Incorporating Wind Generation Forecast Uncertainty into Power System Operation, Dispatch, and Unit Commitment Procedures

Conference · Tue Oct 19 04:00:00 EDT 2010

OSTI ID:1000809

Makarov, Yuri V; Etingov, Pavel V; Huang, Zhenyu; Ma, Jian; Subbarao, Krishnappa

In this paper, an approach to evaluate the uncertainties of the balancing capacity, ramping capability, and ramp duration requirements is proposed. The approach includes three steps: forecast data acquisition, statistical analysis of retrospective information, and prediction of grid balancing requirements for a specified time horizon and a given confidence level. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on histogram analysis, incorporating sources of uncertainty of both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures) nature. A new method called the "flying-brick" technique is developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation process is used to validate the accuracy of the confidence intervals. To demonstrate the validity of the developed uncertainty assessment methods and its impact on grid operation, a framework for integrating the proposed methods with an EMS system is developed. Demonstration through integration with an EMS system illustrates the applicability of the proposed methodology and the developed tool for actual grid operation and paves the road for integration with EMS systems from other vendors.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1000809
• Report Number(s): PNNL-SA-74448; 600303000
• Country of Publication: United States
• Language: English

Related Subjects

17 WIND ENERGY
24 POWER TRANSMISSION AND DISTRIBUTION
ACCURACY
ALGORITHMS
CAPACITY
DATA ACQUISITION
FORECASTING
PERFORMANCE
POWER SYSTEMS
START-UP
WIND POWER
WIND POWER PLANTS