Space-time wind speed forecasting for improved power system dispatch
KAUST DepartmentApplied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Spatio-Temporal Statistics and Data Analysis Group
KAUST Grant NumberKUS-C1-016-04
Online Publication Date2014-02-27
Print Publication Date2014-03
Permanent link to this recordhttp://hdl.handle.net/10754/563405
MetadataShow full item record
AbstractTo support large-scale integration of wind power into electric energy systems, state-of-the-art wind speed forecasting methods should be able to provide accurate and adequate information to enable efficient, reliable, and cost-effective scheduling of wind power. Here, we incorporate space-time wind forecasts into electric power system scheduling. First, we propose a modified regime-switching, space-time wind speed forecasting model that allows the forecast regimes to vary with the dominant wind direction and with the seasons, hence avoiding a subjective choice of regimes. Then, results from the wind forecasts are incorporated into a power system economic dispatch model, the cost of which is used as a loss measure of the quality of the forecast models. This, in turn, leads to cost-effective scheduling of system-wide wind generation. Potential economic benefits arise from the system-wide generation of cost savings and from the ancillary service cost savings. We illustrate the economic benefits using a test system in the northwest region of the United States. Compared with persistence and autoregressive models, our model suggests that cost savings from integration of wind power could be on the scale of tens of millions of dollars annually in regions with high wind penetration, such as Texas and the Pacific northwest. © 2014 Sociedad de Estadística e Investigación Operativa.
CitationZhu, X., Genton, M. G., Gu, Y., & Xie, L. (2014). Space-time wind speed forecasting for improved power system dispatch. TEST, 23(1), 1–25. doi:10.1007/s11749-014-0351-0
SponsorsThe work of the first two authors was supported in part by NSF Grant DMS-1007504. The work of the last two authors was supported in part by NSF ECCS Grant 1150944. This publication is partly based on work supported by Award No. KUS-C1-016-04 made by King Abdullah University of Science and Technology (KAUST).