High-performance hybrid genetic algorithm to solve transmission network expansion planning

In this study, a high-performance hybrid genetic algorithm (HGA) is proposed to solve static and multistage transmission network expansion planning (TNEP) problem. The main features of the HGA are: (i) it avoids homogenised solutions by using a special genetic algorithm as the backbone of the procedure, (ii) uses a powerful path-relinking algorithm for the deep exploration of local solutions, (iii) employs an efficient constructive heuristic algorithm for finding high-quality initial solutions and for improving solution qualities and (iv) uses a fast relaxation strategy for solving the linear programming problems required for calculating the fitness functions. This procedure will result in an intelligent exploration of a large search space in less amount of time. The proposed methodology is tested with three electrical systems: South Brazilian 46-bus, Colombian 93-bus and the North-Northeast Brazilian 87-bus.

Keywords

power transmission planning, genetic algorithms, linear programming, high-performance hybrid genetic algorithm, transmission network expansion planning, static transmission network expansion planning problem, multistage TNEP problem, high-performance HGA, path-relinking algorithm, constructive heuristic algorithm, fast relaxation strategy, linear programming problem, fitness function, South Brazilian 46-bus electrical system, Colombian 93-bus electrical system, North-Northeast Brazilian 87-bus electrical system