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Decision Support for Negotiations among Microgrids Using a Multiagent Architecture

Author

Listed:
  • 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
    Osaka Institute of Technology, Osaka 535-8585, Japan)

  • Rui Castro

    (INESC-ID/IST, University of Lisbon, 1049-001 Lisbon, Portugal)

  • Zita Vale

    (Polytechnic of Porto (IPP), 4200-465 Porto, Portugal)

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.

Suggested Citation

  • Tiago Pinto & Mohammad Ali Fotouhi Ghazvini & Joao Soares & Ricardo Faia & Juan Manuel Corchado & Rui Castro & Zita Vale, 2018. "Decision Support for Negotiations among Microgrids Using a Multiagent Architecture," Energies, MDPI, vol. 11(10), pages 1-20, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2526-:d:171408
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    References listed on IDEAS

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    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. Fotouhi Ghazvini, M.A. & Morais, Hugo & Vale, Zita, 2012. "Coordination between mid-term maintenance outage decisions and short-term security-constrained scheduling in smart distribution systems," Applied Energy, Elsevier, vol. 96(C), pages 281-291.
    3. Liu, Haifeng & Tesfatsion, Leigh & Chowdhury, A.A., 2009. "Locational Marginal Pricing Basics for Restructured Wholesale Power Markets," ISU General Staff Papers 200901010800001031, Iowa State University, Department of Economics.
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    5. Pinto, T. & Morais, H. & Oliveira, P. & Vale, Z. & Praça, I. & Ramos, C., 2011. "A new approach for multi-agent coalition formation and management in the scope of electricity markets," Energy, Elsevier, vol. 36(8), pages 5004-5015.
    6. 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.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    2. Tovar Rosas, Mario A. & Pérez, Miguel Robles & Martínez Pérez, E. Rafael, 2022. "Itineraries for charging and discharging a BESS using energy predictions based on a CNN-LSTM neural network model in BCS, Mexico," Renewable Energy, Elsevier, vol. 188(C), pages 1141-1165.
    3. Anees, Amir & Dillon, Tharam & Chen, Yi-Ping Phoebe, 2019. "A novel decision strategy for a bilateral energy contract," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    4. Capper, Timothy & Gorbatcheva, Anna & Mustafa, Mustafa A. & Bahloul, Mohamed & Schwidtal, Jan Marc & Chitchyan, Ruzanna & Andoni, Merlinda & Robu, Valentin & Montakhabi, Mehdi & Scott, Ian J. & Franci, 2022. "Peer-to-peer, community self-consumption, and transactive energy: A systematic literature review of local energy market models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    5. Fatima Zahra Harmouch & Ahmed F. Ebrahim & Mohammad Mahmoudian Esfahani & Nissrine Krami & Nabil Hmina & Osama A. Mohammed, 2019. "An Optimal Energy Management System for Real-Time Operation of Multiagent-Based Microgrids Using a T-Cell Algorithm," Energies, MDPI, vol. 12(15), pages 1-23, August.

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