ADViCE：2025年人工智能在脱碳领域的应用状况研究报告

StateofAIfor

Decarbonisation2025

January2026

1

2

Contents

3Introduction

10UnlockingDomesticDecarbonisation

18EnablingNetZeroInfrastructure

27MaximisingFlexibilityinEnergyNetworks

36DecarbonisingManufacturingInputs

44ImprovingManufacturingProcessEfficiency

53OptimisingSoilManagement

62MinimisingMethaneinAgriculture

70OptimisingEVInfrastructureandCharging

78DecarbonisingFreightandFleets

3

2025:lotsofhype,sometangibleprogress

Thehype

AIhasbeenconstantlyintheheadlinesthisyear.Jaw-

droppinglylargeinvestmentsinAIdatacentres

underpinnedUSeconomicgrowth.Securingenough

energyforthosedatacentresbecameamajorconcern

andtechcompaniesstartedfundingnuclearpowerplantsandgridreinforcement.Businessesaroundtheworld

startedintegratinggenerativeAIintotheirsoftwareandprocesses,withmixedresults.Debatesaboutthefutureofworkintensified,withpeopleeitherworriedorexcitedaboutAI’spotentialtoautomatejobs.Evennational

carbonbudgetsstartedincludingassumptionsthatAIcouldsavemillionsoftonnesofCO2emissions.

Butamidstthattorrentofnewsandhypeitcanbehardtofindtheanswertoanimportantquestion:howeffectivelyisAIbeingappliedtokeysocietalchallengeslike

decarbonisation?

Thereality

ThisannualreportanswersthatquestionwithareviewofhowUKapplicationsofAIfordecarbonisationhavematuredoverthelastyear.Thisrangesfrom

applicationsthatarereachingscaleandmeaningfullycontributingtonationaldecarbonisation,throughto

earlierstageresearchthathasmadenotableprogress.

Therehavebeentangiblestepsforwardinsomeareas

thisyear.AI-poweredEVchargingisalreadyplayingasignificantroleinmanagingourlowcarbonelectricitygrid.HeatpumpinstallationsarequickerandcheaperbecauseofAI.SteelfurnacesandcementplantsarereducingemissionsthroughAI-optimisedoperations.

ButinotherareasprogresshasbeensloworhinderedbygenerativeAIhype.Lotsmoreworkisrequiredto

fullyrealisethebenefitsofAIfordecarbonisation.

4

MeasuringprogressonthenineGrandChallenges

Inour

originalreport

ADViCEidentified

sevendecarbonisationGrandChallengeswhereAIcouldmosthaveimpact.

Thisyearwehavealsoaddedtwotransport-relatedGrandChallenges.

EachGrandChallengeisbrokendownintomorespecificchallengesforAItotackleinthe

DecarbonisationChallengeCards

.

ToidentifyprogressthisyeartheADViCE

teamofdomainexpertsreviewedpotentialexamplesofprogressineachspecific

challenge(sourcedusingAI-basedresearchtoolsandconversationswithstakeholders).ForeachGrandChallengetheexpertsthenidentifiedkeythemesandexamplesof

whereAIhasmadeprogressin2025.

5

Overallprogressin2025

In2025AIhadameasurableimpactontheUK’sabilitytooperatealowcarbonelectricitygrid.AI-poweredsolarnowcastingreducedemissionsbyanestimated300,000tonnes,smartEVchargingloweredpeakelectricityusagefromEVsby42%andvirtualpowerplantshelpedbalancethegrid.

ThisyearsawanotablestepforwardinAIapplicationstodomesticdecarbonisationwiththenationalrolloutoftwoAItoolslookingtostreamlinedifferentpartsoftheheatpumpinstallationjourney.

Inmanufacturingandtransport,adoptionofAIforprocessefficiencyandoptimisationcontinuedtogrow.Largerorganisationsstartedtomovefromdemonstrationtodeployment,thoughsmaller

organisationstypicallylaggedbehind.

