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Solving non-convex economic load dispatch problem via artificial cooperative search algorithm

机译:通过人工协同搜索算法解决非凸型经济负荷分配问题

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According to no free lunch (NFL) theorem, a metaheuristic optimization method is superior to other meta heuristic optimization methods when it has focused on specific class of optimization problems. Thus, this paper focuses on developing artificial cooperative search (ACS) optimization algorithm to solve economic dispatch (ED) problems more precisely with less complexity than other metaheuristic optimization methods. ACS is a recently developed two population search algorithm based on coevolution process with high probability of finding optimal solution in complex and non-convex optimization problems. This merit is provided by balancing exploration of the problem's search space and exploitation of better results through use of two advanced evolutionary operators and only one control parameter. The constraint handling strategy of the proposed method for solving economic power load dispatch problems is to generate and work with feasible solutions along all the optimization iterations without any mismatch between the total amount of electric power generation and electricity demand plus network transmission loss. Unlike the penalty method, this strategy is unaffected by parameter setting of applied optimization method that complicates its applicability for solving economic power load dispatch problems. The feasibility of ACS for solving ED problem is tested on different lossy non-convex test systems of various sizes and complexities. The practical aspects such as satisfaction of power demand constraint, generation limits of generators and value-point loading effect are undertaken to solve ED problem in medium to relatively large-scale electric power systems. Obtained results confirm the ACS's capability in converging to a better solution highly robust within the reasonable computational time in all independent trials; all these as compared with other optimization algorithms reported in the literature for solving lossy non-convex ED problems. The results are analyzed statistically in terms of solution quality and computational efficiency. The statistical analyses reveal that ACS is a potential method to solve economic power load dispatch problems as it provides higher quality solution in comparison with other optimization algorithms. (C) 2019 Elsevier Ltd. All rights reserved.
机译:根据无免费午餐(NFL)定理,当针对特定类别的优化问题时,元启发式优化方法优于其他元启发式优化方法。因此,本文着重于开发人工合作搜索(ACS)优化算法,以比其他元启发式优化方法更精确地解决经济调度(ED)问题,并且复杂度更低。 ACS是最近开发的基于协同进化过程的两个种群搜索算法,在复杂和非凸优化问题中极有可能找到最优解。通过使用两个高级进化算子和仅一个控制参数来平衡对问题的搜索空间的探索和更好的结果的利用,可以提供此优点。所提出的用于解决经济电力负荷分配问题的方法的约束处理策略是在所有优化迭代中生成并使用可行的解决方案,而在发电总量与电力需求量以及网络传输损耗之间没有任何不匹配。与惩罚方法不同,该策略不受应用的优化方法的参数设置的影响,这使其在解决经济电力负荷分配问题方面的适用性变得更加复杂。 ACS解决ED问题的可行性在各种大小和复杂性的有损非凸测试系统上进行了测试。为了解决中型到大型电力系统中的ED问题,采取了诸如满足电力需求约束,发电机发电极限和价值点负载效应等实际方面。所得结果证实了ACS在所有独立试验中均能在合理的计算时间内收敛至具有高度鲁棒性的更好解决方案的能力;与文献中报道的其他优化算法相比,所有这些算法都可以解决有损的非凸ED问题。根据解决方案质量和计算效率对结果进行统计分析。统计分析表明,与其他优化算法相比,ACS提供了更高质量的解决方案,是解决经济电力负荷分配问题的潜在方法。 (C)2019 Elsevier Ltd.保留所有权利。

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