  As a typical switching power supply, the DC-DC converter has been widely
applied in DC microgrid. Due to the variation of renewable energy generation,
research and design of DC-DC converter control algorithm with outstanding
dynamic characteristics has significant theoretical and practical application
value. To mitigate the bus voltage stability issue in DC microgrid, an
innovative intelligent control strategy for buck DC-DC converter with constant
power loads (CPLs) via deep reinforcement learning algorithm is constructed for
the first time. In this article, a Markov Decision Process (MDP) model and the
deep Q network (DQN) algorithm are defined for DC-DC converter. A model-free
based deep reinforcement learning (DRL) control strategy is appropriately
designed to adjust the agent-environment interaction through the
rewards/penalties mechanism towards achieving converge to nominal voltage. The
agent makes approximate decisions by extracting the high-dimensional feature of
complex power systems without any prior knowledge. Eventually, the simulation
comparison results demonstrate that the proposed controller has stronger
self-learning and self-optimization capabilities under the different scenarios.
