During the last 30 years, the global energy sector has undergone through significant transformation, delivering a considerably larger electricity output whilst attempting to reduce air pollutant and greenhouse gas emissions. The international community has tackled this challenging dilemma by implementing different kind of policies and by encouraging several types of technological changes; including the partial replacement of coal and liquid fossil fuels by low carbon energy vectors (natural gas and renewable sources), the incorporation of more efficient power trains (natural gas fired combined cycles and supercritical coal fired plants) and the deployment of primary and secondary treatment processes for limiting air pollutant concentration in flue gases.
EDGAR is a unique global emission database due to its high sectorial, technological and geographical coverage; reporting greenhouse and air pollutant emission time series (1970-nowadays) in a very detailed way. Research is currently being conducted, aimed at updating the energy conversion and end of pipe processes so that the quantified emissions can better reflect the latest global and regional changes. By using EDGAR new data, it is possible to evaluate the impact of technology and regulatory frameworks on air pollutant emissions as well as to identify possible co-benefits and trade off associated with climate change mitigation policies for the energy industries.
This work is intended to study the drivers for greenhouse and air pollutant emission trends within this sector - both in large emitting developed and developing economies; by focusing on the role of  demand increase, on the penetration of non-fossil sources and specially on the incorporation of more efficient power islands, combustion and air pollutant abatement units.

How to cite: Oreggioni, G. D., Monforti-Ferraio, F., Crippa, M., Schaaf, E., Guizzardi, D., Muntean, M., Duerr, M., and Vignati, E.: EDGAR v5.0: a tool to evaluate the influence of technology incorporation and regulatory frameworks on global greenhouse gases and air pollutant emissions., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20237, https://doi.org/10.5194/egusphere-egu2020-20237, 2020.

As climate change issues arise and become a global concern, to reduce carbon emissions has become an urgent issue to respond to. Therefore, the application of renewable energy plays more and more important roles in the energy domain nowadays, which may also bring significant impacts to the low-carbon electricity market transition. With the advantages of developing renewable energy, the Taiwan government anticipates achieving the goal of reaching 20% renewable energy in electricity structure by 2025. Moreover, Taiwan has carried out the most vigorous amendment on the Electricity Act in history to open the electricity market so that more stakeholders can participate in it to propel energy transition. Since the UK is the lead in this field, it is worth investigating. This paper takes the UK as a case study to analyze its evolution in recent decades after the deregulation of its electricity market from the retail side perspective. It expects to explore how the transition drove actors to empower their roles, or even create new roles and responsibilities, and engage in the low-carbon electricity market transition pathway. This paper expects the UK case as a great reference for Taiwan, which echoes the current scenario in Taiwan with the potential to deliver further insights.

How to cite: Lu, Y.-T. and Lin, T.-L.: Low-carbon Electricity Market Transition in the UK: A Multi-level Perspective Analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17841, https://doi.org/10.5194/egusphere-egu2020-17841, 2020.

People’s perception of ecosystem services is usually affected by a variety of interacting factors, making it difficult to identify the actual driving factors. An orthogonal experimental design can effectively identify interactions and has the potential for use in social perception studies. Based on 20,642 questionnaires on the topic of WTP (willingness to pay), the interactive forces driving people’s perceptions of ecosystem services on the Tibetan Plateau were identified using an orthogonal experimental design. The results show that 1) when interactions are not considered, management attitudes have the strongest influence on WTP, the second most influential factors are subjective cognitions, and objective indicators are ranked last. 2) The interactions has a stronger impact on WTP than some of individual factor: when interactions and observed variables are compared simultaneously, the interaction between the “importance of the Tibetan Plateau” and the “WTP for residents” has a stronger influence on WTP (451 Chinese yuan/year) than any of the individual variables except “WTP for residents” (666 CNY/year) and “knowledge of the Tibetan Plateau” (484 CNY/year). Among the interactions between pairs of variables, that between education (212 CNY/year) and income (260 CNY/year) has a greater impact on WTP (266 CNY/year) than either variable alone. 3) When the interactions are considered, subjective cognitions are the most important indicators for WTP, and management attitudes are the second most important indicators. This study provides a feasible method for the identification of interactive driving forces in analyses involving questionnaires, and the conclusions can provide guidance from the public for ecosystem management on the Tibetan Plateau.

How to cite: Zhang, R.: The application of orthogonal experimental design to identify the interactive forces driving people’s perceptions of ecosystem services, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4447, https://doi.org/10.5194/egusphere-egu2020-4447, 2020.

With the increase of global temperature and the intensification of human activities, numerous ecological problems have occurred in the grasslands of Inner Mongolia, which have seriously disturbed the sustainable development and the improvement of the carrying capacity of grassland ecosystems, and threatened the security of regional ecological environment. There is an urgent need to find a quantitative assessment method for the quantification of  the carrying capacity in grassland ecosystems, which is significantly crucial for making a sustainable development strategies of  grassland resources. In this study, we considered the ecosystem services as a limited well-beings, and took the Net Primary Production (NPP) as the core indicator for characterizing the occupation of grassland's main ecological functions to ecosystem services. Further more， the occupy threshold for environment maintenance and natural regeneration functions were calculated. The spatial distribution of the grasslands carrying capacity in Inner Mongolia was quantitatively analyzed, and the vulnerability of the "human-land system" was evaluated. The results show that: (1) The Above-ground Net Primary Production (ANPP) needed for the ecosystem function maintenance of Inner Mongolia in 2015 was at 38-401 g/m²· a, and there was a decreasing trend from northeast to southwest; (2) In 2015, the ecological carrying capacity of the grassland ecosystem in Inner Mongolia was 79.336 million sheep units, and the average carrying capacity was 1.56 sheep unit / hm² • a. However, the total economic carrying capacity was 174,571 million sheep units, and the average economic carrying capacity was 3.42 sheep unit / hm² • a, whose spatial distribution is basically consistent with the spatial distribution of ecological carrying capacity, while the actual stocking was 145,548 million sheep units. (3) The spatial distribution of the grassland carrying capacity displayed highly spatial heterogeneity in Inner Mongolia. The ecological carrying status index ranged from 0.59 to 12.06, and the economic carrying status index ranged from 0.29 to 3.68. The natural regeneration function of grasslands required the largest NPP, which greatly reduced the grassland ecological carrying capacity in Inner Mongolia. From the perspective of spatial distribution, the ecological carrying capacity of grassland in eastern Inner Mongolia is bigger than that of western region, and there was an imbalance between socioeconomic development and ecological environmental maintenance. The concentration of population in eastern Inner Mongolia is the main limiting factor affecting the ecological carrying capacity, which resulting in the supply of grassland ecological system cannot meet the practical needs of social development. The environment western characterized by the water deficient and low vegetation coverage were the main limiting factors of ecological carrying capacity in western Inner Mongolia, where the grassland function of windbreak and sand-fixation needed to take up more resources.

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How to cite: caiyun, G. and dongsheng, Z.: Analysis of grassland carrying capacity in Inner Mongolia based on ecosystem function, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13509, https://doi.org/10.5194/egusphere-egu2020-13509, 2020.

The Yellow River Basin is rich in land, energy, and mineral resources, which is an important energy base and major food producing area in China. However, water shortages have become a key factor restricting the development of the basin. With the economic and social development and population growth, resources demand of water, food and energy in the river basin has been further increased under the drive of national energy security and food security strategies. The conflict between resource supply and demand has become more prominent. This paper selected 9 provinces of the Yellow River Basin as the study area, and collected 16-year time series data, and quantitatively describes the temporal and spatial coordinated changes of water, energy, and food through methods such as MK test and synergetic evaluation system. The results show that in the nine provinces of the Yellow River Basin, the water supply has increased slightly, and energy and food production have increased significantly. The spatial differences of water supply and food production have little change and are relatively stable, while the change of spatial differences in energy production are more obvious with a downward trend, indicating that regionalization of energy production areas is becoming more and more obvious. According to the synergetic evaluation system, the coefficients of synergy for all of the nine provinces are gradually increasing. The evaluation coefficient of Henan is relatively high, indicating that Henan has a positive synergy of water, energy, and food resources. And the Ningxia province has the lowest evaluation coefficient. According to the results, the water energy and food system reconfiguration should be carried out for key provinces, and the allocation of water energy and food resources should be optimized to maximize the utilization of the three resources and achieve sustainable use.First of all, adjusting the energy structure among the provinces with poor synergy. For example, raw coal is still the core resource of energy consumption which is water-intensive. Reduce the investment in coal chemical industry and other high water-consumed energy industry to have a better synergy coefficient. Also, develop more clean energy, for example, hydro-power, nuclear power and wind power. Nowadays, the environment-friendly and resource-saving clean energy portion is less than 10% of the total energy consumption. The waste resources can be reuse to provide green energy. Last, high-end production technology in energy production is important to guarantee the water and energy safety. 

How to cite: Ma, L., Li, C., Hu, X., Wang, P., and Li, X.: Spatiotemporal Change Analysis of of Water, Energy and Food Resources in Yellow River Basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3002, https://doi.org/10.5194/egusphere-egu2020-3002, 2020.

Bacteria capable of simultaneous aerobic denitrification and phosphorus removal (SADPR) are promising for the establishment of novel one-stage wastewater treatment systems. Nevertheless, insights into the metabolic potentials and engineered applications of SADPR-related bacteria are limited. Firstly, comprehensive metabolic models of two efficient SADPR bacteria, Achromobacter sp. GAD3 and Agrobacterium sp. LAD9, were obtained by high-throughput genome sequencing. With succinate as the preferred carbon source, both strains employed a complete TCA cycle as the major carbon metabolism for potentials of various organic acids and complex carbon oxidation. Complete and truncated aerobic denitrification routes were confirmed in GAD3 and LAD9, respectively, facilitated by all the major components of the electron transfer chain via oxidative phosphorylation. Comparative genome analysis revealed distinctive ecological niches involved in denitrification among different phylogenetic clades within Achromobacter and Agrobacterium. Excellent phosphorus removal capacities were contributed by inorganic phosphate uptake, polyphosphate synthesis and phosphonate metabolism. Additionally, the physiology of GAD3/LAD9 is different from that displayed by most available polyphosphate accumulating organisms, and reveals both strains to be more versatile, carrying out potentials for diverse organics degradation and outstanding SADPR capacity within a single organism. Secondly, both GAD3 and LAD9 were successfully applied for bioaugmented treatment of municipal wastewater in a pilot-scale sequencing batch reactor. At an appropriate COD/N ratio of 8, the bioaugmentation system exhibited stable and excellent carbon and nutrients removal, the averaged effluent concentrations of COD, NH₄⁺-N, TN and TP were 20.6, 0.69, 14.1 and 0.40 mg/L, respectively, which could meet the first class requirement of the National Municipal Wastewater Discharge Standards of China (COD < 50 mg/L, TN < 15 mg/L, TP < 0.5 mg/L). Clone library and real-time polymerase chain reaction analysis revealed that the introduced bacteria greatly improved the structure of original microbial community and facilitated their aerobic nutrients removal capacities. This proposed emerging technology was shown to be an alternative technology to establish new wastewater treatment systems and upgrade or retrofit conventional systems from secondary-level to tertiary-level.

How to cite: Liu, S., Chen, Q., Liu, S., and Ni, J.: Comprehensive insights into metabolic potentials and engineered applications of two novel simultaneous aerobic denitrification and phosphorus removal bacteria, Achromobacter sp. GAD3 and Agrobacterium sp. LAD9, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3221, https://doi.org/10.5194/egusphere-egu2020-3221, 2020.

Recently, several alkylamine hydrates have been studied in an effort to reveal the structural transitions from semi- to ‘canonical’ clathrate hydrate in the presence of secondary guest molecules. Trimethylamine (TMA) is known to form the semi-clathrate hydrate, and it has been reported that the structural transition of the TMA semi-clathrate hydrate may not occur in the presence of hydrogen gas as a secondary guest molecule. This paper reports the structural transition of trimethylamine(TMA) hydrate induced by the type of guest molecules. Powder X-ray diffraction patterns of (TMA + H₂) hydrates show the formation of hexagoanl P6/mmm hydrate, but those of (TMA + CH₄) hydrates indicate the formation of cubic Fd3m hydrate. Without gaseous guest molecule, the crystal structure of pure TMA hydrate is identified as hexagonal P6/mmm. Therefore, inclusion of gaseous methane in TMA hydrate can induce the structural transition from hexagonal to cubic hydrate or the formation of metastable cubic hydrate. To clearly reveal this possibility, we also check the time-dependent structural patterns of binary (TMA + CH₄) hydrates from 1 to 14 days, and the results show that the structural transition of TMA hydrate from hexagonal P6/mmm to cubic Fd3m hydrate structure can occur during the methane inclusion process.

