Abstract
High-performance computing relies on performance-oriented infrastructures with access to powerful computing resources to complete tasks that contribute to solve complex problems in society. The intensive use of resources and the increase in service demand due to emerging fields of science, combined with the exascale paradigm, climate change concerns, and rising energy costs, ultimately means that the decarbonization of these centers is key to improve their environmental and financial performance. Therefore, a review on the main opportunities and challenges for the decarbonization of high-performance computing centers is essential to help decision-makers, operators and users contribute to a more sustainable computing ecosystem. It was found that state-of-the-art supercomputers are growing in computing power, but are combining different measures to meet sustainability concerns, namely going beyond energy efficiency measures and evolving simultaneously in terms of energy and information technology infrastructure. It was also shown that policy and multiple entities are now targeting specifically HPC, and that identifying synergies with the energy sector can reveal new revenue streams, but also enable a smoother integration of these centers in energy systems. Computing-intensive users can continue to pursue their scientific research, but participating more actively in the decarbonization process, in cooperation with computing service providers. Overall, many opportunities, but also challenges, were identified, to decrease carbon emissions in a sector mostly concerned with improving hardware performance.

Abstract
The integration of electric vehicles is paramount to the electrification of the transport sector, supporting the energy transition. The charging process of electric vehicles can be perceived as a controllable load and targeted with price or incentive-based programs. Demand-side management can optimize charging station performance and integrate renewable energy generation through electrical energy storage. Data flowing through charging stations can be used in computational approaches to solve open challenges and create new services, such as a dynamic pricing strategy, where the charging tariff depends on operating conditions. This work presents a data-driven service that optimizes day-ahead charging tariffs with a bilevel optimization problem and develops a case study around a large-scale pilot. The impact of photovoltaics and battery storage on the dynamic pricing scheme was assessed. A dynamic pricing strategy was found to benefit self-consumption and self-sufficiency of the charging station, increasing over 4 percentage points in some cases.

Abstract
The digital terrain model (DTM), the representation of earth's surface at regularly spaced intervals, is the first input in the computational modelling of atmospheric flows. The ability of computational meshes based on high- (2 m; airborne laser scanning, ASL), medium- (10 m; military maps, Mil) and low-resolution (30 m; Shuttle Radar Topography Mission, SRTM) DTMs to replicate the Perdigao experiment site was appraised in two ways: by their ability to replicate the two main terrain attributes, elevation and slope, and by their effect on the wind flow computational results. The effect on the flow modelling was evaluated by comparing the wind speed, wind direction and turbulent kinetic energy using VENTOS (R)/2 at three locations, representative of the wind flow in the region. It was found that the SRTM was not an accurate representation of the Perdigao site. A 40m mesh based on the highest-resolution data yielded an elevation error of less than 1.4m and an RMSE of less than 2.5m at five reference points compared to 5.0m in the case of military maps and 7.6m in the case of the SRTM. Mesh refinement beyond 40m yielded no or insignificant changes on the flow field variables, wind speed, wind direction and turbulent kinetic energy. At least 40m horizontal resolution - threshold resolution based on topography available from aerial surveys is recommended in computational modelling of the flow over Perdigao.