the future of indian electricity demand - brookings.edu · executive summary • this study...

The future ofIndianelectricitydemandHow much, by whom,and under what conditions?

By | Sahil Ali OCTOBER 2018

How much, by whom,and under what conditions?

OCTOBER 2018

Acknowledgements

I would like to express gratitude to Rahul Tongia for his critical inputs throughout the study and helping bring nuance to the analysis. I relied on the resourcefulness and ready support of Abhishek Mishra and Puneet Kamboj to augment my database and refine my methodology.

The communications team starting with Nitika Mehta initially, and then Zehra Kazmi, Rohan Laik and Karan Seera, greatly helped improve presentation and delivery. Pranav Singhania also helped with multiple reviews and cross-checking references.

I am indebted to all the participants of the multiple review workshops for their trenchant insights into ground realities across different end-use sectors. Special thanks is due to Aayushi Awasthy (University of East Anglia, formerly at TERI), Aditya Chunekar (Prayas) and Narsimha Rao (IIASA) for in-depth reviews of the full draft and constructive feedback that helped enrich the study.

I express sincere thanks to Shamika Ravi for her support and useful feedback.

4 | The Future of Indian Electricity Demand

Executive Summary• This study projects application-wise end-use electricity demand from consuming sectors as categorised by the Central Electricity Authority. It includes both grid-based as well as industrial captive demand in the futurewhichenhancesvisibilityfromtheperspectiveofsupplyplanning. o Theanalysis isbasedon the rangeofpossibilitiesofgrowth in servicedemand (eg. lighting, cooling, industrialproduction,pumpedwaterforirrigation,etc.),andsector-wiseandcross-cuttingtechnology andpolicyoptions.

o The base and terminal years for the analysis are 2015 and 2030. This is keeping in mind at 2015 is the mostrecentyearforavailabledataondisaggregatedofficialbaseline,and2030fitswithanumberof strategicobjectives,includingIndia’sclimatechangecommitments.Baseyearresultsarecalibratedand synced with CEA data.

o ‘New loads’ expected to gain prominence in the future, such as inorganic household demand from newhouseholds constructedunder theaffordablehousingprogramme,electric cooking, andelectric vehiclesareincluded.Apartfromthese,theimpactofair-conditioningandhighmanufacturinggrowth by2030arespecificallyemphasised.

o Overall, nine ‘cases’ofelectricitydemandaregenerated for three scenariosofGDPgrowth (6.5, 7 and 7.5 percent) and three levels of energy efficiency and conservation interventions applied across applications.

• Aggregate electricity demand could grow from 949 TWh in 2015 to between 2074 TWh (lowGDP, high efficiency)and2785TWh(highGDP,lowefficiency)by2030.

o ThiscorrespondstoaCAGRof5.4-7.4percent,comparedwith6.9percentCAGRinelectricitydemand between2000to2015.Aplausiblemid-scenarioCAGRbetween2015and2030isderivedat6.2percent.

o The big changes in sectoral shares (and therefore growth rates) occur in the commercial and agriculture sectors—commercial likely surpassing agricultural (irrigation pumping) demand in 2030when it was less than half of the former in 2015.

o Industrial and domestic remain the largest consumers, with greater uncertainty (range of possible outcomes)aroundthelatter.

o New loadsasdiscussedaboveconstitutelessthan10percentofaggregatedemandby2030.

• GDP growth or sectoral value-added has historically not been a good indicator of electricity demand growth from irrigation pumping, which is based on both exogenous factors (for example, the monsoon quality) and policy choices. Accordingly, its future electricity demand may grow between 3.9 and 4.2 percent CAGR, compared to 5.2 percent in the past. The constraining factor will not be so much as pump-set ownership but service demands—usage requirement and intensity of use, amongst others.

• Inresidentialbuildings,thenewdemandfromaffordablehousingunitswillnotexceed50TWh(lessthan5percentofaggregateconsumptionin2015), ifthetargetsaremetby2030.Separately,the‘organic’growthinownershipandusageofresidentialappliancesmayresultinadditional438-623TWhin2030,leadingtooverallCAGR of 5-7.8 percent. The share in consumption of heating, ventilation and air-conditioning (primarily fansandACsintheIndiancontext)andrefrigerationincreaseswhile lightingandtelevisionreduces.Miscellaneousloads (motors, geysers, inverters, washing machines, etc.) grow faster than the overall average. New demand fromelectriccookingcouldfurtheradd48-72TWh.Regulationsandpoliciesonenergyefficiency(super-efficientappliances,mandatorystarlabelingetc.)andconservation(buildingcodes,thermalcomfortstandards,ToDtariffs)will be key drivers to reduce demands and synchronising with the grid.

The Future of Indian Electricity Demand | 5

• Growth in commercial floor-space area and air-conditioning coverage andusewill be the key influencingfactorsforcommercialelectricitydemandgrowth.Whereascommercialheating,ventilationandair-conditioning(HVAC) already consume over 50 percent of its total demand, this could likely grow up to 75 percent as total commercialdemandgrowsto247-348TWh(7.6-10.1percentCAGR)by2030.Thispointstothebigroleofnewtechnologies incentralisedand inverterbasedair-conditioning,aswellasbuildingshelloptimisation includinginsulation,roofing,windowglazing,windowdesign,anddaylightcontrol.

• Analysisofphysicalproductiongrowthandprocess-wisespecificconsumptionacrosssevenPerformAchieveand Trade (PAT) sectors, and value-added and electricity demand outside these, under different scenarios ofmanufacturingsectorgrowthandenergyefficiency,yieldsanindustrialdemandof881-1098TWhby2030.ThisindicatesCAGRsof5.1-6.6percentover481TWhconsumedin2015.Industryremainsthedominantelectricityconsumer,accountingfor~40percentoftotaldemand.HighmanufacturinggrowthdrivenbyMakeinIndiacouldincreasedemandby15percent(133TWh)atidenticallevelsofenergyefficiency.Steel,cementandaluminiumremainthedominantconsumerswithmaximumpotentialforabsoluteenergysaving.

• AstheIndiangovernmentplanstoincreaseelectrificationofrail-routekilometersfrom40percentpresentlyto77percentby2022,thelevelofelectricityconsumptionachievedby2030couldbe35-43TWh,growingat5.0-6.3percentCAGRfrom17TWhin2015.Whereastheimpactofelectrificationisminoronelectricitydemand,significantsavingsonaccountofefficiencygainsandoilsubstitutionaccrue.

• Thepotentialelectricitydemandfromtransitiontoelectricvehicles(EV)acrossprivateandfleetcategories,even with 100 percent EV sales by 2030 turns out to be less than 100 TWh, which is only 10 percent of aggregate consumptionin2015.However,theaggregateloadpotentialofsuchatransitioncanleadtosignificantvolatilityininstantaneousdemand,withEVspotentiallycontributing50percenttopeakloadby2030incaseofunmanagedcharging.

• Increasingurbanisationfacilitatedbyshrinkinghouseholdsizesandconcentrationofeconomicopportunitiesinurbanareaswillincreasepressureonmunicipalservicessuchaspubliclightingandwater-pumping.Theseplusthe‘miscellaneous’demandasreportedbyCEAarelikelytomorethandoublefrom2015levelsof49TWhto104-115TWh(5.1-5.8percentCAGR)in2030.Hereagain,whilethequantumofelectricitydemandedismanageable(representingabout5percentoftotaldemand)enhancingthesupportinginfrastructuretokeeppacewithurbandemand will be crucial. Land availability for these as well as EV charging infrastructure will be the major challenge.

• Overall, theanalysispointstowardsahigh likelihoodofelectricityconsumptionelasticitytoGDPfallingtofour-fifthsfromthebaseyearlevelof0.95,indicatinganongoingprocessofdecouplingofenergyandGDPgrowth.ThisisowingnotonlytoenhancedconsumptionefficiencybutthecontinueddominanceoftheservicessectorinGDPgrowth.Percapitaelectricityconsumptionislikelytodoubleormore,butstillremainmuchlowercomparedtocurrentglobalaverage.Policiesmustfocusonstimulating‘good’andcurbing‘bad’demands.Theadditionofnewandmorecomplexloads,especiallyincities,indicatesthatthekeybottleneckstomeetingdemandwilllieintherealmofdistributioninfrastructureandregulatoryframeworkstomanageincreasingvolatilityindailyandseasonal loads, rather than expansion of total electricity supply.



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ContentsIntroduction-Whymodelelectricitydemand. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ElectricityDemandProjectionsinIndia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Scope and Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Whymodelonbottom-upandend-usebasis?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Historicalcontextandfuturedriversofelectricityconsumption. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . EconomicGrowthandDemographics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sector-specificFactorsandPolicies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Boundaryconditionsandothersalientfeatures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key determinant of future electricity demand—policiesorprices?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Sector-wiseelectricitydemand:Context,DrivesandOutcomes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IrrigationPumping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ResidentialBuildings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . AffordableHousing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Electric Cooking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CommercialBuildings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Railways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Others . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .ConclusionandDiscussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Appendix: Key results from past studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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List of FiguresFigure1: Top-downassessmentsofeconomy-energygrowthnexus. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure2: Historicalcategory-wiseconsumptioninIndia(2000-2015). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure3: HistoricalGDPandpopulationgrowth(2001-16). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure4: StructureofGrowth(2001-15). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure5: Sectoralsharesunderhighmanufacturingscenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure6: Electricityconsumptionandpump-setsenergised. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure7: PumpingHoursandElectricityDemand. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure8: Appliance-wiseshareofelectricityconsumed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure9: ResidentialElectricityDemand. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure10: Electriccookingpenetrationandelectricityconsumption. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure11: Historical EPIs of Residential and Commercial Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure12: HistoricalandFuturefloor-spaceareaofBuildings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure13: CommercialElectricityDemand. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure14: IndustrialProductioninhighandlowindustrialgrowthscenarios. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure15: EfficiencyImprovementsacrossPATsectorsintheLowandHighEfficiencycases. . . . . . . . . . . . . . . . . . . .Figure16: Industrialelectricitydemand. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure17: Impactofhighmanufacturinggrowthonindustrialelectricitydemand. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure18: ElectricitydemandfromRailways. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure19: VehicularCategory-wiseElectricityDemandbyEVsin2030. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure20: HistoricalshareofOthersinroralelectricityconsumption. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure21: Sector-wisenationalelectricitydemandinUBandLBScenarios. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Figure22: Sector-wiseelectricitydemandgrowth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

12131415162123262829303132333536363739404141

List of TablesTable1: ScenariosofGDPGrowth(2016-30). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Table 2: Present and future demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Table 3: Summary drivers of electricity demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Table4: BoundaryConditionsforDemandCases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Table5: Waterandenergydemandforirrigation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Table6: ApplianceStockinbaseyearand2030(7.0%GDPgrowth). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Table7: ElectricityDemandfromResidentialAppliances. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Table8: AffordableHousingstockadditiontill2030. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Table 9: Phase-wise Electricity requirement for AH units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Table10: ServiceIntensityacrosscommercialsectorapplications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Table11: HVACconsumptioninBuildings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Table12: IndustrialProcesssharesacrosshighandlowefficiencyscenarios. . . . . . . . . . . . . . . . . . . . . . . . . , . . . . . . . . . .Table13: FuturesalesofEVs(Cumulative2017-30). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Table14: HistoricalyearlyelectricityconsumptionCAGRsintheOthersector. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Table 15: Aggregate electricity demand under various cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Table16: Keyresultsfromrecentstudiesonelectricitysector. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Introduction-Why model electricity demand ?

