Date of Award

Spring 5-3-2024

Level of Access Assigned by Author

Open-Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical and Computer Engineering

Advisor

Reinaldo Tonkoski

Second Committee Member

Donald Hummels

Third Committee Member

Yifeng Zhu

Additional Committee Members

Timothy M. Hansen

Ujjwol Tamrakar

Abstract

Microgrids have emerged as a promising solution for ensuring the reliability and resilience of future grids, especially with the increasing integration of distributed energy resources (DERs) such as solar, wind, and energy storage systems (ESSs). However, this integration presents significant challenges in the dynamic control of voltage and frequency. Microgrids exhibit a distinctive characteristic marked by a high R/X ratio, resulting in voltage sensitivity to both active and reactive power, while frequency sensitivity is confined to active power. This unique attribute often leads to interference between voltage and frequency support schemes, potentially triggering protection mechanisms and causing localized or cascaded power outages.

To address these challenges, this dissertation introduces a novel approach based on moving horizon estimation (MHE), model predictive control (MPC), and droop control. MHE provides online estimates of microgrid parameters and dynamic states from noisy measurements, which then serve as inputs for MPC and droop control for the computation of reference inverter currents. The integrated MHE-MPC-droop framework is designed to provide dynamic voltage and frequency support, coupled with steady-state frequency support. Notably, the secondary controller ensures that steady-state frequency support is unnecessary, as it consistently maintains the frequency at the nominal value in steady-state conditions.

One of the strengths of the proposed approach lies in its flexibility, allowing for the tuning of performance based on specific requirements. The simulation study conducted on the Cordova microgrid benchmark from Alaska demonstrates the effectiveness of this approach in providing near-optimal voltage and frequency support while accommodating the physical constraints inherent in ESSs. Different case studies show that the proposed approach reduces the voltage and frequency deviation as well as provides flexibility to prioritize different aspects of voltage and frequency support. Further, assessment of computational traceability shows that it is real-time applicable and robust against computational delay. The research contributes to the advancement of microgrid operations, particularly in the context of the growing penetration of renewable DERs. By mitigating the challenges associated with dynamic voltage and frequency control, the proposed approach offers a robust solution for achieving reliable and resilient microgrids of the future.

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