A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Renewable Energy Planning: 

Multiparametric Cost Optimization 

Preprint 

Andy Walker 

National Renewable Energy Laboratory 

Presented at SOLAR 2008 - American Solar Energy Society (ASES) 

San Diego, California 

May 3–8, 2008 

Conference Paper 

NREL/CP-670-42921 

May 2008 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
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RENEWABLE ENERGY PLANNING: MULTIPARAMETRIC COST OPTIMIZATION 

Andy Walker PhD PE 

National Renewable Energy Laboratory 

1617 Cole Boulevard 

Golden CO 80403 

Andy_Walker@nrel.gov 

ABSTRACT

This paper describes a method for determining the 

combination of renewable energy technologies that 

minimize life-cycle cost at a facility, often with a specified 

goal regarding percent of energy use from renewable 

sources. Technologies include: photovoltaics (PV); wind; 

solar thermal heat and electric; solar ventilation air 

preheating; solar water heating; biomass heat and electric 

(combustion, gasification, pyrolysis, anaerobic digestion); 

and daylighting. The method rests upon the National 

Renewable Energy Laboratory’s (NREL’s) capabilities in 

characterization of technology cost and performance, 

geographic information systems (GIS) resource assessment, 

and life-cycle cost analysis. The paper discusses how to 

account for the way candidate technologies interact with 

each other, and the solver routine used to determine the 

combination that minimizes life-cycle cost. Results include 

optimal sizes of each technology, initial cost, operating cost, 

and life-cycle cost, including incentives from utilities or 

governments. Results inform early planning to identify and 

prioritize projects at a site for subsequent engineering and 

economic feasibility study. 

 

1. INTRODUCTION

Several organizations have goals regarding renewable 

energy use. For example, the federal government has a goal 

of 7.5% renewable energy for its facilities. Green-building 

rating systems set percentage goals such as 2.5%, 7.5% and 

12.5%. Further, some organizations set the goal of “net 

zero” utility energy use for a facility (100% renewable). 

This analysis examines how to meet the goal, whatever it is, 

while minimizing life-cycle cost. Organizations that operate 

a lot of real property need a structured, credible, but 

affordable method of identifying and prioritizing renewable 

energy projects prior to detailed evaluation. A convenience 

food manufacturer, a major brewer, a small town in Kansas, 

a Navy base on an island, and the National Zoo have all 

asked NREL to help them determine how to meet their 

renewable energy goals at minimum life-cycle cost. 

It is important to acknowledge that energy efficiency 

measures are prerequisite to renewable energy measures. In 

this analysis we size renewable energy systems to meet the 

specified load, assuming that cost effective efficiency 

measures have already been taken. 

The best mix of renewable energy technologies at a site 

depends on: renewable energy resources; technology 

characterization (such as installed cost, maintenance costs, 

efficiency); state, utility and federal incentives; and 

economic parameters (discount rate, inflation rates). Early 

in a planning process it is necessary to keep the analysis 

simple and inexpensive, but each of these effects needs to be 

represented for the results to be useful. Previous screening 

efforts have evaluated each renewable energy technology 

independently, but here we account for the interactions 

among multiple technologies at a site. 

Analysis can be conducted on a facility as a whole, or on 

each individual building at a facility. Solar water heating, 

solar ventilation preheating and daylighting are considered 

only to meet their associated end-use loads on individual 

buildings. Photovoltaics could be on a building or a central 

plant. Wind power, solar thermal electric, and the biomass 

energy alternatives are considered in a central plant 

arrangement if a facility has multiple buildings. 

Analysis is performed by MS Excel spreadsheets (with an 

add-in called “Premium Solver”) using the following data 

sources: customer-provided energy use and cost and 

building floor area at each site; GIS databases maintained by 

NREL; other databases such as utility rates from Platts Inc.; 

City Cost Adjustment Factors from RS Means and Co.; and 

incentives from the Database of State Incentives for 

Renewable Energy maintained by University of North 

1
Carolina. Calculations estimate the installed cost, 

incentives, energy performance, cost savings, and life-cycle 

cost. An innovative algorithm based on capacity and 

capacity factor is used to estimate the effect of simultaneous 

generation of multiple renewable energy technologies. The 

solver is used to identify the size of each component (kW of 

PV, kW of wind, square feet of solar thermal, etc.) that 

minimizes life-cycle cost. A constraint, such as percentage 

of energy use supplied by renewable energy, may be 

specified in the optimization. 

