| 引用本文: | 鲍海波,郭小璇.求解含风电相关性区间潮流的仿射变换最优场景法[J].电力系统保护与控制,2020,48(18):114-122.[点击复制] |
| BAO Haibo,GUO Xiaoxuan.Optimal scenario algorithm based on affine transformation applied to interval power flow considering correlated wind power[J].Power System Protection and Control,2020,48(18):114-122[点击复制] |
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| 摘要: |
| 风电场发电功率有很强的不确定性和相关性，影响电力系统不确定潮流分布情况。为了能准确掌握电力系统潮流状态的区间分布特性，区间潮流作为不确定潮流计算工具，需要考虑风电的不确定性和相关性。采用联合采样区域的相关角量化风电出力的区间相关性，构建了考虑风电相关性的区间潮流(Interval Power Flow，IPF)模型，并提出了一种基于仿射变换的最优场景算法(Optimal Scenario Algorithm with Affine Transformation，OSA-AT)加以求解。该算法利用仿射变换先将相关的风电出力区间分布转化为独立的区间变量，然后应用最优场景法将区间潮流转化为一系列确定非线性优化问题，进而采用内点法计算获得潮流状态量的最大值和最小值，即区间分布。IEEE-14和IEEE-118系统的计算结果表明，所提方法可以精确处理区间变量相关性，且与蒙特卡罗方法(Monte Carlo, MC)相比，其计算效率可提高数十倍。 |
| 关键词: 风力发电 不确定性 区间相关性 区间潮流 最优场景法 仿射变换 |
| DOI：DOI: 10.19783/j.cnki.pspc.191330 |
| 投稿时间：2019-10-21修订日期：2019-12-04 |
| 基金项目:南方电网公司重点科技项目资助(GXKJXM20170522) |
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| Optimal scenario algorithm based on affine transformation applied to interval power flow considering correlated wind power |
| BAO Haibo,GUO Xiaoxuan |
| (1. Nanning Power Supply Bureau, Guangxi Power Grid Corporation, Nanning 530031, China;
2. Electric Power Research Institute, Guangxi Power Grid Corporation, Nanning 530023, China) |
| Abstract: |
| The generation power of wind farms is very uncertain and difficult to correlate. It affects the distribution of uncertain power flow in a power system. As a tool for calculating uncertain power flow, Interval Power Flow (IPF) should take into account the uncertainty and correlation of wind power, so as to accurately obtain the interval distribution characteristics of the unknowns in power flow. This paper uses the correlation angle of a joint sampling area to quantify the interval correlation of wind power, constructs an interval power flow model considering the interval correlation, and proposes an Optimal Scenario Algorithm based on Affine Transformation (OSA-AT) to solve it. First, the affine transformation is used to transform the correlated wind power output interval distribution into independent interval variables. Secondly, the optimal scenario method is used to transform the interval power flow into a series of nonlinear optimization problems. Finally, the interior point method is used to calculate the maximum and minimum value of the unknowns in power flow, known as the interval distribution. The numerical results of IEEE 14- and 118-bus systems indicate that the proposed method can deal with the correlation of interval variables accurately. Compared with Monte Carlo (MC) method, the computing efficiency can be improved by several dozen times.
This work is supported by Key Science and Technology Program of China Southern Power Grid Company (No. GXKJXM20170522). |
| Key words: wind power uncertainty interval correlation interval power flow optimal scenario method affine transformation |
