Abstract Intermittency in the output power of renewable and green energy sources (RGES) and low inertia of a standalone microgrid (SMG) result in large frequency deviations. Use of energy storage systems (ESSs) alleviate the SMG frequency deviations in an adorable manner but their high cost and low power density calls for alternative sources to balance the mismatch between power supply and demand. In recent years, utilization of the battery of an electric vehicle (EV) to minimize the frequency deviations has gained a lot of attention. Consequently, this paper proposes a robust and newly developed bio-inspired Salp Swarm Optimization (SSO) algorithm based PI-PD cascade controller for load frequency control (LFC) of the SMG integrated with the EVs. To demonstrate the efficacy of the proposed controller, its performance has been compared with other well-known controllers and algorithms considering diverse SMG operating scenarios. Simulation results distinctly prove the superiority of the proposed controller over the other controllers. Also, robustness of the proposed controller has been tested subject to ±50% variation in certain SMG parameters. Results clearly justify the robustness of the proposed controller. Additionally, operational stability of the SMG has been appraised through Eigenvalue and Bode diagram analysis for all the scenarios.

中文翻译：

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用于包含电动汽车的独立微电网负载频率控制的鲁棒级联控制器

摘要 可再生和绿色能源 (RGES) 输出功率的间歇性和独立微电网 (SMG) 的低惯性导致较大的频率偏差。储能系统 (ESS) 的使用以一种可爱的方式缓解了 SMG 频率偏差，但它们的高成本和低功率密度需要替代能源来平衡电力供需之间的不匹配。近年来，利用电动汽车 (EV) 的电池来最小化频率偏差已引起广泛关注。因此，本文提出了一种基于 PI-PD 级联控制器的鲁棒且新开发的仿生 Salp Swarm 优化 (SSO) 算法，用于与 EV 集成的 SMG 的负载频率控制 (LFC)。为了证明提议的控制器的功效，考虑到不同的 SMG 操作场景，其性能已与其他知名控制器和算法进行了比较。仿真结果清楚地证明了所提出的控制器优于其他控制器。此外，所提出的控制器的鲁棒性已在某些 SMG 参数的 ±50% 变化下进行了测试。结果清楚地证明了所提出的控制器的稳健性。此外，SMG 的运行稳定性已通过所有场景的特征值和 Bode 图分析进行评估。结果清楚地证明了所提出的控制器的稳健性。此外，SMG 的运行稳定性已通过所有场景的特征值和 Bode 图分析进行评估。结果清楚地证明了所提出的控制器的稳健性。此外，SMG 的运行稳定性已通过所有场景的特征值和 Bode 图分析进行评估。