大型次小时单位承诺的分解与协调方法

Jianghua Wu, P. Luh, Yonghong Chen, B. Yan, Mikhail A. Bragin
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引用次数: 1

摘要

亚小时机组承诺问题是提高电力系统效率的重要途径。然而,这些问题比每小时的UC问题要困难得多。这不仅是因为需要考虑的周期数量增加了,而且还因为大大降低了单位爬坡能力,导致了更复杂的凸包。因此,最先进的和实践的方法,如分支和切割,表现不佳。在本文中,我们的替代绝对值拉格朗日松弛(SAVLR)方法克服了标准拉格朗日松弛的主要困难,并通过协同结合有序优化(OO)的概念进行了改进。通过使用OO,解决子问题变得快得多。对Midcontinent ISO (MISO)的问题进行了15分钟的测试,测试时间间隔超过36小时,涉及约1100个单元和15000个虚拟机,结果表明,新方法有效地获得了接近最优的解决方案,显著优于分支切断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Decomposition and Coordination Approach for Large Sub-hourly Unit Commitment
Sub-hourly Unit Commitment (UC) problems have been suggested as a way to improve power system efficiency. Such problems, however, are much more difficult than hourly UC problems. This is not just because of the increased number of period to consider, but also because of much reduced unit ramping capabilities leading to more complicated convex hulls. As a result, state-of-the-art and practice methods such as branch-and-cut suffer from poor performance. In this paper, our recent Surrogate Absolute-Value Lagrangian Relaxation (SAVLR) method, which overcame major difficulties of standard Lagrangian Relaxation, is enhanced by synergistically incorporating the concept of Ordinal Optimization (OO). By using OO, solving subproblems becomes much faster. Testing of Midcontinent ISO (MISO)’s problem with 15 minutes as the time interval over 36 hours involving about 1,100 units and 15000 virtuals demonstrates that the new method obtains near-optimal solutions efficiently and significantly outperforms branch-and-cut.
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