The widespread integration of electric vehicles (EVs) into the electrical grid creates a new opportunity for frequency regulation. In this article, to deal with the penetration of intermittent renewable energy and the time variance of system model, an online evolutionary mechanism is developed for the electrical-grid- electric-vehicle system. With a real-time decoder consisting of the long-short-term memory (LSTM) array, the dispatch center is upgraded from a passive executor to an intelligent analyst, which extracts the rolling features from multiple time scales. Based on the high-dimension decoding information from the LSTM array, a deep neural network (DNN) array is then embedded to provide strategic dispatch commands learning from the evolving memory. The whole decoder-dispatch framework is then upgraded with a unified online adaption technique to achieve gradient optimization and weight evolution. The proposed evolutionary structure is validated on a frequency management system to demonstrate its superior performance.

中文翻译：

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时变电网-电动汽车系统二次频率调节的在线滚动进化解码器-调度框架

电动汽车（EV）广泛集成到电网中为频率调节创造了新的机会。在本文中，为了解决间歇性可再生能源的渗透和系统模型的时间变化问题，开发了一种用于电动汽车系统的在线进化机制。借助由长短期存储器（LSTM）阵列组成的实时解码器，调度中心从被动执行器升级为智能分析器，该分析器从多个时间范围提取滚动特征。基于来自LSTM数组的高维解码信息，然后嵌入一个深度神经网络（DNN）数组，以提供从不断发展的内存中学习的战略调度命令。然后，使用统一的在线自适应技术对整个解码器-分配框架进行升级，以实现梯度优化和权重演化。在频率管理系统上对提出的演进结构进行了验证，以证明其优越的性能。