The World Energy Picture: Where Fuel Cells Fit In

The World Energy PictureThe World Energy Picture
(and where Fuel Cells fit in)(and where Fuel Cells fit in)
Matthew Klippenstein
Feb 26, 2012

1. A successful species
2. Where we are now
3. Where we are going
4. How fuel cells fit in

P A G E 4
…we’re pretty successful…

P A G E 5
Global Population
 6.7 billion humans
 25 billion chickens
 2 billion pigs
 1 billion cattle
 1 billion sheep
 0.5 billion cats
 0.4 billion dogs

P A G E 6
Global Population
 2 billion pigs
 1 billion sheep
domesticated animals

P A G E 7
Global Population
 2 billion pigs
 1 billion sheep
 0.001 billion whales (1,000,000)
 0.001 billion bears (1,000,000)
 0.0005 billion elephants (500,000)
domesticated animals

P A G E 8
Visualizing population
+ +
= 1,000,000 individuals
6700 million
2.5 million

P A G E 9
Our noticeable impact
we’re so successful, we put nature off-balance

all numbers are Gigatonnes CO2 equivalent (billions of tonnes). From IPCC via:
http://www.skepticalscience.com/human-co2-smaller-than-natural-emissions.htm)
(billions of tonnes of CO2)

P A G E 10
Our noticeable impact
we’re so successful, we put nature off-balance

From IPCC via: http://www.skepticalscience.com/human-co2-smaller-than-natural-emissions.htm)
17
12
(billions of tonnes of CO2)

P A G E 11
CO2 levels
rising CO2 primarily drives planet’s warming
http://en.wikipedia.org/wiki/File:Mauna_Loa_Carbon_Dioxide.png

P A G E 12
global warming factors
GHG’s dwarf other effects (except volcanoes)



http://www.skepticalscience.com/global-cooling-mid-20th-century.htm

P A G E 13
global warming data
temperatures rising –
heat in oceans rising much faster
(latent heat of melting, of ice)

http://en.wikipedia.org/wiki/Temperature_record_of_the_past_1000_years
http://www.skepticalscience.com/global-cooling.htm

P A G E 14
faster than expected!
http://climateprogress.org/2010/03/23/greenland-ice-sheet-collapse-global-warming-science/
Globe warming
as per IPCC’s
worst-case
scenario!!
Major bummer.

P A G E 15
Why it all matters
Our civilizations evolved in a “goldilocks” zone
From: http://climateprogress.org/category/best-ppts/

P A G E 16
Maddeningly
affordable to solve
Savings from first steps can pay for almost everything else!!
http://climateprogress.org/2008/12/29/mckinsey-2008-research-in-review-stabilizing-at-450-ppm-has-a-net-cost-near-zero/

P A G E 18
Emissions by sector
http://www.ipcc.ch/pdf/assessment-report/ar4/wg3/ar4-wg3-ts.pdf

P A G E 19
Lots of work to do…
but lots of people are doing work!
e.g. industry,
forestry,
buildings
Emissions by sector
Ballard focuses
on this slice
(electricity)
Entrust remaining
slices to others

P A G E 20
Emissions by sector
but lots of people
are doing work!

P A G E 21
Emissions by sector
but lots of people
are doing work!
Let’s look at
industry,
forestry,
buildings

P A G E 22
Industry Emissions
 1990-2010: DuPont cut GHG emissions 75%*
• 2nd-biggest chemicals manufacturer in the world
• $38 billion in sales (2011)
* 63%, if you exclude a business unit they sold off
http://www2.dupont.com/Sustainability/en_US/Footprint/index.html

P A G E 23
Industry Emissions
 1990-2010: DuPont cut GHG emissions 75%*
• 2nd-biggest chemicals manufacturer in the world
• $38 billion in sales (2011)
 DuPont is not perfect
• still #1 emitter of air pollution in US
 but it’s not alone
• in same timeframe, Dow Chemical cut GHG emissions 40%**
* 60%, if you exclude a business unit they sold off
http://www2.dupont.com/Sustainability/en_US/Footprint/index.html
** http://www.dow.com/commitments/pdf/dow_energy_vision.pdf

P A G E 24
Forestry Emissions
(Deforestation)
Pine beetle has been catastrophic
http://explorethebitterroot.com/wp-content/uploads/2009/01/beetle-kill.jpg

P A G E 25
Forestry Emissions
(Deforestation)
http://climateprogress.org/2008/04/25/nature-on-stunning-new-climate-feedback-beetle-tree-kill-releases-more-carbon-than-fires/
see http://www.for.gov.bc.ca/hre/bcmpb/BCMPB.v6.2009Kill.pdf
From 2000 to
2020, BC forests
will emit more CO2
than they absorb!
Deforestation has
same effect: fewer
trees to absorb CO2

