United States Department of Agriculture

DEPAR TMENT OF AGRICULT URE

Forest 

Service

Pacific Northwest 

Research Station

General Technical Report

PNW-GTR-959

August

2017

Economic Sensitivity and Risk 

Analysis for Small-Scale Wood 

Pellet Systems—an Example 

From Southeast Alaska

David L. Nicholls
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Author

David L. Nicholls is a forest products technologist, Alaska Wood Utilization 

Research and Development Center, 901-C Halibut Point Rd., Sitka, AK 99835.

Cover photos: A wood pellet burner (left) and wood pellet storage silo and warehouse at the Tlingit and Haida Regional Housing Authority in Juneau, Alaska. 

Photos by David Nicholls
Abstract

Nicholls, David L. 2017. Economic sensitivity and risk analysis for small-scale 

wood pellet systems—an example from southeast Alaska. Gen. Tech. Rep. PNWGTR-959. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Research Station. 19 p.

This research models a wood pellet heating system at the Tlingit-Haida Regional 

Housing Authority in Juneau, Alaska, used to provide thermal energy to a 929-m2

warehouse, as an alternative to a heating system that burns more costly fossil fuels. 

Research objectives were to evaluate project economics of the pellet system and to 

conduct cost:benefit analysis on key variables (initial capital cost, fuel oil cost, and 

wood pellet cost). Economic results of interest included net present value, payback, 

internal rate of return, and cost:benefit ratio. Monte Carlo simulations were conducted using RETScreen software with the parameters of heating oil cost, wood 

pellet cost, fuel price escalation, and heating load. Cost:benefit analysis was conducted for capital cost versus wood fuel cost and also versus alternative fuel cost. 

This research found that economic performance was favorable over a wide range 

of normal operating conditions, even when paying a relatively high price for wood 

fuel. A pellet production facility in southeast Alaska could lead to lower wood fuel 

costs and even more favorable regional economics.

Keywords: Wood energy, pellets, Monte Carlo simulation, risk analysis, carbon 

emissions, net present value, internal rate of return.
Contents

1 Introduction

1 Alaska Context and Research Needs

2 Community-Scale Wood Heating

3 Literature Review

3 Combined Heat and Power

3 Techno-Economic Evaluations

4 Optimization and Risk Analysis for Thermal Bioenergy Systems

5 Modelling Small-Scale Thermal Bioenergy Systems

8 Research Objectives

8 Methods

8 Sensitivity Analysis

10 Monte Carlo Simulations

10 Results

14 Discussion and Conclusions

15 English Equivalents

16 Literature Cited
1

Economic Sensitivity and Risk Analysis for Small-Scale Wood Pellet Systems—an Example From Southeast Alaska

Introduction

Alaska Context and Research Needs

Over the past decade, wood energy use in Alaska has grown dramatically. Confronted by high fuel costs, many communities in rural Alaska have committed to 

a path of renewable energy. Since 2000, there have been more than 25 new wood 

energy installations in Alaska, in such locations as schools, government buildings, 

and other facilities. Several of these wood energy systems are pellet systems, the 

subject of this research. Efforts are underway to develop a pellet infrastructure in 

southeast Alaska, capable of producing and distributing wood pellets regionally. 

The Tlingit-Haida Regional Housing Authority (THRHA), based in Juneau, Alaska, 

is one of several organizations that has recently established wood pellet burners for 

thermal applications. Their system provides space heating to a 929-m2 warehouse 

plus domestic hot water. This research models the economic performance of this 

system under several likely scenarios, filling a need to provide information for 

small-scale wood pellet thermal systems in Alaska. 

Numerous studies have considered wood energy modeling, including the energy 

and thermodynamic modeling of pellet systems (Carlon et al. 2015, Roy et al. 2013, 

Verma et al. 2013), agricultural or mixed pellet types (Nunes et al. 2014, Verma et 

al. 2012), and numerical modeling of pellet boilers (Chaney et al. 2012, Porteiro et 

al. 2009). Other work (Maraver et al. 2013, Moreton and Rowley 2012, Sartor et al. 

2014) has investigated gasification or combined heat and power (CHP) at various 

scales. Perhaps the most widely researched area for small bioenergy systems has 

been techno-economic evaluations (Arena et al. 2010, Chau et al. 2009, Huang et 

al. 2013, Patel et al. 2011, Pirraglia et al. 2010), although optimization of biomass 

transportation and supply chains have also been evaluated (Mobini et al. 2013). 

Previous research has covered many important aspects of bioenergy use. 

