renewable energy optimazation

7/27/2019 renewable energy optimazation

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Andy Walker PhD PE

Principal Engineer

National Renewable Energy Laboratory

[email protected]

Acknowledgement: US DOE Federal Energy ManagementProgram

Renewable Energy Optimization
mailto:[email protected]:[email protected]


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2

Combining Renewable Energy MeasuresPhotovoltaics

Daylighting

Biomass Heat/PowerConcentrating Solar

Heat/PowerSolar Vent Air Preheat

Solar Water HeatingWind Power

Ground Source HeatPump

Landfill Gas


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"Everything should be made assimple as possible,

but not simpler."

Albert Einstein


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Consider Even One RE Generator

E from utility (kWh) = E load (kWh) E RE generator(kWh)

kWh load

kWh renewables

kWh from utility


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Two Dimensions, power and time:E

from utility(kWh)=P

load(kW) *{t

load-t

re generator}(hours)

Eto utility(kWh)={Pre generatorPload}(kW)*tregenerator(hours)

t loadt re gen

kW load kW renewables


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Two approaches to integration:

Time Series: Identify state of system at eachtime step, and step through time series (8,760hours/year) to perform integration.

Eg. Hourly Simulations: HOMER, SAM, IMBY,PVWatts, etc.

Polynomial Expansion: Identify states that

system could be in and calculate percentage oftime system is in that state. Eg. REO. Timeperiod is arbitrary (Hourly, Monthly, Annual)


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Your choice: An approximate solution to an exact equation or

An exact solution to an approximate equation.

There is no right answer, only models. Even the performance of the completed

project is only a model of the ideal, not theright answer.

With perfect out of the question, how good isgood enough???


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Whats wrong with hourly simulation?

Lack of hourly resource data Often modeled, not measured (183/239 sites in NSRDB) Often not for the specific site High uncertainty in data

Lack of hourly load data

Not often recorded Often modeled or stipulated

Ever-increasing detail of simulation programs Requires detailed design early in evaluation of a project Often run with default values Expensive

Inaccessible in early planning


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Wind Energy in vicinity of Fairf ield CA

Project Site

Hourly Data Site


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Hourly vs Monthly Solar Radiation

Global Solar Radiation for Albuquerque NM, User's Manualfor TMY2s Typical Meteorological Years Derived from the1961-1990 National Solar Radiation Data Base, WilliamMarion and Ken Urban, June 1995

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 6 12 18 24

H o u r o f D ay

Standard

D

eviation(

%

)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 6 12

M o n t h o f Y e a r

Standard

D

eviation

(%

)


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HOMERHourlyLoad

Inputs


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Integration of Renewables by methodof Polynomial Expansion or Mind

your Ps and Qs

P=the fraction of time the system is in a certainstate

Any number of technologiesAny number of states per technology

States that RE generator may be in:(p+q+r+s+.)=1

States that Load may be in:

(l+m+n+.)=1


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020000

40000

60000

80000

100000

120000

0 2000 4000 6000 8000

Hours

ACPower(W)

ON 66 kW for 3063 hours

OFF 0 kW for 5696 hou

Hourly from PVWatts for 100 kW, 1-axis tracking, Boulder CO

Consider two states (p=fraction time on, q=fraction time off) over time period T(p + q) = 1For seven RE generators:1*1*1*1*1*1*1=1(p1+q1)*(p2+q2)*(p3+q3)*(p4+q4)*(p5+q5)*(p6+q6)*(p7+q7)=1


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(p7*p6*p5*p4*p3*p2*p1)+(p7*p6*p5*p4*p3*p2*q1)+(p7*p6*p5*p4*p3*q2*p1)+(p7*p6*p5*p4*p3*q2*q1)+(p7*p6*p5*p4*q3*p2*p1)+(p7*p6*p5*p4*q3*p2*q1)+(p7*p6*p5*p4*q3*q2*p1)+(p7*p6*p5*p4*q3*q2*q1)+(p7*p6*p5*q4*p3*p2*p1)+(p7*p6*p5*q4*p3*p2*q1)+(p7*p6*p5*q4*p3*q2*p1)+(p7*p6*p5*q4*p3*q2*q1)+(p7*p6*p5*q4*q3*p2*p1)+(p7*p6*p5*q4*q3*p2*q1)+(p7*p6*p5*q4*q3*q2*p1)+(p7*p6*p5*q4*q3*q2*q1)+(p7*p6*q5*p4*p3*p2*p1)+(p7*p6*q5*p4*p3*p2*q1)+(p7*p6*q5*p4*p3*

