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The Value of Geographic Diversity of Wind and Solar: A Vector Analysis

[+] Author Affiliations
Victor Diakov, Walter Short

National Renewable Energy Laboratory, Golden, CO

Paper No. IMECE2011-63880, pp. 1093-1100; 8 pages
doi:10.1115/IMECE2011-63880
From:
  • ASME 2011 International Mechanical Engineering Congress and Exposition
  • Volume 4: Energy Systems Analysis, Thermodynamics and Sustainability; Combustion Science and Engineering; Nanoengineering for Energy, Parts A and B
  • Denver, Colorado, USA, November 11–17, 2011
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5490-7
  • Copyright © 2011 by ASME

abstract

The variability of wind and solar is perceived as a major obstacle in employing otherwise abundant renewable energy resources. Based on the available geographically dispersed data for the WECC U.S. area (excluding Alaska) and eastern U.S., we analyze to what extent the geographic diversity of these resources can offset their variability. It is common to discern baseload (i.e. constantly employed cheaper power generation, as from nuclear and coal plants) from more expensive dispatchable power sources which help meet variable electric load. The spot electricity price depends on the difference between the load and baseload. With significant amounts of power coming from wind and solar, we use generation with low variable cost (GLVC) to include baseload and wind/solar generation. The GLVC will then become variable as well. The electricity price, however, will be determined by the difference between load and GLVC. While the details of future electricity spot-pricing are harder to predict, the overall trend will remain: a higher hourly difference between load and the low-variable-cost generation increases the electricity price. This difference can serve as an approximate measure of the (hourly) revenue from producing electricity. Currently, the variable load follows fairly well defined daily and weekly load cycles. Significant amount of wind-produced power will inevitably alter the cyclic nature for the variable load time-dependence. Additionally, wind and solar farms generation profiles may be expected to poorly correlate with the variable load. We determine the set of wind and PV sites that best matches the load; we also show that the generation from any wind or PV site from the optimal set is positively correlated with the remaining variable load. The geographic distribution of optimal generation sites, e.g. North vs. South, shows features similar to phase transitions.

Copyright © 2011 by ASME
Topics: Solar energy , Wind

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