Network Reconfiguration With New Energy Based On Membrane Optimization Algorithms

In recent decades, with the rapid development of economy, environmental pollution and the greenhouse effect are becoming more and more serious. In the context, it is necessary to develop greatly the wind turbine and electric vehicle. However, the wind power generation output uncertainty and electric vehicle charging and discharging behavior will have a serious impact on the reliability and economic operation of the traditional distribution network.At present, a great deal of research on wind turbine and the electric vehicle access to distribution network problem has been conducted, but it is quite rare for the research of distribution network reconfiguration and fault recovery with wind turbines and electric vehicle network. Therefore, it is important to research on the topic. The main works are as follows:(1) The random fluctuations penalty coefficient is proposed for the wind turbine output uncertainties, and the following method is used for the simulation of electric vehicle charge-discharge behavior. First of all, the electric cars are classified according to the purpose and behavior, and then the load curve is obtained according to the types of electric vehicles, the initial state of charge, charge and discharge requirements, staring charging and discharging time. Finally, the dynamic reconfiguration model of distribution network with dividing time is established to consider the electricity purchasing cost, stochastic volatility, electric vehicle charging and discharging and network loss.(2) In this paper, combining with the membrane computing theory and membrane optimization algorithm. The genetic membrane algorithm(GMA) is presented to overcome the premature and convergence slow problem of the genetic algorithm(GA). And equal sections crossover probability selection method is used to overcome a large amount of duplicate solutions in the reconfiguration.(3) Based on the combination of nested structure membrane optimization method, genetic algorithm(GA) and the distributed computing method, an efficient parallel genetic membrane computing(PGMC) is proposed. In order to improve computing efficiency, an object generation method based on the minimum loop and an equal sections crossover probability selection method are presented considering the radical operation characteristics of distribution network.(4) In order to improve the global search ability and the robustness of genetic membrane algorithm, the improved genetic membrane algorithm(IGMA) based on cross transposition rule and mutation rewrite rule is proposed. An application of IGMA to distribution network fault recovery with new energy is presented. The experimental results show that the algorithm has better feasibility.Finally, the typical examples of 33-nodes net are simulated by the proposed algorithm and model. The effectiveness and correctness of the proposed model and method are verified.