The role of smart grids in the building sector

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ContentslistsavailableatScienceDirect

Energy

and

Buildings

jo u r n al h om ep a g e :w w w . e l s e v i e r . c o m / l o c a t e / e n b u i l d

Engineering

Advance

The

role

of

smart

grids

in

the

building

sector

Dionysia

Kolokotsa

∗

SchoolofEnvironmentalEngineering,TechnicalUniversityofCrete,Kounoudipiana,Crete,Greece

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Availableonline23December2015 Keywords:

Smartgrids Microgrids Smartbuildings Zeroenergybuildings Smartcommunities

a

b

s

t

r

a

c

t

Thesmartgridsaremodernelectricpowergridinfrastructureforenhancedefﬁciencyandreliability throughautomatedcontrol,high-powerconverters,moderncommunicationsinfrastructure,sensing andmeteringtechnologies,andmodernenergymanagementtechniquesbasedontheoptimizationof demand,energyandnetworkavailability.Theroleofbuildingsinthisframeworkisverycrucial.This paperaddressescriticalissuesonsmartgridtechnologiesandtheintegrationofbuildingsinthisnew powergridframework.Themainobjectiveofthispaperistoprovideacontemporarylookatthecurrent stateoftheartinthepotentialofbuildingsandcommunitiestobeintegratedinsmartgridsaswellas todiscussthestill-openresearchissuesinthisﬁeld.Thechallengesforthebuildingsectorarediscussed andfutureresearchprospectsareanalysed.

Contents

1. Introduction...703

2. Smartandzeroenergybuildings...704

2.1. Smartmetering...704

2.2. Demandresponse(DR)...704

2.3. Distributedsystems...705

3. Smartandzeroenergycommunities ... 705

4. Conclusionsandfutureprospects...706

References...707

1. Introduction

Smartgridisadynamicallyinteractivereal-timeinfrastructure

conceptthatencompassesthemany visionsof thestakeholders

ofdiverseenergy systems.[1].Smart gridsare electricalpower

gridsthat are more efﬁcient and more resilient and therefore,

“smarter”thantheexistingconventionalpowergrids.The

smart-nessisfocusednotonlyontheeliminationofblack-outs,butalso

onmakingthegridgreener,moreefﬁcient,adaptabletocustomers’

needs,andtherefore,lesscostly[1,2].Smartgridsincorporatethe

innovativeITtechnologythatallowsfortwo-waycommunication

betweentheutilityanditscustomers/users.Asaresultthesensing

alongthetransmissionlinesandthesensingfromthecustomer’s

sideiswhatmakesthegrid“smart”.

∗ Tel.:+302821037808.

E-mailaddress:[email protected]

LiketheInternet,theSmartGridconsistsofcontrols,

comput-ers,automation,newtechnologies,smartbuildingsandequipment

workingtogether,butin thiscase thesetechnologieswillwork

withtheelectricalgridtoresponddigitallytothequickly

chang-ingenergydemandsoftheusers.Thereforesmartgridscreatean

exceptional opportunity for thesupport ofthe developmentof

smartzeroenergybuildingsandcommunitiesandofferthestep

towardstheInternetofThingsfortheEnergyandBuildingIndustry

[3,4].

SmartGridsopenthedoortonewapplicationswithfar-reaching

inter-disciplinaryimpacts:providingthecapacitytosafely

inte-gratemorerenewableenergysources(RES),smartbuildingsand

distributedgeneratorsintothenetwork;deliveringpowermore

efﬁciently and reliably through demandresponse and

compre-hensivecontrolandmonitoringcapabilities;usingautomaticgrid

reconﬁgurationtopreventorrestoreoutages(self-healing

capa-bilities);enabling consumers tohave greatercontrol over their

electricityconsumptionandtoactivelyparticipateintheelectricity

market.

http://dx.doi.org/10.1016/j.enbuild.2015.12.033

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Smart Grids can create a revolution in the building sector.

Theaccumulatedexperienceofthelastdecadeshasshownthat

thehierarchical,centrallycontrolledgridofthe20thCenturyis

ill-suitedtotheneeds ofthe 21stCentury. The smartgrid can

beconsideredasamodernelectricpowergridinfrastructurefor

enhanced efﬁciency and reliability through automated control,

high-power converters,modern communications infrastructure,

sensingandmeteringtechnologies,andmodernenergy

manage-menttechniques basedonthe optimizationofdemand, energy

andnetworkavailability.Theroleofbuildingsinthisframework

isverycrucial.Thispaperaddressescriticalissuesonsmartgrid

technologiesandtheintegrationofbuildingsinthisnewpower

gridframework[5].Themainobjectiveofthispaperistoprovide

acontemporarylookatthecurrentstateoftheartinthe

poten-tialofbuildingsandcommunitiestobeintegratedinsmartgridsas

wellastodiscussthestill-openresearchissuesinthisﬁeld.Since

thevastmajorityofsmartgrids’potentialcustomersarebuildings

(residential,commercial,retailandindustrial)andcommunities,

thepaperaddressesthechallengesposedbysmartgridsonbuilding

andcommunitylevel.

