Optimum solution of power flow problem based on search and rescue algorithm.

Area of Science:

• Electrical Engineering
• Computational Intelligence
• Optimization Techniques

Background:

• The optimal power flow (OPF) problem is crucial for efficient power system operation.
• Existing optimization methods face challenges in effectively solving complex, multi-objective OPF problems.

Purpose of the Study:

• To introduce and evaluate a unique Search and Rescue (SAR) algorithm for solving the OPF problem.
• To minimize three key objective functions: fuel cost, power loss, and voltage deviation, as a single objective function.

Main Methods:

• Application of the Search and Rescue (SAR) algorithm to solve the OPF problem.
• Testing the SAR algorithm on benchmark power systems: IEEE-14 bus, IEEE-30 bus, and IEEE-57 bus.
• Comparative analysis of SAR against 17 other optimization algorithms (e.g., GA, PSO, GWO).

Main Results:

• The SAR algorithm achieved minimum power losses of 0.4597 MW (IEEE-14 bus) and 2.7129 MW (IEEE-30 bus).
• SAR obtained minimum total fuel costs of 8051.12 $/h (IEEE-14 bus) and 798.20 $/h (IEEE-30 bus).
• Minimum voltage deviation was found to be 0.0358 (IEEE-14 bus) and 0.0978 (IEEE-30 bus) using SAR.

Conclusions:

• The SAR algorithm provides a reliable and straightforward solution for the multi-objective OPF problem.
• SAR significantly outperforms various established optimization techniques in solving OPF problems.
• The study validates the efficacy of SAR for enhancing operational and economic performance in power systems.