Using power system modelling outputs to identify weather-induced extreme events in highly renewable systems
Aleksander Grochowicz, Koen van Greevenbroek, Hannah C. Bloomfield

TL;DR
This paper introduces a novel method using power system shadow prices derived from 40 years of data to identify weather-induced extreme events that challenge highly renewable energy systems, emphasizing the importance of interdisciplinary approaches.
Contribution
It presents a new approach combining power system modelling and meteorological data to better understand multi-day resilience challenges in renewable power systems.
Findings
Large-scale weather conditions like low wind and cold temperatures drive system stress.
Purely meteorological approaches fail to identify the most impactful events.
Interdisciplinary methods including storage and transmission are essential for understanding system stress.
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…
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Taxonomy
TopicsElectric Power System Optimization · Energy Load and Power Forecasting · Integrated Energy Systems Optimization
