Critical Reliability Improvement Using Q-Learning-Based Energy Management System for Microgrids

Lizon Maharjan, Mark Ditsworth and Babak Fahimi ()
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Lizon Maharjan: Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USA
Mark Ditsworth: Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USA
Babak Fahimi: Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USA

Energies, 2022, vol. 15, issue 23, 1-21

Abstract: This paper presents a power distribution system that prioritizes the reliability of power to critical loads within a community. The proposed system utilizes reinforcement learning methods (Q-learning) to train multi-port power electronic interface (MPEI) systems within a community of microgrids. The primary contributions of this article are to present a system where Q-learning is successfully integrated with MPEI to reduce the impact of power contingencies on critical loads and to explore the effectiveness of the subsequent system. The feasibility of the proposed method has been proven through simulation and experiments. It has been demonstrated that the proposed method can effectively improve the reliability of the local power system—for a case study where 20% of the total loads are classified as critical loads, the system average interruption duration index (SAIDI) has been improved by 75% compared to traditional microgrids with no load schedule.

Keywords: multi-port power electronic interface; reliability; reinforcement learning; smart grid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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