Subhourly temporal resolution in PV energy system modeling: effects on design, reliability, and computational efficiency

Energy Conversion and Management X, vol. 31, pp. 102186

Abstract

The shift toward high temporal resolution modeling of energy systems is crucial due to the growing reliance on intermittent renewable energy sources. Hourly data has traditionally been used, but it lacks the temporal detail required to accurately capture total system costs, component sizing, operational feasibility, and reliability. This study investigates the impact of minute-resolution on the design and optimization of photovoltaic-based energy systems at both single- and aggregated-buildings levels, represented by a self-sufficient building and a microgrid, respectively. The objective is to quantify how minute-resolution affects capacity expansion results, system design decisions, and computational runtime. The study also evaluates the impact of lost load through unserved energy on system configuration and cost, highlighting how temporal resolution affects feasibility assessments and design outcomes. To manage the higher computational complexity associated with minute-resolution modeling, several complexity-reduction techniques like averaging, regular sampling, and clustering-based time series aggregation are assessed for their trade-offs between accuracy – total annualized cost and components sizing – and computational runtime. The results show that minute-resolution is essential for accurate photovoltaic-based single building systems, with this effect less pronounced in aggregated building systems. The inverter as a bottleneck between the demand and supply system is mostly affected in both the single and the aggregated building systems, and therefore should be systematically sized. On the complexity part, the results show that carefully selected aggregation approaches can preserve most of the accuracy of minute-resolution models while significantly reducing runtime. In particular, combinations of averaging with regular sampling and clustering-based time series aggregation methods can achieve near-minute-level accuracy with runtimes comparable to hourly models. The findings underline the balance between temporal resolution, computational complexity, and model accuracy. While minute-resolution offers better accuracy, appropriate aggregation methods can mitigate the computational burden. Future research could refine hourly data to enhance temporal detail while ensuring manageable computational complexity.

Authors 4

1. Olalekan Omoyele corresponding Aachen Chair for Fuel Cells Faculty of Mechanical Engineering

   RWTH Aachen University · Forschungszentrum Jülich

   Affiliation as printed

   Forschungszentrum Jülich GmbH, Institute of Climate and Energy Systems – Jülich Systems Analysis (ICE-2), 52425 Jülich, Germany

   RWTH Aachen University, Chair for Fuel Cells, Faculty of Mechanical Engineering, 52062 Aachen, Germany

2. Maximilian Hoffmann

   Forschungszentrum Jülich

   Affiliation as printed

   Forschungszentrum Jülich GmbH, Institute of Climate and Energy Systems – Jülich Systems Analysis (ICE-2), 52425 Jülich, Germany

3. Jann Michael Weinand

   Forschungszentrum Jülich

   Affiliation as printed

   Forschungszentrum Jülich GmbH, Institute of Climate and Energy Systems – Jülich Systems Analysis (ICE-2), 52425 Jülich, Germany

4. Detlef Stolten Aachen Chair for Fuel Cells Faculty of Mechanical Engineering

   RWTH Aachen University · Forschungszentrum Jülich

   Affiliation as printed

   Forschungszentrum Jülich GmbH, Institute of Climate and Energy Systems – Jülich Systems Analysis (ICE-2), 52425 Jülich, Germany

   RWTH Aachen University, Chair for Fuel Cells, Faculty of Mechanical Engineering, 52062 Aachen, Germany