  Model-based Vol/VAR optimization method is widely used to eliminate voltage
violations and reduce network losses. However, the parameters of active
distribution networks(ADNs) are not onsite identified, so significant errors
may be involved in the model and make the model-based method infeasible. To
cope with this critical issue, we propose a novel two-stage deep reinforcement
learning (DRL) method to improve the voltage profile by regulating
inverter-based energy resources, which consists of offline stage and online
stage. In the offline stage, a highly efficient adversarial reinforcement
learning algorithm is developed to train an offline agent robust to the model
mismatch. In the sequential online stage, we transfer the offline agent safely
as the online agent to perform continuous learning and controlling online with
significantly improved safety and efficiency. Numerical simulations on IEEE
test cases not only demonstrate that the proposed adversarial reinforcement
learning algorithm outperforms the state-of-art algorithm, but also show that
our proposed two-stage method achieves much better performance than the
existing DRL based methods in the online application.
