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      PSO-Based Smart Grid Application for Sizing and Optimization of Hybrid Renewable Energy Systems

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          Abstract

          This paper introduces an optimal sizing algorithm for a hybrid renewable energy system using smart grid load management application based on the available generation. This algorithm aims to maximize the system energy production and meet the load demand with minimum cost and highest reliability. This system is formed by photovoltaic array, wind turbines, storage batteries, and diesel generator as a backup source of energy. Demand profile shaping as one of the smart grid applications is introduced in this paper using load shifting-based load priority. Particle swarm optimization is used in this algorithm to determine the optimum size of the system components. The results obtained from this algorithm are compared with those from the iterative optimization technique to assess the adequacy of the proposed algorithm. The study in this paper is performed in some of the remote areas in Saudi Arabia and can be expanded to any similar regions around the world. Numerous valuable results are extracted from this study that could help researchers and decision makers.

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          Selectively-informed particle swarm optimization

          Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors.
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            Author and article information

            Contributors
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, CA USA )
            1932-6203
            11 August 2016
            2016
            : 11
            : 8
            : e0159702
            Affiliations
            [1 ]Electrical Engineering Dept., King Saud University, Riyadh 11421, Saudi Arabia
            [2 ]Electrical Engineering Dept., Minia University, Minia 61519, Egypt
            [3 ]Sustainable Energy Technologies Center, King Saud University, Riyadh 11421, Saudi Arabia
            [4 ]Electrical Engineering Dept., Mansoura University, Mansoura 35516, Egypt
            Beihang University, CHINA
            Author notes

            Competing Interests: The authors have declared that no competing interests exist.

            • Conceived and designed the experiments: MAM AME AIA.

            • Performed the experiments: MAM AME AIA.

            • Analyzed the data: MAM AME AIA.

            • Contributed reagents/materials/analysis tools: MAM AME AIA.

            • Wrote the paper: MAM AME AIA.

            Article
            PONE-D-16-09999
            10.1371/journal.pone.0159702
            4981409
            27513000
            b56a1f0e-d379-4f4e-916b-ae9c0fc5c48d
            © 2016 Mohamed et al

            This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

            History
            : 9 March 2016
            : 7 July 2016
            Page count
            Figures: 14, Tables: 3, Pages: 22
            Funding
            Funded by: funder-id http://dx.doi.org/10.13039/501100005725, National Plan for Science, Technology and Innovation;
            Award Recipient :
            The authors acknowledge the College of Engineering Research Center and Deanship of Scientific Research at King Saud University in Riyadh, Saudi Arabia, for the financial support to carry out the research work reported in this paper.
            Categories
            Research Article
            Physical Sciences
            Mathematics
            Optimization
            Earth Sciences
            Atmospheric Science
            Meteorology
            Wind
            Engineering and Technology
            Energy and Power
            Alternative Energy
            Wind Power
            Physical Sciences
            Mathematics
            Applied Mathematics
            Algorithms
            Research and Analysis Methods
            Simulation and Modeling
            Algorithms
            Engineering and Technology
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            Engineering and Technology
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            Fuels
            Physical Sciences
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            Asia
            Saudi Arabia
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            All relevant data are within the paper.

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