学术论文

      多负荷水平下含风电接入的配电网无功优化

      Reactive Power Optimization of Distribution System Integrated with Wind Power under Multiple Load Levels

      摘要:
      由于风电输出功率的随机性,风电机组的大量接入给配电网无功优化带来更多不确定性因素.为了提高配电网无功优化对风力发电并网的适应能力,建立了多负荷水平下基于场景分析的考虑风电接人的多目标无功优化模型. 该模型综合考虑了节省电能损失费用和节点电压偏差2个指标,将2个指标进行模糊化.采用最大化模糊满意度指标法将多目标优化问题转换为单目标优化问题,然后采用自适应遗传算法进行求解.并以IEEE 33节点测试系统为例,计算和分析了在不同场景时最大负荷、一般负荷和最小负荷3种负荷水平下,电容器投切、系统有功损耗、节点电压以及节省电能费用情况.计算结果表明,所提出的无功模糊优化方法,在不同负荷水平、不同场景下改善电压质量和降损节能效果显著,适合多负荷水平下含风电机组的配电网无功优化需要.
      Abstract:
      Due to the random feature of wind power,the access of large numbers of wind turbines brings high uncertainties to the reactive power optimization of distribution network.In order to improve the adaptability of reactive power optimization to the wind turbines,this paper proposes a new model for multi-objective reactive power optimization of distribution systems integrated with wind power based on scenario analysis under multiple load levels.Two indexes,including net savings and nodes voltage deviation,are synthetically considered in the model.Through fuzzing of the two indexes,the maximum fuzzy satisfaction index method is used to transform the multi-objective optimization problem into a single objective problem,which is then solved by the adaptive genetic algorithm.By using a 33-bus testing system as an example,the capacitor switching,power loss,node voltage and net savings are analyzed with the proposed algorithm under different scenarios and three load levels,including maximum load,normal and minimum load.The case study shows that the proposed model and method can effectively improve the voltage profile of distribution system and significantly reduce the power loss,and can be applied to the reactive power optimization of distribution system integrated with wind power generators under multiple load levels.
      作者: 姜凤利 [1] 张鑫 [2] 王俊 [1] 朴在林 [1]
      Author: JIANG Fengli [1] ZHANG Xin [2] WANG Jun [1] PIAO Zailin [1]
      作者单位: 沈阳农业大学信息与电气工程学院,辽宁沈阳,110866 国网辽宁电力公司营口供电公司,辽宁营口,115002
      刊 名: 中国电力 ISTICPKU
      Journal: Electric Power
      年,卷(期): 2017, 50(3)
      分类号: TM727.1
      在线出版日期: 2017年4月6日
      基金项目: 国家科技支撑计划资助项目,辽宁省教育厅科研项目,辽宁省博士启动基金,the the National Science-Technology Support Plan Projects,the Scientific Research Project of Liaoning Province Department of Education,Doctoral Scientific Research Foundation of LiaoNing Province