapacity

46

Forallthesimulationsconsideredbelow,wewilllookattheglobaleconomicequationofthe

system. Indeed, we do not try to allocate the various investment between each of the

stakeholders involved. This choice has beenmade becausewewant to highlight here the

globalbenefits tothe implementationofsmartgrids.Wedonotclaimtoproposeaviable

distributionofthebenefitsobtained.Thisishighlydependentontheparticularitiesofeach

project. However, these study cases demonstrate rather well how a neighborhood can

benefiteconomicallyfromtheimplementationofsmartgrids.

9.2. “BORDEAUXAMÉDÉE”THERMALINFRASTRUCTURE

9.2.1. CONTEXTANDISSUES

The first study case will be conducted on the neighborhood called “Bordeaux

Amédée”.ThisneighborhoodislocatedinBordeaux,Franceinwhatisnowanunconstructed

land next to railway tracks. In the figure below, we can see an illustration of the future

neighborhood:

FIGURE11:FLOORPLANOFTHENEIGHBORHOOD�EMBIX�2016�

Intotal,theneighborhoodwillrepresent100772constructedsquaremeters,composedof:

ü 42000m²ofoffices

ü 54000m²ofhousing

ü 3370m²ofshops

ü 1402m²ofrestaurants

ü 121publiclights

47

The neighborhood is expected to be connected to a district heating network (geothermal

heatpowerplant)locatedoutsidetheneighborhood,ontheothersideoftherailwaytracks

whichrepresentsthemainconstraint.Indeed,thepipethatwillsupplytheneighborhoodinheatingneedislimitedto2MWofheatpower.

However,theestimatedpowerpeakofheatneededis3,5MW.ThispeakoccursinJanuary,

aswecanseeonthegraphbelowthatrepresentstheaverageheatingneedprofileofthe

neighborhoodforadayinJanuary.

FIGURE12:DAILYHEATANDHOTWATERLOADCURVESIMULATEDWITHTHELOAD-CURVESSIMULATOR

Theelectricneedsoftheneighborhoodhavealsobeensimulatedthankstotheload-curves

simulator.Hereisatypicaldayinwinter:

FIGURE13:DAILYWINTERELECTRICLOADCURVESIMULATEDWITHTHELOAD-CURVESSIMULATOR

0kW200kW400kW600kW800kW1000kW1200kW1400kW

0-1

1-2

2-3

3-4

4-5

5-6

6-7

7-8

8-9

9-10

10-11

11-12

12

-13

13-14

14

-15

15-16

16

-17

17-18

18

-19

19-20

20

-21

21-22

22

-23

23-24

Electricneedsoftheneighborhood

Offices Housing Shops Restaurants

Public lightning EV's Totalelectricneeds

48

9.2.2. OBJECTIVESOFTHESIMULATION

The objective of the simulation will be to analyze the economic influence of the

implementation of smart grids for the neighborhood, according to the thermal constraint

exposedbefore.

Twoconfigurationsofthethermalmixhavebeenexamined:

ü Configuration 1: No smart grids is implemented. The thermal needs of the

neighborhood are met thanks to the district heating network and gas heaters in

complementforpeakhours

ü Configuration 2: A smart grids is implemented. The thermal needs of the

neighborhood are met thanks to the district heating only. A heat storage tank is

mutualized between the different buildings of the neighborhood in order to cover

thepeakhoursneedsofheatandhotwater.

Inthetwosimulations,thepriceofheatandhotwaterissupposedtobekeptthesamefor

end-users.

Inordertofullycapturetheeconomicbenefitsofsmartgrid,twoscenarioshavebeen

studied,correspondingtotwodifferentperimeters.

ü Scenario1:Configuration1and2havebeencompared,consideringonlythe

costs related to the heat and hot water infrastructure. The only improvement

broughtbysmartgridsisheretobeabletocontrolthermalheatstorageandavoid

theimplementationofcomplementarygasheaters.

ü Scenario2:Forthissecondscenario,theideaistocoverawiderrangethanonly the thermal mix. Electric needs of the neighborhood are added to the

simulations.Therefore,inconfiguration2,smartgridsenableto:

ð Control the public lighting needs, reducing the intensity by 30% at

nightbetweenmidnightand5a.m.

ð Controlthechargingstationsinstalledonsitebylimitingthemaximum

powerconsumptionofthegroupof10chargingstations.

TheKPIchosentometerandfollowtheperformanceofthedifferentconfigurationsinthe

twoscenariosare:

ü Therateofreturnoninvestment

ü Thetimeofreturnoninvestment

ü TheCO2emissionsperyearassociatedtotheheatandhotwater

consumptionoftheneighborhood

49

9.2.3. DETAILEDASSUMPTIONSMADEFORTHETWOSIMULATIONS

Inthetablesbelow,wehavelistedthedetailedassumptionsmadeineachscenario.

Forscenario1

Configuration1

Configuration2Summaryoftheassumptions GHGemissions Summaryoftheassumptions GHGemissions

Generalassumptions

Tofulfilltheheatandhotwaterneedsoftheneighborhood,gasheatersareinstalledonsiteincomplementofthedistrictheatingnetwork

-

Generalassumptions

Tofulfilltheheatandhotwaterneedsoftheneighborhood,heatstorageisimplementedonsiteincomplementofthedistrictheatingnetwork;Smartgridsinfrastructureistheninstalledtomonitorandcontrolenergystreams

-

Electricgrid 82gCO2/kWh Electricgrid 82gCO2/kWh

HotWaterProvidedthroughdistrictheating

2MWandgasheaters2MW

Districtheating:50gCO2/kWh

HotWaterProvidedthroughdistrict

heating(2MW)Districtheating:50

gCO2/kWhHeat

Gasheaters:400gCO2/kWh

Heat

Heatstorage Noheatstorage - Heatstorage 1,5MWhofheatstorage -

SmartGridsInfrastructure

Nosmartgridequipment -

SmartGridsInfrastructure

Smartgridsinfrastructureinstalled;

50%oftheequipmentgoundermaintenanceevery8years

-

Publiclighting 82gCO2/kWh Publiclighting 82gCO2/kWh

Electricvehiclesandchargingstations

82gCO2/kWh

Electricvehiclesandchargingstations

82gCO2/kWh

TABLE16:STUDYCASE1,SCENARIO1,DETAILEDASSUMPTIONS

Source:�ADEME�

50

Forscenario2

Configuration1 Configuration2Summaryoftheassumptions GHGemissions Summaryoftheassumptions GHGemissions

Generalassumptions

Tofulfilltheheatandhotwaterneedsoftheneighborhood,gasheatersare

installedonsiteincomplementofthedistrictheatingnetwork

-

Generalassumptions

Tofulfilltheheatandhotwaterneedsoftheneighborhood,heatstorageisimplementedon

siteincomplementofthedistrictheatingnetwork;

Smartgridsinfrastructureistheninstalledtomonitorandcontrolenergystreams

-

Electricgrid 82gCO2/kWh Electricgrid

82gCO2/kWh

HotWaterProvidedthroughdistrictheating2MW

andgasheaters2MW

Districtheating:50gCO2/kWh

HotWater

Providedthroughdistrictheating(2MW)Districtheating:50gCO2/kWh

HeatGasheaters:

400gCO2/kWh

Heat

Heatstorage Noheatstorage - Heatstorage 1,5MWhofheatstorage -

SmartGridsInfrastructure

Nosmartgridequipment -

SmartGridsInfrastructure

Smartgridsinfrastructureinstalled;50%oftheequipmentgoundermaintenance

every8years-

Publiclighting121publiclights,stablebrightnessthrough

thenight82gCO2/kWh

Publiclighting

121publiclights;Smartgridsenabletocontrolthebrightnessthroughthenight(-30%frommidnighttill5

a.m.)

