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What constitutes socially just or unjust energy systems or transitions can be derived from the philosophy and theories of justice. Assessments of justice and utilising them in modelling lead to great differences based on which justice principles are applied. We find that comparisons between the two principles of utilitarianism and egalitarianism dominate in assessments of distributive justice, with the latter most often considered representing a "just energy system". The lack of recognition of alternative and equally valid principles of justice, resting on e.g. capabilities, responsibilities and/or opportunities, leads to a narrow understanding of justice that fails to align with the views of different individuals, stakeholders and societies. More importantly, it can lead to the unjust design of future energy systems and energy systems analysis. In this work, we contribute to the growing amount of research on justice in energy systems modelling by assessing the implications of different philosophical views on justice on modelling results. Through a modelling exercise with a power system model for Europe, we explore different designs of a future net-zero European energy system, and its distributional implications based on the application of different justice principles. In addition to the utilitarian and egalitarian approach, we include, among others, principles of "polluters pay" and "ability-to-pay", which take historical contributions of GHG and the socio-economic conditions of a region into account. We find that socially just energy systems look significantly different depending on the justice principles applied. The results may stimulate a greater discussion among researchers and policymakers on the implications of different constructions of justice in modelling, expansion of approaches, and demonstrate the importance of transparency and assumptions when communicating such results

The submillimeter and millimeter ((sub-)mm) sky contains a vast wealth of information that is both complementary and inaccessible to other wavelengths. Over half the light we receive is observable at (sub-)mm wavelengths, yet we have mapped only a small portion of the sky at sufficient spatial resolution and sensitivity to detect and resolve distant galaxies or star forming cores within their their large-scale environment. For decades the astronomical community has highlighted the need for a large, high-throughput (sub-)mm ($\lambda\sim0.35-10$ mm) single dish; The Atacama Large Aperture Submillimeter Telescope (AtLAST), with its 50-m aperture and $2^\circ$ maximal field of view, aims to be such a facility. We present here the full design concept for AtLAST, developed through an EU-funded project. Our design approach begins with a long lineage of (sub-)mm telescopes, relies on calculations and simulations to realize the optics, and uses finite element analysis to optimize the mechanical structure and subsystems. The demanding design requirements for AtLAST, set by transformative science goals, combine novel concepts with lessons learned from past experience. The result is an innovative rocking chair design with six instrument bays, two of which are mounted on Nasmyth platforms. AtLAST will be capable of $3^\circ\,\rm s^{-1}$ scanning and $1^\circ\,\rm s^{-2}$ acceleration, and aims to feature a surface accuracy of $\leq 20~\mu$m half wavefront error under static deformations, allowing observations up to $\approx 950$~GHz. Crucially, AtLAST will be a sustainable, visionary facility that will allow upgrades for decades to come. While some aspects require further testing, prototyping, and field demonstrations, we estimate that the design will be construction-ready this decade., Comment: This version addresses the first round A&A referee reports

Europe as a whole as well as individual countries have many distinct pathways to net carbon neutrality by 2050. We use novel near-optimal modelling techniques to illuminate trade-offs and interactions between national and continental energy transitions under uncertainty. Our results reveal extensive and robust flexibility at a regional level in renewable and hydrogen investments as well as in hydrogen and electricity exports. However, Europe's energy interconnections lead to significant cross-border effects of national energy strategies. Wind and hydrogen investments can easily be shifted geographically within Europe, and Northern Europe's capacity as energy exporter or importer can shape and be shaped by the remaining system. Solar in Southern Europe and Germany comes out as an enabler, and can unlock design flexibility for the rest of the system. Quantifying these regional trade-offs in energy system planning is crucial in order to facilitate meaningful policy discussion and enable a fair energy transition., Comment: 26 pages, 15 figures, 1 table

Across Europe negative public opinion has and may continue to limit the deployment of renewable energy infrastructure required for the transition to net-zero energy systems. Understanding public sentiment and its spatio-temporal variations is as such important for decision-making and socially accepted energy systems. In this study, we apply a sentiment classification model based on a machine learning framework for natural language processing, NorBERT, on data collected from Twitter between 2006 and 2022 to analyse the case of wind power opposition in Norway. From the 68828 tweets with geospatial information, we show how discussions about wind power intensified in 2018/2019 together with a trend of more negative tweets up until 2020, both on a regional level and for Norway as a whole. Furthermore, we find weak geographical clustering in our data, indicating that discussions are country wide and not dominated by specific regional events or developments. Twitter data allows for detailed insight into the temporal nature of public sentiments and extending this research to additional case studies of technologies, countries and sources of data (e.g. newspapers, other social media) may prove important to complement traditional survey research and the understanding of public sentiment., Comment: 31 pages, 36 figures, 2 tables

In this article, we investigate mismatch of renewable electricity production to demand and how this is affected by flexibility options on the supply side. We assess the impact of spatial and temporal smoothing on reliability of production and whether they can reduce risks of variation. As a case study we pick a simplified (partial) representation of the Norwegian electricity system and focus on wind power. We represent regional electricity production and demand through two stochastic processes: the wind capacity factors are modelled as a two-dimensional Ornstein-Uhlenbeck process and electricity demand consists of realistic base load and temperature-induced load coming from a deseasonalised autoregressive process. We validate these processes, that we have trained on historical data, through Monte Carlo simulations allowing us to generate many statistically representative weather years. For the investigated realisations (weather years) we study deviations of production from demand under different wind capacities, and introduce different scenarios where flexibility options like storage and transmission is available. Our analysis shows that simulated loss values are reduced significantly by cooperation and any mode of flexibility. Combining storage and transmission leads to even more synergies and helps to stabilise production levels and thus adequacy of renewable power systems.

