Digital Real-Time Simulation and Power Quality Analysis of a Hydrogen-Generating Nuclear-Renewable Integrated Energy System

Gautam, Sushanta; Szczublewski, Austin; Fox, Aidan; Mahmud, Sadab; Javaid, Ahmad; Olowu, Temitayo O.; Westover, Tyler; Khanna, Raghav

doi:10.3390/en18040937

Digital Real-Time Simulation and Power Quality Analysis of a Hydrogen-Generating Nuclear-Renewable Integrated Energy System

Journal Article · Sat Feb 15 04:00:00 EST 2025 · Energies

DOI:https://doi.org/10.3390/en18040937· OSTI ID:2519259

; Szczublewski, Austin; Fox, Aidan; ; ; ; Westover, Tyler;

This paper investigates the challenges and solutions associated with integrating a hydrogen-generating nuclear-renewable integrated energy system (NR-IES) under a transactive energy framework. The proposed system directs excess nuclear power to hydrogen production during periods of low grid demand while utilizing renewables to maintain grid stability. Using digital real-time simulation (DRTS) in the Typhoon HIL 404 model, the dynamic interactions between nuclear power plants, electrolyzers, and power grids are analyzed to mitigate issues such as harmonic distortion, power quality degradation, and low power factor caused by large non-linear loads. A three-phase power conversion system is modeled using the Typhoon HIL 404 model and includes a generator, a variable load, an electrolyzer, and power filters. Active harmonic filters (AHFs) and hybrid active power filters (HAPFs) are implemented to address harmonic mitigation and reactive power compensation. The results reveal that the HAPF topology effectively balances cost efficiency and performance and significantly reduces active filter current requirements compared to AHF-only systems. During maximum electrolyzer operation at 4 MW, the grid frequency dropped below 59.3 Hz without filtering; however, the implementation of power filters successfully restored the frequency to 59.9 Hz, demonstrating its effectiveness in maintaining grid stability. Future work will focus on integrating a deep reinforcement learning (DRL) framework with real-time simulation and optimizing real-time power dispatch, thus enabling a scalable, efficient NR-IES for sustainable energy markets.

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Sponsoring Organization:: USDOE

OSTI ID:: 2519259

Alternate ID(s):: OSTI ID: 2558081

Journal Information:: Energies, Journal Name: Energies Journal Issue: 4 Vol. 18; ISSN ENERGA; ISSN 1996-1073

Publisher:: MDPI AGCopyright Statement

Country of Publication:: Switzerland

Language:: English

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