82gCO2/kWh

Electricvehiclesandchargingstations

5newEVseachyearforthefirst10years(50intotal)

1newchargingstationof22kWeachyearforthefirst10years(10intotal)

82gCO2/kWh

Electricvehiclesandchargingstations

5newEVseachyearforthefirst10years(50intotal)

1newchargingstationof22kWeachyearforthefirst10years(10intotal);

Peakconsumptionpowerforthechargingstationslimitedat70kW(insteadof220kW)

82gCO2/kWh

TABLE17:STUDYCASE1,SCENARIO2,DETAILEDASSUMPTIONS

51

9.2.4. RESULTSOFTHESIMULATIONS

For the two scenarios, we observe a better rate of return on investment with theimplementationofsmartgrids.

Thetimeofreturnoninvestmentisoneyearquickerwiththeimplementationofsmartgridsforscenario1,anduptofouryearsquickerwithscenario2wheretheopportunitiesofferedbysmartgridshavebeenmoreexploited.Theresultscanbefoundinthetablesbelow:

Rateofreturnoninvestment

Nosmartgrid Smartgrids

Scenario1 4,79% 5,36%

Scenario2 2,84% 3,41%

TABLE18:RATEOFRETURNONINVESTMENT

Timeofreturnoninvestment

Nosmartgrid Smartgrids

Scenario1 15 14

Scenario2 18 14

TABLE19:TIMEOFRETURNONINVESTMENT

IntermsofCO2emissions,theimplementationofsmartgridsrepresentsanimprovementof

2,5%lessemissionsperyear.Thisimprovementisnotsignificant.Indeed,theonlyreductionconsidered comes from the use of thermal energy storage to replace complement-gasheaters.Therefore,inreality,gasheatersareusedonlyforpeak-hoursanddonotrepresentasignificantenergyproductioncomparedtotheheatandhotwatersuppliedbythedistrictheating.According to the simulation, theyonlyprovide1,16%of theheat andhotwaterneed.

52

CO2emissionsperyearoftheneighborhood

Nosmartgrid Smartgrids

Scenario1&2 232t_CO2/yr 226t_CO2/yr

TABLE20:CARBONFOOTPRINTOFTHENEIGHBORHOODOVERAYEAR

Theimprovementbroughtbysmartgridsisheremainlyeconomic.

Below,wepresentthecashflowrelatedtothedifferentconfigurationandscenariosovera25yearstime-period.Thesecurveshavebeenobtainedthankstotheeconomicsimulator.

53

Forscenario1

FIGURE14:STUDYCASE1,SCENARIO1,FREECASHFLOW

-4000k€

-3000k€

-2000k€

-1000k€

0k€

1000k€

2000k€

3000k€

Amou

ntofcashflo

wso

ccuring(k€)

Durationoftheinvestment(years)

Scenario1- NoSmartGrid- Returnoninvestmen

Revenues Investissement Maintenance Freecashflow

-4000k€

-3000k€

-2000k€

-1000k€

0k€

1000k€

2000k€

3000k€

Amou

ntofcashflo

wso

ccuring(k€)

Durationoftheinvestment(years)

Scenario1- SmartGrids- Returnoninvestment

Revenues Investissement Maintenance Freecashflow

54

Forscenario2

FIGURE15:StudyCase1,Scenario2,FreeCashFlow

-4000k€ -3000k€ -2000k€

-1000k€ 0k€

1000k€

2000k€

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Amou

ntofcashflo

wso

ccuring(k€)

Durationoftheinvestment(years)

Scenario2- NoSmartGrid- Returnoninvestment

Revenues Investment Maintenance Freecashflow

-4000k€ -3000k€ -2000k€

-1000k€ 0k€

1000k€

2000k€

3000k€

Year1 Year2 Year3 Year4 Year5 Year6 Year7 Year8 Year9 Year10 Year11 Year12 Year13 Year14 Year15 Year16 Year17 Year18 Year19 Year20 Year21 Year22 Year23 Year24 Year25

Amou

ntofcashflo

wso

ccuring(k€)

Durationoftheinvestment(years)

Scenario2- SmartGrids- Returnoninvestment

Revenues Investment Maintenance Freecashflow

55

9.3. “LARÉUNION”,ANISLANDTERRITORY

9.3.1. CONTEXT:SPECIFICITIESOFANISLAND

TheIslandof“LaRéunion”isaFrenchislandlocatedintheIndianocean.Asmanyisolatedterritoriesenergyisanissueforseveralreasons.

First, the cost of production of energy is high and uncertain. This is mainly due to themassiveimportationoffossilfuel,byboatstotheisland.

Furthermore,theIslandof“LaRéunion” isquitesteep,withavolcano inthecenteroftheisland.Thisrepresentsagreatchallengeforthetransportanddistributionofelectricity.Theresultisacostoftransportanddistributionofelectricitymuchhigherthaninthemainland.Wecanseethepublictransportanddistributionnetworkof“LaRéunion”below:

FIGURE16:TRANSPORTNETWORKOFTHEISLANDOF“LARÉUNION”

56

Theglobalcostofelectricityontheislandisthereforemuchhigherthaninthemainlandandcanbeseeninthefigurebelow.

FIGURE17:COSTOFELECTRICITYONTHEISLAND�EMBIX�2016��EDFSEI�2015��CRE�2014�

However,theislandbenefitsfromaregulatedpriceofelectricity(158€/MWh)fortheend-users,asinthemainland.Hence,wecanunderstandthatthecurrentbusinessmodelisnotviablebotheconomicallyandenvironmentallyspeaking.

But,asmostoftheisland,“LaRéunion”benefitsfromagreatpotentialforrenewableenergyproduction.Firstandforemost,solarirradiationisveryimportantontheterritoryaswecanseeonthesolarmapoftheIsland:

FIGURE18:GLOBALSOLARIRRADIATIONMAPOFTHEISLAND;SOURCE:�ARER�2010�

57

Otherrenewables(hydro,tidalorwindpower)alsohaveagreatpotentialontheIsland.However,wewillnotstudythemhereandconcentrateonsolarenergy.

9.3.2. OBJECTIVEOFTHESIMULATIONS

The issue we want to address in this study case will be analyzing the benefits of theimplementationofsmartgridsforaneighborhoodonthisIslandinordertomakeitmoreortotallyindependenttowardenergyproductionandusages.

