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In this presentation we detail highlight results obtained from the research work within the European Horizon 2020 project Smart4RES (http://www.smart4res.eu). The project, which started in 2019 and runs until 2023, aims at a better modelling and forecasting of weather variables necessary to optimise the integration of weather-dependent renewable energy (RES) production (i.e. wind, solar, run-of-the-river hydro) into power systems and electricity markets. Smart4RES gathers experts from several disciplines, from meteorology and renewable generation to market- and grid-integration. It aims to contribute to the pathway towards energy systems with very high RES penetrations by 2030 and beyond, through thematic objectives including:Improvement of weather and RES forecasting, Streamlined extraction of optimal value through new forecasting products, data market places, and novel business models; New data-driven optimization and decision-aid tools for market and grid management applications; Validation of new models in living labs and assessment of forecasting value vs costly remedies to hedge uncertainties (i.e. storage). In this presentation we will focus on our results on models that permit to improve forecasting of weather variables with focus on extreme situations and also through innovative measuring settings (i.e. a network of sky cameras). Also results will be presented on the development of seamless approach able to couple outputs from different ensemble numerical weather prediction (NWP) models with different temporal resolutions. Advances on the contribution of ultra-high resolution NWPs based on Large Eddy Simulation will be presented with evaluation results on real case studies like the Rhodes island in Greece.When it comes to forecasting the power output of RES plants, mainly wind and solar, the focus is on improving predictability using multiple sources of data. The proposed modelling approaches aim to efficiently combine highly dimensionally input (various types of satellite images, numerical weather predictions, spatially distributed measurements etc.). A priority has been to propose models that permit to generate probabilistic forecasts for multiple time frames in a seamless way. Thus, the objective is not only to improve accuracy and uncertainty estimations, but also to simplify complex forecasting modelling chains for applications that use forecasts at different time frames (i.e. a virtual power plant - VPP- with or without storage that participates in multiple markets). Our results show that the proposed seamless models permit to reach these performance objectives. Results will be presented also on how these approaches can be extended to aggregations of RES plants which is relevant for forecasting VPP production.

