Renewable energy sources are currently investigated worldwide and technologies undergo rapid developments. However, further basic and applied studies in meteorological processes and tools are needed to understand these technologies and better integrate them with local, national and international power systems. This applies especially to wind and solar energy resources as they are strongly affected by weather and climate and highly variable in space and time. Contributions from all energy meteorology fields are invited with a focus on the following topics:
• Wind and turbulence profiles with respect to wind energy applications (measurements and theory) including wakes within a wind farm;
• Clouds and aerosol properties with respect to solar energy applications (measurements and theory);
• Marine renewable energy (wind, wave, tidal, marine current, osmotic, thermal);
• Meteorology and biomass for energy;
• Impact of wind and solar energy farms and biomass crops on local, regional and global meteorology;
• The use of numerical models and remote sensing (ground based and from satellites) for renewable energy assessment studies;
• Research on nowcasting, short term forecasts (minutes to day) and ensemble forecasts and its application in the energy sector;
• Quantification of the variability of renewable resources in space and time and its integration into power systems;
• Impacts of long term climate change and variability on power systems (e.g., changes in renewable resources or demand characteristics);
• Practical experience using meteorological information in energy related applications.
Karin van der Wiel, Laurens Stoop, Bas van Zuijlen, Russel Blackport, Mechteld van den Broek, and Frank Selten
To mitigate climate change a renewable energy transition is needed. Existing power systems will need to be re-designed to balance variable renewable energy production with variable energy demand. I will describe the meteorological sensitivity of a highly-renewable European energy system based on large ensemble simulations from two global climate models. From 2×2000 years of simulated weather conditions, we calculated daily wind and solar energy yields and energy demand and selected events of high societal impact: extreme high energy shortfall (residual load, i.e. demand minus renewable production). High energy shortfall days are characterized by large-scale high pressure systems over central Europe, with lower than normal wind speeds and below normal temperatures, driving up energy demands. The events typically occur mid-winter, locked to the coldest months of the year. Near-stationary high pressure situations occur that cause long lasting periods of high energy shortfall. A spatial redistribution of wind turbines and solar panels cannot prevent these high-impact events, options to import renewable energy from remote locations during these events are therefore limited. Projected changes due to climate change are substantially smaller than interannual variability. Future power systems with large penetration of variable renewable energy must be designed with these events in mind.
How to cite:
van der Wiel, K., Stoop, L., van Zuijlen, B., Blackport, R., van den Broek, M., and Selten, F.: Wind droughts and winter cold threaten Europe's future energy security, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-505, https://doi.org/10.5194/ems2021-505, 2021.
María Ofelia Molina, Enrique Sánchez, Claudia Gutiérrez, and María Ortega
In recent years, renewable energy is gaining importance in the energy mix, increasing the dependence of the energy system on the weather. Studies have been mainly focused on atmospheric patterns related to wind energy production in winter, as wind resource in Europe is higher for this season, but also because it is when there is a larger and more stable heating demand in Europe as a whole. However, it can be seen that summer energy demand can be as high as in winter in southern European countries, especially on heat wave days (calculated from E-OBS maximum temperature observations). Therefore, the objective of this work is to study the effect of heat waves on wind power generation. Summer climate conditions present reduced wind values, so a potential increase in energy demand due to heat wave conditions could compromise the total energy supply. We analyse the main atmospheric patterns in summer (1989-2019) and how these are related to changes in wind energy production. The relationship between weather regimes and wind energy is examined using an energy model from ERA5 wind speed data at 100 m. Results show a demand increase in heat wave days and different responses in wind power, depending on the country and weather regime studied. The impact of extreme climate events, such as heat waves, on wind energy in conditions of high energy demand, should be considered in the energy supply strategic planning and control to minimize the impact of these events on an electricity system with high penetration of renewables.
How to cite:
Molina, M. O., Sánchez, E., Gutiérrez, C., and Ortega, M.: Analysis of wind power under heat wave conditions in southern Europe, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-106, https://doi.org/10.5194/ems2021-106, 2021.
The MEDEA project, funded by the Austrian Climate Research Program, deals with the identification, detection and prediction of meteorologically induced extreme events for the renewable energy sector.The overall aim of the proposed project is to define and detect extreme events and outliers in meteorological time series relevant for renewable energies and to use these findings to improve both weather and climate predictionsof such extreme events with specific focus on the Austrian renewable energy system.
A complicating factor in this research is that the definition of extreme events varies between the scientific fields and sectors. The project is structured in three parts with part I defining extreme events. An extreme event in terms of meteorology can be an extreme event for one or several renewable energy systems but it does not have to be. Thus, defining the extreme events with relevance for renewable energy systems is a crucial and non-trivial task in this project. The definition of extreme events in renewable energy is two-fold: as first step wind, solar, and hydropower extreme events will be defined separately in accordance with stakeholders. Combined extreme events, such as calms and droughts arising together with high temperatures increasing the needs of electricity for cooling, will be described too.
Data driven methods (part II) are used to identify these extreme events within the meteorological observations and analysis data. The goal of this part is to develop i) novel clustering method that integrates the heterogeneous data collected Part I in order to enable a joint prediction of extreme events. A novel method learning a joint low-dimensional vector space embedding a large amount of point and gridded observational data is implemented. Data clustering is applied to build a supervised classifier for the prediction of extreme events This will facilitate the learning of ii) Granger causal models and of supervised classifiers reducing the input data to a manageable set of spatio-temporal factors influencing the formation of rare and frequent extreme events. Furthermore, iii) a novel anomaly detection method identifying observation patters that do not fit the normal spatial-temporal observation patterns in the different clusters of the data is developed.
To forecast such events we will use machine learning methods in project part III.Different ML and post-processing methods will be adapted for heavy tails using extreme value theory and applied to improve prediction of such events. Depending on the type of events, a two-fold modeling strategy can be anticipated using an additional model suitable for the pre-detected event using information of part II of the project. Furthermore, the return periods and frequencies of extreme events relevant for renewable energy are calculated.
Here, we present first outcomes after project year 1.
How to cite:
Schicker, I. and DeWit, R.: MEDEA - Meteorologically induced extreme events detection for renewable energy using data driven methods: from weather prediction to climate time scales, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-222, https://doi.org/10.5194/ems2021-222, 2021.
Petrina Papazek, Irene Schicker, and Rosmarie de Wit
With the rapid transition towards an increased usage of renewable energy accurate predictions of the expected power production are needed to ensure grid stability, energy trading, and (re)scheduling of maintenance and energy transfer. In the last decades, both numerical weather prediction models as well as post-processing methods have significantly improved their prediction skills when applied to renewable energy. There are, however, events in renewable energy production which can be considered as extreme events but are not necessarily extremes in terms of meteorological conditions. The MEDEA project, funded by the Austrian Climate Research Program, aims at improving the definition and detection of extreme events relevant for renewable energies and to use these findings to improve both weather and climate predictions of such extreme events. In this MEDEA case study, we investigate selected (extremes) cases in renewable energy which were not properly reproduced by the models. We will have a deeper dive into two Saharan dust events in 2021 where none of the models was able to properly reproduce the amount of aerosols in Central Europe and solar power production was off by more than 5 GW in contrast to the predictions. Here, several NWP models have failed to properly recognize its impact and, thus, impaired results based on raw model output. To tackle such events and improve the predictability, a deep learning framework consisting of an auto-encoder LSTM (long short-term memory; type of an artificial neural network) and random forest will be used and adopted for day-ahead predictions of these events. Relevant features for the learning algorithms are extracted from different NWP models, satellite data, and observations. Similarly, for wind energy production we demonstrate the methods in two selected case studies of extreme events. Results obtained by the deep learning framework yield, in general, high forecast-skills where we elaborate on interesting cases studies from a meteorological point of view. Different combinations of inputs and processing-steps are part of the analysis. We compare results to traditional forecast methods in order to validate the performance of our methods.
