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电力大数据:2019,22(8):-
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基于分布式计算的月度机组组合高效算法设计
凌武能1, 莫东1, 张德亮2, 黄红伟2, 毛文照2, 游成彬2
(1.广西电网电力调度控制中心;2.北京清大科越股份有限公司)
An Efficient Algorithm Design for Monthly Unit Commitment Based on Distributed Computing
Ling Wuneng1, Mo Dong1, Zhang Deliang2, Huang Hongwei2, Mao Wenzhao2, You Chengbin2
(1.Guangxi Power Grid Power Dispatching Center;2.Beijing Qingda Keyue Co,Ltd)
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投稿时间:2019-05-15    修订日期:2019-05-20
中文摘要: 月度机组组合是电力系统运行方式安排的重要内容。随着电网规模的不断扩大,月度机组组合计算规模快速增加。传统的月度机组组合计算方法在计算效率上已不能满足当前系统要求。为此,介绍了分布式计算基本概念,提出了一种基于混合维度粒子群算法的分布式计算方法。根据月度机组组合模型实际,设计了面向月度机组组合问题的分布式高效计算方法,通过将传统串行计算转换为并行计算,提升了整体计算效率。最后基于某电网实际构造算例,验证了本文所提出方法的有效性。
Abstract:Monthly unit commitment is an important issue in power system operation mode arrangement. With the continuous expansion of power grid, the monthly unit commitment calculation scale increases rapidly. The traditional calculation method of monthly unit commitment can no longer meet the current requirements on calculation efficiency. To this end, the basic concept of distributed computing is introduced and a distributed computing method based on hybrid dimensional particle swarm optimization is proposed. Based on the monthly unit commitment model, a distributed calculation method for the monthly unit commitment problem is designed. By converting the traditional serial computing model to parallel computing one, the overall computing efficiency could be improved. Finally, a case study is constructed on the actual data in a provincial power grid to prove the effectiveness of the proposed method.
文章编号:     中图分类号:TM732    文献标志码:
基金项目:考虑多市场化交易的长中短期发电调度优化和安全校核技术研究及应用 编号:GXKJXM20170362
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