How Far Can Life Cycle Assessment Be Simplified? A Protocol to Generate Simple and Accurate Models for the Assessment of Energy Systems and Its Application to Heat Production from Enhanced Geothermal Systems.

Life cycle assessments (LCAs) quantify environmental impacts of systems and support decision-making processes. LCAs are however time-consuming and difficult to conduct for nonexperts, thus calling for simplified approaches for multicriteria environmental assessments. In this paper, a five-step protocol is presented to generate simplified arithmetic equations from a reference parametrized LCA model of an energy system and its application illustrated for an enhanced geothermal system for heat generation with very low direct emissions in continental Europe. The simplified models estimate seven environmental impacts (climate change, freshwater ecotoxicity, human health, minerals and metals, and fossil resources depletion, and acidification) based on six technological parameters: number of injection and production wells, power of the production and injection pump, average well length, thermal power output, and eight background parameters defining the European electricity mix. A global sensitivity analysis identified these parameters as influencing the variance of the environmental impacts the most. Ensuring the representativeness of the reference LCA model and the validity of the simplified models requires thorough assessment. This protocol allows to develop relevant alternatives to detailed LCAs for quick and multicriteria environmental impact assessments of energy systems, showing that LCAs can be simplified to system-specific equations based on few, easily quantified, parameters.

A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata.

Photovoltaic (PV) energy generation plays a crucial role in the energy transition. Small-scale, rooftop PV installations are deployed at an unprecedented pace, and their safe integration into the grid requires up-to-date, high-quality information. Overhead imagery is increasingly being used to improve the knowledge of rooftop PV installations with machine learning models capable of automatically mapping these installations. However, these models cannot be reliably transferred from one region or imagery source to another without incurring a decrease in accuracy. To address this issue, known as distribution shift, and foster the development of PV array mapping pipelines, we propose a dataset containing aerial images, segmentation masks, and installation metadata (i.e., technical characteristics). We provide installation metadata for more than 28000 installations. We supply ground truth segmentation masks for 13000 installations, including 7000 with annotations for two different image providers. Finally, we provide installation metadata that matches the annotation for more than 8000 installations. Dataset applications include end-to-end PV registry construction, robust PV installations mapping, and analysis of crowdsourced datasets.