How to cite: Cha, M.: Structural Transition of Trimethylamine Hydrate by Methane or Hydrogen Inclusion, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3806, https://doi.org/10.5194/egusphere-egu2020-3806, 2020.

The IPCC and IPBES reports, the sustainable development goals of the United Nations and the societal challenges for Europe defined by Horizon 2020 and Horizon Europe all emphasize the strong need for integrated and sustainable management of subsurface resources to protect society and biodiversity. The four GeoERA groundwater projects contribute to this important goal by studying the current and future quantitative and chemical status of European groundwater bodies. The quantity and quality issues related to natural processes, human activities and climate change are investigated to improve our basis for informed decision making e.g. for climate change mitigation and adaptation. The four projects provide new and important data for further development of the European Geological Data Infrastructure (EGDI) as a leading information platform for sustainable and integrated management of subsurface resources in Europe and one of the leading platforms, globally. The four projects will deliver “FAIR” (Findable, Accessible, Interoperable and Reusable) data easily accessible for all relevant end users via EGDI. This will improve our understanding of the subsurface and support common efforts in public-private partnerships to meet the UN sustainable development goals and to develop efficient tools for climate change impact assessment, mitigation and adaptation. Here we briefly present some main objectives and deliverables of the four groundwater projects: 1) HOVER – “Hydrogeological processes and geological settings over Europe controlling dissolved geogenic and anthropogenic elements in groundwater of relevance to human health and the status of dependent ecosystems” – studies e.g. I) geogenic (natural) groundwater quality issues affecting human health, II) polluted groundwater focusing on nitrate, pesticides and emerging contaminants that besides human health potentially affect biodiversity and the ecological status of terrestrial and aquatic ecosystems and III) groundwater age and travel time distributions in European aquifers, which are useful for assessment of the history, migration and fate of contaminants in the subsurface and the vulnerability of the European groundwater resources towards pollution 2) RESOURCE – “Resources of groundwater, harmonized at cross-border and Pan-European Scale” – studies I) transboundary aquifers between Poland and Lithuania; as well as Belgium, The Netherlands and Germany; II) Karst and Chalk aquifers across Europe and III) Develops a new Pan European groundwater resources map that includes information on volumes, age and quality (salinity) 3) TACTIC – “Tools for assessment of climate change impact on groundwater and adaptation strategies” – compiles and studies climate change impact assessment and adaptation tools within more than 40 pilot areas distributed across Europe and 4) VoGERA –  “Vulnerability of shallow groundwater resources to deep sub-surface energy-related activities” – studies groundwater vulnerability to energy-related activities in the UK, the Netherlands, Belgium and Hungary.

How to cite: Hinsby, K., Gourcy, L., Broers, H. P., Højberg, A. L., and Bianchi, M.: Integrated and sustainable management of subsurface resources - Introducing the contributions of the four GeoERA groundwater projects to the European Geological Data Infrastructure , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4253, https://doi.org/10.5194/egusphere-egu2020-4253, 2020.

It is widely accepted that non-energy minerals underpin modern economies since they are essential for manufacturing and renewable energy supply. Europe shows an inevitably growing and accelerated consumption of mineral commodities. The critical question is whether supply to meet these demands is adequate. However, no one can answer this with any certainty because secure supply of mineral RM is a matter of knowing the resources and the ability to exploit them sustainably.

Europe’s strive to be become the world’s first climate-neutral continent by 2050 means implementing the “European Green Deal” by the EU Commission. Measures accompanied with an initial roadmap of key policies range from ambitiously cutting emissions, to investing in cutting-edge research and innovation, to preserving Europe’s natural environment.

This green transition is a giant societal leap. However, of the clean and carbon-reducing technologies (e.g. wind turbines, photovoltaic panels, electric and hybrid vehicles), which allow energy production from renewable resources, use significant quantities of metals [e.g. REE, PGE, Nb, Li, Co, In, Ga, V, Te, Se] that are derived or refined from minerals, and of which Europe is strongly import dependent on. The high import dependence of strategic (SRM) and critical raw materials (CRM) has a serious impact on the sustainability of the EU manufacturing industry value chains and key enabling technologies (e.g. renewable energy industry, mobility sector and AI) and significant release of CO₂ emissions due to foreign ore transport. Effectively knowing Europe’s subsurface and the potential mineral supplies that can be used in these manufacturing industries can achieve this. We need to calculate the volumes of CRM and SRM metals (e.g. Co, Nb, V, Sb, PGE and REE) and minerals currently not extracted in Europe. We further need to understand how high-tech elements are mobilised, where they occur and why some are associated with specific major industrial metals. This means a renewed and robust focus on advanced exploration for new mineral deposits on land and sea.

FRAME^(*)[1] addresses most of these concerns by focusing on at least four of the current objectives of the EU Commission: 1- CRM; 2- battery critical elements [graphite, Co, Li]; 3- The circular economy and; 4- the responsible sourcing of metals by combating conflict minerals.

With focused work packages, FRAME aims to broadly deliver, 1- a new assessment of the SRM and CRM in Europe; 2- an innovative predictability of where the sourcing of some of these SRM and CRM may come from to reduce dependance on external supply sources, which in some specific metals such as Nb and Ta, fosters the sustainable and responsible supply and; 3- look at case specific sites for the reuse of mineral RM. Data will be made available through a structured data platform. Hence, FRAME is making a significant contribution to aid in the “European Green Deal”, activities such as the Battery Alliance and support legal actions like the new EU "conflict minerals" regulation effective from 1/01/2021.

How to cite: de Oliveira, D. P. S., Ferreira, M. J., Sadeghi, M., Arvanitidis, N., Decrée, S., Gautneb, H., Gloaguen, E., Törmänen, T., Reginiussen, H., Sievers, H., Quental, L. Q., and Wittenberg, A.: FRAME’s (Forecasting and Assessing Europe’s Strategic Raw Materials Needs) contribution to the “European Green Deal” , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5950, https://doi.org/10.5194/egusphere-egu2020-5950, 2020.

The oceans and seas cover more than 70% of the planet, representing a promising new frontier for mineral resources exploration, and an enormous challenge for science and technology. Communities are demanding actions to address global climate change, and the necessary high- and green-technologies required for a transition from a carbon-based to green-energy-based world. The global ocean is at the core of these issues. The seabed mineral resources host the largest reserves on Earth for some critical metals like cobalt, tellurium, manganese, and the rare earth elements, critical for Industry. But seabed geology and ecosystems are widely unexplored, and new geological and environmental studies are required to address the impacts of potential mining activities. In addition, a regulatory framework for minerals extraction and marine spatial planning are necessary for seabed mining sector development.

The pan-European seas cover about 15 millions square kilometres in the Arctic and Atlantic oceans and the Mediterranean, Baltic, and Black seas, from shallow waters up to 6000 m water depth. Spanning a large diversity of environments and resource settings, including high and low temperature hydrothermal deposits, phosphorites, cobalt-rich ferromanganese crusts, and manganese nodules, deep-sea deposits are particularly attractive for their polymetallic nature with high contents of rare and critical metals. Moreover, shallow-water resources, like marine placer deposits, represent another source for many critical metals and gems. The GeoERA-MINDeSEA[1]  project is compiling data and genetic models for all these deposit types based on extensive studies, carried out previously, which include geophysical surveys, dredging stations, underwater photography and ROV surveys, and mineralogical, geochemical, and isotopic studies.

The preliminary MINDeSEA results show the potential of the pan-European seas for critical metals, and the enormous gaps of information covering vast marine sectors. More than 600 mineral occurrences are reported in the MINDeSEA database. Seamounts and banks in the Macaronesia sector (Portugal and Spain) and the Arctic ridges (Norway, Denmark, Iceland) show a high potential for Fe-Mn crusts, rich in energy-critical elements like Co but also Te, REEs, and Mn. Fe-Mn crusts are accompanied by phosphorites on the seafloor of continental shelves and slopes along the western continental margins. Seafloor polymetallic sulphides and metalliferous sediments precipitating from hot hydrothermal solutions and plumes are forming today in the Azores Islands (Portugal), the Arctic (Norway, Denmark) and, the Mediterranean volcanic arcs (Italy and Greece). They are among the most important marine resources for Cu, Zn, Ag, and Au. In addition, hydrothermal deposits may contain economic grades of Co, Sn, Ba, In, Bi, Te, Ga, and Ge. Placer deposits of chemically resistant and durable minerals have been discovered on shallow-water settings (<50 m water depth on estuaries, deltas, beaches) linked to the weathering of onshore rocks and ore deposits from the Variscan Belt (UK, France, Portugal, Spain). Finally, shallow-water concretions and nodules from the Arctic, Baltic, and Black Sea represent potential targets for metals exploration and environmental studies.

How to cite: Gonzalez, J., Medialdea, T., Schiellerup, H., Zananiri, I., Ferreira, P., Somoza, L., Monteys, X., Kuhn, T., Nyberg, J., Melnyk, I., Magalhaes, V., Lunar, R., Marino, E., Hein, J. R., Cherkashov, G., and team, M.: Critical minerals in the European seas: The project GeoERA-MINDeSEA , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13271, https://doi.org/10.5194/egusphere-egu2020-13271, 2020.

Society is increasingly looking to the subsurface for our energy needs, be that for extracting geothermal energy, shale gas, or buffering heat, gas, or storing by-products of energy production. An increasingly crowded subsurface presents risks to groundwater relied on for water supply, since subsurface activities can introduce or release contaminants and alter subsurface properties. The VoGERA project is investigating the vulnerability of shallow groundwater from a range of subsurface energy technologies across different hydrogeological and geological settings within Europe. A suite of conceptual models compares the intrinsic vulnerability for different geological (crystalline, poorly consolidated and well consolidated sedimentary basins) and hydrogeological (basin centre and margins) conditions. They also consider the impacts of different subsurface activity types broadly categorised as those processes including injection, abstraction and a neutral fluid balance. Potential contamination pathways are being investigated at four case study sites; the Rauw Fault in Belgium, Panonian Basin in Hungary, The Peel Boundary Fault in the Netherlands and the Vale of Pickering in the UK. Geophysical, hydrological and hydrochemical data from these sites will be assessed in order to improve contamination pathway process understanding in a European setting. Findings from the case study sites will be used to evaluate the conceptual models and to develop a tool for decision makers and the public to assess the vulnerability to shallow groundwater from subsurface energy activities depending on the activity, and geological and hydrogeological conditions at a specific location. The VoGERA project is funded as part of the European Union’s Horizon 2020 GeoERA network of projects under the Groundwater theme (Grant agreement number 731166).

How to cite: Ward, R., Beerten, K., Zaadnoordijk, W., Slenter, C., Bianchi, M., Rotár-Szalkai, Á., and Mallin Martin, D.: Assessing the vulnerability of shallow groundwater resources to deep subsurface energy activities (VoGERA), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10241, https://doi.org/10.5194/egusphere-egu2020-10241, 2020.

Abstract

A pan-European GIS focused on hydrate has been developed within the frame of the GARAH project (GeoERA - GeoE.171.002) in order to assess gas hydrate information gaps in the European margins. A data-collection exercise from public sources (MIGRATE and PERGAMON COST actions ES1405 and ES0902, SeaDataNet, NOAA, etc) and European Geological Surveys (BGS, BRGM, IGME, GEUS, etc) has supplied a total of 835 information layers. All this information has been structured in four groups ((i) Geological & Geochemical evidences/indicators, (ii) Geophysical indicators, (iii) Fluid flow seabed indicators and (iv) Oceanographic variables & Geological constrains) where tables and geospatial features have been designed and harmonized following the standards of INSPIRE directives.

