Large-Scale Interoperable Modeling and Simulation of Electric Power Systems

The increasing complexity of modern power systems, driven by emerging grid technologies and high renewable energy penetration, necessitates advanced computational methods for simulation, scheduling, and stability assessment. This dissertation develops a series of modeling frameworks and computational tools to enhance the interoperability, scalability, and efficiency of power system studies.

First, an upgraded Large-Scale Testbed (LTB) with an integrated market simulator is introduced to facilitate the interoperation between scheduling and transient stability assessment. Inspired by movable type printing, LTB adopts a modular, function-based design that enables a What You See Is What You Get modeling approach, reducing development effort while enhancing adaptability.

Second, a dynamics-incorporated scheduling framework is proposed to bridge the gap between steady-state generation scheduling and transient stability analysis. This framework enables extensible, scalable, and interoperable scheduling under high renewable penetration by integrating dynamics into optimization formulations.

Third, a charging-time-constrained control strategy for electric vehicle (EV) aggregation in secondary frequency regulation is developed using a state-space modeling approach. The proposed method ensures reliable frequency response while respecting EV charging constraints, balancing system stability with the preferences of EV owners.

Finally, a hosting capacity analysis of California’s power grid quantifies the limitations of renewable energy integration. The study highlights that hosting capacity is primarily constrained by grid infrastructure, demonstrates the potential of dynamic line rating in alleviating these constraints, and identifies significant disparities in hosting capacity between energy-disadvantaged communities (EDC) and non-EDC.

Collectively, this dissertation advances interoperability in power system modeling and simulation by providing open, extensible frameworks that support cross-domain, large-scale analysis.