Physics-informed Deep Reinforcement Learning-based Control in Power systems

Book · Tue Mar 25 04:00:00 EDT 2025

DOI:https://doi.org/10.1002/9781394334599.ch3· OSTI ID:2544985

Hossain, Ramij Raja ^[1]; Huang, Qiuhua ^[2]; Mahapatra, Kaveri ^[1]; Huang, Renke ^[3]

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1. BATTELLE (PACIFIC NW LAB)
2. Colorado School Of Mines
3. Shanghai Jiao Tong University

Incorporating physics information into the deep reinforcement learning (DRL) process is a promising approach for addressing the challenges faced in learning-based control design problems for physical systems. Power grid dynamics, being a physical system, adheres to specific physical laws, constraints, as well as operational and control rules. Therefore, consideration of such physics-based law improves the learning process drastically. In general, traditional grid control schemes rely on rule-based mechanisms that cannot adapt to changing operating conditions. To improve the adaptability and computation time, recent research has seen a surge of DRL-based applications in power grid control. A generic DRL-based control design imposes the system performance requirements through the design of reward functions. In some cases, some of the important physics information is injected through this reward function. However, due to the complex dynamics and large state-action space, learning an optimal DRL policy often becomes challenging. Inspired by the latest developments in general machine learning (ML) research, power system researchers have been investigating more direct ways of incorporating physics knowledge into DRL training. This chapter specifically focuses on these aspects of physics-informed DRL designs in grid control. It discusses the significance, applications, research gaps, and open problems that need to be addressed in future research.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 2544985
• Report Number(s): PNNL-SA-193438
• Country of Publication: United States
• Language: English

Related Subjects

artificial intelligence
machine learning