Integration of solar energy and optimized economic dispatch using genetic algorithm: A case-study of Abu Dhabi

AKMAL, Muhammad, ALI, Samr, AL KHALIL, Yasmina, IQBAL, Nusrat Binte and ALZAABI, Aalim (2017). Integration of solar energy and optimized economic dispatch using genetic algorithm: A case-study of Abu Dhabi. In: 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017, Washington, DC, USA, April 23-26, 2017. IEEE, 1-5.

[img]
Preview
PDF
Integration of Solar Energy and Optimized Economic Dispatch using GeneticAlgorithm- A case-study of Abu Dhabi Final.pdf - Accepted Version
All rights reserved.

Download (940kB) | Preview
Official URL: https://ieeexplore.ieee.org/document/8085990
Link to published version:: https://doi.org/10.1109/ISGT.2017.8085990
Related URLs:

    Abstract

    © 2017 IEEE. The United Arab Emirates is focusing on cultivating Renewable Energy (RE) to meet its growing power demand. This also brings power planning to the forefront in regards to keen interests in renewable constrained economic dispatch. This paper takes note of UAE's vision in incorporating a better energy mix of Renewable Energy (RE), nuclear, hybrid system along with the existing power plants mostly utilizing natural gas; with further attention for a sound economic dispatch scenario. The paper describes economic dispatch and delves into the usage of Genetic Algorithm to optimize the proposed system of thermal plants and solar systems. The paper explains the problem formulation, describes the system used, and illustrates the results achieved. The aim of the research is in line with the objective function to minimize the total costs of production and to serve the purpose of integrating renewable energy into the traditional power production in UAE. The generation mix scenarios are assessed using genetic algorithm using MATLAB simulation for the optimization problem.

    Item Type: Book Section
    Additional Information: Electronic ISSN: 2472-8152
    Uncontrolled Keywords: Economic dispatch; optimization; renewable energy; genetic algorithm
    Identification Number: https://doi.org/10.1109/ISGT.2017.8085990
    Page Range: 1-5
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 31 Jan 2020 12:27
    Last Modified: 31 Jan 2020 12:30
    URI: http://shura.shu.ac.uk/id/eprint/25015

    Actions (login required)

    View Item View Item

    Downloads

    Downloads per month over past year

    View more statistics