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Research On Distribution Network Planning Considering Distributed Generation

Posted on:2016-07-20Degree:MasterType:Thesis
Country:ChinaCandidate:D HeFull Text:PDF
GTID:2272330470473188Subject:Motor and electrical appliances
Abstract/Summary:PDF Full Text Request
With the promotion of smart and clean grid, and the access to the distributed generation, the distribution network planning has become extremely complex. Especially the access to the distributed generation brought a great impact on the trend of the distribution network, the net loss, voltage, reliability, etc. Therefore it is necessary to research containing distributed generation network. On the basis of the research at home and abroad, this paper mainly studied the following aspects.(1) New hybrid particle swarm optimization algorithm is proposed, in order to applied in the distributed generation points planning and expansion planning.Aiming at the problem that the basic particle swarm optimization is prone to “inert” particle a new hybrid particle swarm algorithm is proposed in this paper. Firstly,chaos theory is used to initialize particle swarm and make the initial solution in the entire search space. And the operation of the crossover mutation and chaotic disturbance is added to help jumping out of the local optimal particles.(2) New hybrid particle swarm algorithm is applied to plan the location and capacity of the distributed generation contained the IEEE33 node.The function that active in the distribution system network losses are minimized is worked as the objective function, and the constraints is introduced as a penalty term to the objective function. With active power distribution system, the objective function is minimum network loss, will be introduced to the constraint condition as a penalty term in the objective function. Results show that the introduction of the distributed generation supply can greatly reduce the distribution system network losses, and in this paper, the new hybrid particle swarm has improved in convergence than the basic particle swarm algorithm increase to a certain extent, so the planning scheme is better.(3) Genetic tissue membrane algorithm is proposed, in order to applied in the distribution network expansion planning.Considering the computing capacity of genetic algorithm, genetic organization membrane algorithm was proposed in this paper. The organization communication rule in the membrane algorithm is introduced into genetic algorithm to improve its ability of calculation. All genetic operators are conducted in the membrane area. The better individual cells are stored in center cell after the individual through the complex communication rules between cells, cells and center cells. The communication rules retained the excellent individuals, increase the population diversity, and enhance the computing power.(4) Proposed double distribution network expansion planning including distributed generation.Containing distributed generation network’s double expansion planning. This paper introduces the government subsidy policy to the objective function, to encourage the use of clean energy. The function which is to save the most line construction costs, network loss costs, electricity purchasing cost, power loss cost, distributed generation installation cost and power cost is chosen to be the objective function model. Under the condition of the introduction of the distributed generation position and capacity, the inner layer use genetic tissue membrane algorithm to make expansion planning of the space truss structure; Outer layer adopts new hybrid particle swarm optimization algorithm to determine the location and capacity of the distributed generation. Finally, the planning results of the containing distributed generation and without distributed generation were compared and analyzed. The results show that the integrated circuit containing distributed power expansion plan is more reasonable, and achieve certain economic benefits.
Keywords/Search Tags:distributed network planning, siting and sizing, new hybrid particle swarm, Genetic Organization Type Membrane Algorithm, double expansion planning
PDF Full Text Request
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