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Transcript of Bnef university solar_wind_bioenergy_geothermal_ccs_energy_smart_technologie
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BNEFUNIVERSITY
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BNEF UNIVERSITY
JENNY CHASEHead of Research, SolarBloomberg New Energy Finance
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PV INDUSTRY: BOOMING IN CRISIS
Falls in price are not purely due to margin compression – they are also due to gains in efficiency and cost
The entire PV value chain has been remarkably adept at reducing prices as incentives come down, and installation volumes continue to grow strongly
We expect installation growth to continue and prices to stabilise in 2013, although consolidation will continue
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EBIT MARGINS OF PV MANUFACTURERS, Q4 2012
Source: Bloomberg New Energy Finance
The largest 17 quoted pureplay PV manufacturers made a total net loss of at least $8.9bn in 2011 and 2012
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THE PV MODULE EXPERIENCE CURVE, 1976–2012 ($/W)
Source: Paul Maycock, Bloomberg New Energy Finance
0.1
1
10
100
1 10 100 1,000 10,000 100,000 1,000,000
experience curve
1976
2006
2012
1985
2003
2012
experience curvehistoric prices (Maycock)Chinese c-Si module prices (BNEF)Thin-film experience curveFirst Solar thin-film module cost
Cost per W (2012$)
Cumulative capacity (MW)
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AVERAGE EFFICIENCY OF PV CELLS, 2010-2012 (%)
Source: Bloomberg New Energy Finance
17.5 17.7 17.216.2 16.3 16.1
18.2 18.1 18.216.8 16.7 16.8
18.6 18.5 18.617.0 17.0 17.0
Mono cellAverage all
mono
China mono Internationalmono
Average allmulti
China multi Internationalmulti
2010 2011 2012
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GERMAN PV COST AND REMUNERATION
0
100
200
300
400
500
600
0.0
1.0
2.0
3.0
4.0
5.0
6.0
Q22006
Q12007
Q32007
Q12008
Q32008
Q12009
Q32009
Q12010
Q32010
Q12011
Q32011
Q12012
Q32012
Q12013
Capex sub 10kW (EUR/W) Feed-in tariff <10kW (EUR/MWh)
Residential power price (EUR/MWh) Commercial power price(EUR/MWh)
Source: BSW-Solar, Eurostat, Bloomberg New Energy Finance
EUR/W EUR/MWh
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GERMAN PV COST AND REMUNERATION
Source: BSW-Solar, Eurostat, Bloomberg New Energy Finance
EUR/W EUR/MWh
0
100
200
300
400
500
600
0.0
1.0
2.0
3.0
4.0
5.0
6.0
Q22006
Q12007
Q32007
Q12008
Q32008
Q12009
Q32009
Q12010
Q32010
Q12011
Q32011
Q12012
Q32012
Q12013
Capex sub 10kW (EUR/W) Feed-in tariff <10kW (EUR/MWh)
Residential power price (EUR/MWh) Commercial power price(EUR/MWh)
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PV BUILD 2007-2012, CENTRAL FORECAST (MW/YEAR)
Source: Grid operations, incentive programme operators, industry associations, Bloomberg New Energy Finance
2.8GW6.6GW 7.7GW
18.2GW
28.7GW 30.6GW
35.7GW
46.6GW
54.5GW
2007 2008 2009 2010 2011 2012 2013 2014 2015Western Europe Eastern Europe Japan USAChina India Rest of World
Historical Forecast
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BNEFUNIVERSITY
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BNEF UNIVERSITY
JUSTIN WUHead of Research, WindBloomberg New Energy Finance
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THE WIND INDUSTRY IN 2013
Margin pressure
Manufacturing overcapacity – 2 times demand
Pricing pressure –27% drop since
2008
Market uncertainty – 17% drop in
demand in 2013
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TRANSFORMATION OF WIND TURBINE MANUFACTURERS?
Equipment manufacturer
Energy service company
GearboxesBlades
Design Siting
O&M
Monitoring
Design
Gearboxes
Blades
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TRANSFORMATION OF WIND TURBINE MANUFACTURERS?
Equipment manufacturer
Energy service company
MW MWh
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TRANSFORMATION OF WIND TURBINE MANUFACTURERS?
Equipment manufacturer
Energy service company
Low margins
High competition
Less product differentiation
One off revenue
Higher margins
More product differentiation
Longer term revenue stream
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HOW TO BECOME AN ENERGY SERVICES COMPANY?
