Bibliothèque des publications
Journal articles
M., David, J., Alonso-Montesinos, J., Le Gal La Salle, P., Lauret (2023). Probabilistic Solar Forecasts as a Binary Event Using a Sky Camera. Energies, 16, 7125. https://doi.org/10.3390/en16207125
Abstract
Avec l’augmentation rapide des énergies solaires, une prévision à court terme de haute qualité est nécessaire pour intégrer en douceur leur production dans les réseaux électriques. Habituellement, les systèmes de prévision prédisent l’énergie solaire future sous la forme d’une variable continue. Mais pour des applications particulières, telles que les centrales à concentration équipées de dispositifs de suivi de la course du soleil, l'exploitant doit anticiper l'atteinte d'un seuil de rayonnement solaire pour démarrer ou arrêter son système. Dans ce cas, les prévisions binaires sont plus pertinentes. De plus, même si la plupart des systèmes de prévision sont déterministes, l’approche probabiliste fournit des informations supplémentaires sur leur incertitude inhérente, essentielles à la prise de décision. L'objectif de ce travail est de proposer une méthodologie pour générer des prévisions solaires probabilistes sous forme d'événement binaire pour des horizons à très court terme compris entre 1 et 30 min. Parmi les différentes techniques développées pour prédire le potentiel solaire dans les prochaines minutes, l’imagerie du ciel est l’une des plus prometteuses. Ainsi, nous proposons dans ce travail de combiner un modèle basé sur une caméra du ciel et un modèle de choix discret pour prédire la probabilité d'atteindre un seuil d'irradiation adapté aux exploitants de centrales solaires. Deux modèles paramétriques à choix discrets bien connus, les modèles logit et probit, ainsi qu'une technique d'apprentissage automatique, la forêt aléatoire, ont été testés pour post-traiter la prévision déterministe issues des images du ciel. Les trois modèles améliorent considérablement la qualité de la prévision déterministe originale. Cependant, la forêt aléatoire donne les meilleurs résultats et fournit surtout des prévisions probabilistes fiables.
P., Lauret, R., Alonso-Suárez, R., Amaro E Silva, J., Boland, M., David, W., Herzberg, J., Le Gall La Salle, E., Lorenz, L., Visser, W., Van Sark, W., & T, Zech (2024). The added value of combining solar irradiance data and forecasts: A probabilistic benchmarking exercise. Renewable Energy, 237, 121574. https://doi.org/10.1016/j.renene.2024.121574
Abstract
Despite the growing awareness in academia and industry of the importance of solar probabilistic forecasting for further enhancing the integration of variable photovoltaic power generation into electrical power grids, there is still no benchmark study comparing a wide range of solar probabilistic methods across various local climates. Having identified this research gap, experts involved in the activities of IEA PVPS T16 agreed to establish a benchmarking exercise to evaluate the quality of intra-hour and intra-day probabilistic irradiance forecasts.
The tested forecasting methodologies are based on different input data including ground measurements, satellite-based forecasts and Numerical Weather Predictions (NWP), and different statistical methods are employed to generate probabilistic forecasts from these. The exercise highlights different forecast quality depending on the method used, and more importantly, on the input data fed into the models.
In particular, the benchmarking procedure reveals that the association of a point forecast that blends ground, satellite and NWP data with a statistical technique generates high-quality probabilistic forecasts. Therefore, in a subsequent step, an additional investigation was conducted to assess the added value of such a blended point forecast on forecast quality. Three new statistical methods were implemented using the blended point forecast as input.
T. A., Randrianantenaina, J. Le Gal La Salle, S. V., Spataru, & M., David, M. (2025). Increasing the self-sufficiency of a university campus by expanding the PV capacity while minimizing the energy cost. EPJ Photovoltaics, 16, 7. https://doi.org/10.1051/epjpv/2024048
Abstract
Microgrids, which promote the production and consumption of renewable energy on site, are a relevant solution to reduce carbon emissions and the price of energy for end users. However, converting an existing building stock into a microgrid powered mainly by renewable energy requires finding a technical and economic optimum while taking into account strong constraints. This work proposes a methodology to achieve this objective on an existing university campus located in La Reunion, a French island in the Indian Ocean. The campus already has three photovoltaic (PV) systems and high-quality measurement data of weather, loads and energy production. The goal of the work is to find an optimal rooftop PV capacity that maximizes campus selfsufficiency while keeping energy price affordable for users. The results do not highlight a unique combination of roofs as a solution to the optimization problem. However, the analysis of possible combinations gives clear rules for defining the total photovoltaic capacity to be installed and selecting the most suitable roofs.
