Displays

In Europe, the system cost minimizing highly renewable power system set-up predominantly relies on wind energy, with minor shares of photovoltaics.

Yet, minimizing system cost neglects negative externalities of wind turbines, such as their impact on wildlife, noise emissions, landscape aesthetics, manifesting in local economic impacts such as a decline of house prices in the vicinity of wind turbines.

To better understand the trade-off between electricity system cost and the negative externalities from wind turbines, we quantify the increase in electricity system cost when the system cost minimizing deployment of wind turbines is reduced in the favor of photovoltaics.

Methodologically, we rely on the power system model medea, an open, techno-economic, numerical model of hourly dispatch and investment, set up to resemble the electricity market in Austria and its largest electricity trading partner Germany in 2030, when Austria aims to generate 90% of its electricity consumption from domestic renewable sources on annual balance.

Depending on the capital cost of renewable energy technologies, the marginal system cost from displaced wind turbines can reach up to 40.000 EUR per MW and year or approximately 20 EUR per MWh. Moreover, CO2 emissions can increase by up to 1.2 million tons per year when wind energy is fully displaced. Producer surplus could increase by up to 220 million EUR per annum at intermediate wind energy displacement but falls back towards initial levels when wind energy is fully displaced.

These numbers compare to estimates of property price declines between 2% and 16% caused by wind turbines, depending on the proximity to, and the visibility of the turbine. For illustration, adding a 3.5 MW wind turbine to a total installed wind power capacity of 12.6 GW in Austria over its lifetime (assuming a 3% discount rate) would generate sufficient social value to compensate affected property worth between 0.8 and 6.7 million EUR.

How to cite: Wehrle, S. and Schmidt, J.: The cost of undisturbed landscapes: on the valuation of wind turbines in Austria, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11328, https://doi.org/10.5194/egusphere-egu2020-11328, 2020.

The decarbonisation of power production is key to achieving the Paris Agreement goal. Wind and solar energy have matured and decreased in cost rapidly into cost-effective decarbonisation solutions. However, the location of renewables effects the impact on the environment and the communities they are sited. Thus, socio-environmental constraints can strongly limit the overall capacity potential affecting the technology choices, resulting costs and political feasibilities of reaching the national emission reduction targets. Nevertheless, socio-environmental acceptance is usually not considered when studying the transition to a net-zero energy system.

Norway has one of the best wind energy potentials in Europe and a large scale deployment in combination with increased interconnection could have effects on the rest of the European power system. However, recent projects have been facing large opposition. This may be surprising as Norway has very low population density but the right to unspoilt nature is strongly anchored in the Norwegian culture and Sami reindeer herding could be disturbed by wind projects.  In 2019 the Norwegian Water Resources and Energy Directorate (NVE) proposed a national framework for wind energy which defined the most suitable areas for wind energy development. After massive protests the framework was recently withdrawn by the government. Offshore wind energy is often seen as a potential solution as socio-environmental opposition is expected to be lower but it is more expensive. However, it is as socio-political decision to choose a more expensive technology, site or mitigation option. A spatially-dependent capacity assessment under different socio-environmental scenarios and their effect on energy system design is missing to allow for such discussion.

Here, we close this gap by analysing the NVE framework, previous concessions and related opinions, literature, newspaper articles and perform interviews with key stakeholders to design three scenarios of socio-environmental acceptability for onshore/offshore wind and solar energy. Based on the here developed scenarios we then conduct a GIS analysis to determine the spatially dependent capacity potential per technology and scenario. Finally, we implement these geospatial capacity scenarios into a high spatial and temporal resolution electricity system model for Europe (“highRES Europe”) to analyse the effects on the Norwegian and European electricity system design in 2050.  

How to cite: Zeyringer, M., Price, J., and Mannino, E.: The effects of socio-environmental constraints on Norway’s renewable energy potential, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20190, https://doi.org/10.5194/egusphere-egu2020-20190, 2020.

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.

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.

How to cite: Price, J., Mainzer, K., Petrović, S., Zeyringer, M., and McKenna, R.: The implications of landscape visual impact on future highly renewable power systems: a case study for Great Britain, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5708, https://doi.org/10.5194/egusphere-egu2020-5708, 2020.

The UK government has made formal commitments to reduce GHG emissions (e.g. under the Climate Change Act 2008 and subsequent amendments) and to protect/improve natural capital and the environment (e.g. as part of the 25 Year Environment Plan published in 2018). Meeting these objectives requires an integrated approach to two parallel challenges i) decarbonising the energy system and ii) better understanding and valuation of natural capital and ecosystem services. From an academic perspective this involves bringing together two substantial, but rather weakly connected bodies of research, while also acknowledging that this integration in a UK setting needs to recognise the international context (i.e. a whole systems perspective).

The ADVENT project (ADdressing Valuation of Energy and Nature Together) has been funded by the UK National Environment Research Council to develop conceptual frameworks and modelling tools which ‘integrate the analysis of prospective UK energy pathways with considerations relating to the value of natural capital’. A methodology has been implemented to downscale the outputs of pathways from national energy system models and incorporate environmental impacts into the assessment of different options. This has required defining spatially-optimised distributions of investments in new energy infrastructure using a range of financial and welfare criteria. These distributions are then compared in terms of their construction, transport and land opportunity costs, as well as the implications for biodiversity, greenhouse gas emissions, recreation, visual amenity and water resources.

This paper will present results from comparing different UK energy pathways through to 2050 in terms of the implications of electricity generation from three types of renewables (bioenergy, solar and onshore wind). The results illustrate that i) individual pathways can vary appreciably in their environmental impacts, ii) overall societal welfare can be enhanced by using spatial modelling to incorporate valuations of such impacts into implementation of pathways and iii) assessment outcomes can be sensitive to modelling assumptions (e.g. regarding the proportion of biomass feedstock from domestic or international sources). More broadly, the results demonstrate how important improvements can be achieved in the integration of environmental considerations into the assessment of future energy pathways at regional and national scales. The approach is now being further refined through the UK Energy Research Centre Phase 4 programme and ADVANCES Landscape Decisions project in the UK, as well as the five-country IRENES project funded by Interreg Europe. 