The method is intended to use information that is readily 

available from an organization’s real-property management 

database and utility procurement database, thus minimizing 

original data collection. At a minimum, the customer would 

provide: the locations (names of facilities, street addresses) 

to be considered in the analysis; the square footage of 

building space; and annual utility use and cost (gas, electric, 

oil, propane, steam) for the previous year at each facility. A 

GIS utility is used to convert the street addresses into GIS 

coordinates for use in the analysis. If the site provides an 

inventory of waste streams from the facility itself, this is 

considered in the biomass fuel assessment along with 

feedstocks from the surrounding area from the GIS data. If 

more detailed information is available, then any of the many 

default values in the analysis may be replaced with other 

information. Examples include a breakdown of types of 

floor space (office, warehouse, etc.), with different rates of 

ventilation air and lighting levels for each space, gallons per 

day of hot water, or other information regarding site energy 

use that may be available. 

2. GIS DATASETS

NREL’s GIS is a computer-based system used to 

manipulate, manage, analyze, and display renewable energy 

resource data linked to a spatial reference. NREL datasets 

used in this analysis include solar radiation (W/m2) on a 

40x40 km grid including global on the horizontal and on a 

tilt equal to local latitude, and beam radiation on tracking 

east/west axis. Wind power density (W/m2) is on a 200m 

x1000m grid for most locations, and a 25 km grid in 

locations where the high-resolution data are not available. 

Heating degree days and cooling degree days are also 

included. 

GIS data layers can be recombined or manipulated and 

analyzed with other layers of information. For example, the 

energy delivery of solar ventilation air preheating is 

estimated by the integral of solar resource on a vertical 

south wall and heating degree days at a location. 

Illuminance data for daylighting calculations are selected 

from the closest city for which Typical Meteorological Year 

weather data is available. 

Utility cost data are usually provided by the site under 

evaluation, but NREL also purchases GIS data sets of 

residential, commercial, and industrial utility rates from 

Platts which could be used if data are not available from the 

site. 

The GIS database manages biomass resource information 

from surrounding areas, reported as tons available within a 

50 mile radius. The breakdown of available biomass 

feedstocks is as follows [all from reference 2]: 

• Crop residues (dry tonnes/year) include corn, wheat, 

soybeans, cotton, sorghum, barley, oats, rice, rye, 

canola, dry edible beans, dry edible peas, peanuts, 

potatoes, safflower, sunflower, sugarcane, and flaxseed 

from Unites States Department of Agriculture (USDA), 

National Agricultural Statistics Service, 2002 data. 

• Orchard and Grape prunings (dry tonnes/year) from 

USDA, 2002 data. 

• Forest residues (dry tonnes/year) from USDA, Forest 

Service's Timber Product Output database, 2002 

• Primary wood mill residues (dry tonnes/year) from 

USDA, Forest Service's Timber Product Output 

database, 2002. 

• Secondary wood mill residues (dry tonnes/year) from 

the U.S. Census Bureau, County Business Patterns, 

2002 data. 

• Urban wood waste (dry tonnes/year) from U.S. Census 

Bureau, 2000 Population data, BioCycle Journal, State 

of Garbage in America, January 2004; and County 

Business Patterns 2002 data. 

• Methane emissions from landfills (tonnes/year) from 

EPA, Landfill Methane Outreach Program, 2003 data. 

• Methane emissions from manure management 

(tonnes/year) including: dairy cows, beef cows, hogs 

and pigs, sheep, chickens and layers, broilers, and 

turkey, from USDA, National Agricultural Statistics 

Service, 2002 data. 

• Methane emissions from domestic wastewater 

treatment (tonnes/year) from the EPA Inventory of U.S. 

Greenhouse Gas Emissions and Sinks: 1990-2003 and 

U.S. Census Bureau, 2000 Population data. 

3. TECHNOLOGY CHARACTERIZATIONS

Initial cost, efficiency, and operation and maintenance cost 

for each of the renewable energy technologies is 

characterized according to the cost and performance data 

reported in edition four of the “Power Technologies Energy 

Data Book” from NREL [2] and also from “Renewable 

2
Energy Technology Characterizations” from Electric Power 

Research Institute [3]. Other sources of information are also 

employed such as the author’s project experience. 

