Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system

Zonggen Yi, Yusheng Luo, Tyler Westover, Sravya Katikaneni, Binaka Ponkiya, Suba Sah, Sadab Mahmud, David Raker, Ahmad Javaid, Michael J. Heben and Raghav Khanna

Applied Energy, 2022, vol. 328, issue C, No S0306261922013708

Abstract: New ways to integrate energy systems to maximize efficiency are being sought to meet carbon emissions goals. Nuclear-renewable integrated energy system (NR-IES) concepts are a leading solution that couples a nuclear power plant with renewable energy, hydrogen generation plants, and energy storage systems, such that thermal and electrical power are dispatchable to fulfill grid-flexibility requirements while also producing hydrogen and maximizing revenue. This paper introduces a deep reinforcement learning (DRL)-based framework to address the complex decision-making tasks for NR-IES. The objective is to maximize revenue by generating and selling hydrogen and electricity simultaneously according to their time-varying prices while keeping the energy flow in the subsystems in balance. A Python-based simulator for a NR-IES concept has been developed to integrate with OpenAI Gym and Ray/RLlib to enable an efficient and flexible computational framework for DRL research and development. Three state-of-the-art DRL algorithms have been investigated, including two-delayed deep deterministic policy gradient (TD3), soft-actor critic (SAC), proximal policy optimization (PPO), to illustrate DRL’s superiority for controlling NR-IES by comparing it with a conventional control approach, particle swarm optimization (PSO). In this effort, PPO has shown more-stable performance and also better generalization capability than SAC and TD3. Comparisons with PSO have demonstrated that, on average, PPO can achieve 13.9% more mean episode returns from the training process and 29.4% more mean episode returns from the testing process when different hydrogen-production targets are applied.

Keywords: Nuclear renewable integrated energy system; Deep reinforcement learning; System control; Operation optimization (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.apenergy.2022.120113

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