Post on 13-Jul-2020
William NelsonNovember 28, 2017
Renewable Energy’sJagged Path to Parity
1
ContentsPV’s Path to Parity
Energy Cost Reductions – Global
A Eulogy for Power Prices – Conceptual
Footprints from the Merit Order Effect – WECC
Wind Meets DA-RT Spreads – ERCOT
Power Grids – A Legacy of Super-Cycles
Think Like Gas Plants – They Appraise Renewables
Think Like Batteries – They Could Support Renewables
Alberta’s Gas Dilemma – Wrong End of the Pipe
What Sets Gas Prices – Every Thursday, 10:30am ET
2
Energy cost reductions
3
$62bn$88bn
$128bn
$175bn$205bn $207bn
$276bn$317bn
$291bn$269bn
$315bn$349bn
$287bn$258bn(estimate)
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
0.00
100.00
200.00
300.00
400.00
500.00
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017Total values include estimates for undisclosed deals. Includes corporate and government R&D, and spending for digital energy and energy storage projects (not reported in quarterly statistics). Excludes large hydro.
Global new clean energy investment and capacity installations
$300 billion
4
$62bn$88bn
$128bn
$175bn$205bn $207bn
$276bn$317bn
$291bn$269bn
$315bn$349bn
$287bn$258bn(estimate)
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
0.00
100.00
200.00
300.00
400.00
500.00
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
100.00
200.00
300.00
400.00
500.00
20GW
88GW
160GW
Total values include estimates for undisclosed deals. Includes corporate and government R&D, and spending for digital energy and energy storage projects (not reported in quarterly statistics). Excludes large hydro.
Canadian investment hovered around $6bn from 2010-14 but dipped to $2.4bn in 2016 and is down again in 2017.
Global new clean energy investment and capacity installations
$300 billion
5
Note: Excludes large hydro Source: Bloomberg New Energy Finance
Renewable energy (ex large hydro) proportion of power generation, 2006-16
Lowest
Mid
Highest
No data
13%
30%Spain
3%12%
Australia
6%18%
Brazil
5% 6%
Canada
6% 10%
China
9%
29%Germany2%
25%UK
4% 6%
India12%
25%
Italy7%
12%
Japan
3% 9%
US
1% 3%
South Africa
6
Source: Bloomberg New Energy Finance; ImagesSiemens; Wikimedia Commons
Unsubsidized clean energy world records since April 2016
Country:Bidder:Signed:Construction:Price:
MoroccoEnel Green PowerJanuary 20162018US$ 3.0 c/kWh
Country:Bidder:Signed:Construction: Price:
MexicoFRVSeptember 20162019US$ 2.69 c/kWh
Solar PV Onshore wind Offshore wind
Country:Bidder:Signed:Construction:Merchant Price:
GermanyDONG/EnBWApril 20172024US$ 4.9 c/kWh
Note: The offshore wind merchant price is estimated based on project LCOE in real 2016 terms
7
0
20
40
60
80
100
120
140
160
180
200
2010 2011 2012 2013 2014 2015 2016
Retail on/off-peak range
Average retail power price
Historic prices of wind in Ontario
(FiT 1.0)(FiT 2.0)
(LRP I)
(Canada wind LCOE)
Ontario wholesale power price
Ontario wind versus wholesale power
C$/MWh
Source: Bloomberg New Energy Finance.
8
Source: Bloomberg New Energy Finance
PV module prices and learning curve calculation
0.1
1
10
100
1 10 100 1,000 10,000 100,000 1,000,000
historic prices (Maycock) Chinese c-Si module prices (BNEF) Experience curve at 28%
2016 $/W
2003
1976
19852008
Cumulative capacity (MW)
2015
2017(estimate)
9
Price benchmark for fixed-axis utility-scale PV systems (2016 $W, DC)
1.851.35
0.93 0.71 0.68 0.58 0.48 0.35 0.32 0.31 0.29 0.28 0.26 0.24 0.23 0.22
3.24
2.65
1.801.58 1.49
1.31 1.140.99
0.93 0.90 0.86 0.83 0.79 0.76 0.73 0.70
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025Module Inverter Balance of plant EPC Other
Note: OEM (original equipment manufacturer). Integrated module cost for mono could achieve lower costs, using new in-house factory. Source: Bloomberg New Energy Finance
10
Source: Bloomberg New Energy Finance.
