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Research On Total Supply Capability For Power Distribution Network Based On Feeder Section

Posted on:2017-02-17Degree:MasterType:Thesis
Country:ChinaCandidate:X B LiFull Text:PDF
GTID:2272330503985195Subject:Electrical engineering
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After the large-scale reconstruction, urban distribution network is still been at low asset utilization level despite of its large scale and satisfactory network structure. With the rapid development of the city, the construction of the substation and the underground passage of feeder lines are more and more difficult because of the shortage of land resources and urban space. Urban distribution network cannot supply electricity to new additional load at times. In a word, the traditional large-scale reconstruction mode is not suitable for urban distribution network any more. Besides, the construction of distribution automation can greatly improve the utilization rate of urban distribution network. Distribution automation makes it possible to maximize the power supply capacity of urban distribution network. In the circumstances of smart grid, evaluating the supply capacity of distribution network scientifically is beneficial to optimize network structure and to guide the lean planning and construction of urban distribution network, and it also can produce enormous social and economic benefits.In this paper, the existing methods for computing the total supply capability(TSC) of distribution network are compared. As a representative, the method based on feeder interconnection is analyzed carefully. Then this paper points out that load transfer of feeder section under N-1 contingencies should be taken into account in the TSC method. Load transfer of feeder section at TSC loading means the reconfiguration with zero load loss. This paper simplifies partial constraints of reconfiguration model according to the calculation condition of the TSC.Total supply capability of a distribution system is the maximum load that it can serve under N-1 security. This paper studies the feeder N-1 security comprehensively and provides the feeder N-1 constraints of TSC model following practical operating requirement. At the same time, some concepts of main-transformer N-1 security are also studied. In order to make main-transformer N-1 verification model more accurate, this paper refines existing models.This paper proposes an accurate method of main-transformer N-1 verification for distribution network, considering the capacity constraints of feeder and main-transformer,practical operating constraints and load transfer constraints. The verification model of transferring the load of N-1 main-transformer through closing normally open switches among feeders is established. This model also considers the situation that the load of N-1 main-transformer is transferred to the other main-transformer within the same substation through the bus connecting the two main-transformers. Combined with the practical work of distribution network planning,the proposed method is applicable to various wiring modes of distribution network and close to the actual network structure. The accuracy of the proposed method is verified by the N-1 simulation of a transformer in practical distribution network.This paper proposes a method to calculate the total supply capability of distribution network based on feeder section, considering the capacity constraints of feeder and main-transformer and the N-1 security constraints. Particle swarm optimization(PSO) is employed to solve the TSC method. To make sure that distribution network can pass the feeder N-1 simulation test at TSC loading, enumeration method is utilized. For the main-transformer N-1 constraints, a hybrid PSO approach composed of the binary PSO and the discrete PSO based on reconfiguration of large-scale distribution network is employed to find the load transfer path of N-1 main-transformer. Finally, two examples are given to demonstrate the effectiveness and accuracy of this proposed method.
Keywords/Search Tags:distribution network, total supply capability, N-1 security, reconfiguration, particle swarm optimization
PDF Full Text Request
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