# Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning Algorithm

arXiv: https://arxiv.org/abs/2305.05484 · HTML: https://mlsanity.com/paper/2305.05484/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2023.

## Built on by (most cited first, 11 of 11)

- [A Review of Safe Reinforcement Learning Methods for Modern Power Systems](https://mlsanity.com/paper/2407.00304/) (2024)
- [RL-ADN: A High-Performance Deep Reinforcement Learning Environment for Optimal Energy Storage Systems Dispatch in Active Distribution Networks](https://mlsanity.com/paper/2408.03685/) (2024)
- [A Constraint Enforcement Deep Reinforcement Learning Framework for Optimal Energy Storage Systems Dispatch](https://mlsanity.com/paper/2307.14304/) (2023)
- [Why Reinforcement Learning in Energy Systems Needs Explanations](https://mlsanity.com/paper/2405.18823/) (2024)
- [Integrating Reinforcement Learning and Model Predictive Control with Applications to Microgrids](https://mlsanity.com/paper/2409.11267/) (2024)
- [Safe Imitation Learning-based Optimal Energy Storage Systems Dispatch in Distribution Networks](https://mlsanity.com/paper/2411.00995/) (2024)
- [Neural Two-Stage Stochastic Optimization for Solving Unit Commitment Problem](https://mlsanity.com/paper/2507.09503/) (2025)
- [Cooperative Energy Scheduling of Multi-Microgrids Based on Risk-Sensitive Reinforcement Learning](https://mlsanity.com/paper/2512.17246/) (2025)
- [EcoNet: Multiagent Planning and Control Of Household Energy Resources Using Active Inference](https://mlsanity.com/paper/2512.21343/) (2025)
- [Topology-Aware Graph Reinforcement Learning for Energy Storage Systems Optimal Dispatch in Distribution Networks](https://mlsanity.com/paper/2603.26264/) (2026)
- [Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings](https://mlsanity.com/paper/2606.02049/) (2026)

## Abstract (arXiv)

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 textitstrictly 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).