Onshore wind is getting cheaperBNEF wind turbine price index
0.99
0.830.75
1.00
1.25
1.50
1.75
2.00
2.25
2.50
1H 2H 1H 2H 1H 2H 1H 2H 1H 2H 1H 2H 1H 2H 1H 2H 1H 2H 1H 2H 1H 2H
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
$million/MW (nominal)
United States
Global
Global average
11
Wind turbines are getting bigger…
202520152000 2005 20101995199019th C
300m
200m
100m
1-12kW0.5 MW
1.2 MW2 MW
4 MW
7 MW 9 MW
13-15 MW
Sources: Various; Bloomberg New Energy Finance
Offshore
12
Source: Bloomberg New Energy Finance. Note: Reports P50 capacity factors.
Global capacity factors U.S. capacity factors
0%
10%
20%
30%
40%
50%
60%
1990 1995 2000 2006 2011 2017
United StatesGermanyIndiaDenmarkSpain
0%
10%
20%
30%
40%
50%
60%
1998 2003 2008 2013 2018
…capacity factors are improving…
13
Gas
6HR
7HR
10HR
Solar PV
Wind
0
50
100
150
200
250
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
USD per megawatt-hour (real 2016)
Low wholesale prices increasecompetition among all types of generators
Average PPA price vs short run marginal cost of gas
Source: Bloomberg New Energy Finance, U.S. DOE (LBNL). Note: HR = ‘Heat Rate’ (MMBtu/MWh); Levelized, time-of-day adjusted contract price.
14
Gas is cheap
15
Gas production is up despite rigs used being down (U.S. production)
Production (Bcfd) Number of rigs
Source: Bloomberg New Energy Finance, EIA, Baker Hughes. Data up through the latest October 2016.
02004006008001,0001,2001,4001,6001,800
0102030405060708090
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016Shale Other lower 48 Rigs
16
A Eulogy for Wholesale PowerWhat’s killing energy prices
17
-
2
4
6
8
10
12
0
20
40
60
80
100
120
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
U.S. wholesale power prices
0.0
20.0
40.0
60.0
80.0
100.0
120.0
1
New England
New York
PJM
California
ERCOT
Southwest
MISO
Northwest
Henry Hub gas
Power prices – Day-Ahead, around-the-clock (ATC) average$/MWh
Gas prices$/MMBtu
18
Factors hurting wholesale power prices
Starting point Supply-side effects Demand-side effects
Fuel prices fall (short-run cost cuts)
Renewable build-out (merit order effect)
Load reductions (energy efficiency)
Demand elasticity injection (consumer empowerment)
Power prices are set by the short-run marginal cost of the marginal generator. On an hourly basis, electricity demand is relatively price insensitive (hence the vertical demand line).
Falling fuel prices reduce marginal costs of generation (for those plants burning fuel). This shifts the supply curve downward and reduces power prices.
Intermittent renewables operate with near-zero short-run marginal costs, placing them in the front of the merit order. This shifts supply curves to the right, undermining prices.
A combination of societal changes and efficiency investments are stalling load growth (especially relative to a growing install base). Econ 101: demand drops, prices fall.
Demand-side solutions aim to boost load’s sensitivity to price spike events, reducing grid ‘peakiness’, saving consumers money, and robbing generators of revenue opportunity.
19
Where are we headed?
