National Renewable Energy Laboratory 

Innovation for Our Energy Future 

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

Office of Energy Efficiency & Renewable Energy

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 

Technical Report 

NREL/TP-710-36774 

May 2005 

Using HOMER® Software, NREL’s 

Micropower Optimization Model, 

to Explore the Role of Gen-sets in 

Small Solar Power Systems 

Case Study: Sri Lanka 

T. Givler and P. Lilienthal 
National Renewable Energy Laboratory 

1617 Cole Boulevard, Golden, Colorado 80401-3393 

303-275-3000 • www.nrel.gov 

Operated for the U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337

Technical Report 

NREL/TP-710-36774 

May 2005 

Using HOMER® Software, NREL’s 

Micropower Optimization Model, 

to Explore the Role of Gen-sets in 

Small Solar Power Systems 

Case Study: Sri Lanka 

T. Givler and P. Lilienthal 

Prepared under Task No.PVP4.9001 
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iii

Abstract 

Stand-alone photovoltaic (PV) systems offer a cost-effective alternative to expensive grid extensions in 

remote areas of the world. In such applications, small solar home systems provide power for small 

fluorescent lighting and other small appliances. According to a survey conducted in Sri Lanka by the 

International Resources Group, most of the solar home systems there use a 50 W PV/battery system to 

power several compact fluorescent lights. Photovoltaic systems can also serve larger home or village 

loads that may include additional households, community centers, and street lighting. In a hybrid power 

system, a back-up diesel generator supplements the PV power for peak loads and during poor resource 

periods and can cost less than a PV-only system. For example, a 20-kW PV/75-kW diesel hybrid system 

in Gaize, Tibet meets a daily energy consumption of 75 kWh for a village of 1000 people. Somewhere 

between these two points the inclusion of a diesel backup improves the economics of the system. This 

study used HOMER to explore the threshold load size at which it is more cost-effective to include a diesel 

than to increase the size of the battery bank or PV array. By performing multiple sensitivity analyses, the 

economic crossover point between these two system types was determined over a range of system sizes, 

solar resources, fuel prices, and reliability requirements. Depending on these factors the crossover varied 

from 3 – 13 kWh day. 

The HOMER software, NREL’s micropower optimization model, can evaluate a range of equipment 

options over varying constraints and sensitivities to optimize small power systems. HOMER’s flexibility 

makes it useful in the evaluation of design issues in the planning and early decision-making phase of rural 

electrification projects. 
iv

TABLE OF CONTENTS 

Abstract..........................................................................................................................................iii 

Introduction..................................................................................................................................... 1 

Assumptions and Model Inputs ...................................................................................................... 2 

Load ............................................................................................................................................ 2 

Solar Resource ............................................................................................................................ 2 

Diesel Fuel Price......................................................................................................................... 3 

Economics................................................................................................................................... 3 

Reliability Constraint.................................................................................................................. 3 

Equipment Considered................................................................................................................ 3 

Photovoltaic Panels..................................................................................................................... 4 

Batteries ...................................................................................................................................... 4 

Converters................................................................................................................................... 4 

Generators................................................................................................................................... 4 

Results and Discussion ................................................................................................................... 5 

Determination of Load Thresholds ............................................................................................. 5 

Effect of Allowing Unserved Load............................................................................................. 5 

Changes in the Global Solar Resource........................................................................................ 7 

Effect of Fuel Price ..................................................................................................................... 8 

Challenges in the Analysis Process............................................................................................. 9 

Conclusion .................................................................................................................................... 10 

Glossary ........................................................................................................................................ 10 

References..................................................................................................................................... 11 

Appendix A: HOMER Input Data ................................................................................................ 12 

Appendix B: Solar Resource Data Maps (Dmaps) for Sri Lanka and Southern Egypt ................ 16 

Appendix C: Cost of Energy vs. Load Graphs ............................................................................. 18 

Appendix D: OST Graphs Showing Maximum Annual Capacity Shortage vs. Load.................. 19 

Appendix E: Hourly Load and Production Profiles...................................................................... 21 