A large portion of astronomy's carbon footprint stems from fossil fuels supplying the power demand of astronomical observatories. Here, we explore various isolated low-carbon power system setups for the newly planned Atacama Large Aperture Submillimeter Telescope, and compare them to a business-as-usual diesel power generated system. Technologies included in the designed systems are photovoltaics, concentrated solar power, diesel generators, batteries, and hydrogen storage. We adapt the electricity system optimization model highRES to this case study and feed it with the telescope's projected energy demand, cost assumptions for the year 2030 and site-specific capacity factors. Our results show that the lowest-cost system with LCOEs of $116/MWh majorly uses photovoltaics paired with batteries and fuel cells running on imported and on-site produced green hydrogen. Some diesel generators run for backup. This solution would reduce the telescope's power-side carbon footprint by 95% compared to the business-as-usual case., Comment: 16 pages, 10 figures

We suggest a new methodology for designing robust energy systems. For this, we investigate so-called near-optimal solutions to energy system optimisation models; solutions whose objective values deviate only marginally from the optimum. Using a refined method for obtaining explicit geometric descriptions of these near-optimal feasible spaces, we find designs that are as robust as possible to perturbations. This contributes to the ongoing debate on how to define and work with robustness in energy systems modelling. We apply our methods in an investigation using multiple decades of weather data. For the first time, we run a capacity expansion model of the European power system (one node per country) with a 3-hourly temporal resolution with 41 years of weather data. While an optimisation with 41 weather years is at the limits of computational feasibility, we use the near-optimal feasible spaces of single years to gain an understanding of the design space over the full time period. Specifically, we intersect all near-optimal feasible spaces for the individual years in order to get designs that are likely to be feasible over the entire time period. We find significant potential for investment flexibility, and verify the feasibility of these designs by simulating the resulting dispatch problem with four decades of weather data. They are characterised by a shift towards more onshore wind and solar power, while emitting up to 50% less $CO_2$ than a cost-optimal solution over that period. Our work builds on recent developments in the field, including techniques such as Modelling to Generate Alternatives and Modelling All Alternatives, and provides new insights into the geometry of near-optimal feasible spaces and the importance of multi-decade weather variability for energy systems design. We also provide an effective way of working with a multi-decade time frame in a highly parallelised manner., Comment: 34 pages, 18 figures, 2 tables

Energy system models underpin decisions by energy system planners and operators. Energy system modelling faces a transformation: accounting for changing meteorological conditions imposed by climate change. To enable that transformation, a community of practice in energy-climate modelling has started to form that aims to better integrate energy system models with weather and climate models. Here, we evaluate the disconnects between the energy system and climate modelling communities, then lay out a research agenda to bridge those disconnects. In the near-term, we propose interdisciplinary activities for expediting uptake of future climate data in energy system modelling. In the long-term, we propose a transdisciplinary approach to enable development of (1) energy-system-tailored climate datasets for historical and future meteorological conditions and (2) energy system models that can effectively leverage those datasets. This agenda increases the odds of meeting ambitious climate mitigation goals by systematically capturing and mitigating climate risk in energy sector decision making., Comment: MTC, JW and LPS contributed equally to this manuscript. 25 pages, 2 figures. Fixed typos in manuscript title. This perspective is based on the discussion held at the 2021 Next Generations Challenges in Energy-Climate Modelling (NextGenEC'21) workshop

Reanalysis data are widely used for simulating renewable energy and in particular wind power generation. While MERRA-2 has been a de-facto standard in many studies, the newer ERA5- reanalysis recently gained importance. Here, we use these two datasets to simulate wind power generation and evaluate the respective quality in terms of correlations and errors when validated against historical wind power generation. However, due to their coarse spatial resolution, reanalyses fail to adequately represent local climatic conditions. We therefore additionally apply mean bias correction with two versions of the Global Wind Atlas (GWA) and assess the respective quality of resulting simulations. Potential users of the dataset can also benefit from our analysis of the impact of spatial and temporal aggregation on simulation quality indicators. While similar studies have been conducted, they mainly cover limited areas in Europe. In contrast, we look into regions, which globally differ significantly in terms of the prevailing climate: the US, Brazil, South-Africa, and New Zealand. Our principal findings are that (i) ERA5 outperforms MERRA-2, (ii) no major improvements can be expected by using bias-correction with GWA2, while GWA3 even reduces simulation quality, and (iii) temporal aggregation increases correlations and reduces errors, while spatial aggregation does so only consistently when comparing very low and very high aggregation levels.