Figure 1: Simple representation of triple-bottom-line power grid appraisal
Optimal grid Today’s grid Tomorrow’s grid
Objective of wholesale power markets
Objective of capacity markets
Objective of environmental
incentives
Source: Bloomberg New Energy Finance
Cheap
CleanReliable
Cheap
CleanReliable
Cheap
CleanReliable
California case study
William NelsonOctober 2, 2017
Footprints from the Merit Order Effect
21
$- (15) 35imports thermal solar wind hydro geothermal Nuclear power price
CAISO power mix and price profile
-$20-$10$0$10$20$30$40$50$60$70$80
(10) (5) - 5
10 15 20 25 30 35 40
12 13 14 15 16 17 18 19 20 21 22 23 24 25April 2012 (24-hour daily profiles)
pow
er m
ix (G
W)
pow
er p
rices
($/M
Wh)
S
P15
day-
ahea
d
-$20-$10$0$10$20$30$40$50$60$70$80
(10) (5) - 5
10 15 20 25 30 35 40
12 13 14 15 16 17 18 19 20 21 22 23 24 25April 2017 (24-hour daily profiles)
pow
er m
ix (G
W)
pow
er p
rices
($/M
Wh)
S
P15
day-
ahea
d
22
California merit order without renewables$/MWh
California merit order with renewables and RECs$/MWh
Renewables suppress wholesale prices in liberalized power markets
14 June 2016
Source: Bloomberg New Energy Finance (Fading value of solar (and midday power) in California)
-$40
-$30
-$20
-$10
$0
$10
$20
$30
$40
$50
$60
- 10 20 30 40 50 60 70 80
50GW of demand clears at $24/MWh
Renewables shift the plant stack
Solar receives a ~$15/MWh REC payment in California
Wind collects RECs and a $23/MWh production tax credit
$0
$10
$20
$30
$40
$50
$60
- 10 20 30 40 50 60 70 80
With demand at 50GW market clears at $40/MWh
Gas
Oil
Cumulative capacity (GW)
23
-
1
2
3
4
5
6
7
8
24-houraverage daily profile
CAISO solar ‘scalar’ degradation
Solar generation (GW) Net load (GW) Implied heat rates (MMBtu/MWh)
Penetration (% of power mix)
Notes: implied heat rates correspond to SP15 Day-Ahead power; SoCal Citygate gas; California carbon; $0 variable O&M
2012
2013
2014
2015
2016
YTD2017 2012
201320142015
2016
YTD 2017
2012
2013
2014
2015
2016
YTD 2017
Evening super-peak power prices are rising as midday prices fall
Net load dropping
Evening ramp rising
midday prices sagging
super-peak climbing
-
5
10
15
20
25
30
24-houraverage daily profile
-
2
4
6
8
10
12
14
16
18
24-houraverage daily profile
24
$-
$5
$10
$15
$20
$25
$30
$35
$40
$45
$50
2011
2012
2013
2014
2015
2016
YTD
201
7
Fading value of CAISO solar (and midday power prices)
Realized power prices ($/MWh) Scalars (realized price relative to ATC) Scalars (realized price relative to ATC)
Penetration (% of power mix)
Solar’s realized power prices are falling with rising levels of penetration
Notes: solar penetration refers to utility-scale solar PV on CAISO’s grid.
Gas
Solar
Around-the-clock average (ATC), SP15 Day-Ahead
Scalar degradation shows how value of solar falls with rising penetration
-50%
-40%
-30%
-20%
-10%
-
+10%
+20%
+30%
+40%
+50%
2011
2012
2013
2014
2015
2016
YTD
201
7
Solar
Gas 20112012
20132014
20152016
2017 (thru Q3)
-50%
-40%
-30%
-20%
-10%
-
+10%
+20%
+30%
+40%
+50%
0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10%
11%
12%
25
California power plants and transmission grid – installed capacity as of April 2016
Gas (CCGT)Gas (OCGT/steam)OilNuclearHydroWindSolar PVSolar thermalGeothermalBiomass / Biogas
Power plant techs
Plant capacity
2GW
1GW
500MW
250MW
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2009
2010
2011
2012
2013
2014
2015
2016
SP15
NP15
ZP26
Cumulative PV capacity by zone,2009-16*
Source: Bloomberg New Energy Finance’s US Plant Stack contains details about each generating unit in California.
San FranciscoSunset at 10.36pmon 21 June
MidlandSunset at 9:54pmon 21 June
Los AngelesSunset at 10.08pmon 21 June
ERCOT DA-RT Spread Case Study
William NelsonOctober 2, 2017
Costs of Intermittency
27
Better Real-Time resolution than Day-Ahead
Real-TimeDay-Ahead
28
-
10
20
30
40
50
60
08 Sep 09 Sep 10 Sep
LoadERCOT, 2017
• DA load forecasts are typically very good at predicting hourly-average load (they are typically accurate within 1-2%). But the grid always requires some intra-hourly true-up to overcome the unrealistic 'blocky-ness' associated with 60-minute DA forecast segments.
Real-Time load, actual (TW)Day-Ahead load forecast (TW)
• In ERCOT, Real-Time dispatch signals improve granularity from 60-minute to 5-minute segments. Any imprecision that arises within 5-minute RT blocks is overcome using Regulation Reserves.
