Research Of Distribution Network Optimization Planning Based On Quantum Artificial Fish Swarm Algorithm

Distribution network is an important part of power system. With the fast development of social economy, the scales of power demand and distribution network system also unceasingly increase, so distribution network is undertaking a more herculean task. The reasonable distribution network can not only enhance the power supply reliability, but also save large amount of investment cost. Therefore, it is essential to optimize distribution network planning.Firstly, based on credibility theory, a new mathematical model of distribution network planning, which can represent double uncertainty characteristics of randomness and fuzziness, has been established. The new model has an overall consideration of various uncertainties such as: the land character and the load of the new load node, the load and the line price of the old load node. Thus, it can simulate the uncertainties factors of distribution network planning more completely and adequately, which lays the solid foundation of optimizing distribution network planning.Secondly, a new algorithm: Quantum Artificial Fish Swarm Algorithm (QAFSA), which can realize the planning model with double uncertainty characteristics of randomness and fuzziness, has been presented. For the insufficiency of Artificial Fish Swarm Algorithm (AFSA), the new algorithm combines Quantum computation theory with AFSA, and realizes the optimization using the renewal strategy of quantum rotating gate and QAF-follow. It can find the optimal solution of the question with smaller fish scale in the short time. Meanwhile, for its fast convergence speed and good overall searching efficiency, the new algorithm has a better performance for optimization of planning model with double uncertainty characteristics of randomness and fuzziness.Finally, Quantum Artificial Fish Swarm Algorithm is applied to distribution network structure optimization planning. Combining with random and fuzzy simulation, this paper has solved the model based on credibility theory. And the calculations show that the proposed methods are correct and superior.