How to cite: Lovett, A., Day, B., Smith, G., Delafield, G., Owen, N., Agnolucci, P., Bateman, I., Beaumont, N., Carver, S., Dockerty, T., Donnison, C., Eigenbrod, F., Ferguson-Gow, H., Hastings, A., Holland, R., Pearson, R., Sünnenberg, G., Taylor, G., and Ziv, G.: Incorporating the value of nature into assessments of future energy pathways, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4176, https://doi.org/10.5194/egusphere-egu2020-4176, 2020.

Wind and solar power are vital for climate change mitigation, producing electricity at much lower greenhouse gas (GHG) emissions than conventional fossil-based technologies. Here, we obtain facility-specific environmental footprints of utility-scale wind and solar power across the globe. We investigate how the GHG footprint of wind and solar power varies across space and across technological characteristics. We will furthermore investigate other environmental footprints such as mineral resource scarcity to assess whether there is a trade-off between low GHG footprints and possibly higher other footprints.

We use facility-specific technological characteristics of ~30,000 wind parks and ~10,000 photovoltaic solar parks across the globe, such as capacity, hub height, rotor diameter or type of panel, to determine the life-cycle environmental impacts per wind or solar park. The produced power per facility over its lifetime is computed based on technological characteristics as well as location-specific hourly climate input from the ERA5 reanalysis dataset. The environmental footprint is then defined as impact divided by power produced, e.g. g CO₂-eq/kWh, to allow for comparison between facilities and across energy sources.

The facility-specific footprints will be shown on maps to indicate spatial variability and range of footprints of both wind and solar power. We will furthermore investigate the variability in footprints using analysis of variance, in order to indicate whether climate (i.e. location-specific wind or radiation) or technological characteristics (i.e. hub height, rotor diameter or type of panel) is the main cause of variability in footprints.

How to cite: Bosmans, J., Dammeier, T., and Huijbregts, M.: Facility-specific environmental footprints of wind and solar power at a global scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8139, https://doi.org/10.5194/egusphere-egu2020-8139, 2020.

This paper addresses the link between geo data models, market design of renewable energy auctions and energy system models. Renewable energy accounts for around 20% of electricity supply in Europe. In countries such as Sweden, Finland and Germany we already reached a share of more than 40%. In these countries renewables became the main energy source. The dash for building renewable energy in Europe will continue with the EU and national climate targets.

The impact of renewables on the grid and system operation will increase. Key elements to build an efficient energy infrastructure in the long term are a good understanding on (1) how renewables will penetrate the energy system (regional investments) and a good perception on (2) the effects renewables have on the energy system including (3) the additional infrastructure required, enabling a secure electricity system.

Since 2005 the DLR uses geo data model ENDAT to predict wind power feed-in and investments in the years up to 2050 based on historic weather data. In order to allow for better modelling of the potentials of wind energy high resolution of wind data and efficient clustering methods are applied to allow a more detailed representation of the long term potentials of wind energy.

In this paper we combine three modelling approaches: The geo data model ENDAT (DLR), a model of the renewable auctions based on a system dynamics model HECTOR (RWTH Aachen) and an energy system model REMix (DLR) – that allows investigating the long term impact of renewables on the electricity system for 2030, 2040 and 2050. The key questions this paper aims to answer are: How will detailed spatial and temporal modelling of renewable energy data as well as auction design influence the predictions for future distribution of wind power plants? What policy recommendations can be drawn from predictions for the years up to 2050 with regard to policy design and investments in wind energy in Germany and Europe?

The paper divides in two parts. The first part investigates different approaches to model potential for wind power investments and power generation based on historic wind data. While in the past ENDAT used to generate time series for wind on a country by country basis or on NUTS-1 level, improved models allow for more detailed representation of wind data. Key element of this part is to understand the benefits of high resolution of wind data for the results of the overall energy system modelling.

The second part of the paper describes how the detailed representation of wind potentials and wind speeds will affect future auction results - and therefore influence long term investments in renewable energy. Model results for the German electricity system will be presented. To benchmark different scenarios, each scenario will be evaluated based on the regional distribution of renewable energies and the resulting impact on the energy system (with regard to grid investments, operation costs and aspects of security of supply).

How to cite: Borggrefe, F., Sheykhha, S., von Krbek, K., and Scholz, Y.: Modelling long term investments in wind energy – benefits of combining high resolution geo data, energy system modelling and auction design, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21830, https://doi.org/10.5194/egusphere-egu2020-21830, 2020.

The variability of renewable power generation is often quantified based on modern reanalyses such as ERA5 or MERRA-2 which provide climatic information over the last few decades. Compared to infrastructure lifetimes, modern reanalyses cover only short periods and may consequently fail to sample relevant longer-term climate variability. While there is evidence for multi-decadal variability in wind power generation [Wohland et al. (2019), Zeng et al. (2019)], hydropower [Bonnet et al. (2017)] and solar energy [Sweerts et al. (2019)], a consistent treatment of multi-decadal variability has not been achieved. 

This knowledge barrier can potentially be overcome using 20th century reanalyses which provide internally consistent fields of energy-relevant variables (e.g., solar radiation, precipitation, temperature and wind). However, the provision of reliable climatic information on these timescales is known to be a challenge due to, for example, the evolution of measurement techniques. Some cases of spurious trends and other shortcomings of the datasets are known. It is therefore of utmost importance to quantify uncertainties prior to usage in energy system studies. To this end, we systematically compare 20CRv3, 20CRv2c, CERA20C and ERA20C with respect to variables needed in renewable energy assessments and report similarities and discrepancies accross the datasets. The focus is given to substantial differences with respect to multi-decadal solar radiation variability in Europe, also known as dimming and brightening. 

