Reducing climate risk in energy system planning: a posteriori time series aggregation for models with storage

TL;DR

This paper introduces a posteriori time series aggregation methods for energy system models with storage, improving accuracy and preserving chronology over traditional a priori methods, thus enabling better climate risk assessment in long-term planning.

Contribution

It presents novel a posteriori aggregation schemes that adapt to system specifics and preserve storage chronology, outperforming traditional methods in accuracy.

Findings

A posteriori methods better identify and preserve extreme events.

These methods improve model accuracy over a priori approaches.

Tools are made publicly available for broader use.

Abstract

The growth in variable renewables such as solar and wind is increasing the impact of climate uncertainty in energy system planning. Addressing this ideally requires high-resolution time series spanning at least a few decades. However, solving capacity expansion planning models across such datasets often requires too much computing time or memory. To reduce computational cost, users often employ time series aggregation to compress demand and weather time series into a smaller number of time steps. Methods are usually a priori, employing information about the input time series only. Recent studies highlight the limitations of this approach, since reducing statistical error metrics on input time series does not in general lead to more accurate model outputs. Furthermore, many aggregation schemes are unsuitable for models with storage since they distort chronology. In this paper, we introduce a posteriori time series aggregation schemes for models with storage. Our methods adapt to the underlying energy system model; aggregation may differ in systems with different technologies or topologies even with the same time series inputs. Furthermore, they preserve chronology and hence allow modelling of storage technologies. We investigate a number of approaches. We find that a posteriori methods can perform better than a priori ones, primarily through a systematic identification and preservation of relevant extreme events. We hope that these tools render long demand and weather time series more manageable in capacity expansion planning studies. We make our models, data, and code publicly available.