AIforsoilmanagementprogressedfromtheoreticalpilotingtoearlycommercialisation,whileapplicationstoagriculturalmethanereductionmadesomeprogressattheresearchstage.

LessprogresswasmadeonapplyingAItodecarbonisingmanufacturinginputsorelectrifyingfreight,partlyduetothehighcapitalcostsinvolved.ThesemayneedadditionalinterventionstoaccelerateAIadoption.

Visualsummaryofprogress

ShownbelowisanassessmentofhowfarsolutionsforeachGrandChallengeprogressedbetween2024and2025,alongwitharoughestimateofpotentialprogressin2026basedontheenablersandblockersidentifiedinthisreport.

NosignificantEarlyexploratoryMajorresearchProofofconceptPilotsinsomeMeaningfulimpactforGrowingimpactNational-scaleMaximumdecarbonisation

decarbonisationimpactimpact

workresearchprogrammesdemonstratorsorganisationsindividualorgsacrosssector

I

Unlockingdomesticdecarbonisation

20242025⃞2026

EnablingNetZeroinfrastructure2024一20252026

20242025

2026

-----⃞

Decarbonisingmanufacturinginputs

Improvingmanufacturingprocessefficiency

Optimisingsoilmanagement

Minimisingmethaneinagriculture

OptimisingEVinfrastructureandcharging

Decarbonisingfreightandfleets

2026

2025

2024

2026

2024

2025

------------⃞

2026

2024—一2025

--------------⃞

2026*

20242025

2026

2024

2025

Maximisingflexibilityinenergynetworks202420252026*

2024

Key

Maturityattheendof2024

Maturityattheendof2025

Potential

maturityattheendof2026

(estimate)

2025

2026

6

*Note:shorterarrowsfor2026in‘National-scaledecarbonisationimpact’arenotanindicatorprogressisexpectedtoslow,butbecausethefigureisfocusedonthegrandchallengesinearlierstages.AI’sdecarbonisationimpactforthemorematurechallengesisexpectedtogrowsteadilyin2026andfuturereportsmaystarttotrackthatmoreclearly.

7

Somekeynumbersfrom2025

79%

ofEVowners

haveasmart

charger

300,000

tonnesofCO2

avoidedeach

yearusingsolar

nowcasting

1500

farmsusing

autonomous

droneflightsto

inspectcrops

2%

reductioninCO2

fromcement

productionusing

AI

50%

reductioninheat

pump

installationtime

usingAItools

Thestartupecosystemcontinuestogrow

TheUKAIfordecarbonisationecosystemhasshownsteadygrowthinrecentyears.Mostcompaniesareintheseedandventurestage,whichbroadlymirrorstheoverallAIecosystemintheUK.

ActiveCompanies

379*

366

7%

343

307

12%

268

Seed

236

45%

Venture

Growth

Established

36%

20202021202220232024Q3*2025

8*DatasourcedfromBeauhurstcompanydatabaseonaselectionof380companiesidentifiedthroughacustomquery.DatacollectedupuntilQ3of2025.Categoriesofevolutionstagesofcompaniescanbefound

here

Isthisreportmissingsomething?

Youcancontactusat

ADViCE@turing.ac.uk

,or

signupto

ourmailinglist

tobenotifiedofthewebinars,workshopsandinperson

eventsthatwehost.MoredetailsaboutthelaunchofthedatabaseofAIfor

decarbonisationsolutionswillbeannouncedshortly.Wealsohavea

knowledge

base

withkeyresources.

AboutADViCE

AIforDecarbonisation’s

VirtualCentreofExcellence(ADViCE)isaprogrammefundedbytheDepartmentforEnergySecurityandNetZero.Itisapartnership

betweenDigitalCatapult,EnergySystemsCatapultandTheAlanTuring

Institute.

ADViCE

existstojoinuptheAIfor

decarbonisationecosystem.We’realwayskeentohearaboutwhatyou’redoinginthisspace.