Asof2017,73percentofelectricitygenerationinIndiawasbasedoncoal(CentralElectricityAuthority,2017).Theelectricitysector (gridandcaptivegeneration)consumesover80percentof thedomesticcoaloff-take inIndia(MinistryofCoal,2017).Dueto itscapital intensiveandpublicgoodnature,electricitysupply in India ishighlyregulated,wherepoliciesandplansarefocusedaroundcreatingadequatesupplycapacityandreserves,togenerate, sell and purchase power. Coal demand for electricity depends on the overall level of electricity demand, itsnature (who isdemanding,atwhat locationandwhattime in theday)andtheavailabilityandpreferenceordering of other sources of supply. Aside from the more immediate issues of supply planning, electricity demand modelling directly feeds into concerns around access, energy security and environmental sustainability.

Electricity demand, as is elaborated later, depends on a number of variables, some with deep uncertainty into the future. For example, India’sGDP growthprojectionstill 2030 vary fromaround 7-8 percent projectedbygovernmentalsourcesandIMF,to5.5-6.5percentasprojectedbycertainbanks,developmentorganisations,andresearchinstitutions(Uehara,Masashi;Tahara,Kengo;etal,2017;MinistryofEnvironment,ForestsandClimateChange,GoI,2015).Thesehaveprofoundlydifferentimplicationsintermsoflevelsandcompositionofeconomicactivityandassociatedenergyintensities(Uehara,Masashi;Tahara,Kengo;etal,2017;MinistryofEnvironment,ForestsandClimateChange,GoI,2015). It thenbecomesnecessary tounderstandplausibleandmarginalyetpossible scenarios of electricity demand to plan for the future electricity grid accordingly.

Electricitydemand forecasts form thebasis for supply (generation capacity and transmissionanddistribution(T&D) infrastructure)planningatthecentralandstate level. In India,thisexercise iscarriedoutonceineveryfewyearsastheCentralElectricityAuthority’s(CEA)ElectricityPowerSurvey(EPS).Therecentlyreleased19thEPSprojectsanelectricitydemandof1743TWh(6.59percentCAGRfrom2017)andpeakloadof299GW(6.32percentCAGR)by2027(CentralElectricityAuthority,2017). NITI Aayog, private think tanks, and other multilateral organisations also undertake electricity projectionexercises,attimesusingproprietarymodels.Insystemplanningexercises,theseprojectionsareusedasmeanstodetermineoptimalsystemconfigurationundervariousscenarios,ratherthanasanendinitself.However,basedonhowmodelsareset-up, significant insightscanbeobtainedregarding thenatureof relationshipsbetweenmacroeconomicdrivers,technologyoptions,policyleversandtheresponsivenessofelectricitydemand.

In the recent past, significant changes have taken place in the demand-supply dynamics as well as policy formulationintheelectricityandrelatedsectors.Energyandpeakdeficitshaveseenaseculardecline,reducingtoaslowas0.3percentinthelastquarterof2016-17(CentralElectricityAuthority,2012-2018).Thermalpowerplantshavebeenoperatingatlowplantloadfactors(PLFs)owingtosuppresseddemandgrowthandutility-offtake,inadditiontocoallinkageissues.Ontheotherhand,renewableenergy(RE)capacityadditionshavepickeduppaceasnewsolartariffskeepfallingunderreverse-bidding.Inaddition,thecentralandstategovernments’policieson24/7PowerforAll,electricitymarketreforms,domesticmanufacturingviaMakeinIndia,electricmobility,andenergyefficiencywillbeinstrumentalininfluencingthelevelandpatternoffuturedemand.

Electricity demand projections in India


Scope and methodology

This study was designed to understand the growth in electricity demand from the individual end-use sectors. It formsabuildingblockforamulti-yearstudyonthefutureofcoalecosystembeingundertakenatBrookingsIndia.Electricitydemandfor2020asassessedinaninterimreportunderthisstudywasestimatedat1,634TWhatbus-bars(8percentCAGRfrom2015)(Sehgal&Tongia,2016).Whilethe2020demandanalysisconsidersplannedgrowthincapacityadditionofthermalandRE,andanotionallifelineconsumptionforunelectrifiedhouseholds,anestimatefor2030necessarilyneedstoconsidergrowthintheelectricitydemandforend-usesectors.Thisisbecausecapacityadditionsaretypicallyplannedforahorizonofthreetofiveyears,forwhichelectricitydemandgrowthisreasonablywellestimated.Fora2030horizon,anestimationoftheservice(end-use)demandgrowththatleadstoelectricityconsumptionaswellasthetechnology-policyinterplaybecomesnecessaryforplanningsupply. Accordingly, this study projects the electricity demand in 2030 from the end-use (agriculture, commercial, residential,industrial,transport,municipallightingandwaterpumpingandmiscellaneous)sectors.

Somestudiesuseatop-downmethodologyforestimatingelectricitydemandbasedonitsrelationship(regressionequationsorelasticityfactors)withmacroeconomicvariables(Saxena,Gopal,Ramanathan,&al,2017).However,thisapproachislimitedwithregardstoaddressingthetransformationalaspectsofsuchrelationshipsoveralongerhorizon,sinceelectricityrequirementisineffectobtainedaseitherasecondorthirdordereffectfromchangesin macroeconomic factors.1Itfollowsthatall-encompassingvariableslikeGDPorsectoralvalueadditionswillfailtoaccountfordynamisminneweconomicactivities,servicedemands,technologies,policies,andinstitutionalframeworks,thatinturnplayamoredirectroleindetermininglevel,compositionandgrowthofdemand.Hence,theroleofchoiceandinnovationinthetechnology-policyspaceneedstobefactoredinordertotranslatechangesinmacroeconomicvariablesintoservicedemandsandfinallyintoelectricitydemand.

ThisisdemonstratedinFigure1,whichplotstheCAGRsofsector-wiseelectricityconsumptionagainstthegrowthofvalue-addedinthesesectorsbetween2001-02and2015-16.Iftheabsolutevaluesforconsumptionandvalue-addedareexamined,theyyieldhighR-squaredrelationships.Butthisaccountsforabsolutechangesyear-on-yearandnottheratesofchange,thelatterbeingtherelationshipofinterestinprojectingfuturevaluesofelectricitydemandbasedonGDPgrowth(orthegrowthsectoralvalue-addedoranyderivedvariablessuchaspercapitaGDP).

Why model on bottom-up and end-use basis?

1 Globally,CO¬2-GDP‘decoupling’(Saha&Muro,2016)hasbeenempiricallyestablishedtodemonstratehowtherate ofgreenhousegas(GHG)emissionsgrowthrelativetoGDPgrowthslowstotheextentthatthetraditionallyunderstood linkage between development and emissions is no longer meaningful for a futuristic analysis. On the other hand, intermediatefactorssuchastechnologicalinnovation,choiceandunderlyingpolicyframeworkbetterexplainemissions growth.


Figure 1: Top-down assessments of economy-energy growth nexus

Note: Y-axis and X-axis in each plot indicate annual growth rates in electricity consumption and value added respectively.Source: CEA General Reviews, RBI Database for Indian Economy and author’s calculations.

Whenwescatter-plottherespectivey-o-ygrowthratesofelectricityversusGDP,theyyieldlowR-squared,whichimplies thatGDPvalue-addedgrowthalone isnotuseful forprojectingelectricitydemandof the future.Thismakes sense intuitively, because the technology-policy interplay, frameworks and choices in the energy andrelatedsectorswillaffectelectricitydemandmoredirectly,andtheoutputwithagreaterlag,ifatall.

Therefore, in order to undertake a meaningful analysis of electricity demand, its underlying drivers and policies needtobeanalysed.Thishelpsbuildasystems-levelviewofhowpolicytweaksinoneormoreparticularsector/application,canimpactthelevelsandtechnologyallocationinboththedemandandsupplysectors.


Figure 2: Historical category-wise consumption in India (2001-2015)

Source: CEA General Reviews.

Theend-usesectorsconsideredinthebase-yearofthisanalysisarebasedonCEA’scategorisation ̶Residential,CommercialAgriculture,Industrial,TractionandOthers/Miscellaneous2.Figure2showsthehistoricalconsumptionandcompoundedannualgrowthrates(CAGRs)inthesesectorsbetween2000and20153.Thisincludesthecaptivecomponentofelectricityconsumption incaseof industrialusers,whichstoodat justover134Terawatthours[TWhorbillionunits(BU)]in2015,andoverallelectricitydemandhasgrownbynearly7percentCAGRinthisperiod(CentralElectricityAuthority,2007-2016).

Historical context and future drivers of electricity consumption

2 Miscellaneousprimarilyincludesmunicipallighting,sewageandwaterpumping.Incertaincases,(likeCEAGeneralReviews) thethreeconsumptionbucketsarereportedseparatelyandmiscellaneousshownasadistinctcategoryunder‘Others’.3 Revised estimates of sector-wise electricity consumption are usually available with a two-year lag. In the period the researchwasconducted,thelatestestimatesavailablewerefor2015,whichisusedasthebase-yearforfutureprojections.

0

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1000000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

GWh

Miscellaneous

Traction

Agriculture

Industrial

Commercial

Domes�c

CAGR 8.0%

9.7%

6.7%

5.2%

5.2%

7.5%

Overall CAGR: 6.9%


FromFigure2,whiletheindustrialsectorconsumesthebulkoftotalelectricityconsumption(~42percent),itistheresidentialandcommercialbuildingsthathavebeendrivingconsumptiongrowth,presentlyataboutathirdof total consumptionandgrowingatover8percentCAGR.Surprisingly,while thecommercial/services sectorcontributes60percenttoGDPgrowth,itonlyaccountsforlessthan9percentinelectricityconsumption.Theservices sectordrivenGDPgrowthandconsequent rise inpercapita incomes,especially inurbanand formalsector households, seems to have provided the impetus for electricity demand growth, even though electricity consumptionintensityislowerinservicessectorcomparedtoagricultureandindustry4.

Theprecedingdiscussions highlight the role and importanceof economic growth, its structures andpatternsininfluencingtheelectricitydemand,notwithstandingthelimitedroleitplaysinexplainingthefullpicture(asdemonstratedinFigure1).Theyear-wiseeconomic,populationandper-capitaincomegrowthisshowninFigure3.TheGDPandpopulationCAGRsinthisperiodare7.1percentand1.5percentrespectively,yieldingapercapitaincomeCAGRof5.6percent.

Economic growth and demographics

4 Nevertheless,commercialsectorelectricitydemandisthefastestgrowingconsumptioncategoryinthepastdecadeanda half,althoughitisstartingoutfromalowerbaseofdemandin2000.

Figure 3: Historical GDP and population growth (2001-16)

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12.0%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Year

ly G

row

th (%

)

GDP (2011-12 prices and factor costs) Popula�on GDP/capita

Sources: RBI Database on Indian Economy, UN Population Monitor and author’s calculations.

-10.0%

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Agriculture Industry Services


Figure4showsthehistoricalgrowthpatternacrossindustry,agricultureandservicessectors,highlightinghowservices(growingat8.5percentCAGR)hasbeenconsistentindrivinggrowth.IndustrialsectorhasgrownattheoverallGDPgrowthrateat7.1percent,butexperiencedmuchmorevolatilityingrowth.Theagriculturevalue-addedgrowthishighlyvolatilesinceitispredicatedonanumberofexogenousfactors,suchasthequalityanddurationofmonsoon.

ComparingacrossFigure3andFigure4,wefindthatsince2008the‘snakeinatunnel’patternfollowedbytheindustrial value-added is reflected inGDPgrowthpattern.However, thediameterof the tunnel isalsohigherinthecaseofindustry,sinceoverallGDPgrowthisalsobuttressedbytherelativelysmoothergrowingservicessector,makingtheGDPgrowthlessvolatile.Thisco-variationisalsoobservedinabsoluteelectricityconsumption(Figure2)owingtothehighshareofindustry.

