ERE2.2

With the need for clean energy that reduces greenhouse gas emissions, the coming decades will see a transition of our energy system towards a higher share of renewable energy sources. With this work we aim at gaining insight in meteorological conditions that lead to extremely low energy generation and extremely high residual load (difference between production and demand) in European countries. We constructed a simplified climate-energy modeling framework in which extreme impact events on the European energy system can be examined. We compute daily electricity demand and the supply from hydropower, wind and photovoltaic solar, based on 2000 years of simulated present and future weather conditions for countries in Europe. From this data the meteorological drivers with high impact on the current energy system were investigated for individual countries and larger regions. Atmospheric blocking in summer can result in heatwaves and droughts that in turn result in long-lasting periods of high energy demands in countries with large cooling capacities (southern Europe) and low energy production in countries that rely on hydropower. In winter, dry and cold periods with lower than normal windspeeds lead to high residual load in northern European countries, especially around the North Sea. These countries have a high share of offshore wind and high installed heating capacities. Dry seasons lead to a higher sensitivity to wind and solar variability due to a decrease in balancing potential of hydropower. Additionally, the co-variability of electricity shortage events between countries is investigated. The goal is to identify balancing potential of transmission between countries. Due to different demand profiles across Europe and the spatial variability of weather the potential of extreme event reduction through cross-border transmission is high.

How to cite: van der Most, L., van der Wiel, K., Bintanja, R., Benders, R., and Leenes, W.: Extreme impacts in the European renewable electricity system as a result of climate variability , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6907, https://doi.org/10.5194/egusphere-egu22-6907, 2022.

Increasing the generation of variable renewable energy sources (VRE), such as wind or solar photovoltaics, is one pivotal element in the decarbonization of our energy systems. VRE availability depends on prevailing weather conditions. During a period with high VRE availability, surplus energy needs to be integrated into the system. Times of VRE shortage require flexibility options that can serve demand. These options need to cover VRE droughts, i.e., long-lasting periods of low VRE availability. They may challenge the security of supply, notably in the case of simultaneity with high demand phases, and vary largely across time and space. This paper evaluates VRE droughts in terms of severity, duration, timing, and simultaneity in Germany and Europe using the Pan-European Climate Data Base by ENTSO-e, which provides VRE availability factors for 35 years.

Two definitions apply to VRE droughts (Ohlendorf & Schill, 2020). First, droughts as periods of consecutive hours with availability factors constantly below a certain threshold (CBT). Second, droughts as periods of consecutive hours with a moving average of VRE availability factors below a certain threshold (mean below threshold – MBT). While the CBT notion identifies drought periods in the narrow sense, the MBT definition also accounts for longer stretches of low VRE availability on average, with short instances of VRE availability above a threshold possible. Contrasting results from these two approaches reveals insights on the short- and long-term need for flexibility, for instance, by different types of storage.

Additionally, there are two options to count VRE droughts: First, a drought window denotes a period of consecutive availability factors with a fixed duration, qualified either according to the CBT or MBT notion. Windows are counted for increasing window size, starting with a few hours up to multiple months. This method identifies flexibility slices that the system needs to provide for all relevant time scales. Second, a drought event is a period of consecutive availability factors with variable duration. The algorithm counts in descending order from the longest event lasting multiple months to events lasting only a few hours. Consequently, each qualified CBT or MBT period is counted only once. Results reveal the number of drought events with a minimum duration that the system needs to balance.

This paper analyzes VRE droughts of individual VRE generation technologies, all VRE generation technologies combined, and, to account for simultaneity with demand peaks, residual load time series to identify periods with an energy deficit in the system (Ruhnau & Qvist, 2022). Insights allow for a distinct assessment of the relevance of energy droughts concerning VRE technologies individually and in terms of simultaneity for the transition of our energy systems, both in the German and European context (Raynaud et al., 2018; Kaspar et al., 2019).

References

Ohlendorf, N., Schill, W.-P., 2020. DOI: 10.1088/1748-9326/ab91e9

Kaspar, F., Borsche, M., Pfeifroth, U., Trentmann, J., Drücke, J., Becker, P., 2019. DOI 10.5194/asr-16-119-2019.

Raynaud, D., Hingray, B., François, B., Creutin, 2018. DOI: 10.1016/j.renene.2018.02.130.

Ruhnau, O., Qvist, S., 2022. (in press).

How to cite: Kittel, M.: Severity of variable renewably energy droughts in Germany and Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9909, https://doi.org/10.5194/egusphere-egu22-9909, 2022.

Climate data sets are widely used for renewable power simulation. While previous generations of global reanalysis data including MERRA-2 and ERA-Interim have been widely assessed for their suitability to simulate variable renewable power systems, more recent datasets such as ERA5 and ERA5-Land lack validation, particularly in regions outside of Europe.

Here, we assess the accuracy and bias of wind power simulation using ERA5 wind speeds in Brazil and New Zealand as well as solar photovoltaic power simulation accuracy using ERA5-Land solar radiation and temperature data in Chile. We compare the performance of ERA5 and ERA5-Land to MERRA-2 based renewable power generation. The reference data sets are capacity factors derived from data measured at individual installations in each country and the performance indicators include the pearson’s correlation coefficient, mean bias error (MBE) and root mean square error (RMSE). For wind power simulation, we also assess a bias correction method using the Global Wind Atlas.