P A G E 26
Forestry Emissions
(Deforestation)
 but some exciting reforestation news, e.g. Groasis
• 3-year trial by University of Oujda in Morocco
• “Waterboxx” improves sapling survival rate: 10.5% → 88.2%

P A G E 27
Forestry Emissions
(Deforestation)
 but some exciting reforestation news, e.g. Groasis
• 3-year trial by University of Oujda in Morocco
• “Waterboxx” improved sapling survival rate: 10.5% → 88.2%
• costs $2. Can be removed after 1st
year (and used again)

P A G E 28
Buildings’ Emissions:
LEED™ buildings
 a modest premium - not a crazy premium
 Cadillac vs. Chevrolet (not Ferrari vs. Ford)
Olympic Athlete’s Village Millenium Water
http://www.vancouversun.com/Business/1709730.bin?size=620x400

P A G E 29
net zero houses
 one has been built nearby!
• Harmony House in Burnaby http://www.harmony-house.ca/index.html
• solar panels to generate electricity equivalent to annual use
crazy premium
(for now)

P A G E 30
net zero houses
 one has been built nearby!
• solar panels generate all its electricity
• why expensive? All parts are imported!
• 20,000 built in Europe since standards set (1996)
crazy premium
(for now)

P A G E 31
but lots of people are doing work!
e.g. industry,
forestry,
buildings
Where Fuel Cells focus
Ballard focuses
on this slice
(electricity)
Let’s go in-depth…

2. Where we are now
b) electricity

P A G E 33
Global Electricity (2010)
mainly wind
http://www.pewclimate.org/technology/overview/electricity Figure 12

P A G E 34
Global Electricity (2010)
http://www.pewclimate.org/technology/overview/electricity Figure 12
coal is dirty:
40% of electricity,
75% of CO2
mainly wind

P A G E 35
Coal…
 cheap
 dirty
 dangerous
http://www.coal-is-dirty.com/files/images/blogentry/smoke%20stack.JPG
http://www.worldcoal.org/coal-the-environment/coal-use-the-environment/
NOx
SOx
mercury
arsenic
uranium (!)
smog

P A G E 36
Coal’s big effect
Two provinces have high per-capita coal use
http://www.ec.gc.ca/pdb/ghg/inventory_report/2008_trends/trends_eng.cfm#toc_3
2008 GHG emissions
(tonnes CO2 / person)
0
10
20
30
40
50
60
70
80
BC AB SK MB ON QC NS NB PEI NF

P A G E 37
Coal’s big effect
If we magically eliminated coal
remove 8 gigatonnes CO2 emissions

17
12
4
http://www.pewclimate.org/technology/ove
rview/electricity Figure 13

P A G E 38
Nuclear
 fewest deaths per kWh
http://www.inference.phy.cam.ac.uk/withouthotair/c24/page_168.shtml
annual coal deaths: 4,000
Fukushima deaths: 0
Chernobyl deaths: 3,000
Chernobyl evacuees: 250,000

P A G E 39
Nuclear
but even before Fukushima there wasn’t a comeback,
because…

P A G E 40
Nuclear
but even before Fukushima there wasn’t a comeback,
because…
it’s unbelievably,
unbelievably,
unbelievably expensive!!
http://climateprogress.org/2009/07/15/nuclear-power-plant-cost-bombshell-ontario/
Cost for Ontario (2.4 GW)
coal: $5 billion
nuclear: $26 billion
plus overruns

P A G E 41
The Achilles’ heel
Electricity use fluctuates during the day…
personal correspondence

P A G E 42
…but coal and nuclear don’t easily turn off:
they aren’t a complete solution
0
5
10
15
20
25
0 2 4 6 8 10 12 14 16 18 20 22 24

P A G E 43
Natural Gas
 cleanest fossil fuel…
still emits CO2
 mainly “peak plants”
• high demand periods
• slow to adjust up/down
 recently, baseline too
• for now, natural gas is
even cheaper than coal!
must start up hours before use
 hits efficiency, cost

P A G E 44
Natural Gas – a new map
 shale gas (natural gas in shale ‘rock’)
• newly-accessible deposits  cheaper
• but wells deplete fast  need to keep drilling
• could replace coal for primary power plants
http://www.aapg.org/explorer/2010/01jan/ShaleGasBasinsNorthAmer300px.jpg
http://www.chartsrus.com/chart1.php?image=http://www.sharelynx.com/chartstemp/free/chartind1CRUvoi.php?ticker=FUTNG

