Challenges and opportunities for energy system modelling to foster multi-level governance of energy transitions

Achieving the swift energy transition necessary to meet global climate ambitions requires concerted action across governance scales, from municipal authorities to national governments. Decision-making is often closely informed by energy system modelling, making energy models a crucial tool to foster a multi-level governance system that is based on mutual understanding and coordination across scales. Here, we review 186 energy modelling studies and identify challenges and opportunities for the energy modelling community to take into account and facilitate multi-level governance systems. We show that current energy modelling practices typically focus on and aim to support a single scale, largely overlooking the multi-level nature of energy governance. Embedding multi-level governance throughout the energy modelling process entails significant obstacles but is crucial for ensuring such approaches continue to provide timely and salient decision-support

Irradiance and cloud optical properties from solar photovoltaic systems

Solar photovoltaic power output is modulated by atmospheric aerosols and clouds and thus contains valuable information on the optical properties of the atmosphere. As a ground-based data source with high spatiotemporal resolution it has great potential to complement other ground-based solar irradiance measurements as well as those of weather models and satellites, thus leading to an improved characterisation of global horizontal irradiance. In this work several algorithms are presented that can retrieve global tilted and horizontal irradiance and atmospheric optical properties from solar photovoltaic data and/or pyranometer measurements. Specifically, the aerosol (cloud) optical depth is inferred during clear sky (completely overcast) conditions. The method is tested on data from two measurement campaigns that took place in Allgäu, Germany in autumn 2018 and summer 2019, and the results are compared with local pyranometer measurements as well as satellite and weather model data. Using power data measured at 1 Hz and averaged to 1 minute resolution, the hourly global horizontal irradiance is extracted with a mean bias error compared to concurrent pyranometer measurements of 11.45 W m−2, averaged over the two campaigns, whereas for the retrieval using coarser 15 minute power data the mean bias error is 16.39 W m−2. During completely overcast periods the cloud optical depth is extracted from photovoltaic power using a lookup table method based on a one-dimensional radiative transfer simulation, and the results are compared to both satellite retrievals as well as data from the COSMO weather model. Potential applications of this approach for extracting cloud optical properties are discussed, as well as certain limitations, such as the representation of 3D radiative effects that occur under broken cloud conditions. In principle this method could provide an unprecedented amount of ground-based data on both irradiance and optical properties of the atmosphere, as long as the required photovoltaic power data are available and are properly pre-screened to remove unwanted artefacts in the signal. Possible solutions to this problem are discussed in the context of future work

Datasets for "Irradiance and cloud optical properties from solar photovoltaic systems"

This dataset contains all the relevant data for the algorithms described in the paper "Irradiance and cloud optical properties from solar photovoltaic systems", which were developed within the framework of the MetPVNet project

Datasets for "Irradiance and cloud optical properties from solar photovoltaic systems" (final version)

This dataset contains all the relevant data for the algorithms described in the paper "Irradiance and cloud optical properties from solar photovoltaic systems", which were developed within the framework of the MetPVNet project. Input data: COSMO weather model data (DWD) as NetCDF files (cosmo_d2_2018(9).tar.gz) COSMO atmospheres for libRadtran (cosmo_atmosphere_libradtran_input.tar.gz) COSMO surface data for calibration (cosmo_pvcal_output.tar.gz) Aeronet data as text files (MetPVNet_Aeronet_Input_Data.zip) Measured data from the MetPVNet measurement campaigns as text files (MetPVNet_Messkampagne_2018(9).tar.gz) PV power data Horizontal and tilted irradiance from pyranometers Longwave irradiance from pyrgeometer MYSTIC-based lookup table for translated tilted to horizontal irradiance (gti2ghi_lut_v1.nc) Output data: Global tilted irradiance (GTI) inferred from PV power plants (with calibration parameters in comments) Linear temperature model: MetPVNet_gti_cf_inversion_results_linear.tar.gz Faiman non-linear temperature model: MetPVNet_gti_cf_inversion_results_faiman.tar.gz Global horizontal irradiance (GHI) inferred from PV power plants Linear temperature model: MetPVNet_ghi_inversion_results_linear.tar.gz Faiman non-linear temperature model: MetPVNet_ghi_inversion_results_faiman.tar.gz Combined GHI averaged to 60 minutes and compared with COSMO data Linear temperature model: MetPVNet_ghi_inversion_combo_60min_results_linear.tar.gz Faiman non-linear temperature model: MetPVNet_ghi_inversion_combo_60min_results_faiman.tar.gz Cloud optical depth inferred from PV power plants Linear temperature model: MetPVNet_cod_cf_inversion_results_linear.tar.gz Faiman non-linear temperature model: MetPVNet_cod_cf_inversion_results_faiman.tar.gz Combined COD averaged to 60 minutes and compared with COSMO and APOLLO_NG data Linear temperature model: MetPVNet_cod_inversion_combo_60min_results_linear.tar.gz Faiman non-linear temperature model: MetPVNet_cod_inversion_combo_60min_results_faiman.tar.gz Validation data: COSMO cloud optical depth (cosmo_cod_output.tar.gz) APOLLO_NG cloud optical depth (MetPVNet_apng_extract_all_stations_2018(9).tar.gz) COSMO irradiance data for validation (cosmo_irradiance_output.tar.gz) CAMS irradiance data for validation (CAMS_irradiation_detailed_MetPVNet_MK_2018(9).zip) How to import results: The results files are stored as text files ".dat", using Python multi-index columns. In order to import the data into a Pandas dataframe, use the following lines of code (replace [filename] with the relevant file name): import pandas as pd data = pd.read_csv("[filename].dat",comment='#',header=[0,1],delimiter=';',index_col=0,parse_dates=True) This gives a multi-index Dataframe with the index column the timestamp, the first column label corresponds to the measured variable and the second column to the relevant sensor Note: The output data has been updated to match the latest version of the paper, whereas the input and validation data remains the same as in Version 1.0.