The neighborhood considered does not exist but is inspired of 6400m2 of an urban arealocated in “Saint-Denisde LaRéunion” called “La citédesarts”. Thebuildings and relatedsurfacearethefollowing:

ü 3160m2ofofficesü 1540m2ofhousing

ü 1700m2ofshopsandculturalequipment

The electricity needs of the neighborhood have been simulated thanks to the load-curvesimulator.�ARER�2010�Wecanseebelowatypicalload-curveforadayinJanuary:

FIGURE19:TYPICALLOAD-CURVEFORADAYINJANUARY

0kW

20kW

40kW

60kW

80kW

100kW

120kW

140kW

160kW

0-1

1-2

2-3

3-4

4-5

5-6

6-7

7-8

8-9

9-10

10-11

11-12

12

-13

13-14

14

-15

15-16

16

-17

17-18

18

-19

19-20

20

-21

21-22

22

-23

23-24

Electricneedsoftheneighborhood

Ventilation Lightning Specificusages

Electricity forhotwater Electricity forcold Totalélec

58

Therefore,toaddresstheissuepreviouslymentioned,wewillrunthreedifferentsimulationstounderstandtheroleandbenefitsthansmartgridscanplayforsuchanisolatedterritory.Thesethreescenariosarethefollowing:

ü Scenario 1: Scenario 1: No smart grid will be implemented and the

neighborhoodwillsimplybesuppliedthroughtheelectricnetworkoftheIsland.ü Scenario 1 bis: It is the exact same scenario as scenario 1, except that theassumption ismadethatsincenomonitoring, is implemented,energyconsumptionwill deviate from its initial target by +20% the first year and then +1% per yearpercent.ü Scenario 2: A smart grid will be implemented, along with a surface of PV-

panelsthatproduce100%(600kWc)oftheelectricneedsoftheneighborhood,eachmonth,overayear.Asnostorageisimplemented,only42%oftheproductionwillbeself-consumed. The other part of the energy needed will come from the publicdistributionnetwork.ü Scenario 2 bis: It is the exact same scenario as scenario 2 except for the

surface of PV installed. In scenario 2 bis, only 150 kWc of PV-panels have beeninstalled.Theyproduceonly25%ontheannualenergyneedsoftheneighborhood,butupto92%ofthisenergycanbeself-consumedwithoutanyenergystorageü Scenario 3: A smart grid will be implemented, along with a surface of PV-panelsthatproduce100%(600kWc)oftheelectricneedsoftheneighborhoodover

a year. Furthermore, electric storagewill be installed to enable the independenceandcompleteself-sufficiencyoftheneighborhoodtoward itsenergyneeds.Costofconnectiontothepublicdistributionnetworkwillthusbeavoided.

For all these simulation, the choicehasbeenmade to consider that therewasno feed-intariff for PV-panels production. Thus, PV-panels’ production should either be consumedlocallyorlost.

Furthermore, it has been assumed that collective self-consumption at the scale of theneighborhoodwasauthorizedandachievedforscenarios2,2bisand3.

Theamountofenergy it ispossible toself-consumeforeachscenariohasbeencalculatedthankstothePVpanelsproductionsimulator.Indeed,thatsimulatornotonlysimulatesPVpanelproduction foraparticularplace,butalsocalculates thematchbetweenproductionandconsumptionwhenitisfedwithloadcurvesfromtheload-curvesimulator

59

Forscenarios2and3,wehaveshownbelowtherelativeproductionandconsumption fortheneighborhoodbelow:

FIGURE20:COMPARISONOFPVPRODUCTIONANDCONSUMPTIONOFTHENEIGHBORHOOD

Furthermore, for scenario 3, we highlight on the graph below the energy supply over atypicaldayinJanuary:

FIGURE21:ILLUSTRATIONOFTHEENERGYSUPPLYOFTHENEIGHBORHOODFORATYPICALDAYINJANUARY

Allthesethreescenariostakeplaceduringaperiodof20years,assumedasthelife-spanofPV-panels.

0kWh

20kWh

40kWh

60kWh

80kWh

100kWh

120kWh

140kWh

1 2 3 4 5 6 7 8 9 10 11 12

Monthlyproduction vs.consumption

Consumption Production

1 6 11 16 21

0kW200kW400kW600kW800kW1000kW1200kW1400kW1600kW1800kW

0kW50kW

100kW150kW200kW250kW300kW350kW400kW

0-1

1-2

2-3

3-4

4-5

5-6

6-7

7-8

8-9

9-10

10-11

11-12

12

-13

13-14

14

-15

15-16

16

-17

17-18

18

-19

19-20

20

-21

21-22

22

-23

23-24

TypicaldayinJanuary

Batterydischarging Batterycharging

Consumption Production

Levelofchargeofthebattery

60

TheKPIchosentometerandfollowtheperformanceofthedifferentscenariosare:

ü TimeofReturnoninvestmentü Levelizedcostofenergyü GHGemissions

61

9.3.3. DETAILEDASSUMPTIONSMADEFORTHETHREESCENARIOSSOURCE:�ADEME�

Scenario1

Scenario1bis

Summaryoftheassumptions GHGemissions

SummaryoftheassumptionsGHG

emissions

General

assumptions

-Nosmartgrid-Theenergyneedsoftheneighborhoodareonlysuppliedbythe

publicdistributionnetwork

General

assumptions

-Nosmartgrid-Theenergyneedsoftheneighborhoodareonlysuppliedbythepublicdistributionnetwork-Asnomonitoringisrealized,the

consumptionis20%morethantargetedthe

firstyear,andkeepdeviatingby+1%each

year

Electricgridand

electricneeds

-900mloflowvoltagecables(120€/ml)toconnecttheneighborhoodtothepublicdistributionnetwork-1transformerstationof200kVA(29000€)-Costofenergy(production,transport,commercialization):289€/MWh

ElectricgridGHG

emissions:780kg/MWh

Electricgrid

andelectric

needs

-900mloflowvoltagecables(120€/ml)toconnecttheneighborhoodtothepublicdistributionnetwork-2transformerstationsof200kVA(29000€each);Astheconsumptiongoalsarenotmet,thepeakpowerneedshavealsoincreased-Costofenergy(production,transport,commercialization):289€/MWh

ElectricgridGHG

emissions:780kg/MWh

PV-panels

PV-panels

-

Batteries

Batteries SmartGrids

Infrastructure

SmartGrids

Infrastructure

62

Scenario2

Scenario2bis

Summaryoftheassumptions GHGemissions

SummaryoftheassumptionsGHG

emissions

General

assumptions

-Asmartgridisimplemented-600kWcofPVpanelsareinstalled-42%oftheenergyproducedislocallycollectivelyself-consumed-Noelectricstorageisinstalled-TheenergyneedsarethussuppliedbyboththepublicdistributionnetworkandthePV-panels

General

assumptions

-Asmartgridisimplemented-150kWcofPVpanelsareinstalled;-92%oftheenergyproducediscollectivelyself-consumed-Noelectricstorageinstalled-TheenergyneedsarethusmetbythepublicdistributionnetworkandthePV-panels

Electricgrid

andelectric

needs

-900mlofcableBT(120€/ml)toconnecttheneighborhoodtothepublicdistributionnetwork-1transformerstationof200kVA(29000€)-Costofenergy(production,transport,commercialization):289€/MWh

ElectricgridGHG

emissions:780kg/MWh

Electricgrid

andelectric

needs

-900mlofcableBT(120€/ml)toconnecttheneighborhoodtothepublicdistributionnetwork-1transformerstationof200kVA(29000€)-Costofenergy(production,transport,commercialization):289€/MWh