3.1 Initial Cost

The initial cost of each technology is estimated from the 

technology characterizations [1,2], staff project experience, 

and cost estimating manuals [5]. Costs are adjusted for the 

city cost adjustment factors for each location from RS 

Means and Co. [5]. The city cost adjustments are for 

composite project cost including both materials and labor. 

The initial cost is reduced by any available incentives 

including the Federal Business Investment Tax Credit (% of 

cost); state tax credit (% of cost); and/or rebate ($/Watt or % 

of cost. Even though the Modified Accelerated Cost 

Recovery Schedule (accelerated depreciation) occurs over 

five years, we model it here as an equivalent reduction in 

initial cost (10% as recommended by calculations by Alicen 

Kandt of NREL). Incentives that may be available at each 

location from state governments, utilities, or others are as 

listed in the Database of State Incentives for Renewable 

Energy maintained by University of North Carolina [6]. Liz 

Brown of NREL compiled the incentives available for each 

location into a spreadsheet that can be read by look-up 

functions. 

Initial cost is represented by the equation: 

C = (cunit,electric Prated,electric)+( cunit,boiler Prated, boiler)+ (cunit,storage

Srated, storage)+ (cunit,distribution Prated,boiler) 

Where cunit,electric = per-unit cost of installed electrical 

generating system ($/kW) 

Prated,electric = rated power (kW) output of electrical system 

 cunit,boiler = cost per-kW (or MBH) of solar thermal or 

biomass boiler 

Prated, boiler = rated thermal output (kW, or MBH) of solar 

thermal or biomass boiler 

cunit,storage = cost per kWh of thermal storage (e.g. tank) 

Srated, storage= capacity of thermal storage tank (kWh thermal) 

cunit,distribution = cost of system to distribute heat from plant to 

buildings (e.g. $250,000/MBH). 

The initial cost is then adjusted for any incentives that may 

be available as follows: 

Cafter incentives = (C *CCA - rebate)*(1-federal tax credit)*(1-

state tax credit) 

Where, 

C = installed cost of system ($) 

CCA = city cost adjustment factor from RS Means and Co. 

cost estimating manuals 

3.2 Annual Fuel and Electricity Savings

At this early planning stage, analysis is conducted using 

annual average load and resource information. Energy 

delivery is calculated as a function of renewable energy 

resources based on annual-average efficiency models 

(energy delivery=resource*efficiency). For some loads, 

such as the manufacturing plants and breweries, this 

introduces little error since the load is essentially constant, 

but in some cases the error may be substantial. In all cases, 

this tool is intended only to focus investment in site visits 

and detailed evaluation using hourly simulation or more 

sophisticated (and expensive) means of confirming both 

engineering and economic feasibility. 

Energy savings consist of savings in natural gas (or other 

fuel) and electricity. Natural gas savings are limited to the 

minimum of: 1) basecase fuel use; or 2) renewable energy 

heat generating capacity. Electric savings are limited to the 

facility average electric demand. Renewable energy 

generation above average demand is sold back to the utility 

and credited at a lower wholesale rate. Electric savings are 

limited to: 1) the minimum of generator size (kW) or 2) in 

the case of solar thermal and biomass heat, limited to 

generating capacity of the plant multiplied by heat-toelectric cogeneration efficiency. Thermal energy as a byproduct of electric generation is added back into the gas 

savings but multiplied by a heat exchanger effectiveness. 

For wind power the energy savings are expressed as: 

Es, electric, wind = Aswept pwind ηwind 

Where Aswept = swept area of wind turbine (m2) 

pwind = annual average wind power density (W/m2) 

ηwind efficiency of wind turbine system. 

For the solar photovoltaic and solar thermal options the 

equations are: 

Es, gas = Ac Iave ηsolar365 *(1- ηcogeneration)*ehx/ ηboiler 

Es, electric = Ac Iave ηsolar365 *ηcogeneration 

where 

Ac = solar collector area (m2) 

ηsolar = efficiency of solar electric system. For PV this is the 

efficiency of the PV panels times 0.77 to account for 

balance-of-system losses. 

365 is the days/year 

I ave = average solar radiation (kWh/m2/day) 

ηcogeneration is the efficiency of the electric generator (zero for 

photovoltaics) 

ehx is the effectiveness of the heat recovery heat exchanger 

ηboiler = auxiliary heater efficiency. 

