An enhanced hunter‐prey optimization for optimal power flow with FACTS devices and wind power integration

Hassan, Mohamed H.; Daqaq, Fatima; Kamel, Salah; Hussien, Abdelazim G.; Zawbaa, Hossam M.

doi:10.1049/gtd2.12879

Cited by 15 publications

(1 citation statement)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results demonstrated the strength and efficacy of the ML-TSO approach to solve the OPF problems. An enhanced hunter-prey optimization (EHPO) approach in [7] was suggested to tune the parameters of a FACTS devices and optimize OPF, with wind and solar power. Moreover, it is confirmed that EHPO is robustness to achieve the global search exploration than the conventional hunter-prey optimization (HPO) and the HPO algorithm as well does not success during attaining the optimum solution for the large-scale power systems.…”

Section: B Literature Reviewmentioning

confidence: 99%

Optimal Power Flow Analysis With Renewable Energy Resource Uncertainty: A Hybrid AEO-CGO Approach

Hassan,

Kamel,

Alateeq

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

Over the last decade, significant advancements have occurred in global electricity networks due to the widespread adoption of renewable energy resources (RES). While these sources offer numerous benefits such as cost-effective operation of solar photovoltaic and wind power stations and reduction of environmental hazards related to traditional power sources, they have also introduced various challenges to power network scheduling and operation. The traditional optimal power flow (OPF) problem, which is inherently complex, has become even more intricate with the integration of RES alongside traditional thermal power generators. This complexity arises from the unpredictable and intermittent nature of those resources. To tackle the intricacies of incorporating RES into conventional electric power systems, this study utilizes a pair of probability distribution functions to predict the power generation of wind and solar PV systems, respectively. The comprehensive OPF, which includes RES components, is expressed as a singular objective problem encompassing multiple goals including reducing fuel costs, emissions, real transmission losses, and voltage deviations. To tackle this challenge, a novel hybrid metaheuristic optimization algorithm (ACGO) is introduced. The ACGO algorithm combines Chaos game optimization (CGO) with the artificial ecosystem-based optimization (AEO) method to obtain the optimum solution for the OPF problem considering stochastic RES. This technique aims to enhance solution precision by increasing solution diversity through an optimization process. The modified optimizer's validation begins by examining its performance using well-known benchmark optimization functions, demonstrating its superiority over CGO, AEO, and other competitive algorithms. Subsequently, the modified optimizer is applied to a combined model of a wind and PV-incorporated IEEE 30-bus system. The ACGO technique proves to be highly effective, yielding the lowest fitness values of 781.

show abstract

Section: B Literature Reviewmentioning

confidence: 99%