Dr. Jérôme Dujardin

Wind energy is one of the potential options to fill the gap in renewable energy production in Switzerland during the winter season when the energy demand exceeds local production capacities. With likely further rising energy consumption in the future, the winter energy deficit may further increase. However, a reliable assessment of wind energy potential in complex terrain remains challenging. To obtain such information, numerical simulations are performed using a combination of the “Consortium for Small-scale Modeling” and “Weather Research and Forecasting” (COSMO-WRF) models initialized and driven by COSMO-1E model, which allows us to simulate the influence of topography at a horizontal resolution of 300 m. Two LiDAR measurement campaigns were conducted in the regions of Lukmanier Pass and Les Diablerets, Switzerland. Observational LiDAR data and measurements from nearby wind sensor networks are used to validate the COSMO-WRF simulations. The simulations show an improved representation of wind speed and direction near the ground compared to COSMO-1E. However, with increasing height and less effect of the terrain, COSMO-WRF tends to overestimate the wind speeds, following the bias that is already present in COSMO-1E. We investigate two characteristic mountain–terrain flow features, namely waves and Foehn. The effect of mountain-induced waves of the flow is investigated through an event that occurred in the area of Diablerets. One-year analysis for the frequency of conditions that are favorable for mountain wave formation is estimated. The Foehn impact on wind was observed in the Lukmanier domain. We attempt quantification of the probability of occurrence using the Foehnix model. The result shows a high probability of Foehn occurrence during the winter and early spring seasons. Our study highlights the importance of incorporating complex terrain-related meteorological events into the wind energy assessment. Furthermore, for an accurate assessment of wind speed in complex terrain, our study suggests the necessity to have a better representation of the topography compared to COSMO-1E.

This study presents an inter-comparison of three structurally different electricity system models (EXPANSE, Nexus-e, and OREES) with sub-national spatial resolution in Switzerland in 2035. We analyze technology and regional implications of three targets for electricity generation from new renewable sources (17 to 25 TWh/year from solar PV, wind, biomass, and waste incineration) and compare results at a national level as well as at a higher spatial resolution of Swiss cantons and municipalities. All three models align on high capacities of solar PV in 2035 as the key technology for reaching the three targets, but there is flexibility where PV can be placed to achieve the targets: either on roofs and facades or also on land. Electricity interconnection with Europe remains of key importance in Switzerland because any increases in electricity demand or lower deployment of new renewable generation are compensated by import. For the rest, the three models provide internally-consistent storylines of future strategies for Switzerland: a future with a diversified range of technologies (EXPANSE), a future with the focus on decentralized rooftop solar PV with batteries (Nexus-e), and a future with the prioritization of most productive areas for wind and solar PV, including open-field PV (OREES).

Wind energy potential in complex terrain is still poorly understood and difficult to quantify. With Switzerland’s current efforts to shift to renewable energy resources, it is now becoming even more crucial to investigate the hidden potential of wind energy. However, the country’s topography makes the assessment very challenging. We present two measurement campaigns at Lukmanier and Les Diablerets, as representative areas of the complex terrain of the Swiss Alps. A general understanding of local wind flow characteristics is achieved by comparing wind speed measurements from a near-surface ultra-sonic anemometer and from light detection and ranging (LiDAR) measurements further aloft. The measurements show how the terrain modifies synoptic wind for example through katabatic flows and effects of local topography. We use an artificial neural network (ANN) to combine the data from the measurement campaign with wind speed measured by weather stations in the surrounding area of the study sites. The ANN approach is validated against a set of LiDAR measurements which were not used for model calibration and also against wind speed measurements from a 25-meter mast, previously installed at Lukmanier. The statistics of the ANN output obtained from multi-year time series of nearby weather stations match accurately the ones of the mast data. However, for the rather short validation periods from the LiDAR, the ANN has difficulties in predicting lowest wind speeds at both sites, and highest wind speeds at Les Diablerets.

