Optimal Deployment of FiWi Networks Using Heuristic Method for Integration Microgrids with Smart Metering

A QoS-Aware Machine Learning-Based Framework for AMI Applications in Smart Grids

The Internet of things (IoT) enables a diverse set of applications such as distribution automation, smart cities, wireless sensor networks, and advanced metering infrastructure (AMI). In smart grids (SGs), quality of service (QoS) and AMI traffic management need to be considered in the design of efficient AMI architectures. In this article, we propose a QoS-aware machine-learning-based framework for AMI applications in smart grids. Our proposed framework comprises a three-tier hierarchical architecture for AMI applications, a machine-learning-based hierarchical clustering approach, and a priority-based scheduling technique to ensure QoS in AMI applications in smart grids. We introduce a three-tier hierarchical architecture for AMI applications in smart grids to take advantage of IoT communication technologies and the cloud infrastructure. In this architecture, smart meters are deployed over a georeferenced area where the control center has remote access over the Internet to these network devices. More specifically, these devices can be digitally controlled and monitored using simple web interfaces such as REST APIs. We modify the existing K-means algorithm to construct a hierarchical clustering topology that employs Wi-SUN technology for bi-directional communication between smart meters and data concentrators. Further, we develop a queuing model in which different priorities are assigned to each item of the critical and normal AMI traffic based on its latency and packet size. The critical AMI traffic is scheduled first using priority-based scheduling while the normal traffic is scheduled with a first-in–first-out scheduling scheme to ensure the QoS requirements of both traffic classes in the smart grid network. The numerical results demonstrate that the target coverage and connectivity requirements of all smart meters are fulfilled with the least number of data concentrators in the design. Additionally, the numerical results show that the architectural cost is reduced, and the bottleneck problem of the data concentrator is eliminated. Furthermore, the performance of the proposed framework is evaluated and validated on the CloudSim simulator. The simulation results of our proposed framework show efficient performance in terms of CPU utilization compared to a traditional framework that uses single-hop communication from smart meters to data concentrators with a first-in–first-out scheduling scheme.

Optimal Performance and Modeling of Wireless Technology Enabling Smart Electric Metering Systems Including Microgrids

This work is focused on the performance analysis and optimal routing of wireless technology for intelligent energy metering, considering the inclusion of micro grids. For the study, a geo-referenced scenario has been taken into account, which will form the structure of a graph to be solved using heuristic-based algorithms. In the first instance, the candidate site of the world geography to perform the case study is established, followed by deploying infrastructure devices and determining variables and parameters. Then, the model configuration is programmed, taking into account that a set of nodes and vertices is established for proper routing, resulting in a preliminary wireless network topology. Finally, from a set of restrictions, a determination of users connected to the concentrator and optimal routing is performed. This procedure is treated as a coverage set problem. Consequently, to establish the network parameters, two restrictions are specifically considered, capacity and range; thus, can be determined the best technology to adapt to the location. Finally, a verification of the resulting network topologies and the performance of the infrastructure is done by simulating the wireless network. With the model created, scenarios are tested, and it is verified that the optimization model demonstrates its effectiveness.

Routing Based Multi-Agent System for Network Reliability in the Smart Microgrid

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.

Capacitated Multicommodity Flow Problem for Heterogeneous Smart Electricity Metering Communications Using Column Generation

This paper addresses the planning and deployment of wireless heterogeneous networks (WHNs) for smart metering, based on a cross-layer solution. We combine the constraints of the network layer that considers routing and flow demands at each link in the WHN, while at the same time, we account for the restrictions of the physical layer referred to the capacity of a short range technology when used in a multi-hop fashion. We propose a model based on a column generation approach to solve the capacitated multicommodity flow problem (CMCF); the model includes wireless links capacities, coverage, and cost. The work integrates the multi-hop routing of packets in a mesh network formed by smart meters and concentrators connected to a cellular network via base stations. The traffic of each link is represented in a multigraph with the occupation percentage, and we build a scalable routing tree on a georeferenced map to represent a real deployment. The results describe the behavior of the proposed model in terms of the traffic load per concentrator, the network coverage, and the reduction of energy consumption. We demonstrate that an infrastructure cost reduction is achieved with the inclusion of multi-hop short range technology, which reduces the number of smart meters that require a direct connection to cellular technology. The model guarantees 100% coverage of the smart meters analyzed in each scenario. The calculation time of the CMCF for advanced metering infrastructure (CMCF-AMI) based on the column generation algorithm as the population increases is reduced by 10%, and this is the expected return when the population is considerable.