2. Smartandzeroenergybuildings

Theenergyconsumptionforbuildingsaccountsfor40%ofthe

energyusedworldwide.Ithasbecomeawidely-acceptedfactthat

measuresandchangesinthebuildingmodusoperandicanyield

substantialenergysavingsminimizingthebuildings’carbon

foot-print[6,7].Moreover,buildingsinthenearfutureshouldbeable

toproducetheamountofenergytheyconsume,i.e.,becomezero

ornearlyzeroenergybuildings(ZEBs)[8,9].Thisisamandatory

requirementbasedonthefactthatby31December2020,allnew

buildingsshallbenearlyzero-energyconsumptionbuildings.New

buildingsoccupiedandownedbypublicauthoritiesshallcomply

withthesamecriteriaby31December2018[8,10].

ZEBsarebuildingsthatworkinsynergywiththegrid,avoiding

puttingadditionalstressonthepowerinfrastructure[11].

Achiev-inga ZEBincludes apart fromminimizing the required energy

throughefﬁcient measuresand covering theminimized energy

needsbyadoptingrenewablesources,a seriesofoptimisedand

wellbalanced operationsbetweenconsumptionand production

coupledwithsuccessfulgridintegration[12].

Information and Computer enabled Technologies (ICT) and

smart gridsimplementationare the keysto achieve the

afore-mentionedzeroenergygoals[13].ICTforenergymanagementin

buildingshasevolvedconsiderablythelastdecadesleadingtoa

betterunderstandingandpenetrationoftheterm“smart

build-ings”[14].Advances in thedesign, operationoptimization and

controlofenergy-inﬂuencingbuildingelements(e.g.,HVAC,solar,

fuelcells, CHP,shading,natural ventilation, etc.)unleashedthe

potentialforrealizationofsigniﬁcantenergysavingsand

efﬁcien-ciesintheoperationofbothnewandexistingdwellingsworldwide.

Smartbuildingsreadytobeinterconnectedwithsmartgridsshould

complywiththefollowingrequirements:

(a)Incorporationofsmartmetering.

(b)Demandresponsecapabilities.

(c)Distributedarchitecture.

(d)Interoperability.

2.1. Smartmetering

Smartmeteringisaprerequisiteandstartingpointforeffective

implementationofsmartgridsandzeroenergybuildings’

perspec-tive.InFinland,theusageofsmartmeteringencouragedconsumers

toincreaseenergyefﬁciencyby7%.Inorderforelectricityproviders

todeliverintelligentservicesforcustomers,bidirectional

meter-inginterfacesshouldbeusedtoobtaincustomers’energydemand

information[15].Moreoverthroughtheadvancesofsmart

meter-ing,sensorsbasedapproachescanbeexploitedtoprovideenergy

loadforecasting [16]. Data collectedfrom smart meters,

build-ingmanagement systems and weather stationscan beusedby

advanced artiﬁcial intelligent techniquesand machine learning

algorithmstoinferthecomplexrelationshipsbetweentheenergy

consumption and various variables such as temperature, solar

radiation,timeofdayandoccupancy[16–21].Duetothefast

devel-opmentandapplicationoflowcostoptionsforenergymetering

inrecentyears,energyloadpredictionisbecomingincreasingly

relevantandcosteffective[16,22].

Smartmeteringwithsensor basedapproaches wasexploited

intheframeworkofGreen@Hospitalproject(

www.greenhospital-project.eu/).Inthisproject,theoutdoortemperaturesand

hospi-tals’energydemandwerepredictedfor4,8,12 and24hahead

[23].Thispredictionisthenusedforoptimalcontrolofthe

hos-pitals’airhandlingunitsleadingtoalmost20%reductionofthe

energyuse.Otherresearchersexploitneuralnetworks’

capabili-tiesfor24h-aheadbuilding-levelelectricityloadforecastingusing

datacollectedfromvariousoperationalcommercialandindustrial

buildingsites[19].Dataminingbasedapproachestodeveloping

ensemblemodelsforpredictingnext-dayenergyconsumptionand

peak powerdemand,withtheaimofimprovingtheprediction

accuracyarealsodeveloped.Thisapproachwasadoptedtoanalyse

thelargeenergyconsumptiondataofthetallestbuildinginHong

Kong[22]withverysatisfactoryresults.Theseensemblemodels

canbevaluabletoolsfordevelopingstrategiesoffaultdetectionand

diagnosis,operationoptimizationandinteractionsbetween

build-ingsandsmartgrid.Moreover,dataprocessingandinterpretation

extractedbythesmartmeteringcanprovideusefulinformation

forthebuildings’energybehaviour.Advancedtechniquessuchas

clusteranalysisareusedbyvariousresearchers[14,24]leadingto

thedeterminationof optimumclustering proceduresaswellas

buildingbenchmarking.