Solar photovoltaic (PV) is the most rapidly expanding renewable resource worldwide. Yet, its full potential may be hindered by mismatches with market demand and correlated production profiles. In this research, we explore a case study of innovative PV placements in alpine regions using two, soft-linked optimization models of Switzerland's electricity system. Using Swissmod, an electricity dispatch and load-flow model, and OREES, an electricity system model employing evolution strategy to optimize PV placement, we simulate market prices of optimized PV placements given multiple years of weather data, various CO2 prices, and considering future electricity infrastructure developments across Europe. Mountain placements result in higher market value and less required area relative to lower-altitude PV placement strategies. The higher market value is driven by better alignment with demand, particularly during winter when demand is highest. We found that optimized alpine placements offer revenues of panel capacity (EUR/kW/year) that are on average 20% higher than revenues from urban PV installations. Furthermore, the Swiss mountains could host more than 1 GW of capacity with even greater revenues (33%). Alpine PV installations, with their higher market values and increased value factors, can potentially be very profitable investments and are also valuable from a system perspective.

Predicting wind flow in highly complex terrain like the Alps is a challenge for all models. When physical processes need to be resolved in a spatially explicit manner, grids with high horizontal resolution of a few hundred meters are often required and drastically limit, in many cases, the extent and duration of the simulations. Many surface process models, like the simulation of heterogeneous snow cover across a season, however, need long time series on large domains as inputs. Statistical downscaling can provide the required data, but no model can reach the desired resolutions effectively and provide temporally resolved wind speed and direction on highly complex topography. The assessment of the potential for wind energy in the Alps, a promising player in the energy transition, is an example where the current shortcomings cause strong limitations. We present "Wind-Topo", a novel approach based on deep learning that discovers some of the interactions between high-resolution topography and coarser-resolution states of the atmosphere to generate near-surface wind fields with a 50-m resolution. In our test case, we use a large number of measurement stations in Switzerland to train the model and an operational weather prediction model (COSMO-1) as predictor. Wind-Topo employs a custom architecture that analyses the state of the atmosphere on various scales and associates it with high-resolution topography. A dedicated loss function leads to good scoring metrics as well as accurate wind-speed distributions at 60 independent stations used for a thorough validation. 50-m resolution wind fields are generated efficiently and exhibit several expected orographic effects like ridge acceleration, sheltering, and deflection. Furthermore, the bias and mean absolute error from COSMO-1 at the alpine validation stations, which are 0.72 and 1.77 m·s⁻¹ respectively, are reduced to −0.07 and 1.21 m·s⁻¹.

Dank genetischer Algorithmen zum Optimum? | Hört man den Ausdruck Evolution, denkt man an Darwin und das Überleben der Stärksten. Dass sich diese Strategie 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.

The influence of drifting and blowing snow on surface mass and energy exchange is difficult to quantify due to limitations in both measurements and models, but is still potentially very important over large areas with seasonal or perennial snow cover. We present a unique set of measurements that make possible the calculation of turbulent moisture, heat, and momentum fluxes during conditions of drifting and blowing snow. From the data, Monin–Obukhov estimation of bulk fluxes is compared to eddy-covariance-derived fluxes. In addition, large-eddy simulations with sublimating particles are used to more completely understand the vertical profiles of the fluxes. For a storm period at the Syowa S17 station in East Antarctica, the bulk parametrization severely underestimates near-surface heat and moisture fluxes. The large-eddy simulations agree with the eddy-covariance fluxes when the measurements are minimally disturbed by the snow particles. We conclude that overall exchange over snow surfaces is much more intense than current models suggest, which has implications for the total mass balance of the Antarctic ice sheet and the cryosphere.

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.

The prospect of a renewable transition seems plausible for many countries, but can be shrouded in risks, costs and challenges. This paper illuminates a path for such a transition with a numerical investigation, aimed at resolving the power dynamics of a country powered only by renewable generators. The focus was Switzerland, with its considerable hydropower infrastructure and plans to phase out nuclear energy. The model uses optimal power flow calculations to compute the transmission of electricity, and also accounts for the movement of water within the hydropower system. Results suggest that the renewable goal is attainable and will not require radical re-building of the country's transmission infrastructure. Under our assumptions, it was found that the transmission grid is placed under slightly lower stress on average, in renewable scenarios. Despite matching supply and demand on average, the fully renewable system required a ∼ 107% increase in electrical exchange with neighbouring countries to compensate for seasonal variability, and additional intermittency of electrical supply. Simulation results are described for three scenarios: Current, Intermediate, and Renewable. The bulk power statistics, temporal dynamics, distributions of line use, and spatial patterns are presented, and the implications of the results are discussed.