ElectricgridGHG

emissions:780kg/MWh

PV-panels

-600kWcofPV-panelsinstalled,producingoverayear100%oftheneedsoftheneighborhood-Only42%oftheenergyproducedcanbeself-consumed-CAPEX:1,68€/Wcinstalled-OPEX:0,338€/Wcinstalled

74kgCO2/KWcinstalled

PV-panels

150kWcofPV-panelsinstalled,producingoverayear25%oftheneedsoftheneighborhood-92%oftheenergyproducedisself-consumed-CAPEX:1,68€/Wcinstalled-OPEX:0,338€/Wcinstalled

74kgCO2/KWcinstalled

Batteries

Batteries

SmartGrids

Infrastructure

-Smartgridinfrastructurecosts2,5€/m2;50%ofthehardwarechangedevery8years-Softwaredevelopmentcosts2,5€/m2;OPEXof0,25€/m^2

SmartGrids

Infrastructure

-Smartgridinfrastructurecosts2,5€/m2;50%ofthehardwarechangedevery8years-Softwaredevelopmentcosts2,5€/m2;OPEXof0,25€/m^2

63

Scenario3 Summaryoftheassumptions GHGemissions

General

assumptions

-Asmartgridisimplemented-600kWcofPVpanelsareinstalled;-Abatteryof3,2MWhisinstalled-ThePVpanelsandthebatteryallowstheneighborhoodtobeself-sufficientinenergy

-100%oftheenergyproducedbythePVpanelsisthususedonsite,eithercollectivelyself-consumedrightawayorstoredinthebattery-TheenergyneedsareonlymetbythePVpanelsandthebattery.-Theneighborhoodisnotconnectedtothepublicdistributionnetwork.

Electricgrid

andelectric

needs

-Neighborhoodnotconnectedtothepublicdistributionnetwork -

PV-panels

-PV600kWctocoverallelectricityneedsoftheneighborhood-CAPEX:1,68€/Wcinstalled-OPEX:0,338€/Wcinstalled

74kgCO2/KWcinstalled

Batteries

-3,2MWhtobeself-sufficient-CAPEX:500€/kWhinstalled-OPEX:10€/KWhinstalled

SmartGrids

Infrastructure

-Smartgridinfrastructurecosts2,5€/m2;50%ofthehardwarechangedevery8years-Softwaredevelopmentcosts2,5€/m2;OPEXof0,25€/m^2

TABLE21:STUDYCASE2,DETAILLEDASSUMPTIONS

64

9.3.4. RESULTSOFTHESIMULATIONS

Theresultsareshowinthetablebelow.

NPVafter20years

LevelizedCostofEnergy

CO2emissionsover20years

ReductionofGHGemissionscomparedto

scenario1

Scenario1 -3933k€ 297€/MWh 11225tCO2 -

Scenario1bis -5118k€ 298€/MWh 14577tCO2 +23%

Scenario2 -4241k€ 323€/MWh 6573tCO2 -32%

Scenario2bis -3907k€ 296€/MWh 8011tCO2 -22%

Scenario3 -3666k€ 282€/MWh 888tCO2 -71%TABLE22:RESULTSOFTHESIMULATION

Wecanseethatscenario3,wheretheneighborhoodisself-sufficientinenergythankstoPVpanelsandelectricstorage,istheoptimumscenariobotheconomicallyspeakingandenvironmentallyspeaking.

The Levelized Cost of Energy produced by the PV-panels is 85€/MWh according to thecalculations.However, theuseofabattery tostore theexcessofenergyproducedby thePV-panelsandsupplytheneighborhoodatnightincreasestheLCOEofthewholesystemupto 282€/MWh. Compared to mainland price of energy, this solution would not beeconomicallyviable.Though,theparticularitiesofanislandterritorymakeitprofitable.

Ithastobenoticedthatfurtherstudiesshouldbeconductedtocompletelyvalidatescenario3.Indeed,thecapacityof3,2MWhchosenforthebatterycorrespondstotheaveragedailyconsumptionoftheneighborhoodoverayear.

Hence,firstadynamicsimulationshouldbeconductedtocheckhourafterhourthelevelofchargeofthebattery.

Secondly, the maximum instantaneous power that the battery can deliver has not beentaken into account. The assumption was made that the battery could always deliver theamountofpowerneededbytheneighborhood.

Intermsoflifetime,westatedthatthebatterywouldonaveragedohalfacycleeveryday,thatwoulddo3650completecyclesafter20years,whichseemsareasonablelife-spangiventhecurrentbatteriesavailableonthemarket

65

10. DISCUSSIONS

Inordertounderstandtheeconomicbenefitsofsmartgridswehavemainlyfocusedontheoptimization and reduction of infrastructure of energy equipment thanks to the toolsbrought by this technology: data collection, monitoring, analysis of causes of failure andcontrolofequipment.

However,wecouldhaveconsideredthatthedatacollectedthroughtheICTtechnologyhadavalueinitselfandcouldbepartofthebusinessmodelofasmartgridsoperator.

Indeed,energydata,bothfromhouseholdsandbuildingshasvocationtobeexchangedandsoldbetweenstakeholders(networkoperators,BtoBandBtoCcompanies,…)inordertocreatesnumericalservicesorstrengthentargetedadvertising.

However, barriers appear as soon as the data collected can be attributed to a particularhumanbeing.TheCNIL,theFrenchcommissionof informaticsand liberties, is inofdefiningthe framework that regulates theseexchangesofdatabetweenstakeholdersandprotectstheindividualsfromabusiveuseofpersonaldata.

TheCNILhasdefinedthreescenariosaroundthedatacollectedfromsmartmeters:

Scenario1:IN->IN

In the first scenario, the data collected in ahousing are not sent outside the apartmentand the end-user keeps a complete controlover the data. The data collected is notcollected by a third party or used by a thirdparty.

Scenario2:IN->OUT

In this scenario, the data collected in theapartmentistransmittedtooneorseveralthird-parties outside the apartment. Thedata is then post-treated by a third partyto offer services to the end-user, but noactionisactivatedintheapartment

Scenario3:IN->OUT->IN

The data collected in the apartment istransmitted tooneormore thirdparties thatanalyze them and use the data to remotelycontrolequipmentoftheapartment.

TABLE23:DIFFERENTSCENARIOSOFDATA-USAGESCOLLECTEDBYSMARTMETERS�CNIL�2014�

Forthescenario2and3,dependingontheserviceofferedbythethirdparty,personaldatacanbecollectedunderrequiredconditiontogettheauthorizationoftheindividual.

Theseprivacyquestionsraisetheissueofthepublicacceptancetowardsmartgrids.Thisisanissuewehavenotdevelopedinthisthesisbutthatmightslowthedevelopmentofsmartgrids.

66

11. CONCLUSIONAlong this thesis, we have tried to properly define what are smart grids for a newneighborhoodandwhichnewtoolsdotheybringtotheequation.Fromthesetools,wehaveanalyzedwhich improvements could be found, economically, environmentally and sociallyspeaking.

We have actually tried to highlight the economic improvements in terms of reduction ofinfrastructurethroughtwostudycases:aneighborhoodinBordeauxandanotheroneontheIslandofLaRéunioninthesouthhemisphere.

Though, the lessons learned from the first eco-districts around Europe show us that theenergyperformanceexpectedareseldomreached.

