Model-based decentralized optimal control of a microgrid

Power networks have experienced dramatic changes with the growth of renewable energy and `smart' grids. To accommodate the challenges posed to traditional power system control architectures, the microgrid concept has gained traction. Microgrids are small-scale power networks that can disconnect from the main grid and operate autonomously if necessary. These systems add robustness and facilitate the incorporation of renewable power, but they face control challenges of their own due to the lack of significant inertial generation. Without the main grid to provide balance, the high proportion of electrically-interfaced power resources can cause significant deterioration in microgrid stability. This dissertation proposes designs to improve decentralized control in microgrids; model based information is incorporated into controllers and estimators to more optimally guide control signals, while still only using local data for real-time computation. We outline the role that microgrid topology can have on stability, and how judicious power injection can mitigate instabilities. These results are extended to a decentralized H-infinity control design for microgrid frequency; even with limited model-based information and controller distribution, the design offers significant improvements over traditional controllers. Building upon the idea of microgrid stabilization, we also present a control method by which a wind turbine can be coordinated for microgrid support. The wind turbine is used as a controllable power source by utilizing the rotational energy stored in its rotor; this design incorporates an aerodynamic wind turbine model and a novel optimal blade pitch angle controller to ensure stable turbine operation. This allows for rapid power injection for grid support. This theme concludes with a decentralized estimation scheme to facilitate coordinated control across a microgrid using only local data. We leverage the frequency synchronization and load-sharing intrinsic to the microgrid so that local measurements can provide insight about grid-wide conditions. This allows for effective implementation of optimal filtering techniques so that remote conditions can be estimated using only local data; this allows for grid-wide coordination and optimization. Together these ideas represent the concept that the microgrid model, even in a limited and inaccurate sense, can be manipulated to provide significant benefits for decentralized control across the network.