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
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
Renewable energy and global warming
Large scale wind energy development
Policies to promote renewable energy
Benefits of renewable energy
Market and contract design
Electricity market reform to introduce wholesale market competition
Price dynamics and volatility
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.
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 has been rising rapidly in the last few years.
-ERCOT has inter-zonal transmission constraints
-Market defined by 4 regional zones: 1999-2010
Rising export of wind generation from the West zone displaces thermal (mainly natural gas) generation in the other zones.
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
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
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.
Because most of the observations have zero value, a simple OLS regression yields a slope coefficient of less than 0.01, an uninformative result
Distribution of drivers when price difference > 0
Distribution of drivers when price difference < 0
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.
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.