Wehavethenproposedanewtool, theenergyperformancecontract inorder todefineanewenergeticdealatthescaleoftheneighborhood.Theexpectationofsuchacontractis:

ü to create amotivating framework for themain actors to complywith theircommitmentsü to enable the emergence of a new actor that will ensure the goodperformanceoftheneighborhoodduringitswayoflife:theperformancesupervisorü Foreachandeverystakeholdertobenefitfromtheenhancedperformancebysizingproperlytheinfrastructureneeded.

Suchacontractisonlypossibleifsmartgridsareimplemented.Smartgridsarenotanend,theyareatooltoimproveourpractices,increaseourknowledgeofenergyusage,andcopewiththesustainabledevelopmentofourcities.

67

12. ANNEXES

12.1. STUDYCASE1:BORDEAUXAMÉDÉE

12.1.1. SCENARIO1

Scenario1:Nosmartgrid

Heatandhotwater Thermalstorage SmartsGridInfrastructure Total

Subscriptionrevenues Bills Investissement Maintenance Freecash

flows Revenues Investissement MaintenanceFreecashflow

Revenues Investissement MaintenanceFreecashflow

Revenues Investissement Maintenance Freecashflow

Year1 123 266 (3047) (188) (2762) - - - - - - 390 (3047) (188) (2762)Year2 123 266 - (188) (2560) - - - - - - 390 - (188) (2560)Year3 123 266 - (188) (2358) - - - - - - 390 - (188) (2358)Year4 123 266 - (188) (2156) - - - - - - 390 - (188) (2156)Year5 123 266 - (188) (1954) - - - - - - 390 - (188) (1954)Year6 123 266 - (188) (1752) - - - - - - 390 - (188) (1752)Year7 123 266 - (188) (1550) - - - - - - 390 - (188) (1550)Year8 123 266 - (188) (1348) - - - - - - 390 - (188) (1348)Year9 123 266 - (188) (1146) - - - - - - 390 - (188) (1146)Year10 123 266 - (188) (944) - - - - - - 390 - (188) (944)Year11 123 266 - (188) (742) - - - - - - 390 - (188) (742)Year12 123 266 - (188) (540) - - - - - - 390 - (188) (540)Year13 123 266 - (188) (338) - - - - - - 390 - (188) (338)Year14 123 266 - (188) (136) - - - - - - 390 - (188) (136)Year15 123 266 - (188) 66 - - - - - - 390 - (188) 66Year16 123 266 - (188) 268 - - - - - - 390 - (188) 268Year17 123 266 - (188) 470 - - - - - - 390 - (188) 470Year18 123 266 - (188) 672 - - - - - - 390 - (188) 672Year19 123 266 - (188) 874 - - - - - - 390 - (188) 874Year20 123 266 - (188) 1076 - - - - - - 390 - (188) 1076Year21 123 266 - (188) 1278 - - - - - - 390 - (188) 1278Year22 123 266 - (188) 1480 - - - - - - 390 - (188) 1480Year23 123 266 - (188) 1682 - - - - - - 390 - (188) 1682Year24 123 266 - (188) 1884 - - - - - - 390 - (188) 1884Year25 123 266 - (188) 2086 - - - - - - 390 - (188) 2086

k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€

68

Scenario1:Smartgrids

Heatandhotwater Thermalstorage SmartsGridInfrastructure Total

Subscriptionrevenues Bills Investment Maintenance Freecash

flows Revenues Investment MaintenanceFreecashflow

Revenues Investment Maintenance Freecashflow Revenues Investment Maintenance Freecash

flow

Year1 123 266 (2406) (131) (2084) (375) (8) (371) (252) (30) (274) 390 (3033) (168) (2729)Year2 123 266 - (131) (1825) - (8) (379) - (30) (304) 390 - (168) (2508)Year3 123 266 - (131) (1566) - (8) (386) - (30) (334) 390 - (168) (2286)

Year4 123 266 - (131) (1307) - (8) (394) - (30) (364) 390 - (168) (2065)

Year5 123 266 - (131) (1048) - (8) (401) - (30) (394) 390 - (168) (1843)Year6 123 266 - (131) (789) - (8) (409) - (30) (424) 390 - (168) (1622)Year7 123 266 - (131) (530) - (8) (416) - (30) (454) 390 - (168) (1400)Year8 123 266 - (131) (271) - (8) (424) (126) (30) (610) 390 (126) (168) (1305)Year9 123 266 - (131) (12) - (8) (431) - (30) (640) 390 - (168) (1083)Year10 123 266 - (131) 247 - (8) (439) - (30) (670) 390 - (168) (862)Year11 123 266 - (131) 506 - (8) (446) - (30) (700) 390 - (168) (640)Year12 123 266 - (131) 766 - (8) (454) - (30) (730) 390 - (168) (418)Year13 123 266 - (131) 1025 - (8) (461) - (30) (760) 390 - (168) (197)Year14 123 266 - (131) 1284 - (8) (469) - (30) (790) 390 - (168) 25Year15 123 266 - (131) 1543 - (8) (476) - (30) (820) 390 - (168) 246Year16 123 266 - (131) 1802 - (8) (484) - (30) (850) 390 - (168) 468Year17 123 266 - (131) 2061 - (8) (491) (126) (30) (1006) 390 (126) (168) 563Year18 123 266 - (131) 2320 - (8) (499) - (30) (1036) 390 - (168) 785Year19 123 266 - (131) 2579 - (8) (506) - (30) (1066) 390 - (168) 1006Year20 123 266 - (131) 2838 - (8) (514) - (30) (1096) 390 - (168) 1228Year21 123 266 - (131) 3097 - (8) (521) - (30) (1126) 390 - (168) 1449Year22 123 266 - (131) 3356 - (8) (529) - (30) (1156) 390 - (168) 1671Year23 123 266 - (131) 3615 - (8) (536) - (30) (1187) 390 - (168) 1892Year24 123 266 - (131) 3874 - (8) (544) - (30) (1217) 390 - (168) 2114Year25 123 266 - (131) 4134 - (8) (551) - (30) (1247) 390 - (168) 2336

k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€

69

12.1.2. SCENARIO2

Scenario2:Nosmartgrid(1/2)

Heatandhotwater Thermalstorage SmartsGridInfrastructure

Subscriptionrevenues Bills Investment Maintenance

Freecashflows

Revenues Investment MaintenanceFreecashflow

Revenues Investment MaintenanceFreecashflow

Year1 123 266 (3047) (188) (2762) - - - - - -

Year2 123 266 - (188) (2560) - - - - - -

Year3 123 266 - (188) (2358) - - - - - -

Year4 123 266 - (188) (2156) - - - - - -

Year5 123 266 - (188) (1954) - - - - - -

Year6 123 266 - (188) (1752) - - - - - -

Year7 123 266 - (188) (1550) - - - - - -

Year8 123 266 - (188) (1348) - - - - - -

Year9 123 266 - (188) (1146) - - - - - -

Year10 123 266 - (188) (944) - - - - - -

Year11 123 266 - (188) (742) - - - - - -

Year12 123 266 - (188) (540) - - - - - -

Year13 123 266 - (188) (338) - - - - - -

Year14 123 266 - (188) (136) - - - - - -

Year15 123 266 - (188) 66 - - - - - -

Year16 123 266 - (188) 268 - - - - - -

Year17 123 266 - (188) 470 - - - - - -

Year18 123 266 - (188) 672 - - - - - -

Year19 123 266 - (188) 874 - - - - - -

Year20 123 266 - (188) 1076 - - - - - -

Year21 123 266 - (188) 1278 - - - - - -

Year22 123 266 - (188) 1480 - - - - - -

Year23 123 266 - (188) 1682 - - - - - -

Year24 123 266 - (188) 1884 - - - - - -

Year25 123 266 - (188) 2086 - - - - - -

k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€

70

Scenario2:Nosmartgrid(2/2)

