NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Technical Manual for the SAM

Biomass Power Generation 

Model

Jennie Jorgenson, Paul Gilman, and 

Aron Dobos

Technical Report

NREL/TP-6A20-52688

September 2011
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

1617 Cole Boulevard

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Technical Manual for the SAM

Biomass Power Generation 

Model

Jennie Jorgenson, Paul Gilman, and 

Aron Dobos

Prepared under Task No. 2940.5009

Technical Report

NREL/TP-6A20-52688

September 2011
NOTICE

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iii

Acknowledgments

This work was supported under a Strategic Initiative by NREL’s Office of Planning and 

Analysis.

The authors would like to acknowledge the SAM development team at NREL, 

specifically Nate Blair for professional guidance and Steve Janzou, Mike Wagner, Ty 

Neises, and Mike Kasberg for technical and software integration support. Additionally, 

special thanks go to Dan Getman (NREL) and Chris Perkins (NREL) for contributing to 

the Biopower Atlas Web service development.

The authors also thank Maggie Mann (NREL), Anelia Milbrandt (NREL), Jerry Davis 

(NREL), Andy Aden (NREL), Scott Haase (NREL), Randy Hunsberger (NREL), Richard 

Bain (NREL), Brad Woods (McHale & Associates, Inc.), and Bob Cleaves (Biomass 

Power Association) for their instrumental guidance in both model development and 

documentation. 
iv

Executive Summary

This technical manual provides context for the implementation of the biomass electric 

power generation performance model in the National Renewable Energy Laboratory’s 

(NREL’s) System Advisor Model (SAM). Additionally, the report details the engineering 

and scientific principles behind the underlying calculations in the model. The framework 

established in this manual is designed to give users a complete understanding of behindthe-scenes calculations and the results generated. 

The addition of a biomass power model to SAM facilitates comparison of many 

renewable technologies regarded as “intermittent” to a “baseload” solid-fueled 

technology. The biomass power performance model is more detailed than the standard 

“generic” system model that is traditionally used for baseload-type generation and 

computes performance metrics such as the gross heat rate and capacity factor. The 

biomass power model generates this performance data based on specifications of the 

system's physical components, as well as information about various biomass feedstocks 

that can be automatically obtained for a particular location from an NREL Web service. 

The model's flexible design allows it to be used to model power systems that burn 

different types of solid fuels, including biomass and coal. 

This technical manual describes the biomass power model's internal calculations and the 

engineering principles that guide them. It supplements the user documentation that comes 

with SAM, which describes the user inputs and how to operate the model. 
v

Table of Contents

List of Figures.............................................................................................................................................vi

List of Tables...............................................................................................................................................vi

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

1.1 SAM Overview....................................................................................................................1

1.2 Biomass Electric Power Generation ....................................................................................1

1.3 Modeling Approach .............................................................................................................3

1.4 Abbreviations and Variable Names.....................................................................................4

2 Feedstock ..............................................................................................................................................7

2.1 Feedstock Availability .........................................................................................................7

2.2 Biomass Properties...............................................................................................................7

2.2.1 Moisture Content ........................................................................................................7

2.2.2 Heating Value .............................................................................................................8

2.3 Transportation......................................................................................................................9

2.4 Handling and Preparation ....................................................................................................9

2.4.1 Equilibrium Moisture Concentration (EMC)............................................................10

3 Combustion System ...........................................................................................................................14

3.1 Introduction........................................................................................................................14

3.1.1 Principles of Combustion..........................................................................................14

3.1.2 Types of Combustors................................................................................................14

3.2 Energy Balance ..................................................................................................................15

3.2.1 Dry Flue Gas Losses.................................................................................................15

3.2.2 Moisture in Fuel........................................................................................................16

3.2.3 Latent Heat................................................................................................................17

3.2.4 Unburned Fuel ..........................................................................................................18

3.2.5 Radiation and Miscellaneous....................................................................................18

3.3 Mass Balance .....................................................................................................................19

4 Steam Rankine Cycle..........................................................................................................................20

4.1 Efficiency Fluctuations......................................................................................................20

4.2 Minimum Load and Maximum Overdesign Operation .....................................................20

4.3 Temperature Correction Mode...........................................................................................21

5 Parasitic Losses..................................................................................................................................23

6 Dispatch Schedule..............................................................................................................................24

7 Model Outputs .....................................................................................................................................25

8 Emissions Comparison ......................................................................................................................27

8.1 Stack Emissions Calculations............................................................................................28

8.2 Ash Production Calculations..............................................................................................28

9 Summary..............................................................................................................................................30

References .................................................................................................................................................31
vi

List of Figures 

Figure 1. Biomass power process flow diagram................................................................. 2

Figure 2. Average EMC for woody biomass in various U.S. locations............................ 13

Figure 3. Example monthly energy output for a theoretical 60 MW biomass power plant 

in Fargo, North Dakota ......................................................................................... 26

Figure 4. Example change in byproducts and emissions by displacing an equivalent coal 

fuel plant by a theoretical 60 MW biomass power plant in Fargo, North Dakota 27

List of Tables

Table 1. Select Biomass Power Plants in the United States ............................................... 2

Table 2. Abbreviations Used in this Document.................................................................. 4

Table 3. Variable Naming Conventions.............................................................................. 5

Table 4. Default Biomass Moisture Contents..................................................................... 8

Table 5. Average Monthly Weather Characteristics of Various U.S. Locations.............. 12

Table 6. Example Performance Metrics for a Theoretical 60 MW Biomass Power Plant in 

Fargo, North Dakota ............................................................................................. 26

Table 7. Average Emissions Rates for Power-Producing Technologies .......................... 28

Table 8. Average Industry Heat Rates for Fossil Fuel Plants (2009) ............................... 29
1

1 Introduction

This report provides detailed documentation regarding the engineering methodology 

associated with the System Advisor Model (SAM) biomass power generation model. 

Broadly, the model can be applied to any power generation technology utilizing a solid 

fuel that can be defined by its content and heating value. A solid-fueled combustion 

power plant can be broken into three discrete operations, each with its own mass and 

energy balances: the feed yard where the fuel is stored, the combustion system, and the 

turbine. This simplified approach makes it possible to model a range of feedstocks. For 

instance, the performance of a biomass-fired power plant can be modeled just as easily as 

a coal-fired power plant or co-fired plant. 

1.1 SAM Overview 

SAM is a system performance and economic model designed to facilitate decision 

making for project developers, financiers, and policymakers, as well as provide robust 

analysis capabilities for energy researchers. SAM models grid-connected power systems 

using biomass, solar, wind, and geothermal electric generation technologies, as well as 

small-scale solar water heating systems. For each technology, SAM pairs a performance 

model with a financial model to calculate the cost of energy over a multi-year analysis 

period. SAM makes economic calculations for residential and commercial projects that 

buy and sell power at retail rates and for larger-scale power plants that sell electricity

through a power purchase agreement (PPA) to a utility.

The model calculates the cost of generating electricity (or the value of energy saved by a 

solar water heating system) based on user-specified inputs describing a system's physical 

configuration, its location, and the cost of installing and operating the system. SAM's 

financial model can represent various complex financing structures, taxes, and incentives.

SAM displays performance and financial model results in numerous tables and graphs, 

which range from a table of basic metrics, such as levelized cost of energy (LCOE) and 

total annual output, to tables of detailed hourly simulation results. A cash flow table 

shows project income, expenses, financing costs, incentive payments, and other details on 

a year-by-year basis over the full multi-year analysis period.

Advanced modeling options include parametric studies, sensitivity, and statistical 

analyses that can help investigate impacts of variations and uncertainty in performance, 

cost, and financial assumptions on model results. SAM's scripting language, SamUL,

makes it possible to automate complex analysis tasks in batch mode.

