Reinforcement learning approach for optimizing load shedding and controller parameter tuning in active islanded power systems

The rising frequency of extreme weather events associated with climate change has made the resilient design an important feature for power systems. Incorporating the capability of parts of the power system to operate as active decentralized systems is one way of increasing the resiliency and enhancing the system reliability. This thesis addresses two significant issues related to decentralized active networks. The first issue concerns the challenge of maintaining the balance between generation and load within an islanded system through under-frequency load shedding (UFLS) during islanding events. The optimal design of UFLS schemes in active networks presents a complex problem, especially with the high penetration of inverter-based resources (IBR) that exhibit complex dynamics and low inertia. It is also important to ensure that the system operates effectively at any given operating point, necessitating the consideration of multiple operating points in the design process. Conventional schemes and control methods may not be sufficient and identifying a suitable scheme can be both time-consuming and labor-intensive, often requires conducting many simulations at various operating points. This thesis presents an optimal load shedding method in active decentralized power systems using reinforcement learning. The effectiveness of these proposed methodologies is demonstrated through the implementation of a UFLS system on a segment of the Manitoba Hydro power network. The second significant issue addressed in this context is the tuning of controllers for active, decentralized systems. These controllers need to be adjusted to function optimally across a wide range of operating conditions, both in grid-connected and islanded modes. This poses numerous challenges due to the systems' complexity and dynamic nature and traditional heuristic controller tuning methods often prove inadequate in managing such complexities. In response, this thesis proposes a novel approach that extend