Since models applying the resulting datasets are based on different spatial and temporal scales, we also aim at finding a relation between the spatial and temporal resolution and simulation quality. We assess the simulation results applying spatial aggregation ranging from individual installations to the country level and temporal aggregation varying from hours to months. This aids to evaluate the reliability of the simulated renewable power generation time series on various spatiotemporal scales for future simulation efforts.

Overall, we find that both datasets, ERA5 and ERA5-Land, perform well in wind and solar photovoltaic power simulations. For wind power simulation, ERA5 shows improved performance compared to MERRA-2 based wind power simulation, while for solar photovoltaic the improvements of ERA5-Land compared to MERRA-2 are minor. Correlation of wind power generation is around 0.8 without correction and MBEs around -0.1. Mean bias correction with the Global Wind Atlas does not consistently improve simulation results. For the solar photovoltaic power simulation, we find correlations above 0.75, while the MBE is between -0.05 and 0.1.

How to cite: Gruber, K., Ramirez Camargo, L., and Schmidt, J.: PV and wind power simulation with ERA5 and ERA5-land – a multi-country analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9702, https://doi.org/10.5194/egusphere-egu22-9702, 2022.

Policy and economic analyses use simple estimates of regional and global wind energy potential, or technical wind energy potential, to evaluate low carbon energy transition pathways. They  neglect the reduction in mean wind speeds due to the extraction of kinetic energy (KE) from the lower atmosphere as a means to reduce the computational complexity. However, climatological analyses of proposed regional wind turbine deployments with capacity densities ranging from 2-10 MW km^-2 show that this assumption leads to significant overestimation of wind energy potentials because the removal of KE does reduce wind speeds and turbine yields. This gap between policy focussed and climatology based estimates implies the need for the former to use a more climatologically descriptive, yet simple, approach to estimating technical potential. Framing regional wind energy potential within the context of a fixed lower atmosphere KE budget which uses variation in mean boundary layer height to define the KE budgets can improve estimates without increasing computational complexity. We evaluate this hypothesis by analysing a set of previously published Weather Research and Forecasting simulations of a hypothetical large scale wind turbine deployment in Kansas, US, which showed that nighttime yields were lower than daytime, despite wind speeds being 40% higher at night. We assess this seemingly counter-intuitive result by estimating day and nighttime yields while constraining them with separate day and night KE budgets. Daytime budgets are defined by higher mean boundary layer heights (2000m) while nighttime budgets by lower heights (900m). The combination of wind speeds boundary layer variations during day and night result in similar budgets. This means that turbines extract more KE from the atmosphere at night than daytime, leading to the nighttime budgets being depleted faster and thus lower deployment yields. Using the standard approach, which discounts the effect of KE removal by wind turbines, leads to a 180% and 600% overestimation in day and nighttime yields, respectively, relative to the weather model simulations. The KE budget approach , in contrast, leads to a significant improvement with day and nighttime bias being reduced to 20% and 60%, respectively. Using this approach, we revaluate existing technical wind energy potentials using the net area available for wind energy deployment in Kansas to show that yield expectations of 2000-3000 TWh yr^-1 could be reduced by almost 50%. Despite this reduction, the technical potential remains almost 3-5 times higher than the state’s 2018 primary energy consumption. We show that framing the yield from regional wind turbine deployments within a fixed lower atmosphere KE budget framework that uses mean boundary layer variations to define the KE budget leads to more climatologically representative estimates of technical potential without increasing computational complexity. These estimates are likely to improve further with the inclusion of nighttime stability effects. Climatologically representative estimates of technical wind energy potential will enable the development of more robust renewable energy transition policy. 

How to cite: Minz, J., Mbungu, N., Kleidon, A., and Miller, L.: Estimates of technical wind energy potentials can be improved by incorporating information about the variation in mean boundary layer heights , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5764, https://doi.org/10.5194/egusphere-egu22-5764, 2022.

How to cite: Luzia, G., Hahmann, A. N., and Koivisto, M. J.: Mesoscale modelling of the spatio-temporal variability in wind and solar time series for power and energy system applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12759, https://doi.org/10.5194/egusphere-egu22-12759, 2022.

Wind power is attracting more attention as an alternative energy in accordance with efforts to reduce carbon emissions, but its stable operation is difficult due to the intermittency of wind. Since the 1990s, many previous studies have suggested forecasting models focusing on accuracy and speed using numerical weather prediction models, statistical approaches, and hybrid techniques. However, although their verification methods and periods are different, most showed high errors of about 15% or more. In this study, we developed and validated a hybrid forecasting model using the mesoscale model Weather Research and Forecasting (WRF) and artificial neural network (ANN) for a wind farm located in Yeongyang-gun, Gyeongsangbuk-do, South Korea, a complex mountainous terrain (36°36ʹ49ʺN, 129°13ʹ21ʺE). In order to simulate more accurate wind at hub-height of wind turbine (i.e., 80 m), roughness sublayer parameterization (Yonsei surface layer scheme; YSL) is used and daily spatio-temporal high resolution wind forecasts are performed for one year in 2020. The simulated wind speed and actual wind power generation at that time are used as training data set for ANN to construct a hybrid forecast model, and it is validated for January and February 2021. Our analysis shows that improved parameterization in the roughness sublayer can significantly contribute to wind power forecasting through more accurate wind speed forecasting.

