This session addresses spatial and temporal modelling of renewable energy systems, both in a prospective as well as in a retrospective manner. Therefore, contributions which model the characteristics of future renewable energy systems are equally welcome as contributions which assess the characteristics of the past performance of renewable energies. Session contributions may reach from purely climate based assessments of simulated renewable generation time series, over assessments of land use to full energy system models used to better understand energy systems with high shares of renewables.
Studies may for instance
Improve our understanding of how climate data can be used to model renewables
Show the spatial and temporal variability of renewable energy sources
Assess the complementarity of different renewable energy sources or locations
Derive land availability scenarios for renewable energies based on climatic, technical, economic, or social criteria
Assess past spatial deployment patterns of renewables
Assess past impacts on land cover and land use change, including impacts on biodiversity and other environmental indicators
Derive integrated scenarios of energy systems with high shares of renewables (Including systems from the local scale e.g. in form of local Energy Communities to the national or continental scale).
The objective of the session is to provide an insight into recent advances in the field of renewable energy system modeling. The session welcomes papers dedicated to climatic and technical issues, environmental impact assessments, and policy-making, forecasting and real time applications concerning renewable energy systems.
vPICO presentations: Fri, 30 Apr
According to the latest IPCC report, 70 to 85% of electricity generation worldwide will need to come from renewable sources of energy by 2050 if countries are to meet internationally agreed greenhouse gas emissions targets. In the rush to decarbonise energy supplies to meet such targets, solar parks (SPs) have proliferated around the world, with uncertain implications for the biodiversity and ecosystem service (ES) provision of hosting ecosystems. SPs necessitate significant land-use change that could disproportionately affect the local environment compared to other low-carbon sources.
In Britain, SPs are commonly built on intensive arable land and managed as grasslands. This offers both risks and opportunities for ecosystem health, yet evidence for assessing ecosystem consequences is scarce. Therefore, there is an urgent need to understand how net environmental gains can be integrated into land-use change for solar energy development to address the current biodiversity and climate crises.
We used vegetation data from over 70 SPs and 50 countryside survey plots (1 km2) in England and Wales to assess the effects of land-use change for SPs on plant diversity and ES provision. We assessed ten habitat indicator variables (e.g., species richness, larval food plants, forage grasses, bird food plants) associated to functionally important plant species that have the potential to enhance ecosystem service delivery.
SPs showed higher diversity of habitat indicator species than arable land and improved grasslands, with vegetation between solar arrays showing higher numbers of species important for ES provision (e.g., N-fixing species important for nutrient cycling) than vegetation under solar panels. Overall, the diversity of habitat indicator species seemed highly dependent on former land-use, showing SPs have the potential to enhance ecosystem services provision if built on degraded agricultural land.
Developing this understanding will enable optimisation of SP design and management to ensure delivery of ecosystem co-benefits from this growing land-use.
How to cite: Carvalho, F., Armstrong, A., Ashby, M., Howell, B., Montag, H., Parker, G., Cruz, J., White, P., and Smart, S.: Assessing the impact of land-use change for solar park development in the UK: implications for biodiversity and ecosystem services, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1271, https://doi.org/10.5194/egusphere-egu21-1271, 2021.
Brazil has seen more than a ten-fold increase in wind power capacity in the last decade and in the past few years, the development of solar PV increased as well. However, little is known about the impacts of variable renewable generation (VRES) in Brazil compared to other world regions, although Brazilian wind power infrastructure is concentrated in the least protected ecosystems that are prone to degradation, desertification and species extinction. Even less is known about solar PV. This study focuses on land-use impacts of past VRES generation development in Brazilian federal states, which cover most of the country's VRES installed capacity. We assessed and compared their spatial installation patterns associated land-use and land cover change in the period before installation until 2019, using a detailed wind turbine and solar PV location database in combination with a high-resolution land-use and land cover map. Also, we explored which drivers contributed to the existing allocation of VRES in Brazil. We found that 62% of the studied wind park area was covered by native vegetation and coastal sands. Overall, 3.2% of the total wind park area was converted from native vegetation to anthropogenic use. Wind parks installed mainly on native vegetation, on average, underwent higher land-use change compared to other wind parks. Similar to wind power, solar PV in some regions e.g., Bahia, occupied mostly native vegetation land, however being installed in closer proximity to anthropogenic land activities than wind power.
How to cite: Turkovska, O. and Schmidt, J.: Land-use impacts of variable renewable energy in Brazil, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15348, https://doi.org/10.5194/egusphere-egu21-15348, 2021.
Solar photovoltaics (PV) is projected to become the dominant renewable, with much capacity being installed as ground-mounted solar parks. Land use change for solar can affect ecosystems across various spatial scales and solar parks offer a unique opportunity for ecological enhancement. One compelling potential benefit put in practice by the solar industry is management for insect pollinators. Specifically, solar parks can provide refuge for pollinators through the provision of suitable habitat, potentially contributing to halting and reversing widespread declines recorded in a number of pollinator groups. There is scope to both manage and design solar parks for pollinators, but understanding is limited. Using a combination of GIS and a process-based pollinator model, we explore how solar park size, shape and management could affect ground-nesting bumblebee abundance inside solar parks and surrounding landscapes in the UK. We show that within solar parks, the floral resources provided by different management practices is a key factor affecting bumblebee abundance, but the impacts are dependent on landscape context. In comparison, solar park size and shape have a lesser impact. Moreover, the effects of both solar park management and design extend into the surrounding landscape, affecting bumblebee abundance up to 1 km away from the solar park. If designed and managed optimally, solar parks therefore have the potential to boost local pollinator abundance and pollination services to surrounding land. Our results demonstrate how incorporating biodiversity into solar park design and management decisions could benefit groups such as pollinators and contribute to the wider environmental sustainability of solar parks.
How to cite: Blaydes, H., Armstrong, A., Whyatt, D., Potts, S., and Gardner, E.: Optimising solar park design and management to boost pollinator abundance, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2199, https://doi.org/10.5194/egusphere-egu21-2199, 2021.
A decarbonized, renewable energy system is generally assumed to represent a cleaner and more sustainable one. However, while they do promise day-to-day reductions in carbon emissions, many other environmental impacts could occur, and these are often overlooked. Indeed, in the two documents that form the EU Energy Union Strategy (COM/2015/080) the words ‘water’, ‘biodiversity’ or ‘raw materials’ do not appear. This ‘tunnel vision’ is often also adopted in current energy systems models, which do not generally provide a detailed analysis of all of the environmental impacts that accompany different energy scenarios. Ignoring the trade-offs between energy systems and other resources can result in misleading information and misguided policy making.
The environmental assessment module ENVIRO combines the bottom up, high resolution capabilities of life cycle assessment (LCA) with the hierarchical multi-scale upscaling capabilities of the Multi-Scale Integrated Assessment of Socioecosystem Metabolism (MuSIASEM) approach in an effort to address this gap. ENVIRO also takes the systemic trade-offs associated with the water-energy-food-(land-climate-etc.) nexus from MuSIASEM while considering the supply chain perspective of LCA. The module contains a built-in set of indicators that serve to assess the constraints that greenhouse gas (GHG) emissions, pollution, water use and raw material demands pose to renewable energy system scenarios. It can be used to assess the coherence between energy decarbonization targets and water or raw material targets; this can be extended to potentially any economic or political target that has a biophysical component.
In this work, we introduce the semantics and formalization aspects of ENVIRO, its integration with the energy system model Calliope, and the results of a first testing of the module in the assessment of decarbonization scenarios for the EU. The work is part of the research developed in the H2020 Project SENTINEL: Sustainable Energy Transition Laboratory (contract 837089).
How to cite: Martin, N., Madrid-López, C., Talens-Peiró, L., and Pickering, B.: How clean is your ‘clean’ energy? The ENVIRO module for energy system models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16017, https://doi.org/10.5194/egusphere-egu21-16017, 2021.
Background: Transparency, reproducibility and reusability of scientific analysis
Researchers are now widely expected to share the data and source code of their work to foster transparency, reproducibility and reusability.
Alas, the quality of data documentation and scientific software scripts can vary substantially. In many instances, metadata and information on the provenance of data are missing or incomplete, and source code often does not include a clear list of dependencies (including version information) or systems requirements. Finally, the source code does not include sufficient inline documentation to be easily understood. As a consequence, even though the data and related scripts may be released under an open-source license, analysis too often cannot be reproduced or adapted with reasonable effort by other researchers.
The pyam package
This talk presents the open-source Python package pyam for energy system scenario analysis and visualization. The aim of pyam is not to provide any ground-breaking new methods or analysis routines. Instead, it provides a reliable, well-tested interface
similar in feel & style to the widely used pandas package, but geared for data formats and applications often used in energy systems analysis
and integrated assessment modelling.
