This paper presents biogeography-based optimization (BBO) and grey wolf Optimization(GWO) for solving the multi-constrained optimal power flow (OPF) problems in the power system. In this paper, the proposed algorithms have been tested in 9-bus system under various conditions along with IEEE 30 bus test system. A comparison of simulation results reveals optimization efficacy of the proposed scheme over evolutionary programming (EP), genetic algorithm (GA), mixed-integer particle swarm optimization (MIPSO) for the global optimization of multi-constraint OPF problems. It is observed that GWO is far better in comparison to other listed optimization techniques and can be used for aforesaid problems with high efficiency.

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The electrical power generation from conventional thermal power plants needs to be interconnected with natural resources like solar, wind, hydro units with all-day planning, and operation strategies to save mother nature and meet the current electricity demand. The complexity and size of the power network are increasing rapidly day by day. The enhanced power transfer from one section to another section in the existing grid system is the subject of available transfer capability (ATC), which is the modern power system's critical factor.