The increasing share of variable renewable energy sources in the power supply system raises questions about the reliability and the steadiness of the production. In this study, we assess the main ...statistical characteristics of “energy droughts” for wind, solar and run-of-the-river hydro power in Europe. We propose two concepts of energy droughts, considering either: Energy Production Droughts (EPD) as sequences of days with low power production or Energy Supply Droughts (ESD) as sequences of days with a high production/demand mismatch.
Using a set of adhoc weather-to-energy conversion models, we characterize energy droughts in 12 European regions from 30-yr series of daily wind, solar, hydro power and energy demand. The characteristics of EPD are very different between sources with short but frequent wind power droughts and rare but long hydro power ones. Solar power droughts are very region-dependent with much longer droughts in Northern Europe. ESD are next characterized in a 100% renewable energy scenario. The features of EPD and ESD differ significantly, highlighting the interplay with the energy demand. Moreover, both duration and frequency of energy droughts decrease when mixing energy sources or when some storage capacity balances the temporal production/demand mismatch.
•Energy droughts are defined as periods of low production or high production/demand imbalance.•Energy droughts present various characteristics within Europe and between energy sources.•The fluctuations of the energy demand impact energy droughts especially for solar and hydro power.•Using several energy sources in an energy mix reduces the frequency and the duration of energy droughts.•Small storage systems limit wind power droughts and Southern Europe solar power droughts.
The penetration rate of Climate Related Energy sources like solar-power, wind-power and hydro-power source is potentially low as a result of the large space and time variability of their driving ...climatic variables. Increased penetration rates can be achieved with mixes of sources. Optimal mixes, i.e. obtained with the optimal share for each source, are being identified for a number of regions worldwide. However, they often consider wind and solar power only.
Based on 33 years of daily data (1980–2012) for a set of 12 European regions, we re-estimate the optimal mix when wild run-of-the-river energy is included in the solar/wind mix. It is found to be highly region dependent but the highest shares are often obtained for run-of-the-river, ranging from 35% to 65% in Belarus and England. High solar shares (>40%) are found in southern countries but solar shares drop to less than 15% in northern countries. Wind shares range from 10 to 35% with the exception of Norway where it reaches 50%. These results put in perspective the optimal 60%–40% wind/solar mix currently used for Europe. For all regions, including run-of-the-river in the mix allows increasing the penetration rate of CREs (from 1 to 8% points).
•Integration of run-of-the river (RoR) power in solar/wind power mix is analyzed.•Integrating RoR power in solar/wind power mix increases the global penetration rate.•Integrating RoR modifies the optimal share between solar- and wind-power.•In Europe, optimal RoR/solar/wind energy mix is region dependent.
In many regions worldwide, the electrification of rural areas is expected to be partly achieved through micro power grids. Compliance with the COP21 conference requires that such systems mainly build ...on renewable energy sources. To deliver a high power and quality service may be difficult to be achieved, especially when micro-grids are based on variable renewable sources. We here explore the multiscale temporal variability of the local solar resource in Africa and its implication for the development of 100% solar systems. Using high resolution satellite data of global horizontal irradiance (GHI) for a 21-year period (1995–2015), we characterize the seasonality and temporal variability of the local resource. We focus on its low percentile values which give a first guess on the size of the solar panels surface required for the micro-grid to achieve a given quality service. We assess the characteristics and especially persistence of the low resource situations, for which the local demand would not be satisfied. We finally assess how the ability of electricity consumers for some day-to-day flexibility (e.g. via the postponement of part of one day as demand to the next), would help to achieve the design level of service quality with a smaller microgrid system.
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•1.1 billion people lack access to electricity in the world, 80% live in rural areas.•100% solar microgrids (MG) are expected to provide electricity access in remote African areas.•The power service quality will depend on the seasonal and daily variability of solar radiation.•Using low percentile GHI values to design MG could help achieving a given power service quality.•Design should account for some clustering of low resource days in specific seasons or time sequences.
Climate related energy sources such as wind, solar and runoff sources are variable in time and space, following their driving weather variables. High penetration of such energy sources might be ...facilitated by using their complementarity in order to increase the balance between energy load and generation. This study presents the analysis of the effect of a 100% renewable energy mix composed by solar and run-of-the-river energy in Northern Italy where these two energy sources are the main alternative energy sources. Along a climate gradient from the Alpine crest (snow melt dominated area) to the Veneto plain (rainfall dominated area), solar power is generated in the flat plain, and run-of-the-river hydropower at two mountainous locations. Covering all possible mixes of these two sources, we analyze their complementarity across different temporal scales using two indicators: the standard deviation of the energy balance and the theoretical storage required for balancing generation and load. Results show that at small temporal scale (hourly), a high share of run-of-the-river power allows minimizing the energy balance variability. The opposite is obtained at larger temporal scales (daily and monthly) essentially because of lower variability of solar power generation, which also implies a lower storage requirement.
•Soar photovoltaic (PV) and run-of-the-river power (RoR) from different hydrological regime area are combined in order to smooth the energy balance in Northern Italy.•The optimal share among solar PV and RoR coming from either Alpine area or rain-fed catchment depends on the time scale.•At small scale, high share of RoR minimizes the energy balance because of high variability of solar PV.•At larger time scales, high share of PV minimizes the energy balance and implies a lower storage requirement.