How to cite: Kim, J., Gim, K., and Hong, J.: Hybrid wind power forecasting model (WRFv4.1.3 and Artificial Neural Network) considering roughness sublayer characteristics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10976, https://doi.org/10.5194/egusphere-egu22-10976, 2022.

The expansion of marine renewable power is a major alternative for the reduction of greenhouse gases emissions. In Europe, however, the high penetration of offshore wind brings intermittency and power variability into the existing power grid. Offshore solar photovoltaic power is another technological alternative under consideration in the plans for decarbonization. However, future variations in wind, air temperature or solar radiation due to climate change will have a great impact on both renewable energy resources. In this context, this study focusses on the offshore energy assessment off the coast of Western Iberia, a European region encompassing Portugal and the Northwestern part of Spain. Making use of a vast source of data from 35 simulations of a research project called CORDEX, this study investigates the complementarity of offshore wind and solar energy sources with the aim of improving the energy supply stability of this region up to 2040. The most pessimistic greenhouse gases emission scenario (RCP8.5) is considered. The 35 simulations are validated by comparing wind speed at 10m, air temperature at 2m and solar radiation values with data from the ERA5 reanalysis database. Although the offshore wind energy resource has proven to be higher than solar photovoltaic resource at annual scale, both renewable resources showed significant spatiotemporal energy variability throughout the western Iberian Peninsula. When both renewable resources are combined, the stability of the energy resource increased considerably throughout the year. The proposed wind and solar combination scheme is assessed by a performance classification method called Delphi, considering stability, resource, risk, and economic factors. The total index classification increases when resource stability is improved by considering hybrid offshore wind-photovoltaic solar energy production, especially along the nearshore waters.

How to cite: Costoya, X., deCastro, M., Carvalho, D., Arguilé-Pérez, B., and Gómez-Gesteira, M.: Offshore wind and solar photovoltaic energy combination to stabilize energy supply in the western Iberian Peninsula in the near future. , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10283, https://doi.org/10.5194/egusphere-egu22-10283, 2022.

Hydrogen is a versatile energy carrier. When produced with renewable energy by water splitting, it is a carbon neutral alternative to fossil fuels. The industrialization process of this technology is currently dominated by electrolyzers powered by solar or wind energy. For small scale applications, however, more integrated device designs for water splitting using solar energy might optimize hydrogen production due to lower balance of system costs and a smarter thermal management. Such devices offer the opportunity to thermally couple the solar cell and the electrochemical compartment. In this way, heat losses in the absorber can be turned into an efficiency boost for the device via simultaneously enhancing the catalytic performance of the water splitting reactions, cooling the absorber, and decreasing the ohmic losses.^[1,2] However,integrated devices (sometimes also referred to as “artificial leaves”), currently suffer from a lower technology readiness level (TRL) than the completely decoupled approach.

Here, we describe our progress in designing integrated solar water splitting devices to power research stations in Antarctica as a first potentially economic competitive implementation of this technology.^[3] In such remote world regions, local and small-scale hydrogen production can become both economically and environmentally favorable, since the logistics for fossil fuels are expensive and environmentally hazardous. One reason for the low TRL of integrated devices is the complex and poorly understood influence of different weather/climate conditions and changes in the solar spectra on their efficiency.^[4] Therefore, we introduce an open-source Python-based model that combines solar cell physics, optical simulations, electrochemistry, as well as atmospheric and climate data as part of the “YaSoFo” environment.^[5] We model and analyze the climatic response of a device based on state-of-the-art AlGaAs/Si dual-junction photoabsorbers in Antarctica. Furthermore, we present a first prototype demonstrating solar water splitting at temperatures as low as -20°C. ^[3]  

Our work gives important insights into the chances and challenges for thermally coupled solar water splitting and lays the foundation for our goal of using these devices in remote world regions with cold climates.

[1] R. van de Krol, B. Parkinson, MRS Energy Sustain., 2017, 4(e13), 1-11

[2] S. Tembhurne, F. Nandjou, S. Haussener, Nat. Energy, 2019, 4, 399–407

[3] M. Kölbach, K. Rehfeld, M.M. May, Energy Environ. Sci., 2021,14, 4410-4417

[4] M. Reuß, J. Reul, T. Grube, M. Langemann, S. Calnan, M. Robinius, R. Schlatmann, U. Rau,  D. Stolten, Sustain. Energy Fuels, 2019, 3, 801-813

[5] M. M. May, D. Lackner, J. Ohlmann, F. Dimroth, R. van de Krol, T. Hannappel, K. Schwarzburg, Sustain. Energy Fuels, 2017, 1, 492-503

How to cite: Kölbach, M., Höhn, O., Barry, J., Finkbeiner, M., Rehfeld, K., and May, M. M.: Climatic response of thermally coupled solar water splitting in Antarctica, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11608, https://doi.org/10.5194/egusphere-egu22-11608, 2022.