Futurescenariosofsectoralvalue-addedandoverallandpercapitaGDP,areusedtodetermineservicedemandsincertain sectors, that in turn determine the employment of various appliances and equipment to convert electricity intoend-useservices(suchaswaterpumped,steelproduced,lightingandairconditioning,etc.).Thegovernmentover successive yearshasprioritised themanufacturing sector todraw surplus labour fromprimaryactivitiesandenhancegrowth,productivityandmeaningfulemployment.ThishasreflectedintheNationalManufacturingPolicy(NMP)aswellasthemorerecentlylaunchedMakeinIndia(MII)campaign.Amongkeyprioritieshasbeenincreasingtheshareofmanufacturingfrom16-17percentatpresentto25percentofGDP(Bhattacharjee,2015).If this isachievedby2030, then industrywillneed togrowat1.5percentagepointshigher thanoverallGDP,assumingservicesretainsitscurrent60percentshare(Figure2).ThisisillustratedinTable1atGDPgrowthratesof6.5,7and7.5percentrespectively,whichhavebeenchosenasthelow,midandhighGDPscenariosforthisstudy.

Figure 4: Structure of Growth (2001-15)

Source: RBI Database on Indian Economy and author’s calculations.


Table 1: Scenarios of GDP Growth (2016-30)

Table 2: Present and future demographics

Figure 5: Sectoral shares under high manufacturing scenario

13% 7%

27% 33%

60% 60%

0%

20%

40%

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80%

100%

2015 2030

Services

Industry

Agriculture

GDP Growth Scenarios (2016-2030)

GDP

Agriculture

Industry

Services

CAGR

6.5%

2.2%

8.0%

6.5%

7.0%

2.7%

8.5%

7.0%

7.5%

3.2%

9.0%

7.5%

Source: Author’s calculations.

Source: Census 2001, Census 2011, UN World Population Prospects, India’s INDC to the UNFCCC and author’s calculations.

Source: RBI Database on Indian Economy and author’s calculations.

Table2 shows thepopulationandhousehold characteristics (sizeanddistribution)basedon theurbanisationlevelsin2015and2030.Over100millionnewhouseholdsareexpectedtobeaddedbetween2016and2030,withthree-fourthsoftheseexpectedinurbanareasowingtorisingurbanisationandshrinkinghouseholdsizes.Thisimpacts the overall electricity and energy use from urban India based on demand for housing, urban infrastructure and related services.

Besides the cross-sectoral impact ofmacroeconomics, and demographics on service and (in turn) electricitydemand,anumberofsector-specificpoliciesandtechnologydecisionsdirectlydeterminetherangeofpossibleelectricity demand from the individual end-use sectors.

DemographicsPopula�on (million #)Urbanisa�on (%)HH Size UrbanHH Size RuralHH Urban (million #)HH Rural (million #)HH Total (million #)

20121262

31.4%4.525.0688

171259

20151311

32.3%4.364.9697

179276

20301528

40.0%3.544.45173206379

CAGR (16-30)1.0%1.4%-1.4%-0.7%3.9%0.9%2.1%


Table 3: Summary drivers of electricity demand

Fromabottom-upperspective,driversofeconomicactivityfromeachend-usesectorareoutlinedbasedonthenatureofevolutionofthesector,currentstatus,andtechnologiesandpoliciesforthefuture.Thesemayhavedirectorindirectimplicationsforelectricitydemand.5 Table 3 summarises such factors considered for the analysis, alongwiththecross-sectoraldriversdiscussedintheprevioussection.

The impact of these drivers on electricity demand and the range of outcomes so obtained are discussed in the followingsection.Thisisfollowedbysector-wiseappraisalofcurrentstatusandfuturecasesconsideredinthestudy.

Sector-specific factors and policies

5 Forexample,whilethePerform,AchieveandTrade(PAT)schemeforindustriesandEnergyConservationBuildingCode (ECBC)forbuildingsdirectlyimpactenergyefficiency,policiesforacceleratingmanufacturingproductionorinfrastructure development(eg.housing)raisethedemandforenergyingeneral.Botharedriversfortheanalysis.

Sectors Drivers

Agriculture

Buildings

Industry

Transport

Others

Cross Sectoral

Net sown area, cropping intensity, surface and micro-irriga�on coverage/ schemes, cropping pa�ern and water requirements, pump-set characteris�cs (size, head, discharge), solar, micro and canal irriga�on policies, Ag-DSM

Floor space area, urbanisa�on and household sizes (rural and urban), affordable housing gap, service demand in low income households, appliance types, technologies and ownership, building shell design efficiency and electrical controls, ECBC, standards and labelling

Na�onal Manufacturing Policy, Make in India, Perform Achieve and Trade scheme (PAT), historical produc�on and consump�on pa�erns, infrastructure/development related demand, sub-sectoral policies and targets (eg. Steel target, NG switching in fer�lizer produc�on), alternate fuels and raw material, waste/scrap recycling, thermal subs�tu�on).

Railway track growth, targets for rail route electrifica�on, EV urban passenger demand (PKM demand, vehicle ownership and usage, EV sales targets, EV technologies, duty cycles and charging provisions)

Historical rates of growth and urbanisa�on, smart ci�es (smart metering)

GDP growth sectoral value added service demands (industrial produc�on, residen�al and commercial floor-space area, appliance stock and ownership)

Demographics housing stock, rural-urban share, household sizes service demand per capita inorganic demand from new households


The approach towards case building in this study is based on deriving the upper and lower bounds for electricity demandbasedondemanddrivers,ambitionand influenceofpolicies,and technological choices.Theboundsobtainedalsoreflecttheunderlying‘plausibleextremes’inthelevelsofthedeterminingvariablesdescribedinTable3.Soacombinationoflowestservicedemand(underalowgrowthpathway)combinedwithhighenergyefficiencywillgivethelowerboundforelectricitydemandinaparticularsector.Further,a‘mid’scenarioisalsocomputedthatisbasedonaplausibleorlikelyconfigurationofeconomicgrowth,infrastructure,policydecisionsand technology deployment that is achieved within the lower and upper bounds.

Table4provides theboundary conditions forderiving the sector-wiseplausible rangesofelectricitydemand.Thesearederivedbasedonourquantitativeandqualitativeassessmentsofstartingsituation,ambition(orlackthereof)andplausibilityofoutcomes,describedingreaterdetailinsectoraldescriptionsthatfollow.Theboundsshouldbeunderstoodinlinewiththeearlierdescription.

Boundary conditions and other salient features

Table 4: Boundary Conditions for Demand Cases

Upper Bound Lower Bound

Agriculture

Buildings

Industry

Cooking

Transport

Others

2/3rd irrigated area by UG pumping

20% Improvement in Pump-set Efficiency

100% housing access by 2030

Medium Penetra�on of Efficient Appliances and ECBC

20% rural and 10% urban HH use electric cooking

Medium penetra�on of efficient technologies, processes and alternate fuels and raw materials

High Non-PAT sector growth due to NMP/MII

90% rail-route kms electrified

55-60% penetra�on of EVs in new sales since 20166

5% energy efficiency improvement

Half irrigated area by Micro and Surface Irriga�on

40% Improvement in Pump-set Efficiency

35% housing access by 2030

High Penetra�on of Efficient and Super- Efficient Appliances and ECBC 5% HH use electric cooking

High penetra�on of energy efficiency measures

Medium Non-PAT sector Growth

75% rail-route kms electrified

30-35% penetra�on

10% energy efficiency improvement

6 Only intra-city transport demand for passenger EVs is considered, owing to range and charging issues for EVs in long distance travel.


AsisevidentfromTable4,analysingdemandthiswaythrowsopenopportunitiestounderstandandtesthowsectorandapplication-wisecompositionofdemandvaries,whichhassignificantimplicationsforsupplyplanningandthrustareasforenergyefficiency.Thisseguesintothequestionofinstantaneousdemandfacingutilitiesinorder for them to reserve energy capacity, and manage day to day loads within their consumer mix.

Thereareexistingandpotential future loadsthatdonotshowupprominently in thecurrentcontextbutwilllikely be significant from the supply perspective7 going forward, including electric cooking, electric vehicles, surgingheating,ventilationandair-conditioning(HVAC)demand,andinorganic(latent)demandfromindividuals/households that do not occupy any dwellings. These form individual components in the analysis.

Further,theend-useapproachfollowedinthestudyallowsforprojectingcaptiveconsumptionfromindustries,whichcurrentlyconstitutesover14percentoftotalend-useconsumptioninIndia(CentralElectricityAuthority,2007-2016).Oncetheentireindustrialelectricitydemandiscomputedthisway,itallowsforusersanddecision-makers to place their preferences on how much of this demand should they target to supply through the grid—a decisionthatholdsmuchsignificanceowingtothestrategicimportanceofthesectortotheoveralleconomyandtheroleitplaysincross-subsidisingresidentialusers.Withrooftopsolar,thepossibilityofmigrationofcommercialusers,whoarepresently paying thehighest rates for grid consumption, is also looming large for distributioncompanies(Discoms). Inthisway,completeness isemphasisedforthisanalysiswhichattemptstoaccountforsuchstructuraltransitions(presentandenvisaged)inthepowersector8.

Finally, any reasonable estimation of future demand needs to achievemaximum coincidencewith base-yearactuals. It is on this basis that individual components of future electricity demand (pertaining to service demands andtechnologypenetration)areprojectedforward.Whiletheunderlyingdatagapsinconductingsuchanalysesiswellacknowledged,thiseffortbuildsuponanumberofstudiestocalibrateandvalidatetheunderlyingassumptionsandintermediateoutputs.Thefinalcalibrationtakesplaceatthelevelofsector-wiseconsumptionobtainedinthebaseyearofthemodelwiththeofficialstatisticsprovidedbytheCEA.Indoingso,theintermediateassumptions/results are cross-checked and validated.9

7 Aswelaterdiscuss,mostofthesecategoriesturnouttobenotsignificantintermsofthequantityofenergydemand,but more so in terms of the underlying infrastructure and investments necessitated to synchronise these with the grid.8 Byincludingbothgrid-baseddemandfromend-usesectorsandcaptivedemandfromindustry,thestudyexploresthefull marketforelectricitydemandthatisusefulattheaggregateleveltoestablishlikelihoodsofpotentialdemandthatwillbe metbyself-generationoroutsidethegrid(viacaptive,rooftop,microgrids,openaccess,etc.).9 Forexample,estimatesonbuiltupfloor-spacearevalidatedagainstenergyconsumptionincommercialandresidential buildings using the energy performance index (EPI) values in terms of units of energy consumed per square foot area annually, forair conditionedandnon-air conditionedbuildings.EPIsareobtained from independentsurveystudiesby privateinstitutions,governmentprescribedcodesandmanuals,andestimatesprovidedbytheBureauofEnergyEfficiency (BEE).Thisalsovalidatestheaverageenergyinputandshareofenergyconsumedbyairconditioninginresidentialand commercialbuildingsandlendscredibilitytofutureprojections.


The extent to which prices impact the choice of technology deployment and use in any model is based on the cost-benefitassessmentof thevariousalternativesor substitutesover their lifetimes.Ultimately the levelsoftechnology deployment and their associated consumption profiles across sectors and end-uses, determineelectricitydemand.Therearetwofactorsatworkhere—capitalandoperationalcosts,thelatterrepresentedbyelectricitytariffsinthesemodels.Whatisnotsufficientlycapturedareinertiaagainstrationalchoice(preferences,tastes,etc.),availabilityofsupportingservices(eg.qualityelectricitysupply),andmostimportantly,differentialdiscount rates across consumer categories (oftenmuch higher than the prevailing weighted average cost ofcapital).10Basedontechnologyandelectricitypricesalone,anoptimisationmodelmayprovidecornersolutionsdisregardingboundedrationalityandlackofperfectforesight,heterogeneitiesinconsumptionpreferencesandlowcapacitytopayhigherfrontcostsforoperationalefficiency,evenwhenenergyefficientinvestmentshavedefinite(discounted)paybackperiodsovertheirlifetimes.Next,electricityremainsaregulatedmarketinIndiaand therefore tariffs (and its adjustments)maynot necessarily reflect the true electricity costs (and changesovertime).Lastly,thecosttrajectoryofnewtechnologiesispredicatedondisruptionsorscaledeployment(also,learningbydoing)—theformerisnoteasytoprojectandlatterposesachicken-and-eggproblem,wherebymassdeploymentcanonlytakeplacewhenanewtechnologyisaprovensubstituteandcost-competitive,asevidencedby its acceptance and use.