By using pyam for their scenario input data processing and analysis workflows, researchers can reduce standard tasks like unit conversion and data validation from a 5-minute effort to 30 seconds - and have the knowledge that their scripts won't break if pandas or another dependency change their APIs, because the pyam community will work to ensure forward-compatibility and continuity. As another side benefit, the pyam package will raise meaningful errors when input data doesn't make sense, whereas own ad-hoc scripts may fail silently or - even worse - return non-sensical values.
Spatial, temporal and sectoral aggregation & downscaling features
To highlight the applicability of the pyam package for the EGU community and energy & climate modellers at large, this talk will focus on the features for spatial, temporal and sectoral aggregation and downscaling. The package include several often-used methods like weighting by proxy variables or deriving indicators based on minimum or maximum values of timeseries data.
Building a community
The pyam package follows best-practice of version control, continuous-integration and scientific-software documentation. This facilitates building on the package by other researchers. The community uses several tools for communication and discussion, including a Slack channel, an email list and a Github repository for issues & pull requests. And of course, we appreciate contributions by colleagues to extend the scope of features and methods based on their own use cases and requirements!
GitHub repo: https://github.com/iamconsortium/pyam
How to cite: Huppmann, D. and Gidden, M.: pyam - an open-source Python package for energy system scenario analysis & visualization, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13096, https://doi.org/10.5194/egusphere-egu21-13096, 2021.
Achieving current electricity sector targets in Central Europe (Austria, Denmark, France, Germany, Poland and Switzerland) will redistribute regional benefits and burdens at sub-national level. Limiting emerging regional inequalities would foster the implementation success. We model one hundred scenarios of electricity generation, storage and transmission for 2035 in these countries for 650 regions and quantify associated regional impacts on system costs, employment, greenhouse gas and particulate matter emissions, and land use. We highlight tradeoffs among the scenarios that minimize system costs, maximize regional equality, and maximize renewable electricity generation. Here, we show that these three aims have vastly different implementation pathways as well as associated regional impacts and cannot be optimized simultaneously. Minimizing system costs leads to spatially-concentrated impacts. Maximizing regional equality of system costs has higher, but more evenly distributed impacts. Maximizing renewable electricity generation contributes to minimizing regional inequalities, although comes at higher costs and land use impacts.
How to cite: Sasse, J.-P. and Trutnevyte, E.: Regional impacts of electricity system transition in Central Europe until 2035, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4505, https://doi.org/10.5194/egusphere-egu21-4505, 2021.
A decarbonised European energy system will require a number of potentially contested decisions on where best to locate renewable generation capacity. Typically, modellers determine the “best” system based on the least cost to society, focussing on a cost-minimising energy system model result to inform planning and policy. This approach neglects potentially more desirable alternative results which might, for example, avoid problematic concentrations of onshore wind power deployment, increase national supply security, or lower the risk of system failure in adverse weather conditions.
In response, we have developed a method to generate spatially explicit, practically optimal results (SPORES) in the context of energy system optimisation. SPORES can be used to explore energy systems which may offer more socially, politically, or environmentally acceptable alternatives. Furthermore, we have developed metrics to aid identification of interesting alternatives, like those which maximise the spatial distribution of wind generation capacity or minimise exposure to multi-year demand and weather uncertainty.
In this presentation, we will detail the application of the SPORES method in two cases of energy system decarbonisation: the Italian power system and the European energy system. We will present technology deployment strategies which are prevalent across SPORES, such as solar photovoltaics coupled with battery storage, as well as those which offer flexibility of choice in location and extent of deployment. To help with the urgent task of planning socially and politically acceptable energy system decarbonisation strategies, our implementation of SPORES in the open-source energy systems modelling framework Calliope makes it accessible to a wide range of potential users; we will also discuss how other research groups can further build on this to accelerate the energy transition.
How to cite: Pickering, B., Lombardi, F., and Pfenninger, S.: Decision support for renewables deployment through spatially explicit energy system alternatives, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16205, https://doi.org/10.5194/egusphere-egu21-16205, 2021.
Renewable energy technologies are most economical when planned at a large scale in a coordinated manner. But local resistance often hinders developments, especially for onshore wind. In these decentralized energy systems, the beauty of landscapes is particularly relevant for acceptance of wind turbines or transmission lines. Thus, by using the scenicness as a proxy for public acceptance, we quantify its impact on optimal energy systems of around 11,000 municipalities. In municipalities with high scenicness, it is likely that onshore wind will be rejected, leading to higher levelized costs of energy up to about 5 €-cent/kWh. Onshore wind would be replaced mainly by solar photovoltaics and the cost-optimal energy systems would be associated with higher CO2 emissions of up to about 120%. The quantitative basis that we have created can be used to first identify municipalities where public resistance to onshore wind could be particularly high. Second, the results regarding the increase in costs and CO2 emissions can be used to convince the citizens in these municipalities towards accepting onshore wind installations.
How to cite: Weinand, J., McKenna, R., Kleinebrahm, M., and Scheller, F.: Quantifying the trade-off between public acceptance and cost efficiency in decentralized energy systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-716, https://doi.org/10.5194/egusphere-egu21-716, 2021.
Power system expansion models are a widely used tool for planning power systems, especially considering the integration of renewable resources. Studies using these models form the basis for far-reaching political decisions. The backbone of power system models is an optimization problem, which depends on a number of economic and technical parameters. Although these parameters contain significant uncertainties, a consistent way to quantify the sensitivity to these uncertainties does not yet exist. Here, we analyze and quantify the sensitivity of a power system expansion model to the meteorological parameter time series based on a novel misallocation metric. We find that the sensitivity to the weather data is in the same order of magnitude as the sensitivity to the definition of cost. By comparing different climatic periods both from a meteorological perspective and with respect to the impacts on the optimal power system design we can, additionally, identify representative weather years and periods which should rather not be used for expansion planning.
How to cite: Schyska, B., Kies, A., Schlott, M., von Bremen, L., and Medjroubi, W.: The Sensitivity of Power System Expansion Models on Climate Scenarios, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-185, https://doi.org/10.5194/egusphere-egu21-185, 2020.
Energy systems are typically modeled with a low spatial resolution that is based on administrative boundaries such as countries, which eases data collection and reduces computation times. However, a low spatial resolution can lead to sub-optimal investment decisions for renewable generation, transmission expansion or both. Ignoring power grid bottlenecks within regions tends to underestimate system costs, while combining locations with different renewable capacity factors tends to overestimate costs. We investigate these two competing effects in a capacity expansion model for Europe’s future power system that reduces carbon emissions by 95% compared to 1990s levels, taking advantage of newly-available high-resolution data sets and computational advances. We vary the model resolution by changing the number of substations, interpolating between a 37-node model where every country and synchronous zone is modeled with one node respectively, and a 512-node model based on the location of electricity substations. If we focus on the effect of renewable resource resolution and ignore network restrictions, we find that a higher resolution allows the optimal solution to concentrate wind and solar capacity at sites with higher capacity factors and thus reduces system costs by up to 10.5% compared to a low resolution model. This results in a big swing from offshore to onshore wind investment. However, if we introduce grid bottlenecks by raising the network resolution, costs increase by up to 19% as generation has to be sourced more locally where demand is high, typically at sites with worse capacity factors. These effects are most pronounced in scenarios where transmission expansion is limited, for example, by low social acceptance.
How to cite: Frysztacki, M., Hörsch, J., Hagenmeyer, V., and Brown, T.: Unravelling the opposing effects of network clustering on electricity system models with high shares of renewables, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10011, https://doi.org/10.5194/egusphere-egu21-10011, 2021.
The integration of renewable energy sources into the power grid is of the utmost importance for achieving the goal of zero carbon emission. Although there are feasibility studies showing that renewable energy might be able to cover 2050 global energy demand using less than 1 % of the world's land for footprint and spacing, see Jacobson and Delucchi (2011), nowadays renewable energy production is known to be highly intermittent due to substantial uncertainties in the weather conditions. One possibility to reduce such uncertainty (besides storage and employing hydrogen technologies) is spatiotemporally diversified allocation of renewable power capacities which (alongside with the transmission infrastructure) should guarantee that the power demand is met at any given time with a certain (high) probability. We treat the question of spatiotemporal diversification of renewable capacities as a Markowitz portfolio problem with the difference that instead of n = 1, …, N stocks we have geographical locations each with a certain expected level of renewable power production (instead of expected returns for stocks) and the corresponding variance. Another difference to a classical Markowitz portfolio problem is that we require additionally that at each given time point t = 1, …, T, we can reach a predetermined level of renewable power production with a certain probability, i. e. we solve so called chance-constrained problem. Finally, instead of solving one-step problem as it is the case with a Markowitz portfolio we reformulate our problem in the optimal control framework in continuous time and solve it with a reinforcement learning algorithm as suggested in Lillicrap et al. (2019). The advantage of this approach is that the optimal capacities (control) are updated continuously as a response to changing weather conditions (state). We exemplify our approach with the data from ERA5 data, see Hersbach et al. (2020), and suggest possible allocation of renewable energy sources across the European Union.
