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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., Comment: 26 pages, 14 figures

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., 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

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 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