Abstract In this article, a new selection technique based on Enhanced Nature-Inspired Meta-Heuristic (ENIMH) optimization algorithm is presented to improve the Microgrid (MG) dynamic performance. Interconnected microgrids have the ability to provide a clean and sustainable energy during normal and emergency operating conditions. The concerned microgrid includes hybrid renewable energy sources (RES) and energy storages systems (ESS). MG achieves a reduced dependency on the electric grid and provides flexible and adaptive energy supply. This paper develops a new selection technique based on ENIMH optimization that distinguishes the degrees of resemblance between the best individual and other individuals of current population. This technique proposes a binary coding of individuals, and is compared to conventional techniques; it allows each individual to occupy a section of the modified roulette wheel selection for the calculated degree of resemblance. This enhanced optimization technique tunes the dynamic PID parameters of microgrid closed loop system. The designed strategy is dependably to locate the arrangement of enhanced parameters to minimize the system frequency fluctuations in the microgrid and to provide the improved dynamic performance by being sensitive to variations for closed loop response under various power and load conditions. The proposed technique has been demonstrated using Matlab/Simulink simulation on the underlined microgrid, where the achieved results confirm the effectiveness of proposed selection method for the reproduction of best individuals to show the improved performance. The proposed technique achieved satisfactory performance for PID-controllers, and provided a good closed loop performance, minimum overshoot and minimum fitness index, in comparison with other well-established methods. The results emphasize that ENIMH optimization algorithm has the exploration and exploitation capability of population best individuals to accomplish the best solutions.

中文翻译：

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用于微电网性能改进的增强的自然启发元启发式算法

摘要 在本文中，提出了一种基于增强自然启发元启发式（ENIMH）优化算法的新选择技术，以提高微电网（MG）的动态性能。互联微电网能够在正常和紧急运行条件下提供清洁和可持续的能源。相关微电网包括混合可再生能源 (RES) 和储能系统 (ESS)。MG 减少了对电网的依赖，并提供灵活和自适应的能源供应。本文开发了一种基于 ENIMH 优化的新选择技术，可区分当前种群中最佳个体与其他个体之间的相似程度。该技术提出了个人的二进制编码，并与传统技术进行了比较；它允许每个人根据计算出的相似度占据修改轮盘选择的一部分。这种增强的优化技术可以调整微电网闭环系统的动态 PID 参数。设计的策略是可靠地定位增强参数的排列，以最大限度地减少微电网中的系统频率波动，并通过对各种功率和负载条件下闭环响应的变化敏感来提供改进的动态性能。所提出的技术已在带下划线的微电网上使用 Matlab/Simulink 模拟进行了证明，所取得的结果证实了所提出的选择方法对最佳个体再现的有效性，以显示改进的性能。与其他成熟的方法相比，所提出的技术为 PID 控制器取得了令人满意的性能，并提供了良好的闭环性能、最小的超调和最小的适应度指数。结果强调ENIMH优化算法具有对种群最佳个体的探索和开发能力，以完成最佳解。