2.2. Demandresponse(DR)

DR[25,26]offersthecapabilitytoapplychangesinthe

elec-tricityusagebytheconsumers fromtheirnormal consumption

patternsinresponsetochangesintheelectricitypricingovertime

[27].Thisleadstolowerenergydemandduringpeakhoursor

dur-ingperiodsthatelectricitygrid’sreliabilityisputatrisk.Therefore

demandresponseisareductionindemanddesignedtoreducepeak

loadoravoidsystememergencies.Hence,demandresponsecan

beamorecost-effectivealternativethanaddinggeneration

capa-bilitiestomeetthepeak andor occasionaldemandspikes.The

underlyingobjectiveofDRistoactivelyengagecustomersin

mod-ifyingtheirconsumptioninresponsetopricingsignals.Demand

responseisexpectedtoincreaseenergymarketefﬁciencyand

secu-rityofsupply,whichwillultimatelybeneﬁtcustomersbywayof

optionsformanagingtheirelectricitycostsandleadtoreduced

environmentalimpact.

Thealready availableDRprogramsare generallycategorized

into incentive-and price-based programs. Incentive-based

pro-grams provide economic incentives for customers to reduce

demandattimesofcapacityshortageorexceptionallyhigh

electric-ityprices,whereasprice-baseddemandresponseprogramsinvolve

dynamictariffratesthatpromotegeneralchangesinpatternsof

electricityuse.Time-of-usetariffs,whichareoneofthemajor

price-based demandresponse programs in useinvolvedifferent unit

priceswithindifferentblocksoftime,andreﬂecttheaveragecost

ofutilitiesduringtheseperiods[26].

TherearesomeeffortsincountryleveltoshowthebeneﬁtsofDR

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caseforDRareanalysedbyBradleyetal.[27].Acost/beneﬁt

analy-sisisperformedinaquantitativemannershowingthatthebeneﬁts

oncountrylevelareclearly verysigniﬁcant,i.e.,2.8% reduction

inoverallelectricityuseanda1.3%shiftinpeakdemand.

More-overtheeconomicviabilityoftheDRmainlydependsonensuring

participationbytheendusers,i.e.,thebuildingsector.Increaseof

participationcanbeensuredbyloweringtheparticipantcostsand

sharingofbeneﬁts.Finallyitisrevealedthattheactualcostsofthe

infrastructurearealsoaffectedbycustomerengagementandtrust.

AnempiricalstudyforSwedenisperformedbyBartuschetal.[26],

inordertoestimatetheendusers’responsetoademand-based

time-of-useelectricity distributiontariffamongSwedish

single-familyresidentialhouses.Thestudyshowedthatinalongterm

theresidentialhouseholdsstillrespondtothepricesignalsofthe

tariffbycuttingdemandinpeakhoursandshiftingelectricity

con-sumptionfrompeaktooff-peakhours.

Energy efﬁcient smart buildings are possible by integrating

smartmeter,smartsockets,domesticrenewableenergygeneration

andenergy storagesystemsforintegrated energymanagement,

andthis integratedsystemsupportsdemandsideload

manage-ment,distributedgenerationanddistributedstorageprovisionsof

futuresmartgrids[28,29].Consequently,theeffectiveintegration

ofbuildingsinsmartgridsrequirestheappropriatelevelsofdigital

technologyandinteroperability[30].Thesuccessful

implemen-tationof DRas mentioned in thepreviousparagraphs requires

near-real-time powermanagement [31] and advanced building

automationand communicationprotocols.Moreinformation on

thevariouscommunicationprotocolsthatcansupporttheZEB

per-spectivecanbefoundin[8].RecentstudiesinterconnectASHRAE

BACNETprotocolwiththewirelessZigBeeprotocoltoprovideDRin

thebuildings’energymanagementside.TheAddendumannounced

byASHRAEspeciﬁesthatthewirelesscommunicationforBACnet

willbebasedonZigBeeprotocol[32].Theinterconnectionof

Bac-netwithZigBeeallowsthemanagementofelectricalresourcesand

devices’load,targetingtooptimiseandreducetheelectricitycost

andreducepeakpowerconsumption.Therealtime

characteris-ticsandfunctionalitiesoftheBACnet–ZigBeeinterconnectionare

examined,showingthepotentialandcriticalroleofrecent

devel-opmentsintheimplementationofsmartgrids’infrastructurein

buildings.Otherresearchersprovideinterconnectionofthe

BAC-netandEnOceanprotocolstomeasureenergymeteringofvarious

appliancesinbuildingsandcontroltheloadusingwireless

commu-nications[25].Additionally,theDRusingthespeciﬁcconnectivity

isshownusingexperimentalfacilitiesthatcanrespondtoreal-time

changesintheelectricityprice.

