| dc.description.abstract | This thesis introduces a multi-layer control architecture for inverter-based resources (IBRs), separating fast local feedback control from slower self-dispatch and system-level market coordination. Existing integration methods for IBRs limit their control flexibility and completely restrict their market participation potential. Two common practices include treatment of IBRs as negative loads and setting a fixed power factor during grid commissioning. Modeling IBRs as negative loads excludes them from dispatch coordination in electricity markets, significantly limiting incentive for contribution to grid reliability and flexibility. Likewise, a fixed power factor prevents the IBR from providing voltage support through reactive power absorption/injection. With a fixed power factor, constant real and reactive power limits are imposed on the inverter, even during voltage transients, ignoring the fact that an inverter’s available capacity can vary significantly due to internal current constraints and the power provided by the renewable energy source. To address the need for reactive power adjustment in IBRs and pave the way for their active participation in electricity markets , this work presents a coordinated control approach that enables IBRs to transition into active, self-dispatching participants. This thesis proposes a first layer hybrid PLL plus Q-V droop based controller in the first layer which governs millisecond-scale autonomous behavior, including low-voltage ride-through and real-time power adjustment based on voltage deviations at the point of common coupling and irradiance fluctuations from the renewable energy source, in this case solar. Given implementation from the first layer and predicted irradiance, Layer 2, which will be implemented in future work, uses a model predictive controller to provide bid functions for both real and reactive power while keeping voltage at the Point of Common Coupling within its limits. Finally, the third layer performs centralized market clearing through a security-constrained optimization by the system operator. By advocating for self-dispatched, constraint aware control, this thesis challenges the prevailing passive modeling paradigm and offers a structured, physics-informed alternative. It demonstrates how IBRs can evolve into reliable, market-integrated assets, enabling smarter renewable integration and a more resilient, cost-effective and decarbonized grid. | |