q2*p1)+(p7*p6*q5*p4*p3*q2*q1)+(p7*p6*q5*p4*q3*p2*p1)+(p7*p6*q5*p4*q3*p2*q1)+(p7*p6*q5*p4*q3*q2*p1)+(p7*p6*q5*p4*q3*q2*q1)+(p7*p6*q5*q4*p3*p2*p1)+(p7*p6*q5*q4*p3*p2*q1)+(p7*p6*q5*q4*p3*q2*p1)+(p7*p6*q5*q4*p3*q2*q1)+(p7*p6*q5*q4*q3*p2*p1)+(p7*p6*q5*q4*q3*p2*q1)+(p7*p6*q5*q4*q3*q2*p1)+(p7*p6*q5*q4*q3*q2*q1)+(p7*q6*p5*p4*p3*p2*p1)+(p7*q6*p5*p4*p3*p2*q1)+(p7*q6*p5*p4*p3*q2*p1)+(p7*q6*p5*p4*p3*q2*q1)+(p7*q6*p5*p4*q3*p2*p1)+(p7*q6*p5*p4*q3*p2*q1)+(p7*q6*p5*p4*q3*q2*p1)+(p7*q6*p5*p4*q3*q2*q1)+(p7*q6*p5*q4*p3*p2*p1)+(p7*q6*p5*q4*p3*p2*q1)+(p7*q6*p5*q4*p3*q2*p1)+(p7*q6*p5*q4*p3*q2*q1)+(p7*q6*p5*q4*q3*p2*p1)+(p7*q6*p5*q4*q3*p2*q1)+(p7*q6*p5*q4*q3*q2*p1)+(p7*q6*p5*q4*q3*q2*q1)+(p7*q6*q5*p4*p3*p2*p1)+(p7*q6*q5*p4*p3*p2*q1)+(p7*q6*q5*p4*p3*q2*p1)+(p7*q6*q5*p4*p3*q2*q1)+(p7*q6*q5*p4*q3*p2*p1)+(p7*q6*q5*p4*q3*p2*q1)+(p7*q6*q5*p4*q3*q2*p1)+(p7*q6*q5*p4*q3*q2*q1)+(p7*q6*q5*q4*p3*p2*p1)+(p7*q6*q5*q4*p3*p2*q1)+(p7*q6*q5*q4*p3*q2*p1)+(p7*q6*q5*q4*p3*q2*q1)+(p7*q6*q5*q4*q3*p2*p1)+(p7*q6*q5*q4*q3*p2*q1)+(p7*q6*q5*q4*q3*q2*p1)+(p7*q6*q5*q4*q3*q2*q1)+(q7*p6*p5*p4*p3*p2*p1)+(q7*p6*p5*p4*p3*p2*q1)+(q7*p6*p5*p4*p3*q2*p1)+(q7*p6*p5*p4*p3*q2*q1)+(q7*p6*p5*p4*q3*p2*p1)+(q7*p6*p5*p4*q3*p2*q1)+(q7*p6*p5*p4*q3*q2*p1)+(q7*p6*p5*p4*q3*q2*q1)+(q7*p6*p5*q4*p3*p2*p1)+(q7*p6*p5*q4*p3*p2*q1)+(q7*p6*p5*q4*p3*q2*p1)+(q7*p6*p5*q4*p3*q2*q1)+(q7*p6*p5*q4*q3*p2*p1)+(q7*p6*p5*q4*q3*p2*q1)+(q7*p6*p5*q4*q3*q2*p1)+(q7*p6*p5*q4*q3*q2*q1)+(q7*p6*q5*p4*p3*p2*p1)+(q7*p6*q5*p4*p3*p2*q1)+(q7*p6*q5*p4*p3*q2*p1)+(q7*p6*q5*p4*p3*q2*q1)+(q7*p6*q5*p4*q3*p2*p1)+(q7*p6*q5*p4*q3*p2*q1)+(q7*p6*q5*p4*q3*q2*p1)+(q7*p6*q5*p4*q3*q2*q1)+(q7*p6*q5*q4*p3*p2*p1)+(q7*p6*q5*q4*p3*p2*q1)+(q7*p6*q5*q4*p3*q2*p1)+(q7*p6*q5*q4*p3*q2*q1)+(q7*p6*q5*q4*q3*p2*p1)+(q7*p6*q5*q4*q3*p2*q1)+(q7*p6*q5*q4*q3*q2*p1)+(q7*p6*q5*q4*q3*q2*q1)+(q7*q6*p5*p4*p3*p2*p1)+(q

