Abstract
A new dynamic reconfiguration method considering distribution generation (DG) was presented. The dynamic reconfiguration problem was solved from three aspects: firstly, the time interval partition of the entire scheduling period was optimized by load distribution variation index; secondly, a plurality of time intervals were optimized and reconfigured by using the multi-objective particle swarm optimization model of multi-period encoding; finally, the optimal time interval numbers was determined by gradually approaching the falling threshold of net loss. If the system contained DG, DG’s output curve was determined by the analysis of its dynamic characteristics. The results of the test showed that the proposed method was effective to solve the dynamic reconfiguration with DG.
Highlights
Distribution Network Reconfiguration contains static reconfiguration and dynamic reconfiguration
Static reconfiguration only take into account the optimization target of distribution network under a certain time cross section while the objective function of dynamic reconfiguration reveals the optimization goal over a period
According to the time interval partition based on the variation of load distribution, the total network loss caused by the dynamic configuration is 972.59kWh while the time interval partition based on the value change of total load creates 983.9kWh of dynamic configuration network loss
Summary
Distribution Network Reconfiguration contains static reconfiguration and dynamic reconfiguration. Static reconfiguration only take into account the optimization target of distribution network under a certain time cross section while the objective function of dynamic reconfiguration reveals the optimization goal over a period. According to the mathematical model of distribution network dynamic reconfiguration, the relation and difference between dynamic and static reconfiguration can be concluded as: The time region of dynamic reconfiguration is a scheduling cycle while the time region of static reconfiguration is only a time section or a short time segment. The decision variables of dynamic reconfiguration is the switch state throughout the scheduling cycle which is described as mixed integer optimization problem of discrete time systems. Time interval segmentation method was applied to decouple the distribution network dynamic reconfiguration problem and time the optimal number of time interval can be determined by the relation between network loss and the time interval number.
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