A genetic evolving ant direction DE for OPF with non-smooth cost functions and statistical analysis

This paper proposes an evolving ant direction differential evolution (EADDE) algorithm for solving the optimal power flow problem with non-smooth and non-convex generator fuel cost characteristics. The EADDE employs ant colony search to find a suitable mutation operator for differential evolution (DE) whereas the ant colony parameters are evolved using genetic algorithm approach. The Newton–Raphson method solves the power flow problem. The feasibility of the proposed approach was tested on IEEE 30-bus system with three different cost characteristics. Several cases were investigated to test and validate the robustness of the proposed method in finding the optimal solution. Simulation results demonstrate that the EADDE provides superior results compared to a classical DE and other methods recently reported in the literature. An innovative statistical analysis based on central tendency measures and dispersion measures was carried out on the bus voltage profiles and voltage stability indices.

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