Principal Investigator: Dr. Roy McCann
The increasing generator interconnection queue for solar PV and wind power stations into the bulk electric power system is accelerating throughout the United States. These inverter-based resources (IBRs) are interconnected into transmission networks through a variety of power electronics-based equipment. These include modular multi-level converters (MMC) for wind and solar resources and HVDC networks for battery energy storage. However, the intermittent nature of renewable resources can bring about new challenges for power system operators and planners. This research develops software analysis tools to assist power system planners and operations staff in optimizing grid management, predicting potential faults or failures, and making decisions to maintain grid resiliency and stability. Conventional transmission planning approaches may not fully account for the effects of IRBs on factors such as resource adequacy, unit commitment, economic dispatch, transient stability, potential for voltage collapse, and the impacts of low inertia IBRs. This research will develop and demonstrate software that solves these issues as a resilient transmission expansion planning (RTEP) toolset. Because of the random nature of power system outages and the variability of renewable energy resources, the software will be developed using AI-based machine learning techniques to ensure the accuracy, efficiency, and decision-making capabilities of the proposed RTEP processes. To evaluate the advantages of the proposed approach, IEEE standard test systems such as the 24 and 30 bus benchmarks will be used for initial development. Random effects of outages and the uncertainty in wind/solar availability will be incorporated based on historical records from synchrophasor data that was collected during prior GRAPES projects. The software tools developed by this project will allow importing PSSE .raw and .dyr files such that larger power systems can be analyzed. Interface to other analysis tools such as PROMOD and PLEXOS. A user-friendly software GUI written in Python will be delivered as the outcome of this research. This software will include Application Programming Interface (API) files that facilitate the interaction between the users and solvers of the RTEP problem. The Python source code and APIs can be further developed based on the users’ expectations and recommendations.
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