3
Fuel savings delivered by the solar ventilation air preheating 

system, Es, gas, SVP, is calculated by the equation 

Es, gas, SVP = A c

 q useful * (#days per week/7)/ η heating

where 

η heating = heating system efficiency. 

Biomass Gas Savings (therms/year) are the minimum of site 

gas use and thermal energy provided by the biomass boiler 

minus that converted to electricity, divided by heat recovery 

steam generator effectiveness. 

Biomass electric delivery (kWh/year) is the minimum of 

electric energy generation as calculated by cogen capacity 

times capacity factor or as limited by boiler capacity, boiler 

efficiency, and boiler capacity factor. For the biomass 

energy alternatives, annual delivery of heat and electricity 

are calculated as follows: 

 

Es, gas, biomass = Pboiler * ηbiomass boiler *8760 * CFboiler (1-

ηcogeneration +(1- ηcogeneration)*ehx/ ηgas boiler

Es, electric,biomass = Pboiler * ηbiomass boiler *8760 * CFboiler 

*ηcogeneration

where 

Pboiler = biomass boiler size (M Btu/h), a variable determined 

by the optimization 

ηbiomass boiler = efficiency of biomass boiler 

CFboiler = capacity factor (% of time operational) 

ηcogeneration is the efficiency of the electric generator 

ehx is the effectiveness of the heat recovery heat exchanger 

ηboiler = auxiliary heater efficiency. 

3.3 Annual Utility Cost Savings

Renewable energy delivered less than the site load is 

credited at full retail value Es Ce . Power delivered in excess 

of the average load (kW) is credited an “avoided cost” 

(wholesale rate) Es Ce, avoided . Where 

Ce = cost of utility energy ($/kWh) 

Ce, avoided = avoided cost paid by utility for excess power 

($/kWh). 

3.4 Annual Biomass Fuel Cost

The tons of biomass fuel used is calculated as the boiler heat 

delivered for both process heat and cogen divided by boiler 

efficiency and divided by heating value of fuel. The radius 

to collect fuel (miles) is calculated from the quotient of fuel 

required (tons) and density (tons/square mile) from the GIS 

database. The per-ton biomass fuel cost ($/ton) is calculated 

as a fixed cost ($/ton) plus trucking cost ($/ton/mile). The 

biomass fuel cost ($/year) is then the fuel used by the 

biomass energy plant minus fuel available onsite times fuel 

cost ($/ton). 

3.5 Operation and Maintenance Costs 

Annual operation and maintenance costs are calculated as an 

fraction of installed cost or as a multiplier on energy 

production as reported in Edition 3 of the Power 

Technologies Energy Data Book [1], or other sources [3] 

including staff project experience. 

3.6 Production Incentives

Production incentives are calculated as the electrical energy 

delivery from each technology times the per-kWh incentive 

available for that particular technology. In most cases the 

production incentives are applied only to power provided to 

the utility. The cash flows associated with production 

incentives are not escalated over time. 

3.7 Life-Cycle Cost 

Life-cycle cost is calculated by adding initial cost to any 

annual costs discounted to their present value. Annual costs 

include maintenance, fuel (as in the case of biomass), 

standby charges from the utility, payments to the utility 

associated with the difference between retail and delivered 

power, and any production incentives or other cash flows. 

The customer would specify the rate at which future costs 

are discounted to their present value. For the federal 

government this rate is specified at 5% in 2007 [4] . A large 

corporation investing in their own facility may have a 7.5% 

time-value of money, and the rate may be much higher for 

third party investors or a small company struggling to make 

payroll. The effect of higher discount rates is to make the 

capital-intensive renewables more expensive in terms of life 

cycle cost. 

Fuel escalation rate (according to census region and fuel 

type), and general inflation rate are also from reference [4] 

for federal projects, but given considerable uncertainty in 

these parameters, most customers ask for results over a 

range of values, from 2% to as high as 15%. It is interesting 

that some corporations view a 15% fuel escalation rate not 

only out of academic interest, but they think rates that high 

are actually possible in coming years. 

