用于调节包含电动汽车的独立微电网虚拟惯性的元启发式算法

Debayani Mishra, M. K. Maharana, Manoj Kumar Kar, Anurekha Nayak, Md. Minarul Islam, T. Ustun
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引用次数: 0

摘要

现代电网,尤其是微电网,获取非常规能源的情况急剧增加。由于使用可再生能源取代了传统的同步发电机,微电网的惯性急剧下降。惯性的降低对使用可再生能源的微电网的动态和性能产生了负面影响,从而降低了微电网的稳定性,尤其是在岛屿上运行时。本研究的主要目的是通过将电动汽车与基于虚拟惯性控制的频率控制技术相结合,增强岛屿微电网的动态安全性。使用修正微分演化(MDE)方法优化创建的比例积分微分滤波常数(PIDFN)控制器是虚拟惯性控制环控制的基础。基于 MDE 的 PIDFN 控制器的有效性得到了检验,考虑到了不同的运行场景,并与传统方法微分演化(DE)和基于教学学习优化(TLBO)的 PIDFN 控制器进行了比较和对比。为提供逼真的仿真设置,纳入了实时风能和太阳能统计数据以及随机负载波动。结果表明,与其他优化策略相比,基于 MDE 的 PIDFN 控制器在参考频率跟踪和减少频率干扰方面表现更好。
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A metaheuristic algorithm for regulating virtual inertia of a standalone microgrid incorporating electric vehicles
Modern electrical networks, particularly microgrids, have seen a sharp rise in acquiring non‐conventional sources. The inertia of the microgrid decreases drastically because RESs have been used in place of traditional synchronous generators. The reduced inertia negatively impacts the dynamics and performance of the microgrid with RESs, which decreases the microgrid's stability, especially in operation on an island. The primary purpose of this research study is to enhance the dynamic security of an island microgrid by merging an electric vehicle with a frequency control technique based on virtual inertia control. A Proportional Integral Derivative Filter Constant (PIDFN) controller optimally created using the Modified Differential Evolution (MDE) method served as the foundation for control in the virtual inertia control loop. The effectiveness of the MDE‐based PIDFN controller was examined considering the diverse operational scenarios are compared and contrasted with those of traditional methods Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO)‐based PIDFN controllers. Real‐time wind and solar power statistics and random load fluctuations were incorporated to provide realistic simulation settings. The outcomes demonstrate that the MDE‐based PIDFN controller performs better in reference frequency tracking and reducing frequency disturbances than the other optimization strategies.
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