Wind generation and zonal-market price divergence: evidence from Texas

Renewable energy conference December 3, 2010

Hong Kong Energy Studies CentreHong Kong Baptist University

C.K. Woo, J. Zarnikau, J. Moore, I. Horowitz

Agenda

Background

Research questions

ERCOT market & Texas wind

Descriptive analysis

Regression analysis

Conclusion

Background

Renewable energy and global warming

Large scale wind energy development

On-going research

• Policies to promote renewable energy

• Benefits of renewable energy

• Grid integration

• Transmission planning

• Marginal costing

• Market and contract design

Electricity market reform to introduce wholesale market competition

On-going research

• Price dynamics and volatility

• Risk management

• Asset valuation

• Market power detection

• Geographic market integration

• Hedging zonal market price spread

• Retail competition and contracting

Little is known about the effect of rising wind generation on zonal market price difference, which reflects the marginal congestion cost between two zones.

Research questions

Does wind generation cause zonal market price divergence?

If “yes”, is the price divergence frequent and large in size?

What else move zonal market price difference?

ERCOT is ideally suited to address the above questions

• Rapid wind development

• Zonal markets defined by transmission constraints

• Zonal market prices determined by least cost dispatch

• Large sample of 15-minute data

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000TX IA CA O

RW

A ILM

N NY

CO ND OK IN WY KS PA N

MM

O WI

WV SD MT

UT

ME ID NE

MI

AZ HI

VT2010

Inst

alle

d W

ind

Capa

city

(MW

)

Texas has nearly 3 times as much wind as the next highest state.

TX 9,707 MW

IA, 3,670

CA, 2,739OR,

1,920WA,

1,914

IL, 1,848

MN, 1,797

The 43 Other States, 12,760

Almost 33% of US wind MW are in TX

Wind generation in Texas

Source: AWEA, Oct 2010

Wind generation in Texas

• Large Transmission investments at same time as wind development.

• Has transmission expansion kept pace with wind development? • If “yes”, there would be few zonal price differences.

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

0

1,500

3,000

4,500

6,000

7,500

9,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Installed wind capacity(MW)ERCOT transmissioninvestment since 1999

Inst

alle

d w

ind

capa

city

(MW

)

Traa

nsm

isio

n in

vest

men

t ($M

M)

Source: ERCOT

ERCOT market

Wind generation has been rising rapidly in the last few years.

Source: ERCOT

For the five Commercially Significant Constraints (CSCs), the simple average of the constraint quantities for all 15-minute intervals of 2009 was:

1. South to North: 1,249 MW

2. North to South: 728 MW

3. North to Houston: 3,198 MW

4. West to North: 1,015 MW

5. North to West: 741 MW

Zones and CSCs

NorthWest

SouthHouston

Module 2Congestion Management

Definitions and Key Concepts

ERCOT market

-ERCOT has inter-zonal transmission constraints

-Market defined by 4 regional zones: 1999-2010Source: ERCOT

Where is Texas’ wind generation?

Rising export of wind generation from the West zone displaces thermal (mainly natural gas) generation in the other zones.

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

West North South Houston

WindHydroCT/PeakersGas CCGTOtherCoalNuclear

Nam

epla

te M

W

ERCOT zone

For the five Commercially Significant Constraints (CSCs), the simple average of the constraint quantities for all 15-minute intervals of 2009 was:

1. South to North: 1,249 MW

2. North to South: 728 MW

3. North to Houston: 3,198 MW

4. West to North: 1,015 MW

5. North to West: 741 MW

Zones and CSCs

NorthWest

SouthHouston

Module 2Congestion Management

Definitions and Key Concepts ERCOT market & constraints

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

Houston North South West

MW

Resources Loads

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

Houston North South West

MW

Resources Loads

All zones are self-sufficient, except for Houston.

Wind generation directly affects the North zone price and indirectly the prices of other zones.

West zone is sparsely populated with relatively low load

Descriptive analysis

The North zone price seems to spike when wind generation explodes (e.g., April 25-27).

But there are other factors that move the North zone price (e.g., April 1-2)

The West zone price can become negative due to federal tax credit

Descriptive analysis

The price difference data pattern is noisy, with 80+% of the 115+K observations having zero value. The price difference seems to positively correlate with wind generation.

Challenge for simple regression of wind & price differences

Because most of the observations have zero value, a simple OLS regression yields a slope coefficient of less than 0.01, an uninformative result

Descriptive analysis

Distribution of drivers when price difference < 0

$0

$2

$4

$6

$8

$10

$12

$14

0500

1,0001,5002,0002,5003,0003,5004,0004,5005,0005,500

Meg

awatt

hou

rs

Hen

ry H

ub n

at g

as p

rice

($/M

MBt

u)

$0

$2

$4

$6

$8

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$12

$14

0500

1,0001,5002,0002,5003,0003,5004,0004,5005,0005,500

Meg

awatt

hou

rs

Hen

ry H

ub n

at g

as p

rice

($/M

MBt

u)

MeanMedian5th to 95th percentile25th to 75th percentile

I

Distribution of drivers when price difference > 0

Positive price difference is more likely to occur when wind generation is relatively high

Effect of other factors is less than clear

Untangling the various effects requires a regression analysis

Descriptive analysis

The distribution of the positive price difference is highly skewed, suggesting the use of a log-linear specification in the regression analysis of non-zero price difference.