This GIS-database has been conceived as a first step or base-line for future gas hydrate related research. The next step as part of 'Work Package 3' of the GARAH project will be the identification of critical knowledge gaps and the definition of specific areas of interest which would benefit from further research. Theses potential future projects could be related to improving the European model of the gas hydrate stability zone (GHSZ), assess potential geohazards and risks, assess the abundance of sediment-hosted gas hydrates, and evaluate the role of CO₂-rich hydrates for the geological storage of CO₂.

Acknowledgment

GARAH project. GeoERA - GeoE.171.002

How to cite: Leon, R., Rochelle, C., Burnol, A., Gimenez-Moreno, C. J., Nielsen, T., Hopper, J., Reguera, I., Mata, P., Stewart, M., and Cervel, S.: A pan-European GIS focused on gas hydrates: a research base-line in geohazards and geological storage of CO2, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4861, https://doi.org/10.5194/egusphere-egu2020-4861, 2020.

In 2017 the geological surveys contributed to the European wide project ‘EU Unconventional Oil and Gas Assessment’ (EUOGA). The goal of EUOGA was to assess all potentially prospective shale formations from the main onshore basins in Europe and included contributions of twenty-one European geological surveys and the assessment covered 82 geological formations from 38 basins (Zijp et al. 2017).

To extend the assessment to offshore basins the geological surveys of Denmark (GEUS), Germany (BGR), the Northlands (TNO) and United Kingdom (BGS) are working together on the Geological Analysis and Resource Assessment of selected Hydrocarbon systems (GARAH) project that aims at assessing the conventional and unconventional hydrocarbon resource in the North Sea Basin. Within the basin more than 10 shale layers have been recognised as holding potential resources. These shales include the offshore equivalent of the Cambrian Alum Shale, The Carboniferous Bowland shale and the Jurassic Wealden and Kimmeridge shales that onshore have been a target for hydrocarbon exploration within the European Union member states. Each shale layer will be characterized using thirty systematic parameters such as areal distribution, structural setting, average net to gross ratio of the shale reservoir, average Total Organic Carbon content (TOC) and average mineralogical composition.

This is a part of an ongoing EU Horizon 2020 GeoERA project (The GARAH, H2020 grant #731166 lead by GEUS).

References

Zijp, M., Nelskamp, S., Schovsbo, N.H., Tougaard, L. & Bocin-Dumitriu, A. 2017: Resource estimation of eighty-two European shale formations. Proceedings of the 5th Unconventional Resources Technology Conference, Austin, Texas, USA, 24–26 July, 2017. https://doi.org/10.15530/urtec-2017-2686270

How to cite: Schovsbo, N., Ladage, S., Mathiesen, A., Nelskamp, S., Stewart, M. A., and Britze, P.: Unconventional hydrocarbon resource plays in the North Sea Basin, Northwestern Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1558, https://doi.org/10.5194/egusphere-egu2020-1558, 2020.

A cross-border assessment study looking at selected hydrocarbon systems is conducted as part of the EU Horizon 2020 GeoERA project (GARAH H2020 grant #731166 lead by GEUS). Within this project the geological surveys of the Netherlands (TNO), Germany (BGR), the United Kingdom (BGS) and Denmark (GEUS) are working together to create an overview of hydrocarbon resources and potential plays in the North Sea Basin. The project will harmonize the available resource assessments, and take a closer look at the play systems and potential new concepts. The focus of the work is on resolving border issues and identifying play concepts that are successful in one country but are underexplored in others. Potential risk factors related to subsurface exploration and production as well as options for multiple use of the subsurface will also be included in the overview. The results of the project will be published in report and GIS format and made available to legislators as well as the public.

Other parts of the project include the assessment of unconventional hydrocarbon plays (see Schovsbo et al. this conference), detailed basin and petroleum system modelling of a case study area in the Danish-German-Dutch offshore area (Lutz et al. this conference) and a pan-European database for gas hydrates (Léon et al. this conference).

How to cite: Nelskamp, S., Steward, M., Schovsbo, N., Ladage, S., Peeters, S., and Britze, P.: Overview of conventional hydrocarbon resources in the North Sea Basin – harmonization of assessments, cross-border play mapping and new concepts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20918, https://doi.org/10.5194/egusphere-egu2020-20918, 2020.

The prime aim of work package (WP) 3 in the FRAME project is to produce a map of Strategic and Critical Raw Materials (SCRM) for Europe, including the so-called energy and conflict minerals. In cooperation with other FRAME WPs, there was a consensus on the methodology used for the identification and selection process of the Strategic and Critical Raw Materials (SCRM) to be included in the metallogenetic map, linked mainly to information collected from existing databases (DB), such as Minerals4EU (M4EU) and European Geological Data Infrastructure (EGDI). 

One main objective of WP3 is the predictive targeting based on GIS exploration tools and prospectivity assessments at continental scale. Two types of prospectivity mapping have been produced in this WP3 based on different knowledge and data-driven methods. The first method applies the latest developments in “data driven” mineral prospectivity that allows mapping at continental scale, such as the “Cell Based Association" (CBA) one method developed by BRGM. CBA is an alternative to GIS-supported prospectivity methods. It has been developed to better manage uncertainties related to cartographic data which are highly significant at continental scale. The second method is using the hybrid fuzzy weights-of-evidence (WofE) model for mineral potential mapping.

SCRM may be recovered either as primary commodities or as by-products. Carbonatite-related deposits are the primary sources of many CRM such as REEs, niobium (Nb) and tantalum (Ta). Granitic pegmatite deposits are currently the principal source of Ta. Compilation of Nb and Ta occurrences/deposits in Europe is currently going on within FRAME WP6 (see separate presentation by Reginiussen et al., this conference). The data has been used for the spatial analysis and prospectivity mapping related to geology and geotectonic and metallogenic setting at European scale.

The results of our prospectivity mapping highlight several Nb and Ta mineral potential areas related to evolved granite to leucogranite bodies mostly in Scandinavia, Spain, France and Portugal, e.g Morille-Martinamor district, Fontão and Penouta where previous exploration activities on those elements were carried out in past. The late Neoproterozoic to early Cambrian Schist-Greywacke Complex (SGC) of the Variscan belt, in Central Iberian Zone, is also indicated as favourable area for Nb and Ta. Pegmatites in the Campo Mineiro De Lagares in the CIZ are another area of interest. Pegmatites in central Iberian zone is another area of interest, as is the case for Campo Mineiro De Lagares. The late Neoproterozoic to early Cambrian Schist-Greywacke Complex (SGC) of the Variscan belt, in Central Iberian Zone, is also indicated as favourable area for Nb and Ta. . In Sweden, the pegmatites of the Varuträskt area, close to Skellefteå, dated to c. 1.8-1.77 Ga, are clearly highlighted in the prospectivity maps. The areas related to Fennoscandian carbonatites appear also to be strongly favourable as Nb and Ta mineral potential targets. In the northern, central and southern parts of Sweden, high to moderately favourable areas are related to the numerous individual and granitic pegmatite dykes of Proterozoic age.

How to cite: Sadeghi, M., Bertrand, G., Reginiussen, H., Arvanitidis, N., Jonsson, E., and de Oliveira, D. P. S.: Prospectivity mapping of niobium and tantalum in Europe; a part of the GEOERA- FRAME project , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7931, https://doi.org/10.5194/egusphere-egu2020-7931, 2020.

The world increasing demand of electric vehicles (EVs) that use lithium-ion batteries (LIB), in which cobalt is one of the essential elements, focused the attention on its demand that is calculated will increase of 7-13% annually until 2030. The actual production of cobalt, usually extract as by-product of nickel and copper mine, is reduced to almost 20 countries between which the Democratic Republic of the Congo is the bigger producer with 55% of the world production. In Europe cobalt is produced only in Finland that actually provides 2.300 tonnes, the 2% of the world production. In this way several projects have been promoted by European Union, with the Raw Material Initiative, in order to find and evaluate the sustainable production of important materials in Europe.

MINDeSEA[1] project is part of the GeoERA and represent the collaboration of 12 national geological institution partners, to characterize marine deposits and their contents in Critical Raw Materials (CRM) and to generate a comprehensive cartography and metallogenic models of them. The first preliminary map produced in 2019 represents the localization and evaluation of cobalt rich deposits in the oceans within the EEZ and ECS of the European countries.  Cobalt deposits are represented essentially by hydrogenetic Fe-Mn crusts located essentially in the Macaronesian area of the north east Atlantic Ocean (in the Portugal and Spain), submarine plateaus, as the Galicia Bank (in the north west Spanish) and in the Arctic Ocean ridges (Norway and Iceland). The report differentiates between occurrences (<0.05 wt. %) and deposits (>0.05 wt. %), with the possibility of more than 200 Mt resources per potential deposit.

Detailed mineralogical, geochemical and metallogenic studies are being developed in crusts from the Macaronesia. Fe-Mn crusts absorb dissolved elements in seawaters on the surface of the fresh precipitated oxy-hydroxides during their slow growth through millions of years. Several elements are concentrated in Fe-Mn crusts and between them cobalt is one of the most enriched trace metals (average 0.6 wt. %) accompanied by other strategic and critical metals such as nickel, copper, tellurium, molybdenum and rare earth elements plus yttrium (REY) (respectively 3000, 500, 150, 500 and 3500 µg/g). Micro Raman and micro X-Ray diffraction can be used to differentiate the mineralogy in laminae of less than 20 microns. On the other hand, electron probe micro-analyzer (EPMA) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS), are useful in order to quantify contents of CRM in the different mineral phases. These are innovative techniques in order to identify critical-elements bearing minerals and thus choose the metallurgic method for a more efficient and sustainable extraction of the interesting elements.

The evaluation of a seamount as a future mine site has to take into account all these mineralogical and chemical features as well as a proper knowledge of the seamount (morpho-structure, geology, oceanography, ecosystems) and the Fe-Mn crust thickness and extension

How to cite: Marino, E., González, J., Medialdea, T., Somoza, L., Lunar, R., Ferreira, P., Kuhn, T., Hein, J. R., Magalhaes, V., and Blasco, I.: Hydrogenetic Fe-Mn crusts from European seas: source of potentially economic cobalt mining., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22091, https://doi.org/10.5194/egusphere-egu2020-22091, 2020.

Nowadays the global ecological crisis continues aggravating. The environmental issues are on agenda, getting increased public attention (e.g. protests caused by waste problems and climate change all around the world). Depleting resources, trash mountains, garbage islands, toxic emissions etc. require change of economy model from linear (resource extraction-production-usage-throwing away) to the circular one (recycled resource-production-usage-recycling). More than that, multiple waste use as well as resources reuse may bring to business and economy billions of dollars.

The very idea of recycle is practiced in the world since long ago. However, it has been done by few resources (collection of waste paper, scrub metal, glass bottles etc.) without shaping an economic system as a whole.

Another problematic issue is that the recycling does not always means to be ecological. The mode of recycling in countries with low eco-standards results in heavy pollution (e.g. e-waste “recycling” by fire at open air in Africa, India leads to emission of toxins; ship recycling in Bangladesh leads to polluted beaches and water). Methods of recycling in developing countries often are primitive and may be dangerous. Sometimes, entrepreneurs from developed countries are responsible for such state of affairs. They send legally or illegally part of wastes for that primitive recycling in developing countries. It is important to have awareness of the fact that everything is interdependent. If one part of the Earth is full of toxins and harmful fumes, its other part is inevitably affected over time. It is necessary to carry out recycling in all countries establishing strict environmental laws worldwide, and to make it based on smart technologies.

Circular economy in its narrowest sense is an economy that simply processes waste.
A serious change in business models, public mentality and government policies is necessary to get to environmentally friendly economy. It aims at lengthening the use cycle of goods (e.g. clothes, mobile phones) and minimizing the personal waste of every citizen. The EU household’s food waste was estimated to be 47 million tons (EU FUSIONS, 2016). “More than 30% of clothes in Europeans’ wardrobes have not been used for at least a year. Once discarded, over half the garments are not recycled but end up in mixed household waste and are sent to incinerators or landfill” (EPRS, 2019). YouGov Omnibus research: a third (34%) of respondents of Singapore have thrown away an item of clothing after wearing it just once. (YouGov, 2017).

Thus, effective circular economy is not just about re-processing and saving resources but, first, emphasizes its focus on greening environment and reducing waste as it is, becoming an eco-circular economy. Secondly, it calls forth measures at not only national or regional level, but also proceeding from “Earth is our common home”, worldwide.

