Renewable Energy’s Jagged Path to Parity · Think Like Gas Plants – They Appraise Renewables....

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

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Energy cost reductions

3

$62bn$88bn

$128bn

$175bn$205bn $207bn

$276bn$317bn

$291bn$269bn

$315bn$349bn

$287bn$258bn(estimate)

0.00

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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

Presenter

Presentation Notes

~85GW of RE auctioned to date globally. 400MW underway in Alberta.

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

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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

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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…

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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.

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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

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4

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12

0

20

40

60

80

100

120

2005

2006

2007

2008

2009

2010

2011

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2013

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2015

2016

2017

2018

2019

2020

U.S. wholesale power prices

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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

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$- (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)

https://www.bnef.com/core/insight/14600

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-

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

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5

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$-

$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

http://www.bnef.com/core/insight/12851

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


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.

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12

08 Sep 09 Sep 10 Sep -

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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


10

20

30

40

50

60


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.

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10

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60

08 Sep 09 Sep 10 Sep-2

-1

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6


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

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08 Sep 09 Sep 10 Sep-20

-15

-10

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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)


http://bloom.bg/2di2XaH

35

U.S. generators – online, 2017

1GW500MW100MW

Plant capacity

0

200

400

600

800

1,000

1,200

1,400

2017


0200400600800

1,0001,200

2005

2007

2009

2011

2013

2015

2017

Operational capacity (GW nameplate)




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


Build (GW nameplate)




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


Retirements (GW nameplate)




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)

https://www.bnef.com/core/insight/16389

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)

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2011

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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


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


Capacity factors (%)

-20

-10

0

10

20

30

40

50


0%10%20%30%40%50%60%70%80%90%

100%



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



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


Gross margins ($/MW)


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


http://bloom.bg/2f4JCPH

51

Energy-to-power ratios for the U.S. battery fleet

0

5

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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

Presenter

Presentation Notes

Polar Vortex – Lots of LNG

55


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

Presenter

Presentation Notes

Flows reversing - out of App towards Gulf to help supply exports Exports via LNG and pipes to Mexico Minimal imports Less from Canada Power demand grown- gas overtakes coal

56


(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

Presenter

Presentation Notes

Driven by pricing HH around $3 App <$2 Northeast currently $2.62 – summer so low demand Can see things like Florida, dawn (Toronto markets) fairly attractive

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.

https://bloom.bg/2ti0urP





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)


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.

https://bloom.bg/2sISsYa



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





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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Coverage.

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Bloomberg New Energy Finance is a research firm that helps energy professionals generate opportunities. With a team of experts spread across six continents, BNEF provides independent analysis and insight, enabling decision-makers to navigate change in an evolving energy economy.

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