1.2 Biomass Electric Power Generation

Many viable technologies convert biomass into electricity. As of 2009, biomass-powergenerating capacity in the United States totaled about 12 GW, representing about 1.1% of 

the total capacity [1, 2]. Table 1 shows a sampling of current biomass power plants in the 

United States. Total biomass generation capacity in the world is approximately 50 GW

[3]. Most of these power plants are well below 100 MW and are primarily located in the 

United States and Europe. However, biomass power capacity is expected to rise in future 

years [3]. 
2

Table 1. Select Biomass Power Plants in the United States

Plant Location Capacity 

(MW) Feedstock(s) Year Online

Kettle Falls, WA 46.0 Primary Mill Residues 1983

New Meadows, ID 5.8 Wood Wastes 1983

Bethlehem, NH 15.0 Wood Wastes 1987

Tracy, CA 23.0 Wood Wastes 1990

Hurt, VA 79.5 Mill, Forest Residues 1994

Eagar, AZ 3.0 Forest Residues 2004

Snowflake, AZ 24.0 Wood Wastes, Mill Residues 2008

Currently, the primary approach for biomass conversion to electricity is also the most 

time-tested: combustion. Other biomass-to-power technologies are less straightforward 

and not yet as commercially demonstrable [3]. For example, biomass gasification 

converts biomass into a volatile “synthesis gas” (or syngas), which is combustible [3]. 

Another technology, anaerobic digestion of biomass, results in the formation of methane, 

which can be refined into various chemicals or similarly combusted [3]. The SAM 

biomass power model is limited to simple biomass combustion due to the known viability 

of the technology compared to more recent gasification and anaerobic digestion methods

[4], as well as its direct applicability to generating electricity. 

Biomass power plants are very similar in operation to coal-fired power plants, as 

illustrated in Figure 1. The biomass is delivered to the plant where storage piles or silos 

accommodate extra biomass that is not immediately fed to the plant. Biomass can 

undergo various preprocessing steps including size reduction, separation, and drying 

before being fed to the combustor. In the combustor, biomass oxidation occurs under 

excess air. The exothermic reaction heats the combustion gases, which generate steam via 

heat exchangers to power a typical Rankine-cycle turbine and electric generator.

Figure 1. Biomass power process flow diagram
3

Biomass-fired power plants employ the same basic unit operations as a coal-fired power 

plant, making co-fired power plants that can run on either feedstock (or both in parallel)

financially appealing. Since coal is more energy dense and the resource is more 

geospatially concentrated relative to biomass, coal transport can be economical over 

greater distances. Thus, most coal power plants are at least an order of magnitude larger 

than biomass power plants. Coal power plants can scale more easily, resulting in reduced

expenses per megawatt (MW) capacity added. Additionally, larger plants can utilize 

higher pressures and temperature in steam cycles, leading to higher energy conversion 

efficiencies [5]. 

The SAM biomass power model can be applied to solid-fueled plants of any size and 

with any mix of biomass or coal fuels. Although the SAM model has no explicit limits on 

plant size, users should be aware of the feedstock input, component cost, and 

performance considerations and scale them appropriately to the system they are 

modeling. 

1.3 Modeling Approach

The model consists of four primary modules, each represented by an input page in the 

user interface. 

• The Climate page specifies the weather data that goes into hourly performance 

calculations. The ambient dry bulb temperature, atmospheric pressure, and 

relative humidity data from the weather file affect the performance of the 

plant.

• The Feedstock page specifies the biomass resource properties, availability,

and maximum radius for collection. The user can also describe additional nonstandard feedstock types by supplying the elemental composition, moisture 

content, and heating value. 

• The Plant Specification page allows users to specify components of the 

system, such as the drying method, percent excess air, and combustor system 

type. It also estimates various efficiency losses that apply to the combustion 

system, which change in real-time to reflect user-made modifications. Users 

can see the direct effect of various inputs, such as percent excess air, on boiler 

efficiency. Actual efficiencies will be calculated during simulations. The user 

also specifies the rated cycle conversion efficiency, minimum load, and max 

over design operation of the Rankine cycle. Additionally, the user can enable 

a time of dispatch schedule, which allows the user to define an operation 

schedule for the plant over the course of a year.

• The Emissions Comparison page inputs do not affect the modeled 

performance of the plant but generate data about avoided emissions and ash 

production when compared to a displaced power generation source.

During simulations, SAM calculates performance metrics such as electricity produced, 

capacity factor, heat rate, and thermal efficiency. The specifics of these outputs and 

associated calculations are detailed in Section 7. 
4

Broadly, SAM's biopower model requires inputs, such as location and system design 

parameters, and calculates the estimated capacity of a potential biomass power plant as 

well as its capacity factor, heat rate, and energy produced. This distinguishes SAM from 

other biopower models, which typically require that the plant's performance parameters 

be specified as inputs. For example, the Electric Power Research Institute (EPRI)

BIOPOWER Model requires the capacity factor and heat rate as inputs and generates 

biomass usage and thermal efficiency outputs [5]. SAM, in a sense, operates in reverse

and is useful in cases where plant metrics such as heat rate are not already known. This 

makes the SAM model particularly appropriate for scoping and screening various 

possible plant configurations, locations, and costs to help navigate towards a feasible and 

economic overall design. Advanced built-in analysis capabilities, such as parametric 

simulation coupled with detailed financing structures, allow users to explore a wide 

variety of options and assess the various tradeoffs involved.

The internal calculations in SAM's biopower model use a different timescale than the 

solar and wind models, which operate on hourly timescales (or smaller) to capture the 

variability of the solar and wind resources. However, biopower is typically a baseload 

power source that runs almost constantly on its fuel source, and biomass fuel properties 

generally remain constant over the hourly timescale. Therefore, the moisture content

instead is calculated over a monthly timescale. Hourly ambient temperature influences 

both boiler and turbine efficiency, so the efficiencies are calculated hourly and monthly

and averaged before incorporating the efficiency loss due to biomass moisture, which is 

presumed to remain constant over the course of an hour. 

1.4 Abbreviations and Variable Names

Italicized phrases in this manual refer to terms found in the SAM user interface. 

Table 2. Abbreviations Used in this Document

Abbreviation Description

ABMA American Boiler Manufacturer's Association

Btu British thermal unit

EMC equilibrium moisture content

EPA Environmental Protection Agency

EPRI Electric Power Research Institute

FBC fluidized bed combustor

HHV higher heating value

LHV lower heating value

MACT Maximum Achievable Control Technology

NREL National Renewable Energy Laboratory

O&M

psi

operation and maintenance

pounds per square inch (pressure)

SAM System Advisor Model
5 

Table 3. Variable Naming Conventions 

Name Description Units Units Abbrv. 

a Percent excess fed air (molar basis) Percent % 

A Ash weight percent (dry) Percent % 

ash Ash production tons per year ton/yr 

ܾሶ Biomass feed rate Pounds per hour lb/hr 

C Carbon weight percent (dry) Percent % 

CF Capacity factor Percent % 

Cgross Gross generating capacity Megawatts MW 

Cnet Net generating capacity Megawatts MW 

EMC Equilibrium moisture content weight 

percent (dry) Percent % 

ex

Efficiency loss as a percent of total 

heat input due to x

Percent % 

H Hydrogen weight percent (dry) Percent % 

H2

Diatomic hydrogen weight percent 

(dry) Percent % 

HHV Higher heating value British thermal units per 

pound Btu/lb 

HRgross Net heat rate Million Btu per megawatt-hr MMBtu/MWh 

HRnet Gross heat rate Million Btu per megawatt-hr MMBtu/MWh 

Hx Enthalpy of species x British thermal units per hour Btu/hr 

LHV Lower heating value British thermal units per 

pound Btu/lb 

Mdb Moisture content (dry basis) Percent % 

Mwb Moisture content (wet basis) Percent % 

݉ሶ x Mass flow rate of species x Pounds per hour lb/hr 

N Nitrogen weight percent (dry) Percent % 

݊ሶ Mass flow rate of dry gas per fuel 

input Pounds per Btu fuel lb/Btu 

O Oxygen weight percent (dry) Percent % 

Ogross Gross annual output Kilowatt-hours kWh 

Onet Net annual output Kilowatt-hours kWh 

P Parasitic load, as percent of gross 

capacity Percent % 

Pb

Barometric pressure from weather 

file Pounds per square foot psi 

Pvd

Saturated pressure of water vapor 

at ambient temperature Pounds per square foot psi 

ܳሶ

x Heat transfer rate British thermal units per hour Btu/hr 

R Relative humidity Percent % 

r Relative humidity Fraction (%/100) 

TA

Monthly average ambient 

temperature Degrees Celsius °C 

TC Temperature Degrees Celsius °C 

TF Temperature Degrees Fahrenheit °F 
6

TFG Flue gas temperature Degrees Celsius °C

TK Temperature Kelvin K

Tn

Hourly weather file temperature (nth

hour) Degrees Celsius °C

ηx

Efficiency as a fraction of heat input 

for component x Fraction (%/100)
7 

2 Feedstock 

2.1 Feedstock Availability 

Biomass feedstock availability is highly dependent on location, climate, and season. 

