Optimizing CEED Problems using Walrus and Red-Tailed Hawk Optimization Algorithm

• Published in: Al-Salam Journal for Engineering and Technology Vol. 4; no. 2; pp. 30 - 46
• Main Authors: Khlaif, Rafid Z., Atyia, Thamir H.
• Format: Journal Article
• Language: English
• Published: 23.08.2025
• ISSN: 2958-0862; 2790-4822; 2790-4822
• DOI: 10.55145/ajest.2025.04.02.003

Combining economics emissions power dispatch (CEED) challenging optimization issue that involves is a lowering the entire cost of electricity production yet satisfying overall environment emissions constraints. The issue at hand is complex because of the nonlinear and not convex character of the function's goals and restrictions.  Fuels are the primary form of electrical power production; coal, is the globe's principal fuel, accounting for 42% of the entire electrical power produced internationally.  Energy from electricity is excessively pricey because of the substantial amounts invested by generation corporations as a consequence of the high reliability of fuel for generating electricity. A new two ways to solve CEED problem is to use a Algorithms for Red-Tailed Hawk Optimize (RTHOA) and Walrus Optimize (WOA). By the advantages of nature-inspired metaheuristic techniques, we can reduce generation costs and reduce emissions, enhancing power system efficiency and sustainability. As inspired by walruses' social behavior and movement patterns, the WOA demonstrates significant potential for exploring and exploiting the solution space. RTHOA, which mimics hawks' hunting strategy and sharp vision, is just as good. Three examples have been validated by a simulated research investigated. The IEEE 30-bus with six generators in Case 1 has a power consumption of 2.834 p.u., the 10-unit in Case 2 has a power requirement of 2000 MW, and the 40-unit in Case 3 has a demand of 10,500 MW.   Comparison with alternative approaches documented in the published works, the simulation outcomes of the created techniques showed interest in the area of lowering emission and the expenses of electric generation.  With a tiny standard deviations and a significant correlation between the optimum and poorest fitness figures, the WOA demonstrated strong performance and great consistency, especially in Case 1.  As demonstrated in instance 1, the RTHOA also demonstrated strong features, particularly in preserving stability and attaining targeted fitness goals. But in Case 3, the RTHOA showed more fluctuations, suggesting a wider capacity for investigation.  These results imply that the two algorithms provide useful methods for solving the CEED phenomenon.