Abstract

This paper proposes a new approach based on the moth flame optimizer algorithm. Moth flame optimizer simulates the natural fervent navigation technique adopted by moths looking for a source of light. The proposed method is further improved by priority list-based ordering; the unit commitment problem (UCP) is a non-linear, non-convex, and combinatorial complex optimization problem. It contains both continuous and discrete variables. This further increases its complexity. Moth flame optimizer is very good at obtaining a commitment pattern: allocation of power on the committed units obtained by mixed-integer quadratic programming method. Heuristic search strategies are used to adopt for the repair of minimum up and downtime, and spinning reserve constraints. MFO effectiveness is tested on the standard UCP reference IEEE model buses 14 and 30, and 10 and 20 units. The efficiency of the projected algorithms is compared to classical PSO, PSOLR, HPSO, PSOSQP, hybrid MPSO, IBPSO, LCA-PSO, NPSO, PSO-GWO, and various other evolutionary algorithms. The comparison result shows that MFO can lead to all methods reported earlier in literature.

中文翻译：

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基于蛾类火焰优化器的电力系统机组承诺和发电调度问题的解决方法

摘要

本文提出了一种基于飞蛾火焰优化器算法的新方法。飞蛾火焰优化器模拟寻找光源的飞蛾所采用的自然热导技术。通过基于优先级列表的排序，进一步改进了该方法。单元承诺问题（UCP）是非线性，非凸和组合复杂的优化问题。它包含连续变量和离散变量。这进一步增加了其复杂性。飞蛾火焰优化器非常擅长获得承诺模式：通过混合整数二次编程方法获得的承诺单元上的功率分配。启发式搜索策略用于修复最小的正常运行时间和停机时间以及旋转备用限制。MFO有效性在标准UCP参考IEEE模型总线14和30上进行了测试，和10和20个单位 将投影算法的效率与经典PSO，PSOLR，HPSO，PSOSQP，混合MPSO，IBPSO，LCA-PSO，NPSO，PSO-GWO和其他各种进化算法进行了比较。比较结果表明，MFO可以导致文献中较早报道的所有方法。