Research On Active Distribution Network Planning Based On Hybrid Algorithm Of Tabu-Quantum Particle Swarm Optimization

As the deterioration of the natural environment and energy shortage,the use of renewable energy distributed power(DG)as a flexible,once appear green,efficient power generation technology has received widespread attention.Due to the high permeability of DG connected to the power grid,the traditional distribution network has been transformed from one-way passive power grid to two-way active power grid.In order to adapt to the randomness and volatility of DG output,the traditional passive power distribution network has been transformed to active power distribution network(ADN).Faced with people’s increasing demand for power supply and the improvement of DG permeability,ADN planning has become a new research focus.In this paper,the ADN network planning algorithm and model are studied,the specific content is as follows:Firstly,the algorithm of distribution network planning model is analyzed.At At present,the commonly used algorithms include the traditional mathematical programming algorithm and the emerging intelligent algorithm.Based on the quantum particle swarm optimization(QPSO),this paper improves the defects of the algorithm which are prone to premature convergence,and uses the randomness and ergodic property of chaos optimization to improve the global search capability of the algorithm.In order to ensure the convergence efficiency of the algorithm,the improved algorithm is mixed with tabu search algorithm,and the mixed strategy of the algorithm is put forward.The feasibility of the algorithm is analyzed by testing some classical test functions,and the performance superiority of the hybrid algorithm is verified by comparing with QPSO.Secondly,the two-layer programming model of ADN programming is established with the optimal economic cost as the optimization objective.In the upper layer planning model,the annual comprehensive cost of distribution network considering the reliability power shortage cost is used as the optimization objective function,and the hybrid algorithm proposed above is used as the model solution method.The lower planning model takes the minimum removal amount of DG output as the optimization objective function,and uses prior-dual interior point method as the model solution method.Finally,a practical 29 node ADN programming example is used to verify the effectiveness of the two-layer model,and the iterative process of the hybrid algorithm is compared with other intelligent algorithms to verify the effectiveness of the hybrid algorithm.