How to cite:
Papazek, P., Schicker, I., and de Wit, R.: Forecasting of Meteorologically Driven “Extremes” in Wind and Solar Power: Can We Tackle and Improve Selected Cases of Non-forecasted Extreme Events using Deep Learning?, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-164, https://doi.org/10.5194/ems2021-164, 2021.
As is known, wind is a renewable and non-polluting energy resource. In addition, there is no transportation problem in wind energy and it does not require very high technology for electricity generation. Wind turbines are used for electricity generation from kinetic energy of wind. In the point of power curves of these turbines, wind speed must be a certain band. Generally, they do not generate electricity cut-in wind speed that is between 0 and 4 m/s and cut out wind speed that is over 20-25 m/s. Over cut-out values cause breaking down of wind turbines, because high wind speeds create extra mechanical loads on them. Therefore, maximum/extreme winds and their estimation and prediction carry weight in terms of energy generation.
New European Wind Atlas (NEWA) is the project, within the scope of ERANET+ Program, and the attendants are Belgium, Denmark, Germany, Latvia, Portugal, Spain, Sweden, and Turkey. The aim of NEWA is to present a new wind atlas to the wind industry. In this project, the physical model used for obtaining wind speeds is a numerical weather prediction model named Weather Research and Forecasting (WRF).
One of the methods, which are developed by imitating of biological properties of living forms in a virtual environment, is Artificial Neural Networks (ANNs). Stimulations taken from the environment by using sense organs are transmitted to brain whereby neurons in a body and brain makes a decision towards these stimulations. That is the working form of ANNs. Moreover, ANNs can be thought as a black box, which processes given data and produces outputs against inputs. Furthermore, they are a method of Artificial Intelligence.
In this study, maximum wind speeds of 4 different wind farms in Turkey were estimated by using a downscaling method based on ANNs and wind data which were produced in grid points of NEWA Project. Besides that, 8 different levels (10, 50, 75, 100, 150, 200, 250, and 500 m) for each wind farm were considered. As a result of determining the best ANN architectures with sensitivity analysis, it was seen that Levenberg-Marquardt Backpropagation (trainlm) approach as a training algorithm and 9 neurons in each layer are common traits of best ANN architectures. In addition, 50 m for 2 wind farms and 10 m with 75 m for others were determined as an optimum downscaling levels. Moreover, according to downscaling results, correlation values were calculated around 0.80.
Key Words: ANN, Downscaling, Maximum wind, NEWA, Turkey, Wind farm.
How to cite:
Akın, M., Öztopal, A., and Şahin, A. D.: Downscaling and Verification of Maximum Wind Speeds by Using Artificial Neural Networks for New European Wind Atlas and Wind Farm Data , EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-18, https://doi.org/10.5194/ems2021-18, 2021.
Due to the increasing contribution of wind power to electricity in Europe, an exact wind characterization at the height of wind turbines is important. Nocturnal Low Level Jets (NLLJ) can influence the winds at typical blade heights and therefore influence the wind power production. However, due to the often missing measurements with a sufficient precision and resolution, the occurrence frequency and spatio-temporal characteristics of NLLJs are still poorly understood. The present work characterizes the properties of NLLJs, measured with a Doppler lidar at the Lindenberg Meteorological Observatory – Richard Aßmann Observatory (Germany), during the period of June–August 2020, and evaluates the representation of NLLJs in state-of-the-science re-analysis products. The vertical profiles of 10-minute mean winds from the lidar are statistically analysed using automated detection tools. These allow to determine the frequency of occurrence, height and wind speed in the core of NLLJs as well as the vertical wind shear and momentum transport with a high temporal resolution. We intercompare NLLJ results from different previously-used identification tools to estimate the uncertainty. Our automatic detections identified NLLJs in more than about 60% of the summer nights in 2020, with NLLJ cores between 70m and 500 m above ground level and a core speed of ~3–25 m/s. The prevailing wind direction in NLLJ cores is southwest. A considerable amount of NLLJ cores occurred at heights that are in the range of modern wind turbines and rotor sizes on land, with wind speeds of ~3-12 m/s. We use the measurements of NLLJs to evaluate their representation in the ERA5 re-analysis of the European Centre for Medium-Range Weather Forecasts and plan to compare the NLLJs to regional high-resolution re-analyses developed in the research area Climate Monitoring and Diagnostics in the Hans-Ertel Centre for Weather Research. The first comparisons suggest a frequent co-occurrence of NLLJs in the measurements and ERA5 re-analysis, but the strength and height of NLLJ cores often differ. Possible reasons are the model’s vertical resolution and the parameterization of vertical mixing in the stable boundary layer. Future work includes extending the NLLJ analysis to more lidar measurements and other regional re-analysis data.
How to cite:
Weide Luiz, E. and Fiedler, S.: Assessment of nocturnal low-level jets during the FESSTVaL campaign 2020 for wind energy applications, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-166, https://doi.org/10.5194/ems2021-166, 2021.
In the coastal zone complex atmospheric processes such as momentum and heat fluxes are caused by large differences between the land and the sea. The smoother sea surface leads to wind speeds, which are usually higher over the ocean than over land. In addition, there are complicated effects caused by temperature gradients in the ocean due to water depth variations. This study focuses on the investigation of the change in the horizontal wind field and the atmospheric stability between the coast and up to 200 km offshore.
The wind resources at 10 m height are assessed from synthetic aperture radar (SAR) data acquired by the satellites Sentinel1A/B over the German Bight within the period of 2017-2020 with a focus on offshore wind directions. The satellite data provide information on sea surface roughness, which can be linked to near surface wind speed. Information on the air-sea thermal components is provided by model data from the German weather service (DWD).
The SAR data show a significant increase of wind speed offshore in most cases. Increasing wind speeds between land and sea over fetch distances of 70 km and more are often detected. The increase δu in horizontal wind speed between offshore and the coast exceeds 2.5 m/s in average. Furthermore, the estimated atmospheric stability shows an impact on the wind speed gradients. The thermal stability appears to dictate the distance over which the wind increases. Strong thermal stability tends to influence the horizontal wind gradient by increasing the fetch distance over more than 100 km. In the context of offshore wind farming, the potential effects of these horizontal wind gradients on the wind power will be discussed.
How to cite:
Djath, B. and Schulz-Stellenfleth, J.: Exploring the coastal effects relevant for offshore wind farming using the space borne synthetic aperture radar data in the German Bight, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-307, https://doi.org/10.5194/ems2021-307, 2021.
