文章摘要
汪超群,韦化,吴思缘.求解大规模水火最优潮流问题的近似牛顿方向解耦算法[J].电力系统自动化,2018,42(5):48-55. DOI: 10.7500/AEPS20170516011.
WANG Chaoqun,WEI Hua,WU Siyuan.Approximate Newton Direction Based Decomposition Algorithm Applied to Large-scale Hydrothermal Optimal Power Flow Problems[J].Automation of Electric Power Systems,2018,42(5):48-55. DOI: 10.7500/AEPS20170516011.
求解大规模水火最优潮流问题的近似牛顿方向解耦算法
Approximate Newton Direction Based Decomposition Algorithm Applied to Large-scale Hydrothermal Optimal Power Flow Problems
DOI:10.7500/AEPS20170516011
关键词: 水火电力系统  最优潮流  分解—协调  近似牛顿方向  精确解耦法  最优性
KeyWords: hydrothermal power system  optimal power flow  decomposition and coordination  approximate Newton direction  accurate decomposition method  optimality
上网日期:2017-09-22
基金项目:国家重点基础研究发展计划(973计划)资助项目(2013CB228205);国家自然科学基金资助项目(51667003)
作者单位E-mail
汪超群 广西大学电气工程学院, 广西壮族自治区南宁市 530004 wangchaoqun2014@sina.com 
韦化 广西大学电气工程学院, 广西壮族自治区南宁市 530004  
吴思缘 国网浙江省电力公司杭州供电公司, 浙江省杭州市 310016  
摘要:
      分解后计算效率低和解的最优性差一直是困扰大规模水火最优潮流(HTOPF)研究与应用的两个关键问题。针对这些问题,提出了一种求解HTOPF的精确高效的解耦算法。基于近似牛顿方向直接对原问题KKT(Karush-Kuhn-Tucker)条件解耦的思想,将含梯级电厂的 HTOPF问题分解为火电问题和水电问题。火电问题分解为单时段最优潮流问题,并进一步划分为多区域子问题;根据水电厂类型的不同将水电问题分解为单个固定水头、单个变化水头水电厂子问题以及梯级水电厂群优化子问题。求解过程中,每个子问题只迭代一次而不用求其最优解,极大地提高了计算效率。仿真计算结果表明:所提算法具有良好的适应性和稳定性,不仅显著减少了内存占用,而且在串行求解时CPU计算时间缩短了3~4倍,在并行计算条件下可获得10~20倍甚至1 000倍以上的加速比,并保证所得最优目标值与准确值之间的误差在10-8以下,确保了分解协调结果的最优性。
Abstract:
      The low computational efficiency and poor optimality of solutions are all along two key problems plaguing the research and application of large-scale hydrothermal optimal power flow(HTOPF). To solve these problems, an accurate and effective decomposition algorithm for HTOPF is proposed. Based on approximate Newton direction method that decouples the first order Karush-Kuhn-Tucker(KKT)conditions of the original problem, a HTOPF problem with cascaded hydro plants is decomposed into a thermal plant sub-problem and a hydro plant sub-problem. The thermal sub-problem is decomposed into T period optimal power flow(OPF)problems over a certain time horizon, and each OPF problem is further divided into multi-area sub-problems. According to different types of hydro plants, the hydro plant sub-problem is combined with fixed head hydro plant sub-problems, variable head plant sub-problems and one cascaded plant sub-problem. Each sub-problem needs only a single iteration instead of optimal solution, which results in a very high efficiency. In order to verify the effectiveness of the proposed algorithm, numerical tests are performed on three large-scale test systems of. Test results show the proposed algorithm has excellent performance in convergence and stability. Not only is memory usage significantly reduced, but also the central processing unit(CPU)time is decreased by about 65%~75%. Under the condition of parallel computing, it is capable of achieving 10-20 times or even 1 000 times faster without loss of optimality and ensuring the error between optimal target value and exact value is below 10-8, which ensures the optimal result of decomposition and coordination.
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