DocumentCode :
574839
Title :
Optimal sharing of quantity risk for a coalition of wind power producers facing nodal prices
Author :
Bitar, E.Y. ; Baeyens, E. ; Khargonekar, Pramod P. ; Poolla, K. ; Varaiya, Pravin
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., U.C. Berkeley, Berkeley, CA, USA
fYear :
2012
fDate :
27-29 June 2012
Firstpage :
4438
Lastpage :
4445
Abstract :
It is widely accepted that aggregation of geographically diverse wind energy resources offers compelling potential to mitigate wind power variability, as wind speed at different geographic locations tends to decorrelate with increasing spatial separation. In this paper, we explore the extent to which a coalition of wind power producers can exploit the statistical benefits of aggregation to mitigate the risk of quantity shortfall with respect to forward contract offerings for energy. We propose a simple augmentation of the existing two-settlement market system with nodal pricing to permit quantity risk sharing among wind power producers by affording the group a recourse opportunity to utilize improved forecasts of their ensuing wind energy production to collectively modify their forward contracted positions so as to utilize the projected surplus in generation at certain buses to balance the projected shortfall in generation at complementary buses. Working within this framework, we show that the problem of optimally sizing a set of forward contracts for a group of wind power producers reduces to convex programming and derive closed form expressions for the set of optimal recourse policies. We also asses the willingness of individual wind power producers to form a coalition to cooperatively offer contracts for energy. We first show that the expected profit derived from coalitional contract offerings with recourse is greater than that achievable through independent contract offerings. And, using tools from coalitional game theory, we show that the core for our game is non-empty.
Keywords :
convex programming; game theory; power generation economics; power markets; pricing; wind power plants; coalitional game theory; convex programming; geographically diverse wind energy resources; nodal pricing; optimal sharing; quantity risk; recourse opportunity; spatial separation; two-settlement market system; wind power coalition; wind power producers; wind power variability; Forward contracts; Loading; Production; Vectors; Wind energy; Wind power generation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference (ACC), 2012
Conference_Location :
Montreal, QC
ISSN :
0743-1619
Print_ISBN :
978-1-4577-1095-7
Electronic_ISBN :
0743-1619
Type :
conf
DOI :
10.1109/ACC.2012.6315524
Filename :
6315524
Link To Document :
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