Solar and wind power curves typically exhibit inverted daily and annual cycles. However, their monthly anomalies show both positive and negative low correlation values across Europe, which compromises the effectiveness of their integration in the energy grid. This is because the well-known asymmetric response of the resources to the main large-scale teleconnection patterns vanishes and/or shows low synchronicity when the compound effect of these patterns is considered, as we show here. So we propose a step-wise method to help narrowing the monthly deviations of the total wind-plus-solar electricity production at the regional level from a given curve (here, the mean annual cycle of the total production), applied here across five continuous European regions but with straight application elsewhere and at other temporal scales. It detects the optimal shares of each power over previously identified sub-regions with homogeneous temporal variability of the monthly anomalies of the wind and solar capacity factors. Results show that, keeping the current total regional shares, just through a smart distribution of the power units, the standard deviation of the monthly anomalies of the total wind-plus-solar production is reduced up to 20% without loss in the mean capacity factor as compared to a base scenario with uniform distribution of the installations. This reduction grows above 50% if the total regional shares also came into the optimization game.

Acknowledgments:

This study was supported by the Spanish Ministry of Science, Innovation and Universities – Agencia Estatal de Investigación and the European Regional Development Fund through the project EASE (RTI2018-100870-A-I00), and by the Fundación Séneca – Agencia de Ciencia y Tecnología de la Región de Murcia through the project CLIMAX (20642/JLI/18).

How to cite: Jerez, S., Barriopedro, D., García-López, A., Lorente-Plazas, R., Somoza, A., Turco, M., and Trigo, R. M.: An action-oriented approach to make the most of the wind and solar power complementarity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12420, https://doi.org/10.5194/egusphere-egu22-12420, 2022.

For the analysis of the expected performance of renewable energy systems of all sizes, nowadays various data bases offer the required time series of irradiances and wind speed. The validation of these sets is mainly based on various comparison schemes of averages or distribution functions on a yearly or monthly scale, forming a comprehensive toolbox for the respective quality assessment.  This allows for a high confidence in predictions of system performance data, as-long-as system operation can be considered as “memory-less”.  This is most prominently not the case for systems containing storage devices whose performance depend on the temporal characteristics of the time series - which had been less in focus for the validation of irradiance sets. 

This contribution aims to discuss which sequential characteristics of the data series govern the storage sizing. This should result in the identification of parameters that are applicable for the validation of irradiance series for this task. As example, photovoltaic + battery systems are analyzed, that are driven by data extracted from different sources made available by the PVGIS (https://ec.europa.eu/jrc/en/pvgis ) service.

How to cite: Beyer, H. G.: Identifying sequential characteristics of irradiance data sets applicable for the validation of sets for the assessment of the performance of renewable energy systems containing storage, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9615, https://doi.org/10.5194/egusphere-egu22-9615, 2022.

Climate change may alter long term averages as well as short term extremes of solar irradiation and wind speed distributions. Resulting changes in the performance of wind and solar power generation capacities can impact returns on investments and security of power supply. To assess this impact and enable robust energy system planning, we use climate scenarios from global and regional climate models on an hourly basis to calculate renewable power generation potentials with our Energy Data Analysis Tool EnDAT. We investigate the development of key parameters of wind and solar power generation: annual capacity factors, minimum and maximum generation, duration of periods with extremely low generation and ramp rates. Results show that while changes are small at European average, significant changes can occur at country level. We present model results and discuss uncertainties associated with the climate scenarios and wind and solar power technology parameters as well as capacity distribution. Future research will include demand scenarios and the impact of climate change on “Dunkelflaute”-events in Europe.

How to cite: Scholz, Y. and Stegen, R.: Climate change impact on wind and PV power generation characteristics in Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8608, https://doi.org/10.5194/egusphere-egu22-8608, 2022.

The Paris Agreement aims at creating the climate-neutral world by the middle of the 21st century. One possible and currently popular solution to achieve this goal is to integrate renewable energies into the existing power grid. However, this is a highly complex task, since renewable energies are intermittent due to their weather dependency and predicting weather long-term both at a sufficient time and spatial resolution is non-trivial. It is often the case that both weather predictions based on historical data and future climate predictions are of very limited precision. To accommodate both for aleatoric and epistemic uncertainties we address the problem of optimal allocation of renewable energy capacities in the context of distributionally robust optimization, where the best possible capacity allocation is found given the worst possible weather conditions. We determine the optimal mix for the future 2050 climate-neutral Europe in the financial portfolio framework, where we view mean production of renewable power plants as expected returns on the assets. In the suggested framework portfolio risk is diversifed based on probabilistic characteristics of the underlying assets. Taking into account probabilistic nature of weather variables provides additional safety guarantees which are of primary importance when modelling renewable-based power systems. We limit our analysis to solar and wind energy capacities and implement suggested approach using both historical data-based forecasts and climate predictions models for the EU. 

How to cite: Sirotko-Sibirskaya, N., Benth, F. E., and Zeyringer, M.: Climate ambiguity and optimal allocation of renewable energy capacities in the 2050 EU, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5615, https://doi.org/10.5194/egusphere-egu22-5615, 2022.