LCC= Cinitial + (Senergy – C O&M –C biomass fuel) pwf 25 + (S prod 

incentive) pwf prod incentive 

Where 

LCC= life cycle cost 

 Cinitial = initial cost of renewable energy system 

4
Senergy = annual savings in electricity and natural gas 

purchased from utility 

 C O&M = annual cost of operating and maintaining 

renewable energy systems 

C biomass fuel = annual cost of delivering biomass fuel to the 

site 

 pwf 25 = present worth factor for future savings stream,(e.g. 

17.41 years for 25 year lifetime and discount rate specified 

by NIST for 2005 in reference [3]). 

S prod incentive =annual revenue from production incentive 

 pwf prod incentive = present worth factor associated with the 

term of the production incentive. 

4. DISPATCH ALGORITHM

Special consideration is required to represent the way the 

electrical systems (PV, wind, solar thermal electric, biomass 

electric, daylighting) interact with each other. In particular, 

it is not possible to save the same kWh twice, and it is 

necessary to determine how much renewable energy is 

generated in excess of the load and supplied back to the 

serving utility under a net metering policy or as wholesale 

electric power. Similarly, it is necessary to calculate how 

much of the load is served directly by the renewable energy 

systems and how much is purchased from the utility. These 

effects are often represented by hourly simulation, but in 

this case we need a means to do it with only annual 

averages. Using the term “integrate” as it is used in calculus 

(area under a curve), the method employed here “integrates” 

power delivery from renewable energy installations with 

respect to time. The “integral” is illustrated as the area of 

the colored boxes in the following figure. The grey 

rectangle represents the load being served by a combination 

of renewable energy and conventional utility power. 

Figure 1. Annual energy quantities are represented as 

rectangles with height proportional to rated power and 

width proportional to operating hours in order to 

account for simultaneous actions of multiple energy 

sources. 

Referring to Figure 1, the energy delivery of each 

technology is represented as its capacity times it operating 

hours. The sum of the capacities (kW) of all the renewable 

energy systems operating concurrently is multiplied by the 

hours that those technologies are operating concurrently. 

Operating hours are calculated according to the energy 

delivery of each system and the temporal relationships 

indicated in Figure 1. Regardless of how the energy 

delivery (kWh/year) of each system was estimated, it is now 

represented as rated capacity times hours of operation. 

Hours of operation are calculated according to the capacity 

factor as described in Section 3 of this paper except that in 

the case of daylighting, hours are stipulated at 2500 

hours/year. It is important to note that the relations of 

Figure 1 do not “describe” how these technologies interact 

at a site but rather “prescribes” the assumed interaction that 

will be used in order to calculate the energy quantities. 

For example, the sum of electrical capacities (kW) of wind, 

solar thermal electric, photovoltaics, daylighting and 

biomass are multiplied by the hours that those five devices 

are operating simultaneously. Then the sum of capacities of 

solar thermal electric, photovoltaics, daylighting and 

biomass are multiplied by the hours that those four devices 

are operating simultaneously. In this way it is possible to 

calculate the energy sold back to the utility and the energy 

purchased from the utility to serve a load with a 

combination of different renewable energy technologies. 

5. OPTIMIZATION TECHNIQUE

The objective of the optimization problem is to minimize 

life-cycle cost. An MS Excel spreadsheet was prepared to 

estimate the cost and savings associated with each 

renewable energy measure and the life cycle cost for energy 

use at each facility. A computer program named “Premium 

Solver” from Frontline Systems Inc was then used to adjust 

each of the 15 variables to minimize life-cycle cost: 1) kW 

of PV; 2) kW of wind power; 3) square feet of solar 

ventilation air preheating; 4) square feet of solar water 

heating; 5) square feet of solar thermal (parabolic troughs); 

6) kW of solar thermal electric; BTU/hour of biomass boiler 

capacity for 7) combustion, 8) gasification, 9) pyrolysis, 10) 

anaerobic digestion; kW of biomass electric for for 11) 

combustion, 12) gasification, 13) pyrolysis, 14) anaerobic 

digestion; and 15) square feet of daylighting aperture for 

each type of space. 

