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

Author:

Irnawan Roni12ORCID,Rizqi Ahmad Ataka Awwalur1,Yasirroni Muhammad1ORCID,Putranto Lesnanto Multa12ORCID,Ali Husni Rois1,Firmansyah Eka1,Sarjiya 12ORCID

Affiliation:

1. Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia

2. Center for Energy Studies, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia

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.

Funder

Indonesian Ministry of Research and Technology/National Agency for Research and Innovation

Indonesian Ministry of Education and Cultur

World Class University Program

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

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