Thisisanannualreportandforfuture

editionswewillbeworkingwiththe

ecosystemtocurateacontinuallyupdateddatabaseofAIfordecarbonisation

solutionsandtheirprogress.

We’dlovetohearfromyouwhetheryou:

•haveadecarbonisationchallengeyouthinkismissing

•areworkingonanAIsolutionthatisdeliveringdecarbonisation

•orjustknowaboutsomethinginterestingwehaven’tcovered.

9

Grand

Challenge1

Unlocking

Domestic

Decarbonisation

10

11

Overview:UnlockingDomesticDecarbonisation

TherearesomewellstudiedAIapplication

areaswhichhavebothacademicresearchandahandfulofearly-stagecommercialofferings,butfewexamplesofsuccessatscaleyet.

BothdataavailabilityandthepotentialmarketforAIaregrowingasdomesticdecarbonisationpicksupspeed.

Residentialheatingisresponsibleformorethan13%ofgreenhousegasemissionseachyear,

andsoisessentialindecarbonisingtheUK

economy.However,decarbonisinghomes

requireschangestobothheatingsystemsandconsumerbehavioursineveryhomeintheUK.Engagingconsumersinthatprocess,financingit,anddeliveringitatpaceareallmajor

challenges.

Therehavebeenanumberoflargepublic

sectorinnovationfundingprogrammesinthisareaoverthelastfewyears,includingtheNetZeroHeatprogramme.

ThisyearsawanotablestepforwardinAI

applicationstothisareawiththenationalrolloutoftwoAItoolslookingtostreamlinedifferent

partsoftheheatpumpinstallationjourney.

12

AIapplicationsthatcouldaddressthischallenge

13

ThemesinAIadoptionfordomesticdecarbonisation

1

2

3

4

5

UseofAIinacceleratingheatingsystemdesignhasmovedfrompilots(e.g.

Geo

’sAISmartHeatPathwayin

2022-24)tonational-scaleproductlaunches(

HeatGeek’sZeroDisruptAI

)thatarehelpingtosignificantlyreduceheatpumpinstallationcosts.

Frictionintheadminprocessforlowcarbontechnology(LCT)installshasbeenreducednationwide,withtheabilitytoprovidesame-dayauto-approvalsforLCTinstallsusing

ENA’sConnectDirect

.

Specialistchatbotsaimedatsupporting

installers

and

consumers

withheatpumpinstallationshavebeendevelopedandtrialled–includingapublic-facingrollout–buthavenotyetseensignificanttraction.

AdoptionofAItoimproveidentificationofvulnerableenergyconsumersisnowwidespread,from

callanalysisby

ScottishPower

,to

fuelpovertyriskmapping

tobettertargetgrantsupport,toSSEN

forecastingfuturevulnerability

atalocallevel.

AItoautomaticallybreaksmartmeterusageintodifferentappliances(knownasnon-intrusiveloadmonitoring,NILM)remainsanactiveareaofacademicresearch.Itisalreadyemployedinanumberofconsumer-facingappsincluding

Loop

,whichhashelped>150kusersreduceenergyusagebyanaverageof15%.

Heatpumpdesign:HeatGeekZeroDisrupt

HeatGeek

haveusedAIextensivelyinautomatingpartsoftheheatingsystemdesignprocess.Thisincludes:

•LiDAR-basedautomatedinternalsurveyingofhomes

•Computervision-basedheatpumpsitingassessment

•Automationofformpopulationandcommunications

•AI-basedselectionofoptimaldesignparameterstoachievetargetefficiencyatminimumcost

Intrialsthishas

reducedcosttocustomers

*by~75%andinstallationtimeby~50%,andhasnowbeenrolledout

nationallyandisinusebyallHeatGeekinstallers.

Thisdirectlyaddressesthecostanddisruptionchallengesthatareslowingheatpumpadoptionandislikelyto

significantlyacceleratetherateofheatpumpinstallations.

