m&v for pay for performance approaches update, resources...
TRANSCRIPT
M&V for Pay for Performance
Approaches: Update, Resources, and New Developments
Presenta(ontoNWEnergyCoali(onOlympia,WA
October24,2016
DavidJump,Ph.D.,P.E.kWEngineering
Agenda • AchievingEnergyEfficiency• AddressingPerformanceRiskswithM&V• NewDevelopments
• M&V2.0• Tools
• LBNLResearch• BestApplica(ons
Achieving Energy Efficiency
Audit
BusinessCase
Approval&Financing
Specifica(on&Procurement
Installa(on&Commissioning
Verifica(on
Performance
Payment
BaselinePeriod Installa(onPeriod PerformancePeriod
Energy
Time
Savings
Risks • Savingsunderes(mated
• Poormodeling• Es(matesnotbasedondata
• ECMsdon’tperform• Misspecifica(onofequipment• Poorinstalla(on,lackofcommissioning
• Savingsdon’tlast• Buildingopera(onschange• Equipmentnotmaintained
• Non-Rou(neEvents• Newloadsadded• Opera(onschange• Occupancychanges
• Result:• Poorreturnoninvestment• Owners,Investors,EveryoneUnhappy!
International Performance Measurement and Verification
Protocol (IPMVP) 4M&VOp(ons:• A:KeyParameterMeasurement• B:AllParameterMeasurement• C:WholeFacility• D:CalibratedSimula(on
RetrofitIsola(on
WholeBuilding
Otherguidelines:sameop(ons-differentemphasis:Technicalmethods–ASHRAEGuideline14Repor(ngRequirements–FEMPSpecificApplica(ons-
BonnevillePower(bestprac(ces)CaliforniaCommissioningCollabora(ve(retro-commissioning)
Option A: Key Parameter Measurement
"#ℎ↓&'() =( "#↓+'&) − "#↓,-&. )× /01↓)&.
Ligh(ng• Measurefixturepower• Agreeonhoursofusees(ma(ons(ownercontrolsopera(ons)
BestApplica(ons• Individual,SimpleECMs
• Constantload&use• LowInterac(onEffects
Costs-Low• Simplemeasurements• Uncomplicatedanalysis
RisksareShared:• ESCOresponsiblefor
performance(kW)• Ownerresponsiblefor
usage(HRS)
Option B: All Parameter Measurement
"#ℎ↓&'() =( "#↓+'&) − "#↓,-&. )× /01↓,-&.
Ligh(ng• Measurefixturepower&hoursofuse
BestApplica(ons• Interac(veECMs• Variableload&use
Costs-Higher• Muchdatarequired• Extensiveanalysis• Technicalreview
Risks:• Datacollec(onerrors• Pooranalysis
Option C: Whole Facility DataSources:• U(litybills• Localweathersta(ons• Produc(onratesRegressionanalysis• HDD/CDD,produc(onrate
"#ℎ↓&'() = "#ℎ↓+'&) (3↓,-&. )− "#ℎ↓,-&. ( 3↓,-&. )
BestApplica(ons• Mul(ple,Interac(veECMs• Savings>15%
Costs-Low• Availabledata• Tools
Risks:• Savings<15%• Non-Rou(neEvents
Option D: Calibrated Simulation DataSources:• As-BuiltDrawings• On-siteaudits• U(litybills• Localweathersta(ons• Muchmore…
Requirements:• Modelcalibra(on(varies)• Experiencedmodelers• Soiware• Time
BestApplica(ons• Mul(ple,Interac(veECMs• Savings<15%
Costs-High• Highdatarequirements• Exper(sewithtools
Risks:• Modelnotcorrect• Non-Rou(neEvents
¨ Rigor¨ Accuracy¨ Confidence¨ Risk¨ QualityAssurance
¨ CostofService
¨ CostofSavedEnergy
Key M&V Principle
M&V 2.0
• DataUses:• Buildingaudits• ECMCommissioning• M&V• Performancetracking
