Dynamics-Incorporated Modeling Framework for Stability Constrained Scheduling Under High-Penetration of Renewable Energy

Abstract

In this paper, a modularized modeling framework is designed to enable a dynamics-incorporated power system scheduling under high-penetration of renewable energy. This unique framework incorporates an adapted hybrid symbolic-numeric approach to scheduling models, effectively bridging the gap between device- and system-level optimization models and streamlining the scheduling modeling effort. The adaptability of the proposed framework stems from four key aspects: extensible scheduling formulations through modeling blocks, scalable performance via effective vectorization and sparsity-aware techniques, compatible data structure aligned with dynamic simulators by common power flow data, and interoperable dynamic interface for bi-direction data exchange between steady-state generation scheduling and time-domain dynamic simulation. Through extensive benchmarks with various usage scenarios, the framework's accuracy and scalability are validated. The case studies also demonstrate the efficient interoperation of generation scheduling and dynamics, significantly reducing the modeling conversion work in stability-constrained grid operation towards high-penetration of renewable energy.