Optimizing economic dispatch problems in power systems using manta ray foraging algorithm: an oppositional-based approach

This paper introduces the manta ray foraging optimization algorithm (MRFO) and its enhanced version, the oppositional-based manta ray foraging optimization algorithm (OMRFO), as effective meta-heuristic approaches for solving challenging economic dispatch (ED) problems in power systems. Specifically tailored for economic power dispatch (EPD), combined economic emission dispatch (CEED), and combined heat and power economic dispatch (CHPED) problems, considering factors like valve-point loading effects (VPL), transmission power losses, and prohibited operating zones (POZs) inherent in real-world power systems. To address MRFO's limitations, including slow convergence, susceptibility to local optima, and limited exploration capacity due to foraging behavior, this study integrates oppositional-based learning (OBL) with MRFO to enhance solution quality, speed up convergence, and improve exploration of the search space. Extensive testing is conducted on benchmark functions and ED problems of four standard test systems with non-convex solution spaces. The comprehensive comparative assessment shows that both MRFO and OMRFO outperform other algorithms in terms of solution quality and system constraint satisfaction. Additionally, OMRFO exhibits significant improvements over MRFO, especially for more complex problems. For instance, for small test systems like the 6-unit test system, both algorithms achieve a cost of 15,441.84 $/h. However, for larger systems with higher complexity, such as the 40-unit test system, OMRFO significantly outperforms MRFO with a cost of 119,733.27 $/h compared to 120,221.34 $/h. Similarly, for a 24-unit test system with VPL only and with VPL and POZs, OMRFO achieves costs of 58,054.78 $/h and 58,191.69 $/h, respectively, surpassing MRFO's costs of 58,125.803 $/h and 58,223.578 $/h.