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As part of its ambitious long term energy strategy, Switzerland plans to phase out nuclear power production and replace most or all of its significant share of national electricity production (40%) by renewables, in particular by photovoltaics (PV) and wind energy. The existing large fraction of hydropower and significant pumped-storage hydro capacity in the mountainous regions of the country will potentially provide valuable balancing and ancillary services for the management of intermittent production from PV and wind. We present a set of calculations based on data from the current Swiss electricity system, which investigate the effect of different PV-wind mixing ratio scenarios on required import and export from foreign neighbors. It is shown that the current hydropower is a good basis to deal with intermittent energy sources and to keep import rates of electricity at current levels in a fully renewable Switzerland as long as the PV-wind ratio is small. For higher PV-shares, forced export and import will increase but appear acceptable if PV-contribution stays below 0.6. Enlargement of reservoir capacity or oversizing of intermittent renewables can further reduce required import effectively. These findings are quantitatively specific for Switzerland but qualitatively transferable to similar mountainous environments with abundant hydropower resources.

Reliable predictions of sea level rise require a quantitative understanding of the mass balance of the Antarctic ice sheet. Water vapor exchange between snow and the atmospheric boundary layer may be an important term in the mass balance equation but current estimates for this process are highly uncertain. The exchange of water vapor becomes particularly strong during drifting and blowing snow events, which are frequent in Antarctica. In these conditions, measured turbulent fluxes based on the Eddy–Covariance (EC) method or the Monin–Obukhov (MO) bulk formula are associated with increased uncertainties that are difficult to quantify. The EC raw data can contain artifacts because blowing snow particles temporarily perturb the measurement signals of ultrasonic anemometers and open–path infrared gas analyzers. The MO bulk approach suffers from the fact that sources or sinks of moisture and heat in the drifting and blowing snow layer violate the assumption of height–constant fluxes. Additionally, strong winds typically result in small vertical differences in temperature and humidity, which makes the MO bulk approach particularly sensitive to instrument–specific biases in temperature and humidity. In view of these limitations, it is difficult to validate models, especially the parametrizations used in large-scale models. Nevertheless, detailed small-scale numerical simulations can help to constrain vapor and heat exchange and to disentangle different sources of uncertainty. In this study, we use Large-Eddy Simulations (LES) as a reference to validate and improve parametrizations of latent and sensible heat fluxes in conditions of drifting and blowing snow. The boundary conditions of the LES simulations are based on field measurements at the Japanese Syowa S17 Station, coastal East Antarctica. Consistent with the almost flat and permanently snow-covered terrain, the LES simulations assume a flat snow surface a the lower boundary of the domain (approximately 38 x 19 x 18 m^3). The transport of snow particles and their interaction with the flat surface is represented by a Lagrangian Stochastic Model coupled with the LES. Vapor and heat exchange between snow particles and the air is based on the energy and mass balance of a spherical ice particle in turbulent flow, neglecting radiative heat transfer. In contrast to most other modeling studies on drifting and blowing snow, the LES simulations do not assume a thermal equilibrium with constant particle temperatures but the model explicitly computes the particle temperatures, which increases the accuracy of the vapor and heat exchange. LES-based steady-state vertical profiles of the latent and sensible heat fluxes are compared with parametrized profiles in a simple one-dimensional model, resembling the approach of existing large-scale models. In the simple model, we focus for simplicity on a suitable representation of the vapour and heat exchange between particles of drifting and blowing snow and the atmosphere, not yet the parametrization of the particle concentration. We propose the following three modifications, which improve significantly the parametrization: (i) additional model levels in the lowest few centimeters above the surface, (ii) prognostic computation of air temperature and specific humidity also at the near-surface levels, and (iii) an empirical expression for the change in particle temperature derived from the LES data. This work is an important step towards reliable parametrizations of drifting snow effects.

