Enhancing Multi-Energy Modeling: The Role of Mixed-Integer Optimization Decisions

TL;DR

This paper examines critical decision points in modeling multi-energy systems with mixed-integer programming, emphasizing their impact on computational efficiency and model clarity, demonstrated through a case study of Berlin's district heating network.

Contribution

It identifies and analyzes key modeling decisions in MIP formulations for multi-energy systems, offering approaches to enhance efficiency and interpretability.

Findings

Two approaches to handle modeling degrees of freedom are compared.

Case study shows impact of modeling choices on solution quality and computational performance.

Highlighting overlooked decisions improves model scalability and user-friendliness.

Abstract

The goal to decarbonize the energy sector has led to increased research in modeling and optimizing multi-energy systems. One of the most promising techniques for modeling (multi-)energy optimization problems is mixed-integer programming (MIP), valued for its ability to represent the complexities of integrated energy systems. While the literature often focuses on deriving mathematical formulations and parameter settings, less attention is given to critical post-formulation decisions. Modeling multi-energy systems as a MIP demands decisions across multiple degrees of freedom. Key steps include reducing a real-world multi-energy network into an abstract topology, defining variables, formulating the relevant (in-)equalities to represent technical requirements, setting objectives, and integrating these elements into a MIP. However, with these elements fixed, the specific transformation of the abstract topology into a graph structure and the construction of the MIP remain non-uniquely. These choices can significantly impact user-friendliness, problem size, and computational efficiency, thus affecting the feasibility and efficiency of modeling efforts. We identify and analyze the additional degrees of freedom and describe two distinct approaches to address them. The approaches are compared regarding mathematical equivalence, suitability for solution algorithms, and clarity of the underlying topology. A case study on a realistic subarea of Berlin's district heating network involving tri-objective optimization for a unit commitment problem demonstrates the practical significance of these decisions. By highlighting these critical yet often overlooked aspects, our work equips energy system modelers with insights to improve computational efficiency, scalability, and interpretability in their optimization efforts.