Introduction

Energy system models have been widely used to foresee medium- and long-term energy transitions. However, most energy system models generally have rough time slices, such as 12 or 16 slices per year. The problem with this rough time slice is that it does not adequately reproduce the short-term flexibility among the flexibility required in response to variable renewable energy penetration. Rough time slices in energy system models are particularly challenging in terms of computing power when modelling the transmission substation units used in energy system modelling studies, which can be classified into more than 350 regions and reflect transmission capacities.

Methodologies

To address these challenges, a new modelling method is proposed that introduces high-resolution analysis for critical periods and processes other periods at a coarse time slice. The steps of the method are as follows:

Definition of critical time slices:

A specific period of time (e.g. the first week of July) with high demand and high fluctuations in renewable energy output is selected and this is modelled at hourly resolution (e.g. 168 slices per week).

The remaining annual periods are aggregated into 16 coarse slices to ensure computational efficiency.

creation of realistic curves:

For the key periods defined, demand curves and solar and wind supply curves are created on an hourly basis to reflect realistic variations.

For other periods, aggregated information based on seasonal and day/night data is used.

incorporation of existing capacity:

Capture the capacity calculated in the previous model run in year t-1 and treat this as the “existing” capacity for year t.

model run for year t:

Provides existing capacity for year t and optimises the additional capacity needed to manage supply and demand variations through high resolution slices and coarse slices in critical periods.

Major Findings

System stress testing: identify periods of extreme stress and assess the suitability of existing and proposed capacity mixes.

Flexibility analysis: assesses the role of storage, demand response and grid coordination to mitigate variability.

Incremental capacity planning: provides actionable insights on annual incremental investments rather than major system modifications.

This hybrid approach bridges the gap between computational feasibility and the need for realistic high-resolution system modelling. By exposing hourly variations during critical periods, it enables the energy system capacity mix to meet real-world challenges and supports a more resilient and cost-effective energy transition.