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 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 (0.25 deg). Our results show that a) weather regimes 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

Hourly historical and near-future weather and climate variables for energy system modelling

Energy systems are becoming increasingly exposed to the impacts of weather and climate due to the uptake of renewable generation and the electrification of the heat and transport sectors. The need for high-quality meteorological data to manage present and near-future risks is urgent. This paper provides a comprehensive set of multi-decadal, time series of hourly meteorological variables and weather-dependent power system components for use in the energy systems modelling community. Despite the growing interest in the impacts of climate variability and climate change on energy systems over the last decade, it remains rare for multi-decadal simulations of meteorological data to be used within detailed simulations. This is partly due to computational constraints, but also due to technical barriers limiting the use of meteorological data by non-specialists. This paper presents a new European-level dataset which can be used to investigate the impacts of climate variability and climate change on multiple aspects of near-future energy systems. The datasets correspond to a suite of well-documented, easy-to-use, self-consistent, hourly- and nationally aggregated, and sub-national time series for 2 m temperature, 10 m wind speed, 100 m wind speed, surface solar irradiance, wind power capacity factor, solar power factor, and degree days spanning over 30 European countries. This dataset is available for the historical period 1950–2020 and is accessible from https://doi.org/10.17864/1947.000321 (Bloomfield and Brayshaw, 2021a). As well as this a companion dataset is created where the ERA5 reanalysis is adjusted to represent the impacts of near-term climate change (centred on the year 2035) based on five high-resolution climate model simulations. These data are available for a 70-year period for central and northern Europe. The data are accessible from https://doi.org/10.17864/1947.000331 (Bloomfield and Brayshaw, 2021b). To the authors’ knowledge, this is the first time a comprehensive set of high-quality hourly time series relating to future climate projections has been published, which is specifically designed to support the energy sector. The purpose of this paper is to detail the methods required for processing the climate model data and illustrate the importance of accounting for climate variability and climate change within energy system modelling from the sub-national to European scale. While this study is therefore not intended to be an exhaustive analysis of climate impacts, it is hoped that publishing these data will promote greater use of climate data within energy system modelling.</p

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 program Subseasonal-to-seasonal forecasting for Energy (S2S4E). The data corresponds 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 is accessible from http://dx.doi.org/10.17864/1947.275, (Gonzalez et al., 2020). The data includes 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 21,210 forecasts) and 11 years (NCEP, a 12 member lagged-ensemble, constructed to match the start dates from the ECMWF forecast. from 1999-2010, totalling 4608 forecasts). The reforecasts containing 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 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 system

Interannual weather variability and the challenges for Great Britain’s electricity market design

Global growth in variable renewable generation has brought significant attention to the challenge of balancing electricity supply and demand. However, inter-annual variability of energy resources has only recently begun to feature in energy system assessments and receives limited recognition in policy discussion, let alone policy design. Meteorological reanalysis datasets that blend modern modelling techniques with historic weather records are seeing increased application in energy system studies. This practice offers insights for market and policy design implications as governments seek to manage the changing energy landscape, as seen with the UK’s introduction of the Electricity Market Reform policy package. Here we apply a concise, Load Duration Curve based approach to consider the market and policy implications of increasing variability in the Great Britain (GB) energy system. Our findings emphasise the growing inter-annual variability in operating opportunity for residual mid-merit and even baseload generation, alongside implications for capacity assurance approaches. The growth in wind generation is seen to bring an accompanying opportunity for increased solar generation, with its lower inter-annual variability and largely uncorrelated annual characteristic. The results underscore the need for an increased recognition of inter-annual variability when addressing market design and incentive mechanisms

Pattern-based conditioning enhances sub-seasonal prediction skill of European national energy variables

Sub-seasonal forecasts are becoming more widely used in the energy sector to inform high-impact, weather-dependent decisions. Using pattern-based methods (such as weather regimes) is also becoming commonplace, although until now an assessment of how pattern-based methods perform compared to gridded model output has not been completed. We compare four methods to predict weekly-mean anomalies of electricity demand and demand-net-wind across 28 European countries. At short lead times (days 0-10) grid-point forecasts have higher skill than pattern-based methods across multiple metrics. However, at extended lead times (day 12+) pattern-based methods can show greater skill than grid-point forecasts. All methods have relatively low skill at weekly-mean national impact forecasts beyond day 12, particularly for probabilistic skill metrics. We therefore develop a method of pattern-based conditioning, which is able to provide windows of opportunity for prediction at extended lead times: when at least 50% of the ensemble members of a forecast agree on a specific pattern, skill increases significantly. The conditioning is valuable for users interested in particular thresholds for decision making, as it combines the dynamical robustness in the large-scale flow conditions from the pattern-based methods with local information present in the grid-point forecasts

