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An Online Machine Learning Multi-resolution Optimization Framework for Energy System Design Limit of Performance Analysis

Oluwamayowa O. Amusat, Luka Grbcic, Remi Patureau, M. Jibran S. Zuberi, Dan Gunter, Michael Wetter

Abstract

Designing reliable integrated energy systems for industrial processes requires optimization and verification models across multiple fidelities, from architecture-level sizing to high-fidelity dynamic operation. However, model mismatch across fidelities obscures the sources of performance loss and complicates the quantification of architecture-to-operation performance gaps. We propose an online, machine-learning-accelerated multi-resolution optimization framework that estimates an architecture-specific upper bound on achievable performance while minimizing expensive high-fidelity model evaluations. We demonstrate the approach on a pilot energy system supplying a 1 MW industrial heat load. First, we solve a multi-objective architecture optimization to select the system configuration and component capacities. We then develop an machine learning (ML)-accelerated multi-resolution, receding-horizon optimal control strategy that approaches the achievable-performance bound for the specified architecture, given the additional controls and dynamics not captured by the architectural optimization model. The ML-guided controller adaptively schedules the optimization resolution based on predictive uncertainty and warm-starts high-fidelity solves using elite low-fidelity solutions. Our results on the pilot case study show that the proposed multi-resolution strategy reduces the architecture-to-operation performance gap by up to 42% relative to a rule-based controller, while reducing required high-fidelity model evaluations by 34% relative to the same multi-fidelity approach without ML guidance, enabling faster and more reliable design verification. Together, these gains make high-fidelity verification tractable, providing a practical upper bound on achievable operational performance.

An Online Machine Learning Multi-resolution Optimization Framework for Energy System Design Limit of Performance Analysis

Abstract

Designing reliable integrated energy systems for industrial processes requires optimization and verification models across multiple fidelities, from architecture-level sizing to high-fidelity dynamic operation. However, model mismatch across fidelities obscures the sources of performance loss and complicates the quantification of architecture-to-operation performance gaps. We propose an online, machine-learning-accelerated multi-resolution optimization framework that estimates an architecture-specific upper bound on achievable performance while minimizing expensive high-fidelity model evaluations. We demonstrate the approach on a pilot energy system supplying a 1 MW industrial heat load. First, we solve a multi-objective architecture optimization to select the system configuration and component capacities. We then develop an machine learning (ML)-accelerated multi-resolution, receding-horizon optimal control strategy that approaches the achievable-performance bound for the specified architecture, given the additional controls and dynamics not captured by the architectural optimization model. The ML-guided controller adaptively schedules the optimization resolution based on predictive uncertainty and warm-starts high-fidelity solves using elite low-fidelity solutions. Our results on the pilot case study show that the proposed multi-resolution strategy reduces the architecture-to-operation performance gap by up to 42% relative to a rule-based controller, while reducing required high-fidelity model evaluations by 34% relative to the same multi-fidelity approach without ML guidance, enabling faster and more reliable design verification. Together, these gains make high-fidelity verification tractable, providing a practical upper bound on achievable operational performance.

Paper Structure

This paper contains 51 sections, 32 equations, 13 figures, 10 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Pilot System Description
  3. Architectural Exploration via Nonlinear Programming
  4. Energy System Control
  5. Modelica model
  6. Rule-based control
  7. ML-Accelerated Optimal Control
  8. General Optimal Control Formulation
  9. ML-Accelerated Multi-Resolution Strategy
  10. Algorithm Notation
  11. Algorithm Parameters
  12. Main Control Loop
  13. ML-Driven Terminal Target Determination
  14. Two-Stage Optimization Cascade
  15. Pilot System Implementation
  16. ...and 36 more sections

Figures (13)

  • Figure 1: Three platform layers used in the Platform-Based Design of the industrial plant and its controller.
  • Figure 2: Schematic diagram of the energy system.
  • Figure 3: Charging and discharging of the battery (green), and hot water tank charging (orange). The electricity price and the equivalent for gas are shown with dotted lines.
  • Figure 4: The flowchart of the ML-Accelerated multi-resolution control strategy.
  • Figure 5: Pareto front for multi-objective problem. The squares represent individual designs on the solution front. The shaded areas highlight the three distinct regimes observed. The red square represents the design to be considered in the limit of performance analysis.
  • ...and 8 more figures