In such a scenario, experience with new technologies in India shows that policies geared towards increasing uptake bymeansofeitherdirectintervention(massprocurement,productionsubsidy,concessionalfinancing,etc.)orsignaling (standardsandcodes,performanceguaranteesandpromotions)hashelpedtransformtheefficiencyfloor,ceilingandmedianofthetechnologiesinuse(AliM.S.,ThePathtoLight:AcaseforLEDs,2015).Duetotherigiditiesandimperfectionspreviouslydiscussed,price-focusedmechanismsfailtocapturereasonablescenariosof technology deployment. Thus optimisation models face a choice between fidelity to the least-cost optimisation logic or reasonabledeploymentscenariosoverthemodellinghorizon.Toestablishpolicyrelevance,thelatterrouteisemployedviaconstraining the outcomes within reasonable bounds. This forces the model to engineer outcomes, which is an exercisethatcanbeundertakenbymoretransparentscenarioplanningtools,wherebythesystemofequationsthat translates inputs to outputs is under full knowledge and control of the analyst/decision-maker.

The key takeaways can be summarised as:

i) Policies rather than prices function as better determinants of future technology configurations toobtainrealistic(usable)modeloutcomes.

Key determinant of future electricity demand—policies or prices?

10 Forexample,acasualanalysisofRefrigeratorpopularity (controlling forsizesandbrands)onthepopulare-commerce platformFlipkartrevealsanoverwhelmingpreferencefor1-2starrefrigeratorsthathavelowerfrontcostsbutturnout muchmoreexpensiveinlifecyclecostscomparedtotheirmoreefficientbuthigherpriced/counterparts.Similarly,Bureau of Energy Efficiency certified efficient irrigationpump-sets aremuchmore expensive and less resilient to fluctuations in electricity supply than locally manufactured ones, so that even if an average farmer with a landholding of less than 5 acreswaschargedfortheelectricitytheyconsume,theircashflowsandfinanceswouldrenderthisupgradeunviable, giventheyover-valuefrontcosts(cashinhand)comparedtolife-cyclebenefitsofefficientpump-sets(AliM.S.,Energy EfficientIrrigationPumping:96GWhofpowercanbesaved,2015).ItisinrecognitionofthisgapthatAg-DSMschemewas launched.


Figure6showshowabsolutesofelectricity(Yaxis)andpump-sets(Xaxis)showastrongcorrelation(Rsq.=0.95),whiletheplotofyearlygrowthratesconveysaverydifferentpicture(Rsq.=0.20).

Irrigation pumping

11 Atraditionalprescriptive‘scenario’approachwouldbebasedonacounterfactualpolicynarrative,forexample,‘business asusual’versus‘lowcarbon’inwhichallvariablesmovebasedonthatnarrative.Inthisexercise,however,policyimplications are modelled directly, and therefore embedded within outcomes so that their impact on electricity demand can be studied bothinisolationandintermsofcumulativeeffectonthesystem.Thisallowsexaminationofunlikelybutplausiblecases— progressivepolicyoutcomesonenergyefficiencywithlowoveralleconomicgrowthandservicedemandsthatmayobtain, butnotnecessarilyfitwithinascenarionarrative.Thisisadvantageous,especiallygiventherecentcontextofsuppressed electricitydemandandrevisedprojectionsbyCEAindicatinglowerfuturedemandthanpreviouslyestimated,asevidenced fromthe18thand19thEPS.

Sector-wise electricity demand: Context, drives and outcomes

In2015,Indiaisestimatedtohaveapproximately20millionenergisedpump-setsconsuming169TWhofelectric-ity,withdecadalCAGRsof3.2and6.9percentrespectively(NITIAayog,2015;CentralElectricityAuthority,2007-2016).Thisindicatesthatintensityofpump-setusehasincreasedfasterthanpump-setsenergisedover-time.

ii) Scenario planning tools offermore flexibility and transparency over optimisationmodels to specify policytransmissionmechanismsdirectly,ratherthanbyrestrictingoutcomes.

Accordingly, the analysis is undertaken in a transparent environment that scenario planning tools and calculators offer,wheretherationaleforpricing(capitalcostsandelectricityprices)isembeddedinscenariodesign,informedbyhistoricaltrendsintechnologyadoptionandpricetrajectories.Moreimportantly,thestudyemploys‘cases’rather thanmoreprescriptive ‘scenarios’11,derivedascombinationsofdifferent levelsofgrowth ineconomicoutput, service demands and market penetration of end-use technologies. Together, the three GDP growthscenariosoutlinedearlier(6.5,7.0and7.5percentCAGR)andthethreescenariosofconservationandefficiencygiveninescenariosofelectricitydemandinthisstudy.Thethresholdsobtainedfromthesolutionset,providetherange of uncertainty over which economic and policy outcomes will play out.

Figure 6: Electricity consumption and pump-sets energised

Source: NITI Aayog IESS Agriculture Documentation, CEA General Reviews and author’s calculations.


Accordingly,projectionsoffuturescenariosforthisanalysisisnotbasedonmerelypump-setgrowthandenergyinput,butmorefundamentallyonthelandundercultivation,croppingpattern,waterrequirementandalternatesourcesofirrigation.Indiahasapproximately141millionhectaresoflandundercultivation(netsownarea)witha cropping intensity12of139percent (DepartmentofAgriculture,Cooperation&FarmersWelfare,2017)13. Of this,morethan50percentlandisrain-fed,whichpartlyexplainsthevolatilityinagriculturalelectricitydemandand economic output. Of the irrigated area, more than two-thirds is via groundwater sources using tube-wells andtanks.India’snetsownareaandaveragelandholdingsizeshavefallensince1970s,withthelatterfacingaseculardecline(DepartmentofAgricultureandCooperation,2013).Thisindicatesthatthegrowingneedforfood,fiber,andintermediateproducehasbeenmetthroughenhancedproductivity.However,biggapsin improvingproductivitythroughsoilandwatermanagementandscientificfarmingpracticesremain.Thecroppingintensityhasalsoincreasedsteadily.Waterrequirementperhectarefromcereals,pulses,fruits,vegetables,oilseeds,fiber,tobaccoandothercrops,representingoverfour-fifthsofthegrosscroppedareaisexaminedalongwithirrigatedareaforprincipalcropstoarriveatanannualwaterrequirementperhectareof840mm14 (IIT Kanpur, 2010).

Basedon these trends, it is assumed that futureexpansion ingross croppedareawillhappenvia increase incroppingintensitytonearly150percentby2030andincreaseinirrigationcoverage.Inresponsetohigherwaterrequirementsanddepletingwatertables,theaveragesizeofpump-setsinoperationwilllikelyincreasefromjustover 5HP in 2013 to 7.5 HP by 2030, with discharge rates around 700-100 litres per minute (lpm), compared to 300-600lpmfor5HPpumps.Groundwater-useefficiencyistakentoimprovefromlessthan15percentcurrentlyto25percentby2030, in linewithNITIAayog’sassessmentofa33percentefficiencypotential15 (NITI Aayog, 2015).

Furtherdistinctionsaremadeforlowerandupperboundcasesofelectricitydemandin2030:

• Irrigatedareabysurface,micro,andsolarirrigation:Anadditional12millionhectaresiscoveredundersurfacewaterschemesandmicroirrigation(MI),with20percentcoverageundersolarandsolar-MIschemesinthelowerbound for electricity demand (PPPAU NITI Aayog, 2017)16. In the upper bound, two-thirds of the irrigated land continuestobesuppliedbyundergroundpumping,withlimitedexpansioninalternatemeans(10percentsolarandsolar-MIirrigation).

• Pump-setefficiency:TheefficiencyofaverageIPsets(understandardtestingconditions)improvesfrom50percent in2015to60and70percentrespectively intheupperandlowerboundcases,thelatterbasedonanaggressivemarkettransformationfacilitatedbyAgriculturalDemandSideManagement(AgDSM).

The key results obtained for the rangeof irrigationpumping demandby 2030 are shown in Table 5. Table 6compares the hours of pumping and electricity demand across 2015 and 2030.

12 Related to the number of cropping cycles in a year.13 NetsownareaxCroppingIntensity=GrossCroppedArea14 A5-10percentdiversionfromfoodtocashcropscausesnegligiblechangeinconsumptionrequirement.15 Thishappensduetoimprovedwatertransporttechnologiesinnew/retrofittedpump-setinstallations,enhancedapplication ofmicro-irrigationandgovernmentpoliciesaimedatsustainablewateruse.16 Ofthe70MhaofMIpotential,lessthan1Mhaiscurrentlydeployed.

Gross Irrigated Area groundwater (Mha)Pumping water requirement (million m3)Million pumping hoursAverage Input (kW) Electricity Demand (TWh; CAGR)

77622,37846, 1027.46358 (4.85%)

2030 UB 2030 LB63530,43539,2926.39251 (3.19%)

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2015 2030 UB 2030 LB

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on H

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Billion Pumping Hours

Electricity Demand TWh


Figure 7: Pumping Hours and Electricity Demand

Table 5: Water and energy demand for irrigation

Whiletheenergydemandmaygrowbetween3.2and4.9percentCAGRby2030,theaggregateloadofpump-sets isestimatedtogrowfrom76Giga-watt(GW)to175-206GW(5.8-6.9percentCAGR), indicatingthatthefundamentaldifferencebetweenpastandfuturegrowthinelectricitydemandwillcomefromthepump-setusagepatternandintensityandavailabilityofviablealternatives,ratherthanelectricityaccessorpump-setownership.

Residentialsectorconsumesnearaquarterof totalelectricity in India,havinggrownat8percentCAGRsince2000 (Central Electricity Authority, 2017). The principal components of consumption are lighting (point andtubular),refrigerators,televisions,fans,roomairconditionersandotherloadssuchaswashingmachines,geysers,residentialwater-pumps,battery-invertersystems,etc.Historically,lightingandfanshaveaccountedforoverhalfofthesector’sconsumption,withhighpeakcoincidence.ButtherapidfallinpricesforfirsttheCFLsandthenLEDs facilitatedbymarketaggregationschemes likeBachatLampYojana (BLY)andDomesticEfficientLightingProgramme(DELP),haveensuredthattheshareof inefficientincandescentbulbsdroptolessthanhalfoftheestimatedpoint lighting stock (AliM.S., ThePath to Light:A case for LEDs,2015)17. On the other hand, the

Residential buildings

17 Theshareinnewsalesismuchlower.Thelightingequipmentmanufacturingassociation(ELCOMA)hasannouncedaplan to phase out incandescent bulbs in the near future.


18 PartofthereasonforsuchhighCAGRisthelowbaseofstock,andwhilethegrowthratesmayslowdowninthefuture, all indicationssuggesttheabsolutestockadditionwillkeepgrowingowingtomassivegapinownership,growthinper capitaincomes,urbanisationandurbanislandinganddecliningtechnologycosts.Wewilllaterseehowitbecomesthe mostsignificantconsumingcategoryby2030.19 Thisanalysis is calibratedat2012since it is the latestyear that stockestimatescanbecross-verifiedviaanumberof sources/studies available. These are simply projected forward to the study base year (2015).

Table 6: Appliance Stock in base year and 2030 (7.0 percent GDP growth)

Point Ligh�ngTubular Ligh�ngRefrigeratorsRACCTVFans

687258474

123305

1137433211101343673

2.661.000.180.020.481.18

3.471.320.640.311.052.05

2.8%2.9%8.7%

19.5%5.9%4.5%

0.7%0.8%6.4%

17.0%3.7%2.3%

2012 2030 2012 Stock CAGR Ownership CAGR2030Total stock (million) Ownership (per HH stock) CAGR

Sources: Quality of Life for All (Byravan, Ali, et al 2015), Avoiding 100 power plants by Increasing efficiency of Room ACs (Phadke, Abhyankar et al 2015), Expert Group on Low Carbon Inclusive Growth (2014), IESS 2047 (NITI Aayog 2015), The case for LEDs (Ali 2015), EFFECT-India Model (World Bank ESCAP 2010), Realising efficiency savings from household appliances in India (Parikh and Parikh 2016), other miscellaneous datasets and author’s calculations.

demandforspaceconditioninghasbeengallopingatCAGRsof20percentperannumsince2010,andislikelytoregistersignificantdeploymentby2030(Byravan,Ali,&al,2017;Phadke,Abhyankar,&Shah,2014)18.

