Security constrained optimal power flow solution using new adaptive partitioning flower pollination algorithm

• A new formulation of critical optimal power flow problem based on partitioning structure is proposed.
• A dynamic adjustment of switching parameter is proposed to enhance the performance of the standard FPA.
• Three objective functions: fuel cost, power loss, voltage deviation are optimized considering faults.
• The standard FPA is improved in term of solution quality and number of generation required.
• Robustness of the proposed strategy adaptive partitioning FPA is tested and validated on IEEE 30-Bus and IEEE 57-Bus.

In this paper, a flexible power system planning strategy using a novel population-based metaheuristic algorithm inspired by the pollination process of flowers named adaptive flower pollination algorithm (APFPA) has been proposed. The proposed power system planning strategy implemented and successfully applied for solving the security optimal power flow (OPF) considering faults at critical generating unit. The main particularity of the proposed variant is that the control variables are optimized based on an adaptive and flexible structure. Also the performances of the standard FPA is improved by dynamically adjusting their control parameters, this allows creating diversity and balance between exploration and exploitation during search process. The robustness of the proposed planning strategy, is demonstrated on the IEEE 30-Bus, and IEEE 57-Bus tests power system for different objectives such as fuel cost, power losses, and voltage deviation. Considering the quality of the obtained results compared with various recent methods reported in the literature, the proposed strategy seems to be a competitive tool for solving with accuracy the security OPF considering critical situations.

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