Climate change and geopolitical risks call for the rapid transformation of electricity systems worldwide, with Europe at the forefront. Wind and solar are the lowest cost, lowest risk, and cleanest energy sources, but their variability poses integration challenges. Combining both technologies and integrating regions with dissimilar generation patterns optimizes the trade-off between maximizing energy output and minimizing its variability, which respectively give the lowest levelized cost and lowest integration cost. We apply the Markowitz mean-variance framework to a rich multi-decade dataset of wind and solar productivity to quantify the potential benefits of spatially integration of renewables across European countries at hourly, daily and monthly timescales. We find that optimal cross-country coordination of wind and solar capacities across Europe's integrated electricity system increases capacity factor by 22% while reducing hourly variability by 26%. We show limited benefits to solar integration due to consistent output profiles across Europe. Greater wind integration yields larger benefits due to the diversity of regional weather patterns. This framework shows the importance of considering renewable projects not in isolation, but as interconnected parts of a pan-continental system. Our results can guide policymakers towards strategic energy plans that reduce system-wide costs of renewable electricity, accelerating the clean energy transition.

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Aircraft performance has always been a focus of attention in aviation. The work of aircraft designers, certifying agencies, aircraft operators, and air traffic controllers relies on aircraft performance models. Current aircraft performance models are based on performance data of brand-new aircraft, independent of airline configuration and customizations. Nonetheless, over time aircraft suffer structure, engine and aerodynamic deterioration, as well as maintenance actions. These factors, which vary with tail number, make aircraft performance deviate from the theoretical and create the need for aircraft performance monitoring, and ultimately for aircraft performance tailoring. This research work proposes a novel approach to develop up-to-date, tail-specific performance models based on the use of Quick Access Recorder (QAR) data and machine-learning techniques. In particular, a methodology was designed to calibrate Base of Aircraft DAta (BADA), a widely consolidated physics-based performance model. As a result, more accurate performance models are generated, maintaining the same applicability over the entire flight envelope and during all phases of flight as BADA nominal models.@en