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

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          Abstract

          The unpredictable increase in electrical demand affects the quality of the energy throughout the network. A solution to the problem is the increase of distributed generation units, which burn fossil fuels. While this is an immediate solution to the problem, the ecosystem is affected by the emission of CO 2. A promising solution is the integration of Distributed Renewable Energy Sources (DRES) with the conventional electrical system, thus introducing the concept of Smart Microgrids (SMG). These SMGs require a safe, reliable and technically planned two-way communication system. This paper presents a heuristic based on planning capable of providing a bidirectional communication that is near optimal. The model follows the structure of a hybrid Fiber-Wireless (FiWi) network with the purpose of obtaining information of electrical parameters that help us to manage the use of energy by integrating conventional electrical system with SMG. The optimization model is based on clustering techniques, through the construction of balanced conglomerates. The method is used for the development of the clusters along with the Nearest-Neighbor Spanning Tree algorithm (N-NST). Additionally, the Optimal Delay Balancing (ODB) model will be used to minimize the end to end delay of each grouping. In addition, the heuristic observes real design parameters such as: capacity and coverage. Using the Dijkstra algorithm, the routes are built following the shortest path. Therefore, this paper presents a heuristic able to plan the deployment of Smart Meters (SMs) through a tree-like hierarchical topology for the integration of SMG at the lowest cost.

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          Clustering of time series data—a survey

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                19 August 2018
                August 2018
                : 18
                : 8
                : 2724
                Affiliations
                [1 ]Electrical Engineering, Universidad Politécnica Salesiana, Quito EC170146, Ecuador; mcampana@ 123456ups.edu.ec
                [2 ]Department of Telecommunications, Universidad Pontificia Bolivariana, Medellín 050031, Colombia; roberto.hincapie@ 123456upb.edu.co
                [3 ]Faculty of Engineering, Universidad Tecnológica Equinoccial, Quito EC170147, Ecuador; omoscoso@ 123456ute.edu.ec
                Author notes
                [* ]Correspondence: einga@ 123456ups.edu.ec ; Tel.: +59-302-396-2913
                Author information
                https://orcid.org/0000-0002-0837-0642
                https://orcid.org/0000-0003-4157-9381
                https://orcid.org/0000-0003-3233-2133
                Article
                sensors-18-02724
                10.3390/s18082724
                6111294
                30126233
                b85de290-17ab-4678-a48e-b6d53bdb5341
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 02 July 2018
                : 15 August 2018
                Categories
                Article

                Biomedical engineering
                optimization,smart metering,iot,microgrid,heuristic,sensor networks
                Biomedical engineering
                optimization, smart metering, iot, microgrid, heuristic, sensor networks

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