The Pareto-Optimal Temporal Aggregation of Energy System Models

TL;DR

This paper introduces advanced clustering techniques for temporal aggregation in energy system models, significantly improving computational efficiency and accuracy over existing methods for large-scale applications.

Contribution

It presents novel, generally applicable clustering algorithms that optimize the tradeoff between temporal resolution and accuracy, outperforming current state-of-the-art approaches.

Findings

Proposed methods outperform existing approaches in Pareto-optimality.

Achieve two orders of magnitude speedup with only 2% cost deviation.

Validated on diverse energy system models, including building and European scenarios.

Abstract

The growing share of intermittent renewable energy sources, storage technologies, and the increasing degree of so-called sector coupling necessitates optimization-based energy system models with high temporal and spatial resolutions, which significantly increases their runtimes and limits their maximum sizes. In order to maintain the computational viability of these models for large-scale application cases, temporal aggregation has emerged as a technique for reducing the number of considered time steps by reducing the original time horizon down to fewer, more representative ones. This study presents advanced but generally applicable clustering techniques that allow for ad-hoc improvements of state-of-the-art approaches without requiring profound knowledge of the individual energy system model. These improvements comprise the optimal tradeoff between the number of typical days and inner-daily temporal resolutions, as well as constituting a representation method that can reproduce the value distribution of the original time series. We prove the superiority of these approaches by applying them to two fundamentally different model types, namely a single-node building energy system and a European carbon-neutral energy scenario, and benchmark these against state-of-the-art approaches. This is performed for a variety of temporal resolutions, which leads to many hundreds of model runs. The results show that the proposed improvements on current methods strictly dominate the status quo with respect to Pareto-optimality in terms of runtime and accuracy. Although a speeding up factor of one magnitude could be achieved using traditional aggregation methods within a cost deviation range of two percent, the algorithms proposed herein achieve this accuracy with a runtime speedup by a factor of two orders of magnitude.