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Transmission Expansion Planning Based On Multi Scenarios Technology For Power System With Wind Farms

Posted on:2018-08-26Degree:MasterType:Thesis
Country:ChinaCandidate:M WangFull Text:PDF
GTID:2322330518958184Subject:Engineering
Abstract/Summary:PDF Full Text Request
At present,wind power has been developed and applied worldwide.With its rapid developmen,the consumptive situation is not satisfactory and there is much wind power abandoned.The main reason is that wind power development plan focuses on centralized planning,and the lack of specific wind power output and consumption project.Moreover,grid construction and wind power development can not be synchronized.On the other hand,considering wind farm output natural factors is uncertain,This paper proposes a new multi-objective transmission expansion planning(TEP)in order to find optimum location of lines/transformers by considering intermittent nature of wind power and variable load structure.To reveal the benefit of wind power in the context of TEP,curtailed wind energy is considered as one of the objective functions besides the sum of the investment costs and penalty for energy not supplied(ENS).By this way,a trade-off between investment cost and curtailed wind energy is established using fuzzy satisfying approach while maintaining the adequacy of power system.The multi-objective nature of the proposed method is handled by using the Non-dominated Sorting Genetic Algorithm-II combined with the DC-optimal power flow which is performed several times to obtain curtaile d wind energy and ENS.The nature of load and wind power is incorporated into the methodology by using agglomerative hierarchical clustering to reduce computational effort.The proposed methodology is illustrated on the different configurations of modified IEEE-RTS 24 bus test system.Numerical case studies indicate the effectiveness of the proposed method for reduction of total investment cost in TEP of power systems with wind power integration.
Keywords/Search Tags:transmission expansion planning, wind power, clustering, multi-objective optimization, fuzzy satisfying
PDF Full Text Request
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