Load Frequency Control in Microgrids using target adjusted Model Predictive Control
Problem and Research Question
Islanded microgrids — local energy systems that operate disconnected from the main grid — must maintain stable frequency despite volatile renewable generation and uncertain demand. Unlike large grids where inertia dampens fluctuations, small microgrids have low rotational mass and experience rapid frequency deviations when generation or load changes. The central question: how can a controller regulate frequency in real time when it must act before the full impact of disturbances is known?
Conventional fixed-target MPC optimizes toward a single operating point, but renewable generation and demand shift continuously. A controller that does not adjust its targets online cannot adapt to these changing conditions.
Method
This paper developed a target-adjusted Model Predictive Control (MPC) formulation for load frequency control in islanded microgrids. The approach combines:
- Receding-horizon optimization — solving a constrained optimal control problem at each time step over a prediction window, so the controller re-plans as new measurements arrive.
- Target adjustment — modifying the control objective online based on updated operating conditions (renewable forecasts, load estimates), allowing the controller to adapt its target trajectory rather than tracking a fixed setpoint.
- Stochastic handling of uncertainty — incorporating forecasts and uncertainty ranges for renewable generation and demand into the optimization, rather than assuming deterministic values.
The formulation was implemented in Python using CasADi for numerical optimization and validated in simulation against benchmark microgrid configurations with realistic wind and solar generation profiles.
Personal Contribution
Frederik Banis was the first author. He developed the target-adjusted MPC formulation, designed and ran the simulation experiments, and wrote the manuscript. Co-authors Daniela Guericke, Henrik Madsen, and Niels Kjølstad Poulsen 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, demonstrating reduced frequency deviations and faster settling times after load disturbances.
- Simulation results on representative microgrid test cases with realistic wind and solar profiles confirmed that online target adjustment allows the controller to track shifting operating conditions more effectively than a fixed reference.
- The approach was validated against benchmark configurations, showing measurable improvements in frequency regulation quality.
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, retrieved 2026-03-24).
Related Outputs
- Conference paper: Utilizing flexibility in Microgrids using MPC, MEDPOWER 2018. DOI: 10.1049/cp.2018.1856
- PhD thesis: Efficient Operation of Energy Grids, DTU, 2020. DOI: 10.11581/DTU.00000334
- Related software: SINDYc and MCMC Framework (Zenodo) — used in subsequent prosumer modeling work
Collaborators and Institutions
- DTU Compute, Technical University of Denmark — Henrik Madsen, Niels Kjølstad Poulsen (supervisors)
- Daniela Guericke (co-author, DTU Compute)
Status and Next Steps
Status: Published (2019). The target-adjusted MPC framework was extended in subsequent work on hierarchical microgrid control (Beus et al., 2020) and SINDYc-based prosumer modeling (Banis et al., 2020).
Transfer to current research: The sequential-decision framework — constrained optimization under uncertainty, acting before full information is available — is the same methodological base that now drives adaptive waveform design for biomedical sensing.
See the related project page for the broader research context and the Methods page for the methodological framework.