14*afterboiler

upgradeschemegrantsof£7.5karetakenintoaccount

15

Connectionapproval:ENAConnectDirect

The

EnergyNetworkAssociation

haveintegratedAIintothenationalconnectionapplicationservicefordomesticlow

carbontechnologies(LCTs)likeheatpumpsandEVchargers.

Itutilisescomputervisionto

reviewphotosofcut

-

outs

(essentiallythefusebetweenahomeandthegrid)to

removetheneedforahumantorevieweveryphotoandenableinstantapprovalofapplicationswhereitwasclearnocut-outupgradewasrequired.

Ithasreducedthetimeandcostofcompletingpaperworkforinstallersandhasbeenusedtospeedupover

185kLCT

approvals

forconsumers.

16

Enablersforthenext12months

Heatpumpinstallationsincreasing

HeatpumpinstallationratesareincreasingandtheUKnowhasover

300,000heatpumps

installed.

ThismeansthereisbothmoredataavailablefortrainingAI(including

freedatafromtrials

)andalargermarkettodriverevenuesforAIinnovators.

Smartmeterdatastartstobecomemoreavailable

Severalongoinginitiatives

tomakeaccesstosmartmeterdataeasierareunlikelytohaveanimpactin2026,but

syntheticsmartmeterdata

isavailablenow.

GenerativeAItoolingmatures

AsgenerativeAImodels(andassociatedtooling)mature,itbecomesincreasinglyfeasibleforthemtobeusedatscaletosupportconsumersindecarbonisingtheirhomes.

17

Remaininggapsandbarriers

Electricityremainsmoreexpensivethangas

Despiterecentgovernmentmovestoreducepolicycostsonelectricity,electricityremainsmorethan4xas

expensiveasgas.Thismakesitextremelydifficultfor

heatpumpstobecostcompetitivewithgasboilers(eventhoughtheyare3-4xmoreefficient).

ThisincreasestheimportanceofusingAItoimproveheatpumpoperation–bothimprovingefficiencyandshiftingusagesohouseholdscanbenefitfromtime-of-usetariffs.

Interoperabilityisacontinuingchallenge

LackofopenAPIsandinteroperablestandardsfordataandcontrolsremainsasignificantbarriertoapplyingAItodomesticheating.

Grand

Challenge2

EnablingNetZero

Infrastructure

18

19

Overview:EnablingNetZeroInfrastructure

SomeestablishedareasofAIusage,particularlyinoptimisingroutesandlayoutsfornewassets,havecontinuedtoseesteadygrowthinadoption.

Continuedpoliticalfocusonthisareameansitis

likelywewillcontinuetoseestrongfundingforAI

applicationsinthisareaoverthenexttwelvemonths(includingviatheStrategicInnovationFundand

GreenIndustriesGrowthAccelerator),butthe

potentialforbreakthroughremainsconstrainedby

challengesintegratingwithhard-to-change

bureaucraticprocesses,aswellasinsufficientdata.

Electrificationofheatingandtransportation,

combinedwithincreasedrenewables,meanswe

needbothsignificantexpansionofourelectricity

networksandwaystomanagenetworkconstraints.Deliveringattherequiredscale–andpace–isa

realchallenge,withnewrenewableprojectsheldupbydelaysoruncertaintyinnetworkconnections.

Despitesignificantattentionfromgovernmentandindustry,therehasbeenlittleprogressonapplyingAItothegridconnectionqueuethisyear.

ApplicationsofAIforreal-timeoptimisationandcontrolhavereachedreal-worldpilotstagein

offshorewindanddistributionnetworks.

20

AIapplicationsthatcouldaddressthischallenge

21

ThemesinAIadoptionforNetZeroinfrastructure

1

2

3

4

5

BigpromisesaboutAIbeingabletostreamlinegridconnectionsandaccelerateapprovalshaveyettodeliver,withthegovernment-announced

Connect

tool(matchingcapacitytodemand)havingbeenpaused.

AItoolstooptimiseinfrastructureplacementarerelativelymatureandwidelyadopted(e.g.