However, current research complements prior work by filling important knowledge gaps regarding risk analysis and economic feasibility of small-scale wood 

energy systems. A second theme of this paper is the use of wood energy in rural, 

northern climates. Over the past decade, tremendous growth has occurred in wood 

thermal systems, owing in part to the high cost of heating fuels. Although small 

in size, rural wood energy systems are collectively important as their adoption 

becomes more widespread. Thus, there is a strong need for research to document 

the economic performance of small-scale pellet systems under a range of operating 

conditions, a primary goal of the current research. This research is expected to 

have broader implications for rural wood energy systems, not only in Alaska, but in 

northern regions internationally. 
2

GENERAL TECHNICAL REPORT PNW-GTR-959

Community-Scale Wood Heating

The THRHA, established in 1973 and headquartered in Juneau, Alaska, serves 

close to 29,000 tribal members throughout southeast Alaska (CCTHITA 2014). Its 

programs include funding for home ownership, rentals, house repairs, weatherization service, assistance for elders, and youth programs (THRHA 2014). Through 

its wood energy systems in southeast Alaska, THRHA is empowering the communities it serves to use local resources sustainably, and to create new employment 

opportunities. Many of these communities have fewer than 1,000 residents and 

require small decentralized heating systems. Typically, either a single building 

(such as a school) is heated, or several buildings are connected by hot water piping 

to a single wood burner. Often, these community-scale systems are owned by a 

municipality, school, government agency, or Native organization. 

The THRHA has one of the first wood pellet systems in Alaska that is larger 

than residential scale. One of the primary motivations for using a wood pellet 

system has been the high cost of alternative fuel sources (primarily heating oil) 

in the villages the THRHA serves. The cost for the THRHA pellet combustion 

system was about $65,000 (table 1). This included components integral to the 

Table 1—Base case scenario for Tlingit Haida Regional Housing Authority wood pellet 

economic analysis using RETScreen software

Input Units Value used in RETScreen analysis

Building structure None Single building

Process water? Yes or no No

Heated area Square meters 943

Domestic hot water demand Percentage of total 0

Base case system Fuel type Heating oil

Base case system fuel cost Dollars per liter 1.19

Base case efficiency Percentage 85

Wood fuel type Pellets

Wood fuel cost Dollars per tonne 331

Capital cost Dollars 65,000

System capacity Kilowatts 56

Heating load Kilowatts per square meter 0.09

Efficiency Percent 75

Peak load system None

Operation and maintenance costsDollars per year 800

Salvage value Dollars 10,000

Project life Years 25

Discount rate Percentage annually 4

Inflation rate Percentage annually 3

Fuel cost escalation rate Percentage annually 3

The THRHA has one 

of the first wood pellet 

systems in Alaska 

that is larger than 

residential scale. 

One of the primary 

motivations for using 

a wood pellet system 

has been the high 

cost of alternative fuel 

sources in the villages 

the THRHA serves.
3

Economic Sensitivity and Risk Analysis for Small-Scale Wood Pellet Systems—an Example From Southeast Alaska

burner, including combustion chamber, exhaust stack, augers, fans, and a small 

“day storage” bin for wood pellets. However, when the control system, distribution system, outdoor pellet silo, and other accessories are considered, the total 

system cost was closer to $215,000. This combined system is typical of the 

design, scale, and seasonal heating variations of other wood pellet systems used 

in rural settings. 

Economic simulation of biomass energy systems has been an extensive area 

of research. However, many of these studies have addressed larger scale systems, 

including municipal and industrial electric power generators, and large district 

heating loops sometimes serving entire cities. By contrast, relatively little research 

has been conducted on community-scale thermal systems heated with wood pellets. 

The current study is motivated by this information gap and will help inform wood 

energy development in Alaska as well as similar environments, including rural 

energy projects in northern latitudes internationally.

Literature Review

Combined Heat and Power

Huang et al. (2013) considered small-scale biomass-fueled CHP applications, 

which they modeled as Organic Rankine systems and also as biomass gasification 

systems. System size for all simulations was 150 kilowatts of electrical energy 

(kWe). They found that breakeven electricity prices for the Organic Rankine 

systems ranged from £40 to £50 per megawatt-hour (MWh) (about $59 to $74 per 

MWh); considerably lower than the biomass gasification systems (with electricity 

prices ranging from £87 and £97 per MWh [$109 to $121 per MWh] ). Maraver 

et al. (2013) also evaluated small-scale biomass CHP systems. They considered 

lesser used technologies such as Stirling engines and Organic-Rankine systems. 

Techno-Economic Evaluations

Moreton and Rowley (2012) conducted multiparametric techno-economic 

analyses for biomass energy systems for horticultural glasshouses at scales up 

to 5 MW. Combined heat and power applications were found to offer significant 

promise in the United Kingdom, especially when financial support mechanisms 

such as sales tariffs and capital support were available. Patel et al. (2011) performed techno-economic analysis of biomass energy systems at different scales. 

