Multi-objective Optimization Reconfiguration Of Active Distribution Network With Distributed Generation And Electric Vehicles

In recent years,the traditional energy is drying up,the growth of electricity demand is driving the power grid to develop in a sustainable way,and promoting the country to develop the smart grid.The increase of electric load makes power production need more energy to balance the power supply capacity.On the side of the distribution network,the use of distributed generation(DG)can alleviate the dependence on traditional fossil energy,and the rise of the electric vehicle(EV)is also the trend of the modernization development.After the large-scale DG and EV access to the side of distribution network,the flow direction becomes diversified and the mode of system operation becomes more complex,and it may exceed the load capacity of the distribution network,but the traditional distribution network can not accept the new energy in high permeability.Therefore,the concept of active distribution network(ADN)is proposed.Besides,the large-scale access of DG and EV in ADN will also bring a series of unfavorable factors,such as voltage fluctuation,harmonic pollution and so on.Network reconfiguration is one optimization method to improve the stable operation of the system by the state of branch switch;especially the DG and EV in ADN at the high penetration level,network reconfiguration is the effective measure to improve the reliable operation of the system.And the network reconfiguration of ADN includes static reconfiguration and dynamic reconfiguration,in this paper,the two methods will be used to analysis respectively.In the static reconfiguration,this paper considers the access of DG,the network loss,voltage quality index(VQI)and the number of switching operation are set up as the multi-objective optimization functions,the improved niche multi-objective particle swarm optimization(INMPSO)is proposed to solve the reconfiguration model of ADN,and the global optimal position is updated by the niche sharing mechanism which maintains the diversity and distribution uniformity of the population.In the dynamic reconfiguration,this paper takes the DG and EV into account in the ADN.The sitting and sizing of DG is determined by the network loss sensitivity,the multi time probability model is constructed with the output of DG and charging of EV.The network loss,voltage quality index and the number of switching operation are set up as the multi-objective optimization functions to determine the best reconstruction scheme,and the INMPSO is used to calculate the model considering the EV disordered charging and intelligent charging.The IEEE33 node system and IEEE69 node system are used as an example to simulate the multi-objective optimization problem in this paper,the multi-objective optimization problem is solved by non-dominated sorting technique,and the fuzzy satisfaction evaluation decision method is used to select the optimal compromise solution from Pareto solution set.The results of INMPSO and MPSO algorithm are compared,and the effectiveness of the improved algorithm is proved.The different charging modes of EV and the network reconfiguration are analyzed,and the results show that the reconfiguration method used in this paper is more obvious for the optimization results that can provide a new way of thinking for the optimized operation of the actual ADN system.