2.3. Distributedsystems

Distributedsystemsplayacrucialpartineffectingsmart

build-ings’ demandresponse as theyprovidethe necessarytechnical

infrastructure [33]. Moreover distributed systems support the

effectiveexploitationofenergystorage.Sincesmartgridsmay

inte-graterenewableenergysources,energystorageisoftenseenas

necessaryfor theelectric utilitysystemswithlargeamountsof

solarorwindpowergenerationtocompensatefortheinabilityto

schedulethesefacilitiestomatchpowerdemand.

Loadshiftingthrougheffectivethermalenergystorageor

elec-tricitystorageisamajorpartofsmartgridsandalreadyexploited

byvariousstudiesforgeneration–consumptionmatchingandzero

energytargeting[32,33].Thereforeasthemajorpowerconsumers

atdemandside,buildingscanactuallyperformasdistributed

ther-malstoragestohelprelievepowerimbalanceofagrid.Thisrequires

accuratepredictionofpossiblepowerdemandvariationsof

build-ingsandenergyinformationfromthegridsideforinteractionand

optimization[36]based onmeasurementsacquiredfromsmart

metering.

Finallysmartgridscanplayasigniﬁcantroleinthebuilding

sec-torduetothefactthattheycreateaphysicalproximitybetween

consumers and energy production that may help increase end

users’awarenesstowardsamorerationaluseofenergyfor

build-ings.Studiesshowthattheusersawarenesscombinedwithsmart

gridscandecreasetheenergycostsby15%[37].

3. Smartandzeroenergycommunities

Movingfromthebuildingtothecommunitylevel,therequests

ofthefuture communitiesareverydemanding.Theyshouldbe

placesofadvancedsocial progressandenvironmental

regenera-tion,aswellasplacesofattractionandenginesofeconomicgrowth

basedonaholisticintegratedapproachinwhichallaspectsof

sus-tainabilityaretakenintoaccount.

As mentioned in the Leipzig Charter (http://ec.europa.

eu/regionalpolicy/archive/themes/urban/leipzigcharter.pdf),

sustainablecommunitiesandcitiesshould:

• Createthesuitableenvironmentforurbanpopulationsthatcan

attractindustry,businesses,tourismandworkforces.

• Modernizetheinfrastructurenetworks.Thisapproachisabout

achievingahigh-qualityandaffordableurbantransportsystem

thatincludesanetworklinkingthecitytotheregion,aswellas

providingandimprovingsupplynetworkssuchasenergy,

waste-watertreatmentandwatersupply.

• Ensuretheefﬁcientuseofnaturalresources,economicefﬁciency

andtheenergyefﬁciencyinnewandexistingbuildings.

TheNational RenewableEnergyLaboratory deﬁnedthe zero

energycommunityas“onethathasgreatlyreducedenergyneeds

throughefﬁciencygainsuchthatthebalanceofenergyforvehicles,

thermal,andelectricalenergywithinthecommunityismetby

renew-ableenergy”[12]and“communityscenarioscouldlinktransportation,

homeandtheelectricgridaswellasenablelargequantitiesof

renew-ablepowerontothegrid”.

Therefore, a smart zero energy community (http://www.

smartcommunities.org) is a regionwhere itscitizens and local

authoritiesareexploitingtheinformationtechnologyto“transform

lifeandworkinsigniﬁcantandfundamentalratherthan

incremen-talways”tomeettheaforementionedstrategiesandobjectives.As

mentionedintheSmartCommunitieswebsite:“Thegoalofsuchan

effortismorethanthemeredeploymentoftechnology.Ratheritis

aboutpreparingone’scommunitytomeetthechallengesofaglobal,

knowledgeeconomy”.

SmartGridscanbethebasisofsmartzeroenergycommunities

offering:

(1)Reliabilityandsecurityastheprominenceofinformation

tech-nology.

(2)Optimal operation thus contributing to a

generation–consumption matching [8] through the full

exploitationofLowZeroCarbon(LZC)emissions’technologies

[12,36].

(3)AdaptationtotherapidlyevolvingICTtechnologies,aswellas

exploitationoftheinternetcapabilitiesandsmartdevicesand

applications[4].

(4)Thenecessarybalancebetweentheenergydemandandenergy

production oncommunity,neighbourhoodand districtlevel

contributingtothesmartandsustainableurbanenvironment.

Effective management of the intermittency of Renewable

EnergySources powergenerationaswellas ofoperationaland

capacityreserveinconjunctionwiththebuildings’energydemand

proﬁles. In order tounderstand more clearly therole of smart

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Fig.1.Thesmartgrid’scomponents.

thenextparagraphs.MoredetailsontheSmartGridspenetration

worldwidecanbefoundin[39].

