A learning-assisted DAES framework uses a surrogate model for GFM BESS frequency dynamics to achieve frequency-secure scheduling with better accuracy and BESS utilization than analytical methods.
Linearization of ReLU Activation Function for Neural Network-Embedded Optimization:Optimal Day-Ahead Energy Scheduling
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Learning-Assisted Day-Ahead Energy Scheduling for Frequency-Secure Inverter-Dominated Grids with Grid-Forming Battery Energy Storage Systems
A learning-assisted DAES framework uses a surrogate model for GFM BESS frequency dynamics to achieve frequency-secure scheduling with better accuracy and BESS utilization than analytical methods.