Modern transmission network expansion planning (TNEP) is carried out with AC network model, which is able to handle voltage and voltage stability constraints. However, such a model requires optimization with iterative AC power flow model, which is computationally so demanding that most of the researchers have ignored the vital (N-1) security constraints. Therefore, the objective of this research work is to develop an efficient, two stage optimization strategy for solving this problem. In the first stage, a DC expansion planning problem is solved which provides an initial guess as well as some very good heuristics to reduce the number of power flow solutions for the second stage of AC transmission and reactive expansion planning. A modified artificial bee colony (MABC) algorithm is used to solve the resulting optimization problem. Static AC TNEP results for Garver 6 bus, IEEE 24 bus and IEEE 118 bus test systems have been obtained with the proposed and rigorous approaches and wherever possible, compared with similar results reported in literature to demonstrate the benefits of the proposed method. Also, multi-stage dynamic AC TNEP for the Garver 6 bus system is solved to show the applicability of the methodology to such problems.

Summary Transmission network expansion planning (TNEP) problem is an essential part of power system expansion planning, and it is an extremely complex nonlinear, nonconvex, mixed‐integer optimization problem. Solution to such a computationally intensive problem is a challenge for any optimization algorithm. Consideration of security constraints makes the problem even more formidable. Although various conventional and metaheuristic methods have been used in the past to solve such problem, scope for better optimization techniques always remain. The artificial bee colony (ABC) algorithm is one of the newest swarm intelligence‐based optimization algorithms, which has delivered promising results in solving numerical optimization problems. However, the algorithm is quite less efficient in solving real‐life constrained engineering problems. In this paper, a modified ABC (MABC) algorithm is formulated by incorporating the idea of global attraction, universal gravitation, and by introducing modified ways of searching in various bees' phases of the ABC algorithm. The MABC is able to get better results in a very efficient manner, when used for solving various benchmark functions. The efficiency and effectiveness of the MABC algorithm in solving constrained engineering problems is demonstrated by solving TNEP problems for different systems. The proposed method is tested on IEEE 24 bus system, South Brazilian 46 bus system, Colombian 93 bus system for direct current TNEP model, and Garver 6 bus system for alternating current TNEP model. Results confirm that MABC can be an attractive alternative to the existing optimization algorithms for solving very complex nonlinear engineering optimization problems in a real‐world situation.

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