Therole ofnetenergymetering and thetimeof useratein

theelectricitydemandofazeroenergyapartmentcommunityare

investigated[40].Theapartments’communityisplacedinWest

VillageinDavis,California.Smartschedulingisappliedinorderto

achievepeakloadshavingbyusingthevarioushousehold

appli-ancesduringoff-peakhours.Thepeakdemandisreducedby18%,

thepart-peakdemandby32%andtheoff-peakdemandisincreased

by12%.Sincetheapartmentsareusingphotovoltaicsfor

electric-ity,thecommunityoccupantscanbeneﬁtsigniﬁcantlyifthesurplus

generationbythephotovoltaicsismaximizedandsoldtothegrid

duringpeakhours(Fig.1).

MoreoveralowenergycommunityconﬁgurationfortheIsland

ofHawaiiisproposedin[41].Theoverallworkincludedenergy

pro-ductionusingphotovoltaics,storagesystemsusingbatteriesand

compressedairsystems,demand responseof buildings,passive

coolingtechniquesforenergyefﬁciencyaswellasmicroclimate

andlandscapeassessment.

Anadvancedenergymanagementandoptimizationmodelfor

theoperationof theLeaf MicrocridCommunityis proposedby

Kolokotsaetal.[42].Theaimofthemodelistominimizetheenergy

costof themicrogrid byperforming ageneration–consumption

matchingusing geneticalgorithms.Theoptimizationprocedure

issupported byenergy loadforecasting and energy production

prediction (bythephotovoltaics and hydroelectric plant) using

artiﬁcialneuralnetworks.Whiletheoptimizationhorizonis24h

ahead, the optimization and management procedure leads to

almost6% reduction of the energy costsfor the microgrid and

signiﬁcantcostsavingsforthecommunitywithoutextraenergy

investment.

Similarworksincludeapartfromgeneticalgorithms[43,44]the

useoffuzzylogicandParticleSwarmOptimization(PSO)method

[45]aswellasnon-linear constrainmultiobjectiveoptimization

method[46].

Anotherpotentialapplicationofsmartgridsincommunitylevel

istheoperationofsmallandmediumenterprises(SMEs)which

havemultipleoperationalcharacteristicsrangingfromofﬁce

build-ings,retail,industrial,etc.anddifferentenergypatternsandneeds

[47].ThoughthepotentialforcarbonreductionofSMEsisunclear

duetothediversityoftheenduses,smartgridscanbeaviable

toolforSMEsdecarbonizationsinceadditionaldatacanbeavailable

throughdetailedsmartmetering.Thereforetheelectricityloadsof

theSMEscannediscretizedandtheproportionsthatareavailable

forloadshiftingorshavingcanbemoreeasilyidentiﬁed.

Thesecanbespaceheating,coolingandhotwaterinthe

com-mercialsectorandheatingorcoolinginindustry.

Lastbutnotleast,thesocialaspectsofsmartgridsincommunity

levelshouldbeclearlyexamined.Thefastpromotionofsmartgrids’

capabilitiesandpotentialapplicationswillbemucheasilydigested

bythelocalcommunitiesifsocialinclusionistakenintoaccount.

Theroleofsocialexpertsinthisaspectisofgreatimportance[48].

4. Conclusionsandfutureprospects

SmartGridscanbeconsideredverypromisingfortheenergy

andbuildingenvironment industryduetothefactthatcreatea

physicalproximitybetweenconsumersandmicroenergysources

thathelpincreaseconsumerawarenesstowardsamorerational

useofenergy.

Moreoversmartgridscanoffernewopportunitiesforthe

reduc-tionofgasemissionsbycreatingtechnicalconditionsthatincrease

theconnectionofdevicesandrenewableenergyresourcesatthe

lowvoltagelevel.

Inaddition,smartgridsinthebuildingsectorofferagreat

oppor-tunityforimprovingthepowerqualityand reliabilityofenergy

sourcesduetothefactthatitoffersdecentralizationofsupply,

bet-tersupplyanddemandmatching,reductionoftransmissionlosses

andminimizationofdowntimes.Thusenergyinvestmentscanbe

shiftedfromtheexpansionoftransmissionandlargescale

gener-ationsystemstotheenergyefﬁciencyinthebuildingsector,i.e.,

improvingbuildingfabric,increasinggreeninfrastructureinthe

community,improvingindoorandoutdoorenvironment

interac-tionbylandscapesolutions.

Inaddition,widespreadapplicationofmodularmicro

genera-tionsourcesoncommunitylevelmaycontributetothereduction

oftheenergypriceinthepowermarket.Further,pricereduction

maybeachievedbyoptimizingmicro-generationoperationand

performingbuildingloadforecastingwhichispossibleduetothe

availabledatafromthemeteringprocess.Asaresultthesmartgrids

canbeviewedasaggregatorsofbuildings,consumersand

com-munitiesthatwillbeempoweredwithbetterpricesandvaluable

opportunities.

