An approach for the estimation of the aggregated photovoltaic power generated in several European countries from meteorological data

Classical approaches to the calculation of the photovoltaic (PV) power generated in a region from meteorological data require the knowledge of the detailed characteristics of the plants, which are most often not publicly available. An approach is proposed with the objective to obtain the best possible assessment of power generated in any region without having to collect detailed information on PV plants. The proposed approach is based on a model of PV plant coupled with a statistical distribution of the prominent characteristics of the configuration of the plant and is tested over Europe. The generated PV power is first calculated for each of the plant configurations frequently found in a given region and then aggregated taking into account the probability of occurrence of each configuration. A statistical distribution has been constructed from detailed information obtained for several thousands of PV plants representing approximately 2 % of the total number of PV plants in Germany and was then adapted to other European countries by taking into account changes in the optimal PV tilt angle as a function of the latitude and meteorological conditions. The model has been run with bias-adjusted ERA-interim data as meteorological inputs. The results have been compared to estimates of the total PV power generated in two countries: France and Germany, as provided by the corresponding transmission system operators. Relative RMSE of 4.2 and 3.8 % and relative biases of −2.4 and 0.1 % were found with three-hourly data for France and Germany. A validation against estimates of the country-wide PV-power generation provided by the ENTSO-E for 16 European countries has also been conducted. This evaluation is made difficult by the uncertainty on the installed capacity corresponding to the ENTSO-E data but it nevertheless allows demonstrating that the model output and TSO data are highly correlated in most countries. Given the simplicity of the proposed approach these results are very encouraging. The approach is particularly suited to climatic timescales, both historical and future climates, as demonstrated here

Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives

Knowledge of the spatial and temporal characteristics of solar surface irradiance (SSI) is critical in many domains. While meteorological ground stations can provide accurate measurements of SSI locally, they are sparsely distributed worldwide. SSI estimations derived from satellite imagery are thus crucial to gain a finer understanding of the solar resource. Inferring SSI from satellite images is, however, not straightforward, and it has been the focus of many researchers in the past 30 to 40 years. For long, the emphasis has been on models grounded in physical laws with, in some cases, simple statistical parametrizations. Recently, new satellite SSI retrieval methods have been emerging, which directly infer the SSI from the satellite images using machine learning. Although only a few such works have been published, their practical efficiency has already been questioned. The objective of this paper is to better understand the potential and the pitfalls of this new family of methods. To do so, simple multi-layer-perceptron (MLP) models are constructed with different training datasets of satellite-based radiance measurements from Meteosat Second Generation (MSG) with collocated SSI ground measurements from Météo-France. The performance of the models is evaluated on a test dataset independent from the training set in both space and time and compared to that of a state-of-the-art physical retrieval model from the Copernicus Atmosphere Monitoring Service (CAMS). We found that the data-driven model's performance is very dependent on the training set. Provided the training set is sufficiently large and similar enough to the test set, even a simple MLP has a root mean square error (RMSE) that is 19 % lower than CAMS and outperforms the physical retrieval model at 96 % of the test stations. On the other hand, in certain configurations, the data-driven model can dramatically underperform even in stations located close to the training set: when geographical separation was enforced between the training and test set, the MLP-based model exhibited an RMSE that was 50 % to 100 % higher than that of CAMS in several locations.</p

Further validation of the estimates of the downwelling solar radiation at ground level in cloud-free conditions provided by the McClear service: the case of Sub-Saharan Africa and the Maldives Archipelago

Being part of the Copernicus Atmosphere Monitoring Service (CAMS), the McClear service provides estimates of the downwelling shortwave irradiance and its direct and diffuse components received at ground level in cloud-free conditions, with inputs on ozone, water vapor and aerosol properties from CAMS. McClear estimates have been validated over several parts of the world by various authors. This article makes a step forward by comparing McClear estimates to measurements performed at 44 ground-based stations located in Sub-Saharan Africa and the Maldives Archipelago in the Indian Ocean. The global irradiance received on a horizontal surface (G) and its direct component received at normal incidence (BN) provided by the McClear-v3 service were compared to 1 min measurements made in cloud-free conditions at the stations. The correlation coefficient is greater than 0.96 for G, whereas it is greater than 0.70 at all stations but five for BN. The mean of G is accurately estimated at stations located in arid climates (BSh, BWh, BSk, BWk) and temperate climates without a dry season and a hot or warm summer (Cfa, Cfb) or with a dry and hot summer (Csa) with a relative bias in the range [−1.5, 1.5] % with respect to the means of the measurements at each station. It is underestimated in tropical climates of monsoon type (Am) and overestimated in tropical climates of savannah type (Aw) and temperate climates with a dry winter and hot (Cwa) or warm (Cwb) summer. The McClear service tends to overestimate the mean of BN. The standard deviation of errors for G ranges between 13 W m−2 (1.3 %) and 31 W m−2 (3.7 %) and that for BN ranges between 31 W m−2 (3.0 %), and 70 W m−2 (7.9 %). Both offer small variations in time and space. A review of previous works reveals no significant difference between their results and ours. This work establishes a general overview of the performances of the McClear service.</p

Quantifying the sensitivity of European power systems to energy scenarios and climate change projections

