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Siting And Sizing Of DG In Distributed Network Planning

Posted on:2009-08-16Degree:MasterType:Thesis
Country:ChinaCandidate:Q ZhangFull Text:PDF
GTID:2132360245994728Subject:Power system and its automation
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Distributed generation(DG) is attracting more and more attention with its wide application in the peak clipping, spare power station or combined heat and power(CHP) station building and the independent generation in the out-of-the-way area. To satisfy the demand of the rapid economic development in China, Vigorously developing DG technique based on existing central power stations and grids will become an inevitable trend for the power system development in the future. As the extension of DG, it has a great impact on node voltage, power flow, short circuit and reliability of distribution network. The degree of impact is closely relative with the site and size of DG. It will be an essential challenge for network planning and grid must consider the influence of DG. In addition, DG can reduce the energy loss, and delay or reduce the cost of grid upgrading, however, if the site and size of DG is not reasonable, on the contrary, it may lead to the increasing of energy loss and some nodes' voltage dropping or the over voltage in the power system. Even it may change the magnitude, duration and the direction of the fault current. So, the optimal siting and sizing of DG is a mass multi-objective problem, and in many cases, these sub-objective contradict each other and cannot handled by conventional single optimization techniques.. To obtain the right solution, the optimization techniques should be able to evaluate the various influence of DG to the power system, and provide the optimal site and size of DG, so that the utilities can manage these networks, maintaining adequate levels of security and economy along with increasing penetration of DG.In this paper, the whole capacity of candidate DG is determined by total load when considering the growth of system load. In the condition that DG's number, site and unit size are uncertain, the multi-objective formulation with variable weights, which is convenient to apply for the grid planners, for the optimal sizing and siting of DG which is convenient to apply for the grid planners. All the sub-objectives are the cost of distribution network upgrading, the investment of DG and the cost of power losses respectively. This paper suggests an improved multi-population genetic algorithm ,which can be used to solve mass multi-objective problems, aiming at multi-objective DG planning: real coding is presented in the algorithm to avoid the famous Hamming cliffs. This paper improves the structure of the multi-population genetic algorithm, and sets up father population which is corresponding to the normalized objective function and son population which is corresponding to the independent sub-objective functions. The quintessence population adopts optimal individual preservation method to preserve the optimal individual of father and son populations which are taken as convergence basis of the algorithm. And this method guarantee the global convergence of the algorithm. The father and son populations adopt the improved adaptive SGA, seeking for the optimal solution independently, and exchange the information with certain frequency by the immigrating operators, so it can avoid "sealing competition" and premature convergence effectively.The result from calculation example shows that the convergence and searching performance based on the algorithm proved better than traditional GA. This paper obtains the reasonable solution about site and size of DG. And the variable weight for individual objective meets different actual requirements. It can also be known that the connection of DG with distribution network can defer and reduce grid upgrading and reduce power losses by comparing result of distribution network planning with and without DG. DG brings great social and economic benefit.
Keywords/Search Tags:distributed generation, distribution network planning, siting and sizing, multi-population genetic algorithm, multi-objective
PDF Full Text Request
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