In order to reach the aforementioned goals, several

chal-lengesanddrawbacksshouldbeovercome.Technicalchallenges

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competencetooperatesuccessfullythemicrogenerationenergy

sourcesaswellastointeractwiththebuildingoccupants.These

aspectsrequireextensiveresearcheffortswithaninterdisciplinary

perspective that will examine smart grids’ real time

manage-ment,protectionandcontrol.Simultaneously,theroleoftheend

usersand buildingoccupants shouldbeenhanced and

success-fullyincorporated.Internet of thingscanplay a signiﬁcantrole

inthisdirectionasitcansupportawarenessraisingofendusers

andprovideuserfriendlyapplicationsfordemandresponseand

loadmanagement capabilities. Inorder toachieve this, speciﬁc

telecommunicationininfrastructuresaswellasopen

communi-cationprotocolsshouldbeprovidedtohelpmanaging,operating

andcontrollingbuildingswithinsmartgrids.

Anothersigniﬁcantchallengeisrelatedtotheincreasedinitial

installationcostwhichconstitutesagreatdisadvantage.Speciﬁc

policiesandincentivesshouldbeprovidedbythegovernmental

bodiestoencourageinvestmentinordertoreachnationalcarbon

reductiongoals.

Finally,signiﬁcanteffortshouldbeputinthedevelopmentof

standardsconcerningtheoperationalcharacteristics,thepower

quality,realtimeoptimizationandoverallmanagement.

References

[1]M.E.El-hawary,Thesmartgrid—state-of-the-artandfuturetrends,Electric PowerCompon.Syst.42(2014)239–250,http://dx.doi.org/10.1080/ 15325008.2013.868558.

[2]C.F.Covrig,A.Mengolini,SmartgridprojectsinEurope:lessonslearnedand currentdevelopments2012update,2013.

[3]L.Chen,Z.C.Huang,P.Wu,C.Liu,Transmissionlineconditionmonitoring basedonInternetofthingsinsmartgrid,Appl.Mech.Mater.336–338(2013) 2488–2493,http://dx.doi.org/10.4028/www.scientiﬁc.net/AMM.336-338. 2488.

[4]Y.Zhen,X.Li,Y.Zhang,L.Zeng,Q.Ou,X.Yin,Transmissiontowerprotection systembasedonInternetofthingsinsmartgrid,Proceedingsofthe7th InternationalConferenceonComputerScience&Education(ICCSE),IEEE, 2012,pp.863–867,doi:10.1109/ICCSE.2012.6295205.

[5]V.C.Güngör,D.Sahin,T.Kocak,S.Ergüt,C.Buccella,C.Cecati,etal.,Smartgrid technologies:communicationtechnologiesandstandards,IEEETrans.Ind. Inform.7(2011)529–539,http://dx.doi.org/10.1109/TII.2011.2166794. [6]S.Deng,R.Z.Wang,Y.J.Dai,Howtoevaluateperformanceofnetzeroenergy

building—aliteratureresearch,Energy71(2014)1–16,http://dx.doi.org/10. 1016/j.energy.2014.05.007.

[7]M.Santamouris,D.Kolokotsa,Passivecoolingdissipationtechniquesfor buildingsandotherstructures:thestateoftheart,EnergyBuild.57(2013) 74–94,http://dx.doi.org/10.1016/j.enbuild.2012.11.002.

[8]D.Kolokotsa,D.Rovas,E.Kosmatopoulos,K.Kalaitzakis,Aroadmaptowards intelligentnetzero-andpositive-energybuildings,SolarEnergy85(2011) 3067–3084.

[9]E.Pyloudi,S.Papantoniou,D.Kolokotsa,Retroﬁttinganofﬁcebuilding towardsanetzeroenergybuilding,Adv.Build.EnergyRes.(2014)1–14, http://dx.doi.org/10.1080/17512549.2014.917985.

[10]M.Kapsalaki,V.Leal,Recentprogressonnetzeroenergybuildings,Adv.Build. EnergyRes.5(2011)129–162.

[11]D.H.W.Li,L.Yang,J.C.Lam,Zeroenergybuildingsandsustainable developmentimplications:areview,Energy54(2013)1–10,http://dx.doi. org/10.1016/j.energy.2013.01.070(**Thespeciﬁcpaperprovidesadeep reviewontheavailabletechnologiesandcasestudiesforthedevelopment andimplementationofzeroenergybuildings.Moreoverasummaryof recentlydevelopedZEBisprovided).

[12]N.Carlisle,O.VanGeet,Deﬁnitionofa“ZeroNetEnergy”Community,NREL, Colorado,USA,2009.