P A G E 45
Natural Gas – a new map
 shale gas (natural gas in shale ‘rock’)
• newly-accessible deposits  cheaper
• but wells deplete fast  need to keep drilling
• could replace coal for primary power plants
• but fugitive emissions could make it worse *
http://www.aapg.org/explorer/2010/01jan/ShaleGasBasinsNorthAmer300px.jpg
http://www.chartsrus.com/chart1.php?image=http://www.sharelynx.com/chartstemp/free/chartind1CRUvoi.php?ticker=FUTNG
* http://www.eeb.cornell.edu/howarth/GHG%20emissions%20from%20Marcellus%20Shale%20--%20with%20figure%20--%203.17.2010%20draft.doc.pdf

P A G E 46
How Shale Gas becomes worse than coal
most people run the faucet awhile,
before filling their water bottles
the first bit of water
goes down the drain…
the rest is captured
for later use.

P A G E 47
shale gas drilling operations kinda do the same thing
post-frack, methane
dissolves into the
fracking fluid…
which is pumped
out. The methane
comes out of
solution and enters
the atmosphere.
Once the fracking
fluid is out, the well
is connected to
existing pipelines
(which leak a bit).

P A G E 48
It’s easy to capture the methane from the fracking fluid –
but gas is so cheap that few companies do it.
It’s a scaled-up case of how we tend to waste tap water.

P A G E 49
30-95 years
In the short term, methane emissions have a much, much
higher Global Warming impact than CO2.
“Fugitive” methane means shale gas could be worse
than coal, on a per-unit-of-combustion-energy basis!
Not a technical issue: a financial issue (can be fixed
with policy / incentives).

P A G E 50
Hydro
can turn up/down instantly (“Holy Grail”)
• “load following”

P A G E 51
0
5
10
15
20
25
0 2 4 6 8 10 12 14 16 18 20 22 24
Daily Electric Usage
natural gas and hydro fill the gap
natural gas, hydro

P A G E 52
Plus and Minus
Different energy types have different “features”
Pollution
(operating)
Follows Load
coal HIGH -
nuclear - -
natural gas LOW ½
hydro - Y

P A G E 54
Renewable Energy
Two future giants

P A G E 55
Renewable Energy
wind: 2.1% …doubling every 3 years
solar: 0.3% …doubling every 2 years
exponential growth

P A G E 56
Exponential Growth
 does amazing things
 good examples:
• compound interest (savings - hopefully!)
• computers double in speed every 18 months
(“Moore’s Law”)
 bad examples:
• compound interest (credit cards, student loans)
• cancers

P A G E 57
Wind
 not a fluke
• exponential growth
for 15+ years
 20% of grid
in Denmark
 China is #1 manufacturer
http://www.ren21.net/pdf/RE_GSR_2009_update.pdf, page 11

P A G E 58
Wind
 Grouse Mountain
• has viewing pod!
 student club
activity?

P A G E 59
Solar
 not a fluke
• also has 15+ years’
exponential growth
 lots of Silicon
Valley money
 China is #1 manufacturer
http://www.ren21.net/pdf/RE_GSR_2009_update.pdf, page 12

P A G E 60
Solar
http://www.eia.doe.gov/conference/2010/plenary/chu.pdf page 14
[US Energy Secretary] Steven Chu proves that winning a Nobel
Prize doesn’t mean you know how to make a legible graph…

P A G E 61
Solar
 when total install-base doubles,
panel cost drops 20% (past trend)
 install-base doubles every 2 years
ie. drops to 80%
of prior value

P A G E 62
Solar
 when total install-base doubles,
panel cost drops 20% (past trend)
 install-base doubles every 2 years
 in 2 years, cost is 80%
ie. drops to 80%
of prior value

P A G E 63
Solar
while panel cost has dropped…
…overall cost still high (system, labour)
http://eetd.lbl.gov/EA/emp/reports/lbnl-2674e.pdf page 12

P A G E 64
0
5
10
15
20
25
0 2 4 6 8 10 12 14 16 18 20 22 24
Wind and solar are both intermittent
• not a 24/7 solution (23/6 if widely deployed)
0
5
10
15
20
25
0 2 4 6 8 10 12 14 16 18 20 22 24

P A G E 66
Analogues
Follows Load
coal -
nuclear -
natural gas ½
hydro Y
complete solution requires
natural gas, hydro
0
5
10
15
20
25
0 2 4 6 8 10 12 14 16 18 20 22 24