A multireplicate multimodel ensemble of hydrological simulations covering the 1860–2099 period has been produced for the Upper Durance River basin (French Alps). An original quasi‐ergodic analysis of ...variance was applied to quantify uncertainties related to General Circulation Models (GCMs), Statistical Downscaling Models (SDMs) and the internal variability of each GCM/SDM simulation chain. For temperature, GCM uncertainty prevails and SDM uncertainty is nonnegligible. Significant warming and in turn significant changes are predicted for evaporation, snow cover and seasonality of discharges. For precipitation, GCM and SDM uncertainty components are of the same order. A high contribution of the large and small‐scale components of internal variability is also obtained, inherited, respectively, from the GCMs and the different replicates of a given SDM. The same applies for annual discharge. The uncertainty in values that could be experienced for any given future period is therefore very high. For both discharge and precipitation, even the sign of future realizations is uncertain at a 90% confidence level. These findings have important implications. Similarly to GCM uncertainty, SDM uncertainty cannot be neglected. The same applies for both components of internal variability. Climate change impact studies based on a single SDM realization are likely to be no more relevant than those based on a single GCM run. They may lead to poor decisions for climate change adaptation.
Key Point
The internal variability of climate scenarios is very high
VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, ...process‐based, etc.). Here we describe the participating methods and first results from the first experiment, using “perfect” reanalysis (and reanalysis‐driven regional climate model (RCM)) predictors to assess the intrinsic performance of the methods for downscaling precipitation and temperatures over a set of 86 stations representative of the main climatic regions in Europe. This study constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods, covering the three common downscaling approaches (perfect prognosis, model output statistics—including bias correction—and weather generators) with a total of over 50 downscaling methods representative of the most common techniques.
Overall, most of the downscaling methods greatly improve (reanalysis or RCM) raw model biases and no approach or technique seems to be superior in general, because there is a large method‐to‐method variability. The main factors most influencing the results are the seasonal calibration of the methods (e.g., using a moving window) and their stochastic nature. The particular predictors used also play an important role in cases where the comparison was possible, both for the validation results and for the strength of the predictor–predictand link, indicating the local variability explained. However, the present study cannot give a conclusive assessment of the skill of the methods to simulate regional future climates, and further experiments will be soon performed in the framework of the EURO‐CORDEX initiative (where VALUE activities have merged and follow on).
Finally, research transparency and reproducibility has been a major concern and substantive steps have been taken. In particular, the necessary data to run the experiments are provided at http://www.value‐cost.eu/data and data and validation results are available from the VALUE validation portal for further investigation: http://www.value‐cost.eu/validationportal.
The largest and most comprehensive to date intercomparison of statistical downscaling methods is presented, with a total of over 50 downscaling methods representative of the most common approaches and techniques. Overall, most of the downscaling methods greatly improve raw model biases and no approach is superior in general, due to the large method‐to‐method variability. The main factors influencing the results are the seasonal calibration of the methods and their stochastic nature, for biases in the mean and variance.
Autonomous micro-grids based on solar photovoltaic (PV) are one of the most promising solutions to provide electricity access in many regions worldwide. Different storage/PV capacities can produce ...the same level of quality service, but an optimal design is typically identified to minimize the levelized cost of electricity. This cost optimization however relies on technical and economic hypothesis that come with large uncertainties and/or spatial disparities.
This article explores the sensitivity of the optimal sizing to variations and uncertainties of such parameters. Using data from Heliosat and ERA5, we simulate the solar PV production and identify the least cost configurations for 200 locations in Africa.
Our results show that the optimal configuration is highly dependent on the characteristics of the resource, and especially on its co-variability structure with the electric demand on different timescales. It is conversely rather insensitive to cost hypotheses, which allow us to propose simple pre-sizing rules based on the only characteristics of the solar resource and electricity demand.
The optimal storage capacity can be estimated from the 75th percentile of the daily nocturnal demand and the optimal PV capacity from the mean demand and the standard deviation of the daily power difference between solar production and demand.
•The least-cost configuration of 100 % PV micro-grids in Africa are relatively robust to economic assumptions•These optimal configurations are highly sensitive to the co-variability of the solar resource and the electric demand•Simple sizing rules can approach these optimal microgrid configurations using characteristics of this co-variability•These rules can accurately estimate the levelized cost of electricity for different load profiles and economic assumptions
In most meteorological or hydrological models, the distinction between snow and rain is based only on a given air temperature. However, other factors such as air moisture can be used to better ...distinguish between the two phases. In this study, a number of models using different combinations of meteorological variables are tested to determine their pertinence for the discrimination of precipitation phases. Spatial robustness is also evaluated. Thirty years (1981–2010) of Swiss meteorological data are used, consisting of radio soundings from Payerne as well as present weather observations and surface measurements (mean hourly surface air temperature, mean hourly relative humidity, and hourly precipitation) from 14 stations, including Payerne. It appeared that, unlike surface variables, variables derived from the atmospheric profiles (e.g., the vertical temperature gradient) hardly improve the discrimination of precipitation phase at ground level. Among all tested variables, surface air temperature and relative humidity show the greatest explanatory power. The statistical model using these two variables and calibrated for the case study region provides good spatial robustness over the region. Its parameters appear to confirm those defined in the model presented by Koistinen and Saltikoff.
This paper presents a combined downscaling and disaggregation weather generator developed for multisite generation of hourly precipitation and temperature time series over complex terrain. Daily ...regional weather variables are first generated from Generalized Linear Models based on daily atmospheric circulation indices from NCEP reanalysis. They are then disaggregated to the required spatial and temporal scales using a
K-nearest neighbour approach.
The weather generator is applied to the Upper Rhone River basin in the Swiss Alps. It successfully reproduces standard statistics for temperature as well as total and liquid precipitation at the temporal and spatial resolutions required for hydrological modelling of the system (3
h, 100
km
2) and at lower resolutions down to those relevant at the river basin scale (3
days, ∼5500
km
2). In addition, it reproduces the monthly distributions of the 1
°C isotherm elevation and of maximum precipitation amounts while preserving the spatial heterogeneity of weather variables and their spatial and temporal correlations. The weather generator can also be used to produce weather scenarios for the studied basin in a future climate.
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