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 |
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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 |