Microgrids have drawn substantial consideration due to high quality and reliable mix sources of electricity. This paper articulates the implication of innovative algorithms for cognitive microgrid. It perceived the algorithms that are backed by artificial intelligence (AI) are quite efficient due to the precision, convergence speed, and less computation time as compared to the conventional heuristic methods. Solar PV/Battery grid‐connected MG is modeled to achieve optimum size, supreme power quality, reduced fluctuations in voltage and frequency, reduced settling time, eliminate short transient currents, seamless power, least annual cost and high reliability as an objective function under wavering weather condition and dynamic load changes. Four broad categorizations of metaheuristic algorithms, that is, evolutionary, swarm intelligence, physics, and human intelligence‐based algorithms are well elaborated in this study. The optimal solution to the fitness function by using a hybrid optimization method also directed in the study. This paper gives deep insight to readers working in the area.

中文翻译：

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对应用于混合微电网系统的优化技术的现代趋势的全面回顾

由于高质量和可靠的混合电源，微电网已经引起了广泛的考虑。本文阐述了认知微电网创新算法的含义。与传统的启发式方法相比，由于精度，收敛速度和更少的计算时间，由人工智能（AI）支持的算法非常有效。太阳能光伏/电池并网MG的建模旨在实现最佳的尺寸，最高的电能质量，减少的电压和频率波动，减少的建立时间，消除短暂的瞬态电流，无缝的电力，最低的年度成本和高可靠性，这是摇摆不定的目标函数天气状况和动态负载变化。元启发式算法有四大类，即进化，群体智能，物理学，这项研究很好地阐述了基于人类智能的算法。研究中还针对使用混合优化方法的适应度函数的最佳解决方案。本文为该领域的读者提供了深刻的见解。