SAM biopower can use the NREL Biopower Atlas to estimate regional biomass resource 

availability. Primarily, biomass residues calculated by Biopower Atlas fall into two 

categories: crop and woody residues. Crop residues can be further broken down into field 

residues and process residues. Bagasse is a residue that is a product of sugarcane and 

sorghum processing. Barley straw, corn stover, rice straw, and wheat straw are all field 

residues that remain after harvest. Woody residues can come from a variety of sources. 

Forest residues usually refer to lumber that is unfit for sawmill processing, such as 

smaller-diameter branches or stumps, misshapen trees, and undergrowth that may fuel 

forest fires. Primary mill residues are wastes generated by mill processes. Urban wood 

waste includes prunings from residential areas, as well as woody construction materials 

and used pallets. 

By entering a weather file location on the Climate page and specifying a collection 

radius, SAM can directly query the NREL’s Biopower Atlas for feedstock data at a 

particular location. The Web service will return annual resource potential for the eight 

standard biomass resource types in tons per year, assuming that a certain amount of 

harvest residue must remain on the field to prevent soil erosion and maintain nutrients 

[6]. These data become visible to the user as resource available (in bone dry tons per 

year). However, it may not be realistic to gather all available residues due to supply and 

demand considerations. Thus, the monthly obtainability adjusts the available biomass 

feedstock (at 100% obtainability) to the realistically obtainable biomass feedstock. The 

default resource obtainability factor is 50%; however, the value is highly region and 

feedstock specific. The U.S. Energy Information Administration publishes biomass 

supply curves that may be helpful when estimating the amount of biomass that is 

economically obtainable [7]. 

2.2 Biomass Properties 

2.2.1 Moisture Content 

Biomass moisture content is highly variable and dependent on biomass type, season, and 

location. Additionally, moisture content is expressed on a dry or wet basis [8]. Dry basis 

moisture (Mdb) content is a ratio of the weight of water to the dry biomass weight. Wet 

basis moisture (Mwb) content is the ratio of the weight of water to the wet biomass weight. 

The two moisture contents are related in the following manner [8]: 

ܯ௪௕ ൌ 

௕ௗܯ

ሺ1 ൅ ܯௗ௕ሻ

ܯௗ௕ ൌ 

௕௪ܯ

ሺ1 െ ܯ௪௕ሻ

The moisture content in the SAM user interface is based on the wet moisture content and 

represents the yearly average of the moisture content of biomass as collected. The 
8 

simulation engine adjusts the moisture content based on monthly average ambient 

temperature and humidity. In the SAM user interface, the wet moisture content is 

required since its definition is slightly more intuitive. However, many publications report 

the dry basis weight, so conversion may be necessary. Table 4 shows the default moisture 

content corresponding to biomass type. Note that these values are hardly universal and 

are only shown for basis of comparison. 

Table 4. Default Biomass Moisture Contents 

Mwb Mdb

Bagasse [9] 50% 100% 

Barley Straw [10] 16% 19% 

Corn Stover [8] 30% 42% 

Rice Straw [11] 67% 200% 

Wheat Straw [8] 12% 14% 

Forest Residues [12] 44% 78% 

Primary Mill Residues [12] 48% 91% 

Urban Wood Residues [13] 12% 14% 

2.2.2 Heating Value 

The heating value of a fuel describes the amount of energy released upon combustion per 

unit mass of fuel. Heating values can either be described as higher (gross) heating value 

or lower (net) heating value. The higher heating value (HHV) takes into account the 

latent heat of vaporization of water, meaning that it assumes all water vapor generated 

during combustion condenses back into water. The condensation of water increases the 

theoretical amount of energy available for use. More practically, however, the latent heat 

will not be recovered and the water remains in vapor form. In this case, the lower heating 

value (LHV) is more appropriate. SAM displays both the HHV and LHV. When only the 

LHV is known, the following equation can give an approximation for the HHV, which is 

a required SAM input for additional fuels [14]: 

ܪܪܸ ൌ ܪܮܸ ൅ 10.30 ሺܪଶ ൈ 8.94ሻ

where the heating values are in British thermal units per pound (Btu/lb) and H2 represents 

the mass percent of diatomic hydrogen in the fuel. 

Empirical relations exist between heating value and elemental composition. Thus, the 

heating value can be calculated if the elemental composition is known using the 

following formula [15]: 

20,600 ൅ 131ܰ ൅ ܪ ൈ ܥ51.2 ൅ ܪ2,230 െ ܥ232 െ ଶܥ3.55 ൌ ܸܪܪ

where C, H, and N are the dry biomass weight percent of carbon, hydrogen, and nitrogen, 

respectively. Online databases have vast collections of biomass compositions [16, 17]. 
9 

2.3 Transportation 

Increasing the collection radius generally increases the amount of available biomass 

determined by the Biopower Atlas but may cause feedstock prices to be prohibitively 

high due to transportation costs. Transporting biomass differs from transporting fossil 

fuels in two ways. Biomass has a significantly lower energy density and generally higher 

moisture content compared with coal and other fossil fuels. Additionally, biomass 

resources are more spatially distributed than fossil fuels, which are largely collected from 

extraction points such as mines or wells. Since long-distance biomass transportation is 

currently unfeasible, most biomass is delivered nearby via diesel trucks. At larger 

distances, railway transport may become more cost effective. Mahmudi and Flynn offer a 

comparison of railway- and truck-hauling costs for fuels [18]. A similar analysis with 

2008 diesel prices shows that rail transport becomes economical at a hauling distance of 

60 miles [19]. 

2.4 Handling and Preparation 

After delivery, biomass feedstock undergoes preparation for combustion. In general, the 

process involves four steps: receiving, processing, storage, and transport [20]. Each step 

requires capital and energy expenditures. Dryers, grinders, conveyors, separators, and 

storage bins may be necessary depending on feedstock. Additionally, biomass decay may 

become a consideration even if the biomass is stored in piles for a relatively short amount 

of time (1–2 months). Biomass decay may result in dry matter loss and thus reduced 

energy content. However, with proper storage techniques, the storage losses are minimal 

[21]. Good storage practices include covering biomass piles, rotating biomass on a “first 

in, first out” schedule, and maintaining a large particle size distribution within the pile 

[21]. When modeling a co-fired power plant with biomass and coal fuels, the user should 

note that the fuel preparation requirements are different (for instance, coal fuel requires 

pulverizing), which may result in additional capital and operating costs. 

The SAM biopower model assumes that biomass is properly stored and thus dry matter 

weight and heating value do not change over time. This assumption is reasonable for 1–2-

month storage periods. For longer storage times (6 months or more), losses become more 

significant and the heating value should be adjusted accordingly [22]. Studies indicate 

that 6 months of storage may reduce energy content by up to 20% [23, 24]. 

Biomass moisture content is highly seasonal and regional. Biomass feedstock can be 

handled in a few different ways. SAM provides three feedstock-handling options, which 

can be specified on the Plant Specs page: 

• Fed as received: The biomass does not undergo any substantial drying before 

being fed to the combustor. This option avoids drying costs but penalizes the 

boiler efficiency since evaporation of biomass moisture requires energy input. 

The season in which biomass is harvested also affects moisture content. 

Biomass harvested in the summer months can be up to 10% drier based on 

operating experience. This applies even to biomass that does not undergo 

intentional drying. However, this option means that all biomass will be fed at 

the moisture content specified in the user interface without implementing 

a dryer. 
10 

• Dry to equilibrium moisture content (EMC) assumes that the biomass is 

exposed to the ambient atmospheric conditions for a sufficient amount of time 

to reach EMC. The EMC calculations are detailed in Section 2.4.1. 

• Drying to specified moisture content involves including a dryer as an 

additional capital expenditure. Generally, adding a dryer will also increase the 

parasitic load of the plant and may add incremental operation and 

maintenance (O&M) costs. In theory, adding a dryer can increase the boiler 

efficiency by several percent, but this practice is not always used due to the 

other considerations [12]. 

