Research On Distribution Network Reconfiguration Method Considering Uncertainty Of Distributed Power

In recent years,distributed generation technology develops rapidly due to its flexible and environment-friendly nature.However.when a large number of distributed generations are connected to the system,their influence on the reliability and economy of distribution network cannot be ignored.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.In this thesis,considering the uncertainty of distributed generation,a reconfiguration optimization model aiming at network loss,load balance and voltage static security is established,and the problem of distribution network reconfiguration is solved by improved whale optimization algorithm.First,the probability calculation model of random load,micro gas turbine and photovoltaic power generation system is constructed.In the power flow calculation method,the probability power flow equation is linearized,a random power flow model is established by the semi-invariant method and the Gram-Charlier series,and the semi-invariant method is verified on the IEEE33 node test system,indicating that the addition of appropriate Photovoltaic power generation can increase the voltage static unsafe probability of the system.In the static reconstruction method of distribution network,an optimized reconstruction model with network loss,load balancing,and voltage static safety as the goal is established.By using heuristic rules to improve the coding method,the solution space can be effectively reduced.The traditional whale optimization algorithm.and the chaotic sequence generated by the piecewise Logistic chaotic map is used to initialize the population position to maintain the initial population diversity:a nonlinear adaptive weighting strategy is introduced to coordinate the global search and local development capabilities.In order to verify the optimization effect of the improved whale optimization algorithm,three typical benchmark functions were tested and compared with the traditional whale optimization algorithm and the whale optimization algorithm based on reverse learning.According to the improved whale optimization algorithm proposed in this thesis.IEEE33 node system and Taipower84 node system of large actual distribution network were selected for simulation analysis.First,the optimized reconstruction scheme proposed in this thesis is used to reconstruct the netw ork of single and multi-objective functions.Secondly,in order to further verify the superiority of the algorithm,the performance of the optimization algorithm proposed in this thesis is compared with several other given algorithms.The simulation results show that the optimized reconfiguration strategy proposed in this thesis significantly improves the three indicators of reducing network loss,maintaining load balance,and reducing the static probability of voltage static.The iteration time and number of iterations required to obtain the optimal solution Less.while also raising the system voltage to a certain extent,enhancing the stability of the system.