Multi-Dimensional Techno-Economic Operation in Power Systems via Enhanced Dhole Optimization Algorithm

This paper proposes an enhanced Dhole Optimization Algorithm (eDOA) for the techno-economic operation of power systems. The proposed eDOA variant improves upon the original DOA by incorporating adaptive parameter control, refined exploration–exploitation mechanisms, and dynamic leader–follower strategies inspired by cooperative hunting and vocal communication behaviors of dholes. The eDOA was utilized to solve the Optimal Power Flow (OPF) problem under various techno-economic scenarios, including minimizing fuel costs, reducing active power losses, enhancing the voltage profile, and improving voltage stability. The effectiveness of the proposed method was validated on three standard IEEE benchmark systems: IEEE 30-bus and IEEE 57-bus test systems. The simulation results are compared with competitive metaheuristic algorithms. It is shown that the efficiency of the proposed eDOA achieves superior convergence rates, robustness behavior, and accurate scheduling of the generation units for all cases studied. Added to that, statistical analyses including Best, Worst, and Mean alongside error-based metrics confirmed the high-quality reliability in handling the non-linearity and complex OPF optimization problem subject to different operational constraints. Significant techno-economic power system improvements are achieved while keeping the operational constraints within acceptable power system operation.