7*q6*p5*p4*p3*p2*q1)+(q7*q6*p5*p4*p3*q2*p1)+(q7*q6*p5*p4*p3*q2*q1)+(q7*q6*p5*p4*q3*p2*p1)+(q7*q6*p5*p4*q3*p2*q1)+(q7*q6*p5*p4*q3*q2*p1)+(q7*q6*p5*p4*q3*q2*q1)+(q7*q6*p5*q4*p3*p2*p1)+(q7*q6*p5*q4*p3*p2*q1)+(q7*q6*p5*q4*p3*q2*p1)+(q7*q6*p5*q4*p3*q2*q1)+(q7*q6*p5*q4*q3*p2*p1)+(q7*q6*p5*q4*q3*p2*q1)+(q7*q6*p5*q4*q3*q2*p1)+(q7*q6*p5*q4*q3*q2*q1)+(q7*q6*q5*p4*p3*p2*p1)+(q7*q6*q5*p4*p3*p2*q1)+(q7*q6*q5*p4*p3*q2*p1)+(q7*q6*q5*p4*p3*q2*q1)+(q7*q6*q5*p4*q3*p2*p1)+(q7*q6*q5*p4*q3*p2*q1)+(q7*q6*q5*p4*q3*q2*p1)+(q7*q6*q5*p4*q3*q2*q1)+(q7*q6*q5*q4*p3*p2*p1)+(q7*q6*q5*q4*p3*p2*q1)+(q7*q6*q5*q4*p3*q2*p1)+(q7*q6*q5*q4*p3*q2*q1)+(q7*q6*q5*q4*q3*p2*p1)+(q7*q6*q5*q4*q3*p2*q1)+(q7*q6*q5*q4*q3*q2*p1)+(q7*q6*q5*q4*q3*q2*q1) =1


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Stochastic Integration of Renewable Energy Technologies by themethod of Polynomial Expansion (SIRET)

This simplified figure shows seven possible states the system could be in,but the model actually has 128 states for seven technologies.

100%


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What is the best integration period?

Pros Cons

Annual

(year/1)

Resource and Load dataavailable.

No seasonal or diurnalvariation

Hourly

(Year/8760)

Seasonal and DiurnalVariation

Resource and Load variableand/or unavailable

Monthly

(Year/12)

Seasonal variation

Load from Utility Bills

Resource more certain

No diurnal variation

Monthly Day/Night

(Year/24)

Load information available

Resource more certain

Seasonal Variation

Diurnal Variation

Utility bill may not tabulateby time-of-use.


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Best Mix of Renewable Energy

Technologies Depends on: Renewable Energy Resources

Technology Characterization Cost ($/kW installed, O&M Cost)

Performance (efficiency)

Economic Parameters Discount rates

Fuel Escalation Rates

State, Utility and Federal Incentives

Mandates (Executive Order, Legislation)


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Technology Characteristics

Heuristic ModelsCost

(Size, m2)*(Unit Cost, $/m2)

Performance

(Size, m2)*(Resource, kWh/m2)*(efficiency)


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Comparison of TEAM REO and Site Visi t Reports for DOE Sites


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Optimization Procedure

Site Data

Geographical Information System (GIS) Data

Incentive Data from DSIREUSA.ORG

PV SVPWind DaylightingSWH CSP Biomass

Dispatch AlgorithmDispatch Algorithm

Life Cycle CostLife Cycle Cost

Technology Characteristics.