Appendix F: Total Electrical Output and Generator Output vs. Load Graphs ............................. 23 
1

Introduction 

Stand-alone solar photovoltaic (PV) systems offer a cost-effective alternative to expensive grid extensions 

in remote areas of the world. In such applications, solar home systems provide power for a few small 

fluorescent lights and other small appliances. According to a survey conducted in Sri Lanka by the 

International Resources Group, most of the solar home systems there use a 50-W PV/battery system to 

power several compact fluorescent lights [1]. Solar home systems may be quite cost-effective when 

compared to the operation, maintenance, and fuel cost of electricity from diesel generators. As the load 

requirements increase, however, the addition of a back-up diesel generator often makes a PV/diesel 

hybrid system a more viable option, even with high fuel costs. The load for a village system may include 

households as well as a community center, schools, and street lighting. For example, a 20-kW PV/75-kW 

diesel hybrid system in Gaize, Tibet meets a daily energy consumption of 75 kWh for a village of 1000 

people [2]. The addition of a backup generator to the PV system serves load peaks and provides power 

when cloudy weather limits the solar resource. The generator allows for smaller PV and battery 

components, which reduces system costs, while the PV and batteries limit the diesel fuel consumption. 

The HOMER software, NREL’s micropower optimization model, can evaluate a range of equipment 

options over varying constraints to optimize small power systems. This type of analysis could aid in the 

planning of large-scale rural electrification projects. The results could then serve as a starting point for the 

design of individual installations. 

This study explored the role of backup generators to reduce overall system costs. We focused on Sri 

Lanka because it has an extensive program of small solar home systems with continuing efforts to 

electrify rural areas. HOMER simulated the operation of thousands of different system designs, with and 

without a backup generator. It was then able to identify the least cost system as a function of load size and 

other variables. This paper uses those results from HOMER to analyze the load threshold above which a 

hybrid PV/diesel/battery system becomes more cost-effective than a simpler PV/battery system. HOMER 

makes clear the sensitivity of this threshold to several factors. 

Of the energy-modeling software available, HOMER’s capabilities provide the best option for modeling 

and investigating such scenarios. The program first runs an hourly simulation of all possible 

configurations of system types. The speed of processing these simulations allows for the evaluation of 

thousands of combinations. This hourly simulation also provides improved accuracy over statistical 

models that typically evaluate average monthly performance of a system. HOMER also models the partial 

load efficiency of diesel generators. This more accurately simulates the lower efficiency of a generator 

when it is not operating at full capacity. 

After running the simulations, HOMER sorts the feasible cases in order of increasing net present (or 

lifecycle) cost. This cost is the present value of the initial, component replacement, operation, 

maintenance, and fuel costs. HOMER lists the optimal system configuration, defined as the one with the 

least net present cost, for each system type. HOMER’s sensitivity analysis then repeats this optimization 

as user-defined factors, such as fuel price, load size, reliability requirement, and resource quality are 

varied.
2

Assumptions and Model Inputs1 

Load 

The load profile is based on a 

hypothetical single home. Figure 1 

illustrates this profile. A small base 

load of 5 W occurs throughout the 

day and night. Small peaks of 20 W 

occur in the morning and at noon, 

while the majority of the load 

occurs in the evening. This evening 

load, with a peak load of 40 W, 

would likely include compact 

fluorescent lighting and a radio. 

The total daily load averages 305 watt-hours per day. 

HOMER can perform a sensitivity analysis by accepting multiple values for a particular input variable 

such as the average load. By scaling the annual average value of kWh/d, HOMER models the I pact of 

increasing loads. This analysis determines how changes in the input variable affect the performance of the 

system and the relative ranking of different systems. By performing the sensitivity analysis over a large 

range of load sizes, the study simulates a range of load types from a single home to a large community. 

While this scaling may represent the overall total load of a larger village, in reality the profile would not 

necessarily remain the same shape. Variations in individual home loads would tend to smooth out the 

overall profile. Additionally, daytime loads such as microenterprises, schools, and clinics would most 

likely change the shape of the profile. For simplicity of analysis, the load profile was not modified as the 

load was increased, but rather kept constant in shape and scaled in size. The sensitivity analysis was 

performed over a scaled range from 0.3 kWh/d to 16 kWh/d. Further analysis is required to determine the 

effect of changes in the load shape. 

