Transition towards carbon-neutral power systems has necessitated optimization of power dispatch in active distribution networks (ADNs) to facilitate integration of distributed renewable generation. Due to unavailability of network topology and line impedance in many distribution networks

physical model-based methods may not be applicable to their operations. To tackle this challenge

some studies have proposed constraint learning

which replicates physical models by training a neural network to evaluate feasibility of a decision (i.e.

whether a decision satisfies all critical constraints or not). To ensure accuracy of this trained neural network

training set should contain sufficient feasible and infeasible samples. However

since ADNs are mostly operated in a normal status

only very few historical samples are infeasible. Thus

the historical dataset is highly imbalanced

which poses a significant obstacle to neural network training. To address this issue

we propose an enhanced constraint learning method. First

it leverages constraint learning to train a neural network as surrogate of ADN's model. Then

it introduces Synthetic Minority Oversampling Technique to generate infeasible samples to mitigate imbalance of historical dataset. By incorporating historical and synthetic samples into the training set

we can significantly improve accuracy of neural network. Furthermore

we establish a trust region to constrain and thereafter enhance reliability of the solution. Simulations confirm the benefits of the proposed method in achieving desirable optimality and feasibility while maintaining low computational complexity.