Research On Reconfiguration Of Distribution Network With Distributed Generation

Because of more social demand for energy and power quality, distributed generation has been more and more used in distribution network. The structure of distribution network was originally radial with single power. Now it turns into the weak ring network of multiple power supplies on account of the introduction of DG. Also the installation position and capacity of distributed generation have some impacts on the node voltage, network loss and line flow distribution, which make the reconfiguration problem of distribution network become more complex. The originals like objective function, rules and constraints of distribution network reconfiguration are no longer viable. In order to ensure the system supply power securely and reliably and make DG better work, research on Distribution network reconfiguration with DG is significant.Firstly, take a simple distribution network model for example. It systematically analyses the installation location, capacity of the distributed power and their influence on distribution network loss. Using scenario analysis methods set up the stochastic model of the wind power system, and reactive power compensation of the shunt capacitor scheme is proposed. In flow calculation, wind power is processed into negative load with certain active power and reactive power changing with the node voltage of installation. Without modifying the flow calculation program, get the specific impact of DG on distribution network loss by the quantitative analysis of numerical example. In order to effectively reduce system loss, use the genetic algorithms on the optimal allocation of the DG installation location and capacity. And examples prove that it is feasible.Finally, establish reconstruction target based on the scenario occurrence probability. To study distribution network reconfiguration with wind power generator, we pay attention to two sides respectively. The first is distribution network reconfiguration with the installation location and capacity of wind power generator determined. The second is to perform the optimal allocation of wind power generators and network reconfiguration at the same time for seeking a network structure with minimum network loss when the installation location and capacity are all uncertain. The result proves that distribution network reconfiguration based on scenario probability model has some applicability. Active power loss of network structure in the second mode is minimum.