Towards time series aggregation with exact error quantification for optimization of energy systems

TL;DR

This paper presents a novel aggregation method for energy system models that allows exact error quantification, balancing computational efficiency with modeling accuracy by intelligently merging time periods with different active constraints.

Contribution

It introduces a new aggregation approach that extends existing methods by aggregating data across different active constraint sets and provides a way to exactly quantify the resulting error.

Findings

Exact error quantification without re-solving the optimization problem.

Significant reduction in model size while maintaining control over accuracy.

Applicability to energy markets with varying temporal granularity.

Abstract

Energy system optimization models are becoming increasingly popular for analyzing energy markets, such as the impact of new policies or interactions between energy carriers. One key challenge of these models is the trade-off between modeling accuracy and computational tractability. A recently proposed mathematical framework addresses this challenge by achieving exact time series aggregations merging time periods sharing the same active constraint sets. This aggregation, however, is insufficient when the number of unique active constraints is large. We overcome this issue by aggregating data points from different active constraint sets. While this further reduces model size, it inevitably introduces an error compared to the full model. Yet, we show how this error can be exactly quantified without re-solving the optimization problem, enabling users to trade off computational efficiency and model accuracy proactively. This may be especially useful in energy markets to accommodate varying granularity across short- and long-term time horizons.