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

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

Gaussian copula modeling of extreme cold and weak-wind events over Europe conditioned on winter weather regimes

A transition to renewable energy is needed to mitigate climate change. In Europe, this transition has been led by wind energy, which is one of the fastest growing energy sources. However, energy demand and production are sensitive to meteorological conditions and atmospheric variability at multiple time scales. To accomplish the required balance between these two variables, critical conditions of high demand and low wind energy supply must be considered in the design of energy systems. We describe a methodology for modeling joint distributions of meteorological variables without making any assumptions about their marginal distributions. In this context, Gaussian copulas are used to model the correlated nature of cold and weak-wind events. The marginal distributions are modeled with logistic regressions defining two sets of binary variables as predictors: four large-scale weather regimes (WRs) and the months of the extended winter season. By applying this framework to ERA5 data, we can compute the joint probabilities of co-occurrence of cold and weak-wind events on a high-resolution grid .Our results show that (a) WRs must be considered when modeling cold and weak-wind events, (b) it is essential to account for the correlations between these events when modeling their joint distribution, (c) we need to analyze each month separately, and (d) the highest estimated number of days with compound events are associated with the negative phase of the North Atlantic Oscillation (3 days on average over Finland, Ireland, and Lithuania in January, and France and Luxembourg in February) and the Scandinavian blocking pattern (3 days on average over Ireland in January and Denmark in February). This information could be relevant for application in sub-seasonal to seasonal forecasts of such events

Drivers of extreme wind events in Mexico for windpower applications

In this study, we use a k-mean clustering approach to investigate the weather patterns responsible for extreme wind speed events throughout Mexico using 40 years of the ERA-5 atmospheric reanalysis. Generally, we find a large geographical split between the weather patterns that generate the strongest winds across the country. The highest wind power production periods therefore occur at different times in different regions across the country. In the South, these are associated with cold surge events, where an anticyclone is present in the Gulf of Mexico resulting in a strong Northerly flow across the Isthmus of Tehuantepec. In the North-East, Easterly trade winds are responsible for the strongest wind events, whereas in the North-West, it is the proximity of the North Pacific High. However, the weakest winds and lowest power production periods occur at the same times for all stations with the exception of Baja California Sur, meaning that low wind power production may be unavoidable at these times. The El Ni\uf1o Southern Oscillation is found to influence wind speeds at some locations across Mexico at sub-seasonal time-scales. We report that statistically stronger wind speeds are observed during the Summer during El Ni\uf1o months than during La Ni\uf1a months for both sites in Chiapas and Oaxaca. 10.1002/joc.684

Sub-seasonal forecasts of demand and wind power and solar power generation for 28 European countries

Electricity systems are becoming increasingly exposed to weather. The need for high-quality meteorological forecasts for managing risk across all timescales has therefore never been greater. This paper seeks to extend the uptake of meteorological data in the power systems modelling community to include probabilistic meteorological forecasts at sub-seasonal lead times. Such forecasts are growing in skill and are receiving considerable attention in power system risk management and energy trading. Despite this interest, these forecasts are rarely evaluated in power system terms, and technical barriers frequently prohibit use by non-meteorological specialists.This paper therefore presents data produced through a new EU climate services programme Subseasonal-to-seasonal forecasting for Energy (S2S4E). The data correspond to a suite of well-documented, easy-to-use, self-consistent daily and nationally aggregated time series for wind power, solar power and electricity demand across 28 European countries. The data are accessible from https://doi.org/10.17864/1947.275 (Gonzalez et al., 2020). The data include a set of daily ensemble reforecasts from two leading forecast systems spanning 20 years (ECMWF, an 11-member ensemble, with twice-weekly starts for 1996–2016, totalling 22 880 forecasts) and 11 years (NCEP, a 12-member lagged-ensemble, constructed to match the start dates from the ECMWF forecast from 1999–2010, totalling 14 976 forecasts). The reforecasts contain multiple plausible realisations of daily weather and power data for up to 6 weeks in the future.To the authors’ knowledge, this is the first time a fully calibrated and post-processed daily power system forecast set has been published, and this is the primary purpose of this paper. A brief review of forecast skill in each of the individual primary power system properties and a composite property is presented, focusing on the winter season. The forecast systems contain additional skill over climatological expectation for weekly-average forecasts at extended lead times, though this skill depends on the nature of the forecast metric considered. This highlights the need for greater collaboration between the energy and meteorological research communities to develop applications, and it is hoped that publishing these data and tools will support this