-
10
20
30
40
50
60
08 Sep 09 Sep 10 Sep
Each block predicts load over the course of 1 hour.ERCOT improves upon its blocky forecasts and makes necessary adjustments in Real Time.
29
Wind productionERCOT, 2017
• Hourly-average wind forecasts slot into ERCOT’s Day-Ahead operating plan. These forecasts are financially binding. If a wind farm (or any ERCOT generator) fails to deliver according to its Day-Ahead commitment, it is responsible for buying (or selling) its shortfall (or surplus) in the Real-Time power market.
Actual wind production (TW)Day-Ahead wind forecast (TW)
• Of course, wind farms do not produce flat hourly blocks. Actual generation varies over the course of an hour, and these fluctuations expose plants to Real-Time prices, even if the hourly average forecast is 100% accurate. Wind farms cannot avoid exposure to DA-RT spreads; dispatchable generators can.
-
2
4
6
8
10
12
08 Sep 09 Sep 10 Sep -
2
4
6
8
10
12
08 Sep 09 Sep 10 Sep
30
Power pricesERCOT Hub Average, 2017
• DA prices are derived from supply-demand forecasts. DA power pricesare financially binding. Generators dispatched Day-Ahead earn DA powerprices and are obligated to provide power or buy it Real-Time. Most loadis cleared Day-Ahead.
Real-Time power price ($/MWh)Day-Ahead power prices ($/MWh)
ERCOT-wide data from 2017
• RT power prices only come into play for generators whose output differsfrom DA commitments. (Wind forecasting error subjects wind farms to RTprices.) In most hours, RT prices are lower than DA prices; in some hoursRT prices can spike significantly higher than DA.
-
10
20
30
40
50
60
08 Sep 09 Sep 10 Sep -
10
20
30
40
50
60
08 Sep 09 Sep 10 Sep
This is a typical Day-Ahead price profile.Power prices are highest midday, when the grid is expected to be under the most strain.
31
Implications of wind forecasting error
• On Sept. 8, the ERCOT wind fleet underestimated its generation. (Actual generation eclipsed the DA forecast throughout most of the day.) Surrounding generators (probably gas) elected to ramp down their production to accommodate the high winds.
• On Sept. 9 at 7 p.m., wind farms over-estimated. The missing generation needed to be replaced (probably by gas turbines) in Real-Time.
DA versus actual wind production(TW)
DA versus RT power prices($/MWh) – ERCOT Hub Average
DA versus RT revenues (and costs) for wind farms ($k/minute)
• RT prices sagged below DA prices throughout most of Sept. 8, when grid supply exceeded expectations (due to strong winds).
• The opposite issue sent Real-Time prices soaring at 7 p.m. Sept. 9. In a physical sense, power prices had to surge, in order to bring enough gas online on short notice to replace the missing wind.
• A similar situation induced RT price spikes on Sept. 10 when wind again under-produced.
• Like most generators, wind makes most of its money in the DA market. Production surpluses (above and beyond DA forecasts) are sold at RT prices. Generation shortfalls incur RT costs, as wind farms are financially responsible for replacing their missing megawatt-hours.[1]
• During the underproduction event on Sept. 9, wind farms spent $214/MWh buying missing generation from the Real-Time market.
[1] Note that many wind-farm owners offload the operating risk of their asset to a third party, through a traditional Power Purchase Agreement (PPA) or another form of financial hedge. Nonetheless, the risks associated with operating a wind turbine in ERCOT or any wholesale power market can ultimately be traced back to the underlying asset. Operating risks affect wind-farm economics regardless of who foots the bill.
-
2
4
6
8
10
12
08 Sep 09 Sep 10 Sep -
10
20
30
40
50
60
08 Sep 09 Sep 10 Sep-2
-1
0
1
2
3
4
5
6
08 Sep 09 Sep 10 Sep
Over-generation
Under-generation
RT price spike
Backfill costs
32
DA-RT delta math
Costs of Forecasting Error = ∆P * ∆Qwhere:
– ∆P = Power Price DA – Power Price RT
– ∆Q = Production forecast – Production actual
• The above equation is very important. It applies to all generators in wholesale power markets and defines risks and opportunities associated with RT deviations from DA dispatch commitments.