Extreme cold environments such as the polar regions and high mountain areas are characterised by unknown precipitation quantities and a high variability of snow and ice masses. A key player in understanding the surface mass balance in these areas is drifting and blowing snow. Apart from the surface mass balance, drifting and blowing snow may perturb the surface structure of the boundary layer resulting in feedback loops that have not been sufficiently explored, especially at large scales. In this contribution we review recent advances in measuring and modelling drifting and blowing snow as well as their effects on the atmosphere and the surface mass balance. In particular, we suggest that current models may underestimate drifting snow sublimation because they assume saturation close to the surface and do not adequately represent the effects of transient (turbulent) wind fields. We highlight the importance of knowing not only the frequency of snow transport occurrence but also the height distribution of both mass flux and humidity for an accurate assessment. Therefore, we present first ground-based micro rain radar measurements of blowing snow clouds in Antarctica and show examples of blowing snow events which are not represented in current weather and climate models. Additionally, recent advances made is simulations of blowing snow clouds in Antarctica are present and compared with measurements. These simulations play the critical role of understanding the effect of drifting and blowing snow at continental scale. Results from such simulations, particularly on vapor contribution due to blowing snow sublimation will be presented as examples to highlight their utility.

The knowledge that wind power is very productive in winter and that photovoltaic (PV) installations can produce more electricity in alpine environments than in the valleys is not completely new. Yet these insights have remained little appreciated or acted upon. But with the energy transition pushing forward into a renewable future, this knowledge is of critical importance. As Switzerland is facing a substantial winter energy deficit with its nuclear power plants going offline, SCCER-SoE modelling results provide vital information as to how smart choices of installation location can help to fill this deficit with solar and wind power from the mountains.

One of the major problems with solar PV in the context of a fully renewable electricity production at mid-latitudes is the trend of higher production in summer and lower production in winter. This trend is most often exactly opposite to demand patterns, causing a seasonal mismatch that requires extensive balancing power from other production sources or large storage capacities. Which possibilities do we have to bring PV production into closer correlation with demand? This question motivated our research and in response we investigated the effects of placing PV panels at different tilt angles in regions with extensive snow cover to increase winter production from ground reflected short wave radiation. The aim of this project is therefore to quantify the effect of varying snow cover duration (SCD) and of panel tilt angle on the annual total production and on production during winter months when electricity is most needed. We chose Switzerland as ideal test site, because it has a wide range of snow cover conditions and a high potential for renewable electricity production. But methods can be applied to other regions of comparable conditions for snow cover and irradiance. Our analysis can be separated into two steps: 1. A systematic, GIS and satellite-based analysis for all of Switzerland: We use time series of satellite-derived irradiance, and snow cover characteristics together with land surface cover types and elevation information to quantify the environmental conditions and to estimate potential production and ideal tilt angles. 2. A scenario-based analysis that contrasts the production patterns of different placement scenarios for PV panels in urban, rural and mountainous areas. We invoke a model of a fully renewable electricity system (including Switzerland’s large hydropower system) at national level to compute the electricity import and storage capacity that will be required to balance the remaining mismatch between production and demand to further illuminate trade-offs between the different placement scenarios. Our results show that in regions with extended periods of snow cover the winter production can be 10% higher without sacrifices on the annual total production. This helps significantly in reducing the energy gap mentioned above; annual required import can be lowered by 10%-20% and forced export due to overproduction during summer months reduces to less than half.

Dank genetischer Algorithmen zum Optimum? | Hört man den Ausdruck Evolu-tion, denkt man an Darwin und das Überleben der Stärksten. Dass sich diese Stra-tegie auch auf erneuerbare Energie anwenden lässt, mag überraschen. Doch was Fauna und Flora geholfen hat, sich an die Umwelt anzupassen, ist auch geeignet, um unsere eigene Zukunft zu optimieren.

