Error message

  • Notice: Undefined offset: 223 in user_node_load() (line 3697 of /home/tcipgpro/public_html/modules/user/user.module).
  • Notice: Trying to get property 'name' of non-object in user_node_load() (line 3697 of /home/tcipgpro/public_html/modules/user/user.module).
  • Notice: Undefined offset: 223 in user_node_load() (line 3698 of /home/tcipgpro/public_html/modules/user/user.module).
  • Notice: Trying to get property 'picture' of non-object in user_node_load() (line 3698 of /home/tcipgpro/public_html/modules/user/user.module).
  • Notice: Undefined offset: 223 in user_node_load() (line 3699 of /home/tcipgpro/public_html/modules/user/user.module).
  • Notice: Trying to get property 'data' of non-object in user_node_load() (line 3699 of /home/tcipgpro/public_html/modules/user/user.module).

Reinforcement Learning Techniques for Controlling Resources in Power Networks

Kowli, A.
Citation:

Ph.D. Dissertation, University of Illinois at Urbana-Champaign, August 2013.

Visit Publisher Online Entry:
Abstract:

As power grids transition towards increased reliance on renewable generation, energy storage and demand response resources, an effective control architecture is required to harness the full functionalities of these resources. There is a critical need for control techniques that recognize the unique characteristics of the different resources and exploit the flexibility afforded by them to provide ancillary services to the grid. The work presented in this dissertation addresses these needs. Specifically, new algorithms are proposed, which allow control synthesis in settings wherein the precise distribution of the uncertainty and its temporal statistics are not known. These algorithms are based on recent developments in Markov decision theory, approximate dynamic programming and reinforcement learning. They impose minimal assumptions on the system model and allow the control to be "learned" based on the actual dynamics of the system. Furthermore, they can accommodate complex constraints such as capacity and ramping limits on generation resources, state-of-charge constraints on storage resources, comfort-related limitations on demand response resources and power flow limits on transmission lines. Numerical studies demonstrating applications of these algorithms to practical control problems in power systems are discussed. Results demonstrate how the proposed control algorithms can be used to improve the performance and reduce the computational complexity of the economic dispatch mechanism in a power network. We argue that the proposed algorithms are eminently suitable to develop operational decision-making tools for large power grids with many resources and many sources of uncertainty.

Publication Status:
Published
Publication Type:
Ph.D. Dissertation
Publication Date:
08/22/2013
Copyright Notice:

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

  1. The following copyright notice applies to all of the above items that appear in IEEE publications: "Personal use of this material is permitted. However, permission to reprint/publish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from IEEE."

  2. The following copyright notice applies to all of the above items that appear in ACM publications: "© ACM, effective the year of publication shown in the bibliographic information. This file is the author’s version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in the journal or proceedings indicated in the bibliographic data for each item."

  3. The following copyright notice applies to all of the above items that appear in IFAC publications: "Document is being reproduced under permission of the Copyright Holder. Use or reproduction of the Document is for informational or personal use only."