How to cite: Zeyringer, M., Sirotko-Sibirskaya, N., and Benth, F. E.: Machine learning-based allocation of renewable power production, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6563, https://doi.org/10.5194/egusphere-egu21-6563, 2021.
The energy provided by sun or wind changes with time and cannot be regulated. This creates problems currently because society demands energy when it needs it, largely ignoring availability. Transmission grid or energy storage capacity expansion and demand management are proposed strategies to deal with this. They can be used in a mix or can at least partially substitute for one another. By 2050, large amounts of wind and solar power capacity is expected to be deployed in an effort to meet the goals of the EU’s “Green Deal” . Norway is in a position to contribute to a stable European grid due to its hydropower resources as well as excellent wind resources allowing for spatial diversification with wind in the rest of Europe and technological diversification with solar in the South of Europe. Spreading out wind over a larger area or combining it with other technologies can reduce the variability. Yet, a conflict of interest is possible from the Norwegian perspective, as increased interconnection might lead to higher power prices in the country.
Previous research has taken transmission capacity expansion into account. A frequent conclusion was that improved transmission capacity requires less energy storage. Yet to our knowledge, no study has examined the optimal level of Norwegian transmission capacities to reach Europe’s climate goals in a model that embeds Norway into a representation of the whole European system. Also, the above mentioned tension between the European and the Norwegian perspective has not been discussed.
This work closes the gap by improving the representation of Norway in the MIT licensed European investment and dispatch power system model (highRES-Europe).
Using it, we study the cost-optimal transmission grid in Norway and interconnection to neighbouring countries to meet European Climate targets. This novel approach, allows investigating spatial diversification and technological diversification effects over a large geographical area. The process includes power generation estimates from reanalysis weather data and demand estimates based on historic electricity demand statistics. Being an optimization model, highRES then takes these inputs to design a power system that satisfies the demand at least cost.
The cost-optimal amount of transmission grid expansion to reach European Climate targets is the main expected conclusion.
When looking at the development of system costs in different countries, conclusions about the benefits from grid expansion are expected. Here we also compare the Norwegian perspective to the European perspective to identify possible target conflicts.
It is anticipated that the larger spatial coverage of the model leads to a lower need for storage expansion and that investment into interconnection between Norway and its neighbours are proposed to allow for import and storage of renewable overproduction in other countries.
Further insights into the amount and duration of electricity stored in Norway, supporting the deployment of renewable energy in Europe, are expected.
How to cite: Roithner, M., Price, J., Schmidt, J., and Zeyringer, M.: Optimizing the Norwegian power grid to meet European climate targets, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12166, https://doi.org/10.5194/egusphere-egu21-12166, 2021.
South Korea’s current energy system heavily relies on fossil fuels in particular coal-fired generation followed by nuclear. Currently, the country is defining its long-term energy strategy and latest Basic Electric Power Supply and Demand Plan proposes to increase the share of renewable energies to 26% by 2034, while converting most of their older half of coal plants to LNG. However, to be consistent with Paris Agreement compatible pathways, more ambitious coal phase out schedules to retire the entire coal fleet until 2030 are also discussed. We consolidate such a schedule with an expansion plan for wind and solar capacities derived from open-source renewable resource and energy system models.
For the analysis of integrating renewable energies into South Korea’s future energy system, we perform a detailed assessment of the technical potential of renewable energy sources by applying a temporally and spatially resolved modelling. A comprehensive set of geographical constraints and land exclusion factors are applied to derive the suitable area for placement of wind onshore and offshore turbines as well as PV installations. The land eligibility analysis is followed by the simulation of generation profiles from wind turbines and PV units from ERA-5 weather data, deriving the hourly capacity factors and the corresponding levelized cost of electricity for each location.
We optimize the expansion and operation of renewable energies and storage in South Korea’s electricity system for a Paris Agreement compatible coal phase out until 2030. The model chooses from the renewable expansion potentials and their cost characteristics derived in the resource assessment to balance an hourly-resolved demand scenario for each year. Flexibility needs are met with an optimized dispatch of the existing gas power plants and additional short-term and long-term storage capacities. The detailed modelling approach at a high temporal and spatial resolution allows to have a realistic assessment of the power system integration impacts of varying renewable sources and to evaluate the system adaptation needs in terms of required storage capacities.
How to cite: Hörsch, J., Aboumahboub, T., Ganti, G., Gidden, M., Bir Shrestha, H., Welder, L., and Zimmer, A.: Replacing Coal with Wind and Solar in South Korea’s electricity system, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11085, https://doi.org/10.5194/egusphere-egu21-11085, 2021.
Floating solar photovoltaics (FPV) systems have become an attractive RE option due to their potential energy, environmental, and social benefits. FPV systems have been deployed as standalone systems and hybridized with other generation or energy storage technologies. Hybrid FPVs, especially those paired with hydropower plants, are of specific interest because of potential cost and performance benefits such as improved system operation at different time scales, additional energy storage opportunities, improved transmission utilization, reduced solar PV curtailment, and water conservation. Despite the interest in hybrid hydropower-FPV systems, there is limited research quantifying the operational benefits of these hybrid systems. To help address this research gap, this study analyzes the potential grid-level operational benefits of a generic hybrid hydropower-FPV system through a modeling exercise. Using a solar resource time-series and resource data for an example hydropower plant, we quantify the potential curtailment reduction, increased transmission utilization, and changes in seasonal and diurnal electricity generation for the hybrid FPV system compared to stand alone systems. Results suggest that depending on the seasonality of hydropower resources and the ratio of the size of the FPV system to hydropower plant sizes, the hybrid hydropower-FPV system could reduce curtailment and lead to more optimal use of limited water resources.
How to cite: Gadzanku, S., Dyreson, A., and Lee, N.: Analysis of the operational benefits of hybrid hydropower-floating solar photovoltaic systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6783, https://doi.org/10.5194/egusphere-egu21-6783, 2021.
Concentrated Solar Power (CSP) can shift electricity over time using cheap Thermal Energy Storage (TES). However, the cost of CSP is still high. Conversely, the cost of Photovoltaic (PV) systems have fallen. However, the Battery Energy Storage (BES) used to mitigate the generation variability is uneconomical to utilize as a grid-scale storage. Moreover, in order to increase the operating hours of both solar technologies, one has to increase both TES capacity and CSP solar field compared to the electricity-generating turbine, as measured by the Solar Multiple (SM), and increase the BES capacity and PV module size relative to a fixed inverter capacity, as measured by the Inverter Loading Ratio (ILR). This increase the investment costs although the Levelized Cost of Electricity tends to be lowered by the higher capacity factor (CF). These differences between solar technologies must be accounted when designing an optimal prospective power supply system based on renewable energies (RE). Particularly, the utilization of CSP and PV with storage is widely suggested within the Moroccan strategy that aims at deploying 20% of its electrical capacity from solar energy by 2030. However, the share between PV and CSP and the amount of storage associated is still to be found. This study discuss objectively scenarios for solar integration in the electricity mix by evaluating the impact of rental cost and storage of CSP  and PV on the optimal mixes together with the role of time-space complementarity in reducing the adequacy risk. To do so, we simulate hourly CFs and load curves adjusted to observations for the four Moroccan electrical zones. We analyze mixes along Pareto fronts using the Mean-Variance approach -implemented in the E4CLIM model - in which the total cost of a mix is constrained to be lower than that of the actual 2018 mix . We find that wind gains a higher shares compared to solar technologies because wind is regular on average which involves less capacity to install. However, at low penetrations, the addition of TES to CSP decreases the risk – the more as SM is increased keeping the mean CF fixed – which makes CSP less variable than wind and favors its installation compared to PV. To prevent reaching the maximum-cost sooner at high penetrations, the share of CSP decreases compared to PV and wind. However, the larger the ILR, the larger the share of PV compared to wind and CSP-TES, particularly for SM<4 and CSP tends to replace PV with high ILRs at high penetrations. We also show that a strong RE variability reduction is achieved through spatial diversification and by taking into account correlations between PV and CSP capacities, but less so as the surplus of energy available for TES and BES is increased.
