Enhanced Aquila Optimizer for Economic Environmental Dispatch with Cubic Fuel Cost and Emission Models

The economic environmental dispatch (EED) problem often utilizes linear or quadratic models, which fail to capture the nonlinear relationship between fuel costs and emissions. This study hypothesizes that a cubic model will better approximate these complexities. We propose an Improved Aquila Optimizer (IAO), which eliminates dependency on the best solution and incorporates a regeneration mechanism. The proposed (IAO) model gains the ability to perform a more thorough exploration of the search space, allowing for a broader range of potential solutions to be considered. Also, every dimension of the newly created solutions is examined to guarantee that the algorithm's generated solutions stay viable and legitimate inside the constraints of the problem. The IAO is rigorously tested against CEC 2017 benchmarks and validated using the IEEE 30-bus power system, showing significant improvements in minimizing fuel costs and emissions. For a 225 MW load, the IAO achieved a mean fuel cost of 16,784.45/h, outperforming other algorithms. Also, the validation is conducted for a large-scale 160-unit system where the proposed IAO demonstrates significant improvements in minimizing fuel costs, emissions, and achieving the best compromise costs with reductions of 7.68%, 47.42%, and 57.39% respectively compared to the AO algorithm.