Optimizing reserve-constrained economic dispatch: Cheetah optimizer with constraint handling method in static/dynamic/single/multi-area systems

Managing the power-generating units on the horizon of one-day scheduling considering all practical equality, inequality, and realistic constraints has always been a significant challenge in power systems. The constraints, such as valve-point effects, prohibited operation zones, transmission losses, and ramp rate limits corresponding to dynamic economic dispatch, change the optimization problem to a complex, nonlinear, non-smooth, high-dimensional, and non-convex one. Therefore, an efficient algorithm and a suitable constraint handling method are needed to solve practical constrained dynamic economic dispatch (DED). This paper proposes a newly developed Cheetah optimizer (CO) that coincides with a backward-forward constraint handling method to tackle the optimum operational cost. The CO algorithm’s performance is verified using eight DED and ED test cases from five different systems. The suggested technique is compared with several state-of-the-art optimization algorithms regarding the effectiveness of achieved results. Numerical results evaluate the performances of the CO advantages on the benchmarks and the DED cases where the results of 5-,10- and 30-unit systems are enhanced in different cases. To achieve a higher level of realism in modeling the ED and DED problem, adopting a multi-area DED (MADED) approach has emerged as a promising strategy. In this paper, three distinct cases of MAED and MADED problems are investigated to demonstrate the effectiveness of the proposed method. Specifically, in cases involving DED-10 and 30 units, two-area 40 units ED, and four-area 40 units DED, significantly improved solutions were obtained compared to previous studies.