Wind generation and zonal market price divergence evidence from texas
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Wind generation and zonal-market price divergence: evidence from Texas. Renewable energy conference December 3, 2010 Hong Kong Energy Studies Centre Hong Kong Baptist University. C.K. Woo, J. Zarnikau, J. Moore, I. Horowitz. Agenda. Background Research questions

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

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Wind generation and zonal market price divergence evidence from texas

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

Renewable energy conference

December 3, 2010

Hong Kong Energy Studies Centre

Hong Kong Baptist University

C.K. Woo,

J. Zarnikau,

J. Moore,

I. Horowitz


Agenda

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

Background

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

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


Wind generation in texas

Wind generation in Texas

Source: AWEA, Oct 2010

Almost 33% of US wind MW are in TX

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


Wind generation in texas1

Wind generation in Texas

Source: ERCOT

  • 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.


Ercot market

ERCOT market

Source: ERCOT

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


Ercot market1

ERCOT market

-ERCOT has inter-zonal transmission constraints

-Market defined by 4 regional zones: 1999-2010

Source: ERCOT


Where is texas wind generation

Where is Texas’ wind generation?

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


Ercot market constraints

ERCOT market & constraints

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

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 analysis1

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

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 analysis2

Descriptive analysis

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

Distribution of drivers when price difference < 0


Descriptive analysis3

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.


Regression analysis

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

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

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 (b1 > 0)

  • Rising natural gas price tends to increase the likelihood of a positive price difference (b2 > 0)

  • Rising nuclear generation tends to increase the likelihood of a positive price difference (b3 > 0)

  • The likelihood of a positive price difference depends on non-West loads (b4-6 ≠ 0)

  • Rising West load tends to reduce the likelihood of a positive price difference (b7 < 0 for the North-West pair)


Stage 2 regression results also support our hypotheses

Stage-2 regression results also support our hypotheses

Interpretation

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

  • Rising wind generation tends to increase the positive price difference (q1 > 0)

  • Rising natural gas price tends to increase the positive price difference (q2 > 0)

  • Rising nuclear generation tends to increase the positive price difference (q3 > 0)

  • The positive price difference depends on non-West loads (q4-6 ≠ 0)

  • Rising West load tends to reduce the size of a positive price difference (q7 < 0)

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


Conclusion

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

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

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.


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