A Strategy for Multi-Objective Energy Optimization in Smart Grid Considering Renewable Energy and Batteries Energy Storage System

Ahmad Alzahrani; Mujeeb Ur Rahman; Ghulam Hafeez; Gul Rukh; Sajjad Ali; Sadia Murawwat; Faiza Iftikhar; Syed Irtaza Haider; Muhammad Iftikhar Khan; Azher M. Abed

doi:10.1109/ACCESS.2023.3263264

IEEE Access (Jan 2023)

A Strategy for Multi-Objective Energy Optimization in Smart Grid Considering Renewable Energy and Batteries Energy Storage System

Ahmad Alzahrani,
Mujeeb Ur Rahman,
Ghulam Hafeez,
Gul Rukh,
Sajjad Ali,
Sadia Murawwat,
Faiza Iftikhar,
Syed Irtaza Haider,
Muhammad Iftikhar Khan,
Azher M. Abed

Affiliations

Ahmad Alzahrani: ORCiD; Electrical Engineering Department, College of Engineering, Najran University, Najran, Saudi Arabia
Mujeeb Ur Rahman: Department of Electrical Engineering, University of Engineering and Technology, Mardan, Pakistan
Ghulam Hafeez: ORCiD; Department of Electrical Engineering, University of Engineering and Technology, Mardan, Pakistan
Gul Rukh: Department of Electrical Engineering, University of Engineering and Technology, Mardan, Pakistan
Sajjad Ali: ORCiD; Department of Telecommunication Engineering, University of Engineering and Technology, Mardan, Pakistan
Sadia Murawwat: Department of Electrical Engineering, Lahore College for Women University, Lahore, Pakistan
Faiza Iftikhar: Department of Electrical Engineering, Lahore College for Women University, Lahore, Pakistan
Syed Irtaza Haider: ORCiD; College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
Muhammad Iftikhar Khan: Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan
Azher M. Abed: Air Conditioning and Refrigeration Techniques Engineering Department, Al-Mustaqbal University College, Babylon, Iraq

DOI: https://doi.org/10.1109/ACCESS.2023.3263264
Journal volume & issue: Vol. 11
pp. 33872 – 33886

Abstract

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Multi-objective energy optimization is pivotal for reliable and secure power system operation. However, multi-objective energy optimization is challenging due to interdependent and conflicting objectives. Thus, a multi-objective optimization model is needed to cater to conflicting objectives. On this note, a multi-objective optimization model is developed, where a non-dominated genetic sorting algorithm is employed to optimize objectives pollution emission, operation cost, and loss of load expectation (LOLE) considering renewable energy sources (RES). RES, like wind and solar, are intermittent and uncertain, which are modelled using a beta probability density function (PDF). The developed method’s effectiveness and applicability are analyzed by implementing it on the 30-bus system, and the results are compared for two cases. Findings reveal that the developed multi-objective optimization model minimizes operation cost, pollution emission, and LOLE by 59%, 7%, and 2.67%, respectively, compared to existing models.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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