Approximate Model Predictive Control for Microgrid Energy Management via Imitation Learning

• URL: http://arxiv.org/abs/2510.20040v1
• Date: Wed, 22 Oct 2025 21:39:18 GMT
• Title: Approximate Model Predictive Control for Microgrid Energy Management via Imitation Learning
• Authors: Changrui Liu, Shengling Shi, Anil Alan, Ganesh Kumar Venayagamoorthy, Bart De Schutter,
• Abstract summary: This paper proposes an imitation learning-based framework to approximate mixed-integer Economic Model Predictive Control (EMPC) for microgrid energy management.<n>The proposed method trains a neural network to imitate expert EMPC control actions from offline trajectories, enabling fast, real-time decision making without solving optimization problems online.
• Score: 9.044455355747482
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Efficient energy management is essential for reliable and sustainable microgrid operation amid increasing renewable integration. This paper proposes an imitation learning-based framework to approximate mixed-integer Economic Model Predictive Control (EMPC) for microgrid energy management. The proposed method trains a neural network to imitate expert EMPC control actions from offline trajectories, enabling fast, real-time decision making without solving optimization problems online. To enhance robustness and generalization, the learning process includes noise injection during training to mitigate distribution shift and explicitly incorporates forecast uncertainty in renewable generation and demand. Simulation results demonstrate that the learned policy achieves economic performance comparable to EMPC while only requiring $10\%$ of the computation time of optimization-based EMPC in practice.

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