2.4.1 Equilibrium Moisture Concentration (EMC) 

Biomass is hygroscopic, meaning that water absorbs and desorbs freely into biomass as 

dictated by the surrounding conditions. The properties of a specific biomass and the 

surrounding temperature and humidity determine the moisture concentration of biomass 

in equilibrium with its surroundings (the EMC). SAM calculates the EMC when the 

allow feedstock to air-dry option is selected on the Plant Specifications page. For this 

option, the biomass undergoes ambient drying during storage. SAM assumes that the 

storage system is equipped with a proper biomass-mixing device so that all biomass is 

equally exposed to ambient airflow. During storage, however, moisture composition does 

not change instantly. Depending on the type of biomass, pile shape, presence of bark, and 

size of chip, reaching the EMC may require anywhere from hours to months [25]. Thus, 

the equilibrium moisture levels are calculated on a monthly basis. Due to differences in 

desorption characteristics, EMC calculations differ for each type of biomass. Since the 

relations are empirical, equations also have different temperature and humidity 

conventions, as shown in the equations below. 

The weather data has monthly values for ambient temperature (in Celsius) and relative 

humidity. As mentioned, biomass properties change on a longer timescale. The following 

equation converts hourly temperatures to a monthly average temperature: 

ܶ஺ ൌ ∑ ܶ௡ ேൈଶସ

௡ୀ଴

ܰ ൈ 24

where TA is the monthly weather file ambient temperature (in degrees Celsius), Tn is the 

temperature of the n

th hour of the month, and N is number of days in the month. 

Calculations for a monthly averaged relative humidity utilize the same approach. 

2.4.1.1 Bagasse Equilibrium Moisture Concentration Calculation 

The following empirical equation determines the EMC of bagasse [26]: 

1,800 ൌ ܥܯܧ

ܴܭ ቈ ܹ

ሺ1 െ ܭܴሻ ൅

ଶܴଶܭଶܭଵܭ2 ൅ ܪܭଵܭ

቉ଶܴଶܭଶܭଵܭ ൅ ܪܭଵܭ൅1

The constants W, K, K1, and K2 are described by: 

ܹ ൌ െ57.70 െ 0.1982ܶ௄ ൅ 22.305ܶ௞

଴.ହ
11 

ܭ ൌ െ2,778.14 െ 2,042.09ܶ௄ ൅ 5,238.88ܶ௄

଴.ହ

ܭଵ ൌ െ70.42 െ 13.68ܶ௄ ൅ 180.22ܶ௄

଴.ହ

ܭଶ ൌ 194.01 ൅ 0.62ܶ௄ ൅ 51.48ܶ௄

଴.ହ

where EMC is represented on a percent dry basis, TK is the absolute temperature in 

Kelvin, and R is the relative humidity (%). 

2.4.1.2 Barley Straw Equilibrium Moisture Concentration Calculation 

Barley straw EMC calculations use the modified Chung-Pfost equation [27]: 

ܥܯܧ ൌ ൬ 1

െ0.14843൰ כ ln ൬

ܶ஼ ൅ 71.996

െ474.12 ൈ ln ሺܴሻ൰

where EMC is represented on a percent dry basis, TC is the absolute temperature in 

Celsius, and R is the relative humidity (%). 

2.4.1.3 Corn Stover Equilibrium Moisture Concentration Calculation 

Corn stover moisture calculations utilize the modified Oswin equation [28]. The values 

for the constants were described using the “stalk” component of corn stover, which 

includes both the stalk skin and stalk pith: 

ܥܯܧ ൌ ሺ10.9137 ൅ ሺെ0.0746 ൈ ܶ஼ሻሻ ൈ ൤ ܴ

ሺ1 െ ܴሻ൨

ଵ

ଶ.ସଵଵ଺

where EMC is represented on a percent dry basis, TC is the absolute temperature in 

Celsius, and R is the relative humidity (%). 

2.4.1.4 Rice Straw, Wheat Straw, and Woody Biomass Equilibrium Moisture 

Concentration Calculation 

Rice, wheat, and woody biomass (primary mill residue, forest residue, and urban wood 

residue) all use the same calculation. Rice straw desorbs marginally less moisture, 

however, making its EMC higher by about 10% [8]. Note that this equation is similar to 

the bagasse equation but with different constants [29]: 

1,800 ൌ ܥܯܧ

ݎܭ ቈ ܹ

ሺ1 െ ݎܭሻ ൅

ଶݎଶܭଶܭଵܭ2 ൅ ݎܭଵܭ

቉ଶݎଶܭଶܭଵܭ ൅ ݎܭଵܭ൅1

The constants W, K, K1, and K2 are described by: 

ܹ ൌ 330 െ 0.452ܶ௄ ൅ 0.00415ܶ௄

ଶ

௄0.0000000844ܶ ൅ ௄0.000463ܶ ൅ 0.791 ൌ ܭ

ଶ

ܭଵ ൌ 6.34 ൅ 0.000775ܶ௄ െ 0.0000935ܶ௄

ଶ
12 

ܭଶ ൌ 1.09 ൅ 0.0284ܶ௄ െ 0.0000904ܶ௄

ଶ

where EMC is represented on a percent dry basis, TK is the absolute temperature in 

Kelvin, and r is the fraction relative humidity (%/100). 

2.4.1.5 Equilibrium Moisture Concentration Calculation Example for Various 

U.S. Locations 

The following case study illustrates the effect of ambient temperature and relative 

humidity on the EMC for woody biomass. Table 5 shows the relevant weather file data in 

Fargo, North Dakota; Boulder, Colorado; Olympia, Washington; and Phoenix, Arizona. 

Table 5. Average Monthly Weather Characteristics of Various U.S. Locations 

Fargo, ND Boulder, CO Olympia, WA Phoenix, AZ 

Temp 

(°C) 

Relative 

Humidity

(%) 

Temp 

(°C) 

Relative 

Humidity

(%) 

Temp 

(°C) 

Relative 

Humidity 

(%) 

Temp 

(°C) 

Relative 

Humidity

(%) 

January -13.2 73.0 -1.0 57.0 3.7 88.9 12.1 48.1

February -11.6 75.7 0.6 59.7 5.3 82.7 12.4 44.9

March -1.4 75.6 3.6 52.8 6.3 76.0 18.5 37.8

April 6.8 65.4 9.2 49.0 8.1 77.7 21.8 26.8

May 13.6 61.6 13.6 51.9 12.1 75.1 25.9 22.7

June 18.6 64.6 18.3 49.4 15.8 71.7 32.3 20.8

July 21.4 67.1 23.1 48.9 18.0 68.6 33.3 35.3

August 20.1 67.6 21.2 50.5 18.0 68.2 32.4 34.2

September 15.5 72.6 16.9 43.3 14.5 75.3 29.6 36.1

October 8.1 70.8 10.8 49.2 9.7 86.9 22.7 36.6

November -2.3 82.5 3.4 58.8 6.3 89.8 17.3 46.9

December -10.9 74.6 -1.6 53.2 3.8 89.3 11.4 46.0

Figure 2 shows the corresponding EMCs for each month. Note that the EMC is calculated 

on a monthly basis, and thus, the lines between data points only indicate trends. The 

graph shows the dramatic effect that ambient conditions can have on biomass moisture 

content. 
13

Figure 2. Average EMC for woody biomass in various U.S. locations
14 

3 Combustion System 

3.1 Introduction 

3.1.1 Principles of Combustion 

After processing, the fuel is fed to the combustion system. Here, the term combustion 

system encompasses the combustion chamber and boiler where steam generation occurs. 

Combustion occurs when fuel-bound carbon reacts with an oxidant (oxygen gas, in this 

case). Generally, the reaction takes the form: 

ݐ݄݁ܽ ൅ ଶܱܥ ՜ ଶܱ ൅ ܥ

Breaking chemical bonds is an endothermic process, meaning it requires energy input. 

Conversely, forming new chemical bonds releases energy and thus is an exothermic 

process. Since combustion releases energy in the form of heat, breaking chemical bonds 

in the fuel must require less energy than the energy generated in making new bonds. A 

combustion system or furnace is simply a process unit that combines all necessary aspects 

of combustion: a fuel source (biomass), an oxidant (here, oxygen supplied by combustion 

air), and a spark. 

The model computes the efficiency of a combustion system as specified by the user. 

Here, the main inputs are moisture content, the biomass feedstock handling option, 

combustion system type, percent excess fed air, flue gas temperature, number of boilers, 

steam grade, and boiler overdesign factor. These inputs generate the system efficiency. 

The system efficiency and the heat content of the fuel determine the rate that steam is 

supplied to the turbine. 

