  The stochastic and dynamic nature of renewable energy sources and power
electronic devices are creating unique challenges for modern power systems. One
such challenge is that the conventional mathematical systems models-based
optimal active power dispatch (OAPD) method is limited in its ability to handle
uncertainties caused by renewables and other system contingencies. In this
paper, a deep reinforcement learning-based (DRL) method is presented to provide
a near-optimal solution to the OAPD problem without system modeling. The DRL
agent undergoes offline training, based on which, it is able to obtain the OAPD
points under unseen scenarios, e.g., different load patterns. The DRL-based
OAPD method is tested on the IEEE 14-bus system, thereby validating its
feasibility to solve the OAPD problem. Its utility is further confirmed in that
it can be leveraged as a key component for solving future model-free AC-OPF
problems.
