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Optimal Reactive Power Planning In Power Market

Posted on:2006-03-22Degree:MasterType:Thesis
Country:ChinaCandidate:K Q XiaFull Text:PDF
GTID:2132360212982391Subject:Power system and its automation
Abstract/Summary:PDF Full Text Request
In power market, there is a financial compensation trend for reactive power ancillary service, with the reactive power price's influence to the operation of system and reactive power planning. Based on the research to reactive power ancillary service acquaintance mode in most power markets, a new reactive power optimal planning in power market is modeled,taking into account the influence of reactive power pricing and reactive power ancillary service purchase cost.And a new self-adaptive genetic algorithm is applied into the reactive power optimal planning in power market, and a synthetically sensitive method is applied as well to find the candidate point of reactive compensating facilities. The optimization results in several reactive power pricing modes are compared and analyzed, and at last some significant results emerge.The results of examples show that the self-adaptive weight sum method will guarantee the multi-orientation searching to the optimization orientation. And the self-adaptive punitive function is approved effective to deal with the inequation. Therefore, the self-adaptive genetic algorithm can be applied to deal with multi-objective reactive optimization. And through example result comparison with traditional fixed weight sum and fixed punitive weight algorithm, the better performance of self-adaptive genetic algorithm is proved.IEEE 14-bus examples of reactive power optimal planning in 3 diverse reactive power modes are calculated respectively, and the good effect of the optimal planning method in this paper is proved as well. The results show that different reactive pricing mode will lead to different planning results. Optimal planning taking in account the reactive price will result to the higher investment cost, yet the lower reactive purchase cost. Thus the excessive investment cost can be compensated in short term. Furthermore, the broadening of restriction on the reactive power of generator can reduce the reactive compensating facility's investment cost as well.
Keywords/Search Tags:Power market, Reactive power, Reactive ancillary service, Optimal reactive power planning, Self-adaptive Genetic Algorithm, Multi-objective optimization
PDF Full Text Request
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