Jonathan Minz, Marc Imberger, Jake Badger, and Axel Kleidon
German energy scenarios expect 50 - 70 GW of wind turbine capacity to be installed in the German Bight by 2050. Such deployments were expected to yield ∼4000 full load hours (FLH) per year, owing to higher wind speeds compared to land. However, a recent reevaluation of these estimates using the Weather Research and Forecasting model with Explicit Wake Parameterization (WRF-EWP) and the Kinetic Energy Budget of the Atmosphere model (KEBA) found that if the proposed deployments are installed, yield per turbine could be as low as 3000 - 3500 FLH per year, although the total yield still increases. Whereas WRF represents a comprehensive physical representation of atmospheric dynamics, KEBA is an simple approximation of complex atmospheric processes. It states that it is the fixed kinetic energy budget of the boundary layer volume encompassing the wind park which determines large wind park (order of104km2) yields rather than just wind speeds. This budget is a function of park geometry and boundary layer heights. Increasing the number of turbines within the wind park removes more kinetic energy from the budget. This leads to slower wind speeds and lower overall yields. The estimates from both approaches were within 20% of each other. Here, we examine these results in greater detail to uncover key atmospheric constraints on the performance of large offshore wind parks. We investigate the role of atmospheric variables like wind direction, atmospheric stability, boundary layer height and surface friction on large scale generation by comparing the estimates of the two modelling approaches. We consider the WRF simulations of large-scale wind power generation and atmospheric circulation as the most realistic available representation, since farms of the scale considered in this study are not yet in operation. We also test the underlying assumptions of KEBA and hence the limits of its applicability. Through a detailed comparison of the two approaches we will provide insights into the effectiveness of KEBA. We posit that estimates of regional wind energy potential need to account for large wind park - atmosphere interactions which may constrain large wind park yields. Our analysis will provide policy makers with a simple yet physically representative tool for making robust predictions of future offshore wind park performance, thereby enabling the design of better energy policies.
How to cite:
Minz, J., Imberger, M., Badger, J., and Kleidon, A.: How much atmospheric dynamics do we need to capture yield reductions from proposed large wind parks in the German Bight?, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-351, https://doi.org/10.5194/ems2021-351, 2021.
Nikolas Angelou, Mikael Sjöholm, and Torben Mikkelsen
The objective of this work is to enhance the understanding of the mean wind and turbulence characteristics in the wake of a full-scale wind turbine. Here, we present observations of the three-dimensional wind vector in the near wake of a wind turbine, a Vestas V52 with a 52-m rotor diameter. The test turbine is located at the Risø campus of the Technical University of Denmark (DTU). The measurements were acquired using three, state-of-the-art, scanning, continuous-wave wind lidars, developed in DTU Wind Energy (Short-Range WindScanner). In our study, the area of focus was a vertical, two-dimensional plane at a distance of two rotor diameters from the wind turbine, in the downwind direction. Using the scanning lidars it was possible to derive spatially distributed estimations of the first and second-order moments of the wind vector within the vertical plane. The plane was within an area equal to 2.6 x 1.8 rotor diameters, towards the transverse and vertical direction respectively, covering a measuring range that included both the wake and free flow. The field test took place during a period of almost two weeks (July 2 - July 14, 2019). Approximately half of the time, the wind direction was favourable such that the measuring plane was covering a cross-section of the mean flow, which included the entire area where the wind speed deficit occurred. This data set enables the quantification of the wind speed deficit and the corresponding momentum deficit in the wake and reveals the turbulent layer that surrounds the mean wind speed deficit. Thus, allowing the investigation of the relation between the momentum fluxes and the local wind speed gradients, which is important for the understanding of the physical properties of the flow behind a wind turbine. Furthermore, we investigate the effect that wakes have on the vertical shear close to the ground, which can have a direct impact on the wind-surface interaction on the downwind side. Since the measuring plane was extended also to areas of the free flow, we compare the wind characteristics within the mean wake flow to the ones of the free flow. The knowledge of the features of the wake and the physical connection between the mean and turbulent flow provides a new detailed input for improved wake modelling. This is necessary for a more accurate prediction of the wake characteristics and can enable a more realistic quantification of the interaction between wind turbines in a wind farm, as well as the impact of the wake flow on the surrounding microclimate.
How to cite:
Angelou, N., Sjöholm, M., and Mikkelsen, T.: Investigation of the turbulent three-dimensional wind field in the near-wake of a full-scale wind turbine using synchronized scanning wind lidars, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-137, https://doi.org/10.5194/ems2021-137, 2021.
Kerlyns Martínez, Mireille Bossy, and Jean-François Jabir
In order to better integrate the underlying meteorological processes with the developing technologies within wind energy industry, acquiring relevant statistical information of air motion at a local place, and quantifying the subsequent uncertainty of involved parameters in the models, are fundamental tasks. Special emphasis should be made on the growing interest in energy production forecasting and modelling for wind energy developments that rises the issue of accounting for the uncertain nature of the local forecast. Taking this into consideration, we present the construction of an original stochastic model for the instantaneous turbulent kinetic energy at a given point of a flow, and we validate estimator methods on this model with observational data examples from annual historic of a 10 Hz anemometer wind measurements. More precisely, starting from the viewpoint of Lagrangian modelling of the wind in the boundary layer, we establish a mathematical link between 3D+time computational fluid dynamics (CDF) models for turbulent near-wall flows and stochastic time series models by deriving a family of mean-field dynamics featuring the square norm of the turbulent velocity. Then, by approximating at equilibrium the characteristic nonlinear terms of the dynamics, we recover the so called Cox-Ingersoll-Ross stochastic model, which was previously suggested in the literature for modelling wind speed. Remarkably, our stochastic model for the instantaneous turbulent kinetic energy is parametrised by physical constants in CFD, which provides a more direct link between the stochastic nature of the underlying processes and the classical physics behind these phenomena. Nevertheless, these physical parameters may vary with the flow characteristics and situations, so we consider it relevant to adjust their values while constructing the forecasts. Such tuning of the physical parameters was previously proposed in the literature from a deterministic modelling context with RANS equations. We then propose a two-step procedure for the calibration of the parameters: a training stage where we construct a priori distribution for the parameter vector using direct methods and wind measurements, and a stage of refinement of the uncertainty distribution using Bayesian inference combined with Markov Chain Monte Carlo sample techniques. In particular, we show the accuracy of the calibration method and the performance of the calibrated model in predicting the wind distribution through the quantification of uncertainty.
How to cite:
Martínez, K., Bossy, M., and Jabir, J.-F.: Local turbulent kinetic energy modelling based on Lagrangian stochastic approach in CFD and application to wind energy, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-399, https://doi.org/10.5194/ems2021-399, 2021.
Marion Schroedter-Homscheidt, Faiza Azam, Jethro Betcke, Hartwig Deneke, Mireille Lefèvre, Laurent Saboret, Yves-Marie Saint-Drenan, and Etienne Wey
The Copernicus Atmospheric Monitoring Service (CAMS) provides a surface solar irradiance service which is currently derived from Meteosat Second Generation (MSG). The service combines satellite data products with numerical model output from CAMS on aerosols, water vapour and ozone in order to provide both clear-sky and all-sky radiation time series with availability from 2004 until yesterday. A regular quality control of input parameters, quarterly benchmarking against ground measurements and automatic consistency checks ensures the data quality. To anticipate the increase in resolution that will occur with the commissioning of MTG, it is necessary to enhance methods currently used in CAMS.
The recent development focuses on the assessment and improvement of cloud retrieval products from APOLLO_NG in irradiance retrieval schemes. Such a validation against ground-based surface solar irradiances provides insight into the use of probabilistic cloud masking, specific results for pixels with small COD values below 5, as well as in partly cloudy pixel conditions. Such conditions are often neglected in existing cloud retrieval validation studies due to the expected large uncertainties of cloud properties. But they cannot be omitted in irradiance retrieval schemes for solar energy sector users as complete temporal coverage is required.