Climate simulations consistently show an increase in European near-surface air temperature by the late 21st century, although projections for near-surface wind speeds and irradiance differ between models, and are accompanied by large natural variability. These factors make it difficult to estimate the effects of physical climate change on power system planning. Here, the impact of climate change on future European power systems is estimated. We show for the first time how a set of divergent future power system scenarios lead to marked differences in Europe’s total energy balance (demand-net-renewable supply) by 2050, which dominate over the uncertainty associated with climate change (~50% and ~5% respectively). However, within any given power system scenario, national power systems may be subject to considerable impacts from climate change, particularly for seasonal differences between renewable resources (e.g., wind power may be impacted by ~20% or more). There is little agreement between climate models in terms of the spatio-temporal pattern of these impacts, and even in the direction of change for wind and solar. More thorough consideration of climate uncertainty is therefore needed, as it is likely to be of great importance for robust future power system planning and design

Creating a proof-of-concept climate service to assess future renewable energy mixes in Europe: an overview of the C3S ECEM project

The EU Copernicus Climate Change Service (C3S) European Climatic Energy Mixes (ECEM) has produced, in close collaboration with prospective users, a proof-of-concept climate service, or Demonstrator, designed to enable the energy industry and policy makers assess how well different energy supply mixes in Europe will meet demand, over different time horizons (from seasonal to long-term decadal planning), focusing on the role climate has on the mixes. The concept of C3S ECEM, its methodology and some results are presented here. The first part focuses on the construction of reference data sets for climate variables based on the ERA-Interim reanalysis. Subsequently, energy variables were created by transforming the bias-adjusted climate variables using a combination of statistical and physically-based models. A comprehensive set of measured energy supply and demand data was also collected, in order to assess the robustness of the conversion to energy variables. Climate and energy data have been produced both for the historical period (1979–2016) and for future projections (from 1981 to 2100, to also include a past reference period, but focusing on the 30 year period 2035–2065). The skill of current seasonal forecast systems for climate and energy variables has also been assessed. The C3S ECEM project was designed to provide ample opportunities for stakeholders to convey their needs and expectations, and assist in the development of a suitable Demonstrator. This is the tool that collects the output produced by C3S ECEM and presents it in a user-friendly and interactive format, and it therefore constitutes the essence of the C3S ECEM proof-of-concept climate service

Towards an improved nowcasting method by evaluating power profiles of PV systems to detect apparently atypical behavior

The installed capacity of PV plants has increased dramatically in the past years. A common approach to determine the actual power of an ensemble of PV systems within a specific region typically employs data from measured reference plants. Obviously the precision of the power estimation depends on having representative reference plants, which are not influenced by strong individual characteristics. The goal of this contribution is to detect such apparently atypical behavior of PV systems by comparing their measured power to simulations based on a nearby weather station and clear sky irradiance. Deviations are studied in the course of each day for the year 2012 and 48 PV systems, indicating systematic characteristics independent from meteorological conditions. Additionally, an approach is presented to detect such unexpected deviations automatically. This can be the basis for a dynamic nowcasting algorithm, which selects the reference units based on their (temporal) suitability

Solar Radiation Nowcasting Using a Markov Chain Multi-Model Approach

Solar energy has found increasing applications in recent years, and the demand will continue to grow as society redirects to a more renewable development path. However, the required high-frequency solar irradiance data are not yet readily available everywhere. There have been endeavors to improve its forecasting in order to facilitate grid integration, such as with photovoltaic power planning. The objective of this study is to develop a hybrid approach to improve the accuracy of solar nowcasting with a lead time of up to one hour. The proposed method utilizes irradiance data from the Copernicus Atmospheric Monitoring Service for four European cities with various cloud conditions. The approach effectively improves the prediction accuracy in all four cities. In the prediction of global horizontal irradiance for Berlin, the reduction in the mean daily error amounts to 2.5 Wh m−2 over the period of a month, and the relative monthly improvement reaches nearly 5% compared with the traditional persistence method. Accuracy improvements can also be observed in the other three cities. Furthermore, since the required model inputs of the proposed approach are solar radiation data, which can be conveniently obtained from CAMS, this approach possesses the potential for upscaling at a regional level in response to the needs of the pan-EU energy transition. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Global assessment of the merit-order effect and revenue cannibalisation for variable renewable energy

The rapid growth of wind and solar power has been a major driver for decarbonisation worldwide. They tend to reduce wholesale electricity prices, both the time-weighted average (the merit‑order effect) and their own output-weighted average (price cannibalisation). Whilst these effects have been widely observed, most previous studies focus on single countries. Here, we compare 37 electricity markets across Europe, North America, Australia and Japan and explore variations between them.Merit-order and cannibalisation effects are observed in nearly all countries studied. However, only in Germany, Spain, Poland, Portugal, Denmark and California can renewable output explain more than 10% of variation in wholesale electricity prices. The global average merit‑order effect is €0.68±€0.54 /MWh per percentage point increase in variable renewable energy penetration, and this falls with higher penetration. Revenues captured by wind farms decrease by 0.23% (€0.16 /MWh) for each percentage point increase of wind penetration and by 1.94% (€0.90 /MWh) for solar PV