Publiclightning ChargingstationsforEvs Total

Subscription Bills Investment MaintenanceFreecashflows

Subscription Bills Investment MaintenanceFreecashflows

Revenues Investment MaintenanceFreecashflow

Year1 (2) (1) (42) (2) (44) (0) (0,40) (24) (2) (26) 390 (3114) (193) (2831)

Year2 (2) (1) - (2) (47) (0) (0,79) (24) (5) (56) 390 (24) (195) (2661)

Year3 (2) (1) - (2) (50) (0) (1,19) (24) (7) (88) 390 (24) (198) (2492)

Year4 (2) (1) - (2) (53) (0) (1,59) (24) (10) (123) 390 (24) (200) (2326)

Year5 (2) (1) - (2) (57) (0) (1,99) (24) (12) (161) 390 (24) (202) (2163)

Year6 (2) (1) - (2) (60) (0) (2,38) (24) (14) (202) 390 (24) (205) (2001)

Year7 (2) (1) - (2) (63) (1) (2,78) (24) (17) (246) 390 (24) (207) (1843)

Year8 (2) (1) - (2) (66) (1) (3,18) (24) (19) (292) 390 (24) (210) (1686)

Year9 (2) (1) - (2) (69) (1) (3,57) (24) (22) (341) 390 (24) (212) (1532)

Year10 (2) (1) - (2) (72) (1) (3,97) (24) (24) (393) 390 (24) (214) (1381)

Year11 (2) (1) - (2) (75) (1) (3,97) - (24) (421) 390 - (214) (1205)

Year12 (2) (1) - (2) (78) (1) (3,97) - (24) (449) 390 - (214) (1029)

Year13 (2) (1) - (2) (81) (1) (3,97) - (24) (477) 390 - (214) (854)

Year14 (2) (1) - (2) (84) (1) (3,97) - (24) (505) 390 - (214) (678)

Year15 (2) (1) - (2) (88) (1) (3,97) - (24) (533) 390 - (214) (503)

Year16 (2) (1) - (2) (91) (1) (3,97) - (24) (561) 390 - (214) (327)

Year17 (2) (1) - (2) (94) (1) (3,97) - (24) (589) 390 - (214) (151)

Year18 (2) (1) - (2) (97) (1) (3,97) - (24) (617) 390 - (214) 24

Year19 (2) (1) - (2) (100) (1) (3,97) - (24) (645) 390 - (214) 200

Year20 (2) (1) - (2) (103) (1) (3,97) - (24) (673) 390 - (214) 375

Year21 (2) (1) - (2) (106) (1) (3,97) - (24) (701) 390 - (214) 551

Year22 (2) (1) - (2) (109) (1) (3,97) - (24) (729) 390 - (214) 726

Year23 (2) (1) - (2) (112) (1) (3,97) - (24) (757) 390 - (214) 902

Year24 (2) (1) - (2) (115) (1) (3,97) - (24) (785) 390 - (214) 1078

Year25 (2) (1) - (2) (119) (1) (3,97) - (24) (813) 390 - (214) 1253

k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€

71

Scenario2:SmartGrids(1/2)

Heatandhotwater Thermalstorage SmartsGridInfrastructure

Subscriptionrevenues Bills Investment Maintenance

Freecashflows

Revenues Investment MaintenanceFreecashflow

Revenues Investment MaintenanceFreecashflow

Year1 123 266 (2406) (131) (2084) (375) (8) (371) (252) (30) (274)

Year2 123 266 - (131) (1825) - (8) (379) - (30) (304)

Year3 123 266 - (131) (1566) - (8) (386) - (30) (334)

Year4 123 266 - (131) (1307) - (8) (394) - (30) (364)

Year5 123 266 - (131) (1048) - (8) (401) - (30) (394)

Year6 123 266 - (131) (789) - (8) (409) - (30) (424)

Year7 123 266 - (131) (530) - (8) (416) - (30) (454)

Year8 123 266 - (131) (271) - (8) (424) (126) (30) (610)

Year9 123 266 - (131) (12) - (8) (431) - (30) (640)

Year10 123 266 - (131) 247 - (8) (439) - (30) (670)

Year11 123 266 - (131) 506 - (8) (446) - (30) (700)

Year12 123 266 - (131) 766 - (8) (454) - (30) (730)

Year13 123 266 - (131) 1025 - (8) (461) - (30) (760)

Year14 123 266 - (131) 1284 - (8) (469) - (30) (790)

Year15 123 266 - (131) 1543 - (8) (476) - (30) (820)

Year16 123 266 - (131) 1802 - (8) (484) - (30) (850)

Year17 123 266 - (131) 2061 - (8) (491) (126) (30) (1006)

Year18 123 266 - (131) 2320 - (8) (499) - (30) (1036)

Year19 123 266 - (131) 2579 - (8) (506) - (30) (1066)

Year20 123 266 - (131) 2838 - (8) (514) - (30) (1096)

Year21 123 266 - (131) 3097 - (8) (521) - (30) (1126)

Year22 123 266 - (131) 3356 - (8) (529) - (30) (1156)

Year23 123 266 - (131) 3615 - (8) (536) - (30) (1187)

Year24 123 266 - (131) 3874 - (8) (544) - (30) (1217)

Year25 123 266 - (131) 4134 - (8) (551) - (30) (1247)

72

k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€

Scenario2:SmartGrids(2/2)

Publiclightning ChargingstationsforEvs Total

Subscription Bills Investment MaintenanceFreecashflows

Subscription Bills Investment MaintenanceFreecashflows

Revenues Investment MaintenanceFreecashflow

Year1 (2) (1) (42) (2) (44) (0) (0,11) (24) (2) (26) 390 (3099) (173) (2729)

Year2 (2) (1) - (2) (47) (0) (0,23) (24) (5) (55) 390 (24) (176) (2508)

Year3 (2) (1) - (2) (50) (0) (0,34) (24) (7) (86) 390 (24) (178) (2286)

Year4 (2) (1) - (2) (53) (0) (0,46) (24) (10) (120) 390 (24) (180) (2065)

Year5 (2) (1) - (2) (57) (0) (0,57) (24) (12) (157) 390 (24) (183) (1843)

Year6 (2) (1) - (2) (60) (1) (0,69) (24) (14) (196) 390 (24) (185) (1622)

Year7 (2) (1) - (2) (63) (1) (0,80) (24) (17) (238) 390 (24) (188) (1400)

Year8 (2) (1) - (2) (66) (1) (0,91) (24) (19) (282) 390 (150) (190) (1305)

Year9 (2) (1) - (2) (69) (1) (1,03) (24) (22) (328) 390 (24) (192) (1083)