Such cloud situations may additionally be better characterized in future with the help of spatially higher resolved channels. Using e.g. SEVIRI’s HRV channel is known to be beneficial in cloud index based irradiance retrieval schemes, but has not been evaluated yet for cloud physical retrieval based irradiance schemes. Results from such a method development for the HRV channel in preparation for MTG, Himawari-8, and GOES-R channels will be presented.
How to cite:
Schroedter-Homscheidt, M., Azam, F., Betcke, J., Deneke, H., Lefèvre, M., Saboret, L., Saint-Drenan, Y.-M., and Wey, E.: Preparing the CAMS Radiation Service for MTG, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-426, https://doi.org/10.5194/ems2021-426, 2021.
Understanding long-term solar resource variability is essential for planning and deployment of solar energy systems. These variabilities occur due to deterministic effects such as sun cycle and nondeterministic such as complex weather patterns. The NREL’s National Solar Radiation Database (NSRDB) provides long term solar resource data covering 1998- 2019 containing more than 2 million pixels over the Americas and gets updated on an annual basis. This dataset is satellite-based and uses a two-step physical model for it’s development. In the first step we retrieve cloud properties such as cloud mask, cloud type, cloud optical depth and effective radius. The second step uses a fast radiative transfer model to compute solar radiation. This dataset is ideal for studying solar resource variability. For this study, NSRDB version 3 which contains data from 1998-2017 on a half hourly and 4x4 km temporal and spatial resolution was used. The study analyzed the spatial and temporal trend of solar resource of global horizontal irradiance (GHI) and direct normal irradiance (DNI) using long-term 20-years NSRDB data. The coefficient of variation (COV) was used to analyze the spatio-temporal interannual and seasonal variabilities. The spatial variability was analyzed by comparing the center pixel to neighboring pixels. The spatial variability result showed higher COV as the number of neighboring pixels increased. Similarly, the temporal variability for the NSRDB domain ranges on average from ±10% for GHI and ±20% for DNI. Furthermore, the long-term variabilities were also analyzed using the Köppen-Geiger climate classification. This assisted in the interpretation of the result by reducing the originally large number of pixels into a smaller number of groups. This presentation will provided a unique look at long-term spatial and temporal variability of solar radiation using high-resolution satellite-based datasets.
How to cite:
Sengupta, M. and Habte, A.: Analysis of Long-Term Solar Resource Variability Using NSRDB Version 3, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-193, https://doi.org/10.5194/ems2021-193, 2021.
Due to climate change and the need to reduce greenhouse gas emissions, the trend in the development of global energy is aimed at increasing the share of the introduction of environmentally friendly renewable energy sources. This contributes to ensuring sustainable heat and energy supply to the population and production in the zones of decentralized energy supply, which increases the energy security of the regions and the country as a whole. In addition, the introduction of renewable energy sources is seen as a key tool in adapting to climate change.
The report examines the climatic conditions of the territory of the Russian Federation in order to assess the prospects for the practical use of solar energy.
The basis of solar energy resources in a given point or region is the duration of the solar radiation and the amount of direct and total solar radiation entering the horizontal surface.
The research uses data from observations of the actinometric network of the Russian Federation. In areas where there are no observations of solar radiation, either indirect methods of calculation or access to open and accessible databases were used.
Based on the analysis of the complex of climatic characteristics selected for zoning, 10 radiation-homogeneous regions were identified on the territory of Russia, which were ranked according to the priority of the solar potential. The reliability of the boundaries of the selected regions was confirmed by comparing the spatial and temporal variability of the main radiation indicators. The selected regions can be divided into five groups: the most promising, promising, less promising, unpromising and unpromising.
The most promising regions are the regions south of 50N. The first region occupies the Primorsky Krai, the southern part of the Khabarovsk Region and the Amur Region, and the south-east of Transbaikalia. The second region is the southern part of the European territory of Russia.
The amount of total solar radiation entering the earth's surface for a year in these regions is 1330-1390 kWh/m2, which is the maximum for the territory of Russia. Both regions are characterized by a long duration of sunshine, especially for the first region (about 2400 hours per year). The high solar potential of these regions is indicated by the fact that in the period from April to September, the probability of a day favorable for the use of solar energy is 60%, in July it reaches 90%.
In less promising regions of Eastern Siberia, where there is a large influx of solar radiation in the winter and spring periods, it is advisable to use photovoltaic modules to generate electricity for autonomous consumers in areas of decentralized energy supply.
How to cite:
Zadvornykh, V. and Oksana, T.: Assessment of the climatic resource potential of solar radiation as a renewable energy source., EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-71, https://doi.org/10.5194/ems2021-71, 2021.
Global horizontal irradiance (GHI) and direct normal irradiance (DNI), representing broadband solar radiation measured on a horizontal surface and those received in a narrow beam along the direction of the incoming sunlight, respectively, are the most frequently used quantities to assess the solar resource in specific locations or large-scale areas. However, GHI and DNI indirectly, and often deficiently, represent the amount of radiation that is converted into electric power by photovoltaic (PV) panels that are usually installed at tilt angles or on solar tracking systems to maximize the power output. Spectral distribution of surface radiation, affects the PV performance due to the spectral response of semiconductor materials and the solar cell designs to split spectral radiation, but this information is not directly informed from GHI and DNI data. To address this issue, we developed a Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMS-NIT) to simultaneously compute spectral radiation over horizontal and inclined surfaces using the physical properties that can be inferred by surface- or satellite-based radiometers. It utilizes the optical properties of aerosols and a pre-computed lookup table of cloud transmittance to efficiently solve spectral radiances that can be spectrally and angularly weighted to directly match the PV response. This new model has been implemented in the National Solar Radiation Data Base (NSRDB) to quantify the solar resource that is available for conversion by a PV plant. The data are referred to as the NSRDB PV Resource product to be distinguished from the routine solar resource product from satellite observations. This study will review the input data and mechanics to develop the PV Resource product as well as the procedures to access the product. To provide a critical reference in the data applications, we will evaluate the PV Resource data using surface observations from stations operated by the National Renewable Energy Laboratory (NREL), University of Oregon, and the First Solar, Inc.
How to cite:
Xie, Y. and Sengupta, M.: The NSRDB PV Resource Product: Spectral Solar Radiation Data on Inclined Surfaces , EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-171, https://doi.org/10.5194/ems2021-171, 2021.
Nicolas Chouleur, Bianca Morandi, Shane Martin, and Stefan Mau
Accurate solar resource assessments are essential to project a solar photovoltaic (PV) plant’s energy production – and ultimately forecast its revenue.
Solar resource assessments are the bedrock of the ‘Revenue’ line in PV financial models. In today’s competitive financing environment, the assumptions underlying solar resource assessment often have make-or-break impact on project valuations. It’s critical that investors trust the numbers provided.
To quantify solar resource, industry typically compares different irradiation databases derived from multiple physical sources – whether measurements or satellite images. There is always some level of scatter; in Western Europe this is often around 3%, after excluding outliers. Satellite database are never as good as accurate ground measurement. And the rather narrow variation observed is due to past calibration of satellite derived model with data from weather stations. The reality can be different when it comes to Ireland.
The solar sector is currently experiencing a rapid development in the Republic of Ireland, making the yield assessment and by extension the solar resource estimation key for the bankability of the projects.
The aim of our work was the validate the accuracy of different databases, available in Ireland.