Foranalysingvariouscasesofservicedemandandefficiency,thefollowingmeansareemployed:

• Datasetof28appliancetechnologiesacross7applicationsand15ECBCinterventions • Varying appliance stock (based on per capita income growth) and energy efficiency (lumens/ Watt, Energy Efficiency Ratio, etc.), and stock shares (across appliance types, eg. window and split AC, etc.)

Differential application of efficiency and conservationmeasures across upper and lower bound of electricitydemand. Table 6 shows the appliance stock in 201219 and 2030 in a mid-growth scenario. The growth of room air- conditioners(RAC)standsoutasadefiningtrendforthefuture.Thestockof‘others’isnotestimated;instead,othersaremodelledasaresidualafterdeductingthebaseyearconsumptionofthebelowappliancesfromtotalresidentialconsumptionreportedbytheCEA.


Acrosstheseapplications,amixoflowmediumandhighefficiencytechnologies20isspecifiedinthebaseyearbased on Bureau of Energy Efficiency’s (BEE) Verified Energy Savings reports. Based on historical revisions inappliance-wiseefficiencyfloorandceilingsasperupdatestoBEE’sstarratingguidelines,historicalsalesofstar-ratedappliancesreportedbyBEE,impactofenergyefficiencypoliciesandhistoricalcostcurves,anassessmentofplausibletechnologyconfigurationsinupperboundandlowerboundofelectricitydemandaredetermined.Interventionspertainingtobuildingdesignandcontrol21aresimilarlyapplieddifferentiallyacrossthelowerandupperbounds,whichservetoreducetheintensityofHVACandlightingloads̶thekeyfactorsbeingpenetrationandcompliancewithstar-ratings,andrelativecostsversusbenefitsofvariousalternativesforinsulation,roofing,windowglazing,windowdesign,anddaylightandlightingcontrol(TheWeidtGroup,2011a;TheWeidtGroup,2011b;TheWeidtGroup,2012)22. Basedontheabove,theapplication-wiseupperandlowerboundsofelectricitydemand23 is shown in Table 7. Lightingandfansoffer thehighestpotentialsincethemarketsaremature, thebestefficient technologiesareknownandthereforeripeforupgrade.Overall,variationsinGDPgrowthandenergyefficiencyandconservationcombined, showsup a differenceof 183 TWhbetween theUBand LB,which is almost identical to the totalresidentialelectricitydemandin2013.

20 Low andmid technologies broadly correspond to representativemedian BEE 3-star and 5-star rated appliances as of August2014,shortlyafterwhichthelistofaccreditedappliancemodelsandtheirtechnicalcharacteristicswasnolonger publicontheBEEwebsite.HighefficiencyappliancespertaintoLEDs,inverterACs,superenergyefficientfans(forwhich request for expressions of interest have beenfloatedbyBEE in the past), refrigerators and televisions basedon best availabletechnologiesthathavesignificantmarketpotential,20-40percentsavingsover5-starappliancesandhavelow commercialpenetrationduetohighcosts.21 Alsoreferredtoas‘ECBCinterventions’inthisreport—namedaftertheEnergyConservationBuildingCode(ECBC)that mandatesenergyefficientbuildingdesign(apartfromuseofenergyefficientequipment).22 Theseareappliedontheresidentialfloor-spaceareaandworktoreducetheEPI(kWh/sq.m.)ofthebuildings.23 Unlike irrigationpumpingdemand, theownershipof residentialappliances isaffectedbypercapita incomegrowth in themodel.Accordingly,thelowerboundisthe6.5percentGDPgrowthandhighenergyefficiencycase,whiletheupperbound isthe7.5percentGDPgrowthandlowenergyefficiencycase.Unlessexplicitlyspecified,LBandUBfortheforthcomingsectors orapplicationswillbedenotedthisway.

Table 7: Electricity Demand from Residential Appliances

Point Ligh�ngTubular Ligh�ngRefrigeratorsRACCTVFansOthersTotal

4.6 (-10.91%)18.9 (2.75%)81.6 (8.60%)

279.6 (21.04%)46.1 (5.16%)69.3 (2.57%)

120.9 (7.56%)620.9 (7.42%)

1.5 (-16.40%)13.9 (0.93%)60.5 (6.82%)

209.3 (19.10%)36.2 (3.76%)29.2 (-2.23%)87.3 (5.63%)

437.6 (5.36%)

68.2%27.4%25.8%25.1%21.5%57.8%27.8%29.5%

2030 UB 2030 LB3.25.2

21.170.39.9

40.133.6

183.3

TWh PercentageDemand in TWh (CAGR) Difference (LB vs UB)


Figure 8: Appliance-wise share of electricity consumed

Figure8showshowthesharesofdifferentend-useapplicationsarelikelytochangeby203024.

19.03%19.59% 19.5%

11.2%

7.4%

13.1%

3%

0.7%

45.0%

6.77%

10.79%25.67%

7.26%

Residen�al Appliance Consump�on Shares (2015)Total Consump�on: 217 TWh

Residen�al Appliance Consump�on Shares (2030)Total Consump�on: 619 TWh

Point Ligh�ngTubular Ligh�ng

RefrigeratorsRAC

CTVFans

Others

24 Results arepresented for theUBcase. Thereareminor variationsbetween LBandUBbut thegeneral trend remains the same.

FromFigure8,air-conditioninggrowsmorethansixtimesinshare,whilelightingreducesfromalmost27percenttolessthan4percentandfansbymorethanhalf.Thishasimplicationsfordistributionoftheload,whichwilllikelybemuchmoreurban-centricthaninthepast,andmaycauselocalcongestionissuesfortheDiscoms.

Sofarwehavediscussedtheorganicdemandgrowth,representedinutilities’supplyplans,andaccountingforelectrificationandenergisation.Thereishowever,anunaccountedcomponentofelectricitydemandfromavastnumberofhomelessfamilies,forwhichthegovernment’sAffordableHousingProgrammepromisesdwellingsby2022.Oftenitisstressedthatowingtoasignificantlatentelectricitydemandfrom300-400millionpeople,Indiangridmaynotbeabletoabandoncoal-basedexpansion.Thisisanattempttoinvestigatethelatentdemandfromthenewhouseholdsofthefuturethatarecurrentlyoffthemap.

Affordable housing


Table 8: Affordable Housing stock addition till 2030

Phase I (15-22)

Phase II (22-30)

50% (UB)10% (LB)50% (UB)25% (LB)

1512268

1411633

5.41.04.42.0

AH target met Total21638

17679

Yearly Million Homes Yearly

Affordable Housing (AH) floor-space Construc�on (million m2)

WefindthatevenwiththerelativelymodestLBtarget,theyearlyrateofhousingstockadditionissteep,andmuchhigherthanthepresentrateofconstruction

NationalHousingBoardestimatesahousinggapof18.78millionurbanand43.67ruralmillionhousesin2012(Byravan,Ali,&al,2017;NationalBuildingsOrganisation,2015).Approximately18percentofurbanhouseholdsareestimatedtobeinslumsthatneedrehabilitationandregularelectricitysupply.By2022,theexpecteddeficitinurbanhouseholdsmaybeinexcessof34million(Mint,2015).Thisraisesthecumulativehousingdeficitto78millionhouseholds,growingatover8percentperannuminurbanareas.Ontheotherhand,yearlystockadditiongrowthaspertwolatestCensushasbeenat5percentperannum,whichislowerthantherateofdeficitgrowth(CentralStatisticsOffice,2011).Thisimpliesthatamassivepushinbuildinghousesforthedeprivedpopulationisrequired.

AsperAffordableHousing (AH)guidelinesavailable invariouspolicydocuments (eg.RajivAwaasYojana), thebuilt-upfloor-spacegapforurbanandruralhouseholdscombinedwillbenearlyover3,100millionsq.meter,ofwhichtheurbandemandwillbenearly44percent(JNNURMMissionDirectorate,2012).Consideringthesteeprequirements, theAHtarget is relaxedtobemetby2030 intwophases (2017-22and2022-30)andresultingelectricitydemandfromthesehouseholdsisobtained.Table8showstherateofyearlyfloor-spaceconstructionundertwospecificcases—LB,whenonly35percentofthetargetismetby2030andUBwhentheAHgapiswipedout by 2030.

Table9 shows theassumptionsonelectricity consumptionperhousehold for theseunits. From500kWhperannum,householdsbuiltinthefirstphaseareassumedtoreachanannualconsumptionof700kWhby2030,whilethoseconstructedbetween2022and2030reach600kWhperannumofconsumptionby203025.

Table 9: Phase-wise Electricity requirement for AH units

Electricity (kWh) /HH/annumEPI (kWh/sq. m/annum)**All India Residen�al EPI in 2015- 15.2 kWh/sq. m

50012.50

70017.5

60015

2022 203050012.5

2022 2030Annual Electricity demand per AH dwelling

Houses in Phase I Houses in Phase II

25 TheIntegratedEnergyPolicymentionsalifelineuseof1kWh/perdayforbasiclighting,ventilationandcharging.Weextend thisto500kWhannually(SPARC,2016)andfurtheraddproductiveuseofmoresophisticatedappliancesandequipment inthefutureforthesehouseholds.Forbenchmarking,all-IndiaresidentialEPIof15.2kWh/sq.min2015isused.


From217TWhin2015,residentialelectricitydemandmaygrowtobetween452(5.0percentCAGR)and669(7.8percentCAGR)TWhbasedonpercapitaincomegrowth,applianceownership,energyefficiency,andstockofhouseholdselectrified.Thiswillraisetheaveragehouseholdconsumptionby1.5-2.25timestheconsumptionin2015(787kWhperannum).WhileenergyefficiencyoftheappliancemiximprovescomparedtobaseyearinbothLBandUB,theaspirationalmotivesforcomfortandconvenienceimplythatfuturegrowthcouldturnoutnearhistoricalgrowthratesinconsumptionifsatisfactoryoutcomesinenergyefficiencyandconversationarenotachieved.

TheaggregateelectricitydemandfromAHunitsisobtainedas48TWhand14TWhrespectivelyintheUBandLBcases.Thisshowsthatevenwithfullhousingaccessby2030,theadditionalelectricitydemandgeneratedwillbelessthan5percentoftheaggregateelectricityconsumptionin2015,indicatingthatthebigchallengeliesnotinthequantumofelectricitydemandisbutcreationofsustainableframeworkstoensureelectricityaccess.

Figure9showsaconsolidatedsnapshotoftheelectricitydemandfrom‘organic’growthinresidentialappliancesandthe‘inorganic’componentofAH.

Figure 9: Residential Electricity Demand

0

100

200

300

400

500

600

700

800

2015 2030 UB 2030 LB

TWh

Point Ligh�ng

Tubular Ligh�ng

Refrigerators

RACCTV

FansOthers

AH demand

669CAGR (7.7%)

452 (5%)

217

Whilecook-stovesformapartofresidentialconsumption,theyaretreatedseparatelyowingtolackofhistoricalprecedent.Withenhancedelectricityaccessandemphasisoncuttingdownhouseholddrudgery,mortalityandmorbidityduetoinefficientandpollutingbiomasscook-stovesinruralareas,electriccookinghasemergedasanimportantoptionforthefuture.Thisalsohelpsavoidimportsofcrudeoilandnaturalgasforcookingandcanserveasaviablesecondoptionincaseofnon-availabilityofLPG/PNG,especiallyforruralhouseholds.

