Economic emission dispatch (EED) of a three-unit stand-alone microgrid system supported by a wind farm is percolated in this paper. The adverse effects of stochastic and uncertainty nature of wind energy in raising the generation cost of the microgrid system are studied in this article. Unit commitment (UC) of the generating units is taken into account which helps in reducing the generation cost and provides relaxation time to the generation units. Three cases are contemplated for the study. For the first two cases, the generation cost of the test system was minimized without and with the involvement of wind power, respectively. The third case considered the involvement of wind power along with the UC of the conventional generation units. A novel hybrid of recently developed superior optimization algorithms, viz. grey wolf optimizer (GWO), sine–cosine algorithm (SCA) and crow search algorithm (CSA), is implemented to perform EED, and the results are compared with basic GWO and other hybrid algorithms. Results are then analysed to compare and contrast among these cases and justify the reliable and profitable one. Statistical analysis claims the superiority of the proposed hybrid MGWOSCACSA over other hybrids and GWO.

中文翻译：

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基于混合MGWOSCACSA的考虑风和负荷不确定性的基于机组承诺的微电网系统经济排放调度

本文对由风电场支持的三单元独立微电网系统的经济排放调度 (EED) 进行了渗透。本文研究了风能的随机性和不确定性在提高微电网系统发电成本方面的不利影响。考虑了发电机组的单位承诺 (UC)，这有助于降低发电成本并为发电机组提供松弛时间。本研究考虑了三个案例。对于前两种情况，测试系统的发电成本分别在没有和有风力发电的情况下最小化。第三种情况考虑了风电与传统发电机组UC的参与。最近开发的高级优化算法的新颖混合，即。灰狼优化器（GWO），正弦-余弦算法 (SCA) 和乌鸦搜索算法 (CSA) 被实现来执行 EED，并将结果与​​基本 GWO 和其他混合算法进行比较。然后分析结果以在这些案例之间进行比较和对比，并证明可靠且有利可图的案例的合理性。统计分析声称所提议的混合 MGWOSCACSA 优于其他混合和 GWO。