European electricity production is increasingly based on renewable energy sources, inspired by ambitious climate politics. Hence, the power system must adapt to larger shares of less flexible electricity sources like wind and solar, which depend on fluctuating weather. Here, hydro power inflow, wind power, solar power and electricity demand in Norway are estimated based on meteorological data for the period 1961-2020. The installed capacity of the production technologies is kept constant at 2020 levels throughout the analyses. Mean annual power production is higher than mean annual electricity demand. However, the variability in production potentials is large for all renewable energy sources at time scales ranging from hourly to annual, and power deficits occur occasionally even at the annual scale. Hydro power inflow shows an increasing trend during the period studied, and the relative increase is largest during the winter season. Wind and solar power production are only marginally affected by climate differences in the study period. Electricity consumption decreases somewhat during the 60-year period, due to increasing average temperatures. The combined effect of production and consumption changes is an increase in mean annual surplus of electricity during the period studied. However, although a surplus of electricity exists at the mean annual level, additional available electricity in the form of reservoir storage or import is needed to maintain security of supply within the country.

How to cite: Haddeland, I. and Sidelnikova, M.: How weather and climate affect renewable electricity sources in Norway, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4481, https://doi.org/10.5194/egusphere-egu22-4481, 2022.

The European Union set the goal to meet 32% of its final energy consumption from renewable sources by 2030. Other than fossil or nuclear electricity generation, VRE produce electricity not at a few individual locations, but distributed throughout a country. Even though solar PV can be generated on urban infrastructure, e.g. on rooftops or above parking spaces, this potential is limited and costs are usually higher than those for ground mounted PV. It is therefore likely that substantial amounts of solar PV have to be deployed as ground mounted PV and land use conflicts may arise from this infrastructure expansion. Mainly land which is currently under agricultural use would be available for that purpose. As this would imply a competition between food, feed, and electricity production, expansion on that kind of land is discussed controversially. One option for minimizing this conflict is an integrated use of agricultural land, producing both agricultural products and electricity from PV. To better understand the synergies and trade-offs, A thorough analysis of integrated PV and agricultural production on a large scale is necessary.

This work presents a simulation framework to determine electricity as well as crop production of APV systems at high temporal and spatial resolution. Radiation data, digital height data and land cover data function as input data to find suitable areas and simulate the APV system power output.

How to cite: Mikovits, C., Krexner, T., Kral, I., Bauer, A., Schauppenlehner, T., Schönhart, M., Schmid, E., and Schmidt, J.: Energy production simulation of Agrivolatic Systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9953, https://doi.org/10.5194/egusphere-egu22-9953, 2022.

 The global electricity consumption is increasing at a faster rate than total global power production. Non-renewable sources contributed more than 63.3% of global electricity production in 2020, and such a high reliance on non-renewable sources could lead to electricity shortages and environmental issues. This alarming situation is on the doorstep as various countries in the world are facing electricity crises. Solar energy can be an alternative to tackle this problem and to achieve the Sustainable Development Goal (SDG) of affordable and clean energy. There are several environmental factors that affect the efficiency of solar panel. Thus, based on the nature and working principle of photovoltaic cell, there is a need to prioritise these factors to locate suitable locations to increase the efficiency of both transmission and generation. Solar panel being expensive capital component, meticulous planning of such projects is necessary as it has long payback period, substantial investment, and relatively lower returns on investment. This study emphasises on two components, first the method of finding a suitable location with several required parameters through geospatial data analysis and the second being financial feasibility by identifying suitable locations of execution and maintenance costs that will be as minimal as possible without compromising the efficient solar locations. Higher efficiency and lower cost locations will together increase sustainability and affordability that contribute to achieving the SDG of affordable and clean energy. 

How to cite: Mudholkar, L. and Haridas Aithal, B.: Multi-Criteria Decision Making for Site Suitability of a Solar Farms , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1074, https://doi.org/10.5194/egusphere-egu22-1074, 2022.

From a superficial reading of the literature on land-use requirements for renewable energy systems, one may conclude that the community has a very clear understanding of how much land is necessary to deploy renewable energy generation technologies. In particular for solar PV and wind power (VRES), the technologies with the highest growth potentials in the coming decades, abundant literature is available. However, a systematic overview is lacking, in particular, concerning approaches used for estimating the requirements, underlying factors that affect the results, and the regional differences.

There is no standard metric that is applied for estimating the land area necessary to accommodate a certain amount of renewable energy capacity. Several different metrics are used for this purpose, including land-use efficiency, land-use intensity, power density, and land occupation, to name a few. E.g., land-use efficiency in one paper can refer to the land area needed to install a certain capacity for energy generation, whereas another paper considers land-use efficiency as the area needed to generate a certain amount of electricity. Therefore, estimates of land-use requirements quantified with the same metrics vary greatly.  

The variability of the land-use requirements is also rooted in the underlying assumptions regarding the area, power-related component, and time of the chosen metrics. However, a systematized review of all aforementioned assumptions on land-use by renewable energies is absent.

