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

Abstract

Abstract. 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.

The impact of future UK offshore wind farm distribution and climate change on generation performance and variability

“…Examining the impact of climate change, we note that mean annual capacity factor under all future climate models is slightly reduced compared to historical (figure 2(a) shows reductions of 2.3%, (p = 0.003) averaged across all models, individual models shown in appendix b), with the climate change impact distinctly significant only during the summer months (summer = − 3.6% (p = 0.0003); winter = − 1.5% (p = 0.1559)) (figure 2(a)), as seen in Bloomfield et al's original study [35], as well as others [19,20]. Inter-model variation is low (see appendix b) however, a detailed analysis of the individual future climate models is not undertaken in this study.…”

“…ERA5 data are initially bias corrected against Global Wind Atlas data [34] to account for sub-gridscale topographic effects. These bias-corrected wind speeds are then scaled to 92 m (average offshore turbine hub height in the UK in 2021) using a wind profile power law [35,36], before being passed through representative onshore and offshore wind farm power curves to provide the wind capacity factor at each ERA5 gridpoint and timestep.…”

Capacity Value of Interconnectors for Resource Adequacy Assessment in Multi-Region Systems

“…The capacity value of interconnectors depends on the coincidence of stress events in interconnected regions, which is partially driven by the weather within those regions. This paper takes advantage of a 70-year reanalysis data set which has been developed specifically for use by energy system modellers [14]. Weather driven models of demand and renewable generation were developed; demand was modelled as a function of ambient temperature [15] whilst the renewable generation models were taken directly from [14].…”

“…This paper takes advantage of a 70-year reanalysis data set which has been developed specifically for use by energy system modellers [14]. Weather driven models of demand and renewable generation were developed; demand was modelled as a function of ambient temperature [15] whilst the renewable generation models were taken directly from [14]. Time-series of conventional generator available capacities were calculated using the models presented in [16].…”

“…When the Celtic Interconnector is installed, there is a rapid decrease in EENS in Ireland, a small decrease in EENS in France, and no change in EENS in GB. This behaviour is partially due to the regularisation term in (14), which uses the weightings (7) to allocate the ENS based on the peak demand of the regions. Ireland initially had a disproportionately high EENS relative to its peak demand, so the additional interconnection is used to reduce shortfalls there before reducing those in France (which has a higher absolute, but lower relative, EENS).…”