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

$0

$20

$40

$60

$80

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$180

y ln y

"

Pric

e di

ffer

ence

($/M

Wh)

Nat

ural

log

of p

rice

diff

eren

ce

y = price difference (North - West) | y>0

MeanMedian5th to 95th percentile25th to 75th percentile

I

Regression analysis

Data sample peculiarities

• Up to 14% with positive values

• 80+% with zero values

• Up to 4% with negative values

Generalized econometric model with selectivity

• Stage 1: Ordered logit regression for the probability of price difference being > 0, = 0, or < 0. This explains why congestion occurs.

• Stage 2: Log-linear regression for the size of price difference. This explains the severity of the congestion, conditional on its occurrence.

Hypotheses

Price difference between a non-West zone and the West zone is residually time-dependent beyond the effects of the factors listed below

Rising wind generation increases the likelihood and size of a positive price difference because it congests the North-West interface

Rising nuclear generation increases the likelihood and size of a positive price difference because it hinders wind export from the West zone

Rising natural gas price increases the likelihood and size of a positive price difference because it magnifies the thermal generation cost in the non-West zones

Price difference depends on non-West loads because they affect wind import by the non-West zones

Rising West load reduces the likelihood and size of a positive price difference because it reduces wind export

Stage-1 regression results support our hypotheses

Interpretation

Estimates (not shown) for timing indicators confirm time-dependence

Rising wind generation tends to increase the likelihood of a positive price difference (1 > 0)

Rising natural gas price tends to increase the likelihood of a positive price difference (2 > 0)

Rising nuclear generation tends to increase the likelihood of a positive price difference (3 > 0)

The likelihood of a positive price difference depends on non-West loads (4-6 ≠ 0)

Rising West load tends to reduce the likelihood of a positive price difference (7 < 0 for the North-West pair)

Stage-2 regression results also support our hypotheses

InterpretationEstimates (not shown) for timing indicators confirm time-dependence

Rising wind generation tends to increase the positive price difference (1 > 0)

Rising natural gas price tends to increase the positive price difference (2 > 0)

Rising nuclear generation tends to increase the positive price difference (3 > 0)

The positive price difference depends on non-West loads (4-6 ≠ 0)

Rising West load tends to reduce the size of a positive price difference (7 < 0)

An unobserved factor that increases the likelihood also enlarges the size of a positive difference ( < 0)

Conclusion

Based on 15-minute data from ERCOT, there is strong empirical evidence that rising wind generation causes zonal market price divergence

Positive price divergence is relatively frequent (up to 14%) and can have very large size (up to $3500/MWh)

Natural gas price, nuclear generation, and zonal loads also contribute to the likelihood and size of positive price difference

While wind generation may help reduce GHG emissions, it can cause severe transmission congestion

Implications for Renewable Development

High levels of wind development in remote areas with limited transmission to cities may cause severe congestion, as measured by large zonal price differences

When promoting wind development, one should consider the ensuing congestion and price spikes, whose resolution may require large transmission investments

C.K. (Chi-Keung) Woo, Ph.D. (Economics, UC Davis)

Dr. Woo specializes in public utility economics, applied microeconomics, and applied finance. With 25 years of experience in the electricity industry, he has direct experience in electricity market reform and deregulation in California, Texas, British Columbia, Ontario, Israel, and Hong Kong.

He has testified and prepared expert testimony for use in regulatory and legal proceedings in California, British Columbia and Ontario. He has also filed declaration for and testified in arbitration in connection to contract disputes.

He has published over 90 refereed articles in such scholarly journals as Energy Policy, Energy Law Journal, The Energy Journal, Energy, Energy Economics, Journal of Regulatory Economics, Journal of Public Economics, Quarterly Journal of Economics, Economics Letters, Journal of Business Finance and Accounting, and Pacific Basin Finance Journal.

Recognized by Who’s Who in America, Who's Who in Finance and Business, and Who’s Who in Science and Engineering, he is (a) an associate editor of Energy and their guest editor of a 2006 special issue on electricity market reform and deregulation and a 2010 special issue on demand response resources; (b) a member of the editorial board of The Energy Journal and their guest editor for a 1988 special issue on electricity reliability; (c) a guest editor for a forthcoming special issue of Energy Policy on renewable energy.

He is an affiliate of the Hong Kong Energy Studies Centre and an adjunct professor of Economics at the City University of Hong Kong.