Predictive skill of teleconnection patterns in twentieth century seasonal hindcasts and their relationship to extreme winter temperatures in Europe

European winter weather is dominated by several low-frequency teleconnection patterns, the main ones being the North Atlantic Oscillation, East Atlantic, East Atlantic/Western Russia and Scandinavian patterns. We analyze the century-long ERA-20C reanalysis and ASF-20C seasonal hindcast datasets and find that these patterns are subject to decadal variability and fluctuations in predictive skill. Using indices for determining periods of extreme cold or warm temperatures, we establish that the teleconnection patterns are, for some regions, significantly correlated or anti-correlated to cold or heat waves. The seasonal hindcasts are however only partly able to capture these relationships. There do not seem to be significant changes to the observed links between large-scale circulation patterns and extreme temperatures between periods of higher and lower predictive skill

Sunny windy Sundays

Rapid expansion of wind and solar capacity in Great Britain presents challenges for managing electricity systems. One concern is the reduction in system inertia during periods where renewables provide a high proportion of demand which has led to some networks imposing system nonsynchronous penetration limits. However, given the lack of operational data, the relationship between renewable generation and demand for the full range of meteorological conditions experienced in Great Britain is poorly understood. This study uses reanalysis datasets to determine the proportion of demand from renewable generation on an hourly resolution for a 36-year period. The days with highest penetration of renewables tend to be sunny, windy weekend days between May and September, when there is a significant contribution of both wind and solar generation and demand is suppressed due to human behaviour. Based on the current distribution of wind and solar capacity, there is very little curtailment for all system non-synchronous penetration limits considered. However, as installed capacity of renewables grows the volume of generation curtailed also increases with a disproportionate volume occurring at weekends. The total volume of curtailment is highly dependent on ratio of wind and solar capacity, with the current blend close to the optimum level

Differential Distribution of Retinal Ca2+/Calmodulin-Dependent Kinase II (CaMKII) Isoforms Indicates CaMKII-β and -δ as Specific Elements of Electrical Synapses Made of Connexin36 (Cx36)

AII amacrine cells are essential interneurons of the primary rod pathway and transmit rod-driven signals to ON cone bipolar cells to enable scotopic vision. Gap junctions made of connexin36 (Cx36) mediate electrical coupling among AII cells and between AII cells and ON cone bipolar cells. These gap junctions underlie a remarkable degree of plasticity and are modulated by different signaling cascades. In particular, Ca2+/calmodulin-dependent protein kinase II (CaMKII) has been characterized as an important regulator of Cx36, capable of potentiating electrical coupling in AII cells. However, it is unclear which CaMKII isoform mediates this effect. To obtain a more detailed understanding of the isoform composition of CaMKII at retinal gap junctions, we analyzed the retinal distribution of all four CaMKII isoforms using confocal microscopy. These experiments revealed a differential distribution of CaMKII isoforms: CaMKII-α was strongly expressed in starburst amacrine cells, which are known to lack electrical coupling. CaMKII-β was abundant in OFF bipolar cells, which form electrical synapses in the outer and the inner retina. CaMKII-γ was diffusely distributed across the entire retina and could not be assigned to a specific cell type. CaMKII-δ labeling was evident in bipolar and AII amacrine cells, which contain the majority of Cx36-immunoreactive puncta in the inner retina. We double-labeled retinas for Cx36 and the four CaMKII isoforms and revealed that the composition of the CaMKII enzyme differs between gap junctions in the outer and the inner retina: in the outer retina, only CaMKII-β colocalized with Cx36-containing gap junctions, whereas in the inner retina, CaMKII-β and -δ colocalized with Cx36. This finding suggests that gap junctions in the inner and the outer retina may be regulated differently although they both contain the same connexin. Taken together, our study identifies CaMKII-β and -δ as Cx36-specific regulators in the mouse retina with CaMKII-δ regulating the primary rod pathway