  Recently, frequency security is challenged by high uncertainty and low
inertia in power system with high penetration of Renewable Energy Sources
(RES). In the context of Unit Commitment (UC) problems, frequency security
constraints represented by neural networks have been developed and embedded
into the optimization problem to represent complicated frequency dynamics.
However, there are two major disadvantages related to this technique: the risk
of overconfident prediction and poor computational efficiency. To handle these
disadvantages, novel methodologies are proposed to optimally design the neural
network structure, including the use of asymmetric loss function during the
training stage and scientifically selecting neural network size and topology.
The effectiveness of the proposed methodologies are validated by case study
which reveals the improvement of conservativeness and mitigation of computation
performance issues.
