• 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