Multi-objective Optimal Reconfiguration Of Distribution Network Containing Distributed Generation

With the gradual improvement of life quality and electrification degree in urban residents,the construction and development of distribution network,as the most closely part connected with grid users,is facing many problems and challenges in the context of the new era.The reconfiguration of distribution network is not only an important means to improve power quality,the reliability and flexibility of power supply,but also plays a key role in reducing network loss and raising operating economy.Due to the good environmental and economic benefits of distributed generation,it has been widely used in distribution network,but the following questions arise:power reversal,misoperation of relay protection devices,decline of power quality,etc.The traditional optimization algorithm of distribution network reconfiguration has been unable to adapt to the grid-connected access of high-permeability distributed generation,so the research of distribution network reconfiguration containing distributed generation has become one of the hot spot issues in current academic research.In order to solve the problem of distribution network reconfiguration with distributed generation,a reconfiguration model aiming at system loss,load balancing and voltage deviation is established.An artificial fish swarm algorithm with efficient parallel optimization is adopted to solve the problem of distribution network reconfiguration with distributed generation.In order to overcome the problem of "dimensionality disaster" caused by binary coding,three major network simplification principles are proposed to improve the computational efficiency of the algorithm.When the algorithm falls into the cycle of "premature convergence",the mechanism of local learning and reverse learning are introduced.Part of fish according to the differential results of the fish in optimal position dynamically adjusted the direction,and work in coordination with optimal population to strengthen the local searching;another part of the fish along the worst position start to reverse learning,flee the local optimal area in time and effectively improve the diversity of population.In order to further accelerate the optimization efficiency of the algorithm,the adaptive adjustment is made in the vision and step size.Aiming at the artificial fish swarm algorithm with the mechanism of local learning and reverse learning in the distribution network reconfiguration proposed in this thesis,the simulation analysis of IEEE 16 nodes system and IEEE33 nodes system are carried out by using MATLAB software.Firstly,the reconfiguration of single objective and multi-objective functions are carried out by using the optimized reconfiguration strategy proposed in this thesis,and the influence of distributed generation access on the reconfiguration results is considered.Secondly,in order to further verify the superiority of the algorithm,the proposed optimization algorithm is compared with other given algorithms.The simulation results show that the proposed optimizing reconfiguration strategy has significant improvements in reducing network loss,maintaining load balance and reducing voltage deviation.It takes less time and iterations to obtain the optimal solution,at the same time,the system voltage is raised to a certain extent,and the stability of the system is enhanced.