文章摘要
耦合KL理论与调度特征的大规模水电站群优化调度降维方法
Dimension Reduction Method for Optimal Operation of Large-scale Hydropower Plants with Karhunen-Loève Theory and Dispatching Features
投稿时间:2020-08-18  修订日期:2020-10-21
DOI:
中文关键词: KL理论  特征调度  降维  水电站群  优化调度
英文关键词: KL theory  dispatching features  dimension reduction  hydropower plants  optimal operation
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
作者单位E-mail
申建建 大连理工大学水利学院水电及水信息研究所 shenjj@dlut.edu.cn 
张博 大连理工大学水利学院水电及水信息研究所 zhangbo311@126.com 
程春田 大连理工大学水利学院水电及水信息研究所  
李秀峰 云南省电力调度控制中心  
蒋燕 云南省电力调度控制中心  
张聪通 云南省电力调度控制中心  
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中文摘要:
      大规模水电站群优化调度计算效率是水电及电力系统运行面临的最棘手问题之一,是突破超百座水电站高维复杂系统求解的理论和技术障碍。本文提出一种耦合KL理论与调度特征的水电优化调度降维方法,通过对水电站群长系列实际调度样本进行主成分分析,识别调度过程中的库水位变化特征值与其对应的特征函数,采用KL展开方法将库水位描述为多个水位变化特征项的组合函数,引入Kullback-Leibler散度以根据问题特点确定调度特征项随机系数的概率分布及初始值;构建了两阶段逐步迭代寻优框架,通过动态搜索水位特征项的随机系数实现大规模水电站群优化调度的高效求解。提出的方法以云南电网超百座水电站群调度问题进行了验证,通过不同算例对方法的有效性、高效性、随机系数概率分布和参数敏感性进行了分析,与经典动态规划及其改进算法相比,本文方法在有效保持结果精度的条件下显著提高了计算效率。
英文摘要:
      The computing efficiency of optimal operations for large-scale hydropower plants is among the toughest problem facing hydropower and power system operations. It is one of main theoretical and technical barriers. This study presents a dimension reduction method for optimal operation of large-scale hydropower plants with Karhunen-Loève (KL) theory and dispatching features. Based on many historical operation data, the principal component analysis is used to identify characteristic value of forebay water level change and corresponding characteristic function. The KL expansion method is then introduced to represent random variables of forebay water level at any a period as a linear function of characteristic terms of water level change. Thus, the operation decision of a specified reservoir inflow process can be determined by a combination of random coefficients of all terms. A framework of iterative optimization is constructed, where a solution procedure for finding random coefficients of water level characteristic terms is given to realize an efficient solution for optimal operations of large-scale hydropower plants. The presented method is implemented on the long-term operations of Yunnan Provincial hydropower system with more than 100 plants. The validity, efficiency, and sensitivity of this method are respectively demonstrated by different case studies. A comparison with typical dynamic programming and its modifications shows that the method contributes a substantial improvement of computational efficiency while ensuring basically same solution accuracy.
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