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.

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Official URL: https://ieeexplore.ieee.org/document/8085990
Link to published version:: https://doi.org/10.1109/ISGT.2017.8085990

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: 18 Mar 2021 02:47
URI: https://shura.shu.ac.uk/id/eprint/25015

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