They evaluated three types of biomass fuels and seven system variables. A key 

finding of this work was that both forestry residue fuels and rapeseed oil fuels 

led to conditions where generating efficiency, capital costs, and operating costs 

were the most significant variables. Of the three biomass fuels evaluated, rapeseed oil had the highest internal rate of return (IRR) (25 percent) followed by 

Relatively little 

research has been 

conducted on 

community-scale 

thermal systems 

heated with wood 

pellets. The current 

study is motivated 

by this information 

gap and will help 

inform wood energy 

development in 

Alaska as well as 

similar environments, 

including rural 

energy projects in 

northern latitudes 

internationally.
4

GENERAL TECHNICAL REPORT PNW-GTR-959

forestry residues (17 percent) and solid recovered fuel (10 percent). Upadhyay et 

al. (2012) analyzed the economic feasibility of biomass gasification for community-scale applications in Ontario, Canada. Major variables evaluated included 

costs of harvesting, biomass delivery and storage, plant construction, operation 

and maintenance, and labor. Plant size and location were identified as important 

factors influencing the cost of electricity produced.

Wood and Rowley (2011) considered the techno-economic feasibility of a 

number of biomass CHP systems for community housing. Six systems were analyzed using actual demand data, along with technical performance and cost data, on 

the various biomass CHP systems. The best economic performance of these CHP 

systems occurred for high-load factors when both the electricity and the heat that 

were sold onsite were maximized.

Arena et al. (2010) considered gasification-based, biomass-to-energy cogenerators in the 100- to 600-kWe range. The techno-economic performances of a gas 

engine and an externally fired gas turbine were evaluated. This study found that 

the internal combustion engine offered higher reliability and higher IRR across this 

range of electrical output. Mobini et al. (2013) simulated wood pellet supply chains, 

from raw materials to consumers, for existing supply chains in British Columbia, 

Canada. Cost estimates were provided for procurement, distribution, production, 

and transportation of pellets. Cost reduction opportunities for strategic use of bark 

in drying and pelletizing were key findings of this research.

Optimization and Risk Analysis for Thermal Bioenergy Systems

Iqbal et al. (2014) reviewed 11 optimization algorithms and tools for renewable 

energy systems; they found increased use for optimizing diverse renewable energy 

sources, including hydropower, geothermal, biomass, and grid-connected energy. 

Nonetheless, significant barriers remain for renewable energy project development, 

including high initial costs, high transaction costs, and performance and technical 

risks (Arnold and Yildiz 2015). Monte Carlo simulation is an approach to risk management that can give considerable advantages to ordinary net present value (NPV) 

methods or sensitivity analysis. A Monte Carlo simulation uses random numbers to 

select samples of input variables, then carries out large numbers of simulations to 

generate output values. Probability density functions are created from these output 

values. Frequently, Monte Carlo simulations are used to quantify the probability 

that a capital investment project will achieve a certain threshold value for financial 

metrics such as NPV or IRR (Platon and Constantinescu (2014).
5

Economic Sensitivity and Risk Analysis for Small-Scale Wood Pellet Systems—an Example From Southeast Alaska

Mirkhani and Saboohi (2012) applied stochastic energy supply models to distributed power systems, comparing these results to those of a deterministic model. 

They found that fuel price (in this case natural gas) was an important variable, as 

was the price volatility and drift in fuel prices. Da Silva Pereira et al. (2014) used 

the Monte Carlo simulation to model risk analysis of power generation with renewable energy. Although they modelled small-scale photovoltaic solar systems, they 

noted that their methods could be easily adapted to systems having larger installed 

capacity or other types of renewable energy, including hybrid systems.

An important aspect of wood energy system financial performance is ensuring 

a steady fuel supply, and this could be particularly important in Alaska and similar 

regions where smaller scales of operation could result in disruptions in supply. Rauch 

(2010) used stochastic simulations of forest fuel-sourcing models under risk. He found 

that supply chain risks could be reduced by adopting procurement practices that 

included wood storage in addition to long- and short-term supply contracts. When 

these practices were implemented, fuel costs could be reduced by 1 to 3 percent. Goh 

et al. (2014) evaluated renewable energy project management risks in Malaysia. Using 

a dynamic modelling approach, they found that the most significant risk factors were 

government policies, energy demand, geographical location, and fund management. 

Michelez et al. (2011) evaluated risk quantification and management for renewable 

energy projects. They relied on a risk mapping technique, evaluating economic, 

social, technological, and political factors. Here, the probability of occurrence for risk 

factors was plotted against the expected impact of each. Strauss (2012) conducted a 

risk analysis for a wood pellet manufacturing project. Four primary risk factors were 

identified: wood cost, future increases in pellet prices, the initial pellet price, and 

plant capacity utilization. This analysis assumed that heating oil costs would not only 

outpace inflation, but also outpace price increase on wood pellets. 

Clearly, analysis is an important element of renewable energy project development, including modeling financial performance. By modelling wood energy systems 

under a range of possible scenarios, planners will be better prepared for future 

changes in key conditions such as wood fuel prices and capital costs for new systems.

