Decision Support for Negotiations among Microgrids Using a Multiagent Architecture

Tiago Pinto, Mohammad Ali Fotouhi Ghazvini, Joao Soares, Ricardo Faia, Juan Manuel Corchado, Rui Castro and Zita Vale
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Tiago Pinto: BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain
Mohammad Ali Fotouhi Ghazvini: 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
Joao Soares: 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
Ricardo Faia: 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
Juan Manuel Corchado: BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain
Rui Castro: INESC-ID/IST, University of Lisbon, 1049-001 Lisbon, Portugal
Zita Vale: Polytechnic of Porto (IPP), 4200-465 Porto, Portugal

Energies, 2018, vol. 11, issue 10, 1-20

Abstract: 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.

Keywords: local electricity markets; microgrids; multiagent systems; smart grids; transactive energy (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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