Multiobjective swarm optimization for operation planning of electric power systems

Multiobjective swarm optimization for operation planning of electric power systems

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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).

Chapter Contents:

  • Abstract
  • 23.1 Introduction
  • 23.2 Objective
  • 23.3 Optimal power flow and static security analysis
  • 23.3.1 Formulation of the problem
  • 23.3.2 Security constrained optimal power flow
  • 23.3.3 Formulation of the SCOPF problem
  • 23.4 Multiobjective optimization
  • 23.4.1 Pareto-optimal solutions
  • 23.4.2 Goals in multiobjective optimization
  • 23.4.3 Differences in the monoobjective optimization
  • 23.5 Multiobjective swarm and evolutionary optimization
  • 23.5.1 Non-dominated sorting genetic algorithm
  • 23.5.2 Multiobjective evolutionary particle swarm optimization
  • 23.6 Problem formulation
  • 23.6.1 Mathematical modeling
  • 23.6.2 Algorithms implemented for SCOPF resolution
  • 23.7 Tests and simulation
  • 23.7.1 Chromosome encoding
  • 23.7.2 Results
  • First implementation (I1)
  • Second implementation (I2)
  • 23.8 Conclusion
  • References

Inspec keywords: power system planning; load flow; particle swarm optimisation; power system interconnection; power system security; genetic algorithms; power system stability

Other keywords: security constrained optimal power flow; minimizes operation costs; multiobjective evolutionary particle swarm optimization; single-objective optimization problems; multiobjective swarm optimization; hybrid metaheuristic evolutionary particle swarm optimization; multiobjective approach; system stability; metaheuristics multiobjective presented improvement; economic operating point; Brazilian National interconnected system; nondominated sorting genetic algorithm; Euclidean distance; electric power systems; multiobjective metaheuristics; multiobjective evolutive optimization techniques; MOEPSO; operation planning

Subjects: Power system management, operation and economics; Power system planning and layout; Optimisation techniques; Power system control

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