: Bouramdane, A.-A.; Tantet, A.; Drobinski, P. Adequacy of Renewable Energy Mixes with Concentrated Solar Power and Photovoltaic in Morocco: Impact of Thermal Storage and Cost. Energies 2020, 13, 5087.
How to cite: Bouramdane, A., Tantet, A., and Drobinski, P.: Sensitivity of the Moroccan mix to the integration of Thermal and Battery Storage combined with Concentrated Solar Power and Photovoltaics: Design, Dispatch and Optimal Mix analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8755, https://doi.org/10.5194/egusphere-egu21-8755, 2021.
To fulfil ambitious targets for reducing CO2-emissions in the building sector, the design of new neighbourhoods or the retrofitting of existing buildings requires an increasingly high use of renewable energy (REN). The coupling of heat and electricity in hybrid energy systems hereby plays a key role, as it allows to cover the needs of both sectors using renewable sources. Existing case studies of hybrid energy systems for individual buildings or neighbourhoods are often highly specific to a given location, and it is difficult to draw generalisable conclusions. This work hence aims at the development of a hybrid energy systems model based on large-scale databases of renewable energy potential with high spatial and temporal resolution, in this case for Switzerland. The resulting model may be used to obtain comparable results for case studies across the country or scaled up to the national level. For this, our approach integrates national-scale databases of hourly solar photovoltaic (PV) potential  and ground-source heat pump (GSHP) potential  for individual buildings with their modelled heat and electricity demand.
The presented work consists of three steps. First, hourly energy demand for heat and electricity of the residential and service sectors is derived for the entire Swiss building stock. The hourly demand model combines a top-down modelling of annual energy demand with a bottom-up mapping of hourly demand profiles. Second, the energy demand profiles are matched with the renewable energy potentials in hybrid energy systems, at the scale of individual buildings and neighbourhoods. We further add flexibility options to these systems, such as thermal energy storage. Third, the size of the renewable technologies and the storage options are optimised such as to maximise the autonomy level of the resulting hybrid energy systems. The autonomy level is obtained through the modelling of the system dynamics at monthly-mean-hourly temporal resolution, i.e. at hourly resolution for a typical day per month. This reduces the computational complexity of the approach and assures its scalability to the national level.
The above workflow is tested on a neighbourhood in Geneva, Switzerland, and the resulting optimal system configurations are compared across different building types in the residential and service sector, and for different shares of REN generation. We show how different system configurations, such as the combined use of PV and GSHPs, as well as the addition of flexibility through the use of a thermal energy storage, impact the self-sufficiency and autonomy level of buildings and neighbourhoods. While the presented work focuses on one neighbourhood only, future extensions will aim at applying the model to the Swiss national scale using all data in the national REN databases. This will allow to compare the feasibility of different system configurations with high REN shares across the country.
 Alina Walch, Roberto Castello et al. ‘Big Data Mining for the Estimation of Hourly Rooftop Photovoltaic Potential and Its Uncertainty’. Applied Energy 262 (2020).
 Alina Walch, Nahid Mohajeri, et al. ‘Quantifying the Technical Geothermal Potential from Shallow Borehole Heat Exchangers at Regional Scale’. Renewable Energy 165 (2021).
How to cite: Walch, A., Sibuet, R., Castello, R., and Scartezzini, J.-L.: Assessment of hybrid urban renewable energy potential through sector coupling of photovoltaic electricity and geothermal heat, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13411, https://doi.org/10.5194/egusphere-egu21-13411, 2021.
US Wind power generation has grown significantly over the last decades, driven by more and larger turbines being installed. However, less is known about how other factors affect the expansion of wind power. In this study, we use historical wind power generation time series, data on installed wind turbines and wind speed time series from the ERA5 data set to quantify driving factors of the growth of US wind power generation. By use of index-decomposition techniques and a regression analysis, we show how different factors affect the output of wind power generation in the US. These include changes in the number of installed turbines, average swept area, park efficiency, location choice, and hub height. Based on this, we discuss potential consequences for the future expansion of wind energy. As expected, the total rotor swept area is responsible for the largest part of the increase in generated power, due to a larger number of installed turbines and larger rotor sizes in particular. Unexpectedly, turbine efficiency slightly declined in the last decades. Wind speeds available to wind turbines have slightly increased. This is a result of larger hub heights, but also of new wind turbines being installed at windier locations.
How to cite: Regner, P., Gruber, K., Wehrle, S., and Schmidt, J.: Driving factors of the growth of US wind power generation - A decomposition of historical on-shore wind power generation data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11266, https://doi.org/10.5194/egusphere-egu21-11266, 2021.
Suitability maps for solar photovoltaic (PV) parks are key in estimating potential supply of these resources. These maps are typically created based on expert judgement using variables such as resource availability (irradiation), topography (e.g. slope), socio-economic factors (e.g. distance to urban areas), infrastructure (e.g. distance to roads and transmission lines), land cover type, and / or ecological functions (e.g. protected areas). However, such priori expert-based suitability maps do not necessarily match up with the actual spatial distribution of solar PV or wind parks.
Here we aim to understand the determinants of the actual global distribution of solar PV parks by relating the locations of utility-scale solar PV parks worldwide to the above-mentioned variables. Specifically, we develop a generalized linear mixed-effects model to predict the probability of occurrence of a PV park at a certain location, based on variable values at each PV location as well as randomly selected locations where PV parks are absent. We furthermore include country as random effect to take into account inter-country differences in renewable energy policies. We then use the model to create a global 1km resolution map of the likelihood of finding a solar PV park and identify the most important determinants of their distribution.
Finally, we compare our findings to the suitability maps currently used by the integrated assessment model IMAGE, which are based on expert judgement using land cover, ecological functions, infrastructure, socio-economic factors and topography. From this comparison and our identification of the most important determinants, we can deduce what drives geographical patterns in the actual distribution of renewable energy facilities, which can be used to improve suitability maps in future integrated assessments of the energy transition.
How to cite: Bosmans, J., Cengic, M., Schipper, A., Gernaat, D., van Vuuren, D., and Huijbregts, M.: Determinants of the global distribution of solar PV parks , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2796, https://doi.org/10.5194/egusphere-egu21-2796, 2021.
Keywords: Market value, Technological diversification, Geographical diversification, Spatial value factor distribution
Ambitious climate and energy targets require environmentally compatible energy generation with a high utilisation of renewable energy sources. However, due to the intermittent appearance of wind and PV feed-in, variable renewable energy (VRE) reveals significantly lower market values than conventional dispatchable power (Joskow, 2011). Additionally, with higher VRE shares a significant market value drop of wind and solar power has been observed in recent years as a result of the merit order effect (Hirth, 2013). Moreover, results by Engelhorn and Müsgens (2018) and Becker and Thrän (2018) have indicated regional disparities in empirical market values for Germany. This poses interest on what exactly drives and how to quantify the development and spatial distribution of VRE market values.
Against this background, an electricity market model is applied to trace the development of spatial market values based on model-endogenous electricity prices. A special feature of the model is the inclusion of highly regionally disaggregated weather data which allows to investigate effects of different geographical and technological VRE diversification strategies in Germany until 2035 (Eising et al., 2020). The results of this research are threefold:
- Technological diversity: results show a significant decrease in PV and onshore wind value factors as VRE shares increase. Replacing onshore wind energy by offshore wind energy reduces the volatility and counteracts the value drop of onshore wind, offshore wind and PV.
- Geographical diversity: results indicate that geographical diversification does not necessarily mitigate decreasing VRE value factors. Under specific circumstances, a higher concentration at sites with lower full-load hours and corresponding higher feed-in volatility potentially mitigates positive effects from more spatially distributed generation.
- Spatial distribution of value factors: for all mitigation strategies and for wind and PV the spatial value factor distribution shows future increases in regional disparities. However, regional value factor disparities are most distinct in case of onshore wind. The analysis reveals two significant drivers: first, a negative relationship between the regional wind capacity density and their regional value factors can be observed. Second, results indicate a negative relationship between site-specific wind feed-in volatility and the value factor.
Summarising, the analysis highlights the importance of considering spatial market values in efficiently designing future electricity markets.
Becker, R., Thrän, D., 2018. Optimal Siting of Wind Farms in Wind Energy Dominated Power Systems. Energies 11, 978. https://doi.org/10.3390/en11040978
Eising, M., Hobbie, H., Möst, D., 2020. Future wind and solar power market values in Germany — Evidence of spatial and technological dependencies? Energy Econ. 86, 104638. https://doi.org/10.1016/j.eneco.2019.104638
Engelhorn, T., Müsgens, F., 2018. How to estimate wind-turbine infeed with incomplete stock data: A general framework with an application to turbine-specific market values in Germany. Energy Econ. 72, 542–557. https://doi.org/10.1016/j.eneco.2018.04.022
Hirth, L., 2013. The market value of variable renewables: The effect of solar wind power variability on their relative price. Energy Econ. 38, 218–236.