Drifting and blowing snow is a dominant process shaping snow-covered surfaces in particular in extreme environments such as high mountains or polar regions. Because the process is complicated to describe, as it covers scales from below 1 mm to several kilometers, it is typically not considered in models or greatly simpli^ed. This impedes our ability to correctly describe the mass- and energy exchange above snow covered surfaces and therefore also to understand the relationship with precipitation patterns. Our contribution addresses this problem by reviewing the current state of understanding. We show based on measurements and large-eddy simulations (LES) that typical bulk Monin–Obukhov formulations for turbulent auxes do not work in the presence of snow transport. More specifically, surface exchange is much more intense than predicted by current large-scale models during snow transport. We further present a new model coupling, CRYOWRF, which introduces the snow model SNOWPACK and a detailed representation of blowing snow to the Weather and Forecasting model (WRF) in order to allow a more accurate representation of surface exchange. We show how this coupled version is able to reproduce observed patterns of blowing snow and its inauence on surface exchange. This includes cases of blowing snow over mountain ridges and large blowing snow clouds from katabatic winds, an hydraulic jump and waves in Antarctica (Figure). Overall, we find that the influence of drifting and blowing snow on surface exchange may have been underestimated in previous (model) assessments and that the highly dynamic exchange associated with drifting and blowing snow has not only implications for the surface mass and energy balance, but potentially also for the isotopic composition of deposited snow.

With large parts of the world moving toward renewable energies, there is an urgent need to organize this large-scale transition effectively. This paper presents a new methodology to guide the planning and siting of renewable electricity generation for countries or larger geographical regions. Its flexible approach accounts for the specific boundary conditions, constraints and available resources of the region of interest and enables solutions that optimize the interplay between the various types of generation. Evolution strategy permits a simultaneous optimization of the placement and the share of renewable electricity generation technologies that are to be added to a system, while most efficiently combining the new with the existing electricity generation and respecting the constraints of the electrical grid. Using Switzerland as case study, we demonstrate the method's ability to devise national installation scenarios that are efficient, realistic with respect to land use and grid infrastructure and reduce significantly the need for seasonal storage. We show how the spatio-temporal variability of weather-driven electricity generation can be exploited to benefit the electrical system as a whole.

Only 42 % of the mean annual production of large hydropower plants in the Swiss Alps is stored in their reservoirs. Once the reservoirs are full at the end of the summer semester, plants are operated as run-on-the-river in order to avoid spilling. A significant part of the hydropower resources are thus sold at discount prices and cannot be transferred to the winter semester. It is precisely in the winter semester that Switzerland imports large volumes of electricity from its neighbours. In order to support the nuclear power phase-out planned in the Swiss Energy Strategy 2050, hydropower must not only maintain its 2012 production level but mainly step-up its role in the grid and improve its remuneration. Following this strategy, the present production of the nuclear plants will be mainly replaced by renewables and the total electricity storage capacity must therefore be increased by 10 to 30%. Hydropower schemes face two challenges: to reduce the lack of electricity production in the winter semester and to lay a suitable foundation for the increased storage demand of intermittent renewables such as wind and solar. In this work, we propose a methodology to evaluate the potential increase in energy storage capacity of a portfolio of large reservoirs in Alpine regions, considering future climate scenarios and future reservoir operation scenarios corresponding to different economic conditions (e.g. more/less PV and wind production) and reservoir management time scales (seasonal, multiannual). In addition to exploring the performance of existing reservoirs in different future conditions, we assess the possibility to link reservoirs (mutualising the use of storage) and/or to raise dams (increasing storage and annual runoff regulation). In fact, several large dams provide the structural capacity to be heightened by at least 10% of their height. Additionally, there are strategic locations that could be used to create new large reservoirs. This methodology is applied to a test site located on the left bank of the Rhône River in Switzerland. Our method uses both recent and future hydrology, as well as operational data to determine the optimal reservoir live storage capacity for a hydropower scheme. In addition to intra-annual energy and water transfer, we also consider the transfer in between years to account for adverse climate conditions. In order to apply this methodology to the Grande Dixence hydropower scheme, we show two opportunities to increase the seasonal storage capacity: heightening of the main dam and the creation of a new upstream reservoir. Our study shows that the largest Swiss reservoir with 400 hm3 can provide more seasonal storage in all climate scenarios, however a full heightening of 10% may not be optimal regarding the different intra-annual operation modes investigated. However, should the operator wish to guarantee production in extreme dry years the reservoir heightening would also provide the possibility of establishing a pluriannual reserve or extra room for pumped storage.

