NDLI: Deep Q‐network application for optimal energy management in a grid‐tied solar PV‐Battery microgrid

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Deep Q‐network application for optimal energy management in a grid‐tied solar PV‐Battery microgrid

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Grace Muriithi Sunetra Chowdhury
Abstract	Abstract This paper presents a deep Q‐network (DQN) technique to optimally manage energy resources in a microgrid in which the algorithm learns tasks in the same way as humans do. Every move the agent makes in the environment generates feedback which then motivate the agent to learn more about the environment and perform far more intelligent steps later in its learning stages. This paper proposes a DQN‐based energy management system that learns system uncertainties, including load demand, grid prices and volatile renewable power supply to ensure that energy is optimally dispatched in such a setting. The method uses experience replay and target network to increase learning speed and improve stability in previous research. The performance of the system is evaluated with slow, medium, and fast fluctuating load profiles and their combinations and also with different sets of actions. The system is designed to minimize both grid transactional costs and battery degradation costs. Results shows that the proposed system successfully learns from experience to raise the battery state of charge and optimally shift loads from a one‐time instance, reducing aggregate peak load and cost of energy purchased from the utility grid while maximizing revenue from energy exported to the grid.
Related Links	https://doi.org/10.1049/tje2.12128
e-ISSN	20513305
DOI	10.1049/tje2.12128
Journal	The Journal of Engineering
Issue Number	4
Volume Number	2022
Language	English
Publisher	Wiley
Publisher Date	2022-01-01
Publisher Place	United Kingdom
Access Restriction	Open
Subject Keyword	Engineering (general). Civil Engineering (general)
Content Type	Text
Resource Type	Article

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