| 引用本文: | 丁明,宋晓皖,孙磊,黄冯,张舒捷,杜德贵.考虑时空相关性的多风电场出力场景生成与评价方法[J].电力自动化设备,2019,39(10): |
| DING Ming,SONG Xiaowan,SUN Lei,HUANG Feng,ZHANG Shujie,DU Degui.Scenario generation and evaluation method of multiple wind farms output considering spatial-temporal correlation[J].Electric Power Automation Equipment,2019,39(10): |
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| 摘要: |
| 含多个风电场的场景生成技术可为电力系统中长期规划和运行提供所需基础数据。为在场景生成过程中计入多风电场风电出力的时空相关性，提出两阶段场景生成方法：在第一阶段，采用Copula函数对多个风电场出力的空间相关性建模，获得多风电场出力的初始场景；在第二阶段，运用随机微分方程对风电场出力波动随机性建模，通过重构初始风电出力场景，使得最终获得的场景中风电序列较好地保留原始序列的时间相关性。为评估生成场景的有效性，构建场景有效性评价指标体系；引入多重分形去趋势波动分析方法，提供刻画风电序列的自相关特性和动态波动特性的多维度指标。以某区域风电场为例，生成风电季度出力场景，结果表明所提方法能够复现原始风电序列的时空相关性。 |
| 关键词: 风电 时空相关性 Copula函数 随机微分方程 多重分形 评价指标 |
| DOI：10.16081/j.epae.201909024 |
| 分类号:TM614 |
| 基金项目:国家电网公司科技项目(5228001600DX) |
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| Scenario generation and evaluation method of multiple wind farms output considering spatial-temporal correlation |
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DING Ming1, SONG Xiaowan1, SUN Lei1, HUANG Feng1, ZHANG Shujie2, DU Degui3
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1.Anhui New Energy Utilization and Energy Saving Laboratory, Hefei University of Technology, Hefei 230009, China;2.Key Laboratory of Photovoltaic Power Generation and Grid Integration, State Grid Qinghai Electric Power Research Institute, Xining 810008, China;3.State Grid Qinghai Electric Power Company, Xining 810008, China
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| Abstract: |
| The scenario generation technology for multiple wind farms can provide basic data for medium-and long-term planning and operation of power system. In order to consider the output spatial-temporal correlation of multiple wind farms in the process of scenario generation, a two-stage scenario generation method is proposed. In the first stage, the output spatial correlation of multiple wind farms is modelled by Copula function to obtain the initial output scenario of multiple wind farms. In the second stage, the randomness of wind power fluctuation is modelled by the stochastic differential equations, and the initial wind power scenario is reconstructed to ensure the wind power series in the finally obtained scenario better preserve the temporal correlation of the original series. An index system is constructed to evaluate the effectiveness of the generated scenario. The multi-fractal detrended fluctuation analysis method is introduced to provide multi-dimensional indexes for describing the autocorrelation and dynamic fluctuation characteristics of wind power series. A regional wind farm is taken as an example to generate quarterly output scenario, and results show that the proposed method can preserve the spatial-temporal correlation of original wind power series. |
| Key words: wind power spatial-temporal correlation Copula function stochastic differential equation multi-fractal evaluation index |