References

Bonnet, R., Boé, J., Dayon, G. & Martin, E. Twentieth-Century Hydrometeorological Reconstructions to Study the Multidecadal Variations of the Water Cycle Over France. Water Resour. Res. 53, 8366–8382 (2017).

Sweerts, B. et al. Estimation of losses in solar energy production from air pollution in China since 1960 using surface radiation data. Nat Energy 4, 657–663 (2019).

Wohland, J., Omrani, N. E., Keenlyside, N. & Witthaut, D. Significant multidecadal variability in German wind energy generation. Wind Energ. Sci. 4, 515–526 (2019).

Zeng, Z. et al. A reversal in global terrestrial stilling and its implications for wind energy production. Nat. Clim. Chang. (2019).

How to cite: Wohland, J., Bloomfield, H., Brayshaw, D., Pfenninger, S., and Wild, M.: Understanding multidecadal variability for energy system studies: can current 20th century reanalyses do the job?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3008, https://doi.org/10.5194/egusphere-egu2020-3008, 2020.

Optimal location and sizing of small hybrid systems in micro-grid system using Volunteer Geographic Information

This study presents an optimization model for the optimal location and sizing of small hybrid systems in simulated micro-grids. By using an optimization model - in combination with COSMO-REA2 weather data - various micro-grids local energy systems are simulated using the Calliope energy simulation model. The Calliope optimization and simulation model is feed with GIS-data from different Volunteered Geographic Information projects, including OpenStreetMap. These allows to automatically allocate specific demand profiles to diverse OpenStreetMap building categories. Moreover, based on the characteristics of the OpenStreetMap data, a set of possible distributed energy resources) including renewables and fossil fueled generators are defined for each building category. The optimization model is applied for a set of scenarios based on different electricity prices and technological characteristics. This allows to assess the impact and profitability of the different technological options on the micro-grid configuration. Moreover, in order to assess the impact of each of the scenarios on the current distribution infrastructure, the results of the simulations are included on an existing model of the low and middle voltage network for Lower Bavaria, Germany. Finally, to facilitate their dissemination, the results of the simulation are stored in a PostgreSQL database, before they are delivered by a RESTful Laravel Server and displayed in an Angular Web-Application.

How to cite: Valdes, J., Wöllmann, S., and Zink, R.: Optimal location and sizing of small hybrid systems in micro-grid system using Volunteer Geographic Information, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21859, https://doi.org/10.5194/egusphere-egu2020-21859, 2020.

The increase of variable renewable energy sources (VRE), i.e. wind and solar power, may lead to a certain mismatch between power demand and supply. At the same time, in order to decarbonise the heat and transport sectors, power-based solutions are often seen as promising option, through so-called sector coupling. At times when VRE power supply exceeds demand, the surplus power could be used for producing liquid and gaseous electrofuels. The power is used for electrolysis, producing hydrogen, which can in turn be used either directly or combined with a carbon source to produce hydrocarbon fuels.

Here, we analyse the potential development of surplus power for the case of Germany, at an ambitious VRE expansion until 2050 and perform a cost analysis of electrofuels at different production levels using sorted residual load curves. These are then compared to biofuels and electric vehicles with the aid of an optimisation model, considering both cost- and greenhouse gas (GHG)-optimal options for the main transport sectors in Germany.

We find that, although hydrocarbon electrofuels are more expensive than their main renewable competitors, i.e. biofuels, they are most likely indispensable in addition for reaching climate targets in transport. However, the electrofuel potential is constrained by the availability of both surplus power and carbon. In fact, the surplus power potential is projected to remain limited even at currently ambitious VRE targets for Germany and carbon availability is lower in an increasingly renewable energy system unless direct air capture is deployed. In addition, as the power mix is likely to contain fossil fuels for decades to come, electrofuels based on power directly from the mix with associated conversion losses would cause higher GHG-emissions than the fossil transport fuel reference until a very high share of renewables in the power source is achieved. In contrast, electric vehicles are a more climate competitive option under the projected power mix with remaining fossil fuel fractions, due to a superior fuel economy and thereby lower costs and emissions.

As part of the assessment, we quantify the greenhouse gas abatement costs for different well-to-wheel pathways and provide an analysis and recommendations for a transition to sustainable transport.

How to cite: Millinger, M., Tafarte, P., Jordan, M., Hahn, A., Meisel, K., and Thrän, D.: Power usage in the transport sector – potential, costs and greenhouse gas abatement of different well-to-wheel pathways, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3399, https://doi.org/10.5194/egusphere-egu2020-3399, 2020.

Many countries in the developing world have immense, but underexploited, renewable electricity potentials. A good example are the countries in the Economic Community of West African States (ECOWAS). Historically, renewable power generation in West Africa has focused on hydropower, which produces around 20% of the region’s overall electricity generation, with natural gas providing most of the remainder; future capacity expansion plans for the region are also focused to a large extent around gas and hydropower.

However, dropping costs for modern renewable power sources, primarily solar photovoltaic and wind power, are expected to break the West African gas-hydro-paradigm in the near future. Given the currently low levels of generation and strongly increasing power demand in many countries, they can be seen as “greenfields” for integrating variable renewable energy (VRE) sources into stable power mixes and planning transmission capacity expansion to the benefit of VRE sources.

Such planning requires a nuanced view of the role that different resources can play in a power mix. Solar and wind power are clean and have low environmental impact, but show pronounced diurnal and seasonal cycles, which requires increased power system flexibility across a wide range of time scales. Globally, such flexibility is currently mostly delivered by natural gas, whose use in the future must be limited to comply with the goals of the Paris Agreement. Reservoir hydropower is an alternative source of flexibility, but only if adequately managed across all involved time scales and without endangering environmental flow requirements.

