| Penulis/Author |
Ir. Roni Irnawan, S.T., M.Sc., Ph.D., SMIEEE. (1) ; Ahmad Ataka Awwalur Rizqi, S.T., Ph.D (2); Muhammad Yasirroni (3); Ir. Lesnanto Multa Putranto, S.T., M.Eng, Ph.D., IPM., SMIEEE. (4); Husni Rois Ali, , S.T., M.Eng., Ph.D., DIC., SMIEEE. (5); Ir. Eka Firmansyah, S.T., M.Eng., Ph.D., IPM. (6); Prof. Ir. Sarjiya, S.T., MT., Ph.D., IPU. (7) |
| Abstrak/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 |
