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Crisscross optimization algorithm for large-scale dynamic economic dispatch problem with valve-point effects

机译:具有阀点效应的大规模动态经济调度问题的交叉优化算法

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DED (Dynamic economic dispatch) considering valve-point effects is a complicated non-convex optimization problem in power system. For large-scale DED problem with hundreds of generators, how to avoid the curse of dimensionality remains a big challenge due to the exponential growth of search space. In allusion to this problem, this paper presents a promising heuristic approach named CSO (crisscross optimization) algorithm, which generates high quality solutions in large space by applying two interacting search operators, namely horizontal crossover and vertical crossover. The former has powerful global search ability while the latter can effectively alleviate the premature convergence problem. Their combination leads to a magical effect on improving solution quality and convergence rate especially for large-scale DED problems with valve-point effects. The feasibility and effectiveness of the proposed CSO algorithm is validated by seven test systems consisting of different numbers of generators. The results are compared with those of other heuristic methods reported in the literature. It is shown that the proposed method is capable of yielding higher quality solutions. To examine the availability of CSO in large power system, three new systems with 200-1000 generators are also tested, the obtained results confirm its suitability for large-scale DED problem. (C) 2015 Elsevier Ltd. All rights reserved.
机译:考虑阀点效应的DED(动态经济调度)是电力系统中一个复杂的非凸优化问题。对于具有数百个生成器的大规模DED问题,由于搜索空间呈指数增长,如何避免维数的诅咒仍然是一个很大的挑战。针对这一问题,本文提出了一种很有前途的启发式方法,称为CSO(十字交叉优化)算法,该方法通过应用水平交互和垂直交叉两个交互的搜索运算符在大空间中生成高质量的解决方案。前者具有强大的全局搜索能力,而后者则可以有效地缓解过早收敛的问题。它们的结合对提高解决方案质量和收敛速度具有神奇的效果,尤其是对于带有阀点效应的大规模DED问题。所提出的CSO算法的可行性和有效性通过由不同数量的发电机组成的七个测试系统进行了验证。将结果与文献中报道的其他启发式方法进行比较。结果表明,所提出的方法能够产生更高质量的解决方案。为了检查大型电力系统中CSO的可用性,还测试了三个200-1000台发电机的新系统,所得结果证实了其适用于大规模DED问题。 (C)2015 Elsevier Ltd.保留所有权利。

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