| 引用本文: | 刘颖杰,陈红坤,田 圆,高 鹏.基于投资组合高阶矩分析的电力系统灵活性评估[J].电力系统保护与控制,2024,52(5):116-127.[点击复制] |
| LIU Yingjie,CHEN Hongkun,TIAN Yuan,GAO Peng.Flexibility assessment of a power system based on higher-moment analysis of an investment portfolio[J].Power System Protection and Control,2024,52(5):116-127[点击复制] |
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| 摘要: |
| 针对现有灵活性指标形式主要集中于低阶统计量，仅能反映系统灵活性的平均水平与集中程度，而忽视了灵活性的高阶特征这一问题，引入投资组合高阶矩分析理论对电力系统灵活性进行刻画，以揭示系统灵活性的调节潜力与风险。首先，通过分析投资组合的均值-方差-偏度-峰度模型(mean-variance-skewness-kurtosis model, MVSK Model)，给出了灵活性单元组合的定义，基于多元Copula函数构建考虑空间相关性的灵活性单元概率模型。其次，基于灵活性单元组合的各阶矩建立灵活性评估指标，并给出基于核密度估计与蒙特卡洛模拟的指标计算方法。最后，通过FTS-213测试系统与德国某电网的历史数据对所提指标进行了测算和验证。算例表明所提指标能够反映系统灵活性调节能力的平均水平、稳定程度、潜力与风险，且能量化评估灵活性资源种类与投建地区对系统灵活性的影响，为后续的灵活性资源规划提供理论支持。 |
| 关键词: 电力系统灵活性 投资组合 高阶矩 MVSK模型 Copula函数 指标评估 |
| DOI：10.19783/j.cnki.pspc.231173 |
| 投稿时间：2023-09-08修订日期：2023-11-09 |
| 基金项目:国家电网公司华中分部科技项目资助(5214GH210006) |
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| Flexibility assessment of a power system based on higher-moment analysis of an investment portfolio |
| LIU Yingjie,CHEN Hongkun,TIAN Yuan,GAO Peng |
| (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China) |
| Abstract: |
| Flexibility metrics predominantly focus on low-order statistical measures, only reflecting the system’s average flexibility level and concentration while overlooking the higher-order characteristics of flexibility. In response to these limitations, this paper introduces a portfolio high-order moment analysis theory to characterize the flexibility of power systems. The objective is to unveil the adjustment potential and risks associated with system flexibility. First, by analyzing the mean-variance-skewness-kurtosis model (MVSK Model) of the portfolio, the definition of a flexible unit portfolio is given. A flexible element probability model considering spatial correlation is constructed based on the multivariate Copula function. Secondly, a flexibility evaluation index is established based on each order moment of the flexible element combination, and the index calculation method based on kernel density estimation and Monte Carlo simulation is given. Finally, the proposed index is measured and verified by the FTS-213 test system and the historical data of a power grid in Germany. The example shows that the proposed index can reflect the average level, stability degree, potential and risk of the system flexibility adjustment ability. It can also quantify the impact of the types of flexible resources and the construction area on system flexibility, providing theoretical support for subsequent flexible resource planning. |
| Key words: power system flexibility investment portfolio higher-moments MVSK model Copula function indicator evaluation |
