Variability of interconnected wind plants: correlation length and its dependence on variability time scale

Journal Article · Thu Apr 02 00:00:00 EDT 2015 · Environmental Research Letters

DOI:https://doi.org/10.1088/1748-9326/10/4/044004· OSTI ID:1220776

St. Martin, Clara M. ^[1]; Lundquist, Julie K. ^[2]; Handschy, Mark A. ^[3]

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1. Univ. of Colorado, Boulder, CO (United States). Dept. of Atmospheric and Oceanic Sciences
2. Univ. of Colorado, Boulder, CO (United States). Dept. of Atmospheric and Oceanic Sciences; National Renewable Energy Lab. (NREL), Golden, CO (United States)
3. Enduring Energy, LLC, Boulder, CO (United States)

The variability in wind-generated electricity complicates the integration of this electricity into the electrical grid. This challenge steepens as the percentage of renewably-generated electricity on the grid grows, but variability can be reduced by exploiting geographic diversity: correlations between wind farms decrease as the separation between wind farms increases. However, how far is far enough to reduce variability? Grid management requires balancing production on various timescales, and so consideration of correlations reflective of those timescales can guide the appropriate spatial scales of geographic diversity grid integration. To answer 'how far is far enough,' we investigate the universal behavior of geographic diversity by exploring wind-speed correlations using three extensive datasets spanning continents, durations and time resolution. First, one year of five-minute wind power generation data from 29 wind farms span 1270 km across Southeastern Australia (Australian Energy Market Operator). Second, 45 years of hourly 10 m wind-speeds from 117 stations span 5000 km across Canada (National Climate Data Archive of Environment Canada). Finally, four years of five-minute wind-speeds from 14 meteorological towers span 350 km of the Northwestern US (Bonneville Power Administration). After removing diurnal cycles and seasonal trends from all datasets, we investigate dependence of correlation length on time scale by digitally high-pass filtering the data on 0.25–2000 h timescales and calculating correlations between sites for each high-pass filter cut-off. Correlations fall to zero with increasing station separation distance, but the characteristic correlation length varies with the high-pass filter applied: the higher the cut-off frequency, the smaller the station separation required to achieve de-correlation. Remarkable similarities between these three datasets reveal behavior that, if universal, could be particularly useful for grid management. For high-pass filter time constants shorter than about τ = 38 h, all datasets exhibit a correlation length $$\xi $$ that falls at least as fast as $${{\tau }^{-1}}$$ . Since the inter-site separation needed for statistical independence falls for shorter time scales, higher-rate fluctuations can be effectively smoothed by aggregating wind plants over areas smaller than otherwise estimated.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• Grant/Contract Number: AC36-08GO28308; IIP-1332147
• OSTI ID: 1220776
• Report Number(s): NREL/JA-5000-64529
• Journal Information: Environmental Research Letters, Vol. 10, Issue 4; Related Information: Environmental Research Letters; ISSN 1748-9326
• Publisher: IOP Publishing
• Country of Publication: United States
• Language: English

Related Subjects

17 WIND ENERGY
wind power
variability
geographic diversity