Deep reinforcement learning-based network for optimized power flow in islanded DC microgrid

Pitchaimani, Jeyaraj; Asokan, Siva Prakash; Kathiresan, Aravind Chellachi; Nadar, Edward Rajan Samuel

doi:10.1007/s00202-023-01835-1

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Learning Control Policy For Opf1

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2024

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Cited by 2 publications

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“…This islanded operation depends on the effective operation of connected distributed renewable energy sources (DRESs). In [71][72][73], deep learning reinforcement agent was employed to provide optimum power dispatch, and accurate control of connected DRES enabled the grid to restore service.…”

Section: Learning Control Policy For Opfmentioning

confidence: 99%

Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review

Jiang,

Wang,

et al. 2024

Energies

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Optimal power flow (OPF) is a crucial tool in the operation and planning of modern power systems. However, as power system optimization shifts towards larger-scale frameworks, and with the growing integration of distributed generations, the computational time and memory requirements of solving the alternating current (AC) OPF problems can increase exponentially with system size, posing computational challenges. In recent years, machine learning (ML) has demonstrated notable advantages in efficient computation and has been extensively applied to tackle OPF challenges. This paper presents five commonly employed OPF transformation techniques that leverage ML, offering a critical overview of the latest applications of advanced ML in solving OPF problems. The future directions in the application of machine learning to AC OPF are also discussed.

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Section: Learning Control Policy For Opfmentioning

confidence: 99%