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Opportunities for Hybrid Modeling Approaches in Energy Systems optimization

Mohamed Tahar Mabrouk, Shri Balaji Padmanabhan, Bruno Lacarrière, Benoit Delinchant, Sacha Hodencq, Xavier Roboam, Bruno Sareni, Mathieu Vallee

TL;DR

This paper analyzes the computational hurdles in energy systems optimization arising from model complexity, solver requirements, and uncertainty handling. It reviews effectiveness of complexity-reduction techniques and uncertainty-management frameworks, and discusses their scaling and data needs. It argues that hybrid modeling—combining mechanistic physics with data-driven components—offers a path to leverage the strengths of both paradigms while mitigating weaknesses. The authors outline concrete directions for hybrid scenario generation, model calibration, and optimization guidance to tackle MES complexity and uncertainty in future energy systems.

Abstract

This paper surveys the primary computational hurdles of Energy Systems optimization coming from different sources: model-induced complexity, optimization algorithm requirements, and uncertainties handling (both aleatoric and epistemic). Techniques to reduce complexity such as time-series and spatial aggregation, model order reduction, and specialized optimization strategies are reviewed for their effectiveness in balancing computational feasibility and model fidelity. Furthermore, Various uncertainty-management frameworks, including scenario-based approaches, robust optimization, and distributionally robust methods, are reviewed and their limitations in scaling and data requirements are discussed. The potential of hybrid modeling emerges as a key avenue: by fusing mechanistic and machine learning elements, hybrid techniques for modelling and optimization can harness the strengths of both worlds while mitigating their respective drawbacks. The paper highlights several directions for further research to develop advanced methods to tackle the complexity of MES.

Opportunities for Hybrid Modeling Approaches in Energy Systems optimization

TL;DR

This paper analyzes the computational hurdles in energy systems optimization arising from model complexity, solver requirements, and uncertainty handling. It reviews effectiveness of complexity-reduction techniques and uncertainty-management frameworks, and discusses their scaling and data needs. It argues that hybrid modeling—combining mechanistic physics with data-driven components—offers a path to leverage the strengths of both paradigms while mitigating weaknesses. The authors outline concrete directions for hybrid scenario generation, model calibration, and optimization guidance to tackle MES complexity and uncertainty in future energy systems.

Abstract

This paper surveys the primary computational hurdles of Energy Systems optimization coming from different sources: model-induced complexity, optimization algorithm requirements, and uncertainties handling (both aleatoric and epistemic). Techniques to reduce complexity such as time-series and spatial aggregation, model order reduction, and specialized optimization strategies are reviewed for their effectiveness in balancing computational feasibility and model fidelity. Furthermore, Various uncertainty-management frameworks, including scenario-based approaches, robust optimization, and distributionally robust methods, are reviewed and their limitations in scaling and data requirements are discussed. The potential of hybrid modeling emerges as a key avenue: by fusing mechanistic and machine learning elements, hybrid techniques for modelling and optimization can harness the strengths of both worlds while mitigating their respective drawbacks. The paper highlights several directions for further research to develop advanced methods to tackle the complexity of MES.

Paper Structure

This paper contains 24 sections, 2 equations, 3 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. Model-based energy systems optimization
  3. Applications
  4. Mathematical formulations and solving paradigms
  5. Challenges
  6. Algorithmic and computational complexity
  7. Model-induced complexity
  8. Optimization-induced complexity
  9. Uncertainty-induced complexity
  10. Uncertainties
  11. Aleatoric uncertainties
  12. Epistemic uncertainties
  13. Classic approaches to tackle these challenges
  14. Complexity reduction
  15. Temporal reduction
  16. ...and 9 more sections

Figures (3)

  • Figure 1: Decision making levels VS energy system scales, adapted from (WEIDNER2022107883)
  • Figure 2: Optimization solving paradigms: coupled structure Vs. iterative structure
  • Figure 3: Model's tractability based on complexity determinants and the targeted application : example of the control of a heat pump