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A complete decomposition and coordination algorithm for large-scale hydrothermal optimal power flow problems

机译:大规模水热功率流问题的完整分解和协调算法

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摘要

This paper presents a complete decomposition and coordination algorithm to solve large-scale hydrothermal optimal power flow (HTOPF) problems. Based on the approximate Newton directions method, which decouples the first-order Karush-Kuhn-Tucker conditions of the original problem, an HTOPF problem with cascaded hydro plants is decomposed into a thermal plant subproblem with independent optimal power flow solutions for each time period and a hydro plant subproblem combined with fixed and variable heads and cascaded plants issues. In order to verify the effectiveness of the proposed algorithm, numerical tests are performed on three large-scale test systems with up to 3120 buses and 7 531 915 primal-dual variables over 168 time periods. Test results show that the proposed algorithm gives excellent performances in convergence and stability. It not only reduces memory usage significantly but also decreases CPU time by about 65-75%. With parallel computing, it is capable of achieving 10-20 times or even 1000 times speed without loss of optimality. (c) 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
机译:本文提出了一种完整的分解和协调算法,以解决大规模的水热最佳功率流(HTOPF)问题。基于牛顿方向方法的近似方法,该方法将原始问题的一阶Karush-kuhn-tucker条件分解了,将级联水力植物的HTOPF问题分解为具有独立的最佳动力流解决方案的热植物子问题水电植物子问题与固定和可变的头部和级联植物问题相结合。为了验证所提出的算法的有效性,在三个大规模测试系统上进行数值测试,该系统在168个时间段内具有最多3120辆总线和7 531 915原始二次变量。测试结果表明,所提出的算法在收敛和稳定性方面具有出色的性能。它不仅大大减少了内存使用量,而且还将CPU时间降低了约65-75%。通过平行计算,它可以实现10-20次甚至1000倍的速度,而不会丧失最佳性。 (c)2017年日本电气工程师研究所。由John Wiley&Sons,Inc。出版

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