Decentralized DC MicroGrid Monitoring and Optimization via Primary Control Perturbations
This work addresses the need for communication-free monitoring and optimization in DC MicroGrids, which is important for system operators seeking reliable and scalable control in high-penetration power electronic environments.
The paper proposes a decentralized method for DC MicroGrids to estimate generation capacities, load demands, and line conductances using only local voltage measurements and primary control perturbations, eliminating the need for external communication. The approach enables autonomous optimal economic dispatch, with evaluations confirming its effectiveness.
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.