Solar Resource 

The solar resource was used for a site in Sri 

Lanka at a location of 7° 30' N latitude and 

81° 30' E longitude. Solar radiation data for 

this region was obtained from the NASA 

Surface Meteorology and Solar Energy web 

site [3]. The annual average solar radiation for 

this area is 5.43 kWh/m2

/d. Figure 2 shows the 

solar resource profile over a one-year period. 

Additional runs were made using solar profiles 

from Southern Egypt. This provided insights 

into the effect of higher solar radiation on the 

system type. Appendix B illustrates the solar 

resources for Sri Lanka and Southern Egypt. 

 

1

 Appendix A provides additional detail for model inputs. 

Figure 1. Hourly load profile 

Figure 2. Solar Radiation profile for 

Sri Lanka 
3

Diesel Fuel Price 

The study included a sensitivity analysis on the price of diesel fuel. This price can vary considerably 

based on region, transportation costs, and current market price. Price information from both the World 

Bank and the International Energy Agency (IEA) show that average diesel prices ranged from $0.40/L to 

$0.70/L in 2000 [4,5]. Prices of $0.30/L to $0.80/L were evaluated in increments of $0.10/L. 

Economics 

A real annual interest rate of 6% was assumed. The real interest rate is equal to the nominal interest rate 

minus the inflation rate. The appropriate value for this variable depends on current macroeconomic 

conditions, the financial strength of the implementing entity, and concessional financing or other policy 

incentives. HOMER converted the capital cost of each component to an annualized cost by amortizing it 

over its component lifetime using the real discount rate. 

Reliability Constraint 

The economic performance of a renewable energy system can be significantly improved if a small portion 

of the annual load is allowed to go unserved. For example, a solar array and battery bank that do not have 

to meet an occasional large load may be significantly smaller than those that must meet the load at all 

times. This is especially true for those extreme cases with a peak load that occurs after several cloudy 

days. If it is acceptable for the system to be down for a small fraction of the year, or if unnecessary loads 

can be shed when the battery bank is low, significant capital cost may be saved. HOMER models this 

scenario with the maximum annual capacity shortage constraint. Set to 0% by default (in which the 

system must meet all of the load all of the time) a sensitivity analysis on this variable shows that the 

optimal system type might change if a small amount of the annual load (1/2% to 5%) is allowed to go 

unserved. 

Equipment Considered 

The search space, list of system component sizes, that HOMER considered for this analysis is outlined in 

Table 1. All equipment details, including the generator fuel efficiency curves, can be found in Appendix 

A. 
4

Component Size Capital 

Cost ($) 

Replacement 

Cost 

O&M Cost ($) Lifetime 

PV Panels 0.05 – 5.0 

kW (see 

Appendix A 

for specific 

sizes) 

$7,500/kW $7,500/kW 0.00 20 years 

Trojan T-105 

Batteries 

225 Ah / 6 

volt (bank 

size: 1 – 54 

batteries) 

$75/battery $75/battery $2.00/year 845 kWh 

of throughput 

per battery 

Converter 0.1 – 4.0 kW $1,000/kW $1,000/kW $100/year 15 years 

Generator 4.25 kW $2,550 $2,550 $0.15/hour 5000 hours 

Photovoltaic Panels 

Photovoltaic panels were specified with capital and replacement costs of $7.50/W. This cost includes 

shipping, tariffs, installation, and dealer mark-ups. Some maintenance is typically required on the 

batteries in a PV system, but very little is necessary for the panels themselves. A derating factor of 90% 

was applied to the electric production from each panel. This factor reduces the PV production by 10% to 

approximate the varying effects of temperature and dust on the panels. The panels were modeled as fixed 

and tilted south at an angle equal to the latitude of the site. 