The increase of the share of intermittent renewable energy in the energy mix raises issues related to the predictability of electricity production. Especially, at the seasonal scale, the transmission system operators (TSOs) are required to make projections of the availability of means of production as well as to predict the consumption in order to guarantee the security of energy supply during the coming winter or summer. However, current projections are mainly based on historical data (climatology) of temperatures (consumption), wind speed (wind energy production), or solar radiation (photovoltaic production). The thesis presents 4 studies: three within the framework of seasonal forecasts, and one study on the realism of the surface wind speed modelled by the Numerical Weather Prediction model of the European Center of Medium-range Weather Forecasts.If the wind energy forecasts at short timescales going from the minute to several days as well as the wind trends at climatic scale have been thoroughly studied, forecasts of wind energy at the intermadiate scale going from a fortnight to the seasonal horizon have recieved little attention. Predictability at midlatitude and at those long term horizons is indeed still an open question. However, several studies have shown that Numerical Weather Prediction models (NWP) are able to bring valuable information on the large scale atmospheric circulation via the forecast of large scale atmospheric oscillations such as ENSO in the Pacific region, or the NAO in the North Atlantic. It has also been demonstrated that these oscillations have a strong influence on precipitations, temperatures, and surface wind speed.Building the relation between such indicators of the large scale atmospheric circulation and the surface wind speed in France allows to take into account the interannual variability of the surface wind speed, which is not the case of climatology by construction. This is the idea developed in the three studies concerning the seasonal forecasts. In order to forecast the wind energy ressource at the seasonal scale, two probabilistic models are proposed. A parametric model based on the forecast of the surface wind speed seasonal distribution at different location in France estimated by the theorical Weibull distribution ; and another non-parametric based on the estimation of the daily surface wind speed distribution knowing the state of the atmosphere. The third study propose to reconstruct the joint probability of the French national consumption and production, allowing to measure the risk of imbalance between supply and demand.; L'augmentation de la part des énergies renouvelables intermittentes dans le mix énergétique génère des problématiques liées à la prévisibilité de la production d'électricité. Notamment,à l'échelle saisonnière, les gestionnaires du réseau de transport d'électricité sont contraints de projeter la disponibilité des moyens de production ainsi que de prévoir la demande. Cela permet de garantir l'approvisionnement pour le prochain hiver ou été. Néanmoins, les projections actuelles sont principalement basées sur des données historiques (climatologie) de températures (consommation), vents (production éolienne), ou encore de rayonnement solaire (production photovoltaïque). La thèse présente 4 travaux : trois dans le cadre de la prévision saisonnière, et une étude sur le réalisme du vent de surface tel qu'il est modélisé; par le modèle de prévision du temps du Centre Européen.Si la prévisions de l'énergie éolienne aux échelles de temps courtes allant de la minute à quelques jours ainsi que la tendance des vents aux échelles climatiques ont été largement étudiées, la prévision de la production éolienne l'échelle de temps intermédiaire allant d'une quinzaine de jours à la saison n'a reçu que peu d'attention. La prévisibilité; du temps aux moyennes latitudes à ces horizons lointains est en effet encore une question ouverte. Cependant, plusieurs études ont montré que les modèles numériques de prévision saisonnières étaient capable d'apporter de l'information sur la variabilité de la circulation atmosphérique de grande échelle via la prévision des oscillations de la circulation grande échelle, comme ENSO dans le Pacifique, ou encore la NAO en Atlantique Nord. Il a aussi été démontré que ces oscillations ont un impact fort sur les précipitations, les températures, et les vents de surface.Construire la relation entre ces indicateurs de la circulation atmosphérique grande échelle et le vent de surface en France permet donc de prendre en compte la variabilité; interannuelle du vent de surface, ce dont n'est pas capable par définition la climatologie. C'est là l'idée développée dans les 3 études concernant la prévision saisonnière. Afin de prévoir la ressource et la production éolienne à l'échelle saisonnière, deux modèles probabilistes sont développés. L'un paramétrique, basée sur la prévision de la distribution saisonnière du vent de surface, à différents endroits en France ; l'autre non paramétrique, basé sur l'estimation de la de la densité de probabilité du vent de surface journalier conditionnel à l'état de l'atmosphère. La troisième étude propose de reconstruire la probabilité jointe de la consommation et de la production nationale française, permettant ainsi de mesurer le risque de déséquilibre entre l'offre et la demande.

We build and evaluate a probabilistic model designed for forecasting the distribution of the daily mean wind speed at the seasonal timescale in France. On such long-term timescales, the variability of the surface wind speed is strongly influenced by the atmosphere large-scale situation. Our aim is to predict the daily mean wind speed distribution at a specific location using the information on the atmosphere large-scale situation, summarized by an index. To this end, we estimate, over 20 years of daily data, the conditional probability density function of the wind speed given the index. We next use the ECMWF seasonal forecast ensemble to predict the atmosphere large-scale situation and the index at the seasonal timescale. We show that the model is sharper than the climatology at the monthly horizon, even if it displays a strong loss of precision after 15 days. Using a statistical postprocessing method to recalibrate the ensemble forecast leads to further improvement of our probabilistic forecast, which then remains sharper than the climatology at the seasonal horizon.

Smart4RES is a European Horizon2020 project developing next generation solutions for renewable energy forecast- ing. This paper presents highlight results obtained during the first year of the project. Data science is used throughout the proposed solutions in order to process the large amount of heterogeneous data available to forecasters, and derive model-free approaches of forecasting and decision-aid tasks. This paper presents a series of solutions addressing relevant for Photovoltaics (PV) and storage applications. High-resolution Numerical Weather Predictions and regional solar irradiance forecasting provide detailed information on local weather conditions and their variability. PV power forecasting benefits from such new data sources, but also the proposed collaborative data exchange. Finally, data-driven methods simplify decision-making for trading in short-term markets and for grid management., This paper (presentation file) was presented at the 11th Solar & Storage Integration Workshop and published in the workshop's proceedings