  The massive integration of renewable-based distributed energy resources
(DERs) inherently increases the energy system's complexity, especially when it
comes to defining its operational schedule. Deep reinforcement learning (DRL)
algorithms arise as a promising solution due to their data-driven and
model-free features. However, current DRL algorithms fail to enforce rigorous
operational constraints (e.g., power balance, ramping up or down constraints)
limiting their implementation in real systems. To overcome this, in this paper,
a DRL algorithm (namely MIP-DQN) is proposed, capable of \textit{strictly}
enforcing all operational constraints in the action space, ensuring the
feasibility of the defined schedule in real-time operation. This is done by
leveraging recent optimization advances for deep neural networks (DNNs) that
allow their representation as a MIP formulation, enabling further consideration
of any action space constraints. Comprehensive numerical simulations show that
the proposed algorithm outperforms existing state-of-the-art DRL algorithms,
obtaining a lower error when compared with the optimal global solution (upper
boundary) obtained after solving a mathematical programming formulation with
perfect forecast information; while strictly enforcing all operational
constraints (even in unseen test days).