Batteries 

HOMER uses the Kinetic Battery Model and represents batteries as a “two tank” system. One tank 

provides immediately available capacity while the second can only be discharged at a limited rate. Trojan 

T-105s were chosen because they are a popular and inexpensive option. HOMER considered up to 54 of 

these batteries. In a real installation a smaller number of larger batteries, such as the Trojan L-16, would 

be preferable. The economic analysis performed by HOMER would not be significantly affected by this 

distinction. 

Converters 

The inverter and rectifier efficiencies were assumed to be 90% and 85% respectively for all sizes 

considered. HOMER simulated each system with power switched between the inverter and the generator. 

These devices were not allowed to operate in parallel. In this simple system, power cannot come from 

both the generator and the batteries at the same time. 

Generators 

A vast range of diesel generators is available. The various manufacturers and distributors provide 

different information that can be difficult to compare. The partial load efficiency is an important 

parameter that HOMER requires when simulating this component. The generators were not allowed to 

operate at less than 30% capacity. Operation and maintenance costs for the generators are listed per hour 

of operation. HOMER determines the amount of time the generator must be used in a year and calculates 

the total operating costs from this value. The costs used for this study are very conservative and may be 

much higher in reality. Only one generator was allowed per system and that generator had to be large 

enough to meet the peak load. HOMER considered two different types of control strategies. Under the 

Table 1. System Components Considered
5

load-following strategy, the generator provides only the power necessary to meet the load at the time. 

With the cycle-charging strategy, once the generator is operating, it uses as much power as possible to 

charge the batteries in addition to meeting the load. 

Results and Discussion

The cost of energy was first calculated and plotted for the three system types over increasing loads to 

identify the load threshold between PV and a hybrid system. Variations in reliability, solar resource, and 

diesel price shift this threshold. 

Determination of Load Thresholds 

As the system load increases, the cost of energy curves identify specific load thresholds for different 

system types. Figure 3 shows the results for systems with 100% reliability, global solar radiation of 5.43 

kWh/m2

/d, and a diesel price of $0.50/L. The PV/battery system has the lowest cost of energy for small 

loads up to about 3.5 kWh/d at a cost of energy (COE) of $0.85/kWh. At this point, the hybrid 

PV/diesel/battery curve intersects the PV/battery curve. Loads above this threshold are best served by a 

hybrid system. At higher loads, the diesel/battery system has a lower cost of energy than the PV/battery 

system. However, the hybrid system is less expensive than either technology on its own. Appendix C 

includes additional graphs of the cost of energy vs. load for various combinations of reliability, solar 

radiation, and diesel price. 

$0.60

$0.70

$0.80

$0.90

$1.00

$1.10

0 2 4 6 8 10

LOAD (kWh/d)

COE ($/kWh)

DIESEL BATT

PV BATT

PV DIESEL BATT

Note: The assumptions used to create these results can be viewed by downloading files: diesel batt system 2.hmr; pv 

only system 3.hmr; pv diesel system 6.hmr at http://www.nrel.gov/homer/ 

Effect of Allowing Unserved Load 

By introducing some annual capacity shortage to the HOMER simulations, the threshold where 

PV/diesel/battery systems become more cost-effective than PV/battery system increases to higher loads. 

Allowing some of the load to go unserved throughout the year means that the PV array and battery bank 

do not need to be sized for extreme, worst-case scenarios of extended cloudy weather and peak loads. The 

Figure 3. Cost of energy for (3) different system types 

over increasing load 
6

lower capital cost allows such systems to compete with PV/diesel/battery hybrid systems when serving 

larger loads. This can be seen in Figure 4, which shows the HOMER Optimal System Type (OST) graph 

for various values of load and maximum annual capacity shortage. As the allowable capacity shortage 

increases on the y-axis, the area where PV/battery systems are the least-cost solution expands over larger 

loads. When the diesel fuel price increases to $0.80/L in the graph on the right, the PV system becomes 

even more competitive with the hybrid system. The graphs also show that the fuel price has much less 

effect on small systems that require 100% reliability than on systems that allow a little bit of capacity 

shortage. This reflects the fact that systems that are primarily PV but require 100% reliability may only 

need to run the diesel for a very few number of hours to achieve that level of reliability. With that little 

diesel run-time, the diesel fuel price does not have a large effect on the overall economics. 