Toarriveatthisdemand,anannualhouseholdconsumptionequivalentto10LPGcylindersof15kgisassumed,which is equivalent to 1371 kWh per annum of useful energy requirement per household26.Fortheupperbound,

Electric cooking

26 BasedonacalorificofLPGas47tera-jouleperkilo-tonneand70percentefficiencyofLPGcook-stoves.Efficiencyofelectric cook-stovesistakenat84percent(Jain,Choudury,&Ganesan,2015;Byravan,Ali,&al,2017).


27 Equivalenttofullswitch-overtoelectriccooking.Inpractice,penetrationmaybehigherwithmixeduse.28 ItshouldbestressedthataverageEPIsofbothresidentialandcommercialbuildingsarestillverylow.Modernapartment complexesconsumebetween40-60kWh/sq.m/annumandhigh-endfullyairconditionedcommercialbuildingsinIndia canconsumeupto250-400kWh/sq.m(TheWeidtGroup,2011a).

energy requirement equivalent to complete switching by 15 percent rural and 10 percent urban households is estimated.Forthelowerbound,two-thirdsofthisswitchingisenvisagedtoaccountforpredominantuseasasecondary rather than primary fuel for cooking.

Figure10showstheresultsobtainedintheupperandlowerboundcasesforhouseholdpenetrationofelectriccooking27 and resultant electricity demand.

Figure 10: Electric cooking penetration and electricity consumption

0

10

20

30

40

50

60

Rural Urban Rural Urban

Million HH Consump�on (TWh)

2030 UB 2030 LB

31

21

50

34

21

149

13

Therefore, electric cookingmayaddanother48 to72TWh inelectricitydemandbutbringwith it significantbenefitsinhealthandproductivity,especiallyinruralareas.

Incontrasttoitscontributionineconomicgrowth,thecommercialsectorinIndiaconsumeslessthan10percentelectricity, owing to low energy intensity of its output. However, the sector has registered the highest growth in electricityconsumptionatnearly10percentCAGRsince2000(CentralElectricityAuthority,2017).

Compared to residentialBuilt-uparea, commercial sectorhasamuchhigherenergy consumptionper squarefoot,whichboilsdowninlargeparttoaccessibilityanddifferenceinpayingabilitiesbetweenthetwosectors,asidefromthecriticalneedofelectricitytoruncommercialactivity.Figure10showshowcommercialelectricityconsumptionpersquarefootwasalmostfivetimesthanthatofhouseholdsin2015(NITIAayog,2015;CentralElectricityAuthority,2007-2016)28.

Commercial buildings


Figure 11: Historical EPIs of Residential and Commercial Buildings

A significant reason for low EPIs of Indian buildings has been the lack of air-conditioning services comparedtodevelopedcountries.ButdemandforHVAChasbeengrowingrapidlyaswitnessedintheresidentialsectordiscussions. However, the density of AC coverage usage is much higher in the commercial sector, as evidenced inFigure10.Despitethis,ourcalculationsshowthataverageannualACcoverage inthecommercialsector inIndiaremainsaslowas8.5percentofthefloor-spacearea29, which explains the low EPI of Indian commercial buildings.Also,asizeablenumberofIndiancommercialbuildingsandbuilt-uparea,particularlyintheretailtradeandrestaurantbusinessisoccupiedby‘OwnAccountEnterprises’—commercialenterprisesthatareindividuallyowned or run by family having no hired employees. These are invariably small-scale, numerous and characterised bylackofair-conditioning,whichfurtherkeepstheaveragecommercialEPIslow(Kumar,Deshmukh,Kamnath,&Manu, 2010). Owingtothediversityofappliancesemployedinthecommercialsector, itsenergyconsumptionisdistributedacrossHVAC,lightingandotheruses30(PlanningCommission,2014).Evenwithinthesecategories,solutionsfromhouseholdleveltomuchlargerscaleapplicationareavailable,soservicedemandsareprojectedonthebasisofapplicationtototalfloor-spacearearatherthanasstockofappliances.Insuchacase,growthincommercialfloor-space area and service-intensity combine to give the total service demand.

Figure12showsthehistoricalandprojectedlevelsofresidentialandcommercialfloor-spacearea(at7.5percentGDPgrowth)31.In2015,commercialsectorbuilt-upareaisonly7.7percentoftheresidentialarea.Onaccountofhigherfuturegrowthrates incommercialarea,thisratio isprojectedto increaseupto12percentby2030(Byravan,Ali,&al,2017;NITIAayog,2015).

29 Notallofthebuilt-upareanecessarilyrequiresairconditioning(suchasshafts,staircases,hallways,passages,rest-rooms, storage)inanair-conditionedbuilding.30 Thesepertaintointernalserverloads,laptop,printers,andothersector-specificequipment.31 InthelowGDPscenario,therespectiveCAGRsforresidentialandcommercialsectorsare3.4percentand6.4percentrespectively.

9.415.2

64

74

0

10

20

30

40

50

60

70

80

90

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

EPI (

kWh/

sq. m

/yea

r)

Residen�al Commercial


32 IncaseofACs,thispertainstothestandardareacoveredpertonofair-conditioners,calibratedwithend-useconsumption basedonweightedaveragewattageandEERsofrepresentativetechnologies.Forlighting,thisisbasedonlumens/wattfor LCD and LED lighting technology mix. The share of electricity consumed by respective end-use applications is 55 percent, 25 percent and 20 percent for HVAC, lighting and others in 2012 (Planning Commission, 2014). This is forward projected to 2015 based on assumed growth in appliance mix and service intensity till 2030. CAGR of others till 2030 is taken as an intermediate value between HVAC and lighting intensitygrowthsuchthattheelectricitydemandobtainedin2015isbalancedwithofficiallyreportedvalues.

Figure 12: Historical and Future floor-space area of Buildings

10643

28544

563

3245

0

500

1000

1500

2000

2500

3000

3500

0

5000

10000

15000

20000

25000

30000

2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030

Com

mer

cial

(mill

ion

sq. m

)

Resid

en�a

l (m

illio

n sq

. m)

Residen�al Commercial

CAGRs: Residential- 4.2% Commercial- 7.4%

Further,thegrowthinserviceintensitybetween2015and2030isshowninTable10.Serviceintensityinthebaseyearisarrivedatbyconvertingtheunitsofelectricityconsumedinusefulservice32.

Table 10: Service Intensity across commercial sector applications

HVAC Coverage (%)Ligh�ng (lumens/sq. m)Others

8.5%290

-

22.5%350

-

6.7%1.3%4.0%

2015 2030 CAGR

Forprojectionsonenergyefficiencyandconservation,adifferentialpenetrationofhigh,mediumandlowefficiencytechnologiesacrosstheapplications(9technologiesoverall)and19ECBCrelatedinterventionsisappliedacrossthe lower and upper bound scenarios.

Basedonthis,thecommercialelectricitydemandderivedisshowninFigure12.Theimpactofairconditioninggrowthfiguresprominentlyastotalelectricitydemandacrossapplicationsgrowsfrom82TWhin2015to247TWhby2030intheLB(7.6percentCAGR)and348TWhintheUB(10.1percentCAGR).


Ingeneral,HVACgrowthemergesasamega-trendacrossresidentialandcommercialbuildingsasevidencedinTable11.Fromaquarteroftotalbuildings'demandin2015,theshareofHVACislikelytomorethandoubleby2030,withCAGRsof12and14percent respectivelyacross theLBandUBscenarios.This calls foraggressiveinterventions inthisspace,particularlyasaverageACEnergyEfficiencyRatios(EERs) in India lagmuchfartherbehind those in the developedworld.Making inverter based star-ratedACs cost competitivewith their non-invertercounterpartsthroughdirectgovernmentinterventionsandeasylow-costfinanceschemescanhelpfosterenergyefficiencyinthisspace.

Figure 13: Commercial Electricity Demand

Table 11: HVAC consumption in Buildings

4819 16

82

266

2557

348

183

1748

247

0

50

100

150

200

250

300

350

400

HVAC Ligh�ng Ohters Total

TWh

2015 2030 UB 2030 LB

CAGR: 9.4%-12.1%

2.0% -0.5%8.8%7.5%-

10.1%7.6%-

Electricity demand (TWh)Residen�al HVACCommercial HVACTotal HVACHVAC: Total buildings demand

254872

25%

28026654654%

20918339256%

2015 2030 UB 2030 LB

While the industrial sector is the largest consumerof gridpower, it alsohas substantial and growing captivegeneration (Central Electricity Authority, 2007-2016). While efforts to enhance industrial production andemployment have been consistent since the 1950s, the largest scheme for enhancing energy efficiency inthe industrial sectorhasbeen thePAT, forwhich the specificenergy consumption (SEC) targets for individualdesignatedconsumers(DCs)acrosseightenergyintensivesectors–Ironandsteel,cement,aluminium,fertilisers,textiles,pulpandpaper,chlor-alkali,andthermalpowerplants—werefirstgivenin201233.

Industry

33 ThefirstcycleofPATincluded478DCsandcametoanendin2014-15.ThesecondPATincludesthreenewsectors-railways, refineriesandDiscoms,andenergysavingtargetshavebeennotifiedfor621DCs(BureauofEnergyEfficiency,2016).


34 ThisisbarringPATtargetsonefficiencyimprovementforthermalpowerplants,whichisanelectricitysupplyissue.

BasedonthePATclassification,theanalysisforindustrysectorattemptstoaccountforprocess-wiseproductionand SECs for the PAT sectors34.Intotal,26processesaremodelledacrossthesevenPATsectors,withfuel-wiseSECs(electricity,individualfossilfuelandwaste/renewableenergy).Forfutureprojectionsofelectricity/energysaving,sector/process-wiseenergysavinginterventions,andrawmaterial,fuelandprocessswitchinginterventionswereconsidered.Of the total industrial electricity consumption (grid and captive) reported by the CEA, electricityconsumptionfromtheremainingsectorsisobtainedasaresidual‘non-PAT’consumptioncategory,whichmainlyincludessecondaryandsmall-scalemanufacturingindustries’consumptionofelectricity.

Thekeydriverforelectricityconsumptionintheindustrialsectorinthemodelistheeconomicactivityrepresentedby production across individual PAT sectors as shown in Figure 13. Production of all sectors is taken to varyproportionatelyacrossdifferentgrowthscenarios.Thisisasimplifyingassumptionsincespecificsectorsarealsoimpactedbyparticularpoliciesgrowthdrivers,allofwhichneednotnecessarilymoveinthesamedirection.

Figure 14: Industrial Production in high and low industrial growth scenarios (2030)

220

Prod

uc�o

n (M

t)

673

5 18 28 26 6 8 9

280

856

6 23 36 33 7 10 120%

1%

2%

3%

4%

5%

6%

7%

8%

9%

0

100

200

300

400

500

600

700

800

900

Steel Cement Aluminium Ammonia Urea Paper Tex�le Caus�cSoda

Soda Ash

CAGR

(%)

Produc�on Low Produc�on High CAGR (16-30) Low CAGR (16-30) High

Sources: Indiastat.com, ministry, consulting and industry associations’ reports, and other scenario modelling studies mentioned for Table 7.

Forthenon-PATsectors,thegrowthoftheirvalue-addedinproductionistakenasaproxyforphysicalproduction,owingtothediversityofproductionwhichcannotbeneatlycategorisedacrossallsectorsonaweightorvolumebasis.Therefore,theoverallindustrialsectorgrowthscenariosareusedtoprojecttheireconomicactivityby2030.

Each industrial process modelled is associated with its own SEC and underlying fuel mix. The decision to model any process as a separate category is taken if the technologies and raw materials, and therefore energy requirements involved in distinct processes are substantially different. The penetration of high-efficiency technologies andprocessesasmodelledinhighandlow-efficiencyscenariosacrossthePATsectorsarepresentedinTable12.


