• DocumentCode
    115044
  • Title

    Variability and the Locational Marginal Value of Energy Storage

  • Author

    Bose, Subhonmesh ; Bitar, Eilyan

  • Author_Institution
    Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA
  • fYear
    2014
  • fDate
    15-17 Dec. 2014
  • Firstpage
    3259
  • Lastpage
    3265
  • Abstract
    Given a stochastic net demand process evolving over a transmission-constrained power network, we consider the system operator´s problem of minimizing the expected cost of generator dispatch, when it has access to spatially distributed energy storage resources. We show that the expected benefit of storage derived under the optimal dispatch policy is concave and non-decreasing in the vector of energy storage capacities. Thus, the greatest marginal value of storage is derived at small installed capacities. For such capacities, we provide an upper bound on the locational (nodal) marginal value of storage in terms of the variation of the shadow prices of electricity at each node. In addition, we prove that this upper bound is tight, when the cost of generation is spatially uniform and the network topology is acyclic. These formulae not only shed light on the correct measure of statistical variation in quantifying the value of storage, but also provide computationally tractable tools to empirically calculate the locational marginal value of storage from net demand time series data.
  • Keywords
    costing; energy storage; power generation dispatch; statistical analysis; stochastic processes; transmission networks; acyclic network topology; distributed energy storage resource; electricity shadow price; energy storage capacity; generator dispatch; locational marginal value; optimal dispatch policy; statistical variation; stochastic net demand process; transmission-constrained power network; Biological system modeling; Economics; Energy storage; Sensitivity; Stochastic processes; Upper bound; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on
  • Conference_Location
    Los Angeles, CA
  • Print_ISBN
    978-1-4799-7746-8
  • Type

    conf

  • DOI
    10.1109/CDC.2014.7039893
  • Filename
    7039893