A new DG multiobjective optimization method based on an improved evolutionary algorithm

A distribution generation (dg) multiobjective optimization method based on an improved pareto evolutionary algorithm is investigated in this paper. the improved pareto evolutionary algorithm,which introduces a penalty factor in the objective function constraints,uses an adaptive crossover and a mutation operator in the evolutionary process and combines a simulated annealing iterative process. the proposed algorithm is utilized to the optimize dg injection models to maximize dg utilization while minimizing system loss and environmental pollution. a revised ieee 33-bus system with multiple dg units was used to test the multiobjective optimization algorithm in a distribution power system. the proposed algorithm was implemented and compared with the strength pareto evolutionary algorithm 2 (spea2),a particle swarm optimization (pso) algorithm,and nondominated sorting genetic algorithm ii (ngsa-ii). the comparison of the results demonstrates the validity and practicality of utilizing dg units in terms of economic dispatch and optimal operation in a distribution power system. © 2013 wanxing sheng et al.