Study On Dynamic Reconfiguration In Distribution Networks Considering Time Scale And Topology Characteristic

Distribution network reconfiguration is vital to optimize and control the electrical distribution network(DN).Based on the actual application of the customers,the time-variant nature of electrical loads is taken into account in the reconfiguration.With constantly increasing demand for electric power and the spread of energy conservation,the penetration of renewable energy distributed generation(DG)to DSs is used to enhance the system reliability.DG outputs fluctuate along with the time and weather conditions.Since the operation modes of a DN are complicated and variable,the initial topology of network reconfiguration is flexible.These demands bring dynamic environment to the distribution network reconfiguration.They generate inevitable limitation for the conventional methods for static reconfiguration.Moreover,the ability to self-healing is a key feature of distribution automation.The dynamic reconfiguration is an effective way to realize the self-healing function of smart DN.Thus,it is more realistic to study the dynamic reconfiguration of the DN in both theory and engineering.The dynamic features are decomposed from the time scale and topological characteristics according to the actual operation of the DN.This paper is focused on the problems of the dynamic reconfiguration and service restoration.In DNs,load profiles vary from time to time due to the mix and dispersion of customers served.This variation in load profile is one major uncertainty factor in achieving dynamic reconfiguration.This paper forms a multi-objective time interval model which includes the objectives of power loss,voltage stability,and node importance.And then a multi-time dynamic reconfiguration method for DNs is proposed.In the method,determining optimal time intervals and detecting the most proper time points greatly affects the realization of the optimal reconfiguration target.The time interval of the load profile is initially divided by analyzing the threshold of load change and load monotonicity.Under the constraint of the maximum allowable number of switching operations,the proposed integer quantum particle swarm optimization is used to determine the optimal reconfiguration time and network configuration by adjusting the optimal function values.Numerical tests implemented on the DN with actual time-varying load show that the achieved results for dynamic reconfiguration are more realistic comparing to static reconfiguration.The proposed dynamic reconfiguration method can improve economy and security of network operation as well as minimize the risk of power supply.Many encoding schemes of the particle swarm optimization(PSO)are not efficient because the extremely large number of unfeasible solutions appearing at each generation would lead to tedious mesh checks before reaching an optimal solution.A hierarchical encoding particle swarm optimization(HEPSO)is proposed.In this method,an effective encoding scheme based on the mixed loop matrix is used to generate a set of solutions which match the actual radial topologies.The scheme has been successfully applied on the PSO to minimize the size of the search space and avoid infeasible particles.A novel scenario model and bi-level programming method for dynamic reconfiguration of DNs are presented.Considers the DG output and time-varying load,the model aims to minimize power loss of the DN,and it sufficiently improves the absorption rate of DGs while satisfying operation constraints.The HESPSO and standard PSO are combined to obtain the optimal topology,the best reconfiguration point and the acceptance capacity of DGs power,respectively.The illustrate results show that the proposed model and method can effectively improve the resource utilization and optimize the operation of the power system.This paper proposes a dynamic reconfiguration method that considers the real-time initial topology variation under non-normal reconfiguration.If the initial topology changes are detected,a dynamic topology analysis method is applied to update the real-time topological parameters and restore the network connectivity.After updating the initial topology,a dynamic adaptive particle swarm optimization is used to find the optimal configuration for power loss reduction and voltage profile improvement.By simulating different changes in the initial topology of DNs,the proposed method has the ability to update the real-time topological parameters and optimize the operation of the distribution network.In view of recovery in DN with DGs,an effective multi-stage service restoration method based on dynamic reconfiguration is presented.The whole process is composed of five stages which are dynamic topology analysis,matching islanding strategies,restoring the connectivity of network with DGs,network optimization,and load shedding.Once a fault occurs,a dynamic topology analysis method is used to detect the out-of-service areas and determine the restoration scheme.The loads in out-of-service areas can be restored by creating a micro grid with distributed generators or network reconfiguration with main-grid.An islanding partition strategy based on shortest path search algorithm is implemented to quickly form intentional islanding in the former condition.For the latter condition,the aim is to find a reasonable service path to restore the connectivity of the out-of-service loads with the main grid.Whether the network optimization and load shedding are carried out or not which depends on violations of the power flow constraints.And a multi-index decision-making method is used to identify the nodes of load shedding.Illustrative examples verify the effectiveness of the proposed mechanism which can obtain reasonable service restoration plans under different faults.