Office of Research and Development

National Risk Management Research Laboratory, Air Pollution Prevention and Control Division September 29, 2010



# Rebecca Dodder and Dan Loughlin 

# OWB market assessment 

# and scenario analysis

# Outline 1. 

# General market trends 2. 

# Engineering economic assessment 3. 

# Scenario analysis with MARKAL 4. 

# Device projections and spatial allocation

1

# Part 1. General market trends

2

# 1. General market trends:



# Residential space heating National New York

1940 2000 1940 2000 Drivers?
-Convenience * on demand * limited storage - Indoor air quality
- Infrastructure expansion 3

# 1. General market trends



# EIA fuel use projections

4

# 1. General market trends



# Fuel prices and heating demands

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000

1998 2000 2002 2004 2006 2008 2010 Heating degree days

Year

Middle Atlantic Heating Degree Days

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

1998 2000 2002 2004 2006 2008 2010 Energy Use (Quads)

Year Middle Atlantic - Residential Fuel Use Natural gas Electricity Oil Wood

# 1. General market trends



# Sales of OWBs

6 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000

1998 2000 2002 2004 2006 2008 2010 Heating degree days

Year

Middle Atlantic Heating Degree Days

# 1. General market trends:



# Competition with other fuels Natural Gas

52% total 77% max 

# Wood 1% total 24% max 

# Fuel Oil 33% total 67% max 

# Electricity

9% total 32% max 7

# 1. General market trends:



# Take home messages • 

# The residential wood market share has diminished dramatically over the past 50 years • 

# Natural gas is the big winner. Where natural gas supplies are available, it typically is the fuel of choice • 

# Climate and energy security concerns are expected to level off the decline of residential wood use • 

# Residential wood is primarily used for secondary heating, but, • 

# Whole house heating with wood likely is increasing with the introduction and sale of OWBs 8

# Part 2. Engineering economic assessment

9

# 2. Engineering economic assessment:



# Approach • 

## Objective

– Improve understanding of competitiveness of OWBs relative to other technologies
– Gain insights for constructing OWB scenarios for MARKAL analysis 

### • 

## Assumptions

– Comparing alternative technologies for 2500 sf home
– Heating demands from Syracuse heating cycle
– Device costs from market research
– Device efficiencies from project testing
– Fuel costs from EIA, market research
– Device lifetimes of 15 yrs 

### • 

## Net present value calculation Where: NPV is net present value of cost CAP is the capital cost of the technology ANN is the annual cost of the technology, in this case the cost of fuel i is the interest or discount rate n is the number of years that the annual payment is made 10$0

$20,000 $40,000 $60,000 $80,000 $100,000 $120,000 $140,000

$- $50 $100 $150 $200 $250 $300 Net Present Value of Cost
Wood Price ($/cord) Sensitivity to Wood Price Automated pellet boiler Conventional OWB Advanced OWB Electric heat pump Natural gas furnace Fuel oil furnace 

# 2. Engineering economic assessment: 

# Sensitivity to wood price

11$0

$20,000 $40,000 $60,000 $80,000 $100,000 $120,000 $140,000

$- $50 $100 $150 $200 $250 $300 Net Present Value of Cost
Wood Price ($/cord) Sensitivity to Wood Price Automated pellet boiler Conventional OWB Advanced OWB Electric heat pump Natural gas furnace Fuel oil furnace "Free" wood supply Typical

green wood price Range for seasoned wood price $15/hr labor estimate w/o equipment cost 

# 2. Engineering economic assessment: 

# Sensitivity to wood price, cont’d

12$0

$10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000 $80,000 $90,000

0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 Net Present Value of Cost

OWB Efficiency Sensitivity to OWB Operational Efficiency Automated pellet boiler Conventional OWB Advanced OWB Electric heat pump Natural gas furnace Fuel oil furnace 

# 2. Engineering economic assessment: 

# Sensitivity to efficiency

13$0

$10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000 $80,000 $90,000

0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 Net Present Value of Cost

OWB Efficiency Sensitivity to OWB Operational Efficiency Automated pellet boiler Conventional OWB Advanced OWB Electric heat pump Natural gas furnace Fuel oil furnace Efficiency rating for advanced OWB Observed advanced OWB operational efficiency 

