  Energy system models are crucial for planning, supporting, and understanding
energy transition pathways. Flexible energy modelling tools have emerged to
provide practitioners, planners, and decision-makers with various alternatives
to represent diverse energy systems, including green hydrogen or exclusively
renewable-powered storage assets. The increased interaction between energy
sectors, temporal resolution, and extensive geographical scopes have led to
large-scale problems posing significant computational challenges. Despite
improvements in computing power and linear programming (LP) solvers,
large-scale LP models are often simplified, sacrificing fidelity to speed up
solutions. This paper aims to debunk the misconception that an LP model's
simplicity cannot be improved without sacrificing fidelity. We propose
exploiting the graph nature of energy systems using a single building block,
the Energy Asset, to reduce computational complexity. By using only one
building block, the Energy Asset, we avoid intermediary assets and connections,
thus reducing the number of variables by 26% and constraints by 35%. This
approach naturally speeds up solving times by 1.27 times without sacrificing
model fidelity. Our illustrative case study demonstrates these improvements
compared to traditional two-building-block approaches. This paper raises
awareness in the energy modelling community about the quality of LP models and
shows that not all LPs are created equal. Our proposed method speeds up energy
system models regardless of anticipated advances in software and hardware,
allowing for the solution of larger and more detailed models with existing
technology.
