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Saturation-based robustly optimal hierarchical operation control of microgrids

Ujjwal Pratap, Steffen Hofmann

TL;DR

The paper addresses robust operation of islanded microgrids with high renewable penetration by combining a saturating droop-based primary control layer with an outer robust energy-management system that schedules conventional generation via unit commitment within a model-predictive control framework. It proves that constant power setpoints can yield optimal operation under certain conditions and demonstrates a minimax-MPC-based EMS to relax those conditions. A case study shows the proposed hierarchical control outperforms prior robust EMS schemes and closely tracks a prescient benchmark, highlighting practical robustness and cost benefits for small-scale microgrids. The work suggests a promising, implementable approach for improving reliability and economics in renewables-rich islanded microgrids, with future work on providing theoretical guarantees and minimum-setpoint guidance at the primary layer.

Abstract

This paper studies the problem of robustly optimal operation control of microgrids with a high share of renewable energy sources. The main goal is to ensure optimal operation under a wide range of circumstances, given the highly intermittent and uncertain nature of renewable sources and load demand. We formally state this problem, and, in order to solve it, we make effective use of the hierarchical power system control approach. We consider an enhanced primary control layer including droop control and autonomous limitation of power and energy. We prove that this enables the use of constant power setpoints to achieve optimal operation under certain conditions. In order to relax these conditions, the approach is combined with an energy management system, which solves a robust unit commitment problem within a model predictive control framework. Finally, a case study demonstrates the viability of the control design.

Saturation-based robustly optimal hierarchical operation control of microgrids

TL;DR

The paper addresses robust operation of islanded microgrids with high renewable penetration by combining a saturating droop-based primary control layer with an outer robust energy-management system that schedules conventional generation via unit commitment within a model-predictive control framework. It proves that constant power setpoints can yield optimal operation under certain conditions and demonstrates a minimax-MPC-based EMS to relax those conditions. A case study shows the proposed hierarchical control outperforms prior robust EMS schemes and closely tracks a prescient benchmark, highlighting practical robustness and cost benefits for small-scale microgrids. The work suggests a promising, implementable approach for improving reliability and economics in renewables-rich islanded microgrids, with future work on providing theoretical guarantees and minimum-setpoint guidance at the primary layer.

Abstract

This paper studies the problem of robustly optimal operation control of microgrids with a high share of renewable energy sources. The main goal is to ensure optimal operation under a wide range of circumstances, given the highly intermittent and uncertain nature of renewable sources and load demand. We formally state this problem, and, in order to solve it, we make effective use of the hierarchical power system control approach. We consider an enhanced primary control layer including droop control and autonomous limitation of power and energy. We prove that this enables the use of constant power setpoints to achieve optimal operation under certain conditions. In order to relax these conditions, the approach is combined with an energy management system, which solves a robust unit commitment problem within a model predictive control framework. Finally, a case study demonstrates the viability of the control design.

Paper Structure

This paper contains 7 sections, 5 equations, 3 figures, 1 table.

Table of Contents

  1. Simplified EMS with constant setpoints and unit commitment problem
  2. Case study
  3. Simulation setup
  4. A prescient controller
  5. Closed-loop comparison
  6. Conclusion
  7. Acknowledgement

Figures (3)

  • Figure 3: Microgrid topology with a conventional generator, a wind turbine, a storage unit, a pv power plant and a load.
  • Figure 4: Load demand profiles $w_\text{d}^s$ for different scenarios (s) for day $1$ in the case study.
  • Figure 5: Comparison of the closed-loop cost calculated over one week simulation, for different controllers and different disturbance scenarios. Scenario $s$ is on the x-axis and the total cost is on y-axis.