In this research, we combined energy science, meteorology, hydrology and climatology to conduct a scenario-based analysis of smart renewable expansion strategies for West Africa using the REVUB model, considering all time scales ranging from hourly to decadal (including climate change effects) and all spatial scales from point to subcontinental. We show that smart management of hydropower plants, smart designs of solar-wind mixes, and smart planning of regional interconnections can ensure reliable and stable power provision while reducing future natural gas demand and at the same time avoiding ecologically damaging hydropower overexploitation. These results have wide implications for energy policy planning far beyond West Africa, particularly in hydro-dependent developing countries.

How to cite: Sterl, S., Vanderkelen, I., Chawanda, C. J., van Lipzig, N., van Griensven, A., and Thiery, W.: Coupling energy, meteorology, hydrology and climate science to optimize renewable power planning in West Africa, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3644, https://doi.org/10.5194/egusphere-egu2020-3644, 2020.

Weather data is essential to model and optimise energy systems, which are based on high shares of renewable generation sources. However, differences between data sources can be significant and often little emphasis is put on energy-related variables such as hub-height wind speeds.

In this work, we use generative adversarial networks (GAN), a class of machine learning systems, to model weather data for large-scale energy system models and optimise energy systems of different scales and sizes.

We show that generating weather data using GAN saves effort as required for processing large amounts of weather data and that it can reliably reproduce results from using weather data produced by numerical models.

How to cite: Kies, A., Srivastava, N., Zhou, K., Steinheimer, J., and Stoecker, H.: Weather data modelling for energy system models using machine learning, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4682, https://doi.org/10.5194/egusphere-egu2020-4682, 2020.

Results on the statistical properties of internal variability of annual mean surface solar radiation (SSR) and associated decadal scale trends are presented, following in part Folini et al. 2017 (doi:10.1002/2016JD025869). Estimates are based on 43 pre-industrial control (piControl) experiments of the Coupled Model Intercomparison Project Phase 5 (CMIP5). Trends are shown to depend strongly on geographical region and on whether they are quantified in absolute units or relative to the long term mean SSR. Providing one map for absolute and one map for relative trends is sufficient, as approximate analytical relations are shown to hold between trends of different length and likelihood and the standard deviation of the underlying SSR time series. Comparison with present-day observations and inter-model spread suggest an average uncertainty of these estimates of about 30%.  Intermodel spread suggests that regional uncertainties can be up to about three times larger or smaller. Using the model by Crook et al. 2011 (doi:10.1039/C1EE01495A) to translate SSR into PV production, associated internal variability of photo voltaic (PV) energy production is inferred. Results suggest that it is plausible for PV production to change by several per cent over a decade just because of internal variability.

How to cite: Folini, D.: Internal variability of surface solar radiation and associated PV production, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5141, https://doi.org/10.5194/egusphere-egu2020-5141, 2020.

The electrification of the transport sector together with an increasing share of renewable energies has the potential to reduce CO₂ emissions significantly. This transformation requires the roll-out of charging infrastructure, which, as a new and rapidly growing electrical consumer, has an impact on the power grid. For grid planning and dimensioning purposes, it is crucial to assess this impact as accurately as possible. Consequently, the possibility to simulate potential spatial distributions of charging points and their ramp-up is of central importance. We present an approach using socio-economic data such as population size, income levels and age to estimate where electric mobility will be concentrated, especially during the transition phase.

Suitable socio-economic data for Germany is only available for the current population and, in terms of spatial resolution, at the level of streets. Thus, both spatial disaggregation and temporal extrapolation within a demographic model are necessary for more detailed scenario predictions. In our proposed approach, a fuzzy-string comparison method and geographical mapping are used to allocate the socio-economic data to buildings (LOD1). A prediction on demographic changes taking into account recent municipal developments in Germany has been implemented. Age-specific changes at the community level are disaggregated on the household level and merged with socio-economic data. Combined with framework scenarios, we use these criteria based on socio-economic factors to develop spatially disaggregated scenarios. The framework scenarios take into account an increased penetration of renewable energies and a developed TCO approach for the ramp-up of electric mobility.

Predicting future distributions of domestic charging points with such a level of detail in terms of the ramp-up model and spatial resolution is highly beneficial for grid analysis and planning purposes. Typically, distribution grid studies that assess necessary grid investments rely on various simplified assumptions. A more detailed analysis of when and where the power flow at certain building connection points is likely to increase allows for more precise analyses of possible grid congestions. This also makes more efficient grid reinforcement and expansion planning possible, especially in urban areas, where infrastructure changes are expensive and time-consuming.

Another important aspect for demand-driven grid planning is the temporal modeling of charging processes. We use individual driving profiles based on surveys to create charging profiles for different consumer types. We combine them with a holistic model of the energy system including power plant scheduling as well as other (future) local producers and consumers such as photovoltaics and heat pumps. It allows us to consider correlations and simultaneities in their behavior and additionally enables us to explore various flexibility options and their influence on the electricity market and the grid.

How to cite: Gauglitz, P., Ulffers, J., Thomsen, G., Frischmuth, F., Geiger, D., von Bonin, M., Horst, D., and Scheidler, A.: Modeling spatial and temporal charging demands for electric vehicles for scenarios with an increasing share of renewable energies, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7231, https://doi.org/10.5194/egusphere-egu2020-7231, 2020.

