| 引用本文: | 彭春华,于 蓉,孙惠娟.基于K-均值聚类多场景时序特性分析的分布式电源多目标规划[J].电力自动化设备,2015,35(10): |
| PENG Chunhua,YU Rong,SUN Huijuan.Multi-objective DG planning based on K-means clustering and multi-scenario timing characteristics analysis[J].Electric Power Automation Equipment,2015,35(10): |
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| 摘要: |
| 若不考虑分布式电源出力及负荷需求的波动性及不确定性，可能导致分布式电源规划容量偏大或系统电压改善程度降低。深入分析分布式电源出力时序波动特性，并引入K-均值聚类多场景概率分析方法，以降低上述波动性及不确定性对配电网的影响；以最大化年寿命周期收益率和电压分布改善率作为目标函数，建立分布式电源多目标规划模型，并采用多目标复合微分进化算法对其求解和基于最短归一化距离法实现多目标总体最优解决策。以IEEE 33节点配电系统为例进行分布式电源多目标规划，验证了所提方法的有效性和优越性。 |
| 关键词: 分布式电源 规划 时序特性 多场景概率 K-均值聚类 多目标决策 |
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| 基金项目:国家自然科学基金资助项目（51567007，51167005）；江西省科技支撑计划项目（20142BBE50001）；江西省自然科学基金资助项目（20152ACB20017，20151BAB216020） |
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| Multi-objective DG planning based on K-means clustering and multi-scenario timing characteristics analysis |
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PENG Chunhua, YU Rong, SUN Huijuan
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School of Electrical & Electronics Engineering，East China Jiaotong University，Nanchang 330013，China
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| Abstract: |
| If the volatility of DG （Distributed Generation） outputs and the uncertainty of load demands are not considered，the planned DG capacity may become larger or the voltage profile improvement rate lower. The timing volatility of DG output is analyzed and the K-means clustering and multi-scenario probability analysis is adopted to reduce the effect of volatility and uncertainty on the distribution network. A multi-objective DG planning model with the maximum annual life-cycle yield rate and the maximum voltage profile improvement rate as its objectives is built and solved by the multi-objective compound differential evolution algorithm，and the shortest normalized distance method is applied to decide the overall optimal solution. As an example，the multi-objective DG planning is carried out for IEEE 33-bus distribution system and the effectiveness and superiority of the proposed method are verified. |
| Key words: distributed power generation planning timing characteristics multi-scenario probability K-means clustering multi-objective decision-making |
