9.5 Update on Estimating the Economic Impact of Short-Term Wind Forecast Improvements in ERCOT

Wednesday, 9 January 2013: 9:30 AM
Room 6A (Austin Convention Center)
Kirsten Orwig, National Renewable Energy Laboratory, Golden, CO; and V. Banunarayanan, S. Nasir, B. M. Hodge, and J. Freedman

In 2010, DOE and NOAA partnered up to fund improvements to short-term wind forecasts and to determine the economic value of these improvements to grid operators, hereafter referred to as the Wind Forecasting Improvement Project (WFIP). At the 2012 AMS Conference, a poster on the preliminary results of the economic benefits of WFIP for the Electric Reliability Council of Texas (ERCOT) was presented in which draft results for one month of new short term forecasts, was shown. This proposed paper is a supplement to three other papers submitted on this subject - Measurements and ramp events by Jeff Freedman et al., Forecast model performance by John Zack et al., and RAP/HRRR model performance by NOAA. This paper will provide results from updated analyses that assess the economic benefits for 10 months of more accurate short term wind forecast data. The economic evaluation was performed by: 1) developing new metrics to measure forecast error, 2) evaluating the overall forecast and ramp event forecast improvements, 3) determining changes in electric power production cost due to more accurate forecasts, 4) deviations between scheduled and delivered energy and the associated costs, 5) investigating the benefits of selling wind energy in hour-ahead or day-ahead markets compared to real-time markets, and 6) determining the reduction in ancillary service costs (i.e. by reducing reserve and regulation requirements). To ascertain the costs associated with the various forecast approaches, a production cost model will be run to simulate the ERCOT system and their operational decision strategy. The base case will employ a security-constrained unit commitment and dispatch market for the existing forecasting technology. Subsequent cases will be run with the modified/improved forecasts. It is expected that unit commitment and dispatch, as well as ancillary service allocation will be better optimized with the new forecasts. In addition to the production cost modeling, the team will use of intra-day and intra-hour models to assess the impact of improved ramp rate forecasts on operating reserve requirements. The results of the modeling will be compared across all cases to determine the overall value of the new forecasts, and preliminary results will be presented. Additional updates in this paper will include results from scenarios where gas prices (a major driver of economic benefits) will be varied to understand the changes in resulting economic benefits and their sensitivity to fuel prices. These results are expected to advance the knowledge base in assessing the impact of improving forecast accuracy for wind, and the resulting economic payback.
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