SHIRENDA: A long-term high-resolution database of electricity demand and wind, hydro and PV renewable resources for Spain

Abstract

Renewable energies (RES) will play a central role in national energy systems worldwide in the near future, boosted by the climate change issue and the ever-growing competitiveness of these energies. An example is the Spanish roadmap to produce 80% of its electricity from renewables by 2030.However, the transition from the current generation mix to decarbonized energy systems is a formidable challenge, as they must be technically reliable and economically viable. To design such systems, the spatial and temporal variability of RES, combined with proper simulation tools, are determinant. In recent decades, energy system models have emerged as valuable tools for conducting these analyses. These models allow, for a specific region, the analysis of the optimal allocation and sizing of new renewable plants, taking into account the variability of generation and demand, energy costs, integration and the issue of transmission. The key input to these models is a database of RES resources in the study region. However, in many cases, the extent to which these databases represent the actual RES for a given country is far from optimal, reducing confidence in the results. In general, current RES databases face two main problems: 1) low reliability of energy estimates and 2) lack of adequate spatial and/or temporal resolution. In most cases, these problems arise from the lack of actual measurements for model training and validation.In this work, we present SHIRENDA (Spanish High-resolution Renewable ENergies and Demand database), an enhanced open access database of Spanish renewable energies resources and demand. The database consists of hourly values of wind, solar photovoltaic and hydroelectric capacity factors (CF), together with electricity demand, covering the period 1990-2020, for each of the Spanish NUTS3 regions, which is an unprecedented spatial resolution so far. CFs and demand values were derived using state-of-the-art machine learning models based on: 1) actual values of installed RES capacities (Jiménez-Garrote et al, 2023); 2) real energy and demand data derived from the Spanish TSO and 3) meteorological data derived from the ERA5 reanalysis. The database covers the period 1990-2020, with the period 2014-2020 used for model training and validation purposes.The SHIRENDA database has been developed within the framework of the MET4LOWCAR project, funded by the Government of Spain, and aims to gather the desirable characteristics to carry out reliable studies on modeling and analysis of energy systems, thus contributing to an adequate energy transition. Notably, the high spatial resolution allows the very high spatial variability of RES resources in the study region to be properly taken into account. At the same time, the high temporal resolution, along with the temporal coverage, allows for properly assessing the impact of climate variability, extreme meteorological conditions and compound events in a future decarbonized energy systems in Spain.    Reference: Jimenez-Garrote et al, 2023. https://doi.org/10.1016/j.solener.2023.03.009