Spatially and Temporally Explicit Energy System Modelling to Support the Transition to a Low Carbon Energy Infrastructure – Case Study for Wind Energy in the UK

Renewable energy sources and electricity demand vary with time and space and the energy system is constrained by the location of the current infrastructure in place. The transitioning to a low carbon energy society can be facilitated by combining long term planning of infrastructure with taking spatial and temporal characteristics of the energy system into account. There is a lack of studies addressing this systemic view. We soft-link two models in order to analyse long term investment decisions in generation, transmission and storage capacities and the effects of short-term fluctuation of renewable supply: The national energy system model UKTM (UK TIMES model) and a dispatch model. The modelling approach combines the benefits of two models: an energy system model to analyse decarbonisation pathways and a power dispatch model that can evaluate the technical feasibility of those pathways and the impact of intermittent renewable energy sources on the power market. Results give us the technical feasibility of the UKTM solution from 2010 until 2050. This allows us to determine lower bounds of flexible elements and feeding them back in an iterative process (e.g. storage, demand side control, balancing). We apply the methodology to study the long-term investments of wind infrastructure in the United Kingdom

highRES-Europe: The high spatial and temporal Resolution Electricity System model for Europe

The high spatial and temporal resolution electricity system model, highRES, is used to design cost-effective, flexible and weather resilient electricity systems for Great Britain and Europe. The model is specifically designed to analyse the effects of high shares of variable renewables and explore integration/flexibility options. As the proportion of renewables in electricity generation increases, there will be increasing imbalances between electricity demand and supply. highRES is a high-resolution electricity system model that simultaneously considers infrastructure planning (investment) and operational (dispatch) decisions to identify the most cost-effective strategies to cope with growing shares of intermittent renewables. It does this by comparing and trading off potential options to integrate renewables into the system including the extension of the transmission grid, interconnection with other countries, building flexible generation (e.g. gas power stations), renewable curtailment and energy storage. highRES is written in GAMS and its objective is to minimise power system investment and operational costs to meet hourly demand, subject to a number of unit and system constraints. It can model a variety of technical characteristics of thermal generators (e.g. ramping restrictions, minimum stable generation, startup costs, minimum up and down times) depending on the requirements of the research question, their CO2 emissions, and the technical characteristics of a variety of energy storage options. The transmission grid is represented using a linear transport model

Intersecting near-optimal spaces: European power systems with more resilience to weather variability

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 three-hourly temporal resolution and 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 more than 50% less CO2 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 (MGA) and Modelling All Alternatives (MAA), 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. Our implementation is open-sourced, adaptable and is based on PyPSA-Eur

The implications of landscape visual impact on future highly renewable power systems: a case study for Great Britain

Recent long term planning studies have demonstrated the important role of variable renewables (VRE) in decarbonising our energy system. However, cost-optimising models do not capture the visual impact of VREs on the landscape which can act to undermine their public acceptability. Here, we use crowd-sourced scenicness data to derive spatially explicit wind energy capacity potentials for three scenarios of public sensitivity to this visual impact. We then use these scenarios in a cost-optimising model of the GB power system to assess their impact on the cost and design of the electricity system in 2050. Our results show that total system costs can increase by up to 14.2% when public sensitivity to visual impact is high compared to low. It is thus essential for policy makers to consider these cost implications and to find mechanisms to ameliorate the visual impact of onshore wind in local communities

A renewable power system for an off-grid sustainable telescope fueled by solar power, batteries and green hydrogen

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 figure

Impact of different levels of geographical disaggregation of wind and PV electricity generation in large energy system models: A case study for Austria

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

Machine Learning of Public Sentiments toward Wind Energy in Norway

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 table

The direct interconnection of the UK and Nordic power market – Impact on social welfare and renewable energy integration

United Kingdom and the Nordic power market have plans to interlink directly through a sub-sea power transmission line in The North Sea. Such power market couplings have complicated implications for the interconnected energy systems and for different agents in the common power market. We analyse this case by modelling the hourly operation of the Nordic-UK power market coupling, considering the local district heating (DH) system in each country as well. According to the results, after the operation of the new interconnection between Norway and the UK (North Sea Link), the overall socio-economic benefits (social welfare) in the region will likely improve by 220–230 million euro per year, without considering the cost of the interconnector itself. The UK-Nordic market coupling enhances the flexibility of the UK power system in wind integration, irrespective of the share of wind in the Nordic countries. However, increasing wind capacity in the UK will diminish the expected economic benefits of the link. The merit order effect of wind integration in the UK will reduce the price gap between UK and Norway, and so the congestion income of the link in many hours a year when the link is congested from Norway towards the UK