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

Inga, Esteban; Campaña, Miguel; Hincapié, Roberto; Moscoso-Zea, Oswaldo

doi:10.3390/s18082724

Cited by 8 publications

(8 citation statements)

References 76 publications

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“…However, their work ignores the congestion delay occurring at each hop. The studies presented in [32,33] developed optimization models that focus on design parameters such as coverage and capacity in a cost-effective manner. The heuristic in [32] uses a near-optimal clustering topology for the deployment of SMs with a bi-directional hybrid communication (wireless-fiber) system for the integration of smart microgrids.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The studies presented in [32,33] developed optimization models that focus on design parameters such as coverage and capacity in a cost-effective manner. The heuristic in [32] uses a near-optimal clustering topology for the deployment of SMs with a bi-directional hybrid communication (wireless-fiber) system for the integration of smart microgrids. The authors in [33] proposed an evolved network architecture considering SMs, universal DAPs, BS, and an optimal route map based on wireless heterogeneous networks (WHNs) to achieve connectivity and coverage of SMs with the optimum use of technological resources.…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2021

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“…The current scenario of electric grids is focused on the deployment of advanced devices, the use of techniques and tools for system control and monitoring, in addition to the management of data that are the product of this process and the active participation of customers in the electricity market; in this way, a characteristic infrastructure of the smart grid is configured to frame a safe, reliable and quality operation [1][2][3][4]. In addition, the grid configurations have been delineated to adopt distributed generation (DG) and renewable energies.…”

Section: Introductionmentioning

confidence: 99%

“…According to Inga et al in their 2017 paper [14,15], for AMI to be considered an efficient and reliable component of the smart grid (SG), it is necessary to ensure two-way communication between the smart meter (SM) and the electric utility. According to [1,2,16], the key in SG is the integration of an adequate communication infrastructure that allows monitoring and gives way to effective control of the electric grid; in addition, it should be taken into account that the data obtained from AMI are necessary for power quality management, especially if we integrate electric micro grids [7,8,11]; additionally, advanced infrastructures allow metering data to be managed for MG operation, and load forecasting to the system [17,18].…”

Section: Introductionmentioning

confidence: 99%

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

Suarez

Inga

2021

Sensors

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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.

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“…Previous works employed optimization problems to model a system that guarantees the coverage of smart meters in a heterogeneous network or to reduce the energy consumption and latency of end-to-end communications [12][13][14][15][16]. However, such works did not take into consideration the capacity of the wireless links, especially when restricted short range technologies were in use.…”

Section: Introductionmentioning

confidence: 99%

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

2019

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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.

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

Cited by 8 publications

References 76 publications

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

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

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

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

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