The objective of this project has been to explore the potential of the wind over complex terrain, as an energy source during wintertime in Switzerland. A wind assessment method is developed based on short-term wind profile measurements with a wind lidar, long-duration meteorological station measurements, which are connected via machine learning to a specific site. Driving a high-resolution numerical weather model (WRF) with COSMO model as an input, we create a map of the spatial pattern of the local wind speed potential for short episodes of predominating weather patterns. We further use Wind-Topo, a very recent machine learning model, which predicts wind potential at high spatial and temporal resolution, to reproduce first the WRF simulations and then yearly averages. The yearly averages are used as a basis of comparison with the Swiss wind atlas. This report provides the local air flow analysis based on two measurement campaigns at La Stadera, near the Lukmanier Pass, GR, and at Cabane, Glacier3000 near Les Diablerets, VD, as examples of 3D wind assessment in complex terrain. Furthermore, a previous assessment in Eastern Switzerland is re-calculated with Wind-Topo. The results show that the Swiss wind atlas provides a good estimate of wind potential at the two measurement sites and that spatial patterns are comparable but not identical to Wind-Topo. The in-depth analysis of spatial patterns from both, Wind-Topo and WRF, suggest that areas of high wind potential may be missed by the wind atlas in particular in slopes and valleys. The spatial analysis presented here has limited validation and we suggest further investigation of these effects and an update of the Swiss wind atlas at higher temporal and spatial resolution. This appears necessary to assist and promote the transition of the Swiss electricity supply system towards renewable energy resources.

Mountains receive increased solar radiation because of a thinner atmosphere, the presence of snow on the ground and – in many regions such as the Alps – decreased winter cloudiness. In complex terrain, multiple terrain scattering further leads to radiation concentration at certain locations. We quantify this effect and present for the first time a surface radiative transfer model, which combines a bidirectional reflectance distribution function (BRDF) with multiple terrain scattering. It is shown that both effects, the anisotropic scattering properties of snow and the multiple terrain scattering contribute significantly to local radiation maxima and lead to an average increase in area-wide radiation input. This has not only implications for the local surface energy balance but also for PV production in snow covered mountains: high elevation installations of PV panels at steep angles can produce a large amount of winter electricity in mid latitudes. Using the example of Switzerland, it is shown how this can help to close an existing winter energy gap.

Wind modelling in complex topography remains a challenging task because operational Numerical Weather Prediction (NWP) models have a spatial resolution that is too coarse to capture all the meaningful terrain-induced phenomena like channeling, blocking, speed-ups, or thermally driven flows. In addition, higher resolution NWP models are so computationally expensive that it is not possible to generate long time series, which are often necessary for the study of wind-induced phenomena like snow deposition and transport or wind energy potential assessment. Downscaling schemes are often used to generate higher resolution wind fields from coarser resolution models. They can approximately be categorized as dynamic, which use nested NWP models or flow solvers, or as statistical, for which relevant predictors are used to interpolate the wind fields to a higher resolution grid. On the one hand, the computational cost of dynamic downscaling schemes does not allow to generate long time series at high spatial resolution. On the other hand, statistical downscaling suffers from the same inaccuracies as their driving coarse-resolution datasets. We aimed at developing a downscaling scheme with the following capabilities: I. Fast enough to generate long time series of high-resolution (50 m) wind fields for large domains (whole mountain range, 10000 sq. km); II. Utilize the accuracy of measurement station networks. III. Generate wind fields that exhibit terrain dependencies, and not a simple interpolation of the coarser dataset. Our novel downscaling model uses several convolutional neural networks that combine, at multiple spatial resolutions, 2D information from a NWP model and various topographic descriptors. This combination of information is performed at the location of hundreds of measurement stations, which data are used as predictands. When trained, this model can predict at any location the wind vector given the local atmospheric state from the NWP model and the local topography. For our test case, the Swiss Alps, we use data from the NWP model COSMO-1 and from 330 automatic measurement stations. The generated high-resolution wind fields show good agreement against observations and against high-resolution simulations from the Weather Research and Forecasting (WRF) model.

