Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning

A Smart Microgrid consists of physical and communication layered networks. It provides communication services to each connected component and resource through multi-agent system. This paper proposes a reinforcement learning based methodology, Q-reinforcement Learning based Multi-agent based Bellmanf...

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Main Authors: Singh, N., Elamvazuthi, I., Nallagownden, P., Badruddin, N., Ousta, F., Jangra, A.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29223 /
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124122304&doi=10.1109%2fICIAS49414.2021.9642596&partnerID=40&md5=9e9acf69b67deadb43bd568a208b166b
http://eprints.utp.edu.my/29223/
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spelling utp-eprints.292232022-03-25T01:12:04Z Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning Singh, N. Elamvazuthi, I. Nallagownden, P. Badruddin, N. Ousta, F. Jangra, A. A Smart Microgrid consists of physical and communication layered networks. It provides communication services to each connected component and resource through multi-agent system. This paper proposes a reinforcement learning based methodology, Q-reinforcement Learning based Multi-agent based Bellmanford Routing (QRL-MABR), using multiple agents communicating over the microgrid network. It strengthens the decision-making core of the microgrid by improving Quality of service and network reliability of the smart microgrid. The performance analysis of the algorithm is tested over small-scale IEEE microgrid models i.e. IEEE 9 and IEEE 14. The work is tested and compared with four routing oriented decision-making algorithms, Open shortest path first (OSPF), Optimized link state routing (OLSR), Routing information protocol (RIP) and Multi-agent based Bellmanford routing (MABR). The results validate the productivity and learning capabilities of the proposed QRL-MABR algorithm. © 2021 IEEE. Institute of Electrical and Electronics Engineers Inc. 2021 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124122304&doi=10.1109%2fICIAS49414.2021.9642596&partnerID=40&md5=9e9acf69b67deadb43bd568a208b166b Singh, N. and Elamvazuthi, I. and Nallagownden, P. and Badruddin, N. and Ousta, F. and Jangra, A. (2021) Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning. In: UNSPECIFIED. http://eprints.utp.edu.my/29223/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description A Smart Microgrid consists of physical and communication layered networks. It provides communication services to each connected component and resource through multi-agent system. This paper proposes a reinforcement learning based methodology, Q-reinforcement Learning based Multi-agent based Bellmanford Routing (QRL-MABR), using multiple agents communicating over the microgrid network. It strengthens the decision-making core of the microgrid by improving Quality of service and network reliability of the smart microgrid. The performance analysis of the algorithm is tested over small-scale IEEE microgrid models i.e. IEEE 9 and IEEE 14. The work is tested and compared with four routing oriented decision-making algorithms, Open shortest path first (OSPF), Optimized link state routing (OLSR), Routing information protocol (RIP) and Multi-agent based Bellmanford routing (MABR). The results validate the productivity and learning capabilities of the proposed QRL-MABR algorithm. © 2021 IEEE.
format Conference or Workshop Item
author Singh, N.
Elamvazuthi, I.
Nallagownden, P.
Badruddin, N.
Ousta, F.
Jangra, A.
spellingShingle Singh, N.
Elamvazuthi, I.
Nallagownden, P.
Badruddin, N.
Ousta, F.
Jangra, A.
Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning
author_sort Singh, N.
title Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning
title_short Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning
title_full Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning
title_fullStr Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning
title_full_unstemmed Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning
title_sort smart microgrid qos and network reliability performance improvement using reinforcement learning
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124122304&doi=10.1109%2fICIAS49414.2021.9642596&partnerID=40&md5=9e9acf69b67deadb43bd568a208b166b
http://eprints.utp.edu.my/29223/
_version_ 1741197207819780096
score 11.62408

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