DA minus actual wind production (TW) DA-RT spreads; DA minus RT power prices ($/MWh) – ERCOT Hub Average
Costs (and revenues) associated with wind forecasting error ($k/minute)
• Positive DA-RT spreads tell power plants to ramp down, relative to their DA operating plans; negative DA-RT spreads tell power plants to ramp up.
• Wind cannot readily respond to DA-RT spreads because wind is not dispatchable.
• Gas plants can respond to DA-RT spreads, though constant ramping incurs fuel and maintenance costs. RT prices are set (by the market) at levels that allow gas plants to recoup the costs associated with ramping.
• At 7 p.m. Sept. 9, wind farms lost $20k/minute buying Real-Time power they had promised Day-Ahead and then failed to deliver (due to a production overestimate).
-3
-2
-1
0
1
2
3
08 Sep 09 Sep 10 Sep-80
-70
-60
-50
-40
-30
-20
-10
0
10
20
08 Sep 09 Sep 10 Sep-20
-15
-10
-5
0
5
10
08 Sep 09 Sep 10 Sep
Underestimates(Generation surplus)
Overestimates(Generation shortfall)
Higher DA prices
Higher RT prices
Beneficial forecast error
Hurtful forecast error
∆Q ∆P ∆P * ∆Q
33
Power GridsA legacy of technology super-cycles
34
U.S. generators – online, 2005Operational capacity (GW nameplate)
1GW500MW100MW
Plant capacity
0
200
400
600
800
1,000
1,200
1,400
2005
2008
2011
2014
2017
StorageOilGas OC / steamGas CCBituminousSub-bituminousLigniteBiomass / wasteSolar thermalSolar PVWindGeothermalSmall hydroLarge hydroNuclear
0200400600800
1,0001,200
2005
May 22, 2017 All data comes from the U.S. Plant Stack (web | Terminal)
35
U.S. generators – online, 2017
1GW500MW100MW
Plant capacity
0
200
400
600
800
1,000
1,200
1,400
2017
StorageOilGas OC / steamGas CCBituminousSub-bituminousLigniteBiomass / wasteSolar thermalSolar PVWindGeothermalSmall hydroLarge hydroNuclear
0200400600800
1,0001,200
2005
2007
2009
2011
2013
2015
2017
Operational capacity (GW nameplate)
May 22, 2017 All data comes from the U.S. Plant Stack (web | Terminal)
36
U.S. generators – built from 2005-17
Transmission lines
05
101520253035
2005
2010
2015
1GW500MW100MW
Plant capacity
0
200
400
600
800
1,000
1,200
1,400
2017
StorageOilGas OC / steamGas CCBituminousSub-bituminousLigniteBiomass / wasteSolar thermalSolar PVWindGeothermalSmall hydroLarge hydroNuclear
Build (GW nameplate)
May 22, 2017 All data comes from the U.S. Plant Stack (web | Terminal)
37
U.S. generators – retired from 2005-17
Transmission lines
-35-30-25-20-15-10
-50
2005
2010
2015
1GW500MW100MW
Plant capacity
0
200
400
600
800
1,000
1,200
1,400
2017
StorageOilGas OC / steamGas CCBituminousSub-bituminousLigniteBiomass / wasteSolar thermalSolar PVWindGeothermalSmall hydroLarge hydroNuclear
Retirements (GW nameplate)
May 22, 2017 All data comes from the U.S. Plant Stack (web | Terminal)
38
0
10
20
30
40
50
60
70
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Storage
Oil
Gas OC / steam
Gas CC
Bituminous
Sub-bituminous
Lignite
Biomass / waste
Solar thermal
Solar PV
Wind
Geothermal
Small hydro
Large hydro
Nuclear
Source: Bloomberg New Energy Finance (Plant Stack Primer: Insight from the U.S. Install Base)
The incumbent power fleet is a legacy of technology super-cycles
EIA project pipeline
Hydro
Coal
Nuclear
Gas
Wind
Solar PV
Commissioning date of U.S. power fleetGigawatts (AC)
39
Think like gas plantsThey decide energy’s value
40
Carbon allowance prices ($/tCO2e)Power prices and short-run marginal costs of gas-fired generation ($/MWh)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
Power prices and gas costs in Southern California
SP15 day-ahead power prices(on/off-peak spreads)Daily historical data;monthly Fair Value futures
SRMC of burning SoCal CityGate gas(Heat rate assumption = 7.0MMBtu/MWh; Variable O&M = $3.69/MWh;Costs inclusive of covering carbon burden under California cap-and-trade)
Carbon price
Spark spreads(given as power price revenue minus SRMC)
41
Gas prices ($/MMBtu)Spark spreads ($/MWh)
0
1
2
3
4
5
6
7
8
9
10
-25
-20
-15
-10
-5
0
5
10
15
20
25
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
Spark spreads (on/off-peak range)(given as power price revenue minus SRMC)
SoCal CityGate gas prices
Spark spreads and gas prices in Southern California
42
Spark spreads ($/MWh)
0%10%20%30%40%50%60%70%80%90%
100%
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecAverage daily profile (24 hours)
Capacity factors (%)
-20
-10
0
10
20
30
40
50
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecAverage daily profile (24 hours)
Daily spark spread and capacity factor profilesFor a typical CCGT selling day-ahead power into CAISO’s SP15 Hub;buying SoCal CityGate gas and California carbon allowances; operating at a heat rate of 7.0MMBtu/MWh
Jan – Dec 2012
Gas units should strive to maximize output when spark spreads are positive…
…and they should minimize output to avoid incurring negative sparks.