Modelling Small-Scale Thermal Bioenergy Systems

Small-scale bioenergy systems have been modelled for many potential applications, 

ranging from residential use to the community-power scale. The following four 

themes review the status of current research and the key challenges addressed: computational fluid dynamics (CFB) and numerical modelling, CHP, techno-economic 

evaluations, and combustion models. 
6

GENERAL TECHNICAL REPORT PNW-GTR-959

Computational fluid dynamics and numerical modelling—

Computational fluid dynamics modelling can be used to great benefit on a wide 

range of wood energy system sizes, wood fuel types, and technologies. Chaney et al. 

(2012) used CFD to model small-scale fixed-bed biomass pellet boilers. Adjustable 

parameters included the ratio of airflow split between the primary and secondary 

supplies; and the orientation, height, direction, and number of the secondary inlets. 

Findings of this research will be used to optimize a 50-kW boiler with respect to 

these parameters. Porteiro et al. (2009) conducted CFD on small-scale commercial 

pellet boilers. A key finding of their research was that high emission levels can 

result from the interaction of wood particles in the bed and the poor mixing of the 

gases in the furnace, often a problem in small-scale systems. Persson et al. (2009) 

developed mathematical models for wood pellet boilers and stoves, calculating 

energy balances and carbon monoxide emissions in a laboratory setting. Improved 

modelling of the dynamic response for boilers was recommended for future work.

Combustion models—

Nunes et al. (2014) reviewed various combustion models for biomass pellets. They 

considered both pellets and briquettes from mixed biomass sources, including 

transportation and storage issues. Both fixed-bed and fluidized-bed systems were 

considered. A key finding was that mixed pellet products could gain importance for 

bioenergy applications in Portugal. Roy et al. (2013) also studied combustion and 

emission results from small-scale (up to 32 kW capacity) prototype pellet furnaces. 

They considered four biomass pellets (one grass pellet and three wood pellets), 

evaluating five combustion gases as well as ash. A key finding of this work was that 

grass pellets can successfully be combusted while having similar performance and 

emissions to wood pellets. Verma et al. (2012) conducted test burns of wood pellets 

under a range of operating conditions. They compared three wood pellet boiler technologies at two different loads. System size ranged from 15 to 32 kW and included 

top, bottom, and horizontal fuel feeds. They found wide variation in emissions. 

Carbon emissions varied by a factor of about 17, and nitrogen oxide emissions varied 

by a factor of 1.7 under different fuel loads. Combustion efficiency also varied but 

was more sensitive to fuel feed locations. Carlon et al. (2015) simulated residential 

wood fuel use under steady-state and dynamic conditions. One 6-kW boiler and one 

12-kW boiler were modelled. Parameters were modelled by load cycle testing, and 

included heat transfer rates to water, water temperature profiles inside the boilers, 

and water temperature profiles at the boiler outlets. This study found better agreement with experimental data during stationary operation rather than during dynamic 

operation. However, for both operational regimes, the fuel consumption was predicted within a 10-percent deviation from the experimental values.
7

Economic Sensitivity and Risk Analysis for Small-Scale Wood Pellet Systems—an Example From Southeast Alaska

Wood energy applications in rural communities—

Since 2000, several dozen new commercial-scale wood energy systems have been 

installed in Alaska at schools, wood products facilities, federal buildings, libraries, 

senior centers, and other community buildings. This growth has been facilitated in 

part by numerous feasibility studies evaluating the economic, environmental, and 

social importance of community-scale wood energy. State grants have also been an 

integral part of construction funding. Based upon these successes, Alaska is now 

entering a “second generation” of wood energy progress. It is hoped that the projects that once required subsidies will soon be able to stand on their own economic 

merits, attracting financing from private sources.

In many ways, Alaska’s wood energy development path has emulated that in 

other regions of the world, driven largely by rural, community-scale applications. A 

diverse mix of fuel types has been used—including cordwood, chips, and pellets—

with each fuel finding niche applications in the state, often driven by economics 

and community resources. Other northern locations have experienced similar wood 

energy growth. In the Northwest Territories of Canada, the city of Yellowknife has 

made a strong commitment to wood pellet use. Between 2008 and 2010, at least 

11 pellet burners were installed, displacing more than 2.1 million L of heating oil 

(McCloy and Associates 2009).

Other rural locations in the continental United States are worth noting. Two 

successful school energy programs have been prototypes for Alaska’s wood energy 

success. The Fuels for Schools program started in Montana and now includes more 

than 19 wood energy systems in the Western United States (Atkins 2011). Preceding this, a similar program in Vermont established numerous school systems. A 

common success factor for all of these regions has been tailoring energy systems 

to local wood resources, labor markets, and energy demands. For example, in 

Vermont, this was often hardwood chips from local sawmills; in Montana, smalldiameter hazard fuels; and in Alaska, cordwood resources near rural communities.

In Europe, wood energy development has been fairly advanced for several decades. 