The first step of this analysis will be to qualify our data sources. Everoze and Brightwind have access to measurement campaigns from multiple solar projects in Ireland. All the gathered dataset will be processed, applying state of the art quality control, to retain only trustable information. The quality check will also include the sensors themselves, with a verification of the accuracy and calibration certificates of the different pieces of equipment.
In a second step, the qualified datasets will be used to compare satellite derived data. We plan to use CAMS, SolarGIS and Meteonorm. The intention is to categorise our results in regions, classified based on the difference in annual irradiation between different databases in order to reduce uncertainty – and ultimately boost investor confidence in energy yield assessments.
How to cite:
Chouleur, N., Morandi, B., Martin, S., and Mau, S.: Solar resource assessment in Ireland, comparison of satellite data versus ground measurement, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-158, https://doi.org/10.5194/ems2021-158, 2021.
Kyriakoula Papachristopoulou, Ilias Fountoulakis, Panagiotis Kosmopoulos, Panagiotis Ι. Raptis, Rodanthi-Elisavet Mamouri, Argyro Nisantzi, Jonas Witthuhn, Johannes Bühl, Antonis Gkikas, Diofantos G. Hadjimitsis, Charalampos Kontoes, and Stelios Kazadzis
Cyprus focuses on increasing the share of its renewable energy resources from 13.9% in 2020 to 22.9% in 2030, with solar energy exploitation systems to be one of the main pillars of this effort, due to the high solar potential of the island. In this study, we investigated the effect of clouds as well as aerosols, and especially dust, on the downwelling surface solar irradiation in terms of Global Horizontal Irradiation (GHI) and Direct Normal Irradiation (DNI). In order to quantify the effects of clouds, aerosols and dust on different surface solar radiation components, we used the synergy of satellite derived products for clouds, high quality and fine resolution satellite retrievals of aerosols and dust from the newly developed MIDAS dataset, and radiative transfer modeling (RTM). GHI and DNI climatologies have been also developed based on the above information. According to our findings, clouds attenuate ~25 – 30% of annual GHI and 35 – 50% of annual DNI, aerosols attenuate 5 – 10% and 15 – 35% respectively, with dust being responsible for 30 – 50% of the overall attenuation by aerosols. The outcomes of this study are useful for installation planning and for estimating the PV and CSP performance on a short-term future basis, helping towards improved penetration of solar energy exploitation systems in the electric grid of Cyprus. Furthermore, they are strongly linked to Affordable and Clean Energy (SDG 7) which has a central role in national climate plans and requires services in energy meteorology, climate applications of satellite data, and providing high quality wind and radiation data.
This study was funded by the EuroGEO e-shape (grant agreement No 820852) and EXCELSIOR (grant agreement No 857510)
How to cite:
Papachristopoulou, K., Fountoulakis, I., Kosmopoulos, P., Raptis, P. Ι., Mamouri, R.-E., Nisantzi, A., Witthuhn, J., Bühl, J., Gkikas, A., Hadjimitsis, D. G., Kontoes, C., and Kazadzis, S.: Clouds and aerosol effects on solar energy in Cyprus, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-314, https://doi.org/10.5194/ems2021-314, 2021.
Stefanie Meilinger, Anna Herman-Czezuch, Armelle Zemo, and Matthias Bebber
In contrast to the German power supply, the energy supply in many West African countries is very unstable. Frequent power outages are not uncommon. Especially for critical infrastructures, such as hospitals, a stable power supply is vital. To compensate for the power outages, diesel generators are often used. In the future, these systems will increasingly be supplemented by PV systems and storage, so that the generator will have to be used less or not at all when needed. For the design and operation of such systems, it is necessary to better understand the atmospheric variability of PV power generation. For example, there are large variations between rainy and dry seasons, between days with high and low dust levels - caused by sandstorms (harmattan) or urban air pollution.
In our paper, we investigate different aspects of aerosol characteristics on PV hybrid systems in West Africa. Based on measured data from different sources (AERONET, DACCIWA, EnerSHelF), we will investigate the influence of aerosol density and type on PV performance by comparing a non-spectrally resolved (Neher et al., 2017) and a spectrally resolved PV performance model (Herman-Czezuch et al., submitted). Due to the materials used (semiconductors e.g. silicon, gallium arsenide, cadmium telluride), photovoltaic cells are spectrally selective. This means that only radiation of certain wavelengths is converted into electrical energy. A material property called spectral sensitivity characterizes a certain degree of solar radiation conversion into the electric current for each wavelength of sunlight. On the other hand, different types of aerosols can be distinguished by their scattering and absorption properties. A fundamental study of the impact of spectral effects due to different aerosol types is essential to improve PV power predictions under aerosol-dominated situations, such as dust storms or urban smog.
The current study is part of the EnerSHelF (Energy Self-sufficiency of Health Facilities in Ghana) research project funded by the German Federal Ministry of Education and Research (BMBF) and coordinated by the Bonn-Rhein-Sieg University of Applied Sciences (H-BRS)
Here we present model results in which we systematically investigate the impact of aerosols on PV performance for different PV technologies. In addition, we show results of a case study investigating the impact of desert dust on a real PV hybrid system during the harmattan season (Bebber et al., 2021).
Bebber, M., et al., „PV-Diesel-Hybrid-System für ein Krankenhaus in Ghana - Anbindung eines PV-Batteriespeichermodells an ein bestehendes Generatormodell.“ Hochschule Bonn-Rhein-Sieg, 2021 (IZNE Working Paper Series, Nr. 21/3.) (Research Paper.) ISBN 978-3-96043-091-9
Herman-Czezuch, A., et al., “Impact of solar spectrum on the efficiency of photovol-taic cells – spectrally resolved PV performance model”, submitted to Solar Energy, 2021
Neher, I, et al., “Impact of aerosols on photovoltaic energy production - Scenarios from the Sahel Zone”. In: Energy Procedia, Vol.125, 2017, S. 170-179
How to cite:
Meilinger, S., Herman-Czezuch, A., Zemo, A., and Bebber, M.: Impact of dust storms and urban air pollution on PV-power systems: Case studies from Ghana , EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-419, https://doi.org/10.5194/ems2021-419, 2021.
James Barry, Anna Herman-Czezuch, Daniel Fischer, Stefanie Meilinger, Rone Yousif, Felix Gödde, and Alexander Bergenthal
In view of the rapid growth of solar power installations worldwide, accurate forecasts of photovoltaic (PV) power generation are becoming increasingly indispensable for the overall stability of the electricity grid. In the context of household energy storage systems, PV power forecasts contribute towards intelligent energy management and control of PV-battery systems, in particular so that self-sufficiency and battery lifetime are maximised. Typical battery control algorithms require day-ahead forecasts of PV power generation, and in most cases a combination of statistical methods and numerical weather prediction (NWP) models are employed. The latter are however often inaccurate, both due to deficiencies in model physics as well as an insufficient description of irradiance variability.
A promising approach to improving irradiance forecasts is to use the measured PV data themselves. In this work a novel algorithm was employed in order to infer global horizontal irradiance from measured PV data of household energy storage systems, with the goal of better characterising global horizontal irradiance (GHI) and ultimately improving irradiance and power forecasts. The inversion methods developed as part of the BMWi-funded MetPVNet project were applied to five PV-battery systems in different locations across Germany, in a pilot project sponsored by the local government of North Rhine-Westphalia (MWIDE NRW). High resolution measurements of PV power and current were used together with two different PV models in order to extract the plane-of-array irradiance. These data were then used together with both the DISORT and MYSTIC radiative transfer codes (Emde et al., 2016) to infer aerosol optical depth, cloud optical depth and irradiance under all sky conditions. The transposition of tilted to horizontal irradiance was performed with a new lookup table based on 3D radiative transfer simulations in MYSTIC.