• Poten(al• Lowercost–datacollec(onandanalysistools
• Rapidfeedback–morecustomerawareness
OptC.WholeBuilding Opt.BRetrofitIsola(on
• Short-(meintervaldata(e.g.15min,hour,day)andadvancedanaly(cs
M&V 1.0 – Monthly Data
� Linearregressions� 12months/datapointsperyear
� LessAccuracy� 12mo.monitoringdura(on
M&V 2.0 - lnterval Data
� Advancedanaly(cs� 8760hourlypointsperyear� MoreAccuracy� Shortermonitoringdura(on:3or6months
� Applicabletosubsystems
Visualizing Savings
-40.0
-20.0
0.0
20.0
40.0
60.0
80.0
100.0
150
250
350
450
550
650
750
850
950
Deg
F
kWh
` ,
BaselinePeriod PostInstallPeriod
Source:UniversalTranslatorv3
Monitoring Savings Persistence
M&V 2.0 Tools PublicDomain
Universal Translator
EMIS-Proprietary
EnergyChar(ngandMetricsTool
InverseModelToolkit(RP1050)
M&VAnalysisModule
Screen Shots of M&V 2.0 Capability
ImageSource:Lucid
Screen Shots of M&V 2.0 Capability
ImageSource:EnerNOC
Screen Shots of M&V 2.0 Capability
ImageSource:UniversalTranslator3
What is New About M&V 2.0? What is Not New? • M&V2.0toolsarebuiltuponsavingses(ma(ontechniquesthathavebeenusedfordecades
• Comparisongroupanalyses• Whole-buildingandsubmeter-basedpre/post(Op(onC)• Calibratedsimula(onmodeling(Op(onD)
• What’snewis:• Degreeofautoma(onindataacquisi(on,andmodelcrea(on• Granularityandvolumeofdatacanimprovequalityofresult• Poten(alforcon(nuousfeedback• Integra(onofM&Vcapabilitywithotheranalysesforopera(onalefficiency• Soiwareasaserviceofferingsforowners,managers,programadministrators
What are the Potential Benefits of M&V 2.0? What is the Value Proposition?
• Increasevisibility,quicklyobtainongoingandinterimresultsfeedback
• Increasesavingsandenhancecustomerexperience?• ImprovetransparencyandtrustworthinessofEEsavings?
• Automatepartsoftheprocessthatcomputersdowell,streamlinedataacquisi(onandprocessing
• Reduce(meandcosttoquan(fysavings?• Maintain/improveaccuracyinsavings?• Increasethroughput,numberofprojectsgoingthroughthepipeline?
What Questions Are Being Asked*? • Howcanwereducethe(meandcostsnecessarytoquan(fysavings?
• Howcanweknowifamodelorcommercialtoolisrobustandaccurate?
• Howcanwecompareandcontrastproprietarytoolsand‘open’methods?
• Whattestprocedurescanbeusedtoevaluatemodelandtoolperformance,andwhichmetricsaremostimportant?
• CanIuseawhole-buildingapproachformyprogramsandprojects?
*All are asked before a project is conducted; after a project, we want to know how much was saved, what was the uncertainty, how confident are
we in those savings?
What Have We Done to Address These Questions?
n Developedates:ngproceduretoquan(fybaselinemodelaccuracy
n Solicitednewintervalbaselinemodelsfromindustry,tools,andacademiccommuni(es
n Appliedthetestproceduretoevaluatemodelperformance
n Withadvisorygroupiden:fiedmostcri:calperformancemetricsforM&V
n Developedconclusionsregardingpoten:alforwideradop(onofAMIdata+analy(csforM&V
Illustration of Test Procedure
!
