There is an increasing concentration in the influences of nonconventional power sources on power system process and management, as the application of these sources upsurges worldwide. Renewable energy technologies are one of the best technologies for generating electrical power with zero fuel cost, a clean environment, and are available almost throughout the year. Some of the widespread renewable energy sources are tidal energy, geothermal energy, wind energy, and solar energy. Among many renewable energy sources, wind and solar energy sources are more popular because they are easy to install and operate. Due to their high flexibility, wind and solar power generation units are easily integrated with conventional power generation systems. Traditional generating units primarily use synchronous generators that enable them to ensure the process during significant transient errors. If massive wind generation is faltered due to error, it may harm the power system’s operation and lead to the load frequency control issue. This work proposes binary moth flame optimizer (MFO) variants to mitigate the frequency constraint issue. Two different binary variants are implemented for improving the performance of MFO for discrete optimization problems. The proposed model was evaluated and compared with existing algorithms in terms of standard testing benchmarks and showed improved results in terms of average and standard deviation.

中文翻译：

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可再生能源对相关电力系统多区域多源负荷频率控制的影响

随着世界范围内对非常规电源的应用激增，非常规电源的影响越来越集中。可再生能源技术是以零燃料成本，清洁环境发电的最佳技术之一，并且几乎全年都可使用。一些广泛的可再生能源是潮汐能，地热能，风能和太阳能。在许多可再生能源中，风能和太阳能资源由于易于安装和操作而更加受欢迎。由于具有很高的灵活性，风力和太阳能发电单元很容易与常规发电系统集成在一起。传统的发电机组主要使用同步发电机，使它们能够在出现重大瞬态误差时确保过程。如果由于错误导致大型风力发电摇摇欲坠，则可能会损害电力系统的运行并导致负载频率控制问题。这项工作提出了二进制飞蛾火焰优化器（MFO）变体来缓解频率约束问题。实现了两种不同的二进制变体，以提高MFO针对离散优化问题的性能。在标准测试基准方面，对提出的模型进行了评估，并与现有算法进行了比较，并在平均值和标准偏差方面显示出改进的结果。这项工作提出了二进制飞蛾火焰优化器（MFO）变体来缓解频率约束问题。实现了两种不同的二进制变体，以提高MFO针对离散优化问题的性能。在标准测试基准方面，对提出的模型进行了评估，并与现有算法进行了比较，并在平均值和标准偏差方面显示出改进的结果。这项工作提出了二进制飞蛾火焰优化器（MFO）变体来缓解频率约束问题。实现了两种不同的二进制变体，以提高MFO针对离散优化问题的性能。在标准测试基准方面，对提出的模型进行了评估，并与现有算法进行了比较，并在平均值和标准偏差方面显示出改进的结果。