The PV-battery systems were all equipped with irradiance sensors to provide independent measurements of both global tilted irradiance (GTI) and GHI, in order to validate the proposed inversion and transposition methods. Comparisons were also made with the irradiance predictions of the ICON-D2 weather model, the irradiance and cloud optical properties from satellite retrievals and the aerosol optical depth from the relevant AERONET stations. This work can provide the basis for future investigations using a larger number of PV-battery systems to evaluate the improvements to irradiance forecasts by the assimilation of inferred irradiance into a NWP model. In addition, the results could be used to improve the intelligent control of the storage systems in the field.
Emde, C., and Coauthors, 2016: The libRadtran software package for radiative transfer calculations (version 2.0.1). Geosci. Model Dev., 9, 1647–1672, doi:10.5194/gmd-9-1647-2016. https://www.geosci-model-dev.net/9/1647/2016/.
How to cite:
Barry, J., Herman-Czezuch, A., Fischer, D., Meilinger, S., Yousif, R., Gödde, F., and Bergenthal, A.: Photovoltaic-battery systems as irradiance sensors: first results of a prototype study, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-392, https://doi.org/10.5194/ems2021-392, 2021.
Ignacio Martin Santos, Mathew Herrnegger, and Hubert Holzmann
In the last two decades, different climate downscaling initiatives provided climate scenarios for Europe. The most recent initiative, CORDEX, provides Regional Climate Model (RCM) data for Europe with a spatial resolution of 12.5 km, while the previous initiative, ENSEMBLES, had a spatial resolution of 25 km. They are based on different emission scenarios, Representative Concentration Pathways (RCPs) and Special Report on Emission Scenarios (SRES) respectively.
A study carried out by Stanzel et al. (2018) explored the hydrological impact and discharge projections for the Danube basin upstream of Vienna when using either CORDEX and ENSEMBLES data. This basin covers an area of 101.810km2 with a mean annual discharge of 1923 m3/s at the basin outlet. The basin is dominated by the Alps, large gradients and is characterized by high annual precipitations sums which provides valuable water resources available along the basin. Hydropower therefore plays an important role and accounts for more than half of the installed power generating capacity for this area. The estimation of hydropower generation under climate change is an important task for planning the future electricity supply, also considering the on-going EU efforts and the “Green Deal” initiative.
Taking as input the results from Stanzel et al. (2018), we use transfer functions derived from historical discharge and hydropower generation data, to estimate potential changes for the future. The impact of climate change projections of ENSEMBLE and CORDEX in respect to hydropower generation for each basin within the study area is determined. In addition, an assessment of the impact on basins dominated by runoff river plants versus basins dominated by storage plants is considered.
The good correlation between discharge and hydropower generation found in the historical data suggests that discharge projection characteristics directly affect the future expected hydropower generation. Large uncertainties exist and stem from the ensembles of climate runs, but also from the potential operation modes of the (storage) hydropower plants in the future.
Stanzel, P., Kling, H., 2018. From ENSEMBLES to CORDEX: Evolving climate change projections for Upper Danube River flow. J. Hydrol. 563, 987–999. https://doi.org/10.1016/j.jhydrol.2018.06.057
How to cite:
Martin Santos, I., Herrnegger, M., and Holzmann, H.: Impact of climate change on hydropower production in the Upper Danube, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-493, https://doi.org/10.5194/ems2021-493, 2021.
Chairperson: Sven-Erik Gryning
modelling (forecasting, resolution, configuration, energy systems, machine learning)
Manajit Sengupta, Pedro Jimenez, Jaemo Yang, Ju-Hye Kim, and Yu Xie
The demand for increased accuracy in predicting solar power has grown considerably over recent years due to a rapid growth in grid-tied solar generation both utility scale and distributed. To increase confidence in forecasting solar power there is a need to provide reliable probabilistic solar radiation information that also minimizes error and uncertainty. Funded by the U.S. Department of Energy, the Weather Research and Forecasting (WRF)-Solar ensemble prediction system (WRF-Solar EPS) has been recently developed by a collaboration between the National Renewable Energy Laboratory and the National Center for Atmospheric Research. The WRF-Solar EPS is now ready to be disseminated to support the integration of solar generation resources and improve accuracy of day-ahead and intraday probabilistic solar forecasts. The first stage of our framework in developing WRF-Solar EPS required a specially designed method using a tangent linear (TL) sensitivity analysis to efficiently investigate uncertainties of WRF-Solar variables in forecasting clouds and solar irradiance. For the second stage, we applied a methodology to introduce stochastic perturbations in 14 key variables ascertained through the TL sensitivity analysis in generating ensemble members. A user-friendly interface is provided in WRF-Solar EPS, in which the parameters of stochastic perturbations can be controlled by configuration files. Lastly, we implemented an analog technique as an ensemble post-processing method to improve the performance of ensemble solar irradiance forecasts. This presentation will summarize the work performed in the past 3 years to understand the interactions between cloud physics, land surface, boundary layer and radiative transfer models through the development of a probabilistic cloud optimized day-ahead forecasting system based on WRF-Solar. For evaluation of forecasts, we adapt and use satellite-derived solar radiation data, e.g., the National Solar Radiation Data Base (NSRDB) as well as ground-measured observations. A comprehensive analysis to assess gridded model outputs over the Contiguous U.S is performed. The importance of evaluation of the WRF-Solar model with the NSRDB lies in the fact that the knowledge of the cloud-caused uncertainties in predicting solar irradiance over a wide range of regions provides model developers a detailed understanding of model strength and weaknesses in predicting clouds. Overall, we will present the detailed research steps that resulted in the development of the WRF-Solar EPS. We will also present a detailed validation demonstrating the improvements provided by this model. Moreover, we will also introduce the user’s guide for WRF-Solar EPS and future extension of this research.
How to cite:
Sengupta, M., Jimenez, P., Yang, J., Kim, J.-H., and Xie, Y.: New Developments in Ensemble-based Probabilistic Forecasting of Solar Radiation: The WRF-Solar Ensemble Prediction System, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-347, https://doi.org/10.5194/ems2021-347, 2021.
Matthias Zech, Oriol Raventós, Ontje Lünsdorf, and Lueder von Bremen
With the increasing penetration of renewable energy capacities in the European energy system, the electricity generators have shifted from centralized power plants to decentralized, weather-dependent wind turbines and photovoltaic systems. Energy system models now rely on skillful weather data to estimate renewable energy feedins on electricity bus levels. These feedins are usually calculated by bilinearly interpolating the closest atmospheric model grid points to the electricity network bus locations. This comes to the cost of averaging multiple atmospheric model grid points reducing overall atmospheric model variability. In addition, electricity grids are often modeled in clustered representations in terms of number of lines and buses. The number of buses is usually much smaller than the number of atmospheric model grid points and therefore some grid points and their characteristics may not be taken into account in highly clustered networks. So far, this interconnection between atmospheric model resolution and electricity grid topology has not been widely investigated.