# 2. Engineering economic assessment: 

# Sensitivity to efficiency, cont’d

14$0

$20,000 $40,000 $60,000 $80,000 $100,000 $120,000 $140,000

$- $50 $100 $150 $200 $250 $300 Net Present Value of Cost
Wood Price ($/cord) Sensitivity to Wood Price Automated pellet boiler High efficiency indoor wood boiler Conventional OWB Advanced OWB Electric heat pump Natural gas furnace Fuel oil furnace "Free" wood supply Typical

green wood price Range for seasoned wood price $15/hr labor estimate w/o equipment cost 

# 2. Engineering economic assessment: Competition with high efficiency wood devices

15

# 2. Engineering economic assessment:



# Take home messages • 

## Natural gas is the least cost option for space heating • 

## OWBs are most competitive in areas:
–With low cost wood resources
–Where natural gas is not available (e.g., rural, low-density) • 

## OWBs can be cost-competitive with oil and electricity where 

## wood resources are perceived to be “free,” although the perception of free and reality may be quite different • 

## The tested efficiencies of OWBs were much less than their rated efficiencies. This greatly impacted their competitiveness • 

## The hot water heating ability of OWBs does not greatly affect their competitiveness since water heating costs are typically much less than space heating • 

## Hot water storage may have the potential to improve efficiency of OWBs considerably (e.g., approaching the 

## performance of the indoor high efficiency wood boiler) • 

## However, this analysis likely overstates OWB competitiveness since efficiencies will be even lower in shoulder seasons 16

# Part 3. Scenario modeling with MARKAL

17

## • 

# Energy system optimization model • 

# Utilized 9- region model of US

– Focus on Mid -Atlantic Census Division (NY, NJ, PA)

– Modifications made for capturing a broader range of residential wood heating devices

– 5-year model periods, 2000-2030 analyzed 

## • 

# Wood heating units and wood supply

– Device cost and performance (efficiency, lifetime, etc)



## – Wood supply divided into purchased and collected fuel wood 

## • 

# Scenario approach

– Difficult to capture actual wood heating market dynamics in an optimization framework



## – Utilized “what if” scenarios for potential future market trends

– Assessed net impact on residential energy use & emissions 

# 3. Scenario modeling with MARKAL: Model background

18

# 3. 

# Residential space heating market:

High OWHH “baseline” scenario

19

# 3. 

# Wood space heating market:

High OWHH “baseline” scenario

20

# 3. 

# Share of OWHH in total residential PM:



# High OWHH “baseline” scenario Total residential PM emissions by fuel (ktonnes/yr)

21 

### 2005 2010 2015 2030 Wood 50.04 93.8% 58.87 95.1% 73.48 96.2% 56.26 96.1% Heating Oil 2.91 5.5% 2.67 4.3% 2.57 3.4% 2.01 3.4% Natural Gas 0.15 0.3% 0.14 0.2% 0.14 0.2% 0.14 0.2% Kerosene 0.11 0.2% 0.10 0.2% 0.09 0.1% 0.06 0.1% LPG 0.09 0.2% 0.08 0.1% 0.07 0.1% 0.05 0.1% Electricity 0.07 0.1% 0.05 0.1% 0.05 0.1% 0.01 0.0% TOTAL 53.37 61.91 76.40 58.53 OWHH 17.11 31.19 49.89 38.59 % of WOOD 34% 53% 68% 69% % of TOTAL 32% 50% 65% 66%

# 3. Alternative scenarios for wood heat



# and OWHH market penetration

22

# 3. Total residential PM emissions High OWHH “baseline” scenario

23

# 3. Total residential PM emissions Intro of advanced units, slow phase out of older units

24

# 3. Total residential PM emissions Advanced units, with rapid phase out of older units

25

# 3. Total residential PM emissions Rapid phase out + lower emissions on advanced units

26

# 3. 

# PM emissions trends Increasing share of wood heat (shift from heating oil), with 

## advanced OWHH and higher efficiency units introduced

27

# 3. Comparison of PM emissions



# across set of scenarios

28

### • 

## Efficiency of OWHHs is a critical factor



## – Clear need for data on range of operational efficiencies

– To the extent that water storage can improve the efficiency of units, they are more competitive technologies • 