ContinuumOptioneer

,KinewellEnergy’s

cable

and

turbine

layoutoptimisation),reducingprojectdevelopmenttimelinesandcosts.

AIforreal-timecontrolandoptimisationofassetshasseennoticeableprogress,includingoperationalpilotsof

reinforcementlearningforwindfarmcontrol(

AIOLUS

)anddistributedcoordinationandcontrolofnetworkassets(

Constellation

).TheNationalEnergySystemOperator’s

VOLTA

programmeisscopingoutadoptionofAIwithinthenationalcontrolroom.

Cross-sectorandmulti-scaleplanningremainsakeychallengethatisstartingtobeaddressed.Therearenotabledata-sharinganddigitaltwininitiativesinthisspace(

ENSIGN

&

CReDo

+),buttheenablingconditionsforAItohavealargeimpactarenotyetinplace.

GenerativeAIhaslargelybeenlimitedtodataqualityenablers(e.g.

publicsentimentanalysis

and

datadiscovery

)andisnotcurrentlydisplacingcoreengineering/optimisationworkflows.

RouteOptimisation:ContinuumIndustries

Deployingnewenergyinfrastructureisslowandcostlybecauseplannersmustmanuallyevaluatethousandsofrouteoptionsagainstengineering,environmental,andpermittingconstraints.

ContinuumIndustries

isworkingwithmajornetworkoperators,includingNationalGrid,SSENTransmission,SGN,andNationalGasTransmission,tosolvethiswithOptioneer.

TheplatformusesAI-drivengeospatialoptimisationandconstraint-basedsearchevaluatemillionsofrouting

scenariosinminutes,balancingcost,environmentalimpact,andengineeringfeasibility.

Itdelivered

~60%reductioninprogrammetime

forSSEN’s132kVextensionand

~93%reduction

for

NGG’spipelinestudy.Thisreducescostsandacceleratesinfrastructureneededforelectrificationandhydrogentransition.In2025thetoolhasalsobeenextended

tocoverthe

screeningphase

ofrenewabledevelopment.

ContinuumarecurrentlyscalingwithliveUKdeploymentsand£8.2mSeriesAfunding.

22

23

IntelligentWindFarmControl:AIOLUS

Windfarmslose

10

-

20%oftheirpotentialoutputto"wakeeffects"

whereupstreamturbinesslowthewindfordownstreamturbines,butcurrentcontroltechnologiescan'toptimisethewholefarm.

UniversityofWarwickhasdeveloped

AIOLUS

,thefirstEuropeandeepreinforcementlearningsystemforwholewindfarmcontrol.Itusesreinforcementlearningtooptimiseturbinesettingsinreal-timetominimisewakeeffectsandmaximisefarm-wideoutput.

Inthelastyearthishasmovedfromlate-stagedevelopment

(ManchesterPrizefinalistinMay2024and£415kEPSRCgrant)intoareal-worldpilotwithoperationalcontrolofawindfarm.

Thiscoulddelivera3-5%increaseinannualenergyoutput-

equivalenttopowering1millionUKhouseholdsfromexisting

windcapacitywithoutnewinfrastructure.Byoptimisingexistingassets,itreducesneedfornewlandandoffshoredevelopments.

Localgridoptimisation:Constellation

UKPowerNetworkspartneredwithABB,

GeneralElectric/GE

Vernova

,UniversityofStrathclydePNDC,andmoretodevelopthe

world’sfirstsmartsubstations

capableofanalysingmillionsof

datapointsandreconfiguringnetworksettingsinreal-time.

Smartsubstationsforecastandanalyselocalpowerflowsandcommunicatewitheachother(ratherthanrelyingoncentral

control)tofreeupcapacityandincreaseresilience.

MLmodelsaretrainedcentrallythendistributedtosubstationsforautonomousoperation,providingresiliencewhen

communicationsfail.