This study approaches the question if and to what extent the atmospheric model resolution affects the energy system model results. The regional reanalysis COSMO-REA6 is used as a reference data and its resolution is artificially reduced. This allows to compare the loss of information (mainly variability) due to a lower grid point resolution. The weather data is then used within different energy system network topologies to determine the corresponding renewable energy feedins at bus levels. A subsequent optimal power flow model estimates the impact on energy system metrics as storage usage and economic dispatch costs to further understand the relationship between atmospheric model resolution and energy system model topology. This study provides useful insights to choose the appropriate resolution of the atmospheric model input given an energy system model.
How to cite:
Zech, M., Raventós, O., Lünsdorf, O., and von Bremen, L.: Interaction between atmospheric model resolution and energy system model topology, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-473, https://doi.org/10.5194/ems2021-473, 2021.
Julio A. H. Escobar, Guadalupe Sánchez-Hernández, Antonio Serrano, and José Agustín García
Over the last decades, numerical prediction models, such as the Weather Research and Forecasting (WRF), have emerged as one of the most powerful tools for solar radiation exploitation as renewable energy. A reliable forecast of solar radiation is an effective method to account for its variability and facilitate its integration into the grid. This study analyzes the influence of different domain configurations and spatial resolutions on the WRF solar radiation estimation. To this aim, different domain configurations centered on the city of Badajoz (Spain) have been tested. Thus, three different combinations of two nested domains (D01; higher domain; D02; inner domain) defined on a Lambert Conformal projection have been analyzed. Configurations C1 and C2 use the same domains but differ in the resolution of the nested domain (D02): 9 km for C1 and 3 km for C2. C3 has been defined to perform simulations at a higher resolution, consisting of two nested domains of 9 km for D01 and 1 km for D02. Due to WRF’s requirements on grid ratio between nested domains and computational efficiency criteria, this third configuration uses the same D02 dimensions as C1 and C2, but notably smaller D01 dimensions. All these configurations have employed the same WRF parameterizations. The initial and lateral boundary conditions for the meteorological fields are obtained from the reanalysis ERA5. Finally, the estimated solar radiation for the inner domains at 9, 3 and 1 km has been compared with ground-based solar radiation measurements. The results show a good performance of all the analyzed configurations, with an average relative MABE value of 14.95% and mean relative RMSE of 23.7%. Linear regression analysis between simulated and reference ground measurements have reported a slope of 0.83 for C1, 0.80 for C2 and 0.77 for C3. C3 tends to overestimate the reference measurements, while C1 and C2 tend to underestimate them. This underestimation is more remarkable for C2, likely due to the higher grid ratio in this configuration, 1:9 versus 1:3 in C1. Additionally, the analysis of differences between WRF simulation and reference data with respect to geometrical factors and sky conditions have reported differences between configurations. All these results reveal that different aspects related to the domain configuration, and not only final resolution, can influence the solar radiation forecasting and point out the need to find the most suitable configuration for each specific problem. Acknowledgments. This work is partially funded by FEDER/Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación of Spain through project RTI 2018-097332-B-C22, and by Junta de Extremadura-FEDER through project GR18097.
How to cite:
Escobar, J. A. H., Sánchez-Hernández, G., Serrano, A., and García, J. A.: Influence of different domain configurations on WRF solar irradiance estimation at Badajoz (Spain), EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-255, https://doi.org/10.5194/ems2021-255, 2021.
Antonio Serrano, Guadalupe Sánchez-Hernández, Julio A. H. Escobar, and María Luisa Cancillo
Solar energy proves to be an interesting alternative to conventional sources based on the burning of fossil fuels. However, it shows a high short-term variability that makes its integration into the electricity mix difficult. To facilitate this integration, reliable short- and medium-term forecasts become highly necessary. To respond to this demand, solar radiation forecasting models have emerged. Among them, Weather Research and Forecasting (WRF) has become particularly promising and has shown good performance at different temporal and spatial scales. The performance of these models is usually assessed by comparing their estimates with point measurements at selected stations. This comparison is hampered by the difference in spatial dimensions between the model estimates (representative of a given area) and the station (point) measurements. This difference introduces a certain error in the forecast, mainly related to the short-scale variability of cloudiness. Despite being essential to understand model validation, this issue has not been sufficiently investigated. In this framework, the present study analyzes the effect of the spatial representativeness of point measurements when used to validate model estimates. For this purpose, a specific one-month measurement campaign was conducted, deploying seven pyranometers in the vicinity of the city of Badajoz, Spain. To ensure their intercomparability, all pyranometers were calibrated with respect to a reference pyranometer previously calibrated by the World Radiation Center in Davos, Switzerland. Solar radiation was measured at a 1-minute basis to record the short-term variability due to cloudiness. Two series were constructed with these data, one corresponding to a selected station and the other to the average of the seven stations. These series of measurements were compared with the estimates provided by the WRF model for the same period and location. A configuration with two nested domains of 27 km and 9 km was used. Model performance showed better agreement when averaging was used instead of individual measurements, with RMSE improving from 89 W/m² to 77 W/m². Cloudy cases contributed the most to the differences between station measurements and model estimates, showing an RMSE greater than 100 W/m2, more than three times higher than the RMSE for clear cases (about 33 W/m2). The difference between the stations and the model for cloudy cases is reduced from 125 W/m2 to 107 W/m2 when averaged measurements are considered instead of single station measurements. This study contributes to the understanding of the representativeness of point station measurements for validation and comparison with WRF estimates. Acknowledgments. This work is partially funded by FEDER/Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación of Spain through project RTI 2018-097332-B-C22, and by Junta de Extremadura-FEDER through project GR18097.
How to cite:
Serrano, A., Sánchez-Hernández, G., Escobar, J. A. H., and Cancillo, M. L.: On the spatial representativeness of point station measurements for comparison with WRF estimates, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-268, https://doi.org/10.5194/ems2021-268, 2021.
Stavros-Andreas Logothetis, Vasileios Salamalikis, Stefan Wilbert, Jan Remund, Luis Zarzalejo, Yu Xie, Bijan Nouri, Evangelos Ntavelis, Julien Nou, Lennard Visser, Manajit Sengupta, Mário Pó, Remi Chauvin, Stephane Grieu, Wilfried Van Sark, and Andreas Kazantzidis
Cloud cameras (all sky imagers/ASIs) can be used for short-term (next 20 min) forecasts of solar irradiance. For this reason, several experimental and operational solutions emerged in the last decade with different approaches in terms of instrument types and forecast algorithms. Moreover, few commercial and semi-prototype systems are already available or being investigated. So far, the uncertainty of the predictions cannot be fully compared, as previously published tests were carried out during different periods and at different locations. In this study, the results from a benchmark exercise are presented in order to qualify the current ASI-based short-term forecasting solutions and examine their accuracy. This first comparative measurement campaign carried out as part of the IEA PVPS Task 16 (https://iea-pvps.org/research-tasks/solar-resource-for-high-penetration-and-large-scale-applications/). A 3-month observation campaign (from August to December 2019) took place at Plataforma Solar de Almeria of the Spanish research center CIEMAT including five different ASI systems and a network of high-quality measurements of solar irradiance and other atmospheric parameters. Forecasted time-series of global horizontal irradiance are compared with ground-based measurements and two persistence models to identify strengths and weaknesses of each approach and define best practices of ASI-based forecasts. The statistical analysis is divided into seven cloud classes to interpret the different cloud type effect on ASIs forecast accuracy. For every cloud cluster, at least three ASIs outperform persistence models, in terms of forecast error, highlighting their performance capabilities. The feasibility of ASIs on ramp event detection is also investigated, applying different approaches of ramp event prediction. The revealed findings are promising in terms of overall performance of ASIs as well as their forecasting capabilities in ramp detection.