For example, the city of Gussing, Austria, derives much of its energy from renewable 

sources (Guevara-Stone 2013). The city’s transition to renewable energy started with 

an initiative for all public buildings to stop using fossil fuels. Within 15 years, the 

renewable energy sector employed close to 1,000 people in Gussing, with both heat 

and power generated from a range of biomass sources, including sawdust, corn, and 

cooking oil. Not only are all local needs met, but surplus energy is sold to the national 

grid. Similar progress has been experienced in Sweden, where district heating systems 

powered by wood have become common, motivated by a carbon tax on fossil fuels. 

And in the United Kingdom there is a trend toward decentralizing renewable energy 

projects in favor of greater community ownership (Walker 2008). Factors such as 
8

GENERAL TECHNICAL REPORT PNW-GTR-959

pressure to reduce carbon emissions and potentially unreliable sources of energy could 

be drivers for greater local involvement. Similarly, in Canada, more planning decisions 

are being made at the community level, and smaller rural communities show greater 

interest in pursuing multiple sources of renewable energy (St. Denis and Parker 2009). 

The path of wood energy development in Alaska closely follows those in other 

parts of the world. Spurred by demands for low-cost energy alternatives in remote 

communities, thermal wood energy systems have become an avenue for greater 

economic stability, increased employment, and higher levels of local control. Often, 

policy measures have served as a catalyst for wood energy use, whether as a tax 

(such as Sweden’s fossil fuel tax), subsidy (such as Alaska’s renewable energy fund), 

or local initiative (such as Gussing’s public building ordinance). In many ways, 

renewable energy development in remote rural communities, where economic and 

operating conditions can be challenging, can serve as examples for broader implementation elsewhere. In this way, rural energy use can serve as “proving grounds” 

for other locations. The wood pellet system evaluated in this report follows this 

model and could become a blueprint for greater regional use of wood energy, 

particularly among Alaska Native organizations.

Research Objectives

The primary objective of this research is to model the operating and economic conditions of a small, industrial-scale wood pellet burner in Juneau, Alaska. This research 

uses risk analysis to simulate several possible scenarios and evaluate economic 

indicators of interest (including NPV, IRR, and project payback period) that could 

benefit this facility as well as other possible facilities planned by the parent organization. A secondary objective is to provide context for the broader use of wood pellets 

for thermal applications in southeast Alaska and other high-latitude locations.

Methods

Sensitivity Analysis

This research evaluated the project economics for the THRHA wood pellet system 

that was established in 2014. All comparisons were made versus a base case of 

heating oil. Economic performance was evaluated initially for base case conditions, heating with wood pellets under a “business as usual” scenario (table 1). The 

NPV, IRR, payback period, and cost:benefit ratio were modelled for each scenario. 

The NPV is defined as the sum of initial investment (a negative value) and the 

present value of all future cashflows (a positive value) at a particular discount rate. 

For typical wood energy systems, the cashflow includes the initial investment, 

annual operating costs, annual debt payment (assuming some financing), inflation, 

The path of wood 

energy development in 

Alaska closely follows 

those in other parts of 

the world. Spurred by 

demands for low-cost 

energy alternatives in 

remote communities, 

thermal wood energy 

systems have become 

an avenue for greater 

economic stability, 

increased employment, 

and higher levels of 

local control.
9

Economic Sensitivity and Risk Analysis for Small-Scale Wood Pellet Systems—an Example From Southeast Alaska

depreciable capital cost, and taxes. However, for the THRHA system, no debt 

payment is included because financing was not involved.

The RETScreen program was used for all financial analyses, including the 

inputs specified in table 1. RETScreen is a Microsoft Excel®1 add-in used internationally to model several types of renewable energy technologies, including biomass 

energy (NRC ES 2015). A standard RETScreen project consists of a five-step 

analysis: energy model, cost analysis, greenhouse gas-emissions analysis, financial 

summary, and risk and sensitivity analysis. The RETScreen program can be used to 

evaluate the energy production, life cycle costs, and greenhouse gas emissions 

reduction for different biomass energy projects. It can be used for central grid 

connected systems, isolated grid, or stand-alone electrical generation projects, as 

well as thermal systems (the focus of the current study). Climatic data for modelling 

thermal loads were provided through RETScreen’s worldwide database of groundbased weather stations (using data for Juneau, Alaska). 

In sensitivity analyses, RETScreen adjusts the variables over a user-defined 

range, displaying results at 0 percent change, high-level change, and intermediatelevel change (i.e., halfway between 0 and the high level). The sensitivity analysis 

was conducted using the variables listed in table 2. Sensitivity results were presented over a range of plus or minus 20-percent compared to the base case (although 

RETScreen allows for other ranges to be evaluated). Although the 20-percent level 

was arbitrary, it was determined that this level would encompass most realistic 

scenarios for this wood energy system. Sensitivity analyses were conducted for 

response variables of NPV, IRR, and payback period. In general, sensitivity analysis evaluates the influence of selected risk parameters over a range of values, and 

the parameter having the greatest influence in the index can be verified (da Silva et 

al. 2014). Initial system cost and wood pellet cost were considered in a sensitivity 

analysis that evaluated IRR, NPV, and payback at three levels.