Land-use requirements are often derived from the existing VRES facilities. Hence, their values are influenced by such location-specific factors as climatic conditions, orography, land ownership, among others. As land-use requirements are often integrated into the research that estimates potentials for the future deployment of renewable energy, the influence of those factors is implicitly integrated as well. Hence, the potentials for one region may be estimated by applying the data from another region. Although the application of region-specific land-use requirements could reduce the introduced inconsistencies, known land-use requirements are in its vast majority estimated for the US. Therefore, a compilation that in particular gathers literature on land-use of renewable energies in underrepresented world regions is of high importance.

How to cite: Turkovska, O., Gruber, K., Klingler, M., Klöckl, C., Ramirez Camargo, L., Regner, P., Wehrle, S., and Schmidt, J.: An overview of land-use requirements for solar PV and wind power, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11962, https://doi.org/10.5194/egusphere-egu22-11962, 2022.

We construct a geodatabase of spatially resolved PV supply profiles for building, land and water-bound installations under typical meteorological circumstances. This is done for three 2050 energy transition scenarios, which are all in line with the Dutch Climate Agreement, but differ from each other regarding the degree of decentralization of electricity demand and supply.

Hourly global horizontal irradiation (GHI) measurements by 33 Dutch ground observation stations are gathered and linearly interpolated to obtain a raster dataset with a resolution of 25 km. GHI is converted to global tilted irradiation (GTI) for a multitude of slope and azimuth combinations by subsequently applying the Erbs diffuse fraction and Perez transposition models, thus generating a GTI lookup table.

By combining building polygons and a high-density LiDAR height point cloud, roof surface polygons characterized by a slope and azimuth are identified for all buildings in the Netherlands. Using the GTI lookup table, the solar resource on each of the roof surfaces is determined. Scenario-specific national PV capacities for residential and utility buildings are then distributed over neighborhoods proportional to the total yearly solar resource on their corresponding roof surfaces. The resulting neighborhood capacities are sub-distributed over slope-azimuth-positions by applying distribution ratios found in a large dataset on registered Dutch PV systems.

Scenario-dependent national capacities for field-bound PV (agriculture, roadside and dike) and inland water-bound PV are distributed over municipalities proportional to their corresponding suitable land use areas. Offshore PV capacity is kept on national level. All neighborhood and municipality capacities are converted to GTI. The building-bound profiles are then translated to PV supply by applying solar elevation angle dependent performance ratios and an assumed panel efficiency of 20%. The same is done for the land and water-bound profiles, only now assuming a constant performance ratio of 90%. Residential building-bound PV is fully allocated to the low voltage distribution grid. For the utility variant, the portion allocated to low voltage level is equal to the neighborhood floorspace share of utility buildings estimated to have a grid connection capacity of less than 300kW. The remainder is assigned to mid voltage level. Water and land-bound PV supply is apportioned to mid and high voltage level with a 3:1 ratio, except for the offshore category, which is fully allocated to high voltage level.

Research results will be presented in the form of a country map and a cumulative distribution function (CDF) graph for; low voltage PV supply; mid voltage PV supply,  low voltage self-sufficiency,  and mid voltage self-sufficiency. All maps and CDF graphs provide information for all three 2050 scenarios, a 2030 scenario and the present situation. 

The PV supply calculation module described above is part of our Advanced Scenario Management model. This model consists of a number of supply and demand modules (traditional building demand, heat pump demand, electric vehicle demand, PV supply and wind turbine supply), each producing low voltage (neighborhood), mid voltage (municipality) and high voltage (country) level electricity profiles. Together, they allow for supply-demand system analysis for multiple voltage levels and energy transition scenarios.

How to cite: van Sark, W., Nortier, N., and Mewe, A.: Spatio-temporal potential profiles for building, land and water-bound photovoltaic installations for future Dutch energy transition scenarios , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4468, https://doi.org/10.5194/egusphere-egu22-4468, 2022.

In the climate protection act of 2021 Germany has set the goal to become climate neutral by 2045 [1]. To meet this target, the renewable energy sites in Germany must be significantly expanded. At the same time, the acceptance of wind turbines is heavily discussed. For example, the construction of wind turbines inside forests or distances to residential buildings are topics in societal discussions. Furthermore, the different legislation in the individual federal states lead to unequal wind expansion possibilities.

This paper assesses the impact of legislation on the onshore wind energy potential in Germany considering residential buildings for the first time. To this end, different scenarios for high resolution land eligibility analyses are developed with the open-source tool GLAES [2] using a 10 m*10 m resolution and high accuracy GIS-data. Firstly, the impact of different exclusion zones in the analysis is evaluated. The distance to residential land use and the use of forests and protected landscapes are especially influential for the results. Secondly, we investigate the impact of different legislation in the individual German federal states. A comparison to national energy system studies shows that a nationwide application of for example Bavaria’s legislation leads to insufficient wind capacity potentials to reach climate neutrality by 2045. Thirdly, we evaluate the distribution of the wind potential when the current federal states’ legislation is applied which uncovers large inequalities.