3.1.2 Types of Combustors 

Many types of combustion systems are encountered in biomass power plants. Most 

commonly, a grate stoker furnace is designed to feed solid fuel onto a grate where 

burning occurs, with combustion air passing through the grate. Stokers are generally the 

least expensive of the three boiler types available in SAM and are best suited for large 

fuel feed rates (75,000–700,000 lb/hr) [14]. Since the moving grate continuously collects 

the unburned ash, maintenance costs are significantly lower than older combustors that 

simply burn a pile of fuel [5]. 

A fluidized-bed combustor (FBC) features a bed of fuel and sand or other inert substance 

that becomes suspended by the combustion air flowing upward. The combustion air 

functions both as an oxidant and suspension medium. This technology reduces the 

fluctuations in steam production associated with inconsistent feedstocks or switching 

fuels. Additionally, the combustion temperature is lower compared to both grate stoker 

and cyclone furnaces, which results in the reduced formation of pollutants [14]. However, 

capital costs and O&M costs generally increase [20]. 

Cyclone furnaces, like FBCs, allow for flexibility in fuel types and increase combustion 

efficiency over stoker boilers by feeding the fuel in a spiral manner. Cyclone furnaces are 

smaller and have less capital cost than FBCs and thus may be more suitable for smaller 

burn rates [14]. 
15 

For all three types of combustors, hot flue gases produced in the furnace travel upward to 

the boiler. The boiler is a heat exchanger that transfers thermal energy contained in the 

hot flue gas to boiler-feed water to generate steam. The steam goes on to power the 

turbine in a Rankine cycle and after condensation is recycled back to the boiler. 

For SAM calculations, the type of furnace selected solely impacts the percent-unburned 

carbon efficiency loss. In reality, the combustion system choice affects the capital costs 

and O&M costs, but SAM is not designed as a cost-predictive model. However, the 

furnace type also affects the boiler overdesign factor, flue gas temperature, steam grade 

selection, and number of boilers that may be installed. Thus, the user may want to 

consider the following guidelines when selecting a combustor type: 

1. Stoker boilers have the greatest unburned carbon loss at 3.5% [20]. For FBCs, the 

unburned fuel loss is 0.25% [20]. Cyclone furnaces fall in between the two at 3% 

[30]. However, the capital cost for FBCs may be 30%–100% higher [20]. The 

user should adjust the capital costs accordingly. 

2. The lower combustion temperatures in FBCs produce a less “severe” grade of 

steam. Typically, the steam temperature for FBCs is between 750–850°F and the 

pressure is around 600–850 psig [12]. Cyclone and grate stoker furnaces typically 

have higher temperatures of around 900°F and pressures of 1,200–1,500 psig 

[12]. The steam grade input should be adjusted accordingly. 

3. Grate furnaces sometimes require higher than 50% excess air levels [31]. FBCs 

can often operate below 20% excess air [31]. 

4. FBCs are often the best choice for systems where there is a high degree of 

heterogeneity in the properties of the fuel. 

5. Cyclone furnaces are not as commonly seen in biomass power plants but may be 

implemented if boiler size is a concern [14]. They are the smallest furnace and 

often have the lowest capital cost [14]. 

3.2 Energy Balance 

The energy balance on the combustion system in SAM follows the “Btu Method” as 

presented by Babcock and Wilcox [14]. The overall heating value of the fuel is penalized 

by various efficiency losses. The most significant efficiency losses in the combustion 

system are listed in the SAM user interface as: dry flue gas losses, moisture in fuel, latent 

heat, unburned fuel, and radiation and miscellaneous. Each of these losses will be 

explained in detail in Sections 3.2.1–3.2.5. The SAM simulation engine also calculates 

the penalty associated with moisture in air, but the significance on the overall efficiency 

is generally small relative to the other derates and is not displayed in the user interface. 

3.2.1 Dry Flue Gas Losses 

Combustion air enters the furnace at ambient temperature, where it is immediately 

subject to preheating by waste process heat, such as the spent flue gas exiting the stack. 

Regardless of how the air is preheated, a significant loss of enthalpy occurs when the 

combustion gas exits the plant at a much higher temperature than the temperature at 

which it was fed. Additionally, heating the gas liberated during combustion (carbon 
16 

dioxide) requires energy. In general terms, the rate of enthalpy loss can be calculated as 

follows: 

 ܪௗ௥௬ ௚௔௦ ௟௢௦௦ ൌ ሺܪଶ െ ܪଵሻ ݉ሶ

where Hdry gas loss is the enthalpy lost (in units of energy per time, or Btu/hr), ݉ሶ is the mass 

flow rate of dry gas in pounds per hour, and H2 and H1 are the enthalpy of the combustion 

gas before and after combustion. 

The flow rate of dry flue gas is largely determined by the percent excess fed air input. By 

convention, the percent excess air is specified on a volumetric/molar basis. However, 

combustion air from the atmosphere is only 21% oxygen by volume (and the balance 

nitrogen). Thus, much of the enthalpy losses result from heating up the nitrogen that 

accompanies the combustion oxygen. 

The efficiency loss, displayed as dry flue gas losses, is estimated by assuming an input 

combustion air temperature of 80°F and an output temperature as specified by the user 

input, flue gas temperature, which defaults to 390°F [14]. The combustion air 

specification of 80°F is chosen for a simple estimation calculation in the user interface 

before the simulation engine runs. The actual simulation, however, computes the monthly 

average ambient temperature (as detailed in Section 2.4.1) to accurately compute the 

efficiency, since the combustion air is assumed to be fed at the ambient temperature. The 

efficiency derate (as a percent of total heat input) is calculated as follows: 

 ݁ௗ௥௬ ௚௔௦ ௟௢௦௦ ൌ 24 ൈ ሺܶிீ െ ܶ஺ሻ ݊ሶ

and 

݊ሶ ൌ ൭ቆሺ12.7ܥ ൅ 38.1ܪሻ ൈ ቀ1 ൅ ܽ

100ቁቇ െ 0.5ܱ൱ 1

ܸܪܪ

where ݁ௗ௥௬ ௚௔௦ ௟௢௦௦ is the efficiency loss as a percent of total heat input; ݊ሶ is the mass 

flow rate of dry gas in pounds per Btu fuel; TFG and TA are the temperature of the 

combustion gas before and after combustion; C, H, and O are the respective mass 

fractions of carbon, hydrogen, and oxygen in the fuel; a is the percent excess fed air; and 

HHV is in Btu/lb. TFG is also known as the “stack temperature” or “flue gas temperature,” 

which is the user input as flue gas temperature, meaning that all useable heat has been 

collected when the combustion gas is that temperature. At that point they exit the plant 

through the stack. TA is the temperature at which air enters the system, which is the 

ambient temperature. The constants in front of the variables capture the specific heat 

capacity and unit conversions. This calculation follows the Btu Combustion Method 

detailed in Chapter 21 of Babcock & Wilcox’s Steam [14]. Dry flue gas losses are often 

responsible for the greatest efficiency loss in the furnace. 

3.2.2 Moisture in Fuel 

Moisture in fuel adversely affects the plant efficiency in two primary ways. First, water in 

biomass imposes extra mass that must be consequently hauled and processed with the 
17 

biomass itself. Since water supplies no heating value, the moisture is essentially “dead 

weight.” Additionally, the water absorbs heat from the combustion reaction that is 

unlikely to be recovered. The enthalpy lost in biomass moisture is calculated similarly to 

the dry flue gas calculation. When the fuel enters the boiler, the water in the fuel will be 

at the fed-fuel temperature. Upon leaving the combustion system, the water, now 

vaporized, will be at the same temperature as the flue gas. The sensible heat of the flue 

gas imposes another energy penalty on the system. 

The enthalpy of the vaporized moisture leaving the stack as steam at atmospheric 

pressure is calculated in the following equation: 

 ܪଶ ൌ ሺ0.00003958ܶிீ ൅ 0.4329ሻܶிீ ൅ 1,062.2

where H2 is the enthalpy of the steam in Btu/lb and TFG is the steam temperature in 

degrees Fahrenheit, which leaves the stack at the flue gas temperature. Likewise, the 

enthalpy of the water in the biomass at its pre-combustion (or ambient) temperature is 

calculated as below: 

ܪଵ ൌ ܶ஺ െ 32

where H1 is the enthalpy of the water in Btu/lb, and TA is the temperature in degrees 

Fahrenheit of the biomass before combustion, which in this case is the ambient 

temperature. 

