| 引用本文: | 刘故帅,肖异瑶,贺禹强,等.考虑新能源类型的电力系统多目标并网优化方法[J].电力系统保护与控制,2017,45(10):31-37.[点击复制] |
| LIU Gushuai,XIAO Yiyao,HE Yuqiang,et al.Multi-objective optimal method considering types of grid connected new energy of electric power system[J].Power System Protection and Control,2017,45(10):31-37[点击复制] |
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| 摘要: |
| 提出通过优化供应侧的新能源设备选型来应对当前电力系统双侧随机问题的新思路。针对不同类型新能源设备的成本、收益以及容量不同，给配电网带来过度投资、电压降落和网损增加等问题，建立了多目标优化数学模型。基于精英保留策略的多参数遗传算法，考虑配电系统总投资收益率、系统网损以及系统节点电压偏移量，得到新能源并网决策的帕累托最优解空间。通过规格化处理转变为单目标函数，得到整体最优并网策略。以IEEE-30节点系统为例进行模拟分析，验证了所提方法的合理有效性，为供应侧新能源设备选型提供了一定的参考。 |
| 关键词: 新能源 多目标优化 帕累托最优 遗传算法 |
| DOI：10.7667/PSPC160772 |
| 投稿时间：2016-05-30修订日期：2016-09-18 |
| 基金项目: |
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| Multi-objective optimal method considering types of grid connected new energy of electric power system |
| LIU Gushuai,XIAO Yiyao,HE Yuqiang,TU Wenbiao,WU Ziyang,ZHANG Zhonghui |
| (School of Information Engineering, Nanchang University, Nanchang 330031, China;School of Qianhu, Nanchang University, Nanchang 330031, China) |
| Abstract: |
| A concept of optimizing the grid connected new energy is proposed to cope with the bilateral randomness problem of power system. In illusion to the problems such as excessive investment, voltage drop and the ascending loss of active power which are resulted from the differences in cost, revenue and capacity between various kinds of new energy. A mathematical model for multi-objective optimizing with the maximum rate of return on investments, the minimum power loss and the offset of node voltage as its objective is established which is based on the Genetic Algorithm (GA) with the elitist strategy and multi-parameters. This paper reaches Pareto optimal solution space of new energy decisions and the overall optimal strategy through normalized processing. Simulating analysis conducted with the instance of IEEE-30 node system verifies the rationality and validity of this method which could provide certain reference for the equipment selection for the new energy on the supply side. |
| Key words: new energy multi-objective optimization Pareto optimality genetic algorithm |