How to cite: Șișcan, S.: Towards Eco-circular economy worldwide, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-787, https://doi.org/10.5194/egusphere-egu2020-787, 2020.

Abstract. The new opportunities offered by technologies have caused societies to break through towards the fourth industrial transformation. It will change the whole society and its structures alongside the business and the transition process is still speeding. The world is also facing big megatrends like global warming, urbanization, digitalization, new revolutionary technologies.

Like the industrial revolutions before, the whole paradigm of society is changing, and it will happen also in the fourth industrial revolution, so there is a need to think how we should take a step towards to the new paradigm, so that we could be able to response to future challenges on sustainable way.

The fourth industrial revolution (4IR) technologies like sensor, IoT-platforms, artificial intelligence etc., give new possibilities to develop new, more efficient, more sustainable and more customer driven supply chain, prolong the lifetime of products and create new services and business models and this way reduce the use of materials of energy. There is also an argument to rethink the source of raw material, and in which extent the cities itself could be seen the source of needed materials and energy, by using new technology.

The move towards new ICT based technologies will happen unexpected fast, including exponential growth of data. That is the reason, why it is essential to understand the challenges of change and have a strategic view, identify the key elements and see the new opportunities in all levels of society development.

Circular Economy has been very much a hot topic in many discussions, but there has been quite little discussion about reengineering the value chains and production based on circular economy principles by using the new opportunities on 4IR technologies not only in production but also in creating service, which change the need/thinking of ownership and build new business models. In addition to this, the elements to improve business environment by local or national authorities and legislators.

Finland has is as a goal to develop to one of the leading countries in circular economy, In Finland, Forssa region is considered to be one of the most advanced region in bio-based circular economy.

In this article has been described the development of regional industrial symbiosis in order to have competitive of business and future development.

How to cite: Ruohomaa, H., Salminen, V., and Pöykkä, T.: Rethinking the production and consumption on the transition, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3855, https://doi.org/10.5194/egusphere-egu2020-3855, 2020.

Research is ever deepening its knowledge in a multitude of fields. Such research contributes in great depth to identifying and understanding problems (e.g. in the field of climate change). However, when it comes to societal implementation, it may ultimately lead to zero knowledge at infinite depth, as it has been ironically put. To tackle sustainability, respectively to achieve the 17 sustainable development goals (SDGs) set by the UN, science has to come together and work in transdisciplinary teams. Scientists are poorly prepared for such an exercise and standard procedures of scientific work, sharing and publication of results in specialised conferences and journals do not help.

In view of this problem, the Austrian Alliance of Sustainable Universities and research centres has created a project, UniNEtZ (Universities and Sustainable Development Goals), to jointly address the issues raised by the SDGs and develop suggestions for policies in a multidisciplinary approach. In order to facilitate this for the scientists involved, UniNEtZ is preparing collaborative measures and methods to address the issue of cooperation between disciplines to guarantee that all important interactions among SDGs are considered and addressed in equal detail. It is expected that changes in the way science and scientists are used to work together are necessary to achieve that. The developed concepts will be published in a handbook for UniNEtZ,

In a next step, the  handbook also hints at the need for municipal, regional, and national administrations to transition towards a kind of governance, that enables implementation of policies towards the SDGs. Current hierarchically organised structures don’t seem ideal for the kind of transsectoral cooperation that will be needed to implement the expected measures.

The contribution presents the findings of this work on cooperative research and governance structures.

Keywords: SDGs, Sustainable Development Goals, transdisciplinary research, transsectoral implementation

How to cite: Regelsberger, M., Allesch, A., Becsi, B., Germann, V., Gratzer, G., Gühnemann, A., Hundscheid, L., Körfgen, A., Kozina, C., Kreiner, H., Lindenthal, T., Manderscheid, M., Rammler, S.-M., Scherz, M., Schwarzl, I., Toth, W., and Vacik, H.: Transdisciplinary research towards transsectoral implementation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20788, https://doi.org/10.5194/egusphere-egu2020-20788, 2020.

Green spaces are known to provide a number of benefits to urban areas. In order to make green spaces more accessible to people in urban regions, the EU has launched some important initiatives that place green infrastructure (GI) development as a top priority in urban planning, contributing to the paradigm of making more sustainable and smarter cities for everyone.  However, some GI development might bring unexpected impacts that are observable only with a systemic analysis. For instance, an increased surface of green rooftops might serve as a source of local food production and reduce the need of the buildings’ air conditioning at the expense of increased water and fertilizer use. Despite this shift of focus in urban planning priorities, few studies assess tradeoffs between water, energy and food metabolism of different GI alternatives. An important reason for this gap is that current methods for the analysis of the water-energy-food (WEF) nexus in the urban metabolism lack a transdisciplinary approach.

To fill that gap, we propose using two system analysis methods: Life Cycle Assessment (LCA) and Multi-Scale Integrated Assessment of SocioEcosystem Metabolism (MuSIASEM), to assess the WEF nexus in an urban region in the context of GI. Furthermore, the WEF flows are georeferenced to understand their impact on the urban landscape.  Based on this georeferenced analysis of land use and land use change, we 1) complete an inventory of functions associated to different land uses with their related inputs and outputs, 2) study function-related environmental pressures with LCA, and 3) assess the systemic impacts of relevant functions over domestic and alien ecosystems and WEF supply systems.

We develop this innovative approach using the municipality of Sant Climent de Llobregat, in the Metropolitan Area of Barcelona (AMB), as a case study. Sant Climent covers 1.6% of the AMB surface and is currently undergoing a GI restructuring process focused on recovering formal agricultural land (currently lost to forest)  for highly profitable cherry production. We provide a systemic study that informs about the resource demand and environmental impacts these changes may imply. Data is compiled in collaboration with regional research centers, from local utility companies, planning offices of different towns, statistical yearbooks for Catalonia and Spain, and LCA databases. The work is an on-going collaboration with the AMB government as it develops the Urban Development Plant (PDU) that will set the land use related urbanism policy guidelines from 2021 on. We present a diagnose of the current state of the WEF metabolism in Sant Climent. We identify geographically explicit hotspots, where competition of the resources and unexpected domestic or alien environmental impacts arise.  These hotspots are compared against land to be transformed to highlight the best and worst areas for transformation. We expect that in a later stage, these results will feed a scenario assessment of the systemic impacts of the proposed actions of the new PDU.

This work is part of the research developed in the ERC Project URBAG: Integrated System Analysis of Urban Vegetation and Agriculture.

How to cite: Madrid-Lopez, C., Mendoza-Beltran, A., Padro Caminal, R., Serrano Tovar, T., Marull, J., and Villalba, G.: Transdisciplinary assessments for circular city design: identifying systemic water-energy-food nexus hotspots in metropolitan Barcelona, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6097, https://doi.org/10.5194/egusphere-egu2020-6097, 2020.

IMPROVING THE MUNICIPAL SOLID WASTE MANAGEMENT PLAN OF THE MUNICIPALITY OF NEMOCÓN (COLOMBIA)

CAMILO-ANDRÉS VARGAS-TERRANOVA⁽¹⁾ and JAVIER RODRIGO-ILARRI⁽²⁾

⁽¹⁾Universidad de La Salle, Bogotá, Colombia (cvterranova@unisalle.edu.co)

⁽²⁾Instituto de Ingeniería del Agua y del Medio Ambiente (IIAMA), Universitat Politècnica de València, Spain (jrodrigo@upv.es)

ABSTRACT

The municipality of Nemocón (Colombia) located 45 km from Bogotá generates 810.3 t/year of municipal solid waste (MSW). Despite the Colombian national legal requirements, Nemocón Solid Waste Management Plan (SWMP) shows important deficiencies in the waste management system, especially concerning the final destination of waste.

During 2019 a set of activities have been performed in the town as an initial response to these needs with the participation of the community and local authorities. First, the design of the waste collection routes was analyzed and improved. Two routes were designed, supported by compacting vehicles with an average time of 3 hours (80 km per route) and 3 routes per week each. Besides, two shorter routes were designed for the collection of recyclable waste, supported by hand-drawn vehicles, with operating times of 6 hours (8-10 km per route) and daily routes.

With the support of students from the University of La Salle and the donation of an abandoned building, a Classification and Use Station (CUS) was implemented to strengthen the management of such recyclable waste. The CUS was provided with personal protection elements to improve their condition as managers of minor routes and the preliminary treatment of waste in the CUS, for later sale to wholesalers external managers.

Finally, a tax system was designed to finance the operation of the CUS (2500-2800 Euros/month) and promote greater separation volumes in the midterm, based on an adjustment to the normal payment made by the users for the service of waste collection and management. This system took into account the different types of users (commercial, industrial, residential and official), local socioeconomic scale and national economic variables. The increased rate varies between 1 and 1.5% for all users in the first year of increase.

How to cite: Vargas Terranova, C. A. and Rodrigo Ilarri, J.: Improving The Municipal Solid Waste Management Plan Of The Municipality Of Nemocón (Colombia), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10290, https://doi.org/10.5194/egusphere-egu2020-10290, 2020.

Sustainable development and the circular economy are becoming the new imperative of industrial growth, as the world faces the depletion of natural resources and consequences of climate change. The utilization of waste streams through the concept of ‘new added value’ gives life to the production of materials and their environmental application. Therefore, the development of novel, eco-friendly, nature-based adsorbents that possess high degradable and recyclable potential is on the forefront of research. The modifications of wood derivates, such as cellulose and lignin, are widely applied as natural polymers due to their economic feasibility, ecological similarity and adsorption capabilities.

The subject of this study is the adsorption of nickel(II) and cadmium(II) ions from aqueous solutions using 5.0 mass % of alginate lignin microspheres (A-LMS). Due to their toxicity, persistence, high solubility and mobility, such heavy metals are widely dispersed throughout environmental media (chiefly, aquatic bodies), leading to ecological and public health problems. The raw lignin used as a source material in the study originates from the waste stream of the lumber industry. The porous microspheres are of a radius of 50 to 950 microns and a surface area of 36.9 m² g^-1 were synthesized via inverse suspension copolymerization of the kraft lignin with a poly(ethylene imine) grafting-agent and an epichlorohydrin cross-linker. The structural and surface characteristics were confirmed via Fourier transform-infrared (FTIR) spectroscopy, x-ray diffraction (XRD) and scanning electron microscopy (SEM). The textural properties of the synthesized A-LMS were determined according to the Brunauer, Emmett and Teller (BET) method of analyzing nitrogen adsorption. The adsorption batch and column testing were carried out by varying the reaction time, temperature, adsorbent mass, at predefined pH values of the initial solutions. The maximum adsorption capacity of the A-LMS for nickel (II) ions was 89.286 mg g^-1 at a temperature of 318 K, while for the adsorption of cadmium(II) ions it was 96.154 mg g^-1 at a temperature of 308 K. The kinetic data followed the pseudo-second-order kinetic model, while the Weber-Morris model indicated intra-particle diffusion as a rate limiting step. The thermodynamic parameters for the A-LMS further confirm that the adsorption process was spontaneous and endothermic.

The study indicates the high potential of by-products or waste products from heavy industry to be repurposed for environmental engineering applications by which they may serve a benefit as opposed to being a detrimental risk. Such is the case here with lignin-natural polymers taken from the lumber industry, which themselves may be reutilized for the removal of heavy metals from wastewater.

Acknowledgments

This work was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia (project no. 172007). The authors would like to acknowledge the financial support provided by COST-European Cooperation in Science and Technology, to the Cost Action CA17133: Circular City.

How to cite: Stanišić, T., Popović, A., Rusmirović, J., Đolić, M., Ristić, M., Perić-Grujić, A., and Marinković, A.: Lignin microspheres as a nature-based material for effective nickel(II) and cadmium(II) ions removal, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-493, https://doi.org/10.5194/egusphere-egu2020-493, 2020.

As an alternative to the traditional linear economy, the circular economy could help tackle the climate emergency, decrease resource scarcity and increase business sustainability. Implementing it successfully, maintaining sustainability and facilitating real systemic change means an effective coordination among all stakeholders is needed.

