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Open AccessArticle

Decision Support for Negotiations among Microgrids Using a Multiagent Architecture

BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain
GECAD–Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Institute of Engineering, Polytechnic of Porto (ISEP/IPP), 4200-072 Porto, Portugal
Osaka Institute of Technology, Osaka 535-8585, Japan
INESC-ID/IST, University of Lisbon, 1049-001 Lisbon, Portugal
Polytechnic of Porto (IPP), 4200-465 Porto, Portugal
Author to whom correspondence should be addressed.
A preliminary and reduced version of this paper was previously presented at the 19th World Congress of the International Federation of Automatic Control (IFAC), 2014; doi:10.3182/20140824-6-ZA-1003.00566, Available online:
Energies 2018, 11(10), 2526;
Received: 7 August 2018 / Revised: 14 September 2018 / Accepted: 17 September 2018 / Published: 21 September 2018
(This article belongs to the Section Electrical Power and Energy System)
This paper presents a decision support model for negotiation portfolio optimization considering the participation of players in local markets (at the microgrid level) and in external markets, namely in regional markets, wholesale negotiations and negotiations of bilateral agreements. A local internal market model for microgrids is defined, and the connection between interconnected microgrids is based on nodal pricing to enable negotiations between nearby microgrids. The market environment considering the local market setting and the interaction between integrated microgrids is modeled using a multi-agent approach. Several multi-agent systems are used to model the electricity market environment, the interaction between small players at a microgrid scale, and to accommodate the decision support features. The integration of the proposed models in this multi-agent society and interaction between these distinct specific multi-agent systems enables modeling the system as a whole and thus testing and validating the impact of the method in the outcomes of the involved players. Results show that considering the several negotiation opportunities as complementary and making use of the most appropriate markets depending on the expected prices at each moment allows players to achieve more profitable results. View Full-Text
Keywords: local electricity markets; microgrids; multiagent systems; smart grids; transactive energy local electricity markets; microgrids; multiagent systems; smart grids; transactive energy
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Pinto, T.; Fotouhi Ghazvini, M.A.; Soares, J.; Faia, R.; Corchado, J.M.; Castro, R.; Vale, Z. Decision Support for Negotiations among Microgrids Using a Multiagent Architecture. Energies 2018, 11, 2526.

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