Irradiance and cloud optical properties from solar photovoltaic systems

Solar photovoltaic power output is modulated by atmospheric aerosols and clouds and thus contains valuable information on the optical properties of the atmosphere. As a ground-based data source with high spatiotemporal resolution it has great potential to complement other ground-based solar irradiance measurements as well as those of weather models and satellites, thus leading to an improved characterisation of global horizontal irradiance. In this work several algorithms are presented that can retrieve global tilted and horizontal irradiance and atmospheric optical properties from solar photovoltaic data and/or pyranometer measurements. The method is tested on data from two measurement campaigns that took place in the Allgäu region in Germany in autumn 2018 and summer 2019, and the results are compared with local pyranometer measurements as well as satellite and weather model data. Using power data measured at 1 Hz and averaged to 1 min resolution along with a non-linear photovoltaic module temperature model, global horizontal irradiance is extracted with a mean bias error compared to concurrent pyranometer measurements of 5.79 W m−2 (7.35 W m−2) under clear (cloudy) skies, averaged over the two campaigns, whereas for the retrieval using coarser 15 min power data with a linear temperature model the mean bias error is 5.88 and 41.87 W m−2 under clear and cloudy skies, respectively. During completely overcast periods the cloud optical depth is extracted from photovoltaic power using a lookup table method based on a 1D radiative transfer simulation, and the results are compared to both satellite retrievals and data from the Consortium for Small-scale Modelling (COSMO) weather model. Potential applications of this approach for extracting cloud optical properties are discussed, as well as certain limitations, such as the representation of 3D radiative effects that occur under broken-cloud conditions. In principle this method could provide an unprecedented amount of ground-based data on both irradiance and optical properties of the atmosphere, as long as the required photovoltaic power data are available and properly pre-screened to remove unwanted artefacts in the signal. Possible solutions to this problem are discussed in the context of future work

Datasets for "Irradiance and cloud optical properties from solar photovoltaic systems" (final version)

This dataset contains all the relevant data for the algorithms described in the paper "Irradiance and cloud optical properties from solar photovoltaic systems", which were developed within the framework of the MetPVNet project

Entwicklung innovativer satellitengestützter Methoden zur verbesserten PV-Ertragsvorhersage auf verschiedenen Zeitskalen für Anwendungen auf Verteilnetzebene (MetPVNet)

In the research project "MetPVNet", both, the forecast-based operation management in distribution grids and as well as the forecasts of the feed-in of PV-power from decentralized plants could be improved on the basis of satellite data and numerical weather forecasts. Based on a detailed network analyses for a real medium-voltage grid area, it was shown that both – the integration of forecast data based on satellite and weather data and the improvement of subsequent day forecasts based on numerical weather models – have a significant added value for forecast-based congestion management or redispatch and reactive power management in the distribution grid. Furthermore, forecast improvements for the forecast model of the German Weather Service were achieved by assimilating visible satellite imagery, and cloud and radiation products from satellites were improved, thus improving the database for short-term forecasting as well as for assimilation. In addition, several methods have been developed that will enable forecast improvement in the future, especially for weather situations with high cloud induced variability and high forecast errors. This article summarizes the most important project results.Im Rahmen des Forschungsprojektes „MetPVNet“ konnten sowohl die prognosebasierte Betriebsführung in Verteilnetzen als auch die Erzeugungsprognose von dezentralen PV - Anlagen auf der Basis von Satellitendaten und Numerischer Wettervorhersage verbessert werden. Anhand detaillierter Netzanalysen für ein reales Mittelspannungsnetzgebiet konnte gezeigt werden, dass sowohl die Einbindung von Prognosedaten auf Basis von Satelliten und Wetterdaten, als auch die Verbesserung von Folgetagsprognosen auf der Basis numerischer Wettermodelle einen deutlichen Mehrwert für ein prognosebasiertes Engpassmanagement bzw. Redispatch und Blindleistungs-management im Verteilnetz aufweisen. Darüber hinaus wurden Prognoseverbesserungen für das Vorhersagemodell des Deutschen Wetterdienstes durch die Assimilation von sichtbaren Satellitenbildern erreicht, sowie Wolken- und Strahlungsprodukte aus Satelliten verbessert und somit die Datenbasis für die Kurzfristprognose als auch für die Assimilation. Darüber hinaus wurden verschiedene Methoden entwickelt, die zukünftig eine Prognoseverbesserung, insbesondere für Wettersituationen mit hoher wolkenbedingter Strahlungsvariabilität und hohe n Prognosefehlern, ermöglichen. In diesem Artikel werden dies wichtigsten Projektergebnisse zusammengefasst

