Decentralized DC MicroGrid Monitoring and Optimization via Primary Control Perturbations

Marko Angjelichinoski, Anna Scaglione, Petar Popovski, Cedomir Stefanovic

Research output: Contribution to journalArticle

2 Scopus citations


We treat the emerging power systems with direct current (DC) MicroGrids, characterized with high penetration of power electronic converters. We rely on the power electronics to propose a decentralized solution for autonomous learning of and adaptation to the operating conditions of the DC Mirogrids; the goal is to eliminate the need to rely on an external communication system for such purpose. The solution works within the primary droop control loops and uses only local bus voltage measurements. Each controller is able to estimate (i) the generation capacities of power sources, (ii) the load demands, and (iii) the conductances of the distribution lines. To define a well-conditioned estimation problem, we employ decentralized strategy where the primary droop controllers temporarily switch between operating points in a coordinated manner, following amplitude-modulated training sequences. We study the use of the estimator in a decentralized solution of the Optimal Economic Dispatch problem. The evaluations confirm the usefulness of the proposed solution for autonomous MicroGrid operation.

Original languageEnglish (US)
JournalIEEE Transactions on Signal Processing
StateAccepted/In press - Apr 13 2018



  • Control systems
  • direct current MicroGrids
  • droop control
  • Maximum Likelihood
  • Microgrids
  • Monitoring
  • Optimal Economic Dispatch
  • Optimization
  • Perturbation methods
  • Training
  • training
  • Voltage measurement

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Cite this