The efficiency loss (݁௙௨௘௟ ௠௢௜௦௧௨௥௘) is calculated as a percent of total heat input using the 

following relationship: 

݁௙௨௘௟ ௠௢௜௦௧௨௥௘ ൌ ܯௗ௕

ሺܪଶ െ ܪଵሻ

ܸܪܪ

where ݁௙௨௘௟ ௠௢௜௦௧௨௥௘ is the efficiency loss as a percent of total heat input, HHV is in 

Btu/lb, ܯௗ௕ is the percent moisture content (dry basis), and H2 and H1 are the enthalpies 

of the steam leaving the stack at the flue gas temperature and the enthalpy of the water 

initially, as calculated in the equations immediately above. 

3.2.3 Latent Heat 

Solid fuels generally contain about 5% hydrogen by weight. During combustion, 

liberated hydrogen reacts with oxygen: 

2ܪଶ ൅ ܱଶ ՜ 2ܪଶܱ ൅ ݄݁ܽݐ

The reaction shows that the hydrogen in the fuel leaves the stack at the flue gas 

temperature as water vapor, thus requiring the latent heat of vaporization of water as well 

as the sensible heat of the vapor at the flue gas temperature [32]. 
18 

As detailed in Babcock and Wilcox [30], the efficiency loss for the additional steam 

generated upon combustion is determined using the following equation: 

 ݁௟௔௧௘௡௧ ௛௘௔௧ ൌ ܪ ൈ 100 ൈ 8.94 

ܪܪܸ ሺܪଶ െ ܪଵሻ

where ݁௟௔௧௘௡௧ ௛௘௔௧ is the efficiency loss as a percent of total heat input, ܪ is the dry 

weight fraction hydrogen, HHV is in Btu/lb, and H2 and H1 are the enthalpies of the steam 

leaving the stack at the flue gas temperature and the enthalpy of the water initially, as 

calculated in Section 3.2.2. 

3.2.4 Unburned Fuel 

Unburned fuel losses simply result from incomplete combustion in the boiler. In practice, 

the unburned fuel percentage depends on the type of boiler and excess fed air. This 

efficiency loss is one of the hardest to predict. For well-maintained boilers at proper 

levels of excess air, the degree of incomplete combustion should be similar among 

various technologies, as detailed in Section 3.1 and reiterated below [20]. 

ߟ௨௡௕௨௥௡௘ௗ ௖௔௥௕௢௡ ൌ ൝ 3.5% for stoker boilers

0.25% for FBC

3.0% for cyclone combustors

3.2.5 Radiation and Miscellaneous 

The surface of a furnace is at a much higher temperature than its surroundings; therefore, 

furnaces lose heat to the surroundings by radiation and convection. Since smaller 

furnaces have a larger surface area relative to their heat output, radiation losses are more 

significant for smaller boilers [32]. Modern boilers tend to maintain a near-constant 

external temperature despite the fraction of capacity at which they operate [32]. Thus, 

boilers operating at full capacity experience less severe radiation losses relative to their 

heat input [32]. The American Boiler Manufacturer’s Association (ABMA) has released a 

table that estimates radiation losses as a function of full capacity and operating capacity 

[32]. In the SAM simulation engine, the ABMA radiation table is used to calculate the 

radiation efficiency loss, ݁௥௔ௗ. 

The “miscellaneous” category also encompasses various losses that are difficult to 

quantify or predict, such as moisture in air, sensible heat in ash, radiation in ash pit, and 

sensible heat in flue dust. The moisture in air loss depends on location and is calculated 

by the SAM simulation engine. The other derates, however, are lumped together under a 

“manufacturer’s margin” (or ݁௠௔௡௨) derate, which here is taken to be 2.03% [20]. 

In the SAM simulation engine, the efficiency loss due to moisture in the air is determined 

using the following equation: 

݁௠௢௜௦௧௨௥௘ ௜௡ ௔௜௥ ൌ 45 ൈ ݊ሶ ൭0.622 ൈ ൬ ሺ0.01 ൈ ܴ ൈ ܲ௩ௗሻ

ܲ௕ െ ሺ0.01 ൈ ܴ ൈ ܲ௩ௗሻ

൰൱ ሺܶிீ െ ܶ஺ሻ
19 

where ݁௠௢௜௦௧௨௥௘ ௜௡ ௔௜௥ is the efficiency loss as a percent of total heat input, ݊ሶ is the mass 

flow rate of dry combustion gas in pounds per Btu fuel (calculated as in Section 3.2.1), 

TFG and TA are the temperature of the combustion gas after (flue gas temperature) and the 

ambient temperature before combustion, R is the percent relative humidity, ܲ௕ is the 

barometric pressure (in psi, as given in the weather file), and ܲ௩ௗ is the absolute saturated 

pressure of water vapor at the ambient temperature in psi. Again, the constant in the front 

of the equation represents the heat capacity as well as necessary unit adjustments. ܲ௩ௗ

varies with ambient temperature as follows [33]: 

݌௩ௗ ൌ 0.0886݁ቀ

ଵ଻.ଶ଺ଽସ்಴

்಴ାଶଷ଼.ଷ ቁ

where TC is the ambient temperature (in degrees Celsius) and ܲ௩ௗ is expressed in psi 

absolute. 

3.3 Mass Balance 

The mass balance on the combustion system is simple to resolve with two material 

balances: one on the biomass stream and one on the boiler steam stream. After the 

vaporization of the biomass stream through combustion, it becomes flue gas that exits the 

plant through the stack. During combustion, the biomass transfers all of its useful heat to 

the boiler feedwater stream. The amount of useful heat is calculated using the following 

manner: 

ܳሶ ൌ ܾሶ ൈ ܪܪܸ ൈ ሺ100 െ ሺ݁௠௢௜௦௧௨௥௘ ௜௡ ௔௜௥ ൅ ݁௠௔௡௨ ൅ ݁௥௔ௗ

൅ ݁௨௡௕௨௥௡௘ௗ ௖௔௥௕௢௡ ൅ ݁௟௔௧௘௡௧ ௛௘௔௧ ൅ ݁௙௨௘௟ ௠௢௜௦௧௨௥௘

൅ ݁ௗ௥௬ ௚௔௦ ௟௢௦௦ሻሻ/100ሻ

where ܳሶ is the heat transferred to the boiler feedwater in Btu/hr, HHV is in Btu/lb, ܾሶ is 

the dry biomass feed rate in lb/hr, and ݁ describes each of the various efficiency losses 

expressed as a percent of total heat input. 

The spent gas that exits the plant has closed the biomass material balance, but the flue gas 

composition and flow rate remains necessary information for determining environmental 

implications. 

Now we are concerned with the steam that has just been created in the boiler. The amount 

of steam created can be described using the following equation: 

݉ሶ ௦௧௘௔௠ ൌ ܳሶ

௠௔௘௦௧ܪ

where ܳሶ is the heat transferred to the steam as calculated above in Btu/hr, ܪ௦௧௘௔௠ is the 

enthalpy (or grade) of the steam, and ݉ሶ ௦௧௘௔௠ is the required flow rate of steam in pounds 

per hour, and by extension, the required flow rate of the feedwater to the boiler. The 

steam grade can be specified by the user, and the enthalpy available directly determines 

the amount of boiler feedwater fed to the combustion system. The created steam then 

powers the turbine, as detailed next in Section 4. 
20 

4 Steam Rankine Cycle 

The steam Rankine cycle, sometimes referred to as a power cycle, includes the equipment 

necessary to convert the heat contained in boiler steam into electrical energy. For the 

purposes of the biopower model, this entails a conventional steam Rankine cycle and an 

electric generator. Many existing biomass power plants use a combined heat and power 

approach, meaning that some of the boiler steam is directly used in industrial processes 

rather than generating electricity [12]. Since steam cycle conversions are only around 

30%–40% efficient, using heat directly dramatically increases the efficiency of a power 

plant [20]. However, the SAM biopower model focuses only on electricity generation, 

and combined heat and power generation is beyond the scope of the current model. The 

SAM Physical Parabolic Trough Plant Model documentation has a detailed account of 

modeling typical power cycles [34]. 

4.1 Efficiency Fluctuations 

The rated efficiency of the turbine, or “power block,” is the baseline efficiency of the 

conversion. However, the efficiency fluctuates based on the ambient air temperature, as 

well as on the part load (or fraction of capacity) at which the turbine operates. The effect 

of temperature and part load on efficiency can be described as a fourth-order polynomial 

with the coefficients ܨ – ଴ܨସ, as shown in the equations below. These coefficients define a 

polynomial equation to describe η, a fractional value that adjusts the amount of heat 

supplied to the power block based on deviations from full load and design temperature. 