Le Gal La Salle, J., David, M., Lauret, P., & Castaing-Lasvignottes, J. (2025). Concevoir et dimensionner des microréseaux autonomes: L’exemple de TwInSolar. Innovations technologiques. https://doi.org/10.51257/a-v1-in199
Abstract
Several studies suggest that energy networks, which are currently mainly centralized, could be more efficient through a wider integration of microgrids. Questions then arise about their optimal design. The PIMENT laboratory of the university of La Reunion created a new decision support software dedicated to the design of microgrids, that optimally sizes the key components according to specified performance indicators. This stochastic algorithm is based on the genetic algorithm approach. The results of a study case are deeply studied.
Conferences
M., David, M.N. Andriamandroso, M. N., P., Behrensdorff Poulsen, J., Castaing-Lasvignottes, N., Cutululis, K., Das, C., Durif-Aboukali, J., Francou, P., Lauret, J., Le Gal La Salle, E., Lorenz, O., Marc, D., Melgar and S., Spataru (2023). A set of study cases for the massive integration of solar renewables in non-interconnected areas. SWC 2023: ISES Solar World Congress 2023, New-Delhi, 30 Oct. – 4 Nov. https://doi.org/10.18086/swc.2023.05.02
Abstract
E., Lorenz, T., Zech, W., Herzberg, P., Lauret, M., David (2024). Probabilistische Kurzfristvorhersage der Globalstrahlung mittels Analog Ensemble unter Nutzung von satellitenbasierter Einstrahlung. Fachtagung Energiemeteorologie, Bad Staffelstein, Germany, 24 Jan..
Abstract
J., Le Gal La Salle, M., David, P., Lauret (2024). Finding the Optimal Size and Design of a Microgrid Energy System Using Genetic Algorithm. EU PVSEC 2024, Vienna, Austria, 23-27 Sept. 2024.
Abstract
T. A., Randrianantenaina, J., Le Gal La Salle, S. V., Spataru, M., David (2024). Increasing the self-sufficiency of the Terre Sainte campus microgrid by expanding the PV capacity while minimizing the cost. EU PVSEC 2024, Vienna, Austria, 23-27 Sept. 2024.
Abstract
This work is dedicated to enhance the self-sufficiency of the the Terre Sainte campus of the University of La Reunion with onsite solar energy production. The main goal is to boost energy production by integrating additional photovoltaic (PV) panels while minimizing the installation and operation costs. To navigate toward this objective, four distinct tasks have been outlined. The first focus of the study is on validating the collected data before its application. Five thorough quality check tests have been carefully performed. This work clearly explains the significant importance of each test in ensuring the reliability of the dataset. Moving forward, the study dives into the simulation of the current microgrid PV systems, drawing comparisons with recorded data to assess the accurracy and reliability of the model. This step is important in establishing the performance of the simulation tool and its alignment with actual observations. In the next step, the research work provides a comprehensive exploration of the available rooftop areas on the campus, strategically identifying potential expanses for the scaling up of the microgrid PV capacity. By analyzing these available areas, the study lays the foundation for informed decision-making in the pursuit of an optimized and efficient solar energy system. Finally, using the results of the previous tasks, this study focuses on minimizing the Levelized Cost of Energy for self-consumption (LCOEsc) while maximizing the self-sufficiency. This strategic approach aims to help identify the optimal combinations of rooftops suitable for installing the additional PV panels.
Grondin, E., Grondin, D., Delsaut, M., Tang, C., & Morel, B. (2024, November 13). Inter-comparison and validation against in-situ measurments of satellite estimates of incoming solar radiation at Reunion BSRN site. Southern African Sustainable Energy Conference SASEC 2024, Somerset West, South Africa.
Abstract
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