Non-hydroelectric renewable energy sources (RES) are the fastest growing energy generation technologies in terms of new capacity and their penetration is expected to double in the next 20 years. More than half of this growth is expected to come from wind power. However, given the variable nature of RES production linked to climate variability and the need for a constant supply-demand balance, increasing penetration of renewables raises structural, technological and economical issues. In Spain, the correlation of solar and wind climate potential with the seasonal fluctuation of electricity consumption, associated mostly with tourism activity, allows for some ambitious renewable penetration scenarios. This work aims at identifying optimal energy mix scenarios that maximize RES penetration while minimizing distribution risk, using the Markowitz modern portfolio theory as a starting point. We apply the e4clim model to the Spanish electricity system, using reanalysis and electricity data in order to produce scenarios for optimal geographical and technological distribution of RES installed capacity. We conduct a mean-risk analysis and discuss the geographical distribution for the most relevant optimal scenarios. We also provide an interpretation of the optimal RES penetration results in terms of the regional climatic characteristics of Spain. Beyond the large potential of the regional climates of Spain to exploit RES technologies, optimal scenario results reveal interesting regional differences with respect to the current installed capacities, which can be linked to economic and regulatory regional contexts.

How to cite: Maimó-Far, A., Tantet, A., Homar, V., and Drobinski, P.: Application of portfolio theory to the wind-solar energy mix in Spain: climate-related risks and opportunities, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7771, https://doi.org/10.5194/egusphere-egu2020-7771, 2020.

Neural networks are widely applicable for different modelling purposes in the energy sector such as predictions of electricity generation from wind and solar resources as well as electricity demand and prices. However, neural network approaches heavily rely on the availability of sound climate and actual generation data for model training. Sufficiently long and accurate time series of climate data needed for model training and seasonal climate forecasts for the prediction process are often not available from a data source based on the same climate model for the corresponding study area. Most likely data sources based on different climate models also feature different bias and consequently using one data source for model training and using another one for model prediction will produce systematically biased results that need correction.

Therefore, we assess here if a neural network approach can be successfully applied as a means to correct for systematic biases when a neural network is trained on wind power electricity generation while using a different climate data source for the prediction process.

We apply neural networks on climate assimilation data from climate modelling and train the neural network on actual generation from wind power. The trained neural networks are then used with an ensemble of climate input variables from seasonal forecasts to seasonally predict electricity generation from wind power. As the neural network is trained on a different data source, the modelled generation values are systematically biased. A subsequent neural network is applied to reduce this bias and to gain insight into how the bias between the two data sources differs via an analysis of the networks weights as well as a sensitivity analysis.

The neural network’s ability to correct for systematic biases is assessed based on whether the quality of the modelled distributions in terms of their seasonal characteristics and extreme event frequencies is improved compared to not using this bias correcting neural network. Initial model results show that a neural network can in fact be used to correct for systematic biases introduced by using different data sources in model training and prediction to help generate results of improved quality versus not using a bias correcting neural network.

How to cite: Baumgartner, J., Schmidt, J., and Gruber, K.: Using a neural network approach to correct for systematic biases in seasonal wind power electricity generation forecasting, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8154, https://doi.org/10.5194/egusphere-egu2020-8154, 2020.

Rainwater collection and utilization is a common method to relieve soil water pressure in dry dryland orchards. Due to relatively low levels of economic development and population distribution, these areas are unable to develop electricity or import large amounts of energy, resulting in rainwater harvesting often not being fully utilized. Photovoltaic (PV) pumping system is an effective way to ensure the sustainable utilization of soil water in apple orchards. In order to explore the application potential of PV pumping system in the apple suitable area of the loess plateau, this study simulates the rainwater collection amount and the orchard water demand change process in typical hydrological years and conducts a feasibility analysis of the PV pumping system from both technical and economic perspectives. The results found that the precipitation from June to October could not meet the water requirement of the growth of apple tree in the demonstration orchard and the total annual water demand reaches 170 m³. Fortunately, the local solar energy resources can basically meet the demand for solar energy in the PV pumping irrigation system, which ensures sufficiently irrigation water for the apple trees grow. After the completion of the PV pumping irrigation system, the income from the increase in fresh grass production in the demonstration area will reach 8019 CNY/year. The ratio of investment to income is 1:3.0. The investment recovery period is 4 years and it has good economic feasibility. Finally, using spatial geographic information technology, the apple-adapted area is systematically matched with the most suitable planting area for solar irrigation. The land area suitable for solar technology irrigation accounts for 47.6% of the total area, showing promising prospects to be popularized in Western China at large scale.

KEY WORDS: photovoltaic pumping System; Loess plateau; economic benefit; application potential; Apple orchards

How to cite: Sun, M., Zhao, X., Gao, X., and Wang, Y.: The Feasibility of Photovoltaic Pumping System For Apple Orchards Irrigation in Loess Plateau, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9249, https://doi.org/10.5194/egusphere-egu2020-9249, 2020.

Integration of a high share of renewables into the energy system comes with its implications. In order to study long and short-term effects on the electrical system, long time series of power generation with high spatial resolution are necessary. In recent years, reanalysis data have become a popular resource for obtaining these power generation datasets, however with the drawback of a rather coarse spatial resolution of several kilometres (MERRA-2: approx. 50km, ERA5: approx. 31 km). In order to overcome this limitation, reanalysis datasets can be combined with other datasets with a higher spatial resolution.

In the present study, we assess whether applying the Global Wind Atlas (GWA) developed by the Technical University of Denmark with a spatial resolution of 1 km can improve wind power generation simulated from two reanalysis (MERRA-2 and ERA5)  datasets when compared to observed power generation. Furthermore, these two reanalysis datasets are compared to determine how different spatial resolution of underlying reanalysis datasets affects the resulting time series. Wind power generation is simulated from reanalysis wind speeds based on a physical model. For wind speed bias correction to specific locations, mean wind speeds are approximated to GWA wind speeds. A turbine-specific power curve model scaled by the turbine specific power is applied to account for different technical performance. The analysis is conducted for different regions of the world (USA, Brazil, Austria, South Africa) and for different spatial and temporal levels, to determine how different datasets perform on different spatio-temporal scales.

