IDEAS home Printed from
MyIDEAS: Login to save this paper or follow this series

Dealing with Uncertainty in the Smart Grid: A Learning Game Approach

  • Hélène Le Cadre


    (CMA - Centre de Mathématiques Appliquées - MINES ParisTech - École nationale supérieure des mines de Paris)

  • Jean-Sébastien Bedo


    (Orange Labs [Paris] - Telecom Orange)

Registered author(s):

    In this article, the smart grid is modeled as a decentralized and hierarchical network, made up of three categories of agents: suppliers, generators and microgrids. To optimize their decisions concerning prices and traded power, agents need to forecast the demand of the microgrids and the fluctuating productions of the generators. The biases resulting from the decentralized learning could create imbalances between demand and supply leading to penalties for suppliers and for generators. We analytically determine prices that provide generators with a guarantee to avoid such penalties, transferring risk to the suppliers. Additionally, we prove that collaborative learning, through a grand coalition of suppliers in which information is shared and forecasts aligned on a single value, minimizes the sum of their average risk. Simulations, run for a large sample of parameter combinations using external and internal regret minimization, show that the convergence of the collaborative learning strategy is clearly faster than that resulting from distributed learning. Finally, we analyze the suppliers' individual incentives to enter into a grand coalition and the tightness of the learning algorithm's theoretical bounds.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL:
    Download Restriction: no

    Paper provided by HAL in its series Working Papers with number hal-00740893.

    in new window

    Date of creation: 10 Oct 2013
    Date of revision:
    Handle: RePEc:hal:wpaper:hal-00740893
    Note: View the original document on HAL open archive server:
    Contact details of provider: Web page:

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    as in new window
    1. Young, H. Peyton, 2009. "Learning by trial and error," Games and Economic Behavior, Elsevier, vol. 65(2), pages 626-643, March.
    Full references (including those not matched with items on IDEAS)

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:hal:wpaper:hal-00740893. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CCSD)

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.