 

The solver routine calculates the change in life-cycle cost 

associated with a change in the size of each of the renewable 

energy technologies, and then moves in the direction of 

decreasing life-cycle cost by an amount determined by a 

quadratic approximation. The solver routine involved the 

following parameters—precision: value of energy use 0.0 

+/- 0.0001; convergence: change in life-cycle cost less than 

5
$0.0001 for five iterations; quadratic extrapolation to obtain 

initial estimates of the variables in one-dimensional search; 

central derivatives used to estimate partial derivatives of the 

objective and constraint functions; and Newtonian Search 

Algorithm used at each iteration to determine the direction 

to search 

Figure 2. The solver routine finds the minimum Life 

Cycle Cost in 16 variables, but only two variables can be 

illustrated in this two-dimensional figure. An increase 

and decrease in the size of each renewable energy 

component is used to indicate direction of reducing life 

cycle cost. 

6. EXAMPLE: ZERO ENERGY PLANNING FOR 

NATIONAL ZOO

This method was used to determine the combination of 

renewable energy technologies that would provide 100% of 

a Smithsonian Institution (SI) National Zoological Park 

energy on an annual basis at the minimum life-cycle cost. 

The facilities considered in the study include the National 

Zoological Park in Washington D.C. and the associated 

4,600 acre Conservation Research Center in Front Royal 

VA. Results indicate that renewable energy measures could 

be integrated directly into buildings at both sites (PV, solar 

water heating, solar ventilation air preheating) but that 

central plant use of renewables (wind and biomass) would 

be needed to meet the zero energy goal. Table 1 lists the 

sizes of each component that minimize life cycle-cost in this 

example. 

Life-cycle costs are summarized in Table 2. The base case 

of continuing to purchase electricity, gas and propane has 

zero initial cost but high annual cost and a life-cycle cost of 

$52 million. The net zero case has high initial cost of $46 

million but low annual cost and a life-cycle cost of $74 

million. Over a 25 year analysis period, the life cycle cost 

of the zero energy case is higher than the basecase, but the 

life cycle-cost analysis does not include a dollar value for 

emissions, educational value, or other benefits associated 

with the zoo’s zero energy goal. Figure 3 illustrates how the 

use of electricity, gas, and propane would be replaced by 

renewable energy sources in a Net Zero Facility. The 

method may now be used to evaluate alternatives, such as 

biodiesel instead of wood chips, in response to issues that 

emerge during the process of implementation. 

Table 1. OPTIMAL SIZES OF EACH TECHNOLOGY 

IN ZOO EXAMPLE 

 

National. 

Zoological 

Park, D.C. 

Conservation. 

Research 

Center, VA Total 

Photovoltaics 

Size (kW) 638 224 862 

Wind Capacity 

(kW) 0 14,500 14,500 

Solar Vent 

Preheat Area 

(ft2) 10,655 8,075 18,730 

Solar Water 

Heating Area 

(ft2) 7,535 2,180 9,715 

Biomass 

Gasifier Size (M 

Btu/h) 10,996 0.000 10,996 

Biomass 

Cogeneration 

Size (kW) 1,168 0 1,168 

Anaerobic 

Digester Size 

(FT3) 3,723 459 4,182 

Anaerobic 

Digester 

Cogeneration 

Size (kW) 12 0 12 

Daylight 

Aperture 

(Skylight) Area 

(ft2) 21,221 6,476 27,697 

6
-40000

-20000

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

Basecase

RE Case

Annual Energy (Mbtu)

Daylighting (Mbtu)

Anaerobic Digester (Mbtu)

Biomass Gasifier (Mbtu)

Biomass Combustion (Mbtu)

Solar Themal (Mbtu)

Solar Water Heating

Solar Vent Preheat (Mbtu)

Wind (Mbtu)

Photovoltaics (Mbtu)

Other Fuel (Mbtu)

Natural Gas (Mbtu)

Electric (Mbtu)

Figure 3. Annual energy associated with each technology 

for basecase and zero energy case. Extra electric 

generation at the farm in VA (below the zero axis) is to 

offset remaining natural gas use at Zoo in D.C. 

Table 2. LIFE-CYCLE COST OF BASECASE AND 

ZERO ENERGY CASE IN ZOO EXAMPLE 

Name 

Basecase 

Life Cycle 

Cost ($) 

RE Case Life 

Cycle Cost ($) 

Initial Cost $0 $45,858,421 

O&M Cost $0 $13,135,266 

Biomass Fuel Cost $0 $5,762,545 

Gas Cost $17,323,188 $5,713,053 

Electric Cost $34,914,085 $7,196,488 

Production Incentives $0 -$2,887,806 

Total $52,237,272 $74,777,968 

7. CONCLUSIONS

The approach described in this paper offers a means of 

providing actionable information very early in the process 

of planning renewable energy at a facility. Most large 

organizations can point to renewable energy projects that 

they have completed or are planning. But often these 

projects are selected anecdotally, and may not represent the 

best investment. This method offers a structured approach 

to identify and prioritize measures for further evaluation. 

