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Negotiation context analysis in electricity markets

Author

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  • Pinto, Tiago
  • Vale, Zita
  • Sousa, Tiago M.
  • Praça, Isabel

Abstract

Contextualization is critical in every decision making process. Adequate responses to problems depend not only on the variables with direct influence on the outcomes, but also on a correct contextualization of the problem regarding the surrounding environment. Electricity markets are dynamic environments with increasing complexity, potentiated by the last decades' restructuring process. Dealing with the growing complexity and competitiveness in this sector brought the need for using decision support tools. A solid example is MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), whose players' decisions are supported by another multiagent system – ALBidS (Adaptive Learning strategic Bidding System). ALBidS uses artificial intelligence techniques to endow market players with adaptive learning capabilities that allow them to achieve the best possible results in market negotiations. This paper studies the influence of context awareness in the decision making process of agents acting in electricity markets. A context analysis mechanism is proposed, considering important characteristics of each negotiation period, so that negotiating agents can adapt their acting strategies to different contexts. The main conclusion is that context-dependant responses improve the decision making process. Suiting actions to different contexts allows adapting the behaviour of negotiating entities to different circumstances, resulting in profitable outcomes.

Suggested Citation

  • Pinto, Tiago & Vale, Zita & Sousa, Tiago M. & Praça, Isabel, 2015. "Negotiation context analysis in electricity markets," Energy, Elsevier, vol. 85(C), pages 78-93.
    • Handle: RePEc:eee:energy:v:85:y:2015:i:c:p:78-93
    DOI: 10.1016/j.energy.2015.03.017
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    References listed on IDEAS

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    Cited by:

    1. Silva, Francisco & Teixeira, Brígida & Pinto, Tiago & Santos, Gabriel & Vale, Zita & Praça, Isabel, 2016. "Generation of realistic scenarios for multi-agent simulation of electricity markets," Energy, Elsevier, vol. 116(P1), pages 128-139.
    2. Pappachen, Abhijith & Peer Fathima, A., 2017. "Critical research areas on load frequency control issues in a deregulated power system: A state-of-the-art-of-review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 163-177.
    3. Tiago Pinto & Zita Vale & Isabel Praça & E. J. Solteiro Pires & Fernando Lopes, 2015. "Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive Learning," Energies, MDPI, vol. 8(9), pages 1-26, September.

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