Year10 (2) (1) - (2) (72) (1) (1,14) (24) (24) (378) 390 (24) (195) (862)

Year11 (2) (1) - (2) (75) (1) (1,14) - (24) (403) 390 - (195) (640)

Year12 (2) (1) - (2) (78) (1) (1,14) - (24) (428) 390 - (195) (418)

Year13 (2) (1) - (2) (81) (1) (1,14) - (24) (453) 390 - (195) (197)

Year14 (2) (1) - (2) (84) (1) (1,14) - (24) (478) 390 - (195) 25

Year15 (2) (1) - (2) (88) (1) (1,14) - (24) (503) 390 - (195) 246

Year16 (2) (1) - (2) (91) (1) (1,14) - (24) (528) 390 - (195) 468

Year17 (2) (1) - (2) (94) (1) (1,14) - (24) (554) 390 (126) (195) 563

Year18 (2) (1) - (2) (97) (1) (1,14) - (24) (579) 390 - (195) 785

Year19 (2) (1) - (2) (100) (1) (1,14) - (24) (604) 390 - (195) 1006

Year20 (2) (1) - (2) (103) (1) (1,14) - (24) (629) 390 - (195) 1228

Year21 (2) (1) - (2) (106) (1) (1,14) - (24) (654) 390 - (195) 1449

Year22 (2) (1) - (2) (109) (1) (1,14) - (24) (679) 390 - (195) 1671

Year23 (2) (1) - (2) (112) (1) (1,14) - (24) (704) 390 - (195) 1892

Year24 (2) (1) - (2) (115) (1) (1,14) - (24) (730) 390 - (195) 2114

Year25 (2) (1) - (2) (119) (1) (1,14) - (24) (755) 390 - (195) 2336

k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€

73

12.2. STUDYCASE2:LAREUNION

12.2.1. SCENARIO1

Scenario1(1/2)

Publicdistributionnetwork PVpanels Electricstorage

Costofproduction Investmenttoconnecttheneighborhood Maintenance Freecashflow Investment Maintenance

Freecashflows

Revenues Investment MaintenanceFreecashflows

Year1 (191) (112) (295) - - - - - -

Year2 (191) - (486) - - - - - -

Year3 (191) - (678) - - - - - -

Year4 (191) - (869) - - - - - -

Year5 (191) - (1061) - - - - - -

Year6 (191) - (1252) - - - - - -

Year7 (191) - (1444) - - - - - -

Year8 (191) - (1635) - - - - - -

Year9 (191) - (1827) - - - - - -

Year10 (191) - (2018) - - - - - -

Year11 (191) - (2210) - - - - - -

Year12 (191) - (2401) - - - - - -

Year13 (191) - (2593) - - - - - -

Year14 (191) - (2784) - - - - - -

Year15 (191) - (2976) - - - - - -

Year16 (191) - (3167) - - - - - -

Year17 (191) - (3359) - - - - - -

Year18 (191) - (3550) - - - - - -

Year19 (191) - (3742) - - - - - -

Year20 (191) - (3933) - - - - - -

k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€

74

Scenario1(2/2)

InfrastructureSmartGrid Total

Revenues Investment Maintenance Freecashflows Revenues Investment Maintenance Freecash

flow

Year1 - - - (112) (191) (295)

Year2 - - - - (191) (486)

Year3 - - - - (191) (678)

Year4 - - - - (191) (869)

Year5 - - - - (191) (1061)

Year6 - - - - (191) (1252)

Year7 - - - - (191) (1444)

Year8 - - - - (191) (1635)

Year9 - - - - (191) (1827)

Year10 - - - - (191) (2018)

Year11 - - - - (191) (2210)

Year12 - - - - (191) (2401)

Year13 - - - - (191) (2593)

Year14 - - - - (191) (2784)

Year15 - - - - (191) (2976)

Year16 - - - - (191) (3167)

Year17 - - - - (191) (3359)

Year18 - - - - (191) (3550)

Year19 - - - - (191) (3742)

Year20 - - - - (191) (3933)

k€ k€ k€ k€ k€ k€

75

12.2.2. SCENARIO1BIS

Scenario1bis(1/2)

Publicdistributionnetwork PVpanels Electricstorage

Costofproduction Investmenttoconnecttheneighborhood Maintenance Freecashflow Investment MaintenanceFreecashflows

Revenues Investment MaintenanceFreecashflows

Year1 (230) (147) (366) - - - - - -

Year2 (230) - (595) - - - - - -

Year3 (232) - (827) - - - - - -

Year4 (234) - (1061) - - - - - -

Year5 (236) - (1296) - - - - - -

Year6 (237) - (1534) - - - - - -

Year7 (239) - (1773) - - - - - -

Year8 (241) - (2014) - - - - - -

Year9 (243) - (2258) - - - - - -

Year10 (245) - (2503) - - - - - -

Year11 (247) - (2750) - - - - - -

Year12 (249) - (2999) - - - - - -

Year13 (251) - (3250) - - - - - -

Year14 (253) - (3502) - - - - - -

Year15 (255) - (3757) - - - - - -

Year16 (257) - (4014) - - - - - -

Year17 (259) - (4272) - - - - - -

Year18 (260) - (4533) - - - - - -

Year19 (262) - (4795) - - - - - -

Year20 (264) - (5059) - - - - - -

k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€

76

Scenario1bis(2/2)

InfrastructureSmartGrid Total

Revenues Investment Maintenance Freecashflows Revenues Investment Maintenance Freecash

flow

Year1 - - - (147) (233) (368)

Year2 - - - - (233) (601)

Year3 - - - - (235) (836)

Year4 - - - - (237) (1072)

Year5 - - - - (238) (1311)

Year6 - - - - (240) (1551)

Year7 - - - - (242) (1794)

Year8 - - - - (244) (2038)

Year9 - - - - (246) (2284)

Year10 - - - - (248) (2532)

Year11 - - - - (250) (2782)

Year12 - - - - (252) (3034)

Year13 - - - - (254) (3288)

Year14 - - - - (256) (3543)

Year15 - - - - (258) (3801)

Year16 - - - - (260) (4060)

Year17 - - - - (261) (4322)

Year18 - - - - (263) (4585)

Year19 - - - - (265) (4851)

Year20 - - - - (267) (5118)

k€ k€ k€ k€ k€ k€

77

12.2.3. SCENARIO2

Sceanrio2(1/2)

Publicdistributionnetwork PVpanels Electricstorage

Costofproduction Investmenttoconnecttheneighborhood Maintenance Freecashflow Investment Maintenance Freecash

flows Revenues Investment MaintenanceFreecashflows

Year1 (106) (112) (212) (1008) (20) (998) - - -

Year2 (109) - (321) - (20) (1018) - - -

Year3 (112) - (432) - (20) (1039) - - -

Year4 (114) - (547) - (20) (1059) - - -

Year5 (117) - (664) - (20) (1079) - - -

Year6 (120) - (784) - (20) (1099) - - -

Year7 (123) - (907) - (20) (1119) - - -

Year8 (126) - (1033) - (20) (1139) - - -

Year9 (129) - (1161) - (20) (1159) - - -

Year10 (131) - (1293) - (20) (1180) - - -

Year11 (134) - (1427) - (20) (1200) - - -

Year12 (137) - (1564) - (20) (1220) - - -

Year13 (140) - (1705) - (20) (1240) - - -

Year14 (143) - (1848) - (20) (1260) - - -

Year15 (146) - (1994) - (20) (1280) - - -

Year16 (149) - (2142) - (20) (1301) - - -

Year17 (152) - (2294) - (20) (1321) - - -

Year18 (155) - (2449) - (20) (1341) - - -

Year19 (158) - (2607) - (20) (1361) - - -

Year20 (161) - (2768) - (20) (1381) - - -

k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€

78

Scenario2(2/2)