Wind energy has played a major role in the development of our society since 5000 BC when the first sail boats traveled the Nile River. Wind technology continued to advance with the invention of wind-powered pumps and mills in China and the Middle East as early as 200 BC, and by the late 19th century the first electricity-generating wind turbines were built. Today the effects of human driven climate change are pushing the world to transition from a fossil fuel dominated energy diet to a menu composed of a variety of technologies powered by renewable energies, including wind power. Our consumption habits and the historical design of our electricity grids were based on relatively few, centralized, and controllable power plants that use various fossil or nuclear fuels to generate power on demand to match real time use. The weather driven nature and low energy density of renewable sources like solar, wind and hydro lead to a more distributed and fluctuating electricity generation, which results in multiple problems: In Switzerland, under the foreseen future where photovoltaic (PV) panels are predominantly installed in urban areas, the nuclear phase-out will cause large deficits of electricity in winter. This thesis contributes to the solution of these important challenges in two ways: first by presenting a model that combines a variety of renewable energy sources to form a reliable and efficient electrical system, and secondly by improving the assessment of wind power potential in the Alps. The first two parts of this thesis are concerned with the simulation and optimization of the Swiss electrical system, in which the nuclear reactors are replaced by a combination of solar and wind energy sources. A spatially explicit approach is required to consider the spatio-temporal variability in renewable sources. Storage hydropower is a remarkable asset for the energy transition, and thus occupies a central role in the manner the system is modeled and operated (Part I). A novel algorithm explores how the whole system reacts under various combinations of PV and wind energies (Part II). By simultaneously optimizing the generation mix and location of PV panels and wind turbines, while considering the electrical grid and land use, this algorithm showed that a large number of carefully located wind turbines and a small number of PV panels located in the Alps can provide a much more balanced system than the conventional, urban PV scenario. Part III of this thesis addresses the difficulty of wind power potential assessment in mountainous terrain. On such terrain, high horizontal resolution is required in the numerical models that simulate the air flows. This constraint, and the need for long time series, prohibit the simulation of large domains and thus the identification of the best locations for wind turbines in the entire Swiss Alps. Downscaling models for wind are often used to obtain high resolution and long time series, but perform poorly on such terrain. In order to improve these models, a new method based on deep learning is presented. It downscales the outputs of the operational numerical model COSMO-1 used for the Swiss weather forecast, which has a resolution of 1.1 km, to a 50-meter resolution. The validation at 60 measurement stations shows that wind speeds and directions are more accurate than COSMO-1 and present many of the expected orographic effects like deflection, sheltering and ridge acceleration.

