基于安全域理论的含新能源电力系统概率潮流分析方法

李伟琦, 王维庆, 王海云, 武家辉, 张永超

太阳能学报 ›› 2022, Vol. 43 ›› Issue (8) : 1-7.

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太阳能学报 ›› 2022, Vol. 43 ›› Issue (8) : 1-7. DOI: 10.19912/j.0254-0096.tynxb.2020-0453

基于安全域理论的含新能源电力系统概率潮流分析方法

  • 李伟琦, 王维庆, 王海云, 武家辉, 张永超
作者信息 +

PROBABILISTIC POWER FLOW ANALYSIS METHOD FOR POWER SYSTEM WITH RENEWABLE ENERGY BASED ON SECURITY REGION THEORY

  • Li Weiqi, Wang Weiqing, Wang Haiyun, Wu Jiahui, Zhang Yongchao
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文章历史 +

摘要

针对含可再生能源电力系统中概率性潮流分析效率低且不能从整体角度反映波动对系统影响的问题,提出一种基于静态安全域理论的改进概率潮流分析方法。首先采用基于最大熵法的潮流算法计算并分析测试系统的概率潮流情况;然后通过有效边界辨识方法和静态安全距离指标分析可再生能源概率性波动对测试系统静态安全的影响。最后通过对新疆哈密区域电网进行分析,验证了所提方法的可行性和实用性。

Abstract

Aiming at the problem that the original probabilistic power flow analysis method of electric power systems with renewable energy is inefficient and cannot reflect the impact of fluctuations on the system as a whole, this paper proposed an improved probabilistic power flow analysis method based on steady-state security region theory. Firstly, the probability power flow of the tested system is calculated by the maximum entropy methods. Furthermore, the influence of the probability fluctuation of renewable energy on the steady-state security of the tested system is analyzed by the effective boundary identification method and evaluated by the steady-state security distance index. Finally, the feasibility and practicability of the proposed method are verified by analyzing Hami regional power grid in Xinjiang.

关键词

可再生能源 / 电力系统 / 最大熵法 / 静态安全域 / 概率性波动

Key words

renewable energy / electric power systems / maximum entropy methods / steady-state security region / probabilistic fluctuation

引用本文

导出引用
李伟琦, 王维庆, 王海云, 武家辉, 张永超. 基于安全域理论的含新能源电力系统概率潮流分析方法[J]. 太阳能学报. 2022, 43(8): 1-7 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0453
Li Weiqi, Wang Weiqing, Wang Haiyun, Wu Jiahui, Zhang Yongchao. PROBABILISTIC POWER FLOW ANALYSIS METHOD FOR POWER SYSTEM WITH RENEWABLE ENERGY BASED ON SECURITY REGION THEORY[J]. Acta Energiae Solaris Sinica. 2022, 43(8): 1-7 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0453
中图分类号: TM73   

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基金

国家自然科学基金(51667020); 新疆维吾尔自治区重点实验室开放课题(2018D04005); 新疆维吾尔自治区高校科研计划自然科学重点项目(XJEDU2019I009); 教育部创新团队(IRT_16R633)

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