Target-Adjusted MPC for Microgrid Frequency Control

Research Question

How can a microgrid maintain stable frequency under volatile renewable generation and uncertain load, when the controller must act before the full impact of disturbances is known?

Method

This project developed a target-adjusted Model Predictive Control (MPC) framework for microgrid frequency regulation. The approach combines:

• Receding-horizon optimization — solving a constrained optimal control problem at each time step over a prediction window.
• Target adjustment — modifying the control objective online based on updated operating conditions, so the controller adapts to changing renewable generation and load patterns.
• Stochastic handling of uncertainty — incorporating forecasts and uncertainty ranges for renewable generation and demand into the optimization.

The method was implemented in Python using CasADi for numerical optimization and validated in simulation against benchmark microgrid configurations.

Personal Contribution

Frederik Banis was the first author of the primary journal publication. He developed the target-adjusted MPC formulation, designed the simulation experiments, and wrote the manuscript. Co-authors contributed supervision, methodological guidance, and review.

Validation and Key Results

• The target-adjusted MPC improved frequency stability compared to fixed-target MPC under variable renewable generation.
• Simulation results demonstrated reduced frequency deviations and faster settling times after load disturbances.
• The approach was validated on representative microgrid test cases with realistic wind and solar generation profiles.

The method was published in IET Renewable Power Generation (2019), a peer-reviewed journal. As of March 2026, the paper has been cited 20 times (Google Scholar).

Outputs

• Journal paper: DOI 10.1049/iet-rpg.2019.0487
• Conference paper: MEDPOWER 2018 — Utilizing flexibility in Microgrids using MPC.
• Related software: SINDYc and MCMC Framework (Zenodo)
• PhD thesis: Efficient Operation of Energy Grids, DTU, 2020. DOI 10.11581/DTU.00000334

Collaborators

• DTU Compute, Technical University of Denmark — Henrik Madsen, Niels Kjølstad Poulsen (supervisors)
• Daniela Guericke (co-author)

Status and Next Steps

Status: Published (2019). The methodological foundation has been extended in subsequent work on hierarchical microgrid control and SINDYc-based prosumer modeling.

Transfer to current research: The sequential-decision framework developed here — constrained optimization under uncertainty, acting before full information is available — is the same methodological base that now drives adaptive waveform design for biomedical sensing.