Three-Level Hierarchical Microgrid Control

Research Question

How can economic scheduling, frequency support, and local device control be coordinated across their different timescales on a laboratory microgrid?

Method

This project developed a three-level hierarchical control architecture for a grid-connected microgrid:

• Energy management — computing an economic dispatch schedule at a 15-minute resolution.
• Frequency control — using MPC and an observer to reschedule controllable power for primary-frequency-reserve provision.
• Local control — tracking device-level setpoints with classical controllers.

The architecture was demonstrated on DTU’s physical SYSLAB microgrid, with coordination through its SCADA system and MOSAIK. It is best described as a laboratory implementation rather than hardware-in-the-loop: the paper did not connect a controller to a wholly real-time-simulated microgrid.

Personal Contribution

Frederik Banis was a co-author. He contributed to the laboratory implementation and experimental validation, working on the integration of control layers and laboratory setup. The first author (Mateo Beus) led the model development and overall architecture design.

Validation and Key Results

• The three layers were integrated and exercised on a grid-connected laboratory microgrid.
• The experiment showed the frequency-control layer modifying scheduled setpoints in response to measured frequency while the local layer tracked those commands.
• The paper did not test islanded operation, voltage restoration, inverter droop control, or source switching.

The method was published in Electric Power Systems Research (2020), a peer-reviewed journal.

Outputs

• Journal paper: DOI 10.1016/j.epsr.2020.106758
• PhD thesis: Efficient Operation of Energy Grids, DTU, 2020. DOI 10.11581/DTU.00000334

Collaborators

• DTU Compute, Technical University of Denmark — Niels Kjølstad Poulsen (supervisor)
• University of Zagreb — Mateo Beus (first author), Hrvoje Pandžić (supervisor)

Status and Next Steps

Status: Published (2020). A key contribution is the integration of optimization and control layers on a physical laboratory microgrid.

Transfer to current research: The closed-loop experimentation architecture — measure, infer, decide, act, with physical validation — is the same pillar that now drives human-supervised autonomous bioassay platforms. The shift is from energy hardware to biomedical hardware, but the closed-loop validation methodology is shared.

See the Methods page for the closed-loop experimentation pillar and the Research page for the broader research program.