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Shaving the peaks through statistical learning, smart use of solar energy and storage solutions A. Lind, J. Selj, J. A. Tsanakas, P. Arnestad and A. Severinsen
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Motivation
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• Theavailabilityofhourlyenergymetering,inpar6cularforelectricityconsump6on,havegreatpoten6altocontributetomoreefficientenergyuseinbuildings
• Toreachfullpoten6alweneedØ ImproveddatamanagementØ Improvedanalysistovisualizeeconomicpossibili6es
• Wehaveperformedacasestudytodemonstratethepoten6alelectricalenergycostsreduc6onsincommercialbuildings.
• Suchcostsavingsareachievablethrough:Ø peakdemandshavingØ u6lizinglocalPVenergyproduc6onØ storage
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Method
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• Weanalyzedtheloadprofileof600differentstorestoiden6fythosewiththegreatestcostreduc6onpoten6al.
• Theenergyconsump6onisanalysedbyusingthecoefficientofvaria6on(CV)Ø TheCVvaluewasusedtoevaluate600differentstores
• PVsystwasusedtodeterminePVproduc6ononanhourlybasis.Ø Basedonavailableroofareaandlocalirradia6onandclima6ccondi6ons
• TRNSYShasbeenusedtolookatdetailedpowerflowandsimula6onofstorage Ø Basedonthefollowinginforma6on:
Ø ResultsfromPVsystØ HourlyloadprofilesØ Effect-tariffsØ Spotprices
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Grid tariffs
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Commercialcustomer:peak-loadtariffs Price Unit
Fixedchargeperinstalla;on 340 NOK/month
Peakloadcharge(Jan-Feb,Dec) 150 NOK/kW/month
Peakloadcharge(MarandNov) 76 NOK/kW/month
Peakloadcharge(Apr-Oct) 11 NOK/kW/month
Energycharge(Jan-Mar,Nov-Dec) 0.052 NOK/kWh
Energycharge(Apr-Oct) 0.03 NOK/kWh
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Spotpriser 2016 (NO1)
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[NOK/MWh]
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• Energy consumption data from 600 Norwegian retail stores, with a total of 5.3 million hourly observations for 2016. • In order to identify stores with high potential of shaving peaks in their
energy consumption profile, we propose using the coefficient of variation (CV). • The CV is a standardized measure of dispersion, defined as the ratio
of the standard deviation σ to the mean µ (Everitt, 1998):
𝐶𝑉= 𝜎/𝜇
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Selecting the stores with the highest cost saving potential
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Statistics for three selected stores with high and intermediate CV for the winter months
Store ID Average KWh Min KWh Max KWh CV Sum KWh Area (m2)
2448 103 60 160 18,7 227 208 1780 2487 76 26 180 44,7 154 870 2200 2703 151 78 212 17,8 307 298 2237
2448 2487 2703
kWh
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Analysis • For each individual store, three different cases were analysed: • Existing system (i.e. only power from grid) • Battery storage system combined with power from the grid • Battery storage system with power production from PV and electricity
purchased from the grid
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260E O
ut.Inv.[GWh]
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Cumulative PV-production
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2487
2703
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• Hourly production values • Based on local irradiation and
climatic conditions • Modelled on the actual roof
orientation and inclinations
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Battery specifications
CurrentbaNerycost
[US$/kWh]
Op;mis;cbaNerycost
[US$//kWh]
BaNeryefficiency
[-]
Stateofcharge(low)
[-]
776 388 0.9 0.2
From: Irena 2015
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Oseana
PV price assumptions
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Yearly grid supply and production
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Ba_ery Ba_ery+PV Ba_ery Ba_ery+PV Ba_ery Ba_ery+PV
2448 2487 2703
[MWh/year]
PV
Grid
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Store 2487: Reductions in grid effect
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[%]
[kW] Ba_+PV
Ba_
Ba_+PV
Ba_
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020406080100120140160180
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
[kW]
PV Grid Charge Discharge Load
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Store 2487: Powerflow
March 3rd
Battery-strategy Use: (Load – PV) >= 130 kW Charge: Load <= 110 kW (20 kW or PV_surpluss)
020406080100120140160180200
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
[kW]
May 19th
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Store 2487: Powerflow
0102030405060708090100
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
[kW]
PV Grid Charge Discharge Load
May 10th
Battery-strategy Jan, Feb, Dec: Use: (Load – PV) >= 130 kW
Charge: Load <= 110 kW (20 kW eller PV_surpluss)
March: Use: (Load – PV) >= 100 kW Charge: Load <= 80 kW (20 kW eller PV_surpluss)
April - Sep: Use: (Load – PV) >= 70 kW Charge: Load <= 50 kW (20 kW eller PV_surpluss)
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Store 2487: Powerflow
Battery-strategy Jan, Feb, Dec: Use: (Load – PV) >= 130 kW
Charge: Load <= 110 kW (20 kW eller PV_surpluss)
March: Use: (Load – PV) >= 100 kW Charge: Load <= 80 kW (20 kW eller PV_surpluss)
April - Sep: Use: (Load – PV) >= 50 kW Charge: If surpluss from PV (PV_surpluss)
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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
[kW]
PV Grid Charge Discharge Load
May 16th
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Payback time
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776US$/kWh 388US$/kWh776US$/kWh+Øktne_leie
388US$/kWh+Øktne_leie
Payba
ck;me[years]
2448
66kWh
33kWh
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776US$/kWh 388US$/kWh776US$/kWh+Øktne_leie
388US$/kWh+Øktne_leie
Payba
ck;me[years]
2487
66kWh
33kWh
≈ 66 kWh battery 0
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776US$/kWh 388US$/kWh776US$/kWh+Øktne_leie
388US$/kWh+Øktne_leie
Payba
ck;me[years]
66kWh
33kWh
2703
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Summary
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• Thecoefficientofvaria6on(CV)isausefulsta6s6calmethodtoanalysethedispersionofenergyconsump6onincommercialbuildingsØ (thereisaneedtosortoutbuildingsthathasrecentlycarriedthroughenergy
efficiencymeasures!)• Highpeakloadtariffswillgivehighsavingpoten6alforbuildingswithhighCV
values,thoughpeakloadshaving• ThreebuildingsofintermediatetohighCVvalueswereanalysedindetail:
Ø Threescenarios:a)grid,b)gridandba_ery,ogc)grid,ba_eryandPVØ CommercialbuildingswithhighCVvaluesmayprofitfromop6mizedba_ery
systemsalreadytoday.Ø Expectedincreasesinpeakloadtariffsandreduc6onsinba_eryandPVpriceswill
increasetherelevanceofsuchsystems.Ø Moreadvancedba_erystrategyisiden6fiedaskeytoachievethemost
costop6malsolu6onsØ Furtherworkincludesimplemen6ngmachinelearningstrategiesforload
predic6onsandba_erystrategies,inaddi6ontoPVforecas6ng.
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Thank you for the attention!
Josefine Selj, Phd Research scientist, IFE E-mail: [email protected] Arne Lind, PhD Senior research scientist Institute for Energy Technology e-mail: [email protected]