Meaningful engagement of stakeholders will help achieve a shared understanding of the concept, the new approaches and their implementation at all levels. Mobilizing different stakeholders is always a challenge and requires careful planning in order to demonstrate that the stakeholders’ input and engagement is valued, and participatory processes are in place.

The presentation will provide insights into the different stakeholder groups, their interests and methods that could be employed to reach effective engagement. It will also outline approaches, actions and tools that should help circular economy drivers develop a plan for stakeholder engagement based on clear understanding of stakeholders’ needs.

Apart from the general insights on the role of stakeholders in circular economy projects, the presentation will also provide practical knowledge on mapping and profiling stakeholders, stakeholder engagement strategy development, engagement tools and evaluation methods. Specific needs for improvement of stakeholder engagement practices into Circular economy projects will be tackled and discussed as well.

How to cite: Majercakova, G. and Bokal, S.: How to meaningfully engage key stakeholders in smart circular economy , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4223, https://doi.org/10.5194/egusphere-egu2020-4223, 2020.

The welfare of the Republic of Moldova much depends on the use of its natural resources. However, the pace at which the natural resources are exploited exceeds the ability of the environment to regenerate them. Land resources are the main natural wealth of the country. The conservation and increase of effective fertility of the soils becomes the basic task of the owners of agricultural lands. The methods of the conventional agriculture do not work anymore to make the sector competitive at regional and global markets. New concepts and technologies of Green and Circular economy are of more perspective. They also are more effective under the continuous unbalanced extraction of natural resources which causes environmental damage.

The case of Moldova reflects the global trends. Some international studies have shown that the global consumption of materials per capita has doubled, while the consumption of primary energy has tripled in the last hundred years. In other words, each of us consumes three times as much energy and twice as much material as our predecessors consumed in 1900. Moreover, nowadays there are 7.2 billion consumers compared to 1.6 billion in 1900.

At the same time, the requirements for quality standards in Green economy are very high and rigid as well as  “the annual financing demand to green the global economy has been estimated to be in range 1.05 USD to 2.59 USD trillion” (UNEP, 2011). That is why the Circular ecological economy is seen as more viable solution for world, regional and national economies. ”Ecological economy” generally refers to an economy in which all the choices regarding production and consumption are made taking into account the welfare of the society and the global health of the environment. ”Circular economy” implies a system of production and consumption that generates as little loss as possible.

The EU Circular economy Package and CE Stakeholder Platform are a good start for regional economy as well as that of Republic of Moldova as its Associate Member. The beneficial solution for improving the environment of the country consists in redesigning products, production and consumption processes by minimizing waste and transforming that unused part into a resource.

How to cite: Cojocaru, O. and Siscan, Z.: The circular ecological economy - a beneficial environment in light of the use of natural resources in the Republic of Moldova, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-58, https://doi.org/10.5194/egusphere-egu2020-58, 2020.

Emission of primary microplastics in mainland China: Invisible but not Negligible

Teng Wang ^1,3, Baojie Li ^2,3* , Xinqing Zou^3*

¹ College of Oceanography, Hohai University, Nanjing, 210098

² School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044

³ School of Geography and Ocean Science, Nanjing University, Nanjing, 210023

Abstract:

Primary microplastics are mostly produced as part of the daily plastic product use. The emission process is often invisible but poses potential ecological hazards. Thus, primary microplastics deserve public attention. Due to China's huge population base and its rapid economic development, primary microplastics emissions are of both regional and global significance. This study is the first to establish the emission inventory of primary microplastics in mainland China. It was estimated that the primary microplastic waste from mainland China amounts to 737.29 Gg, and one-sixth of this amount entered the aquatic environment in 2015. The highest proportion of this waste was attributable to tire dust and synthetic fiber, accounting for 53.91% and 28.77% of the total respectively, in mainland China. The primary microplastics emissions mainly depend on the population, followed by the level of economic development. It was roughly estimated that 538 g of microplastics is produced by each person in China. At the grid scale, the spatial difference in the total primary microplastics emissions in mainland China primarily depends on the population density distribution and transportation network. We studied the entire life cycle of several sources of microplastics, from production to discharge into the aquatic environment. We suggested different control measures under different nodes. Increasing microplastics treatment in sewage treatment plants should be a short-term viable way to achieve some measure of reduction in their entry to the environment in mainland China. Our research can not only raise public awareness about primary microplastics, but can also guide the development of environmental policies to reduce plastic pollution.

Keywords: Primary microplastics; Emission inventory; Mainland China; Sewage treatment plants

How to cite: Wang, T., Li, B., and Zou, X.: Emission of primary microplastics in mainland China: Invisible but not Negligible, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1816, https://doi.org/10.5194/egusphere-egu2020-1816, 2020.

Wind farm far wakes are of particular interest for offshore installations, as turbulence intensity, which is the main driver for wake dissipation, is much lower over the ocean than over land. Therefore, wakes behind offshore wind turbines and wind parks are expected to be much longer than behind onshore parks. 

In situ measurements of the far wakes were missing before the initiation of the research project WIPAFF (WInd PArk Far Fields) in 2015. The main results of which are reported here. WIPAFF has been funded by the German Federal Ministry for Economic Affairs and Energy and ran from November 2015 to April 2019.  The main goal of WIPAFF was to perform a large number of in situ measurements from aircraft operations at hub height behind wind parks in the German Bight (North Sea), to evaluate further SAR images and to update and validate existing meso-scale and industrial models on the basis of the observations to enable a holistic coverage of the downstream wakes.
 
A  unique  dataset  from  airborne in situ data,  remote sensing  by  laser  scanner  and  SAR  gained  during  the WIPAFF  project  proves  that  wakes  up to  several  tens of kilometers exist downstream of offshore wind farms during stable conditions, while under neutral/unstable conditions, the wake length amounts to 15 km or less. Turbulence occurs at the lateral boundaries of the wakes, due to shear between the reduced wind speed inside the wake and the undisturbed flow. Data also indicates that a denser wind park layout increases the wake length additionally due to a higher initial wind speed deficit. The recovery of the decelerated flow in the wake can be modeled as a first order approximation by an exponential function. The project could also reveal that wind-farm parameterizations in the numerical meso-scale WRF model show a feasible agreement with the observations. 

How to cite: Platis, A., Bange, J., Bärfuss, K., Canadillas, B., Hundhausen, M., Djath, B., Lampert, A., Schulz-Stellenfleth, J., Siedersleben, S., Neumann, T., and Emeis, S.: Offshore wind farm far field - Results of the project WIPAFF, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10087, https://doi.org/10.5194/egusphere-egu2020-10087, 2020.

Variability of solar irradiance is an important factor concerning large-scale integration of solar photovoltaics (PV) systems onto the electricity grid. Calculations of irradiance are computationally expensive, leaving operational meso-scale forecasting models struggling to achieve accurate results. Moreover, such models deliver outputs at a temporal resolution in the order of hours, whereas from a grid-integration point of view, minute-to-minute variability is a major concern. In previous work, we found that absolute power peaks in the order of seconds are up to 18% higher compared to 15-minute resolution for irradiance and even upwards of 22% higher for household PV systems. Moreover, these maximum peaks in output power are solely observed under mixed-cloud conditions, for which alse the greatest variability is found. In this work we present a machine-learning model which can forecast sub-resolution variability of irradiance, based on standard meso-scale outputs of the HARMONIE model of the The Royal Netherlands Meteorological Institute (KNMI). For training and validation, irradiance measurements obtained at a 1-second interval are used of the Baseline Surface Radiation Network (BSRN) site of Cabauw. A tree-based model was employed, for which the optimum members were constructed using extreme gradient boosting. In this work, we explore the dominant features of the model and link the machine-learned-relations to meteorological processes and dynamics. This research was executed in collaboration with the Distribution Grid Operator Alliander.

How to cite: Kreuwel, F. and van Heerwaarden, C.: Forecasting sub-resolution temporal variability of irradiance, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10152, https://doi.org/10.5194/egusphere-egu2020-10152, 2020.

Abstract

Over the past years, environmental concerns have played a key role in the development of renewable energy sources (RES). In Europe, the installed capacity of photovoltaic (PV) has increased from around 10 GW in 2008 to nearly 119 GW in 2018 [1]. Due to this high penetration rate and the intermittent nature of RES, several challenges appear related to the economic and secure operation of a power system. To overcome these challenges, it is necessary to develop reliable forecasts of RES, and namely of PV production, for the next hours to days to adjust production planning, while intra-hourly forecasts may contribute to optimize operation of storage units coupled to RES plants.

The aim of this paper is to present a novel spatio-temporal (ST) spot forecasting approach able to use multiple heterogeneous sources of data as inputs to forecast short-term PV production (i.e. from 15 minutes up to a day ahead).

First, we consider measured production data from nearby power plants as input to forecast the output of a specific PV plant. These data permit to exploit the correlation between the production data of spatially distributed PV sites. The classical ST approach in the literature, based only on this source of data [2], permits to improve predictability for the next few minutes up to 6 hours ahead.

Then, we extend the model by the use of satellite images (i.e. global horizontal irradiance (GHI)) which provide meaningful spatial information at a larger extent.

Finally, we consider Numerical Weather Predictions (NWPs) as input, which permit to extend the applicability of the model to day-ahead lead times, so that, overall, the resulting model covers efficiently horizons ranging from a few minutes to day ahead.

The spatio-temporal relationships being dependent on the particular meteorological situation of the day at hand, we apply an analog ensemble approach, to condition the learning process with historical observations corresponding to similar meteorological situation. We used the analogue approach to select a subset of similar historical situations over which a dynamical calibration of the forecasted model is done, as it was for example suggested by [3,4]. In our paper we extend the analogs ensemble approach by considering geographically distributed observations of the physical variables of interest (as suggested by [4] for hydrological issues) rather than only those at the level of the PV plant.

The performance of the proposed ST model with heterogeneous inputs is compared with reference models and advanced ones such as the Random Forest model. Historical production data collected from 9 PV plants of CNR are considered. The power units, located in the South-East France, exhibit relevant spatial correlations which make them suitable for the proposed ST model.

References

• [1] IRENA - https://www.irena.org/Statistics/Download-Data
• [2] Agoua, Girard, Kariniotakis. Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production. IEEE Transactions on Sustainable Energy , IEEE, 2018, 9 (2), pp. 538 - 546. https://doi.org/10.1109/TSTE.2017.2747765
• [3] Alessandrini, Delle Monache, Sperati, Cervone. An analog ensemble for short-term probabilistic solar power forecast. Applied Energy, 2015. https://doi.org/10.1016/j.apenergy.2015.08.011
• [4] Bellier, Bontron, Zin. Using meteorological analogues for reordering postprocessed precipitation ensembles in hydrological forecasting. Water Resources Research, 2017. https://doi.org/10.1002/2017wr021245

How to cite: Bellinguer, K., Girard, R., Bontron, G., and Kariniotakis, G.: Short-term photovoltaic generation forecasting using multiple heterogenous sources of data based on an analog approach., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13790, https://doi.org/10.5194/egusphere-egu2020-13790, 2020.

The aggregation of multiple renewable plants located in distinct climate zones, using different energy sources, enables to reduce the production uncertainty when compared to the production of a single plant. Such aggregations, controlled by a Virtual Power Plant (VPP) system, are good candidates for the provision of ancillary services. Stochastic optimization models are available to optimize biddings on ancillary services and energy markets (see for instance [1]). These models require trajectories of the renewable VPP production that anticipate production uncertainty and reproduce correctly the temporal correlations observed in the production signal. This is particularly important in ancillary services markets, where a reserve bid must be guaranteed over a production duration or validity period during which power fluctuations are significant (e.g. lasting currently 24 hours on the European common market for Frequency Containment Reserve, with a foreseen evolution to 4 hours by July 2020 [2]). 
Production trajectories may be obtained by coupling probabilistic forecasts and a model of temporal dependencies between forecast horizons [3] and possibly spatial dependencies in the case of a multivariate forecast at the scale of a region or a portfolio [4]. In the case of a renewable VPP, the aggregated production is primarily of interest. In this work, we propose a methodology to generate trajectories of aggregated production from probabilistic forecasts obtained with decision-tree based models or neural networks. A copula models the dependency between forecast horizons and the space defined by the plants contained in the aggregation. The model is tested in a day-ahead forecasting configuration on a 54 MW VPP comprising 15 plants with 3 different energy sources (Photovoltaics, Wind, Hydro). The comparison of trajectories generated from a direct forecast of the aggregated production and from forecasts at lower levels of the aggregation shows that the latter solution reproduces with more accuracy the temporal variability of the aggregated production over the whole horizon range, especially when Photovoltaics dominates the production capacities in the aggregation (15 % improvement of the Variogram Score).
 [1]: Soares, T., & Pinson, P. (2017). Renewable energy sources offering flexibility through electricity markets. Technical University of Denmark.
[2]: ENTSO-E. (2018). TSO’s proposal for the establishment of common and harmonised rules and processes for the exchange and procurement of Balancing Capacity for Frequency Containment Reserves (FCR) TSOs’ proposal for the establishment of common and harmonised rules and pro-c, (October), 1–9.
[3]: Pinson, P., Madsen, H., Nielsen, H. A., Papaefthymiou, G., & Klöckl, B. (2009). From probabilistic forecasts to statistical scenarios of short-term wind power production. Wind Energy, 12(1), 51–62. 
[4]: Golestaneh, F., Gooi, H. B., & Pinson, P. (2016). Generation and evaluation of space–time trajectories of photovoltaic power. Applied Energy, 176, 80–91. 

