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Constrained Economic Dispatch by Combined Genetic and Simulated Annealing Algorithm

机译：遗传与模拟退火算法相结合的约束经济调度

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摘要

This article proposes a combined genetic and simulated annealing algorithm (CGSA) to solve ramp rate constrained economic dispatch (ED) problems for generating units with non-monotonically and monotonically increasing incre- mental cost (IC) functions. The developed CGSA method is tested on the sys- tems with the number of generating units in the range of 10 to 80 over the entire dispatch periods. The obtained solutions are near the optimal solutions of zoom brute force (ZBF) and zoom dynamic programming (ZDP), and are less expensive than those obtained from local search (LS), simulated annealing (SA), genetic algorithm (GA), GA based on SA solutions (GA-SA) and merit order loading (MOL) methods, thereby leading to substantial generator fuel cost savings. The proposed CGSA is effective in solving constrained ED problem in terms of the quality of solutions.
机译：本文提出了一种结合遗传和模拟退火算法（CGSA）的方法，以解决具有非单调和单调递增成本（IC）函数的发电机组的斜率受限经济调度（ED）问题。在整个调度周期内，已开发的CGSA方法在系统中经过测试的发电机组数量在10到80个范围内。所获得的解决方案接近缩放蛮力（ZBF）和缩放动态规划（ZDP）的最佳解决方案，并且比从局部搜索（LS），模拟退火（SA），遗传算法（GA），GA获得的解决方案便宜基于SA解决方案（GA-SA）和绩效订单加载（MOL）方法，从而节省了可观的发电机燃料成本。提出的CGSA可以有效地解决解决方案质量方面的ED问题。

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