article · Energy Reports
Solving the optimal power flow problem allows electricity grid operators to identify the best configurations for power system components, balancing economic, environmental, and technical goals. A Modified Artificial Hummingbird Algorithm has been developed to enhance search performance and address complex, non-linear power system challenges. The method improves exploration and exploitation through bandwidth motion, Levy flight distribution, and fitness-distance balance selection, helping to prevent premature convergence and stagnation in local optima. The modified algorithm was tested against twenty-three standard benchmark functions and benchmarked against six established optimisation techniques. It was subsequently applied to standard electricity network test cases, including thirty-bus, fifty-seven-bus, and one-hundred-and-eighteen-bus systems. The algorithm successfully handled single and multiple objectives, including fuel costs, valve loading effects, power losses, emissions, and voltage profiles, outperforming existing optimisation techniques across all tests.
Power systems must balance operating costs, energy losses, and environmental emissions while maintaining grid stability. Finding the most efficient operating state involves solving complex mathematical problems that often trap conventional optimisation tools. This enhanced algorithm provides grid decision makers with a more reliable method to optimise network settings, supporting cleaner, cheaper, and more stable electricity transmission.
This computational tool is intended for electricity grid operators, utility planners, and decision makers managing power system dispatch and network stability. By optimising fuel use, emissions, and power losses, it could be incorporated into commercial grid management and energy dispatch software. The technology currently represents applied simulation research, demonstrated across standard IEEE benchmark networks but not yet validated in live, operational electricity grids.
AI-generated from the published abstract. Always read the original work before citing.
Optimal power flow (OPF) problem solution is a crucial task for the operators and decision makers to assign the best setting of the system components to obtain the most economic, environmental, and technical suitable state. Artificial Hummingbird Algorithm is a recent optimization algorithm that has been applied to solving several optimization problems. In this paper, a Modified Artificial Hummingbird Algorithm (MAHA) is proposed for improving the performance of the orignal Artificial Hummingbird Algorithm as well as effectivelly solve the OPF problem. The proposed MAHA is based on improving the searching capability by boosting the exploitation using the bandwidth motion around the best solution, while the exploration process is improved using the Levy flight distribution motion and the fitness-distance balance selection. This modified version helps overcome issues such as stagnation, premature convergence, and a propensity for local optima when tackling complex, nonlinear, and non-convex optimization problems like OPF. In order to confirm the effectiveness of the proposed algorithm, a series of tests are conducted on 23 standard benchmark functions, including CEC2020. The resulting outcomes are then compared to those obtained using other algorithms such as fitness-distance balance selection-based stochastic fractal search (FDBSFS), antlion optimizer (ALO), whale optimization algorithm (WOA), sine-cosine algorithm (SCA), fitness-distance balance and learning based artificial bee colony (FDB-TLABC), and traditional artificial hummingbird algorithm (AHA).The proposed algorithm is evaluated by solving the OPF problem with multiple objective functions on the IEEE 30-bus system. These objectives include fuel cost, fuel cost with valve loading effects, power losses, emissions, and voltage profile. Additionally, the algorithm's effectiveness is further assessed by testing it on single objective functions using medium and large-scale IEEE 57 and 118-bus networks.The results obtained by the proposed MAHA demonstrate its power and superiority for solving the OPF problem as well as the standard benchmark functions , surpassing the performance of other reported techniques.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1016/j.egyr.2023.12.053
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