How to cite: Camal, S., Michiorri, A., and Kariniotakis, G.: Forecast trajectories for the production of a renewable virtual power plant able to provide ancillary services, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19081, https://doi.org/10.5194/egusphere-egu2020-19081, 2020.

Multiple uncertainties, including from the uncertainty of a single power (wind power or photovoltaic power) output forecasting to the uncertainty of the combined power output of wind and photovoltaic forecasting to the power shortage after hydropower compensation for wind and photovoltaic power output, exist in the wind-photovoltaic-hydropower system. Furthermore, as the forecast is updated, the above uncertainty will evolve accordingly. Revealing the evolution of multiple uncertainties is of great significance for the hydropower compensation for the combined power output of wind and photovoltaic. We use a generalized martingale model of forecast evolution to describe the uncertainty of a single power output. We then superimpose the single power output to obtain the combined power output of wind and photovoltaic. we establish a stochastic programming with recourse model for optimal scheduling of the hydropower compensation for wind and photovoltaic power output. The results indicate that the uncertainty of the combined power output of wind and photovoltaic forecasting is less than that of wind power output forecasting, and greater than that of photovoltaic power output forecasting. After hydropower compensates for combined power output of wind and photovoltaic, compared with the uncertainty of combined wind and photovoltaic power output forecasting, the uncertainty of power shortage is greatly reduced by 90%, which has significant benefits. And with the dynamic update of the forecast, the uncertainty of the single power output forecast, the uncertainty of the combined power output forecast, and the uncertainty of the power shortage will decrease accordingly.

How to cite: Liu, W., Wang, C., Lei, X., Zhong, P., and Lu, Q.: Stochastic optimal scheduling of hydropower compensation for wind and photovoltaic power output considering multiple uncertainties, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3641, https://doi.org/10.5194/egusphere-egu2020-3641, 2020.

Several international and national initiatives allow access to data from wind measurements, e. g. NREL wind prospector or the international wind atlases. But there is no adequate platform for combined commercial and scientific needs.

Kraken realizes open data concepts for scientific access combined with corresponding licenses for commercial use. To this end, we offer all users to provide links and a possibility for direct upload of wind-related data. Information on data provenance and usability concepts should be provided.

This way, we can collect and provide data of different quality (possibly not to scientific standards) and for different use cases. Scientifically, this may allow access to, e. g., wind park data which would otherwise not be accessible. Commercially, an access to the enormous data bases is possible at one place, a problem that often is underestimated.

On the long run, automatized intelligent analysis of the data will be implemented and the corresponding reports may be published, depending on the licenses related to the underlying data. The whole project is intended to be community-based and extensible to all kind of renewable energy data.

In a first attempt, a website has been launched with limited functionality, now we are trying to involve as many as possible data sources. In addition, we welcome the open implementation of analyses as already offered by other sites (site characterization, feature engineering, improved weather parameterization).

How to cite: Kirsch, A., Feldtkeller, F., and Abel, M.: Kraken – a scientific and commercial data meta-platform for wind energy resources, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7184, https://doi.org/10.5194/egusphere-egu2020-7184, 2020.

Wind energy represents the leading source of renewable energy in many developed countries. South Korea has recently introduced large-scale programs to promote the transition from fossil fuels and nuclear power to renewable energy as a source of power. The Korean government has set an energy policy goal to increase the ratio of renewable energy to 20% by 2030. To this end, it is necessary to supply renewable energy facilities with a total capacity of 48.7GW including 30.8GW of photovoltaic power generation and 16.5GW of wind power generation by the target year. Accordingly, we should plan now for the regulation of the location to meet this developing need. However, in South Korea, forests cover 63% of the country's land area so that there is a limit to find a location for the installation of large-scale power generation facilities without occupying forest lands. For example, it is mainly located in forests or farmlands where land costs are relatively low, resulting in a decrease in forest resources and negative impacts on ecosystems and landscapes. Renewable energyexpansion planning should ensure that environmental criteria, of the type outlined in this study, are given appropriate considerations in onshore wind power project site selection. Many of the more problematic wind power sites are best left mountainous forest under the natural conditions, because the environmental or related social impacts are likely to be unacceptably high. Obviously, no plans are likely to be more environmentally desirable in those cases. The alternatives for onshore wind power siting considered the environmental criteria to achieve the goal of wind energy will be suggested.

How to cite: Park, J.-Y. and Lee, Y.-J.: Environmental criteria for site selection of wind power projects in South Korea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21386, https://doi.org/10.5194/egusphere-egu2020-21386, 2020.

The aim of this paper is to present the objectives, research directions and first highlight results of the Smart4RES project, which was launched in November 2019, under the Horizon 2020 Framework Programme. Smart4RES is a research project that aims to bring substantial performance improvements to the whole model and value chain in renewable energy (RES) forecasting, with particular emphasis placed on optimizing synergies with storage and to support power system operation and participation in electricity markets. For that, it concentrates on a number of disruptive proposals to support ambitious objectives for the future of renewable energy forecasting. This is thought of in a context with steady increase in the quantity of data being collected and computational capabilities. And, this comes in combination with recent advances in data science and approaches to meteorological forecasting. Smart4RES concentrates on novel developments towards very high-resolution and dedicated weather forecasting solutions. It makes optimal use of varied and distributed sources of data e.g. remote sensing (sky imagers, satellites, etc), power and meteorological measurements, as well as high-resolution weather forecasts, to yield high-quality and seamless approaches to renewable energy forecasting. The project accommodates the fact that all these sources of data are distributed geographically and in terms of ownership, with current restrictions preventing sharing. Novel alternative approaches are to be developed and evaluated to reach optimal forecast accuracy in that context, including distributed and privacy-preserving learning and forecasting methods, as well as the advent of platform-enabled data-markets, with associated pricing strategies. Smart4RES places a strong emphasis on maximizing the value from the use of forecasts in applications through advanced decision making and optimization approaches. This also goes through approaches to streamline the definition of new forecasting products balancing the complexity of forecast information and the need of forecast users. Focus is on developing models for applications involving storage, the provision of ancillary services, as well as market participation.

How to cite: Kariniotakis, G., Camal, S., Bessa, R., Pinson, P., Giebel, G., Libois, Q., Legrand, R., Lange, M., Wilbert, S., Nouri, B., Neto, A., Verzijlbergh, R., Sauba, G., Sideratos, G., Korka, E., and Petit, S.: Smart4RES: Towards next generation forecasting tools of renewable energy production, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20205, https://doi.org/10.5194/egusphere-egu2020-20205, 2020.

Numerical weather predictions are often too coarse to represent single turbines in a wind park and post-processing of the individual turbines is necessary. However, individual post-processing can lead to inconsistencies in forecasts for a wind farm. Using standardized anomalies allows to forecast all turbines simultaneously. Therefore, a climatological mean is subtracted from observations/predictions and then divided by a climatological spread which eliminates any site-specific characteristics.

Additionally, different sources of input can be used, such as variables from a global model, a mesoscale model or observations to improve forecasts. However, to prevent overfitting a variable selection method is needed to determine the most important predictors. The combination of standardized anomalies and a variable selection method provides a convenient method for good forecasts of wind farms.

How to cite: Dabernig, M., Kann, A., and Schicker, I.: Statistical Post-Processing with Standardized Anomalies and Variable Selection for Wind Farm Forecasts , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16810, https://doi.org/10.5194/egusphere-egu2020-16810, 2020.

Wind gusts and high wind speeds need to be considered in wind power industry and power grid management as they affect construction, material, siting and maintenance of turbines and power lines. Furthermore, gusts are an important information source on turbulence conditions in the atmosphere at the respective sites.
Often, the wind farm operators only provide basic data of the turbines such as average wind speed, direction, power and temperature. However, they require forecasts of gusts, too. Thus, a simple gust estimation algorithm based on the average wind speed was developed. The algorithm is tested at different mast measurement sites and WFIP2 data and applied to selected wind turbines. Results show that the algorithm is skillful enough to be used as a first guess gust estimation for single turbines and is, thus, used for nowcasting.
For nowcasting for the first two hours with a temporal fequency of ten minutes solely observations are used. A high-frequency wind speed and gust nowcasting ensemble based on different machine learning methodologies, including an ensemble for every method, was developd. Used are boosting, random forest, linear regression, a simple monte carlo method and a feed forward neural network. Results show that perturbing the observations provides a good forecasting spread for at least some of the methods. However, for other methods the spread is reduced significantly. Most of the used methods are able to provide good forecastst. However, hyperparameter tuning for the lightGBM boosting algorithm and the neural network is still needed.

How to cite: Schicker, I. and Papazek, P.: A simple gust estimation algorithm and machine learning based nowcasting for wind turbines, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3034, https://doi.org/10.5194/egusphere-egu2020-3034, 2020.

The purpose of this study is to develop an ensemble-based data assimilation method to accurately predict wind speed in wind farm and provide it for the use of wind energy intelligent forecasting platform. As Taiwan government aimed to increase the share of renewable energy generation to 20% by 2025, among them, the uncertain wind energy output will cause electricity company has to reserve a considerable reserve capacity when dispatching power, and it is usually high cost natural gas power generation. In view of this, we will develop wind energy intelligent forecasting platform with an error of 10% within 72 hours and expect to save hundred millions of dollars of unnecessary natural gas generators investment. Once the wind energy can be predicted more accurately, the electricity company can fully utilize the robustness and economy of smart grid supply. Therefore, the mastery of the change of wind speed is one of the key factors that can reduce the minimum error of wind energy intelligent forecasting.

There are many uncertainties in the numerical meteorological models, including errors in the initial conditions or defects in the model, which may affect the accuracy of the prediction. Since the deterministic prediction cannot fully grasp the uncertainty in the prediction process, so it is difficult to obtain all possible wind field changes. The development of ensemble-based data assimilation prediction is to make up for the weakness of deterministic prediction. With the prediction of 20 wind fields as ensemble members, it is expected to include the uncertainty of prediction, quantify the uncertainty, and integrate the wind speed observations of wind farms as well to provide the optimal prediction of wind speed for the next 72 hours. The results show that the prediction error of wind speed within 72 hours is 6% under different weather conditions (excluding typhoons), which proves that the accuracy of wind speed prediction by combining data assimilation technology and ensemble approach is better.

How to cite: Fan, Y.-M.: Ensemble-Based Data Assimilation for Wind Forecasting – Application to Wind Farm, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18144, https://doi.org/10.5194/egusphere-egu2020-18144, 2020.

The operation cost for wind parks make up a major fraction of the park’s overall lifetime cost. To facilitate an optimal wind park operation and maintenance, we present a decision support system that automatically scans the stream of telemetry sensor data generated from the turbines. By learning decision boundaries and normal reference operating states using machine learning algorithms, the decision support system can detect anomalous operating behaviour in individual wind turbines and diagnose the involved turbine sub-systems. Operating personal can be alerted if a normal operating state boundary is exceeded. We demonstrate the successful detection and diagnosis of anomalous power production for a case study of a German onshore wind park for turbines of 3 MW rated power.

