Modelling the Data Aggregator Positioning Problem in Smart Grids

Rolim, Guilherme; Passos, Diego; Moraes, Igor Monteiro; Albuquerque, Célio

doi:10.1109/cit/iucc/dasc/picom.2015.90

Cited by 36 publications

(16 citation statements)

References 9 publications

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“…Using these variables and the expressions from Sections II-A to II-C, we can write the optimization problem for the DAP placement in (25) (on the next page). According to [21], [23],…”

Section: Problem Formulationmentioning

confidence: 99%

“…In this section, we compare the optimality and time-complexity of our algorithm with the work presented in [21] and [25]. For a fair comparison with [21], we limit the number of hops to H = 4 and compare the solution of our algorithm with the second scenario in [21, Table II] that has a similar number of SMs and poles as the Kamloops scenario.…”

Section: Comparison With Other Workmentioning

confidence: 99%

“…This step has the complexity of O(N SM N 2 poles ). In terms of the memory complexity, the DRAFT February 20, 2018 method from [21] would require 2-306 MB depending on the selected square size.…”

Section: Comparison With Other Workmentioning

confidence: 99%

“…In this paper, we do not adopt the average latency model with fixed or minimum number of hops criteria considered in [20], [21], [24], [25]. Instead, to meet latency requirements of smart grid traffic, in Section II, we compute the probability of achieving a certain latency requirement for both mission-critical and non-critical traffic.…”

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confidence: 99%

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Cost-Efficient QoS-Aware Data Acquisition Point Placement for Advanced Metering Infrastructure

Aalamifar

Lampe

2018

IEEE Trans. Commun.

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In an advanced metering infrastructure (AMI), data acquisition points (DAPs) are responsible for collecting traffic from several smart meters and automated devices and transmitting them to the utility control center. Although the problem of optimized data collector placement has already been addressed for wireless broadband and sensor networks, DAP placement is quite a new research area for AMIs. In this paper, we investigate the minimum required number of DAPs and their optimized locations on top of the existing utility poles in a distribution grid such that smart grid quality of service requirements can best be provided. In order to solve the problem for large-scale AMIs, we devise a novel heuristic algorithm using a greedy approach for identifying potential pole locations for DAP placement and the Dijkstra's shortest path algorithm for constructing reliable routes. We employ the characteristics of medium access schemes from the IEEE 802.15.4g smart utility network (SUN) standard, and consider mission-critical and non-critical smart grid traffic. The performance and time-complexity of our algorithm are compared with those obtained by the IBM CPLEX software for small scenarios. Finally, we apply our devised DAP placement algorithm to examples of realistic smart grid AMI topologies. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible 1 I. INTRODUCTION As part of the broader concept of smart grids, advanced metering infrastructures (AMIs) are being massively deployed almost everywhere in the world. AMIs are responsible for reading the energy consumption from thousands of smart meters (SMs) [1]-[4], monitor the last-mile automated devices for reporting emergency events [3], [4] such as electricity blackout and also unauthorized access to the power system [5]. In an AMI, due to the large number of devices and their distances, data collectors are installed to collect traffic from several endpoints and transmit them to the utility control center on their behalf.The placement of collector nodes, which are known as data acquisition points (DAPs) [4], [6] or aggregators [7], [8] in smart grid communication networks (SGCNs) and as relay station [9], gateway [10] or sink [11], [12] in broadband wireless access networks and sensor networks, respectively, has previously been investigated [13]- [16]. However, there is a combination of features and requirements in AMI that render the problem sufficiently different from the data collector placement in other types of networks so that a new problem formulation and solution for network planning are needed. For example, in sensor networks, the collector nodes can be placed on selected endpoint nodes [10] or in arbitrary locations [13]. Different from this, in a distribution grid with overhead powerlines, the utility poles are ideal locations for DAP placement [4], since this extends network coverage and also eliminates the cost of new tower installations. Moreover, since...

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“…Using these variables and the expressions from Sections II-A to II-C, we can write the optimization problem for the DAP placement in (25) (on the next page). According to [21], [23],…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Comparison With Other Workmentioning

confidence: 99%

Section: Comparison With Other Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Cost-Efficient QoS-Aware Data Acquisition Point Placement for Advanced Metering Infrastructure

Aalamifar

Lampe

2018

IEEE Trans. Commun.

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“…]. DRES are capable of functioning independently or in conjunction with the main electrical network under the concept of microgrid [56,57].…”

Section: Conventional Network and The Need Of Smart Gridmentioning

confidence: 99%

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

Inga¹,

Campaña²,

Hincapié³

et al. 2018

Preprint

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The unpredictable increase in electrical demand affects the quality of theenergy throughout the network. A solution to the problem is the increase of distributedgeneration units which burn fossil fuels.While this is an immediate solution to theproblem the ecosystem gets affected by the emission of CO2.A promising solutionis the integration of Distributed Renewable Energy Sources (DRES) to the conventionalelectrical system, thus, introducing the concept of smart microgrids (SMG) that requirea safe, reliable and technically planned two-way communication system. This documentpresents a heuristic based on planning capable of providing a bidirectional communicationnear optimal route map, following the structure of an hybrid Fiber-Wireless (FiWi) with thepurpose of obtaining information of electrical parameters that help us to manage the useof energy by integrating conventional electrical system to SMG. A FiWi network is basedon the integration of wireless access and optical networks. This integration increases thecoverage and reliability at a lower cost. The optimization model is based on clusteringtechniques, through the construction of balanced conglomerates. The method is used forthe development of the clusters along with the Nearest-Neighbor Spanning Tree Algorithm(N-NST). Additionally, Optimal Delay Balancing (ODB) model will be used to minimizethe end to end delay of each grouping. In addition, the heuristic observes real designparameters such as: capacity and coverage. Using the Dijkstra algorithm, the routes arebuilt following the minimum shorter path. Therefore, this paper presents a heuristic able to plan the deployment of smart meters (SMs) through a tree-like hierarchical topology for theintegration of SMG at the lowest cost.

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A Nested Decomposition Approach for a Large Scale Set Covering Problem: A Model with a Variety of Applications in Industry 4.0

Radman

Eshghi

2019

Optimization in Large Scale Problems

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Modelling the Data Aggregator Positioning Problem in Smart Grids

Cited by 36 publications

References 9 publications

Cost-Efficient QoS-Aware Data Acquisition Point Placement for Advanced Metering Infrastructure

Cost-Efficient QoS-Aware Data Acquisition Point Placement for Advanced Metering Infrastructure

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

A Nested Decomposition Approach for a Large Scale Set Covering Problem: A Model with a Variety of Applications in Industry 4.0

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