Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive Learning

Tiago Pinto, Zita Vale, Isabel Praça, E. J. Solteiro Pires and Fernando Lopes
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Tiago Pinto: Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Institute of Engineering of the Polytechnic of Porto (ISEP/IPP), Rua Dr. António Bernardino de Almeida, 431, Porto 4200-072, Portugal
Zita Vale: Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Institute of Engineering of the Polytechnic of Porto (ISEP/IPP), Rua Dr. António Bernardino de Almeida, 431, Porto 4200-072, Portugal
Isabel Praça: Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Institute of Engineering of the Polytechnic of Porto (ISEP/IPP), Rua Dr. António Bernardino de Almeida, 431, Porto 4200-072, Portugal
E. J. Solteiro Pires: Universidade de Trás-os-Montes e Alto Douro (UTAD), Quinta de Prados, Vila Real 5000-801, Portugal
Fernando Lopes: National Research Institute (LNEG), Estrada do Paco do Lumiar, 22, Lisbon 1649-038, Portugal

Energies, 2015, vol. 8, issue 9, 1-26

Abstract: This paper presents a decision support methodology for electricity market players’ bilateral contract negotiations. The proposed model is based on the application of game theory, using artificial intelligence to enhance decision support method’s adaptive features. This model is integrated in AiD-EM (Adaptive Decision Support for Electricity Markets Negotiations), a multi-agent system that provides electricity market players with strategic behavior capabilities to improve their outcomes from energy contracts’ negotiations. Although a diversity of tools that enable the study and simulation of electricity markets has emerged during the past few years, these are mostly directed to the analysis of market models and power systems’ technical constraints, making them suitable tools to support decisions of market operators and regulators. However, the equally important support of market negotiating players’ decisions is being highly neglected. The proposed model contributes to overcome the existing gap concerning effective and realistic decision support for electricity market negotiating entities. The proposed method is validated by realistic electricity market simulations using real data from the Iberian market operator—MIBEL. Results show that the proposed adaptive decision support features enable electricity market players to improve their outcomes from bilateral contracts’ negotiations.

Keywords: adaptive learning; bilateral contracts; decision support; electricity markets; game theory; multi-agent simulation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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