Preliminary results show that bias correction with the GWA has a positive impact on simulation results for MERRA-2, the dataset with lower spatial resolution, while the effect for ERA5 is ambiguous. The error between simulated and observed wind power generation time series can be decreased by spatial and temporal aggregation and a positive, but not very strong correlation between system size (defined by a wind-correlation indicator) and simulation quality (higher correlation, lower error measures) could be identified.

Based on these results, we recommend applying additional wind speed bias correction on datasets with rather coarse spatial resolution, while the quality of newer datasets with high spatial resolution may be sufficient to be used without additional bias correction.

How to cite: Gruber, K., Baumgartner, J., Klöckl, C., Regner, P., and Schmidt, J.: The role of spatial resolution of climate data for the quality of simulated wind power generation – A multi-country analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9961, https://doi.org/10.5194/egusphere-egu2020-9961, 2020.

To reduce anthropogenic climate change, the energy demand from all energy sectors has to be met by renewable energies, wherever possible.
Shallow geothermal energy usage, powered by green electricity, provides heating/cooling at a high level of efficiency, which is difficult to achieve with renewable energy alone.
We have created a coupling approach, which combines hydrothermal and infrastructure modeling at an urban scale to efficiently position shallow geothermal systems between existing power plants and conflicting groundwater usage, optimised by economical and ecological contraints.
We are using Pflotran, a finite volume Darcy-Richards model for our hydrothermal model.
The implementation of the energy infrastructure is done with urbs, a linear optimisation model for distributed energy systems.
We utilize preCICE, a coupling library for multi-physics simulations, for fully parallel peer-to-peer data exchange between these modeling domains.
Iterative optimization is meant to ensure the convergence of the fully coupled model.

How to cite: Schramm, T., Heller, H., Böttcher, F., Halilovic, S., Odersky, L., Davis, K., Hamacher, T., Mehl, M., and Zosseder, K.: Geo.KW - Coupling hydrothermal and infrastructure modeling at urban scale for an efficient use of shallow geothermal energy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10357, https://doi.org/10.5194/egusphere-egu2020-10357, 2020.

We present and test a parsimonious model to help designing optimized wind and photovoltaic fleets, e.g. guaranteeing that the renewable production closely follows the electricity demand curve or any other optimization criteria. First, time-series of weather variables, from high-resolution gridded datasets, are transformed into time-series of wind and PV power potential production, which can be seen as capacity factor (CF) estimates. Second, a combination of hierarchical and non-hierarchical clustering is performed to identify regions with similar temporal variability of the CF series. Third, a linear combination of the resulting mean regional CF series is constructed to be fitted, for instance, to get the best production-demand adjustment, or under alternative optimization criteria such as minimum cost of installations that guarantee a certain supply. The coefficients obtained for each CF series after the fitting or optimization exercise, to which the condition of being zero or positive must be imposed and which, optionally, could be individually forced to vary within a certain range, will indicate the optimum amount of installed power capacity needed in the each region under the chosen optimization criteria. Illustrating the method, it has been applied over Europe at the monthly time-scale using the ERA5 reanalysis, but its applicability in other spatial and temporal scales is immediate. The results support its utility to design optimized renewable energy scenarios.

ACKNOWLEDGMENTS: This work is supported by the projects CLIMAX (20642/JLI/18) funded by the Fundación Séneca – Agencia de Ciencia y Tecnología de la Región de Murcia, and EASE (RTI2018-100870-A-I00) funded by the Spanish Ministry of Science, Innovation and Universities.

How to cite: Jerez, S., Mellado-Cano, J., Lorente-Plazas, R., Jiménez-Guerrero, P., García-Valero, J. A., and Montávez, J. P.: Evaluation and applicability of the spatio-temporal complementarity of the solar and wind resources for the optimized design of renewable energy scenarios, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14476, https://doi.org/10.5194/egusphere-egu2020-14476, 2020.

The share of renewable energy in the electrical grid will likely increase as mitigation measures for future climate change are put into effect. Renewable energy production, such as wind and solar, is largely dependent on the weather and is thus subject to variability on (sub-)daily, weekly and yearly timescales. To facilitate a smooth transition of the energy system, it is necessary to have a thorough knowledge of such a future energy system on hourly level for multiple years. 

Information on the optimal spatial distribution of renewable energy sources, on the supply patterns of different renewable energy sources, and on the cost-effective operation of such a system can be obtained through a variety of methods. For each of these methods intricate knowledge of the regional electricity demand is essential, as without it you have no insights into the required installed capacity, the regional size and sign of the weather-induced impact, or to what degree units have to be committed. 

While in general the knowledge on (renewable) energy systems has steadily increased, the knowledge on the demand for electricity is still basically the same as 20 years ago. The reason for this is simple: sub-national demand data has not been measured until recently and is still not readily available to the public and thus to researchers. Therefore the possibility to study the sub-national demand for electricity in more detail is limited.

The aim of the presented work is to show that national demand time-series can be spatially disaggregated by taking the population distribution and the spatio-temporal variation of temperature into account. Similar approaches have been used in the past, but they never tested this assumption due to a lack of historical regional data. In our work we use 5-minute measurement data of all transformers of the Dutch transmission grid for the period 2012 until 2019. As there exist a plethora of methods for modelling the national demand for electricity, based on socio-economic data and climate variables, the method presented here focuses only on the spatial distribution of demand. 

Using the data on national demand (EN_t) [ENTSO-E], population (P_x,t) [NASA GPWv4.11] and temperature (T_x,t) [ERA5], a variety of linear regression models were constructed for the regional demand (E_x,t). Each of these models allows a researcher to disaggregate the national demand time-series to the regional time-series in a simple, but effective manner. Based on our data the model with the highest accuracy out of the sample is of the following form: 

E_x,t = EN_t  ( α₁ P_x,t + α₂ T_x,t )

By using this method for the regionalization of electricity demand, a whole new range of research becomes possible. For instance, electricity transmission between regions can be explicitly modelled, enabling the identification of future congestion problems in the network.

