Multiobjective swarm optimization for operation planning of electric power systems
The determination of a safe and economic operating point for a power system lies in the definition of a set of controls (in this case, the optimal dispatch) that minimizes operation costs while ensuring system stability during each preestablished contingency. This is a problem that can be described as a case of multiobjective approach to the security constrained optimal power flow. This paper compares the performance of two multiobjective evolutive optimization techniques on obtaining this safe and economic operating point, according to static security point of view: the traditional nondominated sorting genetic algorithm (GA), based on GAs, and the multiobjective evolutionary particle swarm optimization (MOEPSO) developed in the scope of this chapter in order to exploit the performance gains observed with the hybrid metaheuristic evolutionary particle swarm optimization (EPSO) in single-objective optimization problems. The results of both multiobjective metaheuristics were parameterized so as to make possible the comparison with a monoobjective approach to the same problem. It was verified that both metaheuristics multiobjective presented improvement of performance after the implementation of a previous refinement of the initial solutions. It was also observed that the MOEPSO obtained the best performance among the other metaheuristics tested after the implementation of a methodology to obtain the cultural operators of the swarm based on the calculation of the Euclidean distance. The algorithms were implemented in MATLAB® and tested in a test-case that simulates the conditions of the Brazilian National Interconnected System (from the Portuguese Sistema Interligado Nacional).
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