A method for including daily patterns to synthetic wind speed data for energy systems planning: Validation in the Azores Islands

Suomalainen, Kiti; Silva, C. A.; Ferrão, Paulo; Connors, Stephen R.

doi:10.1063/1.3692220

Cited by 1 publication

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wind-power penetration can increase with less reliance on costly grid-scale electricity storage systems if diurnal patterns produce top wind speeds during daily peak energy demand hours. Favorable matching of wind-power supply and energy-demand patterns also might profitably extend wind projects to sites with relatively low average wind speeds [ 3 ]. Consequently, there is critical need in project evaluation to model wind-speed dynamics accurately reflecting real-world patterns [ 1 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…Embedded Markov chain models with two probability transition matrices separating high-frequency from low-frequency behavior must still choose between adequately representing seasonal or diurnal variability in the low-frequency matrix (see [ 3 ] for an extensive review). The most recent contribution to the probabilistic energy planning literature further refined synthesis of natural diurnal and seasonal patterns by: ( 1 ) modeling transitions among “non-peak” day-types (days without significant wind variability) and “peak” day-types (each having maximum wind energy available in different 6-hour blocks during the day); and ( 2 ) fitting a sine-curve to monthly mean wind speeds [ 3 ]. Progressively complicated pattern restoration methods substantially increase the conceptual and computational complexity of proposed planning frameworks.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Nonlinear Dynamics Approach for Incorporating Wind-Speed Patterns into Wind-Power Project Evaluation

Huffaker

Bittelli

2015

PLoS ONE

View full text Add to dashboard Cite

Wind-energy production may be expanded beyond regions with high-average wind speeds (such as the Midwest U.S.A.) to sites with lower-average speeds (such as the Southeast U.S.A.) by locating favorable regional matches between natural wind-speed and energy-demand patterns. A critical component of wind-power evaluation is to incorporate wind-speed dynamics reflecting documented diurnal and seasonal behavioral patterns. Conventional probabilistic approaches remove patterns from wind-speed data. These patterns must be restored synthetically before they can be matched with energy-demand patterns. How to accurately restore wind-speed patterns is a vexing problem spurring an expanding line of papers. We propose a paradigm shift in wind power evaluation that employs signal-detection and nonlinear-dynamics techniques to empirically diagnose whether synthetic pattern restoration can be avoided altogether. If the complex behavior of observed wind-speed records is due to nonlinear, low-dimensional, and deterministic system dynamics, then nonlinear dynamics techniques can reconstruct wind-speed dynamics from observed wind-speed data without recourse to conventional probabilistic approaches. In the first study of its kind, we test a nonlinear dynamics approach in an application to Sugarland Wind—the first utility-scale wind project proposed in Florida, USA. We find empirical evidence of a low-dimensional and nonlinear wind-speed attractor characterized by strong temporal patterns that match up well with regular daily and seasonal electricity demand patterns.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%