1 The use of trade or firm names in this publication is for reader information and does not 

imply endorsement by the U.S. Department of Agriculture of any product or service.

Table 2—Variables modelled in Monte Carlo simulations of Tlingit Haida Regional 

Housing Authority wood energy system, Juneau, Alaska

Variable Low

Medium

(base case) High

Alternative fuel cost (dollars per liter of heating oil) 0.925 1.19 1.45

Wood pellet fuel cost (dollars per tonne) 220.46 330.69 440.92

Fuel escalation rate (percent per year) 2 3 4

Heating load (percent per year) 0.06 0.09 0.12
10

GENERAL TECHNICAL REPORT PNW-GTR-959

Monte Carlo Simulations

Monte Carlo simulations were conducted to account for uncertainty in future 

operating conditions. Simulations were created for NPV, IRR, payback period, 

and cost:benefit ratio based on financial indicator values generated from randomly 

selected sets of input parameters, within a predetermined user-defined range. In 

the RETScreen simulations, the input parameters related to the base case operating 

parameters that were modelled for a single financial result. The simulation itself 

consisted of (1) generating 500 random values for each input parameter, using a 

normal distribution; and (2) multiplying each random value by a user-specified 

variability. Both the number of simulations and the distribution of input values 

were fixed by the RETScreen software, although it is recognized that a more robust 

analysis would allow for different levels of these parameters.

Because the set of random numbers was fixed, exactly the same results were 

obtained whenever the same input parameters were specified. In the current study, 

a level of risk of 5 percent was specified. Thus the minimum level of confidence 

was the 2.5 percentile of the 500 values generated by the Monte Carlo simulation 

(i.e., the 13th ranked value). The maximum level corresponded to the 487th ranked 

value for each simulation of 500 values. For the THARHA wood pellet simulations, 

parameters were varied between a “high” level and a “low” level (table 2). These 

included alternative fuel cost (heating oil), wood pellet cost, fuel cost escalation, 

and heating load. It was determined that these levels of parameters would accurately reflect conditions encountered under normal operating conditions. 

For each simulation, all parameters were held constant except for the variable 

of interest, which was varied to either the high or low level. Median and 95-percent 

confidence intervals were calculated for each simulation. This process was repeated 

for NPV, IRR, and payback period. Initial system cost was also modelled versus 

alternative fuel cost over this same range. Financial metrics, including NPV, IRR, 

and simple payback, were reported for each pair of values.

Results

The THRHA’s flagship wood pellet energy system in Juneau, Alaska, is already 

providing tangible benefits after just its first year in operation. The base case results 

(i.e., actual operating conditions over the past year) indicate a strong economic 

return, with median IRR of 21.6 percent, median equity payback period of 5.0 

years, and median NPV of $191,189 (table 3) (fig. 1). However, future changes in 

market conditions, such as varying market costs of wood pellets or heating oil, 

could further influence project economics. Indeed, over the past year (2016), heating 

oil prices dropped to levels below those used in this research.

The THRHA’s flagship 

wood pellet energy 

system in Juneau, 

Alaska, is already 

providing tangible 

benefits after just its 

first year in operation.
11

Economic Sensitivity and Risk Analysis for Small-Scale Wood Pellet Systems—an Example From Southeast Alaska

Table 3—Risk analysis for financial results for wood energy base case, Tlingit-Haida Regional Housing 

Authority, Juneau, Alaskaa

Financial result Units

Mean (lower 5 percent CI, 

upper 5 percent CI)

Lower 5 percent 

confidence interval

Upper 5 percent 

confidence interval

Pre-tax internal rate of return Percent 21.6 (14.2, 28.7) 14.2 28.7

Equity payback Years 5.0 (0.1, 7.5) 0.1 7.5

Net present value Dollars 191,189 (127,697, 335,901)

Annual life cycle savings Dollars per year 12,238

Benefit:cost ratio None 3.94

a

 Monte Carlo simulations, n = 500. CI = confidence interval.

600,000

500,000

400,000

300,000

200,000

100,000

0

-100,000

Cumulative cashflows (dollars)

012345678 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Year

This research provides planning information for THRHA to manage risk and 

plan for uncertainty in the event of changes in wood fuel cost or alternative fuel 

cost (i.e., heating oil). By modelling parameters at three different levels, most of the 

expected near-term operating conditions were accounted for while encompassing a 

likely range of risk and uncertainty. 

