Using power system modelling outputs to identify weather-induced extreme events in highly renewable systems

TL;DR

The paper addresses weather induced resilience challenges in Europe’s highly renewable power systems and introduces a shadow price based method derived from a capacity expansion model to identify system defining weather periods. It integrates ERA5 driven meteorology with the PyPSA-Eur model to filter, classify, and validate stress events that trigger investments, distinguishing them from traditional meteorological extremes. By incorporating transmission and storage dynamics, the work shows that system stress patterns cannot be captured by meteorology alone and demonstrates robustness through load shedding validation. The approach provides a practical framework for resilience planning and underscores the need for interdisciplinary modelling in future renewable dominated grids, with open code and data to support replication and extension.

Abstract

In highly renewable power systems the increased weather dependence can result in new resilience challenges, such as renewable energy droughts, or a lack of sufficient renewable generation at times of high demand. The weather conditions responsible for these challenges have been well-studied in the literature. However, in reality multi-day resilience challenges are triggered by complex interactions between high demand, low renewable availability, electricity transmission constraints and storage dynamics. We show these challenges cannot be rigorously understood from an exclusively power systems, or meteorological, perspective. We propose a new method that uses electricity shadow prices - obtained by a European power system model based on 40 years of reanalysis data - to identify the most difficult periods driving system investments. Such difficult periods are driven by large-scale weather conditions such as low wind and cold temperature periods of various lengths associated with stationary high pressure over Europe. However, purely meteorological approaches fail to identify which events lead to the largest system stress over the multi-decadal study period due to the influence of subtle transmission bottlenecks and storage issues across multiple regions. These extreme events also do not relate strongly to traditional weather patterns (such as Euro-Atlantic weather regimes or the North Atlantic Oscillation index). We therefore compile a new set of weather patterns to define energy system stress events which include the impacts of electricity storage and large-scale interconnection. Without interdisciplinary studies combining state-of-the-art energy meteorology and modelling, further strive for adequate renewable power systems will be hampered.

Figures (25)

• Figure 1: An overview over the workflow and the three approaches we compare in this study. For a definition of the approaches, see \ref{['tab:approaches']}.
• Figure 2: An overview of all identified system-defining events in the context of daily system cost. Additionally the week with the highest net load for each year is marked (Approach 1 in \ref{['tab:approaches']}). Only winter months are shown as shadow prices are consistently low during the summer. All costs are in 2013 EUR, but derive from model shadow prices, not actual market prices.
• Figure 3: A summary of key metrics compared to 40-year means. Each dot represents the mean value of the metric in question over one system-defining event. From left to right: (a) renewable production deviation from 40-year mean at the time of each event, (b) load deviation from 40-year mean at the time of each event, (c) mean shadow price of transmission congestion during each event, (d) mean value of stored energy for each event. An overview over all events can be found in Table S1.
• Figure 4: System-defining events are the result of an interplay of low renewable availability, high load, storage constraints and transmission congestion. Inputs in the top row, comparable to a usual meteorological approach (Approach 1). System variables in the bottom row. (a) Average weather in Europe over the example event. Note the wind speed anomalies over the North Sea region and the temperature anomalies in Central Europe in Fig. S11. (b) Time series of wind power production and electricity load around the highlighted event (smoothed with rolling averages of 24 hours). The dashed lines show seasonality deduced from the period 1980--2020. (c) Network map of the European power system with the edge widths showing shadow prices of congestion and the regions shaded with the average electricity price during the event. (d) Time series of electricity prices, value of hydrogen storage (with logarithmic scales), and the hydrogen storage level around the highlighted event (all network averages). All costs are in 2013 EUR.
• Figure 5: Meteorological conditions during system-defining events (a)--(b). For all 32 events, (c)--(j) are the four extracted clusters of events.