The total storage capacity of the reservoirs in Switzerland represents only 42% of their annual production of electricity, but secures approximately 100% of hydropower’s contribution to the winter electricity supply. In order to avoid spilling, the storage power plants therefore need to turbine the supplementary inflows like a run-on-the-river plant when the reservoirs are full at the end of the summer semester. This leads to a significant need to import electricity during the winter semester due to lack of production. The storage capacity of the reservoirs located in Valais contains more than 30% of the total live storage capacity in Switzerland and is strategical for the seasonal energy transfer. This paper presents a methodology to assess the potential to increase seasonal storage and then explores this methodology on a case study. The methodology was first developed and tested using recent hydrology and reservoir operation, and then used to explore alternative ways of operating and adapting the hydropower infrastructure when exposed to predictions of future runoff for the coming decades. In fact, from now to the year 2100, the inflows to these reservoirs are expected to vary considerably both in time and space. For instance, an especially significant increase of the yearly inflows due to glacier recession is expected for the 2045-2074 period. This will lead to new opportunities and new challenges in the operational management of the dams. The application of the methodology to the case study of the hydropower scheme of Grande Dixence illustrates two different options to increase the seasonal storage capacity: the creation of a new reservoir further upstream and the heightening of the Grande Dixence dam. The optimal storage capacity that guarantees the best use of the available future inflows and an adequate balance between water abundance in some years and water scarcity in others is computed considering different ways of using the inflows in a range of consecutive years and runoff predictions obtained for three greenhouse gas emission scenarios.

As part of its 2050 energy strategy, Switzerland plans to phase out nuclear power production and replace most or all of its significant share of national electricity production (40%) by renewables, in particular by photovoltaics (PV) and wind energy. We investigated how the proportion of PV / Wind in the energy mix affects the national energy balance throughout the year and how storage hydropower and pumped-storage hydro interact with the intermittency of those new renewables. We got quantitative results about the amount of import and export required to balance the seasonal mismatch between production and demand and investigated solutions to reduce it.

Drifting and blowing snow is a dominant process shaping snow-covered surfaces in particular in extreme environments such as high mountains or polar regions. Because the process is complicated to describe, as it covers scales from below 1 mm to several kilometers, it is typically not considered in models or greatly simplified. This impedes our ability to correctly describe the mass- and energy exchange above snow covered surfaces and therefore also to understand the relationship with precipitation patterns. Our contribution addresses this problem by reviewing the current state of understanding. We show based on measurements and large-eddy simulations (LES) that typical bulk Monin–Obukhov formulations for turbulent fluxes do not work in the presence of snow transport. More specifically, surface exchange is much more intense than predicted by current large-scale models during snow transport. We further present a new model coupling, CRYOWRF, which introduces the snow model SNOWPACK and a detailed representation of blowing snow to the Weather and Forecasting model (WRF) in order to allow a more accurate representation of surface exchange. We show how this coupled version is able to reproduce observed patterns of blowing snow and its influence on surface exchange. This includes cases of blowing snow over mountain ridges and large blowing snow clouds from katabatic winds, an hydraulic jump and waves in Antarctica (Figure). Overall, we find that the influence of drifting and blowing snow on surface exchange may have been underestimated in previous (model) assessments and that the highly dynamic exchange associated with drifting and blowing snow has not only implications for the surface mass and energy balance, but potentially also for the isotopic composition of deposited snow.

The overall EDGE objective is to fast-track the growth of locally-sourced decentralized renewable energy in Switzerland and to ensure that by 2035 and 2050, when ambitious shares of renewable energy are reached, the Swiss energy system is designed and operated in a technically and economically optimal as well as secure way, and that it is well positioned in the European markets. Specifically, the EDGE consortium aims to move beyond generic designs of decentralized renewable systems and markets to a regionalized analysis that is tailored to the Swiss cities, midlands, and the Alps. The pathways towards largely electrified and multi-carrier energy systems will be examined by analyzing electricity, mobility, and heating sectors. The consortium seeks to combine research with innovation from three Pilot and Demonstration project clusters (P&Ds) to ensure feedback loops between theory and practice, and to use the outcomes for delineating national-level pathways for successful implementation of nearly or fully renewable Switzerland by 2050. As the energy system is a socio-technical system, the EDGE consortium will deliver the essential interdisciplinary and transdisciplinary expertise, ranging from technology development to systems modeling, political science, management, economics, sustainability science, and energy practice, in order to identify the most efficient measures to unlock the full potential of decentralized renewable energy.