Comprehensive assessment of deep reinforcement learning approaches for economic dispatch in nuclear-driven microgrids

Journal Article · Mon Jun 23 00:00:00 EDT 2025 · Computers and Electrical Engineering

DOI:https://doi.org/10.1016/j.compeleceng.2025.110528· OSTI ID:2573806

Arvanitidis, Athanasios Ioannis ^[1]; Talbot, Paul ^[2]; Gatsis, Nikolaos ^[1]; Alamaniotis, Miltiadis ^[1]

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1. University of Texas at San Antonio, TX (United States)
2. Idaho National Laboratory (INL), Idaho Falls, ID (United States)

As the electrical grid integrates more variable renewable energy sources such as wind and solar, the demand for distributed and flexible systems to address this increased variability becomes critical. Nuclear-driven microgrids provide a promising solution by offering stable generation to complement intermittent renewables, ensuring grid reliability and operating efficiency. This paper proposes a recurrent deep reinforcement learning framework for optimal economic dispatch in a nuclear-powered microgrid integrating renewable energy sources, small modular reactors, battery storage systems, and balance-of-plant dynamics. A three-agent control architecture is developed, where demand and renewable energy agents act as forecasters, and a reinforcement learning-based dispatch agent performs real-time energy allocation. A nonlinear programming formulation is first used to generate an optimal baseline for benchmarking. The proposed dispatch controller, based on Proximal Policy Optimization enhanced with Long Short-Term Memory networks, exploits temporal correlations in system dynamics by taking advantage of the time series used as inputs to improve policy robustness under uncertainty. Comparative analysis against established deep reinforcement learning methods, including Proximal Policy Optimization with a feedforward architecture, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient, demonstrates superior performance. Numerical results indicate that the proposed controller achieves a 0.39% cost reduction relative to the nonlinear programming benchmark and outperforms other learning-based methods by generating additional revenue of up to 0.35%. All reinforcement learning controllers compute dispatch actions in less than 0.3 s, resulting in a computational speedup of more than three orders of magnitude over the nonlinear programming baseline. The findings of this paper highlight their applicability for real-time operation and control in nuclear-integrated microgrids under volatile operating conditions.

• Research Organization: Idaho Operations Office, Idaho Falls, ID (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: NE0009278
• OSTI ID: 2573806
• Journal Information: Computers and Electrical Engineering, Journal Name: Computers and Electrical Engineering Vol. 126; ISSN 0045-7906
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

Deep reinforcement learning
Dynamic energy management
Economic dispatch
Non-linear optimization
Nuclear-driven microgrid