IEEE Access (Jan 2023)
Optimal Power Flow Analysis With Renewable Energy Resource Uncertainty: A Hybrid AEO-CGO Approach
Abstract
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 781.1675 ${\$}$ /h and 808.4109 ${\$}$ /h in their respective scenarios for the modified IEEE 30-bus system. Additionally, the proposed ACGO method achieves the optimal total cost of 31623.5 ${\$}$ /h and 31601.55 ${\$}$ /h for the modified IEEE 57-bus system. These results emphasize the accuracy and robustness of ACGO in effectively addressing various instances of the OPF problem. The performance of ACGO in solving the OPF issue is verified through statistical boxplot comparisons, non-parametric tests, and robustness analyses. The evaluations indicate that the ACGO technique outperforms other well-known optimization algorithms in achieving the optimum values for the OPF problem involving stochastic PV and wind power systems. Additionally, the results show that ACGO offers faster convergence rates and higher precision in convergence compared to conventional artificial ecosystem-based optimization, Chaos game optimization, and other recent heuristic, metaheuristic, and hybrid optimization algorithms. The effectiveness of the ACGO technique has been proven to be robust and efficient, making it suitable for multidisciplinary problems and engineering optimization challenges.
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