Entwicklung innovativer satellitengestützter Methoden zur verbesserten PV-Ertragsvorhersage auf verschiedenen Zeitskalen für Anwendungen auf Verteilnetzebene: Schlussbericht

Anhand detaillierter Netzanalysen für ein reales Mittelspannungsnetzgebiet konnte gezeigt werden, dass sowohl die Einbindung von Prognosedaten auf Basis von Satelliten und Wetterdaten, als auch die Verbesserung von Folgetagsprognosen auf der Basis numerischer Wettermodelle einen deutlichen Mehrwert für ein prognosebasiertes Engpassmanagement bzw. Redispatch und Blindleistungsmanagement im Verteilnetz aufweisen. Auch Kurzfristprognosen auf der Basis von Satellitendaten haben einen positiven Effekt. Ein weiterer wichtiger Mehrwert des Projektes ist auch die Rückmeldung der kritischen Prognosesituationen aus Sicht der Anwendungsfälle, so dass wie bereits im Projekt gezeigt und darüber hinaus, Prognosen zielgerichteter auf die Anwendung im Verteilnetzbetrieb ausgelegt und optimiert werden können. Weiterhin konnten Prognoseverbesserungen für das Vorhersagemodell des Deutschen Wetterdienstes durch die Assimilation von sichtbaren Satellitenbildern erreicht werden. Darüber hinaus wurden Wolken- und Strahlungsprodukte aus Satelliten verbessert und somit die Datenbasis für die Kurzfristprognose als auch für die Assimilation. Darüber hinaus wurden verschiedene Methoden entwickelt, die zukünftig zu einer weiteren Prognoseverbesserung, insbesondere für Wettersituationen mit hohen Prognosefehlern, führen könnten. Solche Situationen wurden aus Sicht des Netzbetriebs und mithilfe von satellitenbasierten Analysen der Gesamtwetterlage für die Perioden der MetPVNet Messkampagnen identifiziert. Hierbei handelte es sich insbesondere um Situationen mit starker oder stark wechselhafter Bewölkung. Für die MetPVNet Messkampagnen wurde auf der Basis eines Trainingsdatensatzes und in Abhängigkeit der Variabilitätsklasse die Abweichung der bodennahen Einstrahlung von Satellitendaten oder von Strahlungsprognosen quantifiziert. Diese Art der Informationen bietet zukünftig die Möglichkeit zur Bewertung der Prognosegüte.Detailed network analyses for a real medium-voltage grid area showed that both the integration of forecast data based on satellites and weather data and the improvement of next-day forecasts based on numerical weather models have clear added value for forecast-based congestion management or redispatch and reactive power management in the distribution grid. Short-term forecasts based on satellite data also have a positive effect. Another important added value of the project is the feedback of critical forecast situations from the point of view of the use cases, so that, as already shown in the project and beyond, forecasts can be designed and optimised more specifically for the specific application in distribution grid operation. Furthermore, improvements to the forecast model of the German Weather Service were achieved through the assimilation of visible satellite images. Furthermore, cloud and radiation products from satellites were improved and through this also the data basis for both short-term forecasts as well as data assimilation. In addition, various methods were developed that could lead to further forecast improvements in the future, especially for weather situations with high forecast errors. Such situations were identified from the perspective of grid operation and with the help of satellite-based analyses of the overall weather situation for the periods of the MetPVNet measurement campaigns. In particular, these were situations with heavy or very changeable cloud cover. For the MetPVNet measurement campaigns, the deviation of the near-surface irradiance from satellite data or radiation forecasts was quantified on the basis of a training data set and depending on the variability class. In the future, this type of information will offer the possibility of evaluating the forecast quality