As energy sectors become increasingly interconnected, selecting appropriate representations of physical characteristics in energy system optimization models has become challenging. This study evaluates the necessity of transmission and generator models by systematically excluding each one and assessing the impact on objective values, solution time, and feasibility of the resulting system design. We apply this analysis to two contrasting case studies optimizing the design and operation of: the IEEE 118-bus test power system and a zero-emission multi-energy system of the Netherlands. Results show that modeling Kirchhoff's Voltage Law (KVL) and alternating-current (AC) transmission losses is essential for accuracy and feasibility. KVL prevents unrealistic network loops; hence improving network utilization. Additionally, we evaluate two linearization methods for the AC transmission losses. Our findings indicate that tangent-based linear approximations often lead to infeasibility with three or fewer segments, whereas a piecewise-linear approach with at least two segments ensures accurate and computationally efficient solutions.

In order to achieve a timely transition towards sustainable energy systems within a large landscape of multi-sectors and multi-technologies, decision-makers and industry practitioners can rely on time- and space-discretized energy system optimization models. However, such models are often burdened by the computational costs arising from the growing problem complexity, which is especially due to the time discretization. The common strategy to lower the computational cost is to uniformly reduce the temporal resolution, sacrificing the quality of the solution. In light of this, we propose the concept and a formulation of fully flexible temporal resolution, wherein each decision variable and constraint can have a separate temporal resolution. After introducing the formulation in detail, we demonstrate its capability by applying it to an EU-wide case study optimizing both capacity investment and operation decisions of the inter-connected energy system across the different countries. We show that the proposed flexible formulation allows us to flexibly remove variables and constraints that are not needed without losing accuracy, and to simplify the time discretization (e.g., in space) while pushing the Pareto front by simultaneously speeding up computation and limiting losses in accuracy. In conclusion, we highlight the promise of adopting fully flexible temporal resolution and encourage future research to explore further temporal resolution configurations beyond our examples.