The user inputs all defining coefficients. 

௠௥௢௡ܳଶܨ ൅ ௠௥௢௡ܳଵܨ ൅ ଴ܨ ൌ௤ ߟ

ଶ ൅ ܨଷܳ௡௢௥௠ଷ ൅ ܨସܳ௡௢௥௠ସ

௠௥௢௡ܶଶܨ ൅ ௠௥௢௡ܶଵܨ ൅ ଴ܨ ൌ ்ߟ

ଶ ൅ ܨଷܶ௡௢௥௠ଷ ൅ ܨସܶ௡௢௥௠ସ

when 

ܳ௡௢௥௠ ൌ ܳ௔௖௧௨௔௟/ܳௗ௘௦௜௚௡

ܶ௡௢௥௠ ൌ ܶ௔௖௧௨௔௟ െ ܶௗ௘௦௜௚௡

where ߟ ௤and ߟ் are fractional efficiencies that adjust the heat supplied to the power 

block, ܨ – ଴ܨସ are user-defined inputs that can be specified separately (the part load 

efficiency adjustment and temperature efficiency adjustment coefficients, respectively), 

ܳ௔௖௧௨௔௟ is the actual unadjusted heat supplied to the power block, ܳௗ௘௦௜௚௡ is the designed 

heat load (determined by the Nameplate capacity, gross input), ܶ௔௖௧௨௔௟ is ambient air 

temperature entering the power block, and ܶௗ௘௦௜௚௡ is the specified power cycle design 

temperature. 

4.2 Minimum Load and Maximum Overdesign Operation 

Turbines generally do not operate below a certain fraction of full load since turbine 

performance is difficult to predict and the economics may become unfavorable [14]. The 

fractional value for minimum load represents the threshold below which the turbine will 
21 

not operate. Likewise, the max overdesign operation specification will prevent the 

turbine from operating above a certain fraction of the design load where operation also 

becomes unpredictable and may introduce unwanted physical stress on the device. Since 

biomass is a baseload power source, generation should be relatively constant. In SAM 

biopower, the biomass is assumed to be fed constantly, and only fluctuations in biomass 

moisture and ambient conditions will affect generation, unless the user specifies a 

generation profile as detailed in Section 6. 

If the use estimated max for nameplate capacity option is selected, the turbine size will be 

calculated to utilize nearly all of the thermal capacity of the furnace system. The user 

may choose to specify nameplate capacity. In that case, the plant may utilize the biomass 

feedstock sub-optimally. If the specified capacity is greater than the estimated max gross 

nameplate capacity, the plant will constantly be operating at a fraction of the designed 

capacity. Alternatively, if the specified capacity is less than the estimated max gross 

nameplate capacity, the plant will constantly be producing at maximum in an attempt to 

use all of the biomass feedstock. In that case, the surplus biomass will be wasted. Thus, if 

a user desires a capacity significantly smaller or larger than the estimated max gross 

nameplate capacity, the obtainable feedstock should be adjusted accordingly for a more 

appropriately sized plant. 

The SAM biopower model attempts to approach a true “baseload” plant that constantly 

operates near capacity. In practice, however, fuel shortages, equipment failure, planned 

and unplanned shutdowns, and many other factors may cause a plant to operate at 

reduced load. Increased electric loads, fuel surpluses, or economically advantageous spot 

prices for electricity may induce a plant to operate above the designed capacity. 

4.3 Temperature Correction Mode 

The two options for the temperature correction mode, dry- or wet-bulb, refer to the type 

of cooling employed after steam is spent in the turbine. Dry-bulb temperature refers to 

the temperature of the air that can be found with a standard thermometer. The wet-bulb 

temperature also captures the moisture content of the air and is always less than the drybulb temperature (except at 100% relative humidity when the two are equal). Evaporative 

cooling uses the evaporation of water to cool the process condensate to near the wet-bulb 

temperature, whereas dry-cooling uses ambient air and thus the minimum heat rejection 

is the dry-bulb temperature. In general, air-cooled systems require more capital, are less 

thermodynamically efficient, and use more energy. However, evaporative cooling 

demands significantly more water and might not be possible in some regions. 

The choice of temperature correction mode simply affects what temperature is used as 

the reference temperature—the dry- or wet-bulb temperature. Both temperatures are 

usually specified in the weather files. Choosing the wet-bulb option will yield a higher 

energy output since the cooling is more efficient, but the equipment will generally cost 

more. Note that the SAM biomass power model does not explicitly model a full cooling 

system with condensers, associated pressure drops, and other related considerations. 
22 

The simulation will use the specified rated cycle conversion efficiency to calculate the 

output on an hourly basis, which is summed to calculate the annual energy produced: 

ܱ௡௘௧ ൌ ൫∑ ܳሶ ൈ η ൈ η௤ ൈ η௧ ൈ ݐ ଼଴଺଻

଴ ൯ 

3,412.14

where ܳሶ is the heat supplied to the turbine in the form of steam as calculated in Section 

3.3, η is the user-specified rated cycle conversion efficiency, η௤ and η௧ are the fractional 

efficiencies calculated in Section 4.1, t represents the time step of one hour, ܱ௡௘௧ is the 

net annual output in kilowatt-hours (kWh), and 3,412.14 is the conversion factor from 

Btu to kWh. 

 
23 

5 Parasitic Losses 

Parasitic loss is the load that a power plant requires to operate itself. Fans, conveyors, and 

pumps all impose a load. The SAM biopower model handles the parasitic load as a 

percent of the (gross) nameplate capacity: 

ܥ௘௡௧ ൌ ܥ௢௥௚௦௦ ൈ ൬1 െ

ܲ

100൰

where ܥ௘௡௧ is the net generating capacity in MW, ܥ௢௥௚௦௦ is the gross generating capacity 

in MW, and P is the specified parasitic load as a percent of the capacity. 

Additionally, 

ܱ௡௘௧ ൌ ܱ௚௥௢௦௦ ൈ ൬1 െ

ܲ

100൰

where ܱ௡௘௧ is the net energy output in kWh, ܱ௚௥௢௦௦ is the gross energy output in kWh, 

and P is the specified parasitic load as a percent of the capacity. 

Adding extraneous units that require power will increase the parasitic load. The user 

should be aware that installing a dryer (choosing the option dry to specified moisture 

content) will increase the parasitic load of the plant in practice. SAM will not 

automatically compensate for additional parasitic loads—this is up to the user. 

Additionally, air-cooled systems (see Section 4.3) require more energy than evaporative 

cooling. 

 
24 

6 Dispatch Schedule 

Although biomass has been differentiated from other renewable technologies by its 

“baseload” generation characteristics, some users may wish to model the effects of a 

dispatch schedule. The dispatch schedule will adjust the power output of the plant to 

some fraction of the capacity according to a month-by-hour specification. Biomass power 

plants are “dispatchable” to a certain degree, meaning that their power output can be 

adjusted a certain amount in response to demand. For example, Ridge Generating Station 

in Auburndale, Florida, operates at capacity from 11 a.m. until 10 p.m. and reduces its 

load overnight [12]. Many other plants have similar schedules [12]. However, cycling 

power equipment does have implications on the O&M costs due to thermal strain on the 

materials [35], among other factors. Additionally, equipment performance is generally 

limited by maximum “ramp rates” that determine how quickly power output can be 

increased. A “cold start-up,” or a start-up from zero output, may take up to 12 hours for 

an average-sized biomass plant [12]. A “hot start-up,” or a start-up from a fractional 

output, may take anywhere from 1–5 hours [12]. 

If the user selects the enable time of dispatch schedule option, up to nine periods of 

fractional generation can be specified on a month-by-hour schedule. The periods are 

defined by a fractional generation (of nameplate), representing the fraction of the 

nameplate capacity at which the plant will operate during a particular period. The 

fractional value can be as low as zero, which may occur, for example, during scheduled 

seasonal outages but can also be above one. For instance, if the plant experiences greater 

demand during the summer months, it can operate above capacity. However, the 

generation is limited by the design of the plant—namely the max over design operation 

fraction and boiler overdesign factor. Simulation warnings will be issued if the user 

specifies an impossibly high fraction for operation and modeled power output will be 

clipped. Using the dispatch schedule, the user can compensate for maintenance outages or 

biomass shortages and surpluses. 