Characterizing the variability and meteorological drivers of wind power and solar power generation over Africa

Sub-Saharan Africa (SSA) has the lowest energy access rates in the world, which poses a key barrier to power system development. Deployment of renewables, including wind and solar power, will play a key role in expanding electricity supply across SSA: distributed generation (enabling access for remote communities), cost-effectiveness and low emissions are key advantages. However, renewable generation is weather dependent; therefore, including more renewables increases the amount of meteorologically driven variability in the power system. Two countries in SSA are chosen for detailed investigation of this meteorologically driven variability: Senegal in West Africa and Kenya in East Africa. These are chosen due to being areas of dense population, where there is operational wind and solar power, and plans for regional expansion. In Senegal, solar generation is fairly consistent throughout the year, while wind generation exhibits strong seasonality, with a peak in the boreal spring. Low wind and solar power generation days during the boreal summer are found to be related to the passage of African Easterly Waves. Over Kenya, both wind and solar generation exhibit seasonal variability, with wind generation peaking during boreal autumn, and solar generation at a minimum during boreal summer. Inter-annual variability in generation is greater over Kenya than over Senegal; the El Nino Southern Oscillation is found to impact wind and solar generation over Kenya. El Nino phases are associated with lower wind and solar generation in October–December over Kenya, but higher generation in July–September. This improved understanding of variability will assist system planners in designing reliable future energy systems

Synoptic conditions conducive for compound wind-flood events in Great Britain in present and future climates

Extreme wind is the main driver of loss in North-West Europe, with flooding being the second-highest driver. These hazards are currently modelled independently, and it is unclear what the contribution of their co-occurrence is to loss. They are often associated with extra-tropical cyclones, with studies focusing on co-occurrence of extreme meteorological variables. However, there has not been a systematic assessment of the meteorological drivers of the co-occurring \textit{impacts} of compound wind-flood events. This study quantifies this using an established storm severity index (SSI) and recently developed flood severity index (FSI), applied to the UKCP18 12km regional climate simulations, and a Great Britain (GB) focused hydrological model. The meteorological drivers are assessed using 30 weather types, which are designed to capture a broad spectrum of GB weather. Daily extreme compound events (exceeding 99th percentile of both SSI and FSI) are generally associated with cyclonic weather patterns, often from the positive phase of the North Atlantic Oscillation (NAO+) and Northwesterly classifications. Extreme compound events happen in a larger variety of weather patterns in a future climate. The location of extreme precipitation events shifts southward towards regions of increased exposure. The risk of extreme compound events increases almost four-fold in the UKCP18 simulations (from 14 events in the historical period, to 55 events in the future period). It is also more likely for there to be multi-day compound events. At seasonal timescales years tend to be either flood-prone or wind-damage-prone. In a future climate there is a larger proportion of years experiencing extreme seasonal SSI and FSI totals. This could lead to increases in reinsurance losses if not factored into current modelling

Extreme weather events and the energy sector in 2021

In 2021, the energy sector was put at risk by extreme weather in many different ways: North America and Spain suffered heavy winter storms that led to the collapse of the electricity network; California specifically experienced heavy droughts and heatwave conditions, causing the operations of hydropower stations to halt; floods caused substantial damage to energy infrastructure in central Europe, Australia and China throughout the year, and unusual wind drought conditions decreased wind power production in the United Kingdom by almost 40% during summer. The total economic impacts of these extreme weather events are estimated at billions of USD. Here we review and assess in some detail the main extreme weather events that impacted the energy sector in 2021 worldwide, discussing some of the most relevant case studies and the meteorological conditions that led to them. We provide a perspective on their impacts on electricity generation, transmission and consumption, and summarize estimations of economic losses