Minimum stable output(below this line, plants must shut down)
Maintenance outage
43
Spark spreads ($/MWh)
Capacity factors (%)
-20
-10
0
10
20
30
40
50
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecAverage daily profile (24 hours)
0%10%20%30%40%50%60%70%80%90%
100%
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecAverage daily profile (24 hours)
Daily spark spread and capacity factor profilesFor a typical CCGT selling day-ahead power into CAISO’s SP15 Hub;buying SoCal CityGate gas and California carbon allowances; operating at a heat rate of 7.0MMBtu/MWh
Jan 2015 – Oct 2016
Gas units should strive to maximize output when spark spreads are positive…
…and they should minimize output to avoid incurring negative sparks.
Minimum stable output(below this line, plants must shut down)
44
Spark spreads ($/MWh)
Daily spark spread and capacity factor profilesFor a typical CCGT selling day-ahead power into CAISO’s SP15 Hub;buying SoCal CityGate gas and California carbon allowances; operating at a heat rate of 7.0MMBtu/MWh
Two weeks in March 2012
0%
25%
50%
75%
100%
-20
-10
0
10
20
30
40
9 10 11 12 13 14 15 16 17 18 19 20 21 22Mar 2012
-15-10
-505
10152025
9 10 11 12 13 14 15 16 17 18 19 20 21 22Mar 2012
CCGT output (capacity factor) Spark spreads
Losses associated without-of-the-money energy output
Gains from producing during positive spark-hours
Startup cost ($15/MW)
Capacity factor (%)
Gross margins ($/MW)
45
Spark spreads ($/MWh)
Gross margins ($/MW)
Daily spark spread and capacity factor profilesFor a typical CCGT selling day-ahead power into CAISO’s SP15 Hub;buying SoCal CityGate gas and California carbon allowances; operating at a heat rate of 7.0MMBtu/MWh
Two weeks in March 2016
-15-10
-505
10152025
9 10 11 12 13 14 15 16 17 18 19 20 21 22Mar 2016
0%
25%
50%
75%
100%
-20
-10
0
10
20
30
40
9 10 11 12 13 14 15 16 17 18 19 20 21 22Mar 2016
Capacity factor (%)
CCGT output (capacity factor) Spark spreads
Losses associated without-of-the-money energy output
Gains from producing during positive spark-hours
Startup and shutdown costs
46
Think like batteriesThey could restore the value of intermittent output
47
How batteries can bolster solar economicsExample from CAISO’s power mix and price profile, 2016
No batteries (actual data – average day)
With 6.0GWh of battery storage (hypothetical scenario assuming batteries engage every day in energy arbitrage)
Power mix (GW) Power price ($/MWh) Power mix (GW) Power price ($/MWh) Battery charge/discharge schedule (GW)
Notes: Figure 1 shows annual average SP15 Day-Ahead power prices (Southern California).