The user has three options for specifying the ramp rate at which the plant operation can 

be augmented. First, the user can select do not specify ramp rate, which will take each 

hourly fraction and not adjust when generation changes. In this case, it will be up to the 

user to either: (1) assume that the capacity can be adjusted in much less than an hour or 

(2) to increment the generation gradually. Secondly, the user can select specify ramp rate 

in kW per hour. In this case, when the fractional generation varies, the simulation engine 

will compensate for the required ramping time when computing power output. Specify 

ramp rate in percent of capacity per hour will affect the simulation in the same way but 

gives the user a different manner to specify the ramp rate. 

When deciding to cycle a baseline power plant, the user should take care to adjust O&M 

costs to reflect the increased strain on the plant. With good design, however, the entire 

process can be less affected by changes in the output. 
25 

7 Model Outputs 

After the system is fully specified, SAM provides the user with performance metrics, 

such as the annual energy output (kWh), capacity factor (%), thermal efficiency (%), and 

heat rate (MMBtu/MWh). SAM generates graphs to visualize the monthly energy output 

and the percent change in byproducts and emissions as compared to displaced fossil fuel 

power. The net annual energy output (ܱሶ) calculation is detailed in Sections 4.3 and 5. The 

heat rate, an important indication of plant performance, is then calculated as below: 

ܪܴ௢௥௚௦௦ ൌ bሶ ൈ ܪܪܸ

ܱሶ ൈ 1000

ܪܴ௘௡௧ ൌ bሶ ൈ ܪܮܸ

ܱሶ ൈ 1000

where ܪܴ௢௥௚௦௦ is the gross heat rate in MMBtu/MWh, ܪܴ௘௡௧ is the net heat rate in 

MMBtu/MWh, HHV is in Btu/dry lb, LHV is in Btu/dry lb, ܾሶ is the dry biomass feed rate 

in pounds per year, and ܱሶ is annual output in kWh. Thermal efficiency is a conversion of 

the heat rate: 

ߟ௧௛௘௥௠,ுு௏ ൌ 341.23

௦௦௚௥௢ܴܪ

ߟ௧௛௘௥௠,௅ு௏ ൌ 341.23

௧௡௘ܴܪ

where ܪܴ௢௥௚௦௦ is the gross heat rate in MMBtu/MWh, ܪܴ௘௡௧ is the net heat rate in 

MMBtu/MWh, ߟ௧௛௘௥௠,ுு௏ is the percent thermal efficiency on an HHV basis, and 

ߟ௧௛௘௥௠,௅ு௏ is the percent thermal efficiency on an LHV basis. Finally, the capacity factor 

is calculated as: 

ൌ ܨܥ

ܱሶ

ܥௗ௘௦௜௚௡ ൈ 8760

where CF is the capacity factor percentage, ܱሶ is the annual energy output in kWh, and 

ܥௗ௘௦௜௚௡ is the nameplate capacity of the plant in kW. Table 6 shows the performance 

metrics output table for the default case of a theoretical 60 MW biomass power plant in 

Fargo, North Dakota. Note that economic metrics are not included in the table but are 

available in SAM when the biomass model is used in conjunction with a financial model. 

Figure 3 shows the monthly energy generation for the same case. 
26

Table 6. Example Performance Metrics for a Theoretical 60 MW Biomass Power Plant in Fargo, 

North Dakota

Metric Base

Annual Energy (kWh) 451,843,928

Annual biomass usage (dry tons/yr) 388,686

Annual Capacity Factor (%) 86.5

Gross Heat Rate (MMBtu/MWh) 13.03

Net Heat Rate (MMBtu/MWh) 12.19

Thermal efficiency, HHV (%) 26.2

Thermal efficiency, LHV (%) 28.0

Figure 3. Example monthly energy output for a theoretical 60 MW biomass power plant in 

Fargo, North Dakota

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

30,000,000

35,000,000

40,000,000

kWh

Example Monthly Generation Output
27

8 Emissions Comparison 

The input parameters on the emissions comparison input page inform the implications of 

displacing fossil fuels with biomass power. The default values reflect the current national 

distribution of the listed fuel sources [1]. These emissions inputs result in a graph 

comparing direct emissions of the plant, which include polluting nitrogen compounds 

(chemically known as NOx), sulfur dioxide (SO2), and carbon dioxide (CO2). 

Additionally, the graph compares the amount of ash generated in biomass plants versus 

other conventional power plants. Changing emissions-related inputs do not affect the 

calculated performance of the power plant. These outputs will vary significantly based on 

local conditions, and it is incumbent upon the user to update the generation mix inputs for 

their particular location to get reasonable results. Figure 4 shows a sample analysis for a 

theoretical 60 MW plant in Fargo, North Dakota, displacing coal power.

In addition to being environmentally unfavorable, NOx and SO2 emissions are also 

regulated by the U.S. Environmental Protection Agency (EPA). Recently adopted 

Maximum Achievable Control Technology (MACT) standards may cause coal-burning 

plants to install additional emissions removal equipment or, alternatively, co-fire with 

biomass to avoid the extensive capital cost associated with the new installations. The user 

should also note that this calculation does not capture additional harmful emissions, such 

as mercury, which is not generally associated with biomass combustion [36]. 

Figure 4. Example change in byproducts and emissions by displacing an equivalent coal

fuel plant by a theoretical 60 MW biomass power plant in Fargo, North Dakota
28 

8.1 Stack Emissions Calculations 

The stack emissions data compares current fossil-fueled power plants to current average 

biopower plants. Solid fuels contain elemental sulfur and nitrogen that react during 

combustion to make pollutants such as nitrogen oxides (NOx) and sulfur dioxide (SO2). 

Plants install “scrubbers” to remove pollutants before flue gas exits into the atmosphere. 

However, pollutants still escape from the stack. Table 7 shows the emissions data for 

current plants using each technology [36]. 

Table 7. Average Emissions Rates for Power-Producing Technologies 

Power Plant Fuel 

NOx 

(lb/MWh) 

SO2 

(lb/MWh) 

Agricultural Residues 2.6 0.1 

Wood Residues 2.5 0.3 

Bituminous/Subbituminous Coal 4.8 5.9 

Lignite Coal 3.8 10.3 

Natural Gas 2.4 0.1 

Oil 20.7 2.1 

Carbon calculations assume that 100% of the carbon dioxide emitted from biomass fuels 

is reabsorbed into growing biomass, meaning zero net pounds of carbon dioxide. Note 

that the calculation does not take into account the carbon released in biomass harvesting 

and transportation. However, it also does not consider the avoided emissions associated 

with un-harvested biomass decomposition because the model is only concerned with the 

bounds of the plant. Although this oversimplifies the carbon cycle, it provides useful 

insight into the environmental impacts of biomass power generation.1 

8.2 Ash Production Calculations 

Some types of biomass have higher ash contents than coal; however, the ash may contain 

less potentially hazardous compounds. Most coal ash is currently used as an additive in 

cement and tar production [39]. Some biomass ash is used as a fertilizer to restore 

nutrients to the soil from which it was produced [40]. 

The fossil fuel ash contents are determined using the following equation: 

ൈ ܣ ൌ ݄ݏܽ

௦௦௚௥௢ܴܪ

ܸܪܪ

ൈ

ܱሶ

2,000

where ash is calculated in tons per year, A is the dry ash weight percent, HR is the heat 

rate of the plant in Btu/kWh as calculated in Section 6, HHV is in Btu/lb, and ܱሶ is the 

annual output in kWh/year. Table 8 shows the various heat rates [41]. Note that heat rate 

is the industry standard for measuring plant efficiency per unit of heat input into the 

plant [1]. 

 

1

 For more information regarding a more complete life-cycle approach to biomass power, see Spath & 

Mann [37, 38]. 
29

Table 8. Average Industry Heat Rates for Fossil Fuel Plants (2009)

Power Plant Fuel Heat Rate (Btu/kWh)

Coal 10,404

Fuel Oil 10,921

Natural Gas 8,160
30

9 Summary

This manual presented the aim, development, and technical underpinnings of a biomass 

electric power generation model implemented in SAM. The tool allows various 

stakeholders, including energy analysts, policymakers, project developers, and financiers, 

to better understand the various tradeoffs involved with biomass power generation by 

giving access to detailed system performance parameters and the associated economics. 

The SAM biomass power implementation leverages an established solar, wind, and 

geothermal modeling platform to produce credible analyses in a straightforward manner.
31

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