How to cite: Stoop, L., Feelders, A., and van den Broek, M.: A validated method for estimating regional electricity demand from national time-series, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15541, https://doi.org/10.5194/egusphere-egu2020-15541, 2020.

Hydrogen produced from renewable electricity can play an important role in deep decarbonisation of industry such as steel-production. Steady, full capacity hydrogen production can also be a source of negative balancing capacity to increase flexibility of fully renewable power systems. However, there is a trade-off between attaining economically favorable high-full load hours for the electrolyzer and the provision of system flexibility. To better understand this trade-off, we apply a dispatch model for the Swedish power system with long-term time series 29 years of electricity generation (hydro and wind) and demand at hourly temporal resolution, thus being able to represent climate extreme events.

In our hydrogen demand scenarios for Sweden we limit the hydrogen usage options to two pathways that are currently under development: for hydrotreatment of different bio-based feedstocks in biofuel production, and as use as reductant in fossil-free primary steel-making according to the HYBRIT (Hydrogen Breakthrough Ironmaking Technology) route. We applied three different scenarios that can be seen to represent either different ambition levels for decarbonization, or different time perspectives, and which result in different electrolysis loads on the system:

    • 850 MW electrolyzer capacity, corresponding to 5 TWh·a-1 hydrogen production for biofuels
    • 1700 MW electrolyzer capacity, corresponding to 10 TWh·a-1 hydrogen production for biofuels
    • 3500 MW electrolyzer capacity, corresponding to 10 TWh·a-1 hydrogen production for biofuels and 10.5 TWh·a-1 hydrogen for steel-making.

In comparison to the baseline scenario, more wind power is available in the system, as wind power is scaled accordingly to the average electrolyzer demand.

Results very well present the seasonal differences in demand and how hydro power and partly thermal power are used to balance seasonal differences i.e. extreme events are only observed in the winter months. Also, significant curtailment of wind power capacity is present at some points but less so in the winter months. Extreme events are considerably decreased when increasing electrolyzer capacity, as the electrolyzers are operated flexibly and therefore provide significant positive balancing energy to the system in times of low generation events. In particular, longer events are reduced, creating shorter events to some extent.
Even under the assumption of very low electrolyzer ramping capacities and no dispatch of thermal power for hydrogen production, electrolyzers operate at about 90% of full load and still provide sufficient flexibility to reduce the impact of climatic extreme events on the Swedish power system significantly.

How to cite: Mikovits, C., Schmidt, J., and Wetterlund, E.: Continuous hydrogen production facilitates handling of climatic extreme events in a fully renewable Swedish power system, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16931, https://doi.org/10.5194/egusphere-egu2020-16931, 2020.

Solar photovoltaic (PV) technology has been the fastest-growing renewable energy technology in recent years. Since 2009, it has in fact experienced the largest capacity growth of any power generation technology, with benchmark levelized costs falling by four-fifths [1]. In addition, the global technical potential of PV largely exceeds global primary energy demand [2]. Nonetheless, PV typically only appears as a relatively marginal option in long-term energy modelling studies and scenarios. These include the mitigation pathways evaluated in the context of the work of the Intergovernmental Panel on Climate Change (IPCC), which rely on integrated assessment models (IAMs) of climate change and have in the past underestimated PV growth as compared to observed rates of adoption [2]. Similarly, global energy projections, such as the International Energy Agency's World Energy Outlook, have been relatively conservative regarding the role of solar PV in long-term energy transitions.

In order to better understand the long-term global role of solar PV as perceived by various modeling communities, this work synthesizes a broad ensemble of scenarios for global PV adoption at the 2050 horizon. This ensemble includes 784 IAM-based scenarios from the IPCC SR15 and AR5 databases, and 82 other systematically selected scenarios published over the 2010-2019 period in the academic and gray literature, such as PV-focused techno-economic analyses and global energy outlooks. The scenarios are analyzed using a descriptive framework which combines scenario indicators (e.g. mitigation policies depicted in a scenario), model indicators (e.g. the representation of technological change in the underlying model), and meta-indicators (e.g. the type of institution which authored a scenario). We extend this scenario framework to include a text-mining approach, using Latent Dirichlet Allocation (LDA) to associate scenarios with different textual perspectives identified in the ensemble, such as energy access or renewable energy transitions. We then use a scenario discovery approach to identify the combinations of indicators which are most strongly associated with different regions of the scenario space.

Preliminary results indicate that the date of publication of a scenario has a predominant influence on projected PV adoption values: scenarios published in the first half of the 2010s thus tend to represent considerably lower PV adoption levels. In parallel, higher projected values are more strongly associated with renewable-focused institutions. Increasing the institutional diversity of scenario ensembles may thus lead to a broader range of considered futures [3].

 
References
[1] Frankfurt School-UNEP Centre, “Global Trends in Renewable Energy Investment 2019,” Frankfurt, Germany, 2019.
[2] F. Creutzig, P. Agoston, J. C. Goldschmidt, G. Luderer, G. Nemet, and R. C. Pietzcker, “The underestimated potential of solar energy to mitigate climate change,” Nat Energy, vol. 2, no. 9, pp. 1–9, Aug. 2017, doi: 10.1038/nenergy.2017.140.
[3] E. Trutnevyte, W. McDowall, J. Tomei, and I. Keppo, “Energy scenario choices: Insights from a retrospective review of UK energy futures,” Renewable and Sustainable Energy Reviews, vol. 55, pp. 326–337, Mar. 2016, doi: 10.1016/j.rser.2015.10.067.

How to cite: Jaxa-Rozen, M. and Trutnevyte, E.: Solar futures: a systematic review of long-term global solar photovoltaic adoption scenarios, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18094, https://doi.org/10.5194/egusphere-egu2020-18094, 2020.