P A G E 67
Analogues
Follows Load
coal -
nuclear -
natural gas ½
hydro Y
natural gas, hydro
Follows Load
wind -
solar -
wind + solar are not a
complete solution
0
5
10
15
20
25
0 2 4 6 8 10 12 14 16 18 20 22 24
0
5
10
15
20
25
0 2 4 6 8 10 12 14 16 18 20 22 24

P A G E 68
Analogues
Follows Load
coal -
nuclear -
natural gas ½
hydro Y
natural gas, hydro
Follows Load
wind -
solar -
batteries Y
FUEL CELLS Y
batteries, fuel cells, smart
grid, micro-grids, geothermal, etc.
0
5
10
15
20
25
0 2 4 6 8 10 12 14 16 18 20 22 24
0
5
10
15
20
25
0 2 4 6 8 10 12 14 16 18 20 22 24

P A G E 69
“You complete me”
batteries,
fuel cells,
smart grid,
etc.
wind,
solar

P A G E 70
“You complete me”
wind,
solar
investors
“show me
the money!”
batteries,
fuel cells,
smart grid,
etc.

P A G E 71
Reasons for Optimism
Let’s regraph solar, wind growth curves
Clean Energy Growth Curves
(coal plant = 500 MW. Due to intermittency,
need 1500 MW wind or solar to replace it)
0
5000
10000
15000
20000
25000
30000
35000
40000
1980 1985 1990 1995 2000 2005 2010
MW(peakcapacity)
Wind (per-year installations)
Solar (per-year installations)

P A G E 72
Logarithmic chart looks like this
1
10
100
1000
10000
100000
1980 1985 1990 1995 2000 2005 2010
MW(peakcapacity)

P A G E 73
Logarithmic chart looks like this
1
10
100
1000
10000
100000
1980 1985 1990 1995 2000 2005 2010
MW(peakcapacity)
let’s shift the solar curve, to see how far behind solar is

P A G E 74
Solar is 7 years behind wind, but on same track
1
10
100
1000
10000
100000
MW(peakcapacity)

P A G E 75
Solar is 7 years behind wind, but on same track
1
10
100
1000
10000
100000
MW(peakcapacity)
Fuel Cells are a “little” further down -
but we will follow a very similar track, too

P A G E 76
“Learning Curve” Review
 Learning Curves happen for many industries
 Often linked to
• higher volumes
• better use of materials
(cheaper or less stuff)
http://en.wikipedia.org/wiki/Experience_curve_effects#Reasons_for_the_effect

P A G E 77
FC Learning Curves
For fuel cells to grow, costs have to drop -
and they have!
* cost to build - does not include development costs
Automotive Stacks: build cost
(1990's) (1999) (2000) (2010)
"off-scale" "very high"

P A G E 78
Cost of sample Fuel Cell Component,
various product lines
(2002) (2008) (2009)
FC Learning Curves
Sample component
* cost to build - does not include development costs
design
efficiency
higher
volumes

P A G E 79
How Learning Curves Work
 as volumes increase, overhead-per-piece drops
 process improvements usually happen too
Price vs. Volume
0
20
40
60
80
100
120
Price
Overhead
Material Cost

P A G E 80
How Learning Curves Work
 as volumes increase, overhead-per-piece drops
 process improvements usually happen too
Price vs. Volume
0
20
40
60
80
100
120
Price
Overhead
Material Cost
fuel cell suppliers are here

P A G E 81
“All” that’s left
volume to get the price to get the volume to get the price to get
Price vs. Volume
0
20
40
60
80
100
120
Price
Overhead
Material Cost

It looks like you’re using
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P A G E 83
Wrapping Up…
A) get rid of coal
17
12
4

P A G E 84
Wrapping Up…
A) get rid of coal
B) wind and solar can help

P A G E 85
Wrapping Up…
A) get rid of coal
C) but they’ll need help
0
5
10
15
20
25
0 2 4 6 8 10 12 14 16 18 20 22 24
0
5
10
15
20
25
0 2 4 6 8 10 12 14 16 18 20 22 24

P A G E 86
Wrapping Up…
A) get rid of coal
D) from fuel cells among others

P A G E 87
Wrapping Up…
A) get rid of coal
D) from fuel cells among others
E) our learning curve will
get us there, as we persist
Price vs. Volume
0
20
40
60
80
100
120
Price
Overhead
Material Cost

questions?
comments?
matthew.klippenstein@ballard.com
http://ca.linkedin.com/in/matthewklippenstein

The World Energy Picture: Where Fuel Cells Fit In

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