Transmission expansion planning by meta-heuristic techniques: A comparison of Shuffled Frog Leaping Algorithm, PSO and GA

This paper presents the application of a memetic meta-heuristic optimization technique known as Shuffled Frog Leaping Algorithm (SFLA) to the problem of transmission network expansion planning. The main objective of the proposed problem is to minimize total cost by finding the place, number and type of new transmission lines required to ensure that the power system meets the forecasted demand in the most economic and reliable way. The proposed static transmission expansion planning problem is formulated as a mixed integer programming optimization problem to minimize the total cost comprised of investment cost of building new lines, congestion costs and the cost of load curtailment due to contingencies. The proposed algorithm has been successfully applied to IEEE RTS 24-bus test system and the performance of the proposed algorithm has been compared with other heuristic optimization techniques such as particle swarm optimization (PSO) and Genetic Algorithm (GA). The comparison results testify to the feasibility and efficiency of the developed algorithm in solving the transmission expansion planning problem.