## The model generally “optimizes” toward wood stoves



## – Substantially lower capital cost and “good” efficiencies relative to tested efficiencies of OWHHs

– However, does not capture important personal preferences for space heating characteristics • 

## Systems view of optimal use of “free wood”

– Renewable and possibly “free” but still a limited resource • 

## Water heating was included

– Indications are that only marginal reductions in water heating energy usage (and emissions) would occur (from the viewpoint of total residential energy demand) 

# 3. Insights from MARKAL scenarios: 

# market factors

29

### • 

## At the level of total residential PM emissions, type of fossil 

## fuel displaced has very little impact

– Share of wood heat in total space heating is a key determinant of the total residential PM emissions profile

– Moreover, technology mix within the wood heat market a 

## critical driver • 

## Rapid phase-out of the older OWHHs may help avoid high residential PM levels in the 2010-2015 period

– Note that MARKAL is a 5-yr model period representation, and 

## the year to year dynamics will be important 

# 3. Insights from MARKAL scenarios: 

# potential emissions trends

30

# Part 4. Device projections and spatial allocation

31

# 4. County-level prospects for OWBs:



# Factors influencing OWB penetration Major factors:

–County character • 

# Fraction of housing units classed as exurban or rural



# – Resource availability • 

# Historic fraction wood as primary fuel

–Market potential • 

# Historic fraction of oil as primary fuel • 

# Number of exurban and rural households

–Acceptance • 

# Existing bans or emission limits 32

# 4. County-level prospects for OWBs:



# Confluence of factors High Medium Low Very Low

33

# High OWB Potential Counties County Fraction

Exurban + Rural Fraction

Wood as Primary Fraction

Oil as Primary # of HHs

that are Exurban + Rural High

Count in 2015 (77K total) Note Chenengo 0.66 0.16 0.49 15.5K 1,100 Clinton 0.54 0.12 0.44 18.0K 1,300 Columbia 0.65 0.07 0.60 19.0K 1,300 Delaware 0.74 0.17 0.53 20.9K 1,500 Essex 0.63 0.15 0.60 14.3K 1,000 Whiteface Mountain Base Franklin 0.64 0.16 0.60 14.7K 1,100 Greene 0.73 0.08 0.69 19.3K 1.300 Hamilton 0.67 0.22 0.54 5.1K 400 Lewis 0.72 0.29 0.54 10.6K 800 Madison 0.54 0.10 0.37 15.3K 1,100 Orleans 0.57 0.08 0.34 9.5K 700 Otsego 0.62 0.14 0.54 17.3K 1,200 St. Lawrence 0.57 0.16 0.41 28.1K 2,000 Schoharie 0.76 0.15 0.58 11.8K 800 Schuyler 0.73 0.18 0.32 6.4K 500 Sullivan 0.63 0.07 0.67 28.4K 1,900 Tioga 0.60 0.13 0.44 12.7K 900 Ulster 0.57 0.06 0.63 47.4K 3,100 Washington 0.57 0.14 0.48 15.2K 1,100 

# How to do this 

# credibly?

# 4. County-level prospects for OWBs:



# Using ICLUS land use as a surrogate

35 

# Apportion OWB emissions to grid cell based on 

# number of rural and exurban households ICLUS projections: 2000-2100 10-yr time-steps Scenarios: Baseline A1, A2 

# B1, B2

### • 

## Very limited data with which to make device count and location



## projections • 

## Future sales are difficult to predict

– How is the potential market limited by...? • 

### siting considerations (setbacks, etc.) • 

### availability of “free” wood • 

### low valuation of time/labor • 

### public opinion (for and against) and how it is influenced by perception • 

## Most detailed county- level surveys were from around 2000, when OWB sales were much less than today • 

## Characterized primary heating fuel but not specific device • 

## Device projections are as much social science as they are engineering 

# 4. County-level prospects for OWBs: 

# Take-home points

36

# Additional background slides

373839