PreprintArticleVersion 1This version is not peer-reviewed
Enhanced COVID Optimization Algorithm for Solving Multi-Objective Optimal Power Flow Problems with Uncertain Renewable Energy Sources: A Case Study of the Iraqi High-Voltage Grid
Version 1
: Received: 25 October 2024 / Approved: 28 October 2024 / Online: 29 October 2024 (05:13:58 CET)
How to cite:
ALBaaj, B.; Kaplan, O. Enhanced COVID Optimization Algorithm for Solving Multi-Objective Optimal Power Flow Problems with Uncertain Renewable Energy Sources: A Case Study of the Iraqi High-Voltage Grid. Preprints2024, 2024102167. https://doi.org/10.20944/preprints202410.2167.v1
ALBaaj, B.; Kaplan, O. Enhanced COVID Optimization Algorithm for Solving Multi-Objective Optimal Power Flow Problems with Uncertain Renewable Energy Sources: A Case Study of the Iraqi High-Voltage Grid. Preprints 2024, 2024102167. https://doi.org/10.20944/preprints202410.2167.v1
ALBaaj, B.; Kaplan, O. Enhanced COVID Optimization Algorithm for Solving Multi-Objective Optimal Power Flow Problems with Uncertain Renewable Energy Sources: A Case Study of the Iraqi High-Voltage Grid. Preprints2024, 2024102167. https://doi.org/10.20944/preprints202410.2167.v1
APA Style
ALBaaj, B., & Kaplan, O. (2024). Enhanced COVID Optimization Algorithm for Solving Multi-Objective Optimal Power Flow Problems with Uncertain Renewable Energy Sources: A Case Study of the Iraqi High-Voltage Grid. Preprints. https://doi.org/10.20944/preprints202410.2167.v1
Chicago/Turabian Style
ALBaaj, B. and Orhan Kaplan. 2024 "Enhanced COVID Optimization Algorithm for Solving Multi-Objective Optimal Power Flow Problems with Uncertain Renewable Energy Sources: A Case Study of the Iraqi High-Voltage Grid" Preprints. https://doi.org/10.20944/preprints202410.2167.v1
Abstract
The optimal power flow (OPF) problem is a critical component in the design and operation of power transmission systems. Various optimization algorithms have been developed to address this issue. This paper expands the use of the Coronavirus Disease Optimization Algorithm (COVIDOA) to solve a multi-objective OPF problem (MO-OPF), incorporating renewable energy sources as dis-tributed generation (DG) across multiple scenarios. The main objectives are to minimize fuel costs, emissions, voltage deviations, and power losses. Due to its non-convex nature and computational complexity, OPF poses significant challenges. While COVIDOA has been utilized to solve engi-neering problems, it faces difficulties with non-linear and non-convex issues. This paper introduces an enhanced version, the Enhanced COVID Optimization Algorithm (ENHCOVIDOA), designed to improve the performance of the original method. The effectiveness of the proposed algorithm is validated through testing on IEEE 30-bus and 57-bus systems, as well as a real-world 28-bus system representing Iraq’s Standard Iraq Super Grid High Voltage (SISGHV-28 Buses). The Two-Point Estimation Method (TPEM) is also applied to manage uncertainties in renewable energy sources in some cases, leading to cost reductions and annual savings of ($87772.9374, $391246.87, & $ 2,493,881.40) for the IEEE 30-bus, 57-bus, and reality 28-bus systems, respectively. Thirteen dif-ferent cases were analyzed, and the results demonstrate that ENHCOVIDOA is notably more ef-ficient and effective than other optimization algorithms in the literature.
Keywords
Optimization Algorithm; multi-objective OPF problem; distributed generation; TPEM; Standard Iraq Super Grid High Voltage 28 Buses
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