Additional OST graphs showing maximum annual capacity shortage are included in Appendix D. As 

shown in Figure 5, even a half percent allowance of capacity shortage may result in a significant decrease 

(20%) in the net present cost, making the PV/battery system type more cost-effective. 

Note: The assumptions used to create these results can be viewed by downloading the file: home system PV diesel 

range A11 (Sri Lanka) for figure 4, and file: home system pv diesel range A13.hmr for figure 5 at 

http://www.nrel.gov/homer

Figure 4. Optimal System Type (OST) graph over increasing capacity shortage and load. 

The graph on the right shows how a PV/battery system becomes more competitive as more 

capacity shortage is allowed. When fuel cost is increased from $0.30/liter (left graph) to $0.80/liter 

(right graph), the new graph illustrates that a PV/battery system becomes even more cost 

competitive at higher loads. 
7

Figure 5. Net Present Cost vs Max Annual Capacity Shortage for a load of 1.0 kWh/d and 

a fuel cost of $0.50/L. This graph illustrates that allowing just a small amount of unserved load 

can significantly reduce the cost of the system. Allowing even more unserved load may only 

bring much smaller additional savings. It is important to note that as the capacity shortage 

changes, both the system type and configuration may also change. 

Changes in the Global Solar Resource 

The same simulations were run with variations in the available global solar radiation to evaluate the effect 

of the solar resource on the load thresholds and optimal system types. A solar profile of southern Egypt 

provided a region with higher average solar radiation than Sri Lanka. The load thresholds changed with 

these changes in radiation and affected the load range where a PV/battery system was more cost-effective 

than a PV/battery/diesel hybrid. By increasing the solar resource, the PV/battery system type becomes 

optimal over a larger range of loads. These effects on the optimal system type are illustrated by the graphs 

in Figure 6. Appendix E includes the hourly load and electrical production profiles for a system at 

different times of the year, showing the effect of seasonal variations in solar radiation. 

Fixed 

Global Solar = 5.43 kWh/m2

/d 

Diesel Price = $0.50/L 
8

Note: The assumptions used to create these results can be viewed by downloading the files: home system pv diesel 

range A11 (Sri Lanka).hmr and home system pv diesel range A11-Egypt 8.hmr at http://www.nrel.gov/homer/ 

Effect of Fuel Price 

The OST graph for Sri Lanka in Figure 6 shows that diesel fuel price has little impact on the cost effective 

threshold between a PV/battery and a PV/diesel/battery system. The line separating the PV and hybrid 

systems is nearly vertical. However, if some unserved load is allowed throughout the year, the fuel price 

becomes more important to the optimal system type. Figure 7 shows that when 5% capacity shortage is 

allowed, larger PV/battery systems becomes more cost competitive compared to the hybrid at all fuel 

prices. If 100% reliability is required, a diesel generator is often needed to meet that constraint although 

it may run only a few hours a year. In this case, the fuel price plays a small role in the overall net present 

cost. However, when the reliability constraint is lowered, the diesel would only be included in an optimal 

system if it becomes cost-effective to run it more often. This explains why the results in Figure 7, where 

5% capacity shortage was allowed, show a greater sensitivity to fuel price. 

Figure 6. OST graphs over increasing load and fuel price for both Sri Lanka and Southern Egypt 

solar radiation profiles. The increased solar radiation of the Egypt profile increases the area of the graph 

where a PV/battery system is most cost-effective. 

Sri Lanka Southern Egypt
9

Challenges in the Analysis Process 

When performing a sensitivity analysis on the load, many sizes of each equipment type must be 

considered to meet the range of loads evaluated. To reduce the computation times HOMER runs were 

performed in an iterative process. Initially, the optimization search space considered only a few 

component sizes over a large range. Similarly, the sensitivity analyses covered a large range with few 

points. This helped to decrease the initial run-time. With each successive run, more options and variables 

were added to increase the resolution and fill in the search and sensitivity spaces. For example, the final 

results shown previously illustrate a sensitivity analysis of the diesel price over a range of $0.30/L to 

$0.80/L with $0.10/L increments. However, most iterative runs performed included prices of $0.30, 

$0.50, and $0.80 only. This “coarser” sensitivity space provided adequate information to modify the file 

and rerun the simulations. The 2.1 version of HOMER incorporates the recycling of previous results to 

facilitate this iterative process. 