Product differentiation – turbines designed for specific markets
Increase service revenue – bundle O&M contracts with turbines sales
Flexible manufacturing – quality instead of quantity
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TURBINE PRODUCT STRATEGY
Target specific markets
Low wind speedsHigh altitudeExtreme environmentsOffshore wind
Diversify tech &
component risk
Lower costs & improve
output
Drive train configurationGeneratorsPower converters
Hub heightsRotor diameterImproved operations
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PRICING BY TURBINE TYPE, 2008-13 (EUR M/MW)
1.10 1.10
1.21 1.20
1.061.02
0.980.94
0.900.85 0.82 0.82
0.99 0.99 1.02 0.99
H1 H2 H1 H2 H1 H2 H1 H2 H1 H2 H1 H2
2008 2009 2010 2011 2012 2013
WTPI Old models New models
16% average price gap
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O&M AND TURBINE SALES ANNUAL REVENUE POTENTIAL 2008-15E (EUR BN)
3.4 3.8 4.0 4.3 4.8 5.4 6.2 7.0
18.7
28.526.4
29.230.3
25.1
29.8 29.2
2008 2009 2010 2011 2012 2013 2014 2015
O&M Turbine sales
24%
17%
O&M will be ¼ of turbine
sales revenue by
2015
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FLEXIBLE MANUFACTURING
Cut costs Expand into new markets
Reduce presence in
heavily oversupplied
markets
Meet local needs in new
markets (servicing,
local content requirement)
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THE WIND INDUSTRY’S NEW ROI
Resilience: ability to survive pricing pressure and overcapacity in supply chain by rethinking business model
Optionality: selling more than one product, selling an energy service
Intelligence: specific products targeted to specific customers in specific markets
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BNEF UNIVERSITY
MARK TAYLORHead of Research, Geothermal & CCSBloomberg New Energy Finance
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BNEFUNIVERSITY
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ONLINE GEOTHERMAL POWER ASSETS
Just 11.4GW online globally
196GW estimated potential
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ESTIMATED GEOTHERMAL RESOURCE POTENTIAL (MW)
113GW (est.) in 39 developing countries
Seen as economic development tool
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ANNOUNCED GEOTHERMAL PROJECTS
10.3GW (224 projects) stuck in pre-drilling
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PROJECTS STUCK BECAUSE OF DRILLING RISK
Sticking point
$m
Source: Bloomberg New Energy Finance
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A GLOBAL EXPLORATION DRILLING FUND
LOANS
COMMERCIAL BANK
LOANS
LOANS
DEVELOPMENT FINANCE ORG
GEOTHERMAL EXPLORATION DRILLING FUND
PORTFOLIO
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PROJECT SUCCESS PROBABILITY
DRILLING SUCCESS FACTORS FUND COSTS ($M)
44%
73%
62%55%
75%69%
0.34%
11% 13%
27% 30%
18%
100-150C
150-190C
190-230C
230-300C
300C+ Steamfield
Field success rate Field prevalence
1%5%
34%
60%
549
Set-up Admin Debt service Write-offs Total costs
Source: Bloomberg New Energy Finance, Rinova International, IFC
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INTEREST RATES CHARGED TO SUCCESSFUL PROJECTS
SENSITIVITY TO DRILLING SUCCESS SENSITIVITY TO COST OF CAPITAL
Historical weighted average success
0%
10%
20%
30%
40%
50%
60%
70%
40% 50% 60% 70% 80% 90%Drilling success rate
Multilateral3.5%
Private7%
0%
10%
20%
30%
40%
50%
60%
70%
0% 5% 10% 15%
WACCSource: Bloomberg New Energy Finance, Rinova International
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KEY TAKEAWAYS
Fund is feasible – provide access to capital
Leverage investment 1:18
Catalyse capacity expansion
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BNEF UNIVERSITY
ALBERT CHEUNGHead of Research, Energy Smart TechnologiesBloomberg New Energy Finance
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BNEFUNIVERSITY
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RESILIENCE: IMPACT OF SANDY ON THE POWER GRID
806
630
450300
156 14
LIPA JCP&L ConEd PSE&G CL&P Pepco
Total customers that lost power: 8.6m
SANDY STORM DAMAGE ESTIMATES OF SELECTED UTILITIES ($M)
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RESILIENCE: CAN SMART TECHNOLOGY HELP?
