| In recent years,distributed generation(DG)has developed rapidly with the characteristics of low pollution and strong flexibility.With the continuous expansion of the DGs,the operation economy and power supply quality of the active distribution network put forward higher requirements.In order to reduce the operation costs of the power distribution system and improve the reliability of the system operation,the distributed output power is fully absorbed and the uncertainty is suppressed,this thesis studies the optimization of ADN.According to the operating characteristics of ADN,In this thesis it is divided into regions,and three level control system is constructed:comprehensive decision,global optimization and regional optimization.In order to ensure the continuity of the optimization,a control method combining a spatial scale and a time scale is proposed in this thesis,which includes global optimization in long time scale and regional optimization in short time scale.In order to improve the economics of ADN operation,a global optimization model in a long time scale is proposed in this thesis.The objective function of the model is the lowest system power generation cost.Moreover,the basic particle swarm optimization algorithm is easy to fall into local optimum and slow in convergence.This objective proposes to introduce a differential evolution operator into the basic quantum particle swarm algorithm,which improves the diversity of the group,improves the optimization speed and global search ability of the algorithm.In order to improve the stability of ADN operation and avoid the large-scale load fluctuation caused by long-time interval optimization,that causing voltage overruns and reducing grid reliability,in this thesis it is proposed to establish a regional optimization model in a short time scale.After receiving the power optimization value given by the global optimization in each area,the objective functions are to reduce power deviation and network loss,and quickly realize optimization in a short time.Because of the high requirements on the speed of solving the ADN at this stage,traditional intelligent algorithms cannot meet the requirements.In this thesis,the cone optimization method is proposed to transform the traditional nonlinear model,and the second-order cone programming model is established to solve the problem.The IEEE33 node system and the actual 78 node system is used to simulate and analyze the model.The simulation results show that the operating cost of distribution network can be effectively reduced after using the global optimization model in a long time scale.Compared with traditional particle swarm optimization algorithm,the search accuracy of the algorithm is improved in the solution of the global model,and the optimal solution is got faster.The regional optimization model can effectively reduce power deviation and network loss.It can greatly improve the efficiency of the solution when using the cone optimization method.Compared with global optimization only,using the control mode of global and regional coordination optimization can improve the voltage operation level. |