Insufficient resolution in the HOMER search space can result in odd peaks and valleys in an OST graph. 

Such points result when the program must choose from a limited range of equipment sizes although 

interim sizes would better suit a particular simulation. Smoother curves result on the OST graph when 

HOMER has many equipment sizes to evaluate. Increasing the search space resolution smoothes the area 

boundaries of the OST graph. This is particularly problematic when performing sensitivities over load 

size. A larger load may require a larger genset running at a less efficient operating point. 

Obtaining data for analyses such as these is always challenging. This is especially true for the wide 

variety of gensets that are commercially available. It is often hard to know a priori which inputs deserve a 

lot of effort getting precise data. Rough estimates of variables, such as O&M cost, lifetime, and part-load 

efficiency, can be entered into the program. A HOMER sensitivity analysis on these variables can then 

inform the user of the value of improving the accuracy of that input variable. 

Figure 7. OST graph over increasing load and fuel price for Sri Lanka and a maximum allowable 

capacity shortage of 5%. Compared to the graph shown in Figure 6 for Sri Lanka, this OST graph is 

much more affected by change in diesel price than at 0% capacity shortage. 
10

Conclusion 

PV/battery systems were found to be most cost-effective up to loads ranging from 3 kWh/d to 13 kWh/d 

depending on the reliability, solar resource, and diesel fuel price. Loads above this threshold were best 

served by a hybrid PV/generator/battery system. Reliability requirements, solar resource, and fuel price 

all shift the exact threshold for a particular situation. 

By allowing some small percentage of the load to go unserved throughout the year, the PV/battery system 

becomes more cost-competitive with hybrid systems over a larger range of loads. In applications where 

some capacity shortage is acceptable throughout the year, the capital cost can be significantly reduced. 

Even a small amount (1/2%) of capacity shortage can significantly improve the competitiveness of 

PV/battery systems. 

HOMER confirmed the intuitive notion that locating a system in a region of higher average solar radiation 

favors PV/battery systems relative to hybrid systems moving the threshold to larger loads. 

Where high reliability was required, fuel price had surprisingly little effect on the threshold between 

PV/battery and hybrid systems over the range of small systems considered here. However, if some 

unserved energy was allowed in the system, it became a more prominent factor. 

Glossary 

feasible system: A system that satisfies the specified constraints. 

levelized cost of energy: The average cost of producing one kilowatt-hour of electricity, including 

capital, replacement, fuel, operating and maintenance costs. 

maximum annual capacity shortage: The percentage of the yearly total load that is allowed to go 

unserved by the system.

net present cost: The present value of the cost of installing and operating the system over the lifetime of 

the project (also referred to as lifecycle cost). 

optimal system type: The combination of power-generation technologies with the lowest net present 

cost. 

renewable energy fraction: The portion of a system’s total electrical production that originates from 

renewable power sources. 

search space: The set of all system configurations that HOMER evaluates. 

sensitivity analysis: An investigation into the extent to which changes in certain inputs affect a model’s 

outputs. 

system type: A combination of power-generation technologies.
11

References 

1. International Resources Group. “World Bank/Sri Lanka Energy Services Delivery Project, 

Impacts Assessment and Lessons Learned.” Washington, DC, 2003. 

2. “PV/Diesel Hybrid in Gaize, Tibet.” Renewables for Sustainable Village Power (RSVP) 

Village Power Database, http://www.rsvp.nrel.gov/asp/project.asp?ID=225. Accessed May 

2004. 

3. NASA Surface Meteorology and Solar Energy. http://eosweb.larc.nasa.gov/sse/. Accessed 

May 2004. 

