Extreme events in the European renewable power system: Validation of a modeling framework to estimate renewable electricity production and demand from meteorological data

Most, Lieke van der; Wiel, Karin van der; Benders, Reinerus; Gerbens-Leenes, P.W.; Kerkmans, P.

doi:10.1016/j.rser.2022.112987

Cited by 10 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This makes the renewable energy supply susceptible to extreme weather events. Recent events such as long-lasting high temperatures, droughts, and low wind speeds have already proven to be able to impact energy prices and the security of energy services [117]. And the increasing likelihood of these weather events only stresses the need to consider extreme weather events in future energy-forecasting research, although it is hard to predict how these events will affect energy forecasting and solar forecasting in particular.…”

Section: Extreme Weather Outliers and Aimentioning

confidence: 99%

A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence

Barhmi,

Heynen,

Golroodbari

et al. 2024

Solar

View full text Add to dashboard Cite

Solar energy forecasting is essential for the effective integration of solar power into electricity grids and the optimal management of renewable energy resources. Distinguishing itself from the existing literature, this review study provides a nuanced contribution by centering on advancements in forecasting techniques. While preceding reviews have examined factors such as meteorological input parameters, time horizons, the preprocessing methodology, optimization, and sample size, our study uniquely delves into a diverse spectrum of time horizons, spanning ultrashort intervals (1 min to 1 h) to more extended durations (up to 24 h). This temporal diversity equips decision makers in the renewable energy sector with tools for enhanced resource allocation and refined operational planning. Our investigation highlights the prominence of Artificial Intelligence (AI) techniques, specifically focusing on Neural Networks in solar energy forecasting, and we review supervised learning, regression, ensembles, and physics-based methods. This showcases a multifaceted approach to address the intricate challenges associated with solar energy predictions. The integration of Satellite Imagery, weather predictions, and historical data further augments precision in forecasting. In assessing forecasting models, our study describes various error metrics. While the existing literature discusses the importance of metrics, our emphasis lies on the significance of standardized datasets and benchmark methods to ensure accurate evaluations and facilitate meaningful comparisons with naive forecasts. This study stands as a significant advancement in the field, fostering the development of accurate models crucial for effective renewable energy planning and emphasizing the imperative for standardization, thus addressing key gaps in the existing research landscape.

show abstract

Section: Extreme Weather Outliers and Aimentioning

confidence: 99%

A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence

Barhmi,

Heynen,

Golroodbari

et al. 2024

Solar

View full text Add to dashboard Cite

show abstract

“…PV capacity factors are computed from ERA5 direct and diffuse shortwave radiation influx data using a reference solar panel model, assuming no tracking and a fixed 35 • panel slope. Weatherdependent electricity demand is generated based on historical ENTSO-E data and adjusted for heating or cooling demand using a heating/cooling degree days approach as in [9,23,32].…”

Section: Meteorological Inputs and Energy Variablesmentioning

confidence: 99%

“…As opposed to methods considering only peak load or net load, (i.e. peak mismatch between renewable generation and load) [17][18][19][20]23], using power system optimisation outputs to identify systemdefining events takes the complex interactions between storage and transmission into account. Moreover, we need not make assumptions about the availability of storage and transmission in any particular region.…”

Section: Approachmentioning

confidence: 99%

“…In a renewables-based power system both electricity demand and generation are driven by weather and cannot be considered independently; it is thus becoming common practice to consider times of energy system stress as compound events involving a combination of near-surface temperatures, wind speeds, irradiance and hydrological variables across large geographic and temporal scales [10,23,24]. For example, high pressure systems can cause compound events [17,25], affecting multiple countries simultaneously.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Grochowicz,

van Greevenbroek,

Bloomfield

2024

Environ. Res. Lett.

View full text Add to dashboard Cite

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.

show abstract

“…While a number of methods exists to model and/or select challenging high impact events using basic statistical principles (e.g. [10,[12][13][14][15][16][17][18]), we aim to define a physics based and intuitive to understand metric to quantify energy-meteorological variability across timescales.…”

Section: Introductionmentioning

confidence: 99%

The climatological renewable energy deviation index (credi)

Stoop,

van der Wiel,

Zappa

et al. 2024

Environ. Res. Lett.

View full text Add to dashboard Cite

We propose an index to quantify and analyse the impact of climatological variability on the energy system at different timescales. We define the Climatological Renewable Energy Deviation Index (CREDI) as the cumulative anomaly of a renewable resource with respect to its climate over a specific time period of interest. For this we introduce the smooth, yet physical, hourly rolling window climatology that captures the expected hourly to yearly behaviour of renewable resources. We analyse the presented index at decadal, annual and (sub-)seasonal timescales for a sample region and discuss scientific and practical implications.CREDI is meant as an analytical tool for researchers and stakeholders to help them quantify, understand, and explain, the impact of energy-meteorological variability on future energy system. Improved understanding translates to better assessments of how renewable resources, and the associated risks for energy security, may fare in current and future climatological settings. The practical use of the index is in resource planning. For example transmission system operators may be able to adjust short-term planning to reduce adequacy issues before they occur or combine the index with storyline event selection for improved assessments of climate change related risks.

show abstract

Extreme events in the European renewable power system: Validation of a modeling framework to estimate renewable electricity production and demand from meteorological data

Cited by 10 publications

References 35 publications

A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence

A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence

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

The climatological renewable energy deviation index (credi)

Contact Info

Product

Resources

About