Optimal energy system scheduling using a constraint-aware reinforcement learning algorithm

Optimal energy system scheduling using a constraint-aware reinforcement learning algorithm

Shengren, H. (TU Delft Intelligent Electrical Power Grids)
Vergara Barrios, P.P. (TU Delft Intelligent Electrical Power Grids)
Salazar Duque, Edgar Mauricio (Eindhoven University of Technology)
Palensky, P. (TU Delft Intelligent Electrical Power Grids)

2023

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 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).

Energy management systems
Distributed energy system
Safe reinforcement learning
Machine learning
Nonlinear programming

http://resolver.tudelft.nl/uuid:133b7aef-2d3b-4be4-a6ce-a3553dad4432

https://doi.org/10.1016/j.ijepes.2023.109230

0142-0615

International Journal of Electrical Power & Energy Systems, 152

Institutional Repository

journal article

© 2023 H. Shengren, P.P. Vergara Barrios, Edgar Mauricio Salazar Duque, P. Palensky