PEPCO (DC) – SANDY 2012
• 425,000 smart meters installed and activated at time of storm
• ‘Last-gasp’ functionality for outages
• Meters ‘pinged’ to locate faults
• Power restored to 130,000 homes in two days
VERMONT ELECTRIC COOP:OUTAGE FREQUENCY, 2005-2010
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Jan
-05
Jun
-05
Nov-0
5
Ap
r-0
6
Se
p-0
6
Feb
-07
Ju
l-07
Dec-0
7
May-0
8
Oct-
08
Mar-
09
Au
g-0
9
Jan
-10
Jun
-10
System average interruption frequency index (SAIFI)
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RESILIENCE: UTILITY RECONSTRUCTION PLANS ($M)
PUBLIC SERVICE ELECTRIC & GAS (NJ)CONSOLIDATED EDISON (NY)
240
200
165
106
10510060Upgrading selected ovehead lines
Network reconfiguration
Floodproof 460/208/120 volt equip
Gas system flood protection
Electric/steam flood barriers
Moving selected line underground
Substation flood barriers
976
1,700
1,040
454 215 200 140
60 Move 20 miles of lines undground
Protection of gas metering stations
Improve redundancies
Improve distribution lines
Smart grid deployment
Modernize gas mains
Upgrade switching/substations
3,809
Network reconfiguration
Smart grid deployment
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OPTIONALITY: MICROGRIDS
15
9
9
4.8
2.4110.5
Princeton distributedenergy resources (MW)
Back pressure steam turbine
Demand response
Diesel generation
CHP
PV
Chilled water storage
Steam-driven cooling
Gas turbine / HRSG
Total: 43MW
PRINCETON UNIVERSITY MICROGRID CASE STUDY
• Resilience: • Can provide power, cooling and heat to entire
campus in island mode, when utility is down• Optionality:
• Can use DERs to participate in wholesale and ancillary service markets
• Can purchase power in wholesale markets –arbitrage and hedging
• ‘Safe haven in the storm’ during Sandy
• Key enablers:• Smart microgrid technology• Renewable energy policy• Restructured market
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INTELLIGENCE: SMART ENERGY DATA APPLICATIONS
Geospatial visualisation tools for situational intelligence
Space-Time Insight
BuildingIQ
DataRaker/Oracle
Using near real-time data feeds to: • Predict
renewable generation
• Track outages• Monitor asset
health• Coordinate
emergency response
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INTELLIGENCE: SMART ENERGY DATA APPLICATIONS
Predictive optimisation of building energy use using building data, weather forecasts and demand response programme information
Utility DR programs,
pricing data
BMS system-HVAC
-Lighting-Occupancy
Weather data
Building optimisation
engine
Improved DR capacity and
overall building efficiency
Space-Time Insight
BuildingIQ
DataRaker/Oracle
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INTELLIGENCE: SMART ENERGY DATA APPLICATIONS
Cloud-based, big data tools to mine smart meter data
Using smart meter data analytics to:• Pinpoint energy
theft• Identify faulty
metering conditions
• Build asset utilisation profiles
Space-Time Insight
BuildingIQ
DataRaker/Oracle
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BNEF UNIVERSITY
HARRY BOYLEHead of Research, BioenergyBloomberg New Energy Finance
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BNEFUNIVERSITY
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BIOENERGY MARKET SIZES, 2013 AND 2020
2013: $125bn
111
26
4
23
56
First-gen biofuels
Biomass-to-power
Waste-to-energy
BiochemsNext-gen biofuels
62
0.0011
20
41
First-gen biofuels
Biomass-to-power
Waste-to-energy
Biochems Next-gen biofuels
2020: $220bn
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FIRST-GENERATION BIOFUELS CAPACITY, 2004 – 2013 (M G ALLONS)
Source: Bloomberg New Energy Finance
10 1215
22
3135
39 41 42 42
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Hydrotreatment Transesterification 1-Gen fermentation
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OLD SCHOOL VERSUS NEW SCHOOL BIO -INNOVATION
SURPLUS BIOMASS INCINERATION POWER
SOFT COMMODITIES
FERMENTATION
ESTERIFICATION
ETHANOLBIODIESEL
Limited innovation; uses well known processes
UPSTREAM MIDSTREAM DOWNSTREAM
40 FEEDSTOCKS x 50 TECHNOLOGIES x 20 PRODUCTS = 40,000 THEORETICAL PATHWAYS
ALL BIOMASSMULTIPLEPATHWAYS
TAILORED PRODUCTS
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SELECTED BIOENERGY PATHWAYS
GRAINS;SUGARCANE
AGRICULTURAL RESIDUES; WASTE; WOOD; ENERGY
CROPS
OILS, FATS, WASTE HYDROTREATMENT
GASIFICATION
TRANSESTERFICATION
FERMENTATION
ENZYMATIC / ACID HYDROLYSIS & FERMENT.
