In this paper, we introduce a deep learning aided constraint encoding method to tackle the frequency-constraint microgrid scheduling problem. The nonlinear function between system operating condition and frequency nadir is approximated by using a neural network, which admits an exact mixed-integer formulation (MIP). This formulation is then integrated with the scheduling problem to encode the frequency constraint. With the stronger representation power of the neural network, the resulting commands can ensure adequate frequency response in a realistic setting in addition to islanding success. The proposed method is validated on a modified 33-node system. Successful islanding with a secure response is simulated under the scheduled commands using a detailed three-phase model in Simulink. The advantages of our model are particularly remarkable when the inertia emulation functions from wind turbine generators are considered.

中文翻译：

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使用机器学习逼近轨迹约束——具有频率约束的微电网孤岛

在本文中，我们引入了一种深度学习辅助约束编码方法来解决频率约束微电网调度问题。系统运行条件和频率最低点之间的非线性函数通过使用神经网络来近似，该网络允许精确混合整数公式（MIP）。然后将该公式与调度问题集成以对频率约束进行编码。由于神经网络具有更强的表示能力，因此除了成功孤岛之外，生成的命令还可以确保在现实环境中具有足够的频率响应。所提出的方法在修改后的 33 节点系统上得到验证。使用 Simulink 中的详细三阶段模型在预定命令下模拟具有安全响应的成功孤岛。