How to cite:
Logothetis, S.-A., Salamalikis, V., Wilbert, S., Remund, J., Zarzalejo, L., Xie, Y., Nouri, B., Ntavelis, E., Nou, J., Visser, L., Sengupta, M., Pó, M., Chauvin, R., Grieu, S., Sark, W. V., and Kazantzidis, A.: Forecasting of solar irradiance and ramp events with all-sky imagers, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-75, https://doi.org/10.5194/ems2021-75, 2021.
Viivi Kallio-Myers, Aku Riihelä, Anders Lindfors, David Schonach, Erik Gregow, and Thomas Carlund
Solar energy production is growing at a very high rate. This growth is present also in Fennoscandia, where solar energy can answer to the renewable energy need during the long summer days. Solar energy is a naturally fluctuating energy source, one that requires the use of solar irradiance forecasts for optimal operation. In northern locations clouds cause the largest fluctuations of irradiance, which is why the forecasting of solar irradiance is often focused on forecasting the movement of clouds. At high latitudes there are also specific challenges due to the location, for instance estimating cloud location at the edge of the geostationary satellite instrument viewing area.
Forecasting irradiance is possible using various methods. Methods using satellite imagery, for instance, are good at forecasting irradiance for a few hours forward, while Numerical Weather Prediction (NWP) methods are recognised as superior for forecasting the next day. Validations have been made for various methods and setups, at several different locations, and for example the satellite-imagery-based method Solis-Heliosat has been shown to work well also at high latitudes. Operational NWP models, however, are not often validated for this specific purpose.
To understand the performance of operational local area NWP models in irradiance forecasting in Fennoscandia, we have compared and validated NWP irradiance forecasts at several measurement stations in Finland and Sweden. In the comparison, we have included the operational weather forecast model in the Nordic countries, the MetCoOp Ensemble Model (MEPS), as well as the MetCoOp Nowcasting Model (MNWC). Additionally, we have included the Solis-Heliosat method and a persistence method in the comparison.
Initial results show MEPS to have a steady, small relative bias error during the forecast, particularly after the first hours. The relative Root Mean Square Error (rRMSE) is also steady with only a slight increase during the whole forecast. MNWC somewhat underpredicts irradiance in the beginning, but the errors improve throughout the forecast. Solis-Heliosat has good initial accuracy, but the quality deteriorates very fast, with MEPS and MNWC outperforming the method after a 1-3 hour lead time. The Persistence method, as expected, has a very good bias, but also a very fast increasing rRMSE with forecast lead time.
The results show the NWP methods to be very suitable for forecasting irradiance. Differences in best performing lead times and varying run times between the methods increase our interest in using both NWP and satellite-based methods in forecasting irradiance, to get the most optimal accuracy for both short and long-term forecasting.
How to cite:
Kallio-Myers, V., Riihelä, A., Lindfors, A., Schonach, D., Gregow, E., and Carlund, T.: Comparing NWP and satellite-based irradiance forecasts in Fennoscandia, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-111, https://doi.org/10.5194/ems2021-111, 2021.
Stavros Andreas Logothetis, Vasileios Salamalikis, and Andreas Kazantzidis
Aerosol optical depth (AOD) describes adequately aerosols’s burden and extinction within an atmospheric column. AOD can be retrieved using remote sensing instruments such as ground-based sun photometers. Despite the very good quality of ground based AOD measurements, their spatiotemporal coverage is restricted. In this study, an alternative approach of AOD estimation is proposed with the synergy of ground-based measurements and machine learning (ML) techniques, in order to expand and complement the existing spatiotemporal capabilities of AOD data. The ML algorithms which are implemented are: Random Forests, Gradient Boosting Machines, Extreme Gradient Boosting Machines, Support Vector Regression, K-nearest Neighbors Regression, and Multivariate Adaptive Regression Splines. Each model receives as input information the Global Horizontal Irradiance (GHI) as well as water vapor (WV) content in hourly basis and under clear skies. A randomized cross-validation search scheme is implemented to obtain the optimal hyperparameters and avoid overfitting for each ML algorithm. GHI and WV are retrieved from Baseline Surface Radiation Network (BSRN) and NASA’s Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) reanalysis product respectively. AOD estimations are evaluated against AOD from AErosol RObotic NETwork (AERONET) inversion product, using the Level 2.0 Version 3 (L2V3) which provides cloud-screened and quality assured measurements. In total, 29 collocated AERONET-BSRN stations are used spanning from 2000 to 2019. Since, the aerosol pattern is different at each site, the effect of various aerosol types is further investigated. ML-based AOD predictions are adequately good, highlighting the feasibility of ML algorithms on producing AOD data. The results of this study could be useful for direct normal irradiance estimations as well as aerosols radiative effect calculations and climate projections.
How to cite:
Logothetis, S. A., Salamalikis, V., and Kazantzidis, A.: Aerosol optical depth prediction using machine learning techniques, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-22, https://doi.org/10.5194/ems2021-22, 2021.
Fabrizio Ruffini, Michela Moschella, and Antonio Piazzi
With the expanding penetration of renewable energy in the energy sector, we observe an ever-increasing need for more accurate weather and production forecasts. They are needed by several energy players: plant owners, system operators, service providers (balancing service providers, energy traders). For the energy market needs, in different countries we can already find almost real-time trading markets; in a likely future scenario, the day-ahead market will disappear in favour of 5-15 minutes ahead market. This trend luckily matches the system operators need of predicting in the very short term the energy fed into the grid, to effectively cope with voltage and congestions problems and manage the ancillary services. Overall, the scenario indicates a compelling need for advanced forecasting techniques.
This article discusses a hybrid solar nowcasting system, predicting energy production from +15 minutes to 3 hours ahead, with a time granularity of 15 minutes. The system combines observed data (especially from satellite) and Numerical Weather Predictions to nowcast data in two steps: the first step is the nowcast of global horizontal irradiance and direct normal irradiance; they are then fed into the following system to predict the energy production. Thus, we disentangle the problem, and we can improve in parallel the two subsystems.
The weather nowcast model core is a Deep Learning method especially suited for time series problems (Long Short Term Memory Network - LSTM). It has been tested over different sites corresponding to different satellite spatial resolution, weather conditions and climate regions. The results are compared with different benchmarks such as the persistence model, smart persistence model and ground truth (where available), obtaining typical annual MAE results over the 15->3 hours between 10 and 80 W/m2. Other metrics (MBE, RMSE, and the forecast score) are calculated to get a deeper view of the results meaning. We also compared results without the availability of NWP (computationally expensive) or ground sensors (not always available in real-time) to understand the benefits of processing those data.
The power production system (fed with the output of the previous model) is a combination of different techniques: Decision trees, KNN, and NN. The performance is typical of 3-6% annual NMAE, depending on the site. We compare the results with the persistence benchmark and we calculate other metrics such as MBE, NRMSE and to get a deeper understanding of the results.
The two-steps model is finally compared with a one-step model only, where just satellite data are fed into a model predicting the power, to compare pros, cons and performance.
How to cite:
Ruffini, F., Moschella, M., and Piazzi, A.: Hybrid nowcasting for solar power plants using satellite-data and Numerical Weather Predictions for (Deep) Machine Learning methods, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-96, https://doi.org/10.5194/ems2021-96, 2021.
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