Approximate Model Predictive Control for Microgrid Energy Management via Imitation Learning

2025-10-22

AI Summary

To address the high computational burden of online economic model predictive control (EMPC) optimization in microgrids with high renewable energy penetration—hindering real-time deployment—this paper proposes an uncertainty-aware imitation learning-based approximate EMPC framework. The method first generates offline expert trajectories via mixed-integer optimization, then trains a neural network—incorporating noise injection and epistemic uncertainty modeling—to imitate the optimal decision policy, thereby eliminating online optimization. The learned policy achieves near-optimal economic performance relative to exact EMPC while reducing per-step inference latency to only 10% of that required by conventional EMPC, significantly enhancing real-time responsiveness and engineering feasibility. The core contribution lies in the deep integration of robust imitation learning with EMPC, yielding a microgrid energy management solution that simultaneously ensures high accuracy, low latency, and strong generalization across varying operating conditions and uncertainties.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Search and Optimization: Learning to Search
• Multiagent Systems: Multiagent Learning

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Approximating complex microgrid control via imitation learning
• Enabling real-time energy management without online optimization
• Maintaining economic performance while reducing computation time

Innovation

• Imitation learning approximates model predictive control
• Neural network trained on offline expert trajectories
• Noise injection enhances robustness to uncertainty