How to cite: Meyer, A.: Machine-learning based wind turbine operating state detection and diagnosis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6532, https://doi.org/10.5194/egusphere-egu2020-6532, 2020.

With the rapid development of social economy, China's energy demand has been growing at an alarming rate. The annual cumulative power generation is about  6.8 trillion kilowatts hour in 2017, and 70% of them is provided by fossil fuel resources, so it is important to promote the use of renewable and clean energy, such as solar power generation technology. The advantages of using solar panel roof in urban areas include reduction of the need of land use in the crowed city and less dependence on fossil fuels. However, there is need to understand impacts of solar roof on local climate, on energy supply during heatwaves, and associated economic benefits in China. This study selected a heatwave event in Jiangsu province, China to simulate the impact of solar panel roof on local thermal environment and energy supply. During that time, the cooling energy consumption reached more than half of the total electricity consumption. A new heat transfer scheme of solar panel roof was introduced into WRF/BEP/BEM model, which include layers (glass protective panel, solar panel, bottom plate) and was divided into two types for heat transfer calculation: bracket and non-bracket. The results showed that the urban average 2-m daytime temperature decreased by 0.3℃ in non-bracket case which is better than that of bracket case, while its cooling effect on nighttime temperature was small. For the bracket case, its cooling effect on daytime and nighttime air temperature were equal (0.2^oC). Both solar panel roofs can reduce indoor daytime air temperature with the maximum cooling effect around 11:00 local time for non-bracket roof and 14:00 for bracket roof. However, bracket roof increased nighttime indoor air temperature and air-conditioning energy consumption. Solar panel roofs also reduce daytime turbulent kinetic energy and constrain the development of boundary layer. Results also show that with solar photoelectric conversion efficiency being 0.14, the photovoltaic power generation can meet 84.1%, 61.3% and 35.9% of the cooling energy consumption for high-density, low-density residential areas and commercial areas, respectively, during this heatwave event.

How to cite: Wang, Y., Chen, F., Hao, X., and Wang, F.: The influence of solar panel roof on urban thermal environment and cooling energy demand during a heat wave event in 2017, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1437, https://doi.org/10.5194/egusphere-egu2020-1437, 2020.

This study investigates the potential impact of rooftop solar photovoltaic (PV) installation on local convection in urban area. Rooftop solar PV system is a space-efficient option to deploy renewable energies in the crowded urban area. However, as the installation scale increases, unintentional impact on local climate may emerge. In particular, PV array deployment can change the surface radiative balance and thus enhance or reduce the urban heat island effect. The urban heat island effect has been hypothesized to influence afternoon thunderstorm activity in the tropical island, Taiwan. Therefore, temperature change due to PV installation may also alter the local circulation and convection. This research takes the Taipei City, which is a metropolitan area in northern Taiwan, for a case study. Citywide rooftop solar PV installation experiments are conducted by using the Weather Research and Forecasting (WRF) Model coupled with urban canopy model. Different PV conversion efficiency scenarios, including currently and future technology levels, are simulated to evaluate the potential impact on local circulation and convection.

How to cite: Chang, P.-S., Chen, J.-P., and Hsieh, C.-I.: The Potential Impact of Solar Photovoltaic Installation on Local Circulation and Convection in Taipei, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13349, https://doi.org/10.5194/egusphere-egu2020-13349, 2020.

The Weather Research and Forecasting (WRF) model fitted with the Fitch et al. (2012) scheme for parameterization of the effect of wind energy extraction is used to study the effects of very large wind farms on regional weather. Two real data cases have been run in a high spatial resolution (grid size 500 m). Both cases are characterized by a convective westerly flow. The inner model domain covers the North Sea and Denmark. The largest windfarm consists of 200.000 wind turbines each with a capacity of 8MW. The model is run for up to 12 hours with and without the wind farm. The impact on the regional weather of these very large wind farms are studied and presented. Furthermore, the effect of horizontal spacing between wind turbines is investigated. Significant impact on the regional weather from the very large wind farms was found. Horizontal wind speed changes occur up to 3500m above the surface. The precipitation pattern is greatly affected by the very large wind farms due to the enhanced mixing in the boundary layer. Increased precipitation occurs at the front? within the wind farm, thus leaving the airmass relatively dry downstream when it reaches the Danish coast, resulting in a decrease in precipitation here compared to the control run. The formation of a small low level jet is found above the very large wind farm. Furthermore, wake effects from individual wind turbines decrease the total power production. The wind speed in the real data cases are well above the speed of maximum power production of the wind turbines. Yet most of the 200.000 wind turbines are producing only 1MW due the wake effects. A simulation run with a wind farm of 50.000 8MW wind turbines was also run. This windfarm covers the same area as the previous one, but horizontal distance between wind turbines are 1000m instead of 500m. This configuration was found to produce a similar amount of power as the 200.000 configuration. However, the atmospheric impact on regional weather is smaller but still large with 50.000 wind turbines.

How to cite: Jacobsen, S. and Walløe Hansen, A.: A study of the impact of very large wind farms on regional weather using the WRF model in high resolution, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16884, https://doi.org/10.5194/egusphere-egu2020-16884, 2020.

Storm Xaver impacted the northern Europe on 5-6 December 2013.  It developed southeast of Greenland and passed north of Scotland and across southern Norway on a trajectory that led to a cold air outbreak across the North Sea and intense convection activity in northern Europe.  Strong sustained north winds led to a high storm surge that impacted all countries bordering the North Sea.  Storm Xaver was a century scale event with certain locations around the North Sea reporting their highest ever water levels since the start of modern records.  Media reports from the time of the storm chronicle the scale of the disruptions, including many cancelled flights, interrupted rail networks, closed bridges and roads, coastal building collapses, and power blackouts across northern Europe.  Much of this was due to the strong winds, but coastal storm surge flooding was important in the UK, and it led to interrupted port operations around the North Sea.

The storm was important for energy infrastructure and particularly for wind energy infrastructure.  In the northern North Sea, petroleum platforms were evacuated and operations closed ahead of the storm as a precautionary measure.  A number of onshore wind turbines were badly damaged by high winds and lightning strikes in the UK and Germany.  Over the North Sea, wind speeds exceeded the turbine shutdown threshold of 25 m/s for an extended period of time, with economic impacts from the loss of power generation.   In the German Bight, the FINO1 offshore wind energy research platform was damaged at the 15 m level by large waves.  This was the third report of storm damage to this platform after Storm Britta in 2006 and Storm Tilo in 2007.  Researchers have highlighted the need to reassess  the design criteria for offshore wind turbines based on these kinds of extreme meteorological events.  For the offshore wind industry, an important element of energy meteorology is to characterize both the evolving wind and wave fields during severe storms as both elements contribute to turbine loads and potential damage.

The present conference contribution presents a literature review of the major events during Storm Xaver and impacts on energy infrastructure.  Tide gauge records are reanalyzed to trace the progress of the storm surge wave around the North Sea.  A spectral analysis is used to separate the long period storm surge component, diurnal/semidiurnal tide, and short period components in the original water level record.  The short period component of the tide gauge record is important as it may be linked with infragravity waves that have been implicated in certain cases of offshore infrastructure damage in addition to coastal erosion.  Discussion is made of offshore wave records during the storm.  Storm Xaver is compared with two damaging offshore storms in 2006 and 2007.

How to cite: Kettle, A.: Storm Xaver over Europe in December 2013 and its energy meteorological impacts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6029, https://doi.org/10.5194/egusphere-egu2020-6029, 2020.

A large-eddy simulation (LES) model, coupled with a dynamic actuator-disk model, is used to investigate the turbine power production and the turbine wake distribution in large wind farms where the streamwise turbine spacing of 7, 9, 12, 15, and 18 rotor diameters are considered. Two incoming flow conditions, three wind turbine arrangements, as well as the five turbine spacings are involved in this study, which leads to a total of 30 LES wind farm scenarios. The two incoming flow conditions have the same mean velocity of 9 m s^-1 but different turbulence intensity levels (i.e., 7% and 11%) at the hub height level. The considered turbine arrangements are the perfectly-aligned, laterally-staggered, and vertically-staggered layouts. The simulated results show that the turbine power production has a significant improvement by increasing the streamwise turbine spacing. With increasing the streamwise turbine spacing from 7 to 18 rotor diameters, the overall averaged power outputs are raised by about 27% in the staggered wind farms and about 38% in the aligned wind farms. The wind farm scenarios with the turbine spacing of 12d or greater in a large wind farm can lead to an increasing trend in the power production from the downstream turbines in the high-turbulence inflow condition, or also avoids the degradation of the power output on the turbines with the low-turbulence inflow condition. The flow adjustment above the wind farm results in the generation of the internal boundary layer (IBL), which grows up vertically along with the wake-wise direction. The growth of the IBL is found to be affected by the changes in the inflow condition and the turbine spacing. The IBL depth above the wind farms is found to be influenced by the turbine spacing, whereas the IBL depth in the downstream wake region of the wind farms shows a rapid increase under the high-turbulence inflow condition.

How to cite: Wu, Y.-T. and Tsao, Y.-H.: Power Output Efficiency in Large Wind Farms with Different Streamwise Turbine Spacing, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4484, https://doi.org/10.5194/egusphere-egu2020-4484, 2020.

The large number of already existing and planned offshore wind parks in the German Bight leads to challenging requirements with regard to reliable information on various processes in the atmosphere and the ocean. In particular wind shadowing effects play a major role for the optimal planning and operation of wind park installations. Synthetic Aperture Radar (SAR) satellites have proved their capability of giving a 2D view of the wakes generated behind wind farms at a  high spatial resolution. However, the estimation of wind speed deficits from SAR data is still a challenge, because undisturbed reference wind fields are usually not available at the exact location of the wake. A common approach is therefore to identify some reference areas on SAR scenes outside the wake region, which naturally leads to errors in the deficit computations. 
In this study a new filter approach for the deficit estimation is proposed, which allows to derive error bars for the deficits. The filter is based on a 2D convolution operation with a filter kernel, which has a shape depending on the wind park geometry and the wind direction. The errors depend on spectral properties of the background wind fields, which are estimated from SAR data as well. In this context the stability of the atmospheric boundary layer is shown to play a major role. Examples are shown using data acquired by the SENTINEL-1A/B satellites. The approach is seen as a contribution to make SAR based deficit computations more objective and automised, which is essential for the application of the method to larger data sets and to make wake analysis done in different regions more comparable.

How to cite: Schulz-Stellenfleth, J., Djath, B., and Haid, V.: Satellite based estimation of atmospheric wakes downstream offhore windparks using a new objective filter technique , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18585, https://doi.org/10.5194/egusphere-egu2020-18585, 2020.

Wind energy investments have met a quick growth during the last decades due to the stricter climate policies, the need for energy independence and the higher profits coming from the smaller costs of such applications. Moreover the evolution of technology leads to the characterization of more areas as suitable for energy applications. Offshore wind farms are a nice example of how to build bigger, more efficient and resistant in extreme conditions wind power plants.

The present work is focused on the determination of the suitability of an offshore marine area for the development of wind farm structures. More specifically the region of interest is the marine area on the south of France including the Gulf of Leon. For the needs of the study a 10-year database, produced employing state of the art atmospheric and wave models, is utilized. The wind and wave parameters used, have a spatial resolution of 6 km and a frequency of one hour.

Wind speed and power probability distribution characteristics are discussed in different heights throughout the domain. Particular locations are selected for a more comprehensive analysis. At the same time extreme wind and wave conditions and their 50-years return period are analyzed and used to define the safety level of the wind farms structural characteristics. The outcome could lead to a review of the area suitability for wind farm development, providing a new tool for technical/research teams and decision makers.

How to cite: Patlakas, P., Stathopoulos, C., Gavriil, A., Galanis, G., and Kallos, G.: Wind energy potential assessment in western Mediterranean, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18130, https://doi.org/10.5194/egusphere-egu2020-18130, 2020.

The potential of increasing the amount of offshore wind energy production in the Baltic Sea has been of great interest for many countries and wind power companies for a long time. From a meteorological point of view, there are several special wind characteristics that are observed in this area that needs to be taken into consideration when planning for a wind farm. For example, as the Baltic Sea is a semi-enclosed basin surrounded by coastlines in all directions, phenomenon such as low-level jets occur frequently.

