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Scalable method for administration of resource technologies under stochastic procedures

Author

Listed:
  • Merino, S.
  • Martínez, J.
  • Guzmán, F.
  • Sánchez, F.J.
  • Guzmán, R.
  • Sidrach de Cardona, M.
  • Lara, J.D.

Abstract

During the development of the S3Unica project (Smart Specialisation University Campus) and its application in the ASSET project (Advanced Systems Studies for Energy Transition), both within the European Commission, the resolution of the distributed energy generation model was proposed through the creation of an algorithm that would allow the shared market between producers and consumers. From this premise arose the need to create a replicable system to resolve this situation in the new shared generation environment, using low-cost technologies. This work develops the scalable method for resource management technologies (SMART), based on stochastic procedures, which generates microgrids with an integrated energy market. The interest of this work is based on the incorporation of real-time analysis, applying stochastic methods, and its fusion with probabilistic predictive methods that evolve and harmonise the results. The fact that the process is self-learning also enables the use of metadomotic as a tool for both comfort improvement and energy sharing. The most important results developed were the design of the internal scheme of the low-cost SMART control device together with the developments of both individual and collective resolution algorithms. By achieving the incorporation of internal and external producers in the same numerical procedure, the distributed and hybrid generation models are solved simultaneously.

Suggested Citation

  • Merino, S. & Martínez, J. & Guzmán, F. & Sánchez, F.J. & Guzmán, R. & Sidrach de Cardona, M. & Lara, J.D., 2023. "Scalable method for administration of resource technologies under stochastic procedures," Applied Mathematics and Computation, Elsevier, vol. 440(C).
    • Handle: RePEc:eee:apmaco:v:440:y:2023:i:c:s009630032200724x
    DOI: 10.1016/j.amc.2022.127652
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    References listed on IDEAS

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    1. Merino, S. & Martínez, J. & Guzmán, F., 2015. "Metadomotic optimization using genetic algorithms," Applied Mathematics and Computation, Elsevier, vol. 267(C), pages 170-178.
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