Two-Layer Model Predictive Control of Microgrids: Cost Optimization and Resilience Through Adaptive Setpoint Coordination

Date

2025-01-01

Author

Nizamioǧlu, Tuǧrul
Rahimi, Somayyeh
Göl, Murat

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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With the growing use of energy storage systems, storing surplus energy from renewable sources has become a profitable strategy. Given the rising trend in the use of renewable energy and the installation of battery storage systems, the significance of microgrids has increased. To ensure microgrid networks perform optimally, operators should maintain resilience for emergencies and cost-effectiveness during normal operating conditions. This study introduces a strategy that addresses these issues, focusing on customer flexibility and computational efficiency through a two-layer integrated model predictive controller. In this framework, setpoints from the upper-layer microgrid operator are issued every 15 minutes to lower-layer building managers, who adjust their operations on a minute basis in response to these setpoints, ensuring effective problem-solving. Additionally, the recommended battery setpoint dispatch substantially lessens the computational load of optimization, especially in larger systems, and achieves the required resilience. Throughout the paper, corresponding methodology and simulation results are presented and verified.

Subject Keywords

battery storage, cost optimization, microgrid, mixed-integer linear programming, model predictive control, multi-agent layers, optimization, resilience, setpoints, two-layer control

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105019302431&origin=inward
https://hdl.handle.net/11511/116791

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Department of Electrical and Electronics Engineering, Conference / Seminar