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分时电价下考虑源网荷各侧收益的风电消纳模型
引用本文:黄培东,詹红霞,彭光斌. 分时电价下考虑源网荷各侧收益的风电消纳模型[J]. 电测与仪表, 2017, 54(7). DOI: 10.3969/j.issn.1001-1390.2017.07.009
作者姓名:黄培东  詹红霞  彭光斌
作者单位:西华大学 电气与电子信息学院,成都,610039
摘    要:风电的快速发展,系统调峰能力的不足已造成传统调度方式下风电消纳受限,弃风现象严重。采用峰谷分时电价调整用户削峰填谷,降低负荷低谷时段风电弃风。在深入研究分时电价实施机制的基础上,综合考虑发电侧、电网侧、用户侧利益,建立了风电并网量最大、发电侧收益、电网侧收益最大化的多目标风电消纳模型,并采用粒子群算法对模型进行求解。仿真结果表明该模型既能实现源网荷各侧获利,又能提高系统接纳风电能力,实现多方共赢。

关 键 词:风电消纳  需求响应  分时电价  粒子群算法
收稿时间:2015-12-08
修稿时间:2015-12-08

The Model of Wind Power Accommodation Considering Source Network Load on Each Side Benefits in TOU
Huang Peidong,zhanhongxia and pengguangbing. The Model of Wind Power Accommodation Considering Source Network Load on Each Side Benefits in TOU[J]. Electrical Measurement & Instrumentation, 2017, 54(7). DOI: 10.3969/j.issn.1001-1390.2017.07.009
Authors:Huang Peidong  zhanhongxia  pengguangbing
Affiliation:School of Electrical Information,Xihua University,School of Electrical Information,Xihua University,School of Electrical Information,Xihua University
Abstract:With the rapid development of wind power, low peaking capacity of system power has resulted in limited consumptive wind in traditional dispatch and abandoned wind seriously.In this paper, TOU price can adjust user load shifting in load-peak period and reduce abandoned wind.On the basis of studying deeply the TOU price implementation mechanism, considering the generation side, the power grid side, and the interests of the user side, a multi-objective model of wind power consumption is established which the function is the maximum capacity of wind power accommodation, the maximum benefit of generation side, and maximum benefit of the power grid, and the particle swarm optimization is adopted to solve this model.The simulation results show that it is a win-win solution which not only can realize profit of the generation side the power grid side and the network load side, but also can improve the capacity of wind power accommodation.
Keywords:wind power accommodation  demand response  time-of-use (TOU) price  particle swarm optimization
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