.png){kind=icon}
返回
Routing Based Multi-Agent System for Network Reliability in the Smart Microgrid
.png){kind=icon}
OA
AI
DOI：10.3390/s20102992.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Microgrids help to achieve power balance and energy allocation optimality for the defined load networks. One of the major challenges associated with microgrids is the design and implementation of a suitable communication-control architecture that can coordinate actions with system operating conditions. In this paper, the focus is to enhance the intelligence of microgrid networks using a multi-agent system while validation is carried out using network performance metrics i.e., delay, throughput, jitter, and queuing. Network performance is analyzed for the small, medium and large scale microgrid using Institute of Electrical and Electronics Engineers (IEEE) test systems. In this paper, multi-agent-based Bellman routing (MABR) is proposed where the Bellman-Ford algorithm serves the system operating conditions to command the actions of multiple agents installed over the overlay microgrid network. The proposed agent-based routing focuses on calculating the shortest path to a given destination to improve network quality and communication reliability. The algorithm is defined for the distributed nature of the microgrid for an ideal communication network and for two cases of fault injected to the network. From this model, up to 35%-43.3% improvement was achieved in the network delay performance based on the Constant Bit Rate (CBR) traffic model for microgrids.
Keyword:
distributed energy resources (DERs)
microgrid
multi-agent system (MAS)
network performance
renewable energy sources (RES)
smart grid
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
Low-Latency Communications for Community Resilience Microgrids: A Reinforcement Learning ApproachOAAI
OAAI分享收藏A Bankruptcy Problem Approach to Load-shedding in Multiagent-based Microgrid Operation.png)
SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏
查看更多内容