Data Center Chiller Plant Optimization via Mixed-Integer Nonlinear Differentiable Predictive Control

TL;DR

This paper introduces a scalable, real-time predictive control framework for multi-chiller plants that handles mixed-integer nonlinear dynamics, outperforming traditional methods in energy efficiency and computational speed.

Contribution

It extends Differentiable Predictive Control to mixed-integer problems, enabling efficient, model-based control of complex chiller plant systems.

Findings

Achieves significant energy savings over rule-based control.

Offers orders-of-magnitude faster computation than traditional MPC.

Demonstrates scalability and practicality in real-time applications.

Abstract

We present a computationally tractable framework for real-time predictive control of multi-chiller plants that involve both discrete and continuous control decisions coupled through nonlinear dynamics, resulting in a mixed-integer optimal control problem. To address this challenge, we extend Differentiable Predictive Control (DPC) -- a self-supervised, model-based learning methodology for approximately solving parametric optimal control problems -- to accommodate mixed-integer control policies. We benchmark the proposed framework against a state-of-the-art Model Predictive Control (MPC) solver and a fast heuristic Rule-Based Controller (RBC). Simulation results demonstrate that our approach achieves significant energy savings over the RBC while maintaining orders-of-magnitude faster computation times than MPC, offering a scalable and practical alternative to conventional combinatorial mixed-integer control formulations.