OptimizationOptimization


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Optimization Problem

Determine the least cost

combination of renewable

energy technologies for a

facility

Objective: Minimize Life CycleCost ($)

Variables: Size of EachTechnology (kW of PV, kW ofwind, etc)

Constraints: such as 15% ofenergy from renewables


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$10,000

$100,000

$1,000,000

$10,000,000

$10,000 $100,000 $1,000,000 $10,000,000

Hourly Simulation REOCO Commercial $655,076 $614,862CO Industrial $5,158,624 $5,870,856CO Residential $62,117 $49,463WA Commercial $460,039 $568,455

WA Industrial $3,149,595 $4,533,277WA Residential $53,587 $62,304

AZ Commercial $555,003 $522,448AZ Industrial $4,469,720 $4,891,129AZ Residential $53,567 $41,256

Compare/Contrast with Hourly

Simulation

Comparison of Annual Average and Hourly Simu lation in Renewable Energy Technology System SizingChristine L. Lee, University of Colorado at Boulder, 2009

REO

Simulation

Life Cycle Cost ($)


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Compare/Contrast with Hourly

Simulation

Hourly Simulation REO % Difference

PV Energy, kWh 705,725 740,583 4.9%

Wind Energy, kWh 324,069 291,283 -10.1%Energy from Utility, kWh 1,169,264 1,407,744 20.4%Energy to Utility, kWh 139,478 380,030 172.5%Life Cycle Cost, $ $5,158,624 $5,870,856 13.8%

Percent Renewables 50% 41.80% -16.4%

Comparison of Annual Average and Hourly Simu lation in Renewable Energy Technology System SizingChristine L. Lee, University of Colorado at Boulder, 2009

Industrial Load Profile; Colorado Climate; 2,059,580 kWh/year load;464 kW PV; 493 kW Wind


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Examples of Renewable Energy

Optimization (REO) Analysis Town of Greensburg, KS Frito Lay North America plants (7) National Zoo, DC Anheuser Busch facilities (62) High School in Sun Valley, ID

San Nicolas Island, CA DOE Waste Isolation Pilot Plant, NM DOE Savannah River Plant SC Agricultural Research Stations in TX (8) DOE Laboratories (31) Air Force Bases (85) GSA Land Ports of Entry (121) DHS Land Ports of Entry (32) USCG Bases (3) Presidio of San Francisco Others.


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Results of Renewable Energy Optimization:Technology Sizes

PhotovoltaicsSize (kW)

WindCapacity(kW)

Solar VentPreheatArea (ft2)

SolarWaterHeatingArea (ft2)

SolarThermalArea (ft2)

SolarThermalElectric(kW)

Biomass BoilerSize (M Btu/h)

BiomassCogenerationSize (kW)

DaylightingOffice UtilitySkylight/FloorArea Ratio

DaylightingWarehouseSkylight/FloorArea Ratio

10 0 15258 29,179 0 0 0 0 3.968% 3.036%0 957 0 0 0.00 0.00 1.34 101 0.000% 0.000%

Example: Town of Greensburg KS

BuildingsCentral Plant


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Annual Energy from Each Technology

(with Basecase)

0

20000

40000

60000

80000

100000

120000

Base

case

RE

Case

An

nualEnergy(Mbtu)

Electric (Mbtu) Natural Gas (Mbtu) Other Fuel (Mbtu)

Photovoltaics (Mbtu) Wind (Mbtu) Solar Vent Preheat (Mbtu)

Solar Water Heating Solar Themal (Mbtu) Biomass (Mbtu)

Daylighting (Mbtu)

Example: Town of Greensburg KS


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Wind Energy

Build ing Name Wind Capaci ty (kW)

Wind Annual

EnergyDelivery

(kWh/year)

Capacity

Factor (%)

WindAnnual Cost

Savings ($)

Wind

AnnualO&M Cost

($/year)

Wind

PaybackPeriod

(years)Central Plant 957 2,533,894 30.23% $276,194 $7,560 7.0


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Example: Frito Lay North AmericaMinimum Life Cycle Cost (no constraints)

0

200000

400000

600000

800000

1000000

1200000

Plant#

1Basecase

Plant#

1RE

Case

Plant#

2Basecase

Plant

#2RE

Case

Plant#

3Ba

secase

Plant

#3RE

Case

Plant#

4BaseC

ase

Plant#

4RE

Case

Plant$

5Ba

secase

Plant

#5RE

Case

Plant

#6Basecas

e

Plant

#6RE

Case

Plant#

7Ba

secase

Plant

#7RE

Case

AnnualEnergy(Mbtu)