Thefirstsmartsubstationwasinstalled

Jan2025inMaidstone

,withfivemoretobeinstalledbySeptember2026.ThesolutionstrialledaspartofConstellationcould

savecustomersinGB

£132m

by2030.Constellationestimatestheycanalsosave

17m

tCO2by2050

iffullyrolledout.

24

25

Enablersforthenext12months

Politicalappetiteishigh

Pressuretobothkeepenergybillsdownandspeedup

connections,particularlyfordatacentres,meanspoliticalsupportforAIapplicationsinthisspaceisextremely

strong.The

AIEnergyCouncil

focusesonspeedingup

gridconnectionsandissupportedbyworkatDESNZandNESO.The

CleanPower2030

targetcreatesahard

deadline,incentivisingexperimentationwithAIsolutions.

AIcompaniesneedenergyinfrastructurenow

AccesstoenergyfordatacentresisbecomingabindingconstraintonlargeAIcompanies.Theywillinvestboth

cashandtalentinunlockingthat,andwillbepredisposedtoAI-basedsolutions.Thisislikelytoincludecreationofnewrevenuestreams-e.g.Piclo’sdatacentre

connection

accelerationprogramme

intheUSwhichisexchanging

energyflexibilityservicesforfasterconnection.

Remaininggapsandbarriers

Changingprocessesrequiresmorethantechnology

Manyoftheprocessesinvolvedininfrastructure

developmentareformalisedunderlegislationor

regulation,whichrequiresfocusedpoliticalwilltochangequickly.AImayhelpspeedupcertainelementsbut

cannotstreamlineentireprocesses(orchangecultures)inhighlyregulatedareas.

Planningdataremainsfrustratinglypatchyandopaque

Thecomplexity(andhistoricallymanualnature)ofthe

planningprocessmeansconsistent,goodqualitydataisrarelyavailable.ThismakesitchallengingtobuildAI

toolsinthisarea(see

Yottar’sdevelopmentdiary

).

Therehasbeengradualprogressonthis,andOfgem’s

latestreview

proposesimportantactionsfornetworksthatwouldfurtherclosethisgap.

Grand

Challenge3

Maximising

Flexibilityin

EnergyNetworks

27

28

Overview:MaximisingFlexibilityinEnergyNetworks

thesystemoperator,savinghundredsofthousandsoftonnesofemissionsandtensofmillionsofpounds.

AIadoptionforforecastingandoptimisationremainsmixed.Manyorganisationcontinuetorelysolelyon

statisticalforecastingandmathematicaloptimisationtechniques,butanincreasingnumber(particularly

batteryoptimisers)areutilisingMLandReinforcementLearningforcompetitiveadvantage.

Inthenext12monthsweexpecttoseeAIadoptionatscaledeliveringincreasinglylargeimpacts(both

environmentalandfinancial)acrossthisGrandChallengeduetotherapidlygrowingmarket,andstrongfitforAIcapabilities.

Ahighrenewablesfuturerequiresenergydemandtoflexsoweconsumeandstoreenergywhenthewindisblowingandthesunisshining.Thisisaradical

changeinnetworkandmarketoperation,andis

fundamentallydependentonusingAItoforecastandoptimisedemandandsupplyatmuchmoregranularlevelsthaneverbefore.

2025hasseenanaccelerationinthedeploymentofflexibilityonthenetwork,withAIplayinganessentialroleinthat.Mostoftheimpacthascomefroma

growingmarketusingexistingMLtools,ratherthannewAI-driventechnologicalbreakthroughs.

However,akeydevelopmentisthatdeeplearningbasedsolarforecastswerefullyoperationalisedby

AIapplicationsthatcouldaddressthischallenge

30

ThemesinAIadoptionforenergyflexibility

1

2

3

4

5

AI-basedvirtualpowerplantorchestrationishelpingbalancethegridatscale.Therearenowmultiple

organisations(including

Kraken

,

Kaluza

,

Flexitricity

,and

Arenko

)usingAItoaggregatedistributedenergyresourceslikeEVchargers,batteries,andindustrialloadsintovirtualpowerplantsatuptoGWscale.