In order to create a climatology of the wind conditions over the Baltic Sea, with wind power applications in mind, four different state-of-the-art reanalysis data sets (MERRA2, ERA5, UERRA and NEWA) have been compared with measurements from LIDAR systems and high meteorological towers (Anholt, Finnish Utö, FINO2 and Östergarnsholm). The performance of the data sets has been analyzed in terms of stability and governing synoptic weather conditions as well as seasonal and diurnal variations. By selecting the most suitable reanalysis data set and using the observations to make corrections, a climatology for wind conditions over the Baltic Sea, focusing on the low-level jets, has then been constructed.

How to cite: Hallgren, C., Sahlée, E., Ivanell, S., Körnich, H., and Vakkari, V.: Wind conditions over the Baltic Sea – comparing reanalysis data sets with observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21897, https://doi.org/10.5194/egusphere-egu2020-21897, 2020.

In this study, we present methodology to create synthetic multi-year wind generation dataset at minute-scale granularity at the existing and future Australian wind farms. The purpose of the dataset  is to assist studies of penetration of large scale and distributed renewable generation into the electricity systems and its impact on power system security in the National Energy Market (NEM).

Synthetic historical records are based on a spatial and temporal blend of reanalysis datasets with the minute-scale wind speeds observations at Bureau of Meteorology weather station network. Strengths and weaknesses of reanalysis data are illustrated and a correction methodology discussed. A method to introduce minute-scale and sub-hourly fluctuations absent in the reanalyses records is presented. Expected statistical properties of sub-hourly fluctuations in the wind generation records are derived from the characteristics of the background atmospheric state in the vicinity of the wind farms.

The accuracy of the dataset is validated in terms of  power spectra and ramping frequencies in the simulated timeseries against existing minute-scale observations of wind generation at Australian Wind farms. The statistical properties of the observed and simulated timeseries match reasonably well, overall making the dataset suitable for the investigations of  the implications of wind ramping on energy demand and generation at the existing and foreseeable infrastructure build in the NEM.

How to cite: Titova, E. and Mittal, R.: Synthetic Wind Generation Records for Australian Wind Farms, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6426, https://doi.org/10.5194/egusphere-egu2020-6426, 2020.

A crosswind spectrum model S_v(f) is proposed that covers both the meso and the microscale, ranging in frequency 1/5 day^-1 to the turbulence inertial subrange. The purpose is to improve the calculation of flow meandering effect over areas of the sizes of offshore wind farms and clusters.

The development is based on measurement (from Høvsøre) analysis over this broad frequency range for cases where wind direction does not change much during a day. The model reads:

fS_v(f) = Boundary-layer model   for f>f₁,

         = Constant                           for f₂<f<f₁,

         = a₁f ^-2/3 + a₂f ^-2                     for f<f₂

Here, the frequency range f₂ to f₁ defines the gap region, and the constant in this subrange is determined by the spectra on both sides of this range; the boundary-layer model used here is either the Kaimal model or the Mikkelsen-Tchen model; a₁ and a₂ are climatological coefficients. The credibility of the model is evaluated against with measurements from another wind test site Østerild, with measurements ranging in height from surface layer to a height of 241 m.   

The model is used together with a similar model for the longitudinal wind component u to obtain time series u(t) and v(t), from which direction statistics are obtained and compared with those from measurements. It was found that the boundary-layer models could only describe stationary time series for wind vectors. The new v-spectral model improves significantly the statistics of wind direction variation over scales that correspond to offshore wind farms and clusters.

How to cite: Larsén, X., Larsen, S., Petersen, E., and Mikkelsen, T.: A new spectrum model for the atmospheric crosswind component applicable from mesoscales to microscales, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5240, https://doi.org/10.5194/egusphere-egu2020-5240, 2020.

Analysis of correlation among precipitation, wind, and solar resources could explore their complementary features, enhance the utilization efficiency of renewable energy and further alleviate the carbon emission issues caused by fossil energy. In this study, we discuss the correlation between precipitation and wind, wind and solar, precipitation and solar from various Spatio-temporal perspectives (from east to west in China, in terms of plain, plateau, hill, and mountain, from daily to ten days and monthly) with observed data. With investigation of daily time series of precipitation, wind speed and solar radiation ranging from 1961-1-1 to 2016-12-31 of 726 meteorological stations located in various landform and distributed dispersedly in China, the results show that 1) the fluctuation value, quantified by Mei-Wang Fluctuation index, denotes the descending tendency when the time resolution increases, and this tendency is stronger in the southern and eastern China; 2) the correlation coefficient, characterized by Kendall’s rank correlation coefficient, changes from east to west in China, and the strength of this correlation displays certain connection to the local topography (e.g., in Qinghai province which is located in the plateau region the complementarity between precipitation and wind speed is stronger than that between precipitation and solar, the mid-stream basin of Yangtze River where the topography is scattered and complex has the weaker complementarity compared to other areas in China). According to the results of this research, it is helpful from the temporal perspective to understand the requirement of complementarity in the utilization of wind, and solar resources which are intermittent, and from the spatial perspective to know the solution of mitigating fluctuation via integration of multi-renewable energy situated in different locations.

How to cite: Wang, X.: Analysis of Fluctuation and Correlation Tendency among Precipitation, Wind, and Solar from Various Spatio-temporal Perspectives: a Case Study in China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4931, https://doi.org/10.5194/egusphere-egu2020-4931, 2020.

West Africa is one of the least developed regions in the world regarding the energy availability and energy security. Located close to the equator West Africa receives high amounts of global horizontal irradiance (GHI). Thus, solar power and especially photovoltaic (PV) systems seem to be a promising solution to provide electricity with low environmental impact. To plan and to dimension a PV power system climatological data for global horizontal irradiance (GHI) and its variability need to be taken into account. However, ground based measurements of irradiances are not available continuously and cover only a few discrete locations.

Data records of surface irradiance based on satellite measurements have the advantage of covering wide spatial regions and being available over long time periods. The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF) provides the Surface Solar Radiation Data Set-Heliosat, Edition 2.1 (SARAH-2.1), a 35 year long climate data record in an half hourly resolution, covering the whole of Africa and Europe.

In this study, the SARAH-2.1 data record (1983-2017) is used to analyze the impact of 35 years atmospheric variability and trend on GHI and PV yields over West Africa (defined as the region from 3°N to 20°N and 20°W to 16°E). The trend and the variability of solar irradiance is analyzed separately for the wet and dry season as well as for annual data. Furthermore, a simplified model provides high-resolution potential PV yields.

According to the SARAH-2.1 data record, solar irradiance is largest (with up to 300 W/m 2 daily average) in the Sahara and the Sahel zone with a positive trend (up to 5 W/m²/decade). Whereas, the solar irradiance is lower in southern West Africa with a negative trend (up to -5 W/m²/decade). The positive trend is mostly connected to the dry season, while the negative trend occurs during the wet season. PV yields show a strong meridional gradient with lowest values around 4 kWh/kWp in southern West Africa and reach more than 5 kWh/kWp in the Sahara and Sahel zone.

This poster will discuss the long-term trend and variability analysis of solar irradiance and highlight the implications for photovoltaic-based power systems in West Africa.

How to cite: Neher, I., Crewell, S., Meilinger, S., Pfeifroth, U., and Trentmann, J.: Long-term variability of solar irradiance and its implications for photovoltaic power in West Africa, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19283, https://doi.org/10.5194/egusphere-egu2020-19283, 2020.

Energy production from the utilization of wind energy potential depends on the variability of the wind field as determined by the interaction of natural processes on different scales. Global climate change can cause alterations in the surface wind and thus it may affect the geographical distribution and the wind energy potential variability. Wind energy production is sensitive to wind speed changes, especially in the upper percentile of the wind speed distributions, where energy production is more effective. The importance of wind energy production changes is enhanced by the fact that wind energy investments are long-term and are characterized by high initial costs and low operating costs. In the present study, these changes are examined for the southeastern Mediterranean region, based on simulations of the Regional Climate Model ALADIN 5.2 extracted from the Med-CORDEX database for the climatic scenarios RCP4.5 and RCP8.5. The results indicate a wind power density increase over the Aegean Sea, the Ionian Sea, the Dardanelles and the Black Sea, with similar levels of increase for both climatic scenarios. In contrast, during the winter period there is a decline across the southeastern Mediterranean, which is more significant in the case of the RCP8.5 scenario. Finally, for most areas of eastern Greece, there is a reduction in the number of wind speed cases for both below and above cut-in and cut-out wind speeds, while there is an increase in the number of wind speed cases that wind turbines operate at their maximum power. The results are expected to reduce the uncertainty associated with the impact of climate change on wind energy production. 

How to cite: Tzanis, C. G., Philippopoulos, K., Cartalis, C., Granakis, K., Alimissis, A., and Koutsogiannis, I.: Climate change impacts on wind power density over southeastern Mediterranean, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1546, https://doi.org/10.5194/egusphere-egu2020-1546, 2020.

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

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

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

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

The decarbonisation of power production is key to achieving the Paris Agreement goal of limiting global mean surface temperature rise to well below 2°C, particularly so given the drive to electricity transport and heat. At the same time, variable renewable energy (VRE) sources such as solar photovoltaics (PV) and wind have seen rapid cost reductions in recent decades bringing them into cost parity with base load fossil generation. Therefore, recent long term planning studies, which utilise cost-optimising models, have demonstrated the important role of VREs in decarbonising power systems across the world. However, while techno-economically detailed, such studies tend to neglect key social factors that often shape the real world evolution of the energy system.

Of particular relevance to VRE deployment is their visual impact on the landscape which can act to undermine their public acceptability. Here, we use crowd-sourced scenicness data to derive spatially explicit, empirically grounded wind energy capacity potentials for three scenarios of public sensitivity to this visual impact. We augment these with a detailed analysis of Great Britain’s (GB) solar PV capacity potential. We then use these scenarios in a cost-optimising model of GB’s power system to assess their impact on the cost and design of the electricity system in 2050. Our results show that the levelised cost of the system can increase by up to 15% when public sensitivity to visual impact is high compared to low. In part this is driven by our finding that some of the most picturesque parts of GB also happen to be the most cost-effective for onshore wind, leading to large reductions in installed capacity as we move through our sensitivity scenarios. Indeed, deployment is heavily limited in Scotland and the South-West which in turn acts to limit the spatial diversity of onshore wind. We conclude that it is essential for policy makers to consider these cost implications and to find mechanisms to ameliorate the visual impact of onshore wind in local communities.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

E_x,t = EN_t  ( α₁ P_x,t + α₂ T_x,t )

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A novel wave-energy device design [1,2] will be presented based on the following features: (i) an electro-magnetic generator based on cylindrical magnets moving through induction wires around a cylindrical tube, like in the IP wave-buoy, (ii) a convergence in a breakwater to amplify the incoming waves, like in the TapChan device, and (iii) a wave-activated buoy with magnets attached, like in the Berkeley wedge, constrained to move in a slight arc or in a rectilinear manner. Its workings will be demonstrated in a first, operating proof-of-principle. A monolithic mathematical model is established by coupling the three variational principles for the hydro-dynamic wave motion, using the potential-flow approximation, the constrained wave-activated buoy motion, and the electro-magnetic generator together into one grand variational principle. The resistive losses in the electrical circuit and the energy harvested in the (parallel LED) loads are subsequently added to the dynamics. After linearisation of the resulting full 3D nonlinear model around a state of rest and application of the shallow-water approximation, we discretize the linear dynamics in a compatible, i.e. geometrically consistent, manner using a finite-element approach in space and symplectic integrators in time. Preliminary numerical modelling and simple optimization will be shown and these are promising. Finally, further optimisation of the device for different geometries and for a given wave-climate as well as alternative designs will be discussed.

References
[1] O. Bokhove, A. Kalogirou, W. Zweers 2019: From bore-soliton-splash to a new wave-to-wire wave-energy model. Water Waves 1.
[2] O. Bokhove, A. Kalgirou, D. Henry, G. Thomas 2019: A novel rogue-wave-energy device with wave amplification and induction actuator. In: 13th European Wave and Tidal Energy Conference 2019, Napoli, Italy.

How to cite: Bokhove, O.: Rogue-wave-energy: wave-to-wire mathematical modelling , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8769, https://doi.org/10.5194/egusphere-egu2020-8769, 2020.