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Reconsidering the capacity credit of wind power: Application of cumulative prospect theory

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  • Wilton, Edgar
  • Delarue, Erik
  • D’haeseleer, William
  • van Sark, Wilfried

Abstract

The capacity credit is often erroneously considered to be a time-invariant quantity. A multi-year analysis of the incident wind profile of various potential wind sites uncovered that there exist large differences between annual capacity credit figures. The uniformity of these capacity credit figures is found to decrease with diminishing wind time series interval lengths. In recognition of the resulting uncertainty, decision maker risk propensity toward various capacity credit scenarios was investigated by adopting cumulative prospect theory. The methodology proposed in this paper is an extension of the effective load carrying capability method. It enables the quantitative analysis of the attitudes of decision makers with regard to deviations (gains and losses) from the forecasted capacity credit as a result of the uncertainty of the incident wind profile. Here, gains and losses may not be viewed by decision makers as having equal but opposite effects on the appeal of wind power production. Therefore, it is argued that a decision maker will not have a neutral risk propensity toward changes to the outcome of the capacity credit and will discount increases and decreases of the loss of load expectation according to a non-linear preference. In line with the well-known adagium that losses loom larger than gains the value of the capacity credit is found to be lower than its corresponding least squares forecast.

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  • Wilton, Edgar & Delarue, Erik & D’haeseleer, William & van Sark, Wilfried, 2014. "Reconsidering the capacity credit of wind power: Application of cumulative prospect theory," Renewable Energy, Elsevier, vol. 68(C), pages 752-760.
  • Handle: RePEc:eee:renene:v:68:y:2014:i:c:p:752-760
    DOI: 10.1016/j.renene.2014.02.051
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    3. Zhou, Ella & Cole, Wesley & Frew, Bethany, 2018. "Valuing variable renewable energy for peak demand requirements," Energy, Elsevier, vol. 165(PA), pages 499-511.
    4. Mosadeghy, Mehdi & Yan, Ruifeng & Saha, Tapan Kumar, 2016. "Impact of PV penetration level on the capacity value of South Australian wind farms," Renewable Energy, Elsevier, vol. 85(C), pages 1135-1142.
    5. Wen, Lei & Song, Qianqian, 2023. "ELCC-based capacity value estimation of combined wind - storage system using IPSO algorithm," Energy, Elsevier, vol. 263(PB).
    6. Haoran Zhao & Sen Guo & Huiru Zhao, 2018. "Selecting the Optimal Micro-Grid Planning Program Using a Novel Multi-Criteria Decision Making Model Based on Grey Cumulative Prospect Theory," Energies, MDPI, vol. 11(7), pages 1-24, July.

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