• Energy Research

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

AbstractAcross 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 for developing 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 68,828 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 allow 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.

Abstract Floating offshore wind energy is an emerging technology that provides access to new wind generation sites allowing for a diversified wind supply in future low carbon electricity systems. We use a high spatial and temporal resolution power system optimisation model to explore the conditions that lead to the deployment of floating offshore wind and the effect this has on the rest of the electricity system for Great Britain in 2050. We perform a sensitivity analysis on three dimensions: total share of renewables, floating offshore costs and the impact of waves on operation. We find that all three impact the deployment of floating offshore wind energy. A clear competition between floating offshore wind and conventional offshore wind is demonstrated, with less impact on other renewable sources. It is shown that floating wind is used to provide access to greater spatial diversification. Further, access to more distant regions also affects the optimal placement of conventional offshore wind, as spatial diversification is spread between floating and bottom-mounted sites.

<p>The decarbonisation of power production is key to achieving the Paris Agreement goal of limiting global mean surface temperature rise to well below 2°C, particularly so given the drive to electricity transport and heat. At the same time, variable renewable energy (VRE) sources such as solar photovoltaics (PV) and wind have seen rapid cost reductions in recent decades bringing them into cost parity with base load fossil generation. Therefore, recent long term planning studies, which utilise cost-optimising models, have demonstrated the important role of VREs in decarbonising power systems across the world. However, while techno-economically detailed, such studies tend to neglect key social factors that often shape the real world evolution of the energy system.</p><p>Of particular relevance to VRE deployment is their visual impact on the landscape which can act to undermine their public acceptability. Here, we use crowd-sourced scenicness data to derive spatially explicit, empirically grounded wind energy capacity potentials for three scenarios of public sensitivity to this visual impact. We augment these with a detailed analysis of Great Britain’s (GB) solar PV capacity potential. We then use these scenarios in a cost-optimising model of GB’s power system to assess their impact on the cost and design of the electricity system in 2050. Our results show that the levelised cost of the system can increase by up to 15% when public sensitivity to visual impact is high compared to low. In part this is driven by our finding that some of the most picturesque parts of GB also happen to be the most cost-effective for onshore wind, leading to large reductions in installed capacity as we move through our sensitivity scenarios. Indeed, deployment is heavily limited in Scotland and the South-West which in turn acts to limit the spatial diversity of onshore wind. We conclude that it is essential for policy makers to consider these cost implications and to find mechanisms to ameliorate the visual impact of onshore wind in local communities.</p>

This paper assesses how different levels of geographical disaggregation of wind and photovoltaic energy resources could affect the outcomes of an energy system model by 2020 and 2050. Energy system models used for policy making typically have high technology detail but little spatial detail. However, the generation potential and integration costs of variable renewable energy sources and their time profile of production depend on geographic characteristics and infrastructure in place. For a case study for Austria we generate spatially highly resolved synthetic time series for potential production locations of wind power and PV. There are regional differences in the costs for wind turbines but not for PV. However, they are smaller than the cost reductions induced by technological learning from one modelled decade to the other. The wind availability shows significant regional differences where mainly the differences for summer days and winter nights are important. The solar availability for PV installations is more homogenous. We introduce these wind and PV data into the energy system model JRC-EU-TIMES with different levels of regional disaggregation. Results show that up to the point that the maximum potential is reached disaggregating wind regions significantly affects results causing lower electricity generation from wind and PV.

Energy and climate policy-making requires strong quantitative scientific evidence to devise robust and consistent long-term decarbonisation strategies. Energy system modelling can provide crucial insights into the inherent uncertainty in such strategies, which needs to be understood when designing appropriate policy measures. This study contributes to the growing research area of uncertainty analysis in energy system models. We combine consistent and realistic narratives on several technology dimensions with a global sensitivity analysis in a national, bottom-up, optimizing energy system model. This produces structured insights into the impact of low-carbon technology and resource availability on the long-term development of the UK energy system under ambitious decarbonisation pathways. We explore a variety of result metrics to present policy-relevant results in a useful and concise manner. The results provide valuable information on the variability of fuel and technology use across the uncertainty space (e.g. a strong variation in natural gas demand). We demonstrate the complementarities and substitutability of technologies (e.g. the dependency of hydrogen technologies on the availability of CCS). We highlight critical low-carbon options and hedging strategies (e.g. the early decarbonisation of the electricity sector or the stronger use of renewable sources as a hedging against failure in other technologies) and demonstrate timing and path dependencies (e.g. the importance of early decarbonisation action in the presence of multiple technology uncertainty). The results also show how the availability of a given technology can have wider impacts elsewhere in the energy system, thus complicating the management of a long-term energy transition.