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MASCEM: Optimizing the performance of a multi-agent system

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  • Santos, Gabriel
  • Pinto, Tiago
  • Praça, Isabel
  • Vale, Zita

Abstract

The electricity market sector has suffered massive changes in the last few decades. The worldwide electricity market restructuring has been conducted to potentiate the increase in competitiveness and thus decrease electricity prices. However, the complexity in this sector has grown significantly as well, with the emergence of several new types of players, interacting in a constantly changing environment. Several electricity market simulators have been introduced in recent years with the purpose of supporting operators, regulators, and the involved players in understanding and dealing with this complex environment. This paper presents a new, enhanced version of MASCEM (Multi-Agent System for Competitive Electricity Markets), an electricity market simulator with over ten years of existence, which had to be restructured in order to be able to face the highly demanding requirements that the decision support in this field requires. This restructuring optimizes the performance of MASCEM, both in results and execution time.

Suggested Citation

  • Santos, Gabriel & Pinto, Tiago & Praça, Isabel & Vale, Zita, 2016. "MASCEM: Optimizing the performance of a multi-agent system," Energy, Elsevier, vol. 111(C), pages 513-524.
    • Handle: RePEc:eee:energy:v:111:y:2016:i:c:p:513-524
    DOI: 10.1016/j.energy.2016.05.127
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    References listed on IDEAS

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    1. Li, Hongyan & Tesfatsion, Leigh, 2009. "Development of Open Source Software for Power Market Research: The AMES Test Bed," ISU General Staff Papers 200901010800001391, Iowa State University, Department of Economics.
    2. Meeus, Leonardo & Purchala, Konrad & Belmans, Ronnie, 2005. "Development of the Internal Electricity Market in Europe," The Electricity Journal, Elsevier, vol. 18(6), pages 25-35, July.
    3. Canizes, Bruno & Soares, João & Faria, Pedro & Vale, Zita, 2013. "Mixed integer non-linear programming and Artificial Neural Network based approach to ancillary services dispatch in competitive electricity markets," Applied Energy, Elsevier, vol. 108(C), pages 261-270.
    4. 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.
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    Citations

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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. Claudio Monteiro & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado, 2020. "Predictive Trading Strategy for Physical Electricity Futures," Energies, MDPI, vol. 13(14), pages 1-24, July.
    3. Veiga, Bruno & Santos, Gabriel & Pinto, Tiago & Faia, Ricardo & Ramos, Carlos & Vale, Zita, 2023. "Simulation tools for electricity markets considering power flow analysis," Energy, Elsevier, vol. 275(C).
    4. Gabriel Santos & Tiago Pinto & Isabel Praça & Zita Vale, 2016. "An Interoperable Approach for Energy Systems Simulation: Electricity Market Participation Ontologies," Energies, MDPI, vol. 9(11), pages 1-22, October.
    5. Davarzani, Sima & Pisica, Ioana & Taylor, Gareth A. & Munisami, Kevin J., 2021. "Residential Demand Response Strategies and Applications in Active Distribution Network Management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    6. Davarzani, Sima & Granell, Ramon & Taylor, Gareth A. & Pisica, Ioana, 2019. "Implementation of a novel multi-agent system for demand response management in low-voltage distribution networks," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    7. Chen, Peipei & Wu, Yi & Zou, Lele, 2019. "Distributive PV trading market in China: A design of multi-agent-based model and its forecast analysis," Energy, Elsevier, vol. 185(C), pages 423-436.
    8. Gabriel Santos & Pedro Faria & Zita Vale & Tiago Pinto & Juan M. Corchado, 2020. "Constrained Generation Bids in Local Electricity Markets: A Semantic Approach," Energies, MDPI, vol. 13(15), pages 1-27, August.
    9. Alfonso González-Briones & Fernando De La Prieta & Mohd Saberi Mohamad & Sigeru Omatu & Juan M. Corchado, 2018. "Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review," Energies, MDPI, vol. 11(8), pages 1-28, July.
    10. Hugo Morais & Tiago Pinto & Zita Vale, 2020. "Adjacent Markets Influence Over Electricity Trading—Iberian Benchmark Study," Energies, MDPI, vol. 13(11), pages 1-22, June.

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