[1]    Bundes-Klimaschutzgesetz (KSG). 2021. Accessed: Jan. 05, 2021. [Online]. Available: https://www.gesetze-im-internet.de/ksg/KSG.pdf
[2]    D. Ryberg, M. Robinius, and D. Stolten, ‘Evaluating Land Eligibility Constraints of Renewable Energy Sources in Europe’, Energies, vol. 11, no. 5, p. 1246, May 2018, doi: 10.3390/en11051246.

How to cite: Risch, S., Pflugradt, N., Kotzur, L., and Stolten, D.: Impact of Legislation and Social Acceptance on Wind Potentials in Germany, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9573, https://doi.org/10.5194/egusphere-egu22-9573, 2022.

The variable nature of wind power generation poses many challenges. The scientific community has addressed many of these challenges and made much progress in the recent years. These include, for example, quantifying resource variability, constraining future climate change impacts on wind energy, and suggesting robust system designs. Much of this research, however, has been academic in nature and lacks bi-directional interactions with stakeholders who make real world decisions. As an attempt to facilitate more exchange with industry partners, we present first results of a stakeholder workshop. The workshop theme is the role of climate in wind energy site assessments, including aspects related to climate variability and climate change. In addition to direct yield related parameters, such as capacity factors and their variability, we also plan to address relevant indirect effects that are less extensive researched, such as climate-induced changes in bat activity that trigger generation interruptions and blade icing. We report on the workshop design, highlight lessons learned and illustrate how the stakeholder feedback is used in shaping the precise research questions to be addressed in the course of the KliWiSt project [1]. KliWiSt is a German acronym that stands for the impact of climate change on wind energy site assessments. The general transdisciplinary approach can be adapted and used in other stakeholder-oriented research projects.

How to cite: Schaffer, L., Borowski, J., Dörenkämper, M., Jacob, D., Keup-Thiel, E., Sieck, K., and Wohland, J.: Let’s talk: incorporating stakeholder needs in climate-science based wind resource assessments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4463, https://doi.org/10.5194/egusphere-egu22-4463, 2022.

Increased adoption of local renewable energy sources and larger active participation of energy end-consumers in the energy transition are attributed to Energy Cooperatives (ECoops). These are the most common organizational form of energy communities, which are currently one of the key strategies of the European Union to advance the energy transition. Getting to understand better which factors facilitate and benefit the existence and development of ECoops might help to foster a larger adoption of energy communities. Most studies addressing this are of qualitative character, while large-scale quantitative studies trying to understand general trends are scarce. None of them has paid attention to the impact of the type and quality of renewable energy resources (RES)  available at the location of ECoops.

We conduct an exploratory spatial data analysis on the NUTS2 and NUTS3 regions levels to explore which characteristics of RES availability co-occur with the presence of ECoops across Europe. The characteristics of RES availability include total solar irradiation, average wind speeds, complementarity between solar and wind resources as well as resource droughts. We use multiple decades of ERA5 and ER5-land data to determine the average RES characteristics for each NUTS region. The location of ECoops is derived from the database on ECoops by ReScoop.

Results contribute to the understanding if there is any spatial relationship between the existence of ECoops and RES availability. Clusters of NUTS regions with rather low RES availability and flourishing ECoops, as well as regions with good resources and a low number or no ECoops, are identified. Further, the study identifies hot-spots that require special attention to become auspicious for energy communities due to the rather challenging conditions of RES availability.

How to cite: Lode, M. L., Ramirez Camargo, L., and Coosemans, T.: Is renewable energy resources availability decisive for Energy Cooperatives' existence? A spatiotemporal analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12710, https://doi.org/10.5194/egusphere-egu22-12710, 2022.

Electricity system optimisation models should have a high spatial resolution, because mixing locations with high renewable yield and locations with high electricity demand while ignoring power lines underestimates transmission constraints that can cause congestion. This can result in an infeasible model and is particularly true for configurations with high renewable penetration because the infrastructure of today’s transmission grid is not designed for an uncertain and geographically scattered generation. But spatially high resolved models are computationally intractable, therefore researchers have developed methods to simplify their models on a spatial scale. Common approaches include modeling every country by a single node, or clustering spatially high resolved models using selected clustering methods. Nevertheless, it was not investigated if results obtained from such low resolution or clustered models are feasible when dis-aggregating the results back into a higher resolution. Moreover, no evaluated dis-aggregation method exists to the author’s knowledge. This is a challenging task as the clustering process typically is not bijective and finding a suitable inverse is not intuitive.

Here, we propose a first method to dis-aggregate spatially low resolution model results into higher resolution. The proposed dis-aggregation is a local optimisation problem that minimises the excess of renewable energy for every node within the cluster while respecting land-use restrictions. We define excess as the available renewable energy per node minus local electricity demand minus all transmission capacity that is connected to the node. Electricity storage is distributed proportional to the resulting renewable capacity. Then, the spatially high resolved model is solved as an operational problem where no further capacity expansion is allowed. We investigate if the cost-optimal low resolution investment results of a fully renewable system that consists of wind and solar energy and storage (hydrogen and battery) is feasible when dis-aggregated into a spatially high resolved model of 1250 nodes. Results of our novel approach are benchmarked against the intuitive Ansatz to uniformly dis-aggregate the low resolution modeling results, i.e. distributing the capacity of a clustered node evenly among the nodes in the high-resolution model.

