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An Autonomous Distributed Coordination Strategy for Sustainable Consumption in a Microgrid Based on a Bio-Inspired Approach

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  • Marcel García

    (Laboratorio de Prototipos, Experimental National Universidad de Táchira, San Cristóbal 5001, Venezuela
    CEMISID, Universidad de Los Andes, Mérida 5101, Venezuela)

  • Jose Aguilar

    (CEMISID, Universidad de Los Andes, Mérida 5101, Venezuela
    GIDITIC, Universidad EAFIT, Medellín 050022, Colombia
    IMDEA Networks Institute, 28918 Madrid, Spain)

  • María D. R-Moreno

    (Universidad de Alcalá, Escuela Politécnica Superior, ISG, 28805 Alcalá de Henares, Spain)

Abstract

Distributed energy resources have demonstrated their potential to mitigate the limitations of large, centralized generation systems. This is achieved through the geographical distribution of generation sources that capitalize on the potential of their respective environments to satisfy local demand. In a microgrid, the control problem is inherently distributed, rendering traditional control techniques inefficient due to the impracticality of central governance. Instead, coordination among its components is essential. The challenge involves enabling these components to operate under optimal conditions, such as charging batteries with surplus solar energy or deactivating controllable loads when market prices rise. Consequently, there is a pressing need for innovative distributed strategies like emergent control. Inspired by phenomena such as the environmentally responsive behavior of ants, emergent control involves decentralized coordination schemes. This paper introduces an emergent control strategy for microgrids, grounded in the response threshold model, to establish an autonomous distributed control approach. The results, utilizing our methodology, demonstrate seamless coordination among the diverse components of a microgrid. For instance, system resilience is evident in scenarios where, upon the failure of certain components, others commence operation. Moreover, in dynamic conditions, such as varying weather and economic factors, the microgrid adeptly adapts to meet demand fluctuations. Our emergent control scheme enhances response times, performance, and on/off delay times. In various test scenarios, Integrated Absolute Error (IAE) metrics of approximately 0.01% were achieved, indicating a negligible difference between supplied and demanded energy. Furthermore, our approach prioritizes the utilization of renewable sources, increasing their usage from 59.7% to 86.1%. This shift not only reduces reliance on the public grid but also leads to significant energy cost savings.

Suggested Citation

  • Marcel García & Jose Aguilar & María D. R-Moreno, 2024. "An Autonomous Distributed Coordination Strategy for Sustainable Consumption in a Microgrid Based on a Bio-Inspired Approach," Energies, MDPI, vol. 17(3), pages 1-28, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:757-:d:1333790
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    References listed on IDEAS

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    1. Anvari-Moghaddam, Amjad & Rahimi-Kian, Ashkan & Mirian, Maryam S. & Guerrero, Josep M., 2017. "A multi-agent based energy management solution for integrated buildings and microgrid system," Applied Energy, Elsevier, vol. 203(C), pages 41-56.
    2. Eric Bonabeau & Guy Theraulaz & Jean-Louis Deneubourg, 1998. "Fixed Response Thresholds and the Regulation of Division of Labor in Insect Societies," Working Papers 98-01-009, Santa Fe Institute.
    3. Kofinas, P. & Dounis, A.I. & Vouros, G.A., 2018. "Fuzzy Q-Learning for multi-agent decentralized energy management in microgrids," Applied Energy, Elsevier, vol. 219(C), pages 53-67.
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