INCINERATION; GASIFICATION; CO-FIRING
ANAEROBIC DIGESTION
BIODIESEL, RENEWABLE DIESEL
ETHANOL, BUTANOL
ELECTRICITY, BIOMETHANE
SOLVENTS, PAINTS, LUBRICANTS, COSMETICS,
PLASTICS, RUBBERS
DIE
SE
LG
AS
.P
OW
ER
CH
EM
ICA
LS
FEEDSTOCKS TECHNOLOGIES PRODUCTS
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TECHNOLOGY GLOBAL SHAKE OUT
ENERGY CROPS D1 OILS, MENDEL
CORN / WHEAT
MUNICIPAL WASTEFULCRUM, ENERKEM
JATROPHA
AG. RESIDUESPOET, DUPONT
SUGARCANE
WASTE GASESLANZATECH, JOULE
GASIFICATIONRENTECH, CHOREN, CHEMREC
PYROLYSISDYNAMOTIVE
TRANSESTERIFICATION
TORREFACTION
ENZYMATIC HYDROLYSISDSM, M&G, NOVOZYMES, RAIZEN
ADVANCED FERMENTATION MYRIANT, COBALT, AMYRIS
HYDROTREATMENT NESTE OIL, DIAMOND GREEN
ISOBUTANOL BUTAMAX, GEVO
N-BUTANOL GREEN BIOLOGICS
FARNESENE AMYRIS
HVO NESTE OIL, DYNAMIC
BIOCHEMICALSSOLAZYME, GENOMATICA
BIODIESEL
TORREFIED PELLETS
ALGAL BIOFUELSSYNTHETIC GENOMICS
BIOGASOLINELS9, PRIMUS GREEN ENERGY
FEEDSTOCKS TECHNOLOGIES PRODUCTS
ST
RU
GG
LIN
GS
TIL
L G
OIN
G
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CELLULOSIC ETHANOL COSTS ($ PER GALLON)
2012 2016
3.56
0.95
1.46
0.19 0.56
0.220.15 0.18 -0.17
Fe
ed
sto
ck
Cap
ex
Pre
tre
atm
en
t
Enzym
atic h
yd
roly
sis
Fe
rme
nta
tio
n
Lab
ou
r
Utilitie
s
By-p
rodu
cts
Tota
l
2.55
0.85
1.14
0.10 0.29
0.09 0.090.18 -0.19
Fe
ed
sto
ck
Ca
pex
Pre
tre
atm
en
t
Enzym
atic h
yd
roly
sis
Ferm
enta
tion
La
bo
ur
Utilitie
s
By-p
rod
ucts
To
tal
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NEW TECHNOLOGY GLOBAL LANDSCAPE
SUGARCANE AG. RESIDUES
“SIMPLE” “MORE COMPLEX”
SUGARS
TAILORED FATS / OILSFARNESENELAURIC ACIDOLEIC ACIDBIODIESEL
FUELSETHANOLBUTANOL
RENEWABLE DIESELBIOGASOLINE
CHEMICALSC2: ETHYLENE, MEG, PET
C3: PROPYLENE, ACRYLIC ACIDC4: BUTADIENE, BUTENE, BUTANOL
PR
OD
UC
TM
AR
KE
TF
EE
DS
TO
CK
PHARMACEUTICALS, TRANSPORT, FOOD,
PLASTICS ROAD, AVIATION & MARINE
PLASTICS, PAINTS, SOLVENTS, LUBRICANTS, RUBBER, PLASTICS
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CONCLUSIONS
Do investors understand the dynamics of niche and bulk agricultural commodity markets well enough? Fingers burnt during 1-gen roll out
Bioenergy industry already shown its ability to innovate: now going through a natural shake-out stage, where some technologies pass and some fail
Steel going in ground for a dozen semi-commercial scale cellulosic ethanol plants, with well-capitalised players heavily involved: industry inflection point almost there
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