Multi-Agent Real-Time Advanced Metering Infrastructure Based on Fog Computing

Abstract

This effort to make the power grid more intelligent is tightly coupled with the deployment of advanced metering infrastructure (AMI) as an integral part of the future vision of smart grid. The goal of AMI is to provide necessary information for the consumers and utilities to accurately monitor and manage energy consumption and pricing in real time. Immediate benefits are enhanced transparency and efficiency of energy usage and the improvement of customer services. Although the road map toward successful AMI deployment is clearly defined, many challenges and issues are to be solved regarding the design of AMI. In this paper, a multi-agent AMI based on the fog-computing approach is presented. Architecture follows structural decomposition of AMI functionalities encapsulated in a form of local and area-specific service components that reside at the different tiers of hierarchically organized AMI deployment. Fog computing concepts provide the framework to effectively solve the problems of creating refined and scalable solutions capable of meeting the requirements of the AMI as a part of future smart grid. On the other hand, agent-based design enables concurrent execution of AMI operations across the distributed system architecture, in the same time improving performance of its execution and preserving the scalability of the AMI solution. The real-time performance of the proposed AMI solution, related to the periodic and on-demand acquisition of metering data from the connected electricity meters, was successfully verified during one year of pilot project operation. The detailed analysis of the performance of AMI operation regarding data collection, communication and data availability across the deployed pilot AMI, covering several transformer station areas with diverse grid topologies, is also presented.

Architecture for Smart Buildings Based on Fuzzy Logic and the OpenFog Standard

“…Some of these applications include the communication framework that is applied to security, control, resource management, and risk prevention systems, such as monitoring energy use and expenditure in household appliances [26]. It is also used to analyze environmental conditions to improve wellbeing, prevent machinery or infrastructure failures [27], and assess adverse weather conditions or natural disasters that may directly affect the building [28].…”

A QoS-Aware Data Aggregation Strategy for Resource Constrained IoT-Enabled AMI Network in Smart Grid

“…The proposed method enhances to receive redundant messages from sensors and reducing network congestion at the sink. The work proposed in [32] investigate the limited capabilities of electric meters and network as AMI architectural problem. The authors proposed a hierarchical multi-tiered agent based AMI design using fog computing in order to achieve scalability and realtime performance for periodic and on-demand meter data in the distributed Smart Grid.…”

“…In the aforementioned discussion, various network models such as clustering ( [18], [19], [21], [23], [26], [30], [31], [37]), star ( [22], [25], [36], [38]), tree-based ([27], [28], [34]), and hierarchical ( [24], [29], [32], [39]) models were proposed for AMI network design problem using different data aggregation and data collection techniques as needed for different AMI applications data. In particular, cluster-based network topology uses CH (here, SM and/or DCs) for data collection, processing, data aggregation and relaying purposes in the AMI network.…”

Optimal Placement of UDAP in Advanced Metering Infrastructure for Smart Metering of Electrical Energy Based on Graph Theory

“…Studies on optimizing the location of UDAPs (universal data aggregation points) have been carried out in the literature. Consequently, in studies such as [11][12][13]15,26,27], solutions are proposed based on the problems of distance, network clustering, or latency. The constraints considered are the transmission latency and the number of hops on the link.…”