InfrastructureSmartGrid Total

Revenues Investment Maintenance Freecashflows Revenues Investment Maintenance Freecash

flow

Year1 (32) (2) (33) (1152) (128) (1243)

Year2 - (2) (35) - (131) (1374)

Year3 - (2) (37) - (134) (1508)

Year4 - (2) (39) - (137) (1644)

Year5 - (2) (41) - (139) (1784)

Year6 - (2) (43) - (142) (1926)

Year7 - (2) (44) - (145) (2071)

Year8 (10) (2) (56) (10) (148) (2228)

Year9 - (2) (58) - (151) (2379)

Year10 - (2) (60) - (154) (2532)

Year11 - (2) (62) - (156) (2689)

Year12 - (2) (64) - (159) (2848)

Year13 - (2) (66) - (162) (3010)

Year14 - (2) (67) - (165) (3175)

Year15 - (2) (69) - (168) (3343)

Year16 - (2) (71) - (171) (3514)

Year17 (13) (2) (86) (13) (174) (3701)

Year18 - (2) (88) - (177) (3878)

Year19 - (2) (90) - (180) (4058)

Year20 - (2) (92) - (183) (4241)

k€ k€ k€ k€ k€ k€

79

12.2.4. SCENARIO2BIS

Scenario2bis(1/2)

Publicdistributionnetwork PVpanels Electricstorage

Costofproduction Investmenttoconnecttheneighborhood Maintenance Freecash

flow Investment Maintenance Freecashflows Revenues Investment Maintenance

Freecashflows

Year1 (145) (112) (249) (252) (5) (250) - - -

Year2 (147) - (396) - (5) (255) - - -

Year3 (150) - (546) - (5) (260) - - -

Year4 (152) - (698) - (5) (265) - - -

Year5 (154) - (852) - (5) (270) - - -

Year6 (157) - (1009) - (5) (275) - - -

Year7 (159) - (1168) - (5) (280) - - -

Year8 (162) - (1330) - (5) (285) - - -

Year9 (164) - (1494) - (5) (290) - - -

Year10 (167) - (1661) - (5) (295) - - -

Year11 (169) - (1830) - (5) (300) - - -

Year12 (172) - (2002) - (5) (305) - - -

Year13 (174) - (2176) - (5) (310) - - -

Year14 (177) - (2353) - (5) (315) - - -

Year15 (180) - (2533) - (5) (320) - - -

Year16 (182) - (2715) - (5) (325) - - -

Year17 (185) - (2900) - (5) (330) - - -

Year18 (187) - (3087) - (5) (335) - - -

Year19 (190) - (3277) - (5) (340) - - -

Year20 (193) - (3470) - (5) (345) - - -

k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€

80

Scenario2bis(2/2)

InfrastructureSmartGrid Total

Revenues Investment Maintenance Freecashflows Revenues Investment Maintenance Freecash

flow

Year1 (32) (2) (33) (396) (152) (532)

Year2 - (2) (35) - (154) (686)

Year3 - (2) (37) - (156) (842)

Year4 - (2) (39) - (159) (1001)

Year5 - (2) (41) - (161) (1163)

Year6 - (2) (43) - (164) (1326)

Year7 - (2) (44) - (166) (1493)

Year8 (10) (2) (56) (10) (169) (1671)

Year9 - (2) (58) - (171) (1842)

Year10 - (2) (60) - (174) (2016)

Year11 - (2) (62) - (176) (2192)

Year12 - (2) (64) - (179) (2371)

Year13 - (2) (66) - (181) (2552)

Year14 - (2) (67) - (184) (2736)

Year15 - (2) (69) - (186) (2922)

Year16 - (2) (71) - (189) (3111)

Year17 (13) (2) (86) (13) (192) (3316)

Year18 - (2) (88) - (194) (3510)

Year19 - (2) (90) - (197) (3707)

Year20 - (2) (92) - (200) (3907)

k€ k€ k€ k€ k€ k€

81

12.2.5. SCENARIO3

Scenario3(1/2)

Publicdistributionnetwork PVpanels Electricstorage

Costofproduction Investmenttoconnecttheneighborhood Maintenance Freecash

flow Investment Maintenance Freecashflows Revenues Investment MaintenanceFreecashflows

Year1 - - - (1008) (20) (998) (1600) (32) (1584)

Year2 - - - - (20) (1018) - (32) (1616)

Year3 - - - - (20) (1039) - (32) (1648)

Year4 - - - - (20) (1059) - (32) (1680)

Year5 - - - - (20) (1079) - (32) (1712)

Year6 - - - - (20) (1099) - (32) (1744)

Year7 - - - - (20) (1119) - (32) (1776)

Year8 - - - - (20) (1139) - (32) (1808)

Year9 - - - - (20) (1159) - (32) (1840)

Year10 - - - - (20) (1180) - (32) (1872)

Year11 - - - - (20) (1200) - (32) (1904)

Year12 - - - - (20) (1220) - (32) (1936)

Year13 - - - - (20) (1240) - (32) (1968)

Year14 - - - - (20) (1260) - (32) (2000)

Year15 - - - - (20) (1280) - (32) (2032)

Year16 - - - - (20) (1301) - (32) (2064)

Year17 - - - - (20) (1321) - (32) (2096)

Year18 - - - - (20) (1341) - (32) (2128)

Year19 - - - - (20) (1361) - (32) (2160)

Year20 - - - - (20) (1381) - (32) (2192)

k€ k€ k€ k€ k€ k€ k€ k€ k€ k€ k€

82

Scenario3(2/2)

InfrastructureSmartGrid Total

Revenues Investment Maintenance Freecashflows Revenues Investment Maintenance Freecash

flow

Year1 (32) (2) (33) (2640) (54) (2616)

Year2 - (2) (35) - (54) (2670)

Year3 - (2) (37) - (54) (2724)

Year4 - (2) (39) - (54) (2778)

Year5 - (2) (41) - (54) (2832)

Year6 - (2) (43) - (54) (2886)

Year7 - (2) (44) - (54) (2940)

Year8 (10) (2) (56) (10) (54) (3004)

Year9 - (2) (58) - (54) (3058)

Year10 - (2) (60) - (54) (3112)

Year11 - (2) (62) - (54) (3166)

Year12 - (2) (64) - (54) (3220)

Year13 - (2) (66) - (54) (3274)

Year14 - (2) (67) - (54) (3328)

Year15 - (2) (69) - (54) (3382)

Year16 - (2) (71) - (54) (3436)

Year17 (13) (2) (86) (13) (54) (3503)

Year18 - (2) (88) - (54) (3557)

Year19 - (2) (90) - (54) (3611)

Year20 - (2) (92) - (54) (3666)

k€ k€ k€ k€ k€ k€

83

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