[13]G.Privat,Howinformationandcommunicationtechnologieswillshape SmartGrids,In:SmartGrids,2013,pp.263–280,doi:10.1002/9781118562581 (chapter9).

[14]T.G.Nikolaou,D.S.Kolokotsa,G.S.Stavrakakis,I.D.Skias,Ontheapplicationof clusteringtechniquesforofﬁcebuildings’energyandthermalcomfort classiﬁcation,IEEETrans.SmartGrid3(2012)2196–2210.

[15]M.Bae,H.Kim,E.Kim,A.Y.Chung,H.Kim,J.H.Roh,Towardelectricityretail competition:surveyandcasestudyontechnicalinfrastructureforadvanced electricitymarketsystem,Appl.Energy133(2014)252–273,http://dx.doi. org/10.1016/j.apenergy.2014.07.044.

[16]R.K.Jain,K.M.Smith,P.J.Culligan,J.E.Taylor,Forecastingenergyconsumption ofmulti-familyresidentialbuildingsusingsupportvectorregression: Investigatingtheimpactoftemporalandspatialmonitoringgranularityon performanceaccuracy,Appl.Energy123(2014)168–178,http://dx.doi.org/ 10.1016/j.apenergy.2014.02.057.

[17]H.Zhao,F.Magoulès,Areviewonthepredictionofbuildingenergy consumption,Renew.Sustain.EnergyRev.16(2012)3586–3592,http://dx. doi.org/10.1016/j.rser.2012.02.049.

[18]K.Gobakis,D.Kolokotsa,A.Synnefa,M.Saliari,K.Giannopoulou,M. Santamouris,Developmentofamodelforurbanheatislandprediction usingneuralnetworktechniques,Sustain.CitiesSoc.1(2011) 104–115.

[19]J.G.Jetcheva,M.Majidpour,W.-P.Chen,Neuralnetworkmodelensembles forbuilding-levelelectricityloadforecasts,EnergyBuild.84(2014) 214–223,http://dx.doi.org/10.1016/j.enbuild.2014.08.004.

[20]A.Mellit,A.M.Pavan,A24-hforecastofsolarirradianceusingartiﬁcialneural network:applicationforperformancepredictionofagrid-connectedPVplant atTrieste,Italy,SolarEnergy84(2010)807–821.

[21]S.Papantoniou,D.Kolokotsa,K.Kalaitzakis,D.N.Cesarini,E.Cubi, C.Cristalli,Adaptivelightingcontrollersusingsmartsensors,Int.J.Sustain. Energy(2014)1–17,http://dx.doi.org/10.1080/14786451.2014.

923887.

[22]C.Fan,F.Xiao,S.Wang,Developmentofpredictionmodelsfornext-day buildingenergyconsumptionandpeakpowerdemandusingdatamining techniques,Appl.Energy127(2014)1–10,http://dx.doi.org/10.1016/j. apenergy.2014.04.016.

[23]S.Papantoniou,D.Kolokotsa,K.Kalaitzakis,Buildingoptimizationandcontrol algorithmsimplementedinexistingBEMSusingawebbasedenergy managementandcontrolsystem,EnergyBuild.(2014),http://dx.doi.org/10. 1016/j.enbuild.2014.10.083.

[24]I.P.Panapakidis,T.A.Papadopoulos,G.C.Christoforidis,G.K.Papagiannis, Patternrecognitionalgorithmsforelectricityloadcurveanalysisofbuildings, EnergyBuild.73(2014)137–145,http://dx.doi.org/10.1016/j.enbuild.2014. 01.002.

[25]Y.-C.Li,S.H.Hong,BACnet–EnOceanSmartGridGatewayanditsapplication todemandresponseinbuildings,EnergyBuild.78(2014)183–191,http://dx. doi.org/10.1016/j.enbuild.2014.04.022.

[26]C.Bartusch,K.Alvehag,Furtherexploringthepotentialofresidentialdemand responseprogramsinelectricitydistribution,Appl.Energy125(2014)39–59, http://dx.doi.org/10.1016/j.apenergy.2014.03.054.

[27]P.Bradley,M.Leach,J.Torriti,Areviewofthecostsandbeneﬁtsofdemand responseforelectricityintheUK,EnergyPolicy52(2013)312–327,http://dx. doi.org/10.1016/j.enpol.2012.09.039.

[28]C.Keles,A.Karabiber,M.Akcin,A.Kaygusuz,B.B.Alagoz,O.Gul,Asmart buildingpowermanagementconcept:smartsocketapplicationswithDC distribution,Int.J.Electr.PowerEnergySyst.64(2015)679–688,http://dx. doi.org/10.1016/j.ijepes.2014.07.075.

