• DocumentCode
    1030855
  • Title

    Real-world market representation with agents

  • Author

    Koritarov, Vladimir S.

  • Author_Institution
    Argonne Nat. Laboratory, IL, USA
  • Volume
    2
  • Issue
    4
  • fYear
    2004
  • Firstpage
    39
  • Lastpage
    46
  • Abstract
    As power markets are relatively new and still continue to evolve, there is a growing need for advanced modeling approaches that simulate the behavior of electricity markets over time and how market participants may act and react to the changing economic, financial and regulatory environments in which they operate. A new and rather promising approach is to model the electricity market as a complex adaptive system using an agent-based modeling and simulation (ABMS) approach. The purpose of an ABMS model is not necessarily to predict the outcome of a system but to reveal and understand the complex and aggregate system behaviors that emerge from the interactions of the heterogeneous individual entities. Emergent behavior is a key feature of ABMS and is not easily inferred from the simple sum of the behavior of its components. By relying on both established engineering modeling techniques as well as advanced quantitative economic market principles, the ABMS approach is uniquely suited to addressing the strategic issues of interest to different market participants as well as those of market monitors and regulators.
  • Keywords
    adaptive systems; multi-agent systems; power markets; power system economics; power system simulation; agent-based approach; agent-based modeling and simulation; aggregate system behaviors; complex adaptive system; economic market principles; electric power industry; electricity market; extensive restructuring process; heterogeneous individual entities; market monitors; market regulators; real-world market representation; Adaptive systems; Aggregates; Economic forecasting; Electricity supply industry; Environmental economics; Power generation economics; Power markets; Power system economics; Power system modeling; Predictive models;
  • fLanguage
    English
  • Journal_Title
    Power and Energy Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    1540-7977
  • Type

    jour

  • DOI
    10.1109/MPAE.2004.1310872
  • Filename
    1310872