Optimal power flow analysis considering renewable energy resources uncertainty based on an improved wild horse optimizer

In recent years, electricity networks across the globe have undergone rapid development, especially with the incorporation of various renewable energy sources (RES). The goal is to increase the penetration level of RES in the power grid to maximize energy efficiency. However, the optimal power flow...

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Bibliographic Details
Published in:	IET generation, transmission & distribution Vol. 17; no. 16; pp. 3582 - 3606
Main Authors:	Hassan, Mohamed H., Kamel, Salah, Hussien, Abdelazim G.
Format:	Journal Article
Language:	English
Published:	Wiley 01.08.2023
Subjects:	elite evolutionary strategy optimal power flow stochastic renewable energy sources wild horse optimizer algorithm elite evolutionary strategy optimal power flow wild horse optimizer algorithm stochastic renewable energy sources
ISSN:	1751-8687, 1751-8695, 1751-8695
Online Access:	Get full text
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Summary:	In recent years, electricity networks across the globe have undergone rapid development, especially with the incorporation of various renewable energy sources (RES). The goal is to increase the penetration level of RES in the power grid to maximize energy efficiency. However, the optimal power flow (OPF) problem for conventional power generation with RES integration is highly complex, non‐linear, and non‐convex, and this complexity is further compounded when stochastic RES is integrated into the network. To address this problem, this article proposes an elite evolutionary strategy (EES) based on evolutionary approaches to improve the Wild Horse Optimizer (WHO), creating an enhanced hybrid technique called EESWHO. The proposed technique's effectiveness and robustness were tested on 23 numerical optimization test functions, including seven unimodal, six multimodal, and ten composite test functions. Furthermore, the EESWHO was applied to the modified IEEE‐30 bus test system to demonstrate its supremacy and efficacy in achieving the optimal solution. The simulation results show that the proposed EESWHO algorithm is highly effective and robust in achieving the optimal solution to the OPF problem with stochastic RES. This approach provides a practical solution to the challenges posed by the integration of RES into power networks, allowing for maximum energy efficiency while minimizing system complexity. An elite evolutionary strategy (EES) based on evolutionary approaches to improve the Wild Horse Optimizer (WHO), creating an enhanced hybrid technique called EESWHO. The proposed technique's effectiveness and robustness were tested on 23 numerical optimization test functions, including seven unimodal, six multimodal, and ten composite test functions. Furthermore, the EESWHO was applied to the modified IEEE‐30 bus test system to demonstrate its supremacy and efficacy in achieving the optimal solution. The simulation results show that the proposed EESWHO algorithm is highly effective and robust in achieving the optimal solution to the OPF problem with stochastic RES.
ISSN:	1751-8687 1751-8695 1751-8695
DOI:	10.1049/gtd2.12900