The rising significance of renewable energy increases the importance of representing time-
varying input data in energy system optimization studies. Time-series aggregation, which …

Decarbonized power systems are critical to mitigate climate change, yet methods to achieve
a reliable and resilient near-zero power system are still under exploration. This study …

The advancement of technology has led to the development of smart energy systems,
integrating traditional and innovative energy solutions with technologies such as IoT and AI …

Abstract Generation Capacity Expansion Planning (GCEP) requires high temporal resolution
to account for the volatility of renewable energy supply. Because the GCEP optimization …

Abstract Purpose of Review Future electricity grids will be characterized by the high
penetration of renewables to support the decarbonization process. Yet, this transition will …

Modeling energy storage for a renewables-driven grid using every hour of the year gives
more insight and higher accuracy, but can be computationally demanding. In this study, we …

Given its intrinsic volatility and randomness, the effective design of a building renewable
energy system (BRES) necessitates long-period, short-step boundaries. Nevertheless, this …

The intermittent nature of wind and solar power has led to a scientific consensus in the
international energy research community that a mix of energy sources and carriers is …

Time series aggregation (TSA) is commonly used in energy system optimization to reduce
model complexity and computational expenses by selecting periods to represent the entire …

Power sector capacity expansion models (CEMs) that are used for studying future low-
carbon grid scenarios must incorporate detailed representation of grid operations. Often …