Fornon-PATsectors,whichconsumed47percentoftotalindustrialelectricityconsumptionin2015,conservativeefficiency improvementsof 5-10percent are considered, since theyoffer limitedenergy savingpotential duetoscaleeconomics,frontcostsandfinancingconstraints.Toarriveattheindustrialelectricitydemand,energyefficiencypotentialalongwithspecificshareofelectricityintotalSECsacrosstheprocessesareusedwithprocessshares(Table13)andindustrialproduction(Figure13).

PrevailingcircumstancesineachPATsectorareaccountedfor.Forexample,India’scementindustryisamongthemostefficientintheworld,nextonlytoJapanonaccountofscaleofproductionandtechnologicaladvances,butinnolessmeasureduetohighshare(overfour-fifths)ofblended(PortlandPozzolanaandPortlandSlag)cementsin themix that require less limestoneandenergy inproduction (Krishnan,Vunnam, Sunder,&al,A StudyofEnergyEfficiency intheIndianCementIndustry,2012).Similarly, India’snaturalgasbasedfertiliserproductionishighlyefficientwithlowscopeforfutureefficiencyimprovement,whichisnotthecasewithnapthaorfuel-oilbasedproduction(Bhushan,2010).Ontheotherhand,theironandsteelindustrywithitsheavyrelianceonblastoxidationfurnacebasedproductionhassignificantroomtoshifttowardssmeltingtechnologies(COREXorFINEX)orgas-basedDirectReducedIron(DRI),apartfromutilisingmuchhigherlevelofscrapinproduction.Suchalternativesinsteelofferhighenergysavingpotential,insignificantcasesduetoelectricalswitching,incombinationwithothertechnologiessuchascontinuouscasting,variablefrequencydrives,wasteheatrecoverysystems,etc.

Table 12: Industrial Process shares across high and low efficiency scenarios (2030)

Iron & Steel

Cement

Aluminium

Pulp & Paper

Tex�le

Caus�c Soda

Soda Ash

Fer�lisers(Urea and Ammonia)

BF-BOFCoal DRI-EAFGas DRI-EAFCOREX-BOFScrap-based EAF/IFOPCPPCPSCPre-bakedSoderbergScrap-based Integrated Kra� (Wood/bamboo/agro waste)RCF based (includes market pulp)Natural Gas-basedNaptha-basedFuel Oil- basedIntegrated Tex�le Produc�onMercury CellMembrane CellODCSolvayModified SolvayAkzo Dry Lime

22%15%12%14%37%8%

80%12%60%0%

40%35%65%

100%0%0%

100%0%

80%20%0%

30%70%

ProcessIndustry36%25%12%12%15%20%70%10%75%5%

20%57%43%80%10%10%

100%0%

95%5%

35%25%40%

High Efficiency Low Efficiency


(Krishnan,Vunnam,Sunder,&al,AStudyofEnergyEfficiencyintheIndianIronandSteelIndustry,2013).Figure15showstheweightedaverageefficiencyimprovementsconsideredacrossvariousPATsectorsinthehighand lowefficiencycases.

Figure 15: Efficiency Improvements across PAT sectors in the Low and High Efficiency cases

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

Iron andSteel

Cement Aluminium Fer�liser Pulp andPaper

Tex�le Chlor-Alkali

SEC

Impr

ovem

ent (

%)

Low

High

11.7%

9.4%10.1%

3.5%

5.8%

7.9%8.8% 9.2%

18.6%17.6%

19.3%

14.0%

17.0%

8.5%

TheSECimprovementsarrivedatinFigure14considerthebaselineimprovementsmandatedbythatPATschemeandthegapbetweencurrentdomesticandbestavailabletechnologySECs. Forthelowefficiencyscenario,theSECreductionsmandatedbyPATareassumedtobecarriedforwardthrough2030atadecreasingrate,sincepriceofefficiencyvariesinverselywithitspenetrationaslowhangingfruitsareexhausted.TheSECimprovementsinthehigh-efficiencyscenarioarepredicatedon50-100percentachievementof SECs of the best available technologies available or likely to be adopted in future for commercial deployment (Byravan,Ali,&al,2017).ThevarianceinachievementlevelsacrosssectorsisborneoutofdifferentiallevelsoftechnologicalsophisticationandpolicythrustindifferentPATsectorsasdiscussedabove.

ThetotalindustrialdemandsoobtainedisshowninFigure16.Thebaseyearforthisanalysis(asforresidentialelectricityconsumption)is2012andisforwardprojectedto2015basedonSECimprovementscorrespondingtothe sector actuals.


Figure 16: Industrial electricity demand

Totalindustrialconsumptionthusgrowsfrom418TWhin2015to881TWh(5.1percentCAGR)intheLBcaseto1098TWhintheUBcase(6.6percentCAGR).Steel,cementandaluminiumremaindominantconsumerswithinthePATsectors,whichalsooffergreatestpotentialforabsoluteenergysavings.Thegrowthratesindicatethatthesector will remain the dominant consumer of electricity in the foreseeable future especially with high emphasis on manufacturing.

ThisisreflectedinFigure17whichshowstheelectricitydemandforPATandnon-PATsectorsunderhighgrowth(HG)andlowgrowth(LG)scenariosfordomesticmanufacturing.

7734 24 24 10 6 9

184 169

256

114

3685

29 15 33

568530

196

93

2765

20 9 26

437 444

0

100

200

300

400

500

600

Steel Cement Fer�lisers Aluminium Paper Tex�les ChlorAlkali

Total PAT TotalNon- PAT

2012 2030 UB 2030 LB

CAGR: 5.0 -6.5% 5.5 -6.6%

Figure 17: Impact of high manufacturing growth on industrial electricity demand

214

437 510204

444504

0

200

400

600

800

1000

1200

2015 2030 LG 2030 HG

Elec

trici

ty C

onsu

mpt

ion

(TW

h)

PAT Non-PAT


Figure 18: Electricity demand from Railways

From Figure 17, it can be estimated that the difference in electricity demand is 15 percent higher in a highmanufacturingscenariowhenthelevelsofenergyefficiencyareheldconstant(athighlevelinthiscase)inbothscenarios.Forthebenefitsassociatedwitharobustmanufacturingbase,thisgrowthinelectricityconsumptionshould be facilitated.

Fromtheabove,itcanbeascertainedthattheimpactofongoingelectrificationdriveintherailwayswillhaveonlyaslightimpactonelectricitydemand(8TWh).However,thiscanbringinsignificantefficiencyandimportsubstitutionbenefitsviareplacementoftheolddiesel-basedlocomotives.

Asof2015,theIndianRailwayshadachievedanelectrificationof40percentofits66,030rail-routekilo-meters(RRkm)(MinistryofRailways,2016).WhilethegrossRRkmshavebeengrowingatlessthan0.4percentCAGR,theirelectrificationhasgrownover4percentoverthepastfifteenyears,resultinginanelectricityconsumptiongrowthof nearly 5 percent (Central Electricity Authority, 2007-2016; Central Organization for Railway Electrification,2018).By2021, thegovernmenthasplanned toelectrify24,400RRkms,whichwoulddrastically improve theelectrificationrateto77percent(PressTrustofIndia,2016).

In accordance with the above, the high and low electrification (and therefore electricity demand cases) areconstructedwithannualspecificconsumptionimprovementsof1.5percentand3.5percentannuallybasedonthehistoricalandpotentialratesforimprovementoftraction-basedconsumption,whichhashistoricallycontributed85-90percentofelectricitydemandforrailways.The levelsofelectrification inthehighand lowcasesare90percentand75percentrespectivelyby2030.TheresultingelectricitydemandobtainedisshowninFigure18.

Railways

17

43

35

0%

1%

2%

3%

4%

5%

6%

7%

0

5

10

15

20

25

30

35

40

45

2015 2030 UB 2030 LB

CAG

R

TWh

Consump�on (TWh)

CAGR

CAGR: 5.0-6.3%


The push towards electrification of roadmobility started with the National ElectricityMobilityMission Plan(NEMMP) in2012,which targeted toachieveacertain (albeit) smallpenetrationofbatteryandhybridEVs inthesalesmixby2020.TherationaleforEVsisprovidedintermsofreductionoflocalairandnoisepollutionanddependence on imported oil. However, owing to a number of factors, most notably the low driving range, top speed,acceleration,highfrontcosts(especiallyduetobattery)andlackofadequatecharginginfrastructure,theannualtwoandfourwheelerEVsalesareafractionofdailyaggregatesalesinthesecategories.

More recently, there has been a renewed thrust on EVs, with targets of all vehicular sales by 2030 to be EVs. Whilethisambitionseemstohavebeentemperedatthepolicylevel,manyvehicularmanufacturershavetakenthecueandannouncedplanstosignificantlyexpandEVproductioncapacity.Accordingly,twoscenariosforEVsstock–-ambitiouspenetrationofEVs(100percent)andmodestachievementof33percentEVsalesby2030—areconsideredforestimatingelectricitydemand35.

Further,theelectricitydemandisobtainedforurban(intra-city)passengertransportdemandonlyduetorangeanxiety, relationshipof costswithbattery sizing, lowspeedover longerdistances, andability to supportonlylimitedloadsunsuitableforheavydutyfreightoperation.

TheEVsalessoobtainedfrom2017intheUBandLBscenariosareshowninTable13.

Electric Vehicles

35 Casesonbatteryefficiency (electricity lostduringcharging)ormileage (kmperkWhofcharge) improvementsarenot examined for owing to future uncertainties in battery technology evolution and prices, and relatively small absolute electricitysavingpotentialaslateranalysisshows.

Table 13: Future sales of EVs (Cumulative 2017-30)

(Private) CarsTaxisBuses2W3WTotal/ Wtd. Avg.

20%25%25%20%33%21%

60%65%65%60%65%61%

42.26.82.3

197.824.7274

2030 UB 2030 LB 2030 UB 2030 LB14.12.60.9

65.912.696

% Total Sales Million Numbers

Basedonthis,thecategory-wiseelectricitydemandobtainedisshowninFigure19.TheintermediatestepsandconsiderationsinestimatingelectricitydemandfromtheEVstock,annualvehicularkilometresandmileageareavailableinadetailedpublicationbytheauthorthathighlightsspecificissueswithregardtogridcompatibilitywiththeEVstockandchargingecosysteminthefuture(Ali&Tongia,ElectrifyingMobilityinIndia,2018).


Figure19showshowaggregateelectricitydemandfromEVsmayrange from37to97TWh in theLBandUBscenariosofelectricitydemand.Busesandprivatefour-wheelersarethelargestconsumingcategoriesaccountingfor over half of this demand. However, even with 100 percent EV sales by 2030, electricity demand of less than 100 TWhistimid-only10percentofthetotalelectricityconsumptionin2015.However,meetingtheinstantaneousload of several EVs plugging in simultaneously will likely be the major hurdle for supply planning, especially due to locationalandtime-sensitivenatureofthisdemand.

Theelectricityconsumptioninthe‘Others’categoryincludespubliclighting,publicwaterworksand‘miscellaneous’consumption as reported by the CEA. The year-on-year growth rates in these components have beenmuchmorevolatilecomparedtoothersectors(Table15),whichmayindicateeitherlowreliabilityofdataorstatisticaladjustmentsinreporting(orboth).