Joskow, P.L., 2011. Comparing the Costs of Intermittent and Dispatchable Electricity Generating Technologies. Am. Econ. Rev. 101, 238–241.
How to cite: Eising, M., Hobbie, H., and Möst, D.: Effects of geographical and technological diversification on the development of spatially disaggregated wind and solar power market values , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14563, https://doi.org/10.5194/egusphere-egu21-14563, 2021.
Keywords: direct marketing, post-FiT, wind, spatial analysis
Within the coming ten years, more than 25 GW of onshore wind will reach the end of 20 year feed-in tariff (FiT) scheme according to the German Renewable Energy Law (EEG). This urges operators to take decision on repowering, lifetime extension or shutdown. In order to support the operators’ decision this study discusses and analyses the economic potential of lifetime extension or shutdown. Due to the limited lifetime extension of post-FiT turbines, rather short-term alternative revenue schemes on the day-ahead market, either via direct marketing or via a merchant PPA, appear as a reasonable option.
For these post-FiT business models, the methodology at hand introduces a revenue and cost cascade. The value and cost categories derive from a power system perspective cascade introduced by Hirth et al. (2015) complemented by transaction costs and additional revenue streams, e.g. from Guaranties of Origin (GoO).
The applied spatial economic analysis calculates region-specific contribution margins for post-FiT wind turbines in Germany in two steps:
- Calculation of regionally dispersed value factors [%], market values [€/MWhel] and annual market revenues [€/MW] using hourly day-ahead price time series and hourly wind feed-in time series for German NUTS2 regions.
- Identifying the distribution of wind turbine operational expenditures (OPEX) from the literature and analysing their regional-specific magnitude. Capital expenditures from the initial wind turbine investment or grid connection are considered as sunk costs and can be neglected.
Subtracting spatial OPEX from spatial market values reveals region-specific contribution margins and the economic potential for continuing wind turbine operation. The conclusion is threefold:
- Location-specific market values strongly affect the contribution margin: year-to-year evolution of day-ahead price levels translates into high volatility of contribution margin. Capacity-dense regions show lower empirical market values. This trend of regional disparities will increase (Eising et al., 2020) due to increasing cannibalisation effect at ever-increasing wind market shares.
- Variation in OPEX assumptions influence the locational contribution margins: the literature review on wind turbine OPEX levels reveals a wide assumption range between 21,4 – 46 €/MWhel and a data gap on actual OPEX for post-FiT wind turbines. In addition, the level distribution of cost causation in energy driven [€/MWhel] and capacity driven [€/MW] OPEX together with the regional variation in wind speeds leads to a significant regional OPEX sensitivity.
- In general, wind-intense sites nowadays deliver higher contribution margins. This overperformance arises from higher absolute market revenues [€/MW] and relatively lower OPEX [€/MWhel]. However, relatively lower market values already appear in the observed timeframe 2006 - 2016 at capacity-dense regions in central-northern Germany.
Overall, this study highlights the importance of acknowledging the spatial distribution of market values for analysis of business models, in particular for post-FiT wind turbines, instead of power system analysis as in the vast majority of market value studies.
Eising, Hobbie, Möst, 2020. Future wind and solar power market values in Germany — Evidence of spatial and technological dependencies? Energy Econ. 86. https://doi.org/10.1016/j.eneco.2019.104638
Hirth, Ueckerdt, Edenhofer, 2015. Integration costs revisited – An economic framework for wind and solar variability. Renew. Energy 74. https://doi.org/10.1016/j.renene.2014.08.065
How to cite: Dede, C. and Eising, M.: Spatial economic potential of post feed-in tariff wind turbines in Germany, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16021, https://doi.org/10.5194/egusphere-egu21-16021, 2021.
Policy measures influence the spatial and temporal deployment of wind energy often more than the geo-economical potential. On the national level, the design of renewable energy support schemes mostly determines the most promising locations for investments. These national instruments also interplay with regulation on the local and municipal level, including land development plans, licensing regimes, and local renewable energy targets. While modeling sometimes focuses on the national level, local regulations are most often absent from the analysis. Controversially, measures at the different administrative levels simultaneously foster and hinder the deployment of wind energy in some regions.
The contribution of this paper is threefold. First, we categorize policy measures at different administrative levels that influence the spatial expansion of wind energy. We uncover at which stage of the planning process these instruments play a role and by which mechanism they influence the spatial distribution and the development time. Second, we present and evaluate techniques to reflect such policy measures in spatial and temporal scenario models and discuss how to generalize them across countries and/or jurisdictions. Third, we apply these modelling techniques on the German case to gain insights on the pros and cons of different modelling approaches. The case study incorporates first scenarios from the Kopernikus project Ariadne.
How to cite: Eicke, A., Mieth, S., Pape, C., Reder, K., and Tiedemann, S.: The impact of policy measures on the spatial and temporal expansion of wind energy: a classification of instruments and modeling recommendations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-813, https://doi.org/10.5194/egusphere-egu21-813, 2021.
Assessments of the potential for wind turbine deployment have become a very active research field in spatial and temporal modeling. Initially, such studies assessed geographical, technical and wind resource potentials, with the objective to identify where wind turbines could in principle be erected. Together with further assumptions, for example on grid connection cost, this served as a prime input for power system models, which used results from studies of feasible potentials as upper limits on deployable capacities.
However, increasing opposition against new wind power projects has demonstrated the limitations of such assessments. In response, the research community developed novel methods to include social constraints in assessments of wind energy potentials. In many instances, this amounted to predicting whether wind turbines could be installed at a specific location, ultimately indicating the eligibility of a location for wind power by a binary categorization.
Another strand of literature sought to determine (socially) desirable allocations of wind turbines rather than predicting possible ones. While these attempts also respect binary geographical and technical constraints on wind power deployment, the desirability of a certain allocation of wind turbines results from the trade-off between corresponding benefits and (negative) impacts, assessed either implicitly in a welfare-framework or explicitly within a multi-criteria analysis.
We argue that predictive approaches are not suitable as a basis for further normative analysis in energy system models. Predictive analysis does not consider effects that are external to the modelled agents’ decisions and is thus not compatible with weighing benefits and cost, arising for example from impacts on the environment, in a broader perspective.
To facilitate analysis, we see several avenues for improvement:
- Assessments should clearly state if they aim at predicting the spatial allocation of wind parks or if they model desirable allocations. If resulting wind potentials are used in energy system models, which are designed to model desirable future states of the energy system, we understand that predictive modeling on the side of spatial wind power allocations is incompatible with a general normative modeling approach.
- Binary land-eligibility studies may suffer from conceptual flaws if continuous measures are mapped to binary categorizations. We therefore propose to use binary indicators only in cases when wind turbine deployment is ruled out with high degrees of certainty (such as technical or legal restrictions). This helps to decrease the computational complexity. To integrate trade-offs of different spatial allocations of wind parks in normative energy system models, continuous indicators such as wind resources or impacts of wind parks need to be assessed separately.
- Standard criteria for wind potential assessments should be amended by (i) largely neglected issues of human land-use and land-tenure, which are particularly important in countries where land tenure rights are insecure and different land use interests compete and (ii) assessments of wind park impacts on the quality of neighboring ecosystems. Integrating these insights into prospective modeling studies is of high relevance as climate change mitigation and biodiversity preservation should go hand in hand when modeling the energy transition.
How to cite: Schmidt, J., Klingler, M., Turkovska, O., and Wehrle, S.: Desirable vs. likely: modeling feasible wind power potentials, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14368, https://doi.org/10.5194/egusphere-egu21-14368, 2021.
In the course of the energy transition, spatial and temporal aspects of energy demand and renewable energy supply are increasingly coming to the forefront of scientific studies and political debates. In this context, the use of spatiotemporal models has been identified as a decisive methodology for integrated spatial and energy planning. However, the transformation of spatiotemporal results into concrete spatial planning instruments has not yet been sufficiently discussed. Therefore, this research aims to provide answers by using specific results of a case study in Austria. In the case study evaluation, energy demand is considered in high spatial resolution using statistic data in 250m raster cells as a basis. The results are supplemented with an assessment of high spatio-temporal solar energy potentials. Taking these results as a basis, the following questions are addressed: How can spatial and temporal evaluations of energy demand and supply support the energy transition by means of spatial planning on the local level? What measures with respect to renewable energy generation, storage and grid capacity can be derived and which effects are expected to be achieved? With respect to renewable energy provision, initial results reveal added value for the spatial delimitation of district heating supply areas. Further, building integrated solar energy generation reveals high shares of excess energy – both thermal and electric – which has to be properly used, taking into account different sectors of energy demand. As a consequence, the results of this research also offer the opportunity to reflect on the benefits of sector coupling, as well as the new organization of energy supply via energy communities.
