  Nowadays, the application of microgrids (MG) with renewable energy is
becoming more and more extensive, which creates a strong need for dynamic
energy management. In this paper, deep reinforcement learning (DRL) is applied
to learn an optimal policy for making joint energy dispatch (ED) and unit
commitment (UC) decisions in an isolated MG, with the aim for reducing the
total power generation cost on the premise of ensuring the supply-demand
balance. In order to overcome the challenge of discrete-continuous hybrid
action space due to joint ED and UC, we propose a DRL algorithm, i.e., the
hybrid action finite-horizon DDPG (HAFH-DDPG), that seamlessly integrates two
classical DRL algorithms, i.e., deep Q-network (DQN) and deep deterministic
policy gradient (DDPG), based on a finite-horizon dynamic programming (DP)
framework. Moreover, a diesel generator (DG) selection strategy is presented to
support a simplified action space for reducing the computation complexity of
this algorithm. Finally, the effectiveness of our proposed algorithm is
verified through comparison with several baseline algorithms by experiments
with real-world data set.
