Opportunities for Hybrid Modeling Approaches in Energy Systems optimization

TL;DR

This paper reviews computational challenges in energy systems optimization, discusses techniques to reduce complexity and handle uncertainties, and highlights hybrid modeling as a promising approach combining mechanistic and machine learning methods.

Contribution

It provides a comprehensive survey of existing methods and emphasizes the potential of hybrid models to overcome current limitations in energy systems optimization.

Findings

Hybrid modeling combines mechanistic and machine learning approaches.

Techniques like aggregation and model reduction improve computational efficiency.

Uncertainty management frameworks face scalability and data challenges.

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.