Transmission Expansion Planning Using Zebra Optimization Algorithm

Transmission expansion planning (TEP) is an important and classical problem in electric power system planning. The main goal of TEP is to find suitable locations and new lines to meet increasing power demand in the future. The paper proposes a new bio-inspired meta-heuristic called the Zebra Optimization Algorithm (ZOA) for solving the TEP problem with the objective of minimizing total investment costs. The IEEE 24 bus system is used to prove the effectiveness of the proposed ZOA method. The simulation results using the suggested ZOA approach are compared with Gray Wolf Optimization (GWO) and Differential Evolution (DE). In addition, the proposed ZOA is also compared with the existing other methods. The simulation results show that ZOA is better than 10.96% and 14.1% compared to the CHA and DA algorithms, respectively. Thus, the proposed ZOA is one of the effective and reliable algorithms to solve the TEP problem.