Distribution network reconfiguration considering the random character of wind power generation

A chance constrained programming formulation for distribution reconfiguration with wind power generator (WPG) is proposed that aims at minimum power loss and increment voltage quality. A scenario analysis method is applied to describe the random output of WPG through the scenario probability and scenario output. A multiple objective particle algorithm (MOPSO) is employed to solve the multi-objective discrete nonlinear optimization problem. The dedicated particle encoding with the mesh information of the distribution network can effectively avoid producing a large amount of invalid solutions. With MOSPO, it is possible to obtain the optimum solution set of each objective while helping the operator to choose the most appropriate plan for reconfiguration. Application of the model and MOPSO algorithm to the 69 distribution network has verified their feasibility and correctness.

[1]  Liu Qian-jin Distribution Network Reconfiguration Considering Randomness of Wind Power Generation , 2010 .

[2]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[3]  D. Das,et al.  Impact of Network Reconfiguration on Loss Allocation of Radial Distribution Systems , 2007, IEEE Transactions on Power Delivery.

[4]  Hoyong Kim,et al.  Artificial neural-network based feeder reconfiguration for loss reduction in distribution systems , 1993 .

[5]  Rung-Fang Chang,et al.  Distributed Generation Interconnection Planning: A Wind Power Case Study , 2011, IEEE Transactions on Smart Grid.

[6]  Yuan-Kang Wu,et al.  Study of Reconfiguration for the Distribution System With Distributed Generators , 2010, IEEE Transactions on Power Delivery.

[7]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[8]  J. J. Grainger,et al.  Distribution feeder reconfiguration for loss reduction , 1988 .

[9]  Jiang Han-shu Research on the distribution network reconfiguration with the distributed generation , 2011 .

[10]  D. Shirmohammadi,et al.  Reconfiguration of electric distribution networks for resistive line losses reduction , 1989 .

[11]  Zhou Jia-qi ELECTRICAL DISTRIBUTION NETWORKS RECONFIGURATION USING DYNAMIC PROGRAMMING , 2005 .

[12]  M. Kitagawa,et al.  Implementation of genetic algorithm for distribution systems loss minimum re-configuration , 1992 .