This work presents a modular power system planning and power flow simulation framework for the generation and evaluation of power network models (PNM) using spatially resolved demand data. It targets users who want to study large-scale power grids having only limited information on the actual power system. Besides creating cost minimal PNMs, users are able to flexibly configure the framework to produce PNMs individually tailored to their specific use cases. Both greenfield and expansion planning are possible. The framework further comprises a built-in ac power flow simulation designed to simulate power flows in large-scale networks. This allows users to conduct a great variety of simulation studies on entire power systems, which would otherwise not be possible without access to comprehensive power grid data. Apart from the presentation of the methodology, this work comprises a demonstration of the power system planning process at the example of Singapore. The investigation shows that the framework is capable of generating a network that matches the topological and electrical metrics of the Singapore power grid.
In this paper, we discuss the systematic design of gain scheduling controllers for nonlinear systems. We discuss control signal blending induced windup phenomena and employ an extended Model-Recovery Anti-Windup (MRAW) scheme to mitigate them. Combining control signal blending with MRAW allows to build gain scheduling controllers without imposing the common slow variation assumption regarding the scheduling vectors. In addition, the approach offers a higher flexibility in the choice of the sub-controllers compared to classical approaches and convex optimization allows for a very efficient design of the anti-windup networks.
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