  In this paper, we investigate the scheduling issue of diesel generators (DGs)
in an Internet of Things (IoT)-Driven isolated microgrid (MG) by deep
reinforcement learning (DRL). The renewable energy is fully exploited under the
uncertainty of renewable generation and load demand. The DRL agent learns an
optimal policy from history renewable and load data of previous days, where the
policy can generate real-time decisions based on observations of past renewable
and load data of previous hours collected by connected sensors. The goal is to
reduce operating cost on the premise of ensuring supply-demand balance. In
specific, a novel finite-horizon partial observable Markov decision process
(POMDP) model is conceived considering the spinning reserve. In order to
overcome the challenge of discrete-continuous hybrid action space due to the
binary DG switching decision and continuous energy dispatch (ED) decision, a
DRL algorithm, namely the hybrid action finite-horizon RDPG (HAFH-RDPG), is
proposed. HAFH-RDPG seamlessly integrates two classical DRL algorithms, i.e.,
deep Q-network (DQN) and recurrent deterministic policy gradient (RDPG), based
on a finite-horizon dynamic programming (DP) framework. Extensive experiments
are performed with real-world data in an IoT-driven MG to evaluate the
capability of the proposed algorithm in handling the uncertainty due to
inter-hour and inter-day power fluctuation and to compare its performance with
those of the benchmark algorithms.