48
How grid-scale batteries are used in the U.S.% of capacity (MW) engaged in each activity, 2016 fleet
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
RenewableIntegration
AncillaryServices
EnergyArbitrage
Backup
ResourceAdequacy
49
How PJM batteries make money
Ancillary Services99%
Energy1%
50
05
101520253035404550
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
Ancillary service (AS) price forecastSlide taken directly from our November 2016 publication: California’s ongoing ancillary service price eruption (web | Terminal)
Spark spreads and ancillary service prices – historical and forecast ($/MWh)
SP15 Day-Ahead power prices(on/off-peak range, 30-day rolling average) Spinning
ReservesReg Down
ProjectionHistoric
Reg Up
51
Energy-to-power ratios for the U.S. battery fleet
0
5
10
15
20
25
30
35
40
45
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
Pow
er c
apac
ity (M
W)
Discharge duration (hours)
Given by the ratio of energy capacity to power capacity
Low discharge durations are best for ancillary service provision; bad for storing renewable output.
Projects with high dispatch durations are more beneficial for renewables'Average' U.S. battery project is a
9MW-9MWh system with an energy-to-power ratio of 1 hour. It is ideal for ancillary service provision and provides minimal benefit to renewables.
52
Alberta’s Gas DilemmaOutlook to the East
53
Cheap gas depresses Alberta power
-
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
-
10
20
30
40
50
60
70
80
90
100
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
Power prices (USD per MWh) Gas prices (USD per MMBtu)
54
2013 Natural Gas Flows
(Bcfd) 2013 vs. 2010Production 66.3 7.9Canadian Imports 5.1 -1.8LNG Imports 0.3 -0.9
Power 22.4 2.2RC 22.5 0.8Ind 20.4 1.6Other 7.2 0.9Total Consumption 72.5 5.6
Mex Exports 1.8 1.0LNG Imports 0.0 0.0Source: Bloomberg
Change
55
2016 Natural Gas Flows
Source: Bloomberg
(Bcfd) 2016 vs. 2013Production 72.3 6.0Canadian Imports 5.8 0.7LNG Imports 0.2 0.0
Power 27.3 4.8RC 20.6 -2.0Ind 21.1 0.8Other 6.6 -0.6Total Consumption 75.5 3.0
Mex Exports 3.7 1.9LNG Imports 0.5 0.5
Change
Declining flowsIncreasing flowsUnchanged flows
56
2019 Natural Gas Flows
(Bcfd) 2019 vs. 2016Production 81.0 8.7Canadian Imports 6.5 0.7LNG Imports 0.1 -0.1
Power 28.3 1.0RC 20.1 -0.5Ind 23.2 2.1Other 6.5 -0.1Total Consumption 78.1 2.6
Mex Exports 6.0 2.3LNG Imports 5.5 5.0Source: Bloomberg
Change
Declining flowsIncreasing flowsUnchanged flows
57
Source: Bloomberg; Note: August 2017 forward prices as of July 20, 2017.
Natural gas prices vary across the country
Henry Hub$3.04
Houston Ship Channel
$2.99
FGT Z3$3.00
Sumas$2.39
Malin$2.77
PG&E Citygate
$3.30
SoCal Border$2.92
Waha$2.74
Opal$2.64
Dawn$2.92
Dominion South$1.97
Chicago Citygate
$2.87
Transco Z6 (NY)$2.60
Algonquin Citygate(Boston)
$2.62
TETCO M3
$2.25
Panhandle$2.65
Empress$2.03
58
Toda
y
U.S. established as net exporter
Source: Bloomberg
59
Toda
y
U.S. established as net exporter
Source: Bloomberg
60
What sets gas pricesEvery Thursday at 10:30am ET
61
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
2.25
2.50
2.75
3.00
3.25
3.50
3.75
4.00
4.25
Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct
2011-12
2012-13
2013-14
2014-15
2015-16
2016-17
Gas storage is seasonalU.S. gas storage levels (Tcf)
By November we need ~3.8Tcf of gas storage.
Weather decides how deeply we draw down our storage.
Injection rates (upward slope of this
line) must adjust to winter conditions.
Live Chart:G #BNEF 180 <GO>
Note: Throughout this report we make reference to a ‘3.8Tcf storage target’. This is merely shorthand for ‘a comfortable amount of storage’; in reality there is no precise target. What is ‘comfortable?’ Well, storage operators typically look for 3.5-4.0 Tcf heading into winter, giving themselves a buffer over and above the deepest withdrawals in recent history. During the polar vortex (2013/14) the U.S. burned through 3Tcf.
