Distributed generation planning in distribution network based on hybrid intelligent algorithm by SVM-MOPSO

Regarding stochastic disturbance in power system brought by grid-connected distributed generation(DG), generally considering operational effectiveness, aiming at economy, power quality and environmental efficiency, the optimization model of stochastic chance-constrained programming is built, while using hybrid intelligent algorithm, which simulates the uncertainty functions based on support vector machine(SVM) and solves the model by multi-objective particle swarm optimization (MOPSO), then the Pareto noninferior decision set is obtained. Simulation results show that the planning model can fully take into account randomness, grid-connected probability distribution of DG, and improve the efficiency of the algorithm, then verify the rationality and validity of the proposed approach. Moreover, the introduction of Pareto front gives fully choices to operators and possesses more engineering value.