Several parameters were modelled at levels other than “business as usual” 

levels to account for future uncertainty. They were alternative fuel cost (modelled 

Figure 1—Simple payback period of wood energy system at Tlingit-Haida Regional Housing Authority Warehouse 

(indicating payback period of 5 years for base case). 
12

GENERAL TECHNICAL REPORT PNW-GTR-959

from $0.925 to $1.45 per liter), wood pellet cost (modelled from $220.46 to $440.92 

per tonne), fuel escalation rate (modelled from 2 to 4 percent annually), and heating 

load (modelled from 0.06 to 0.12 kW/m2 (table 2). The most favorable wood energy 

economics occurred under assumptions of high heating oil prices (table 4). 

When heating oil cost was modelled at $1.45 per liter, median project IRR was 

31.0 percent, corresponding to an equity payback of 3.4 years. Similarly, high 

assumed heating loads also resulted in favorable project economics (IRR of 28.5 

percent and equity payback of 3.7 years). Low assumed wood pellet costs ($220.46 

per tonne) also resulted in favorable project economics (IRR of 29.7 percent and 

Table 4—Monte Carlo simulation for parameters described in table 3 (level of risk is 5 percent for all cases) 

Parameter Units Median (upper C.I.*, lower C.I.*) Scenario

Internal rate of return Percent 21.1 (14.2, 28.7) Base case

Equity payback Years 5.1 (0.1, 7.5) Base case

Net present value Dollars 230,417 (127,697, 335,901) Base case

Internal rate of return Percent 9.7 (0.6, 16.9) Low heating oil cost 

Equity payback Years 10.0 (0.0, 19.6) Low heating oil cost

Net present value Dollars 47,862 (-21,735, 126,809) Low heating oil cost

Internal rate of return Percent 31.0 (23.6, 39.9) High heating oil cost

Equity payback Years 3.4 (0.2, 4.5) High heating oil cost

Net present value Dollars 321,728 (223,777, 424,277) High heating oil cost

Internal rate of return Percent 29.7 (23.9, 37.3) Low wood pellet cost 

Equity payback Years 3.6 (0.2, 4.5) Low wood pellet cost

Net present value Dollars 304,258 (225,947, 384,679) Low wood pellet cost

Internal rate of return Percent 11.4 (0.3, 20.2) High wood pellet cost 

Equity payback Years 8.9 (0.0, 19.7) High wood pellet cost

Net present value Dollars 65,486 (-27,320, 168,775) High wood pellet cost

Internal rate of return Percent 19.9 (12.9, 27.3) Low fuel price escalation 

Equity payback Years 5.2 (0.1, 7.8) Low fuel price escalation

Net present value Dollars 156,235 (78,252, 236,602) Low fuel price escalation

Internal rate of return Percent 22.3 (15.2, 29.8) High fuel price escalation

Equity payback Years 5.0 (0.2, 7.2) High fuel price escalation

Net present value Dollars 219,337 (120,198, 321,094) High fuel price escalation

Internal rate of return Percent 13.9 (8.3, 19.3) Low heating load 

Equity payback Years 7.5 (0.1, 11.1) Low heating load

Net present value Dollars 94,445 (35,594, 156,103) Low heating load

Internal rate of return Percent 28.5 (19.8, 38.1) High heating load 

Equity payback Years 3.7 (0.2, 5.4) High heating load

Net present value Dollars 287,309 (172,466, 405,006) High heating load

* 95 percent confidence. CI = confidence interval.
13

Economic Sensitivity and Risk Analysis for Small-Scale Wood Pellet Systems—an Example From Southeast Alaska

equity payback of 3.6 years). Project economics were least favorable under 

assumptions of high wood pellet cost ($440.92 per tonne) and low alternative fuel 

cost ($0.925 per liter). Under the range of conditions evaluated, fuel cost escalation 

was seen to have the least impact on project economics. The sensitivity analysis 

found that the most favorable economic results occurred when low system costs 

were coupled with low wood fuel costs (table 5). 

In this case, IRR was 32.6 percent (versus a base case of 21.3 percent). Not 

surprisingly, high system costs combined with high wood fuel costs resulted in the 

least favorable economics (IRR of 13.1 percent). Initial system cost was also 

modelled versus alternative fuel cost. Here, parameter values were also varied from 

+20 percent to -20 percent using the procedures described earlier. Here, IRR varied 

from 37.3 percent to 9.0 percent when varying heating oil costs over this same 

range (table 5). 