Test Data Set
n 537commercialbuildings¨ 15-minuteelectricloaddata¨ Outsideairtemperaturebasedonzipcode
n Noknownefficiencyinterven(ons,significantchangesinopera(ons,occupancy
Most data from CA Zone 3, and Wash DC Zone 4; some from Seattle Zone 4
Percent Error (NMBE)
26
● ●
●
●
−25.0−22.5−20.0−17.5−15.0−12.5−10.0−7.5−5.0−2.5
0.02.55.07.5
10.012.515.0
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10model
NM
BE (%
)
NMBE (N=441) 12 month training
●●
●●
−25.0−22.5−20.0−17.5−15.0−12.5−10.0−7.5−5.0−2.5
0.02.55.07.5
10.012.515.0
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10model
NM
BE (%
)
NMBE (N=470) 9 month training
● ●
●
●
−25.0−22.5−20.0−17.5−15.0−12.5−10.0−7.5−5.0−2.5
0.02.55.07.5
10.012.515.0
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10model
NM
BE (%
)
NMBE (N=530) 6 month training
● ●
●
●
−25.0−22.5−20.0−17.5−15.0−12.5−10.0−7.5−5.0−2.5
0.02.55.07.5
10.012.515.0
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10model
NM
BE (%
)NMBE (N=537) 3 month training 90th%ile
75th%ile
Mean
Median
25th%ile
10th%ile
Totalnumberofbuildingsinthetestcase
Key Takeaways • AMIdataandintervaldatamodels/toolsholdgreatpromisetoscalewhole-buildingmeasuredsavingscalcula(ons
• Reducing(meandcosts,improvingormaintainingaccuracy
• Errorsinpredic(ngenergyareontheorderofacoupleofpercentformanybuildingsandmanymodels
• Thisisthefloorofperformancefromthefullyautomatedcase,withno‘non-rou(ne’adjustmentsfromanengineer
• 12monthspre/postdatamaynotalwaysberequiredforaccuratewhole-buildingM&V
• Modelseffec(velymeetASHRAEguidelinesinmostcases
Ongoing Work
n Demonstra(onofautomatedapproacheswithu(li(es/programs,andimplementersoranaly(csvendors¨ Usedatafrombuildingsthathavepar(cipatedinwhole-building(preferably)programsorpilots
¨ ApplyautomatedM&ValongsidewhateverM&Vplanwas/isalreadyinplace
¨ Quan(fysavingswithuncertaintyandconfidence¨ Publishandcasestudiesoneffec(veness
LBNL is currently seeking utility/program and implementer or vendor partners who are interested in collaborating in this
work. Please contact [email protected] if you are interested in exploring this opportunity.
Predict/Forecast
TheGood
TheBad
TheUgly
Ø Good buildings: � Predictable operation
Ø Bad buildings � Requires intervention
Ø Ugly buildings � Cannot predict future use
Best Applications – Meter-Based M&V • ‘Predictable’buildings,systems:
Weathersensi(ve,regularlyscheduled• Mul(pleandinterac(veECMs:
Affec(ngmanysystems(HVAC,ligh(ng,etc.)• Deepsavingsprojects:
Savingsare“abovethenoise”• Difficulttoquan(fyECMs:
Ductsealing,envelopeupgrades,etc.• ECMsusingexis(ngcondi(onasbaseline:
RCx,behavioral• SMBsector:otherapproachesnotcost-effec(ve
AB 802 & Meter-Based P4P
PG&E• CommercialWholeBuildingDemonstra(on
Statewide• UC/CSU/IOUPartnershipMBCxProgram
HOPPs• SCE–PublicSector• SoCalGas
• PublicSector• CommercialRestaurant• Mul(-family
• PG&E• On-billfinancing• Residen(alP4P
Meter-Based M&V Approach • TotalSavings:AllECMsavingsbehindmeter,includinginterac(veeffectsandstrandedpoten(al
• LessComplex:fewdatastreamsrequired(energy,weather)
• Tools:publicdomainandvendorsoiware• Accurate:Enableses(mateofaccuracy• PersistenceMonitoring:Ongoingfeedbackonbuildingperformance
• Poten(allyLowerAdminCosts:standardiza(on&automa(onreducesrequired(meforsavingsanalysis&technicalreview
Pay For Performance 1 2 3
EM&V Process Overlap
Step 1: Project Level Savings (M&V) • Gross savings (Customer) • To & Above Code Savings (Regulatory)
Step 2: Attribution • Account for free-ridership
Step 3: Program Level Savings • Determines ‘additionality’ of savings • Program cost effectiveness
• Cumula(vesavings-con(nuoustracking&feedback