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Spatial and geographic heterogeneity of wind turbine farms for temporally decoupled power output

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  • Pearre, Nathaniel S.
  • Swan, Lukas G.

Abstract

Many regions with good wind resources and aggressive renewable energy portfolio targets have achieved installed wind turbine capacity that is difficult for electricity system operators to manage. The purpose of this paper is to compare the measured output of wind farms at various locations throughout Nova Scotia, looking specifically at factors that either facilitate or hamper integration of additional capacity into the existing grid system. Special emphasis is placed on the effects of aggregating wind farms that are separated by distance and geography, and consequently experience different wind conditions at different times. The results are presented using metrics that have significance to electricity grid system operators, and new metrics that are accessible, readily interpretable, and actionable in locating new wind farms. The principle findings are that over a distance of 540 km the majority of potential correlation and 10-min ramp-rate reductions are achieved; that up to 30% improvement in effective load carrying capacity of wind farms is available for the worst electricity load hours by strategic wind farm siting; and a new metric shows several hours time shift between existing farms over this span. This timeshift is equal to or longer in length than most electricity utilities' peak demand periods;

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  • Pearre, Nathaniel S. & Swan, Lukas G., 2018. "Spatial and geographic heterogeneity of wind turbine farms for temporally decoupled power output," Energy, Elsevier, vol. 145(C), pages 417-429.
  • Handle: RePEc:eee:energy:v:145:y:2018:i:c:p:417-429
    DOI: 10.1016/j.energy.2018.01.019
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    Cited by:

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    3. Lobato, E. & Doenges, K. & Egido, I. & Sigrist, L., 2020. "Limits to wind aggregation: Empirical assessment in the Spanish electricity system," Renewable Energy, Elsevier, vol. 147(P1), pages 1321-1330.
    4. Naemi, Mostafa & Brear, Michael J., 2020. "A hierarchical, physical and data-driven approach to wind farm modelling," Renewable Energy, Elsevier, vol. 162(C), pages 1195-1207.
    5. Pearre, Nathaniel & Adye, Katherine & Swan, Lukas, 2019. "Proportioning wind, solar, and in-stream tidal electricity generating capacity to co-optimize multiple grid integration metrics," Applied Energy, Elsevier, vol. 242(C), pages 69-77.
    6. Jurasz, Jakub & Kies, Alexander & Zajac, Pawel, 2020. "Synergetic operation of photovoltaic and hydro power stations on a day-ahead energy market," Energy, Elsevier, vol. 212(C).

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