Model-Free Approach to DC Microgrid Optimal Operation under System Uncertainty Based on Reinforcement Learning

Irnawan, Roni and Rizqi, Ahmad Ataka Awwalur and Yasirroni, Muhammad and Putranto, Lesnanto Multa and Ali, Husni Rois and Firmansyah, Eka and Sarjiya, Sarjiya (2023) Model-Free Approach to DC Microgrid Optimal Operation under System Uncertainty Based on Reinforcement Learning. Energies, 16 (14). ISSN 19961073

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Abstract

There has been tremendous interest in the development of DC microgrid systems which consist of interconnected DC renewable energy sources. However, operating a DC microgrid system optimally by minimizing operational cost and ensures stability remains a problem when the system's model is not available. In this paper, a novel model-free approach to perform operation control of DC microgrids based on reinforcement learning algorithms, specifically Q-learning and Q-network, has been proposed. This approach circumvents the need to know the accurate model of a DC grid by exploiting an interaction with the DC microgrids to learn the best policy, which leads to more optimal operation. The proposed approach has been compared with with mixed-integer quadratic programming (MIQP) as the baseline deterministic model that requires an accurate system model. The result shows that, in a system of three nodes, both Q-learning (74.2707) and Q-network (74.4254) are able to learn to make a control decision that is close to the MIQP (75.0489) solution. With the introduction of both model uncertainty and noisy sensor measurements, the Q-network performs better (72.3714) compared to MIQP (72.1596), whereas Q-learn fails to learn.

Item Type: Article
Additional Information: Library Dosen
Uncontrolled Keywords: DC microgrids; optimisation; Q-learning; Q-network; reinforcement learning
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Engineering > Electrical and Information Technology Department
Depositing User: Rita Yulianti Yulianti
Date Deposited: 02 Jul 2024 06:23
Last Modified: 02 Jul 2024 06:23
URI: https://ir.lib.ugm.ac.id/id/eprint/228

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