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Likewise, the program can chooses the best site out of the sites under study and select the most economic WT for this site. The comparison results are shown in Table 3. However, there are obvious differences between these two methods summed up in the accuracy and the speed of access the optimum solution as the following:. Fig 10 demonstrates the convergence process of the PSO algorithm during the minimization of the LEC for 4 autonomous runs.

As illustrated in this figure, the optimum solution is acquired after around 30 iterations, and the iterations are considered as a reasonable end measure. In addition, it can be noted that the optimum solution almost converges to the same optimum value global minimum for all runs. Fig 11 shows the convergence process for one run of the IOT.

As shown in this figure, the optimum solution is obtained after around iterations and this solution not important to be the optimum one. It is also observed that the time taken to find the optimum sizing by using PSO is lower than the one taken by using IOT. A comparison between these two cases is shown in Fig This figure shows that utilizing load shifting-based load priority reduces the whole system cost, LEC and reduces the size of the HRES components, DG capacity, and the battery capacity.


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Additionally, it lessens the aggregate operation hours of the DG through the system lifetime, and thus diminishes the CO 2 emission and environment contamination. Fig 14 describes the sensitivity analysis of the proposed algorithm with the help of quadratic curve fitting.

The optimization goal was to minimize the system cost with the state of insuring the load demand and satisfying a set of optimization constraints. Load shifting as one of smart grid applications has been introduced to get a distributed load profile, reduce the entire system cost and reduce CO 2 emission. Moreover, a methodology to characterize, manage the dummy energy and their exploitation has been presented.

Sensitivity analysis has been carried out in this paper to predict the system performance under varying operating conditions. The PSO technique has been implemented in this paper to carry out the optimization process. The simulation results affirmed that PSO is the promising optimization techniques due to its ability to reach the global optimum with relative simplicity and computational proficiency contrasted with the customary optimization techniques.

Finally, parallel implementation of PSO has been utilized to speed up the optimization process, and the simulation results confirmed that it can save more time during the optimization process compared to the serial implementation of PSO. National Center for Biotechnology Information , U. PLoS One.

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

Published online Aug Mohamed A. Eltamaly , 3, 4 and Abdulrahman I. Alolah 1. Mohamed 1 Electrical Engineering Dept. Ali M.

DESIGN OF HYBRID PHOTOVOLTAIC POWER GENERATOR WITH OPTIMIZATION OF ENERGY MANAGEMENT

Abdulrahman I. Alolah 1 Electrical Engineering Dept.


  • Molecular and Cellular Effects of Nutrition on Disease Processes;
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  • Optimization of a Standalone Hybrid Renewable Energy System for Telecom Base Station.
  • HYBRID ENERGY GENERATOR WITH OF OPTIMIZATION POWER DESIGN MANAGEMENT PHOTOVOLTAIC OF!
  • Modern Planktonic Foraminifera.
  • Wen-Bo Du, Editor. Author information Article notes Copyright and License information Disclaimer. Competing Interests: The authors have declared that no competing interests exist. Received Mar 9; Accepted Jul 7. 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. 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.

    Introduction In recent years, interest in renewable energy sources RES for power generation is progressively gaining significance in the entire world due to fossil fuel depletion, high cost, and increasing environmental concerns. Open in a separate window. Fig 1.

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    Fig 2. Fig 3. Fig 4. Table 1 The technical characteristics of the WT under study. WT No. This logic is condensed in the following points: 3. Problem Statement The aim of this paper is to introduce an algorithm based on smart grid applications to solve the problem of sizing of HRES to supply the load demand with considering the minimum cost and satisfying a defined reliability index.

    S.C. Gupta

    Cost estimation and reliability assessment of the HRES are detailed in the following subsections: 4. The discussion on the objective function and the constraints is detailed in the following subsections: 5. Fig 5. Fig 6. Fig 7. Fig 8. Fig 9. Hourly solar radiation on optimum tilt angle surface for Yanbu site. Fig The convergence process of the PSO algorithm for 4 autonomous runs. A comparison between full load performance and load shifting performance of HRES with penetration ratio. Sensitivity analysis for DG performance with load shifting rate.

    Data Availability All relevant data are within the paper. References 1. Weitemeyer S. Integration of renewable energy sources in future power systems: the role of storage.

    A local energy management of a hybrid PV-storage based distributed generation for micro grids

    Renewable Energy ; 75 : 14— Mohamed, M. Energy management and renewable energy integration in smart grid system. Fadaee M. Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: a review. Renewable and Sustainable Energy Reviews ; 16 5 : — Bai Q. Analysis of particle swarm optimization algorithm.

    Computer and information science ; 3 1 : — Adequate is better: particle swarm optimization with limited-information. Applied Mathematics and Computation ; : — Gao Y.

    DESIGN OF HYBRID PHOTOVOLTAIC POWER GENERATOR WITH OPTIMIZATION OF ENERGY MANAGEMENT

    Selectively-informed particle swarm optimization. Scientific reports ; 5 : doi: Boonbumroong U. Particle swarm optimization for AC-coupling stand-alone hybrid power systems.