AIisalsobeingusedtomatchrenewablegeneratorstodemand

atalocallevel

,increasingmarginsforrenewablesandmakingthemmoreeconomicallyviable.

Solarnowcasting

(forecastingforthenextfewhours)ismateriallyimprovingcontrol-roomdecisions,savingtensofmillionsongridoperatingcostsand

hundredsofthousandsoftonnesofCO2peryear

.

Forecastingremainsthefoundationforflexibility.ManyorganisationsrelyonMLforforecastsofdemand,generationandprice.Thelastyearhasseen

someprogress

in

research

on

foundationmodels

for

forecastingbutthereal-worldimpactoftheseremainstobeproven.

AIisstartingtomakebuildingsflexiblegridassets.Companieslike

GridEdge

and

CarbonLaces

are

automatingreal-timeloadshiftinganddemandresponsewithAI-ledoptimisationofbuildingenergydeliver15–34%reductionsinpeakdemandintrialsduring2025.

Solarforecasting:OpenClimateFix

Solargeneration'sunpredictabilityforcesgridoperatorstomaintainexpensivefossilfuelbackupcapacity,drivingupcosts.OpenClimateFixpartneredNESOtodeploy

QuartzSolar

Thetechnologyachieved40%improvementoverpreviousforecastsandisfully

.

operationalinNESO'scontrolroom,saving

Adeepneuralnetworkcombinesmeasuredsolar

generation,numericalweatherpredictionandsatelliteimagerytopredictcloudmomentsandsolargenerationacrosstheUKoverthenextfewhours.

inbalancingcosts,withpotentialtoreach£150mby2035.

300,000tonnesofCO2and£30millionperyear

31

32

Localenergymarkets:UrbanChain

Directpeer-to-peerdomesticenergysupplycanmake

energycheaperbydecouplingrenewablesfromvolatile

wholesalegasmarkets,butisnotyetpermittedintheUK.

Instead,UrbanChainactsasaregulatedenergysupplierbutusesAIandblockchaintocreateprivatelocalenergymarketsthatmatchrenewablegeneratorswith

consumers.ThisAI-drivenoptimisationpairsrenewablegeneratorswithconsumersonahalf-hourlybasis,

deliveringupto

upto25%bettermarginsforgenerators

andsavingsforconsumers.

Thebusinessisscalingrapidly,havingrecorded

10x

revenuegrowthto£25m

on

over200GWhofassets

in2024,andispursuinga

£50mSeriesB

in2025.

33

Powersystemsfoundationmodel:GridFM

Managingthegridisdependentonsolvingaseriesof

optimal

powerflow

problems,whichisslowandcomputeintensive.

Researchersfrom

IBM,ImperialCollegeandothers

areworkingonafoundationmodel

pre

-

trainedon300,000+optimalpowerflow

problemstocreateafoundationmodelforgridoperations.

Itusesgraphneuralnetworkstolearnfundamentalpowerflowphysicsandgriddynamicsfromrealandsimulateddata.

Stillintheresearchstate,theopen-sourcecollaborationaimstodeliveranewAI-basedapproachthatprovidesordersof

magnitudefasterpowerflowsimulations.

Ifsuccessful,thishasthepotentialtomakeourexisting

optimisationofgridoperationsmoreeffective,butalsoto

potentiallyunlocknewwaysofoptimisingthegridinnear-realtime.

34

Enablersforthenext12months

TheNationalEnergySystemOperatorcontinuestodigitalise

DigitalisationofNESO’soperationsthroughtheOpenBalancingPlatformisbroadeningaccesstogridservicesfornewentrants,includingthoseusingAIoptimisation.Ithashelpedgeneratorsunderstandthereasonsforoperatingdecisions,andhascontributedtoa

five

-

foldincreaseinbatterydispatch

.The

REVEAL

platformalsoenablestestingofnovel