How to cite: Lichtenwoehrer, P., Neugebauer, G., Abart-Heriszt, L., Suppan, F., and Stoeglehner, G.: The contribution of spatiotemporal modelling to spatial planning instruments on the local level, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15227, https://doi.org/10.5194/egusphere-egu21-15227, 2021.
To date, the spatio-temporal patterns of renewable energies brought about by a deployment that corresponds to internationally agreed climate protection goals, have been neither exactly analysed nor visualised. It is also unknown what land uses would be incorporated into these new energy landscapes due to a lack of spatial restrictions, and what social conflicts these land use changes may give rise to. Moreover, the extent to which existing land use, which is the product of a capitalist order, affects the achievement of a carbon-neutral society, has not been grasped at all. There is no knowledge about the feasibility of altering spatial restrictions for renewable energies in order to identify alternative spatial patterns of sustainable energy transition. Our objective is therefore to model and visualise a regional energy landscape whose greenhouse gas balance in the electricity sector corresponds to the target of the UN Climate Conference. The study provides a detailed analysis of the landscape transformations in rural spaces that would be caused if those forces which strive to link the energy transition to the values of the Paris Agreement were to win through. It is revealed that a precise alignment of the expansion of renewable energies with international climate protection targets would strongly mechanise rural areas and significantly transform their land use patterns.
How to cite: Bosch, S. and Kienmoser, D.: Energy landscapes resulting from climate protection goals – a GIS-based approach to carbon neutrality, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1805, https://doi.org/10.5194/egusphere-egu21-1805, 2021.
Solar photovoltaic (PV) systems will foreseeably be an integral part of future energy systems. Land cover area analysis has a large influence on estimatiin of long-term solar photovoltaic potential of the world in high spatial detail. In this regard, it is often seen in contemporary works, that the suitability of various land cover categories for PV installation is considered in a yes/no binary response. While some areas like natural parks, sanctuaries, forests are usually completely exempted from PV potential calculations, other land over categories like urban settlements, bare, sparsely vegetated areas, and even cropland can principally support PV installations to varying degrees. This depends on the specific land use competition, social, economic and climatic conditions, etc. In this study, we attempt to evaluate these ‘factors of suitability’ of different land cover types for PV installations.
As a basis, the openly available global land cover datasets from the Copernicus Land Monitoring Service were used to identify major land cover types like cropland, shrubland, bare, wetlands, urban settlements, forests, moss and snow etc. For open area PV installations, with a focus on cropland, we incorporated the promising technology of ‘Agri-voltaics’ in our investigation. Different crops have shown to respond positively or negatively, so far, to growing under PV panels according to various experimental and commercial sources. Hence, we considered 18 major crops of the world (covering 85% of world cropland) individually and consequently, evaluated a weighted overall suitability factor of cropland cover for PV, for three acceptance scenarios of future.
For rooftop PV installations in urban areas, various socio-economic and geographical influences come in play. The rooftop area available and further usable for PV depends on housing patterns (roof type, housing density) which vary with climate, population density and socio-economic lifestyle. We classified global urban areas into several clusters based on combinations of these factors. For each cluster, rooftop area suitability is evaluated at a representative location using the land cover maps, the Open Street Map and specific characteristics of the cluster.
Overall, we present an interdisciplinary approach to integrate technological, social and economic aspects in land cover analysis to estimate PV potentials. While the intricacies may still be insufficient for planning small localized energy systems, this can reasonably benefit energy system modelling from a regional to international scale.
How to cite: Yeligeti, M., Hu, W., Scholz, Y., and von Krbek, K.: Identifying land cover suitability factors for photovoltaic installations with focus on cropland and urban areas, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14722, https://doi.org/10.5194/egusphere-egu21-14722, 2021.
Austria aims to meet 100% of its electricity demand from domestic renewable sources by 2030 which means, that an additional 30 TWh per year are required. Solar energy will play a significant role to reach this goal, meaning the need for a substantial increase in photo-voltaic capacity. While some federal states and municipalities released a solar roof-top cadastre, there is lacking knowledge on the estimation of the potential of both, open space installations and roof-top modules, on a national level with a high spatial resolution. Results show significant differences between urban and rural areas, as well as between the Alpine regions and the Prealpine- and Easter Plain areas.
The work includes a framework to automatically process solar PV data and land-use data was developed and openly available for usage. The framework is able to fetch solar data automatically from a defined source, and join, manipulate and alter it with geodata applying various spatial methods.
How to cite: Mikovits, C., Schauppenlehner, T., Scherhaufer, P., Schmidt, J., Schmalzl, L., Hampl, N., and Sposato, R.: Photovoltaics in Austria - open space and rooftop potential analysis on a high spatial resolution, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5648, https://doi.org/10.5194/egusphere-egu21-5648, 2021.
The development of a sustainable and renewable energy system is a significant challenge for Ireland. In line with UN and EU policies, Ireland aims to transition to a competitive, low carbon, climate-resilient and environmentally sustainable economy by 2050 (Project Ireland 2040 National Planning Framework). Ireland is committed to an aggregate reduction in CO2 emissions of at least 80% (compared to 1990 levels) by 2050 across the electricity generation, built environment and transport sectors. Renewable energy can help Ireland reduce GHG emissions and carbon footprint as energy demands grow. It also reduces dependencies on fossil fuels as well as increases energy supply security.
According to the Sustainable Energy Authority of Ireland’s “Energy in Ireland 2020” report, 36.5% of electricity demand was met by renewable energy sources in 2019. Wind energy contributes 32% while solar energy contributes to <1%. Significant investment has been made in Ireland’s wind sector; however, the solar energy sector is relatively new. Ireland has the second-lowest total installed and cumulated solar photovoltaic (PV) capacity in the EU with just 36 MW or 7.3 W per inhabitant. (EurObserv'ER 2019).
Solar prospecting is necessary to identify optimum locations where solar farms can be established. Commercial and industrial building rooftops in urban areas offer a suitable location for establishing rooftop solar farms due to good connectivity with the electricity grid and proximity to users. Here we present an urban solar prospecting study in Dublin, Ireland.
A very high-resolution geospatial dataset was acquired for 47 industrial areas covering 53.3 km2. The data comprises of very high-resolution aerial images (12.5 cm/pixel) and digital surface model (DSM) (25 cm/pixel).
The high-resolution DSMs were used to model solar irradiation on building rooftops in ArcGIS Pro using the area solar analyst tool. These models were optimised for Irish conditions using Met Éireann solar radiation data for Dublin. The maximum solar insolation received in Dublin is 1000-1050 kWh/m2. The results demonstrate that there is potentially a large amount of commercial and industrial rooftop surface area available for PV installation in Dublin. These rooftops can generate a significant amount of electricity and help to offset CO2 emissions.
How to cite: Verma, A., Connolly, J., and O'Connor, N.: Prospecting urban rooftop solar farm potential in Dublin, Ireland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13081, https://doi.org/10.5194/egusphere-egu21-13081, 2021.
Considering climate change, it is essential to reduce CO2 emissions. The provision of charging infrastructure in public spaces for electromobility – along with the substitution of conventional power generation with renewable energies – can contribute to the energy transition in the transport sector. Scenarios for the spatial distribution of this charging infrastructure can help to exemplify the need for charging points and their impact, for example, on power grids. We present an approach based both on the usage frequency of points of interest (POIs) and on the need for charging points in residential areas. This approach is validated in several steps and compared with alternative methods, such as a machine learning model trained with existing charging point utilization data.
Our approach uses two drivers to model the demand for public charging infrastructure. The first driver represents the demand for more charging stations to compensate for the lack of home charging stations and is derived from a previously developed and published model addressing electric-vehicle ownership (with and without home charging options) in households. The second driver represents the demand for public charging infrastructure at POIs. Their locations are derived from Open Street Map (OSM) data and weighted based on an evaluation of movement profiles from the Mobilität in Deutschland survey (MiD, German for “Mobility in Germany”). We combine those two drivers with the available parking spaces and generate distributions for possible future charging points. For computational efficiency and speed, we use a raster-based approach in which all vector data is rasterized and computations are performed on the full grid of a municipality. The presented application area is Wiesbaden, Germany, and the methodology is generally applicable to municipalities in Germany.
