An Efficient Solution for Economic Load Dispatch Using Cheetah Optimizer with Opposition-Based Learning and Adaptive Weighting Factor

The multi-objective economic load dispatch problem (ELDP) with non-smooth cost functions and ramprate limits presents a challenging optimization task in power systems.This paper proposes the use of a modified cheetah optimizer (MCO) that incorporates opposition-based learning (OBL) and a dynamic adaptive weighting factor to efficiently solve this problem.The simulations are conducted on standard test systems using MATLAB programming.A comparative study is performed, evaluating the performance of the MCO against basic CO and other similar heuristics.The results demonstrate the effectiveness of the MCO in achieving optimal solutions for the multi-objective ELDP with non-smooth cost functions and ramp-rate limits.The proposed approach offers a promising solution for addressing the complex optimization requirements in power system operation and planning.The optimal cost is determined using multi-criterion optimization (MCO) in 3-bus system as $6,838.6434/h,$7,738.789/h,and $8,252.033/hfor 700 MW, 800 MW and 850 MW demand levels respectively.The optimal cost is evaluated as $17,988.96/hfor the 13-bus test system considering a total demand of 1800 MW.The optimal cost is evaluated as 121960.30$/hr, for the 40-bus test system considering a total demand of 10500 MW.These outcomes demonstrate the efficacy of MCO in resolving the ELDP with generator and valve controls.