62
Gas demand is more dynamic than supplyDaily North American gas supply-demand balance (Bcfd)
U.S. production+ Canadian imports
Residential / commercial demand
‘Power burn’(demand from the electricity sector)
Industrial demand
Exports to MexicoLNG – net exports
Withdrawal season (demand exceeds supply)
Injection season (supply exceeds
demand)
Live Chart:G #BNEF 252 <GO>
GSFLUSMX IndexGSLIQTOT Index
GSDML48I Index
GSDML48P Index
GSDML48C Index
GSPRODUS Index+ GSFLCTOT Index
Source: Bloomberg’s daily estimates for gas consumption, by sector. We underscore ‘daily’ because this level of granularity is not achievable with public data sources. ‘Official’ consumption figures (from the Department of Energy) provide monthly granularity only – on a time-delayed basis. See NRGZ<GO> for a library of up-to-date, proprietary, daily, regional data highlighting gas flows across North America.
63
Weather versus Res / Com burn
Heating degree days (HDDs) are a common metric used to measure the ‘coldness’ of the winter. Specifically, it tracks the number of degrees that a day’s average temperature falls below 65◦F.
17
19
21
23
25
3,000
3,500
4,000
4,500
5,000
2010
/11
2011
/12
2012
/13
2013
/14
2014
/15
2015
/16
2016
/17
TemperatureHeating degree days
Res/Com gas burnBcfd
Weather decides withdrawalsNatural gas consumption (Bcfd – 30-day moving average)
Source: Bloomberg’s daily estimates for gas consumption, by sector. We underscore ‘daily’ because this level of granularity is not achievable with public data sources. ‘Official’ consumption figures (from the Department of Energy) provide monthly granularity only – on a time-delayed basis. See NRGZ<GO> for a library of up-to-date, proprietary, daily, regional data highlighting gas flows across North America.
0
5
10
15
20
25
30
35
40
45
50
55
60
Nov 2012 Nov 2013 Nov 2014 Nov 2015 Nov 2016
2013-14: very cold
2015-16: very warm
Low power burn after cold winter
High power burn after
warm winter
Industrial
Residential / Commercial
Power
res / com burn
temperature
Live Chart:G #BNEF 252 <GO>
64
Monthly-average power prices versus short-run marginal costs (SRMC) for typical coal and gas-fired generators ($/MWh – real 2015USD)
Assumes gas heat rate = 7MMBtu/MWh; coal heat rate = 10MMBtu/MWh; transport cost from hub = $0/MMBtu; variable O&M = $0/MWh
Source: Bloomberg Terminal function SPRK<GO>, US power and fuel economic data viewer Notes: Short-run marginal costs represent variable costs only. Click here for underlying data
0
20
40
60
80
100
120
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
polar vortex
forwardcurve
history
Hurricane
commodity bubble
financialcrisis
mild winter
mild winter
shale boom basis squeeze?0
20
40
60
80
100
120
140
1
PJM West Hub on/off-peak power price spread
Henry Hub - Tetco M3SRMC basis
SRMC assuming HenryHub Gas price
SRMC assuming TetcoM3 Gas price
SRMC assuming BigSandy (Central App)Coal price
65
Storage levels dictate gas price
Storage levels versus 5-year average(Tcf)
Storage shortfall/surplus vs gas prices Tcf $/MMBtu
Henry Hub gas prices (y-axis: $/MMBtu)vs storage shortfall/surplus (x-axis: Tcf)
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
2012 2013 2014 2015 2016 2017
y = -0.0014x + 3.3682R² = 0.6712
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
-1.0 -0.5 0.0 0.5 1.0-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
2012 2013 2014 2015 2016 2017
Prevailing storage
historical storage levels
(5-year average)
Net shortfall (or surplus)
against 5-year average
Net shortfall(or surplus) against 5-year average
Henry Hub prompt month futures price
Each dot represents an individual month. The deeper the storage shortage, the higher the gas price; and vice versa.
Shortfall SurplusHigh gas price Low gas price
66 Notes: Nominal USDSource: Bloomberg New Energy Finance, Bloomberg Terminal (XLTP XHRC <GO>)
10 January 2017
Henry hub natural gas prices, historical (red line) and market-traded futures (grey lines), 1999-2021 ($/MMBtu)
Actual02468
10121416
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
Period of overly bearish price expectations
Period of overly bullishprice expectations
67
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