The method represents the effects that are most important 

including local resources, utility rates, and incentives. The 

method requires only summary information regarding the 

facility making it affordable to conduct this analysis without 

an expensive data collection effort. The method has proven 

to be effective in early planning to meet renewable energy 

goals in industrial and government applications. 

8. ACKNOWLEDGMENTS

Donna Heimiller, Ray George, and Anelia Milbrandt of 

NREL provided the GIS resources analysis for each site. 

Jorn Aabakken of NREL provided many of the technology 

characteristics. Liz Brown of NREL provided incentives 

offered for each site by utilities, states, or local 

governments. The zoo example was funded by U.S. DOE 

Federal Energy Management Program, Anne Crawley. 

Melinda Humphy Becker and David Hauk of Smithsonian 

Institution supported the study with advice and information. 

9. REFERENCES

1. J. Aabakken, Power Technologies Energy Data Book 

http://www. .nrel. gov/ analysis/ power_ databook/ ) 

March 2006. 

2. A. Milbrandt, “A Geographic Perspective on the 

Current Biomass Resource Availability in the United 

States,” Technical Report NREL/TP-560-39181, 

December 2005. 

3. EPRI, “Renewable Energy Technology 

Characterizations” Topical Report No. TR-109496; 

http://www1.eere.energy.gov/ba/pba/tech_characterizati

ons.html. 

4. “Energy Price Indices and Discount Factors for LifeCycle Cost Analysis, April 2007. Annual Supplement to 

Handbook 135; 

http://www1.eere.energy.gov/femp/information/downlo

ad_blcc.html. 

5. Green Building: Project Planning and Cost Estimating, 

RS Means and Co. 2nd Ed. 2006) 

6. Database of State Incentives for Renewable Energy; 

University of North Carolina; www.dsireusa.org. 

7
F1146-E(12/2004) 

REPORT DOCUMENTATION PAGE Form Approved 

OMB No. 0704-0188

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PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION.

1. REPORT DATE (DD-MM-YYYY)

May 2008 

2. REPORT TYPE

Conference Paper 

3. DATES COVERED (From - To)

 

5a. CONTRACT NUMBER

DE-AC36-99-GO10337 

5b. GRANT NUMBER

4. TITLE AND SUBTITLE

Renewable Energy Planning: Multiparametric Cost Optimization; 

Preprint 

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

NREL/CP-670-42921 

5e. TASK NUMBER

FEO7.3131 

6. AUTHOR(S)

A. Walker 

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401-3393 

8. PERFORMING ORGANIZATION 

REPORT NUMBER

NREL/CP-670-42921 

10. SPONSOR/MONITOR'S ACRONYM(S)

NREL 

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National Technical Information Service 

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13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

This paper describes a method for determining the combination of renewable energy technologies that minimize lifecycle cost at a facility, often with a specified goal regarding percent of energy use from renewable sources. 

Technologies include: photovoltaics (PV); wind; solar thermal heat and electric; solar ventilation air preheating; solar 

water heating; biomass heat and electric (combustion, gasification, pyrolysis, anaerobic digestion); and daylighting. 

The method rests upon the National Renewable Energy Laboratory’s (NREL’s) capabilities in characterization of 

technology cost and performance, geographic information systems (GIS) resource assessment, and life-cycle cost 

analysis. The paper discusses how to account for the way candidate technologies interact with each other, and the 

solver routine used to determine the combination that minimizes life-cycle cost. Results include optimal sizes of each 

technology, initial cost, operating cost, and life-cycle cost, including incentives from utilities or governments. Results 

inform early planning to identify and prioritize projects at a site for subsequent engineering and economic feasibility 

study. 

15. SUBJECT TERMS

NREL; Andy Walker; multiparametric cost optimization; renewable energy technologies; life-cycle cost; photovoltaics; 

PV; wind; solar thermal heat and electric; solar ventilation air preheating; solar water heating; biomass heat; 

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Unclassified 

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Unclassified

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