Improvements in modelling energy systems of populous emerging economies are highly decisive for a successful global energy transition. The models used–increasingly open source–still need more appropriate open data. As an illustrative example, we take the Brazilian energy system, which has great potential for renewable energy resources but still relies heavily on fossil fuels. We provide a comprehensive open dataset for scenario analyses, which can be directly used with the popular open energy system model PyPSA and other modelling frameworks. It includes three categories: (1) time series data of variable renewable potentials, electricity load profiles, inflows for the hydropower plants, and cross-border electricity exchanges; (2) geospatial data on the administrative division of the Brazilian federal states; (3) tabular data, which contains power plant data with installed and planned generation capacities, aggregated grid network topology, biomass thermal plant potential, as well as scenarios of energy demand. Our dataset could enable further global or country-specific energy system studies based on open data relevant to decarbonizing Brazil’s energy system.
Energy system modeling and analysis can provide comprehensive guidelines to integrate renewable energy sources into the energy system. Modeling renewable energy potential, such as wind energy, typically involves the use of wind speed time series in the modeling process. One of the most widely utilized datasets in this regard is ERA5, which provides global meteorological information. Despite its broad coverage, the coarse spatial resolution of ERA5 data presents challenges in examining local-scale effects on energy systems, such as battery storage for small-scale wind farms or community energy systems. In this study, we introduce a robust statistical downscaling approach that utilizes a machine learning approach to improve the resolution of ERA5 wind speed data from around 31 km x 31 km to 1 km x 1 km. To ensure optimal results, a comprehensive preprocessing step is performed to classify regions into three classes based on the quality of ERA5 wind speed estimates. Subsequently, a regression method is applied to each class to downscale the ERA5 wind speed time series by considering the relationship between ERA5 data, observations from weather stations, and topographic metrics. Our results indicate that this approach significantly improves the performance of ERA5 wind speed data in complex terrain. To ensure the effectiveness and robustness of our model, we also perform thorough evaluations by comparing our results with the reference dataset COSMO-REA6 and validating with independent datasets.
The utilization of cropland and rooftops for solar photovoltaics (PV) installation holds significant potential for enhancing global renewable energy capacity with the advantage of dual land-use. This study focuses on estimating the global area suitable for agrivoltaics (PV over crops) and rooftop PV by employing open-access data, existing literature and simple numerical methods in a high spatial resolution of 10 km x 10 km. For agrivoltaics, the suitability is assessed with a systematic literature review on crop-dependent feasibility and profitability, especially for 18 major crops of the world. For rooftop PV, a non-linear curve-fitting method is developed, using the urban land cover to calculate the PV-suitable built-up areas. This method is then verified by comparing the results with open access building footprints. The spatially resolved suitability assessment unveils 4.64 million km2 of global PV-usable cropland corresponding to a geographic potential of about 217 Terawatts (TW) in an optimistic scenario and 0.21 million km2 of rooftop-PV suitable area accounting for about 30.5 TW maximum installable power capacity. The estimated suitable area offers a vast playground for energy system analysts to undertake techno-economic assessments, and for technology modellers and policy makers to promote PV implementation globally with the vision of net-zero emissions in the future.
The tool autumn was developed to address the uncertainty of an evolving technological development in carbon capture in conjunction with different industry, energy and climate scenarios. It consists of a dynamic data pipeline to create a geographically resolved cost and potential distribution of captured CO 2 . It overcomes two challenges of using other solutions: First, users can specify the geographical scope and thus align the cost potential characteristics to the model and research specific scope. Secondly, dynamic technology parameters can be specified in order to describe uncertain political and societal choices in the studied models. autumn provides different spatial and technological resolution in its interface, so that modellers can flexibly adapt the library to their needs.
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