Our results for dis-aggregating a low resolution model of Europe where every country is modeled as a single node are that 12.8% of total demand can not be met with renewable energy (benchmark: 15.3%). If this gap is compensated with gas, carbon emissions would rise by 5% of 1990 emissions (benchmark: 6%) and total annual system costs would increase by 24 billion euros (benchmark: 29 billion). When modelling Europe with 100 nodes, our dis-aggregtion method yields 5.6% of unmet demand (benchmark: 7.3%). This means that carbon emissions would rise by 2.2% (benchmark: 2.7%) compared to 1990s level in case the unmet demand is satisfied with gas and would increase the total system costs by additional 10.6 billion euros (benchmark: 13.2 billion). This result supports other research which shows the importance of modelling at higher resolution than country boundaries in order to avoid unwanted infeasibilities. Approximately one half of unmet demand can be avoided by raising the model resolution in Europe to 100 nodes.

How to cite: Frysztacki, M. and Brown, T.: Inverting spatially low resolved electricity system modeling results: How feasible are they when disaggregated into high resolution?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5827, https://doi.org/10.5194/egusphere-egu22-5827, 2022.

How to cite: Roithner, M., Zeyringer, M., and Price, J.: Evaluation of varying spatial resolution in power system modelling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11025, https://doi.org/10.5194/egusphere-egu22-11025, 2022.

Extreme exploitation of fossil fuels has imposed catastrophic scarcity of energy sources, worldwide. Bioenergy is extremely pertinent to renewable energy alternatives. Municipal solid waste (MSW) and industrial organic waste has sufficient energy potential due to ample organic content. Moreover, landfilling being most economic disposal method offers incubated treatment of solid waste along with production of gases for energy generation. Bioreactor landfills are the new advancements to conventional landfills which accelerates the bioenergy production through incorporation of leachate recirculation. In this research, four simulated anaerobic landfill bioreactors co-disposed with paper mill sludge (PMS) as an industrial organic waste and MSW in three different proportions were installed. One reactor was kept as control (BRL1) with sole disposal of MSW along with other co-landfilled reactors with ratios of PMS and MSW as 1:3 (BRL2), 1:1 (BRL3) and 3:1 (BRL4). Leachate produced from each landfill reactors was utilized for moisture maintenance and degradation enhancement through its recirculation. Periodic analysis of leachate physico-chemical parameters and chromatographic analysis of landfill gas were performed until 300 days of landfilling. Artificial neural network (ANN) modeling was performed to predict biogas production and leachate organic pollutants removal from operating anaerobic landfill simulators. Experimented physico-chemical parameters including pH, electrical conductivity, volatile solids, volatile fatty acids, total alkalinity, ammoniacal and total nitrogen were used as input layer for neural network modeling. Different sets of output layers for leachate pollutants (chemical oxygen demand and total heavy metal concentrations) and biogas yield were decided for individual ANN training. Levenberg-Marquardt algorithm was used to train data sets with 10 number of hidden layers, 15% validation and 15% testing. Moreover, the performance of each landfill reactor was optimized statistically using back propagation network model. Over the completion of landfilling process, BRL3 with equal co-disposal ration of PMS and MSW fetched maximum biogas yield of 146.14 mL/VS/d, which was 1.53, 1.33 and 2.35 times more than that of BRL2, BRL4 and BRL1, respectively. The prediction model could forecast and statistically optimize the biogas content with excellent fitting with experimented data (R² > 0.95). This study expands the dimensions of experimental and mathematical investigations for co-landfilling practices for not only acquiring energy out of simultaneously co-landfilled solid wastes from distinct origins but also supports the attempts for diminishing its leachate pollutant concentrations.

Keywords: Artificial neural network, Bioenergy production, Landfills, Co-disposal, Municipal solid waste, Paper mill sludge

How to cite: Srivastava, A. N. and Chakma, S.: Artificial Neural Network Modeling for Prediction of Bioenergy Production and Organic Pollutant Removal from Simulated Co-Disposed Landfills  , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-118, https://doi.org/10.5194/egusphere-egu22-118, 2022.

Power-to-methane has been identified as a solution for quickly and effectively exploiting surplus electricity potential. Nevertheless, due to the current efficiencies, costs and sizes, it may not be suitable for local energy transitions. This article presents the results of two optimization models developed in Calliope for two case studies regions, with low wind levels in southern Germany with different electricity mixes. The optimization models simulate all available generation sources in the regions and their extensions with an additional Power-to-methane plant. The model minimizes the cost of the overall systems that meet the given demand including the gas, district heating, and electricity systems. Annual gas and district heating demand are generated based on collected data from industries, commerce, and households and standard load profiles, while annual electricity demand is obtained from public statistics. Besides, hourly electricity demand data from industries, commerce, and households are collected. Results are compared with scenarios using standard load profiles for electricity. The results show that the use of Power-to-methane is significantly affected by the load profiles used as well as the existing technological mix.

How to cite: Valdes, J., Bauer, R., and Klaus, G.: Analyzing Input Data Influence in Local Techno-Economic Analyses for Power-to-Methane Plants, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9597, https://doi.org/10.5194/egusphere-egu22-9597, 2022.