Design and optimization of a modular hydrogen-based integrated energy system to maximize revenue via nuclear-renewable sources

Sadab Mahmud, Binaka Ponkiya, Sravya Katikaneni, Srijana Pandey, Kranthikiran Mattimadugu, Zonggen Yi, Victor Walker, Congjian Wang, Tyler Westover, Ahmad Y. Javaid, Michael Heben and Raghav Khanna

Energy, 2024, vol. 313, issue C

Abstract: This paper demonstrates a novel modular distributed framework that uses optimal energy-dispatching strategies to enable greater flexibility and profitability in nuclear-renewable integrated energy systems (NR-IES). Hydrogen is used as a commodity in this framework since its production can improve grid stability and system operational flexibility, decarbonize heavy industry, and create an additional revenue stream for electricity generators, particularly nuclear power plants with high operational expenses. The proposed solution addresses the challenges associated with merging multiple software and services from various domains by using functional mock-up units (FMU) to co-simulate diverse subsystems designed in various platforms. The tightly coupled integrated energy system (IES) is optimized to maximize revenue by utilizing the deep reinforcement learning (DRL) technique to make smart dispatching decisions based on variable electricity prices and the availability of renewable energy. Proximal policy optimization (PPO) algorithm is used in training and testing the DRL agent. Over a period of 120 days, the proposed hydrogen-based IES framework showed about 10% revenue boost compared to a non-hydrogen generating baseline IES while also providing an easily-adoptable framework which can help to improve the flexibility of future generation nuclear power plants.

Keywords: Nuclear renewable integrated energy system; Functional mock-up interface; Co-simulation; Deep reinforcement learning; Hydrogen; Control strategies; Optimization (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:313:y:2024:i:c:s0360544224035412

DOI: 10.1016/j.energy.2024.133763

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