Solar Themal (Mbtu) Biomass (Mbtu) Daylighting (Mbtu)


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Frito Lay North AmericaSolar Thermal at Sunchips Plant

Modesto CA


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Example: Frito Lay North AmericaMinimum Life Cycle Cost (Net Zero constraint)

0

200000

400000

600000

800000

1000000

1200000

Plant

#1Ba

secase

Plant

#1Net

Zero

Plant

#2Ba

secase

Plant

#2Net

Zero

Plant

#3Basecase

Plant

#3Net

Zero

Plant

#4BaseC

ase

Plant

#4Net

Zero

Plant

#5Basecase

Plant#

5Net

Zero

Plant

#6Ba

secase

Plant#

6Net

Zero

Plant#

7

Plant

#7Net

Zero

AnnualEnergy(Mbtu)



Solar Themal (Mbtu) Biomass (Mbtu) Daylighting (Mbtu)


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Example: Frito Lay Optimization

for Seven Manufacturing PlantsConstraint: Net Zero

Photovoltaics

Size (kW)

Wind

Capacity

(kW)

Solar Vent

Preheat

Area (ft 2)

Solar

Thermal

Area (f t2)

Biomass

Boiler Size

(M Btu/h)

Biomass

Cogeneration

Size (kW)

Daylighting

Office Utility

Skylight/Floor

Area Ratio

Daylighting

Warehouse

Skylight/Floor

Area RatioPlant #1 200 491 5456 509196 19 1669 2.2% 2.1%

Plant #2 0 6187 8953 391987 87 3097 3.8% 2.0%

Plant #3 0 3107 13098 469621 44 3180 4.9% 3.6%

Plant #4 1011 1000 10213 1360535 78 4108 3.4% 1.8%

Plant #5 1003 998 10327 704140 44 3327 6.1% 3.4%

Plant #6 0 0 10322 1529609 74 6020 err err

Plant #7 0 3699 10802 673761 43 2193 3.3% 3.7%


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REO Example: Net Zero ZooNational Zoological Park (NZP) and Conservation Research Center

(CRC), Washington DC

-40000

-20000

0

20000

40000

60000

80000

100000

120000

140000

160000

Base

case

RECase

AnnualEnergy(M

btu)

Daylighting (Mbtu)

Biomass (Mbtu)

Solar Themal (Mbtu)

Solar Water Heating

Solar Vent Preheat (Mbtu)

Wind (Mbtu)

Photovoltaics (Mbtu)

Other Fuel (Mbtu)

Natural Gas (Mbtu)

Electric (Mbtu)

Electric

generation

at CRC

Cancels

remaining

gas use at

NZP

Zoo EntranceTai Shan the Panda


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Initial Cost for Renewable Energy Projects ($) $269,634,839Annual Electric Savings (kWh/year) 217,734,776

Annual Gas/Fuel Savings (therms/year) 8,693,565

Annual Cost Savings ($/year) $26,241,533

Simple Payback Period (years) 10.3Rate of Return 11.3%

Sizes of Each Technology:Photovoltaics (kW) 0Wind Energy (kW) 41714Solar Ventilation Air Preheat (sf) 460988

Solar Water Heating (sf) 1274264Solar Thermal Parabolic Trough (sf) 452991

Solar Thermal Electric (kW) 0Biomass Gasification Boiler (MBH) 51Biomass Gasification Cogen (kW) 139

Biomass Anaerobic Digester r (Ft3)) 0Biomass Anaerobic Digester Cogen (kW) 0

Skylight Area (sf) 2602033

31 DOESites

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ergy(Mbtu)

Wind (Mbtu) Solar Vent Preheat (Mbtu) Solar Water Heating

Solar Thermal (Mbtu) Biomass Gasifier (Mbtu) Anaerobic Digester (Mbtu)

Daylighting (Mbtu) Photovoltaics (Mbtu)


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It's so much easier tosuggest solutions

when you don't knowtoo much about the

problem.

- Malcolm Forbes

Thank You!

Andy WalkerSenior Engineer

National Renewable Energy [email protected]

Simplicity is quiteoften the mark ofexcellence-Allen Bennett, SMDC
mailto:[email protected]:[email protected]

renewable energy optimazation

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