%A N. Singh
%A I. Elamvazuthi
%A P. Nallagownden
%A N. Badruddin
%A F. Ousta
%A A. Jangra
%I Institute of Electrical and Electronics Engineers Inc.
%T Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning
%R 10.1109/ICIAS49414.2021.9642596
%D 2021
%L scholars15491
%J International Conference on Intelligent and Advanced Systems: Enhance the Present for a Sustainable Future, ICIAS 2021
%O cited By 1; Conference of 8th International Conference on Intelligent and Advanced Systems, ICIAS 2021 ; Conference Date: 13 July 2021 Through 15 July 2021; Conference Code:175661
%X 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.
%K Decision making; Electric power transmission networks; Network layers; Network routing; Quality of service; Reinforcement learning; Reliability; Smart power grids, Agent based; Bellman-Ford; Microgrid; Multi agent; Network faults; Network reliability; Reinforcement learnings; Routings; Smart grid; Smart Micro Grids, Multi agent systems