4. Bacon, Robert. “Petroleum Taxes.” The World Bank. 

http://rru.worldbank.org/Documents/PublicPolicyJournal/240Bacon-831.pdf, September 

2001. 

5. “Energy Policies of IEA Countries.” http://library.iea.org/dbtwwpd/textbase/nppdf/free/2000/comp2000.pdf, OECD/IEA, 2000.
12

Appendix A: HOMER Input Data 

HOMER Input Summary – Range A 

File Name: Range A.hmr

Notes:

Solar and Diesel with increasing load (0.3-16 kW); does not allow generators that 

are less than the peak load; does not allow multiple generators; inexpensive 

generator 1 with low lifetime; inverter cannot operate simultaneously with 

generator; no fixed cost on pv panels; battery O&M cost added; solar resource for 

Sri Lanka; reduced cost for Trojan batteries per website information; project life 

decreased to 20 years;

AC Load: Primary Load 1 

Data source: Synthetic

Scaled annual average: 0.3, 1.0, 2.0, 8.0, 12.0, 16.0, 4.0, 6.0, 3.0 kWh/d

Daily noise: 15%

Hourly noise: 20%

PV 

PV costs

Size (kW) Capital ($) Replacement ($) O&M ($/yr)

0.04 300 300 0

1.04 7,800 7,800 0

Sizes to 

consider:

0.00, 0.05, 0.10, 0.20, 0.25, 0.30, 0.40, 0.50, 0.75, 1.00, 1.25, 1.50, 2.00, 2.50, 

3.00, 3.50, 4.00, 5.00 kW

Lifetime: 20 yr

Derating factor: 90%

Tracking system:No Tracking

Slope: 7.5 deg

Azimuth: 0 deg

Ground 

reflectance: 20%
13

Solar Resource 

Data source: Synthetic 

Average radiation Month Clearness index

(kWh/m2/d)

Jan 0.522 4.80

Feb 0.606 5.94

Mar 0.617 6.37

Apr 0.573 6.00

May 0.548 5.62

Jun 0.540 5.43

Jul 0.548 5.54

Aug 0.538 5.55

Sep 0.534 5.51

Oct 0.534 5.29

Nov 0.502 4.67

Dec 0.497 4.46

Scaled annual average: 4.46, 6.37, 5.43 kWh/m2/d 

Latitude: 7 degrees 30 minutes North 
14

AC Generator: Generator 1 

Generator 1 costs 

Size (kW) Capital ($) Replacement ($) O&M ($/hr)

4.25 2,550 2,550 0.15

Sizes to consider: 0.00, 4.25 kW

Lifetime: 5,000 hrs

Min. load ratio: 30%

Heat recovery ratio: 0%

Fuel used: Diesel

Fuel curve intercept: 0.0911 L/hr/kW

Fuel curve slope: 0.264 L/hr/kW

Fuel: Diesel 

Price: $ 0.3, 0.5, 0.8, 0.4, 0.6, 0.7/L

Lower heating value: 43.2 MJ/kg

Density: 820 kg/m3

Carbon content: 88.0%

 
15

Battery: Trojan T-105 

Battery costs 

Quantity Capital ($) Replacement ($) O&M ($/yr)

1 75 75 2

Quantities to 

consider: 0, 1, 2, 4, 6, 8, 10, 12, 16, 18, 24, 30, 36, 42, 48, 54

Voltage: 6 V

Nominal capacity: 225 Ah

Lifetime 

throughput: 845 kWh

Converter 

Converter costs 

Size (kW) Capital ($) Replacement ($) O&M ($/yr)

1 1,000 1,000 100

Sizes to consider: 0.00, 0.10, 0.15, 0.20, 0.40, 0.50, 0.75, 1.00, 1.25, 1.50, 2.50, 3.00, 4.00 kW

Lifetime: 15 yr

Inverter efficiency:90%

Rectifier relative 

capacity: 100%

Rectifier 

efficiency: 85%

Economics 

Annual real interest 

rate: 6%

Project lifetime: 20 yr

Capacity shortage 

penalty: $ 0/kWh

System fixed capital 

cost: $ 0

System fixed O&M 

cost: $ 0/yr
16

Appendix B: Solar Resource Data Maps (Dmaps) for Sri Lanka and 

Southern Egypt 

Solar Resource for Sri Lanka 
17

Solar Resource for Southern Egypt 
18

Appendix C: Cost of Energy vs. Load Graphs

These graphs, similar to those shown in Figure 4, plot data points for two additional cases: a 

lower solar resource (Northern India) with a low diesel price ($0.30/liter) and an increased solar 

resource (Southern Egypt) with a high diesel price ($0.80/liter). 