Table 5—Sensitivity analysis showing net present value (NPV), internal rate of return (IRR), and payback 

period for capital costs versus wood fuel cost, alternative fuel cost, fuel escalation rate, and heating load 

(sensitivity range of 20 percent) 

- - - - - - - - - - - - - - - - - - - - - - - - - - - Initial system cost ($) - - - - - - - - - - - - - - - - - - - - - - - - - - - 

52,003 65,003 78,004

-20 percent 0 percent +20 percent

Wood fuel cost

(dollars per year)

IRR

(percent)

NPV

($)

Equity 

payback

(years)

IRR

(percent) NPV ($)

Equity 

payback

(years)

IRR

(percent) NPV ($)

Equity 

payback

(years)

Percent Dollars Years Percent Dollars Years Percent Dollars Years

12,857 -20 percent 32.6 271,133 3.3 26.6 258,132 4.0 22.5 245,132 4.8

16,072 0 percent 26.1 200,087 4.1 21.3 187,087 5.0 17.9 174,086 6.0

19,286 +20 percent 19.4 129,042 5.5 15.7 116,041 6.8 13.1 103,041 8.0

Alternative fuel cost

(dollars per year)

Percent Dollars Years Percent Dollars Years Percent Dollars Years

22,266 -20 percent 14.2 77,053 7.4 11.2 64,052 9.0 9.0 51,052 10.6

27,833 0 percent 26.1 200,087 4.1 21.3 187,087 5.0 17.9 174,086 6.0

33,399 +20 percent 37.3 323,122 2.8 30.4 310,121 3.5 25.7 297,121 4.2
14

GENERAL TECHNICAL REPORT PNW-GTR-959

Discussion and Conclusions

Wood pellet heating has been considered for heating a 929-m2 warehouse in Juneau, 

Alaska. This system provides a good example of small-scale thermal applications 

in temperate climates and is part of an effort to establish wood pellet infrastructure 

in remote areas. The successful operation of this system could catalyze development of similar systems owned by the THRHA in southeast Alaska. As with many 

remote communities, a lack of infrastructure, including limited road access, means 

that wood pellets must be transported long distances (in this case, approximately 

1290 km by water) to reach local markets. However, a strategic goal for wood 

energy development here could be to establish a wood pellet plant in southeast 

Alaska, supplying wood fuel from local resources. Doing so would reduce transportation distances and associated costs, considerably.

Despite these barriers, this research has demonstrated favorable project 

economics when wood pellet use was simulated under a wide range of expected 

operating conditions. Project economics were generally strong for IRR, NPV, 

payback, and cost:benefit ratio. However, if commercial wood pellet production 

were to become established locally in southeast Alaska, and pellets were to become 

less expensive, project economics would become even more favorable. A limitation 

of this research was that the Monte Carlo simulation was limited to 500 trials, and 

specific distributions could not be indicated (other than the normal distribution). 

However, both of these were due to limitations of the software program being used 

(RETScreen), and it is believed that the parameters and economic results obtained 

in this study are realistic for many similarly scaled wood pellet thermal systems. 

This research underscores the importance of local energy production, local 

resource use, and new employment as a strategic goal for rural communities in 

Alaska. This is particularly important for Alaska Native organizations, many of 

which rely on local resources for subsistence purposes, including energy generation. 

As of early 2017, the THRHA has wood energy projects either completed or under 

development in three other communities in southeast Alaska.

This research has 

demonstrated 

favorable project 

economics when 

wood pellet use was 

simulated under a wide 

range of expected 

operating conditions. 

Project economics 

were generally strong 

for IRR, NPV, payback, 

and cost:benefit ratio.
15

Economic Sensitivity and Risk Analysis for Small-Scale Wood Pellet Systems—an Example From Southeast Alaska

This research found several items of practical significance for communities pursuing wood energy projects similar to the one studied here. Economic 

performance was favorable over the range of normal operating conditions, even 

when paying a relatively high price for wood fuel. However, when wood fuel cost 

increased by even 33 percent (i.e., to $440.92 per tonne versus the base case of 

$330.69 per tonne), project IRR dropped considerably. Thus, wood pellet users in 

southeast Alaska will need to pursue options to reduce delivered fuel costs, chief 

among them could be the establishment of local pellet production facilities. Initial 

capital costs and fuel costs were also found to influence project economics over 

a wide range. This is important because in many cases, wood energy systems 

are installed as part of more extensive, yet related, construction projects. Thus, 

accurately accounting for the initial wood energy capital cost will be integral to a 

rigorous economic analysis.

Establishing a local wood pellet production facility in southeast Alaska, and 

the potential for lower cost wood fuel, should bode well for the project economics 

of this wood energy system and others. These results and findings could be relevant 

to pellet systems being installed in other locations in southeast Alaska, such as 

Haines. Similar wood pellet systems may soon be operating in many other regions 

in Alaska and elsewhere, where project feasibility and design studies are being 

conducted. This research should help inform these efforts, especially in rural communities that are just starting to embark on wood energy projects. 

English Equivalents

When you know: Multiply by: To get:

Kilometers (km) 0.621 Miles

Square meters (m2) 10.76 Square feet

Liters (L) 0.265 Gallons

Tonnes 1.102 Tons

Thus, wood pellet 

users in southeast 

Alaska will need to 

pursue options to 

reduce delivered fuel 

costs, chief among 

them could be the 

establishment of local 

pellet production 

facilities.
16

GENERAL TECHNICAL REPORT PNW-GTR-959

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