[29]G.Kayo,A.Hasan,K.Siren,Energysharingandmatchingindifferent combinationsofbuildings,CHPcapacitiesandoperationstrategy,Energy Build.82(2014)685–695,http://dx.doi.org/10.1016/j.enbuild.2014.07.077. [30]J.A.Oliveira-Lima,V.Delgado-Gomes,J.F.Martins,C.Lima,Standard-based

service-orientedinfrastructuretointegrateintelligentbuildingsin distributedgenerationandsmartgrids,EnergyBuild.76(2014)450–458, http://dx.doi.org/10.1016/j.enbuild.2014.03.013.

[31]S.H.Hong,S.H.Kim,G.M.Kim,H.L.Kim,ExperimentalevaluationofBZ-GW (BACnet-ZigBeesmartgridgateway)fordemandresponseinbuildings, Energy65(2014)62–70,http://dx.doi.org/10.1016/j.energy.2013.12.008. [32]ANSI/SHRAEstandard135-2010:adatacommunicationprotocolforbuilding

automationandcontrolnetwork,2012.

[33]G.Chalkiadakis,V.Robu,R.Kota,A.Rogers,N.Jennings,Cooperativesof distributedenergyresourcesforefﬁcientvirtualpowerplants,Proceedingsof the10thInternationalConferenceonAutonomousAgentsandMultiagent Systems,Taipei,Taiwan,2011.

[36]X.Xue,S.Wang,Y.Sun,F.Xiao,Aninteractivebuildingpowerdemand managementstrategyforfacilitatingsmartgridoptimization,Appl.Energy 116(2014)297–310,http://dx.doi.org/10.1016/j.apenergy.2013.11.064. [37]X.H.Li,S.H.Hong,User-expectedprice-baseddemandresponsealgorithmfor

ahome-to-gridsystem,Energy64(2014)437–449,http://dx.doi.org/10.1016/ j.energy.2013.11.049.

[39]G.Bartels,GlobalSmartGridFederationReport,2012(Thespeciﬁcreport underlinestherecenttrendsandpenetrationofsmartgridsinvariouspartsof theworld.Itindicatesthemethodologyforlargescaledeploymentofsmart gridsincities).

[40]K.Gaiser,P.Stroeve,Theimpactofschedulingappliancesandratestructure onbillsavingsfornet-zeroenergycommunities:applicationtoWestVillage, Appl.Energy113(2014)1586–1595,http://dx.doi.org/10.1016/j.apenergy. 2013.08.075.

[41]K.M.Zhang,R.J.Thomas,M.Bohm,M.Miller,Anintegrateddesignapproach forsustainablecommunitydevelopment,Proceedingsofthe42ndHawaii InternationalConferenceonSystemSciences,IEEE,2009,pp.1–10,doi: 10.1109/HICSS.2009.67.

[42]E.Provata,D.Kolokotsa,S.Papantoniou,M.Pietrini,A.Giovannelli,G.Romiti, DevelopmentofoptimizationalgorithmsfortheLeafCommunitymicrogrid, Renew.Energy74(2015)782–795,http://dx.doi.org/10.1016/j.renene.2014. 08.080(*Thespeciﬁcpaperfocusesontheaplicationofartiﬁcialintelligence inmanagementpracticesforsmartgrids.TheLEAFmicrogridcommunity’s resourcesareusedfortheimplementationofadvancedenergymanagement andoptimisationtechniques).

[43]F.A.Mohamed,H.N.Koivo,Onlinemanagementgeneticalgorithmsof microgridforresidentialapplication,EnergyConvers.Manag.(2012) 562–568,http://dx.doi.org/10.1016/j.enconman.2012.06.010.

(6)

[44]H.Z.Liang,H.B.Gooi,Unitcommitmentinmicrogridsbyimprovedgenetic algorithm,IPEC,2010ConferenceProceedings,2010,pp.842–847,doi: 10.1109/IPECON.2010.5697083.

[45]G.Kyriakarakos,A.I.Dounis,K.G.Arvanitis,G.Papadakis,Afuzzylogicenergy managementsystemforpolygenerationmicrogrids,Renew.Energy41(2012) 315–327,http://dx.doi.org/10.1016/j.renene.201111.019.

[46]F.A.Mohamed,H.N.Koivo,Onlinemanagementofmicrogridwithbattery storageusingmultiobjectiveoptimization,ProceedingsoftheInternational ConferenceonPowerEngineering,EnergyandElectricalDrives,2007,doi: 10.1109/POWERENG.2007.4380118.

[47]J.Rawlings,P.Coker,J.Doak,B.Burfoot,Dosmartgridsofferanewincentive forSMEcarbonreduction?Sustain.CitiesSoc.10(2014)245–250,http://dx. doi.org/10.1016/j.scs.2013.04.003.

[48]M.Wolsink,Theresearchagendaonsocialacceptanceofdistributed generationinsmartgrids:renewableascommonpoolresources,Renew. Sustain.EnergyRev.16(2012)822–835,http://dx.doi.org/10.1016/j.rser. 2011.09.006.