India solar resource 

Fuel Price: $0.30/liter 

Egypt solar resource 

Fuel Price: $0.80/liter 

0.00

1.00

2.00

3.00

4.00

5.00

6.00

0 5 10 15 20

LOAD (kWh/d)

COE ($/kWh)

DIESEL ONLY

DIESEL/BATT

PV/BATT

PV/DIESEL/BATT

0.00

1.00

2.00

3.00

4.00

5.00

6.00

0 5 10 15 20

LOAD (kWh/d)

COE ($/kWh)

DIESEL ONLY

DIESEL/BATT

PV/BATT

PV/DIESEL/BAT
19

 Appendix D: OST Graphs Showing Maximum Annual Capacity 

Shortage vs. Load 

Figure 7 illustrates the optimal system type when comparing the maximum annual capacity 

shortage with the system load. The two graphs show this data for two extreme fuel prices of 

$0.30/liter and $0.80/liter. This appendix expands the data shown in Figure 7 with additional 

graphs for diesel prices of $0.40, $0.50, $0.60, and $0.70 per liter. With each successive increase 

in fuel price, the PV/battery system becomes more cost competitive with the hybrid 

PV/diesel/battery system. 

 
20
21

Appendix E: Hourly Load and Production Profiles 

The following graphs show the hourly load and electrical production of the system for two 

different days of the year. For Sri Lanka, March has the highest average solar radiation and 

December has the lowest average. For March 15, the PV array can meet the entire load during 

the day with enough energy stored in the battery bank to meet the nighttime load. On December 

15, the generator runs for one hour in the morning and charges the batteries to assist the PV 

array. 
22
23

Appendix F: Total Electrical Output and Generator Output vs. 

Load Graphs 

The renewable energy fraction of the optimal system can also be shown in graphs comparing the 

total electrical output and the generator output. By graphing these two values simultaneously, the 

difference between the curves represents the amount of the electrical production made up of 

renewables. In reading these graphs, note that the y-axis scales are not the same. 

The first of these graphs plots data for a diesel price of $0.30/liter. Up to a load 3kWh/d, the 

generator is not used at all. The optimal system uses only PV to produce the total electrical 

output. After 3kWh/d, the generator begins to make up a portion of the total output. By 

16kWh/d, the renewable fraction is very small and the generator provides almost all of the 

electrical output. 
24

The second graph plots data for $0.50/liter. The two curves are similar to those in the first graph 

except that the difference between them has increased. As the price of diesel has increased, the 

renewable fraction of the optimal system has also increased. 
25

The third graph, representing a fuel price of $0.80/liter, illustrates an even greater difference 

between the two curves. 
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4. TITLE AND SUBTITLE

Using HOMER Software, NREL's Micropower Optimization Model, 

to Explore the Role of Gen-sets in Small Solar Power Systems; 

Case Study: Sri Lanka 

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NREL/TP-710-36774 

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T. Givler and P. Lilienthal 

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The HOMER software, NREL’s micropower optimization model, can evaluate a range of equipment options over 

varying constraints and sensitivities to optimize small power systems. HOMER’s flexibility makes it useful in the 

evaluation of design issues in the planning and early decision-making phase of rural electrification projects. This 

paper discusses using HOMER to explore the role of gen-sets in small solar power systems in Sri Lanka. 

15. SUBJECT TERMS

HOMER; the HOMER software; micropower optimization model; Sri Lanka; gen-sets; solar home systems; 

photovoltaic (PV) systems; diesel hybrid system 

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