The share of wind power in the generation of electricity has increased significantly in recent years and, despite its volatility, variable energy from wind turbines has become an essential pillar for the power supply in many countries around the world. To investigate the effects of increasing variable renewables on power grids, the environment or electricity markets, detailed power generation data from wind turbines with high spatial and temporal resolution are often mandatory. The lack of freely accessible feed-in time series, for example due to data protection regulations, makes it necessary to determine the wind power feed-in for a required region and period with the help of numerical simulations. Our contribution demonstrates how such a numerical simulation can be developed using publicly available wind turbine and weather data. Herein, a novel model approach will be presented for the wind-to-power conversion, which utilizes a sixth-order polynomial for the specific power curve of a wind turbine. After such an analytical representation is derived for a certain turbine, its output power can be easily calculated using the wind speed and air temperature at its hub height. For proof of concept and model validation, measured feed-in time series of a geographically and technically known wind turbine are compared with the simulated time series at a high temporal resolution of 10 minutes. In order to determine the power generation for larger regions or an entire country the derived numerical simulation is also carried out for an ensemble of almost 26 thousand onshore wind turbines in Germany with a total capacity of about 44 GW. With this ensemble, first simulation results with municipal and hourly resolution can be presented for an annual period.

How to cite: Lehneis, R., Manske, D., Schinkel, B., and Thrän, D.: Modeling of the power generation from wind turbines with high spatial and temporal resolution, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19913, https://doi.org/10.5194/egusphere-egu2020-19913, 2020.

Australia represents an interesting case for energy system transformation modeling.  Wile it currently has a power system dominated by fossil fuels, and specifically with a heavy coal component, there is also vast potential for expansion and use of renewable energy.  Geographically, the country is divided into seven states and territories, two of which have power systems isolated from the rest of the country. Regions have widely differing characteristic energy mixes and resources, ranging from high reliance on brown coal (Victoria), black coal (New South Wales, Queensland), natural gas (Northern Territory, Western Australia) to states that have already moved toward renewable energy-dominant systems (South Australia, Tasmania). Renewable power systems across Australia are experiencing rapid growth, particularly in solar photovoltaics and to a lesser extent with wind power and battery storage. 

In order to better understand the further potential expansion of renewable power systems in Australia, we developed the Australian Energy Modelling System (AUSeMOSYS) based on the open-source OSeMOSYS framework. We apply AUSeMOSYS to investigate cost-optimal transformation pathways towards a carbon-neutral energy system. The model is calibrated carefully to recent past trends in energy generation, including the recent and near-future rapid uptake of renewables in different regions, whether by policy decision or autonomous development.  Beyond the power sector, AUSeMOSYS also provides scenario pathways for the uptake of electric vehicles and hydrogen powered transport, coupled to the power sector with a timeline through 2050. In order to investigate the full extent of renewable energy expansion given Australia’s recognized large renewable energy resource potential, we link selected industrial sectors to the power system model, e.g. steel production, where use of electric generation can further decarbonize Australia’s economy via hydrogen production and use.

In addition to the results showing the potential for large, integrated, cross-sectoral penetration of renewable energy into the Australian energy mix, we investigate modeling sensitivities to key parameters that can affect the uptake and use of renewable energy in the power system. For example, we study sensitivities in the choice of time-step resolution, the availability of trade between states in the National Energy Market (NEM) and the choice of carbon price and carbon cap pathways that can lead to near-zero emissions from the energy system by mid-century.

How to cite: Aboumahboub, T., Brecha, R., Gidden, M., Geiges, A., and Bir Shrestha, H.: Integrating energy sectors in a state-resolved energy system model for Australia , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20428, https://doi.org/10.5194/egusphere-egu2020-20428, 2020.

Energy systems of the future will be highly renewable, but building the required infrastructure will require vast amounts of materials. Particularly, renewable energy technologies are more copper-intensive than conventional ones and the production of this metal is intensive in energy and emissions. Moreover, as mineral resources are being depleted, more energy is required for their extraction, with subsequent increase in environmental impacts. Highly stressed and uncertain water resources only worsen this situation.

In this work, we will first provide a comprehensive review of the limited available energy planning approaches on copper mines, including transferrable learnings from other fields. Our second contribution is to compare the influence of different geographical locations on the optimal design of energy systems to supply the world’s main copper mines. For this, we use a linear energy system optimization model, whose main inputs are hourly time series for solar irradiation and power demand, and projections for energy technology costs and ore grade decline. Our third contribution is to propose a multi-vector energy system with novel demand-side management options, specific to copper production processes, including water demand management, illustrated on a case study in Chile (where mining uses a third of the nationwide electricity).

In the first part, the review, we learned that energy demand models in copper mines have only coarse temporal and operational resolutions, and require major improvements. Also, demand-side management options remain unstudied but could promise large potentials. In general, the models applied in copper energy planning seem overly simplistic when contrasted to available energy decision tools.

For the second part, we observed that in most locations, using local photovoltaic power not only lowers future electricity costs but also compensates for increased energy demand from ore grade decline. Some regions gain a clear competitive advantage due to extremely favorable climatic conditions.

In the third and final part, regarding the demand-side management, we saw how the geography and the spatial design of the mines strongly influence the available options and their performance. Jointly planning flexible water and energy supply seems to be particularly attractive. Also, there is space for smart scheduling of maintenance of the production lines, the hardness of the rock feed, oxygen production, and the hauling (rock transport) fleet.

As an outlook,  we highlight the need for consideration of lifecycle impacts as a design goal, and to further develop demand model’s and their flexibility on the mining side. We expect that implementing these smarter approaches will help secure a cleaner material supply for the global energy transition.

How to cite: Moreno Leiva, S., Haas, J., Nowak, W., and Junne, T.: Flexible energy systems for planning the world’s main copper mines considering geographical conditions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20568, https://doi.org/10.5194/egusphere-egu2020-20568, 2020.