Model-predictive control with admittance matrix estimation for the optimal power sharing in isolated DC microgrids

Santiago Alberto Ramirez-Marin*, Alejandro Garcés-Ruiz, Andres Felipe Cortés-Borray, Angel Perez-Basante, José Emilio Rodríguez-Seco

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Direct current (DC) microgrids are relevant in modern energy systems due to their high efficiency, simplified architecture, and capability of direct integration of distributed resources (DERs). A proportional power-sharing is essential in these grids to balance the power injections according to the capability of each DER. However, the intermittency of DERs, load variations, and the non-linear nature of the model are major challenges. Therefore, this paper proposes a non-linear model predictive control (MPC) alongside an estimator for the nodal admittance matrix. By using MPC, it is possible to achieve optimal operation, considering voltage and power constraints. Moreover, the estimator enables the consideration of load variations with a reduced number of measurements. The robustness of the proposed control strategy is evaluated both in simulations and through a Power-Hardware-in-the-loop (PHIL) implementation. Radial and meshed microgrids were tested with different numbers of nodes. These results validate the practical feasibility and performance of the proposed approach.

Original languageEnglish
Article number111380
JournalElectric Power Systems Research
Volume241
DOIs
Publication statusPublished - Apr 2025

Keywords

  • DC microgrid
  • Model predictive control
  • Power-hardware-in-the-loop
  • Proportional power-sharing
  • Secondary control
  • Supervisory control
  • Tertiary control
  • Voltage regulation

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