The method is compared and validated with alternative approaches on several levels. First, the allocation of parking space based on the raster calculation is validated against parking space numbers available in OSM. Second, the modeling of charging points supposed to compensate for the lack of home charging opportunities is contrasted with a simplified procedure by means of an analysis of multifamily housing density. In the third validation step, the method is compared to an existing machine learning model that estimates spatial suitability for charging stations. This model is trained with numerous input datasets such as population density and POIs on the one hand and utilization data of existing charging stations on the other hand. The objective of these comparisons is both to generally verify our model’s validity and to investigate the relative influence of specific components of the model.
The identification of potential charging points in public spaces plays an important role in modeling the future energy system – especially the power grid – as the rapid adoption of electric vehicles will shift locations of demand for electricity. With our investigation, we want to present a new method to simulate future public charging point locations and show the influences of different modeling methods.
How to cite: Gauglitz, P., Geiger, D., Ulffers, J., and Zauner, E.: Modeling public charging infrastructure considering spatial distribution of e-car ownership and points of interests, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4935, https://doi.org/10.5194/egusphere-egu21-4935, 2021.
In recent years, different approaches have been developed with the aim of defining representative buildings that can be used as a basis for residential building energy system analyses. Due to the coupling of different sectors at the household level, the analysis of future residential energy systems is becoming increasingly complex. On the European level a large amount of data has been published over the last years. This study combines multiple different data sets relevant for energy system analysis at the building level and presents a dynamic methodology for the derivation of representative building/household combinations, which can be used as a basis for residential energy system analyses on a European level. The approach enables representative buildings to be generated dynamically taking into account the parameters relevant to the respective research question. In a first step, various data sets are combined to describe local building properties, weather conditions, economic and ecological framework conditions as well as socio-demographic parameters on NUTS3 level. Based on the developed database, a two-step procedure for the derivation of building household combinations is presented. In the first step, a synthetic European population is generated by using iterative proportional fitting. In the second step different cluster approaches are compared for the derivation of case specific archetype buildings. Finally, the developed methodology is used in an exemplary way for the analysis of the potential of energy self-sufficient single-family buildings in the future European building stock by using a mixed integer linear programming optimization model for the optimal energy system design and dispatch of residential buildings, taking into account relevant framework conditions such as weather conditions, regulatory framework conditions and site-specific building properties.
How to cite: Kleinebrahm, M., Naber, E., Weinand, J., McKenna, R., and Ardone, A.: Development of a dynamic European residential building stock typology for energy system analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8001, https://doi.org/10.5194/egusphere-egu21-8001, 2021.
Bioenergy plays a key role in scenarios limiting global warming below 2°C in 2100 relative to pre-industrial times. Land availability for bioenergy production is constrained due to competition with agriculture, nature conservation and other land uses. Utilizing recently abandoned cropland to produce bioenergy is a promising option for gradual bioenergy deployment with lower risks of potential trade-offs on food security and the environment. Up until now, the global extent of abandoned cropland has been unclear. Furthermore, there is a need to better map bioenergy potentials, taking into account site-specific conditions such as local climate, soil characteristics, agricultural management and water use.
Our study spatially quantify global bioenergy potentials from recently abandoned cropland under the land-energy-water nexus. We integrate a recently developed high-resolution satellite-derived land cover product (European Space Agency Climate Change Initiative Land Cover) with an agro-ecological crop yield model (Global Agro-Ecological Zones 3.0). Abandoned cropland is mapped as pixels transitioning from cropland to non-urban classes. We further identify candidate areas for nature conservation and areas with increased pressure on water resources. Based on climatic conditions, soil characteristics and agricultural management levels, we spatially model bioenergy yields and irrigation water use on abandoned cropland for three perennial grasses. We compute and analyze bioenergy potentials for 296 different variants of management factors and land and water use constraints. By assessing key energy, water and land indicators, we identify optimal bioenergy production strategies and site-specific trade-offs.
We found 83 million hectares of abandoned cropland between 1992 and 2015, equivalent of 5% of today’s cropland area. Bioenergy potentials range between 6-39 exajoules per year (EJ yr-1) (11-68% of today’s bioenergy demand), depending on agricultural management, land availability and irrigation water use. We further show and extensively discuss site-specific trade-offs between increased bioenergy production, land-use and water-use. Our high-end estimate (39 EJ yr-1) relies on complete irrigation and land availability. When acknowledging site-specific trade-offs on water resources and nature conservation, a potential of 20 EJ yr-1 is achievable without production in biodiversity hotspots or irrigation in water scarce areas. This is equal to 8-23% of median projected bioenergy demand in 2050 for 1.5°C scenarios across different Shared Socio-economic Pathways. The associated land and water requirements are equal to 3% of current global cropland extent and 8% of today’s global agricultural water use, respectively.
How to cite: Næss, J. S., Cavalett, O., and Cherubini, F.: Bioenergy potentials from recently abandoned cropland under the land-energy-water nexus, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16111, https://doi.org/10.5194/egusphere-egu21-16111, 2021.
Between February 15th and 16th 2021 a cold spell reached Texas, causing large-scale rolling blackouts in the Texan power system. These were driven in part by a significant increase in electricity demand for heating, and in part by the loss of power generation capacity in the system.
We use (i) ERA-5 temperatures weighted according to population to predict load, (ii) temperatures weighted according to power plant locations, and (iii) temperatures weighted according to Texas gas fields, together with (iv) data on outages of gas and coal power plants, to (a) study the event in February, and (b) estimate the severity of this event based on seven decades of data (1950-2021). To characterise the power demand, we used population weighted temperature and time variables as surrogates.
We find that the February of 2021 event was a very cold year compared to other winters in the period of 2004-2020, from which we use observations of load on the electricity network. There were, however, colder events before 2004. Predicted electricity demand was higher than in any other winter in our simulation, although deeper temperatures were observed before. This is due to the particular timing of temperature fluctuations, with cold episodes coinciding with daily and weekly demand patterns in an unfortunate way. Predicted demand in February 2021 was, however, never higher than the highest observed load during hot hours in summer.
From synoptic signal analysis, we further estimate that a catastrophic failure of gas power plants occurred at temperatures below -7.3°C, and of coal power plants at -9.2°C. However, lower temperatures before 2004 did not cause any catastrophic failures. In contrast, the problems at gas power plants started when the gas field output weighted temperature fell to -10.2°C. This is a temperature never observed since 2004, indicating that the reason for outage may be related to gas and not power production. In the period since 1950-2003, temperatures as low or lower have been observed. However, the 2021 event is exceptional in terms of how long temperatures were below 0°C before the system failed.
Wind power plants also failed to operate due to icing conditions. This seems to be, however, a very rare event. We did not find any other significant difference in wind power generation simulated from ERA5 wind data to observed wind generation in Texas in the period 2016-2019 under freezing conditions. This also points to very special conditions in the 2021 winter event. We find however, that in lower temperatures, capacity factors of the installed wind park tend to decrease.
How to cite: Gruber, K., Gauster, T., Ramirez-Camargo, L., Laaha, G., and Schmidt, J.: The Texas 2021 cold spell in a climate-power system perspective, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7180, https://doi.org/10.5194/egusphere-egu21-7180, 2021.
Wind energy is a promising renewable resource to contribute to the energy transition in many parts of the world. In contrast to solar power, it is available at any time of the day; however, it is highly variable and complex to model. This poses challenges for the planning of future energy systems with high shares of wind power. The quantification of the spatial and temporal variation of wind power and the related uncertainty may hence provide valuable information for energy planners and policymakers. Here we propose an estimation of hourly wind energy potential at the Swiss national scale for pixels of 200 x 200 m2. To this aim, this research is structured into two parts. First, ten years of wind speed measurement collected at an hourly frequency on a set of 208 monitoring stations are interpolated using advanced spatio-temporal techniques, allowing the estimation of wind speed at unsampled locations. Second, the resulting wind field is used to estimate hourly wind power potential on a national scale.
Because of its turbulent nature and its very high variability, wind speed modelling is a challenging task, especially in complex mountainous regions. To face these challenges, the interpolation task is solved as follows. The wind speed data are decomposed through Empirical Orthogonal Functions (EOFs) in temporal basis and spatially dependent coefficients. Then, the spatial coefficients are interpolated. While any regression model could be used to model these coefficients, Extreme Learning Machine (ELM) - a single layer feed-forward neural network with random input weights – was chosen to perform this task, profiting of its high computation speed and of its ability to retrieve reliable and rigorous model uncertainty assessments. Finally, the wind speed time series are reconstructed at any location adopting the interpolated coefficients in the EOFs equation. Uncertainty is quantified by taking advantage of the ELM uncertainty estimates for the spatial coefficients’ models and of the orthogonality of the basis.