Overthepastdecadethesectorhasgrownat4.6percentperannumandhadanaverageshareofabout5percentintotalelectricityconsumption(Figure20).In2015,thesectoraccountedfor49TWhofelectricitydemand

Table 14: Historical yearly electricity consumption CAGRs in the Other sector

Figure 19: Vehicular Category-wise Electricity Demand by EVs in 2030

9

4

11

58

26

10

28

16 16

9.4 9.4

0.8

54.4

19.2

0

10

20

30

40

50

60

0

5

10

15

20

25

30

Cars Taxis Buses 2W 3W

Mile

age

(km

/kW

h)

Elec

tric

ity D

eman

d (T

Wh) 2030 LB

2030 UB

Others

2006200720142015

13%13%6%2%

1%1%

18%-2%

11%11%6%9%

-16%-16%24%10%

Yearly GrowthRates Public Water Works Miscellaneous TotalPublic Ligh�ng

Source: CEA General Reviews


Figure 20: Historical share of Others in rural electricity consumption

1% 1% 1% 1% 1% 1%

2% 2% 2% 2% 2% 2%

2%2%

1% 2%2%

2%

0%

1%

2%

3%

4%

5%

6%

2005 2006 2007 2013 2014 2015

Public Ligh�ng Public Water Works Miscellaneous

Sincethegranularityinconsumptionwithrespecttotechnologyapplicationagainstend-usedemandsisdifficulttoestimate(owingtosketchydataonthesecategories),urbanisationistakenasakeydriverforfuturegrowthsuch that the share in total electricity consumption ismaintainedat 5percent. Further to this, anadditionalelectricitysavingof10percentisappliedinthehigh-efficiencycase.Theresultantelectricitydemandobtainedby2030is115TWh(5.8percentCAGR)intheUBcaseand104TWh(5.1percentCAGR)intheLBcase.Hereagain,the quantum of supply is not the main challenge, but the ability of urban infrastructure to keep pace with the growing demand.

The aggregationof the sector-wise analysis to total end-use electricity demand is shown in Figure 21,whichshows aggregate electricity demand growing from 949 TWh in 2015 to anywhere between 2074 TWh (5.4 percent CAGR)and2785(7.4percentCAGR)—thetwoextremescenarios.Thekeydemandsectors—industry,residential,commercial andagriculture—continue tobe significant contributors toelectricitydemandgrowth,whilst alsoofferingthehighestpotentialforenergyefficiency.Thenew(sub)sectorsanalysedforthisstudy,namelyaffordablehousing, electric cooking and EVs, together account for less than 10 percent of the new demand by 2030, across scenarios.

Conclusion and discussion


Figure22showsthesector-wiseelectricitydemandgrowthandpercentagedifferenceinthelevelsofdemand(withrespecttotheUB).Commercialsectoristhebigmover,partlyonaccountoflowconsumptionbase,butlargely owing to the surging HVAC demand, matching (or even surpassing in some cases) agricultural demand by 203036.Thenewdemand fromaffordablehousing (subsumed inFigure20within the residentialdemand),electriccookingandEVsshowshighvolatilityacrossthecasesonaccountofuncertaintyinservicedemandsandtechnologies,drivenlargelybydedicatedpolicyallocationsandenablinginfrastructure.

Figure 21: Sector-wise national electricity demand in UB and LB Scenarios

Figure 22: Sector-wise electricity demand growth

0

500

1000

1500

2000

2500

3000

2015 2030 UB 2030 LB

TWh

Agriculture Residen�al Commercial Cooking Industry Railways Others EVs

949

CAGR: 5.4% - 7.4% 2785

2074

169 344 271217

66945278

348

247418

1098

881

4.9%

7.8%

10.4%

6.6% 6.4%5.8%

7.4%

3.2%

8.0%

5.1% 5.0% 5.1% 5.3%21%

32%

29% 33%

20%

19%

62%

10%

26%

0%

10%

20%

30%

40%

50%

60%

70%

0%

2%

4%

6%

8%

10%

12%

Agriculture Residen�al Commercial Cooking Industry Railways EVs Others Total

Growth UB Growth LB Diference (%)

5.0%

36 Consumptionfromirrigationpump-setswasmorethantwicethesizeofcommercialconsumptionin2015.


Finally,thekeyresultsoftheninescenariosdescribedinthemethodologysectionearlierarepresentedinTable16.Undereachgrowthscenario, the labels ‘low’, ‘mid’,and ‘high’correspondto levelsofelectricitydemand,whichisinverselyrelatedtoenergyefficiencyinthatscenario.Between2000and2015,electricitydemandgrewby6.9percentCAGRyieldinganelasticityof0.95.By2030,thereisachancethathigherefficiencycanreducetheelasticitybyalmost20percent,signallingadecouplingbetweenelectricitydemandandeconomicgrowth.The achievement on future goals related to electrification (households, cooking andmobility) and enhancingdomesticproductionyieldshigherpercapitaconsumptionofelectricity,whichislikelytodoublebetweenby2030compared to 2015.

Thedistinctionbetweenprescriptive‘scenario-building’exercisesandthe‘cases’ofelectricitydemandexploredinthisstudyisowingtotheobjectiveoffindingplausibleboundstoelectricitygrowthinfutureasaninputtosupplyplanning.Theadvantageofbottom-upanalysesofseparatecomponentsofdemandallowsforflexibilityin generatingandanalysingprescriptive scenarios todevelop roadmaps for sectoral strategies, aswell as theelectricity sector as a whole.

Theanalysispointstothepolicyprescriptionofstimulating‘good’demand(relatedtoaccessandimportedfuelsubstitution,andmanufacturingandjobgrowth)andcurbing‘baddemand’(relatedtoinefficientandprofligateconsumption).Theconcentrationofnewerandmorecomplexloadsinurbanareas,andtheaddedvariabilityofuse(ACsinhotweatherandEVschargingcycles)indicatethatwhileIndia’selectricitycapacityexpansionplansmaybesufficienttocoverelectricitydemandgrowth,peakloadsandlocaldistributioncapacityissuesmaybedrasticallyexacerbated.Thisnotonlycallsformeasurestoenhanceenergyefficiencyatcentralandstatelevel,butgreatercoordinationbetweentheloadgeneratorsandelectricitysuppliers.

Table 15: Aggregate electricity demand under various cases

Electricity Demand (TWh)Growth (CAGR)Elas�cityPer Capita Consump�on (kWh)

24326.5%0.861592

27857.4%0.991823

949-

0.95724

20755.4%0.821358

22515.9%0.911473

25876.9%1.061693

21565.6%0.801411

23386.2%0.891531

26657.1%1.021745

22535.9%0.791475

GDP CAGR by 20302015 LB Mid

6.5%High Low Mid

7% 7.5%High Low Mid High


InstitutfürEnergie-undUmweltforschungHeidelbergGmbH,TheEnergyandResourcesInstitute,SustainableEuropeResearchInstitute,WuppertalInstitute,GIZ–DeutscheGesellschaftfürInternationaleZusammenarbeit.(2013).India’sFutureNeedsforResources.DeutscheGesellschaftfürIInternationaleZusammenarbeit.

Abhyankar,N.,&Gopal,A.etal.(2017).Techno-EconomicAssessmentofDeepElectrificationofPassengerVehiclesinIndia.Berkley,California:LBNL.

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Ali,M.S.,&Tongia,R.(2018).ElectrifyingMobilityinIndia.NewDelhi:BrookingsIndia.

Ali,S.,Goyal,N.,&Srinivasan,S.(2017).SustainableEnergyAccessforAll:BuildingSustainabilityintoUniversalEnergy Access. In D. Bhattacharya, & A. O. Llanos, Southern Perspectives on the Post-2015 InternationalDevelopmentAgenda.NewYork:Routledge.

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Bhattacharya,D.(2011).Emergingtrendsinglobalaluminiumindustry.6thInternationalConferenceonAluminium-INCAL2011.AluminiumAssociationofIndia.

Bhushan,C.(2010).ChallengeoftheNewBalance.NewDelhi:CentreforScienceandEnviornment.BuildingTechnologiesOffice.(n.d.).ApplianceandEquipmentStandardsRulemakingsandNotices.WashingtonD.C.:U.S.DepartmentofEnergy.

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Byravan,S.,Ali,S.,&al,e.(2017).Qualityoflifeforall:AsustainabledevelopmentframeworkforIndia’sclimatepolicyreducesgreenhousegasemissions.EnergyforSustainableDevelopment,48-58.

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Appendix: Key results from past studies

Various past studies have used different approaches towards different ends (energy policy, electricityplanning, greenhouse gas mitigation and climate policy, etc.) to obtain electricity demand and supplyprojections. This section highlights some recent work to place the results of this analysis in thelarger context of existing knowledge. Table 17 shows the pertinent results for electricity growth from thesestudies.Whilethisstudyestimatesend-usedemandthat includes industrialcaptiveconsumption,someprojectionsareonlyreportedatgrossgeneration37ornetgeneration(bus-bardemand)38 level.

FromTable16,while the rangeofdemand (or generation)growthobtained from isolatedelectricitydemandprojections towider scenario-based studies varies between 5.5- 8.7 percent39, it is useful to note that more recentstudiesstudyingelectricitydemand,particularlyNITI’sDraftNEPandCEA’s19thEPS,obtainconsiderablylowergrowth rates for futuredemands thanstudiesconductedearlier (with theexceptionofTERI’sand IEA’sprojections)40.Thisappearstobeacorrectionfrompreviousprojectionsonelectricitydemandthatappearedto

37 Grossgeneration=End-usedemand+T&Dlosses+Auxiliary/ownconsumption.38 ElectricityDemandatbus-bars=NetGeneration=End-usedemand+T&Dlosses.39 The8.7percentdemandgrowth inCSTEP (2015) isobtained ina scenarioofhighmanufacturingandair-conditioning demandgrowth,unrestrictedirrigationpumpinggrowth,withemphasisonserviceprovisionratherthanenergyefficiency orfuel/materialsubstitution.Theseareemployedtoaneconomicallyviabledegree(nolossinoutputperunitspenton energy) in the Sustainable scenario which yields 7.7 percent demand growth. 40 Ingeneral,weshouldexpectthegrowthratesofend-usedemandtobehigherthangenerationrequirementowingto fallingT&Dlossesovertime.

Table 16: Key results from recent studies on the electricity sector

26713350

6.06%7.67%

15

14

18

3175 7.76%

11051105

21042244

5.49%5.87%

804804

28213383

6.41% 7.49%

923923

18

28223343

7.68%8.70%

745745

18

33703465

7.46%7.63%

923923

18

1115

13 2078 6.46%92117 2241 5.53%897

2030TWh

CAGRBase yearTWh#Metric$ Years to

2030Sustainable Development Uncertain�es andClimate Policy (CEEW 2018)~Transi�ons in Indian Electricity Sector(TERI 2017)Dra� Na�onal Energy Policy (NITI 2017)^

19th Electric Power Survey (CEA 2017)*India Energy Outlook (IEA 2015)Energy Emissions Trends and Policy Landscape(Shukkla et al 2015)~Quality of Life for All (CSTEP 2015)

Expert Group on Low Carbon Inclusive Growth(Planning Commission 2014)~

Gross genera�onGross genera�onBus-bar demand/Net genera�onEnd-use demand(incl. cap�ve)End-use demandEnd-use demandGross genera�on

End-use demand(incl. cap�ve)Gross genera�on

$ Reporting is for grid-based demand and generation unless explicitly mentioned to include captive.# Base year refers to the nearest year to the study release that is presented in the tables, graphs and/or narrative.~ Gross Generation taken from official CEA numbers.^ 2030 Values interpolated between 2022 and 2040.* Base year value is the 19th EPS estimated demand for 2017. 2030 value extrapolated using 2022 to 2027 CAGRs.


41 Incertaincases,electricity (gridandnon–grid) isdirectlyreplacedbyon-sitecleanerequivalentsourcessuchassolarirrigationpumpsandwasteheatrecoverysystems.

be a correction from previous projections on electricity demand that appeared to have over-estimateddemand under some implicit driving notion of aspirational per capita consumption of electricity (for example, Planning Commission’s Low Carbon Inclusive Growth report, CEA’s previous EPS, and CSTEP’s QualityofLifeforAllreport),afacthighlightedbyvariousobserversoftheIndianelectricityplanningprocess(Josey, M, & Shantanu, 2017).

This study incorporates the learning frompast experience and reflects the ongoing process of decoupling ofelectricitydemandfromgrowth,whilstaccountingforsubstitutionofelectricity forother fuelsasameanstoachieveanoverallmoreenergyefficientandcleanersystem41. Consequently, while the study projects various casesofelectricitydemandfrom5.4to7.4percentCAGR,themid-valueforthestudy(at7percentGDPgrowthandmediumefficiency)correspondstoaCAGRof6.2percent(seeTable16).

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