In the second part of the research, the modelled spatio-temporal wind field is used to estimate wind power potential, taking into account technical characteristics of horizontal-axis wind turbines as well as national regulatory planning limitations for the installation of power plants. The limitations include restrictions for noise abatement and landscape, natural, ecological and cultural heritage protection plans as provided in the Swiss national wind atlas. The resulting wind power potential represents the first dataset of its type for Switzerland, which may be used to model future energy systems with increased wind power production. Considering the spatial and temporal variability of wind hereby permits to assess the complementarity with other forms of renewables such as photovoltaics, which play a key role in Switzerland’s Energy Strategy.
Amato, Federico, et al. "A novel framework for spatio-temporal prediction of environmental data using deep learning." Scientific Reports 10.1 (2020): 1-11.
Guignard, Fabian, et al. "Uncertainty Quantification in Extreme Learning Machine: Analytical Developments, Variance Estimates and Confidence Intervals." arXiv preprint arXiv:2011.01704 (2020).
Walch, Alina, et al. "Big Data Mining for the Estimation of Hourly Rooftop Photovoltaic Potential and Its Uncertainty". Applied Energy 262 (2020): 114404.
How to cite: Amato, F., Guignard, F., and Walch, A.: Wind of change: predicting wind potentials for the energy transition, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4581, https://doi.org/10.5194/egusphere-egu21-4581, 2021.
The continuous search for affordable and renewable energy resources is a topic of interest for decades. Many large-scale measuring campaigns have been conducted and various different tools have been developed over the years (both numerical and statistical in nature), in order to locate regions with high wind, wave and solar energy potential. Depending on the energy resource, not all regions are performing equally, as expected. To pinpoint regions with high energy gain requires state-of-the-art tools and unremitting research efforts.
The objective of the current research effort is the spatio-temporal wave data analysis, originated from satellite data, and sensor buoy data scattered in the Aegean and Ionian Sea, with the use of geostatistical and dynamic downscaling methods, for estimating the wave energy potential for the Hellenic region. The main areas of interest are the Aegean and Ionian islands, with unsustainable energy production.
WRF model is used to dynamically downscale coarse global climate model output to provide the regional wind forcing for a 40-year hindcast period on a 3 x 3 km grid over the Aegean and Ionian Seas. The calculated wind forcing is used as a driver for the WAVEWATCH-III wave model to calculate the significant wave height and period in the region and subsequently achieve a high-resolution estimation of the wave energy potential spatial distribution and temporal evolution. Model results have been validated with mooring time series of wave parameters in the Aegean Sea and satellite-based along track Significant Wave Height data available through CMEMS Wave Thematic Assembly Center (CMEMS WAVE TAC). To strengthen the results outcome, a spatio-temporal geostatistical methodology has been introduced to validate the computational results and provide a fast and robust estimation of the wave and energy fields. The results between the two different approaches are compared in order to establish either spatial or temporal correlation patterns.
This project has received funding from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under grant agreement No .
How to cite: Kozyrakis, G., Spanoudaki, K., and Varouchakis, E.: Spatio-temporal Modelling of Significant Wave Height and Wave Energy Potential Estimation in the Aegean and Ionian Sea, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1838, https://doi.org/10.5194/egusphere-egu21-1838, 2021.
Smoothing of wind generation variability is important for grid integration of large-scale wind power plants. One approach to achieving smoothing is aggregating wind generation from plants that have uncorrelated or negatively correlated wind speed. It is well known that the wind speed correlation on average decays with increasing distance between plants, but the correlations may not be explained by distance alone. In India, the wind speed diurnal cycle plays a significant role in explaining the hourly correlation of wind speed between location pairs. This creates an opportunity of “diurnal smoothing”. At a given separation distance the hourly wind speeds correlation is reduced for those pairs that have a difference of +/- 12 hours in local time of wind maximum. This effect is more prominent for location pairs separated by 200 km or more and where the amplitude of the diurnal cycle is more than about 0.5 m/s. “Diurnal smoothing” also has a positive impact on the aggregate wind predictability and forecast error. “Diurnal smoothing” could also be important for other regions with diurnal wind speed cycles.
How to cite: Gangopadhyay, A., Seshadri, A. K., and Toumi, R.: Beneficial role of diurnal smoothing for grid integration of wind power, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9226, https://doi.org/10.5194/egusphere-egu21-9226, 2021.
For an efficient integration of wind and solar resources toward sustainable energy systems, it is crucial to consider their fluctuations in space and time. Current spatial wind potential estimations in Japan are limited to the annual average of wind speed. In this study, we evaluate the spatial and temporal evolution of both onshore and offshore wind energy potential in Japan based on 5 km mesh and 1-hour sampling weather forecast data. We then demonstrate the benefits of cross-border sharing on the power output stability and identify important sites having high average potential and low average correlation with other sites for the temporal smoothing of power output.
How to cite: Matsuoka, T., Amano, T., Delage, R., and Nakata, T.: Spatiotemporal wind energy potential estimation and analysis in Japan, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7013, https://doi.org/10.5194/egusphere-egu21-7013, 2021.
In efforts to reduce the carbon intensity of electricity generation, optimizing the use of renewable energy necessitates an understanding of its spatial and temporal variability with respect to the corresponding consumption patterns. Their coupled analysis leads to identifying existing and anticipated discrepancies in supply and demand that can help to guide both the implementation of monitoring and control strategies to balance loads through demand-side management (e.g. shifting space heating, domestic hot water, EV charging) and storage (e.g. battery, thermal, and chemical), as well as plans for further expansion of renewable energy generation. In this study, we analyze PV production and consumption patterns (daily/weekly/seasonal) within and across different utilities for the case study in Switzerland. We analyze three utilities using (1) indicators to assess their PV production and utilization, (2) visualization techniques to observe the varying patterns in consumption and production across day/week/year, and (3) computational methods to balance production surpluses and deficits, estimating the necessary load-shifts for different PV production levels (regardless of the means of shifting this demand). In the first case, we assess balancing areas of different scales---from a handful of prosumers to a residential neighborhood to the full utility area---demonstrating an improvement in their capacity to accommodate higher shares of PV production. We attribute this improvement to reduced variability in aggregated supply and demand, together with the increased diversity in building use (residential, office, retail/restaurant, industrial, etc.). When comparing the production and consumption patterns across the three utilities, similarities in the shapes of the daily profiles, weekday/weekend consumption, seasonal variations (e.g. heating demand) and load-scheduling practices (e.g. domestic hot water charging) are observed. Nevertheless, we observe large differences in their ability to consume the produced PV electricity locally, which appear related to their building stock composition, PV installation types (residential vs commercial), as well as access to higher grid hierarchy levels. These differences demonstrate the need for locally tailored strategies to expand PV production while ensuring their adequate utilization. The general methods and approach presented here aim to assess these differences and inform more effective implementation strategies needed to reach ambitious national renewable energy targets.
How to cite: Vulic, N., Rüdisüli, M., and Orehounig, K.: Spatial and temporal variability in PV generation with respect to consumption patterns, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7945, https://doi.org/10.5194/egusphere-egu21-7945, 2021.
We currently have more than 7500 planned mini grids, most of them in Africa. These will soon connect more than 27 million people and cost about 12 billion dollars . Africa is in a good position for Photo voltaic (PV) mini grid optimization, receiving more than 1800 KWh/m2 Global Horizontal Irradiation (GHI) every year , for most parts of the continent. However, the lack of a coordinated renewable energy monitoring and distribution network works against optimization of PV potential models . This study shows the accuracy of existing photo voltaic potential estimators like renewables ninja , the National Renewable Energy Laboratory (NREL), International Renewable Energy Agency (IRENA), and the global solar atlas , by comparing the modeled values with long term measurements from ground solar stations. This is done for more than 20 stations distributed over Africa. Our results show best correlations  of up to 65.3% from version 2 of the Surface Radiation Data Set from Heliosat (SARAH) derived from the Photovoltaic Geographical Information System (PVGIS). However, we also have correlations as low as 16.2% for models commonly used in off grid simulations. The sensitivities of the modeled cost of a mini grid to the variation in PV potential were tested  using the statistical range in sourced PV potential from the different estimators, giving us cost variation of more than 2.8% that may arise from the different sources.
1. World Bank, ESMAP - Mini grids for half a billion people
3. doi: 10.1016/j.energy.2016.08.060
4. Wikipedia contributors. (2021, January 7). Pearson correlation coefficient. In Wikipedia, The Free Encyclopedia. Retrieved 09:00, January 20, 2021, from https://en.wikipedia.org/w/index.php?title=Pearson_correlation_coefficient&oldid=998963119
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How to cite: Wasike, A. and Cader, C.: Comparison of PV potential models for africa and their potential cost implications., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7386, https://doi.org/10.5194/egusphere-egu21-7386, 2021.
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