SIRTA

Atmospheric Observatory

Renewable energies

The Renewable energies SIRTA’s working group deals with multiple topics related to modelling, characterization, forecasting, experimentation and energy management on renewable energies, in particular solar photovoltaics, wind energy and microgrids. The group is coordinated in collaboration with the Energy4Climate (E4C) Interdisciplinary Center of IP Paris and Ecole des Ponts and has contributions from engineers, professors and researchers from different institutions, including industrial partners. Main technical objectifs are maintaining and developing experimental platforms that are open for innovation, research, teaching and outreaching purposes. 

Laboratories involved: LMD , GeePs, LISN, LPICM , IPVF , SAMOVAR , i3-CRG , CEREA, Météo-France 

 

Activities and projects 

Nuages
View of SIRTA’s main radiative plateform © IPSL

1. Solar and wind resource measurement and estimation

Contact: Johan Parra, Jordi Badosa, Jean-Charles Dupont

Broadband radiation measurements (in short-wave SW and long-wave bands LW) started at SIRTA in 2003 with an installation of a BSRN (Baseline Surface Radiation Network) station (site name: PAL), with the aim of contributing to the climate and satellite community with longterm high quality radiation measurements. On SW (solar radiation) the three irradiance components are measured (global horizontal GHI, direct normal DNI and diffuse horizontal DHI). Other radiation measurements are available at SIRTA with relevance for solar energy studies:

  • Aerosol optical depth and water vapor content with Sunphotometer (in the frame of the AERONET network, site name: Palaiseau)
  • Solar spectrum on a horizontal plane from 350 to 1050 nm.
  • Photosynthetically active radiation (PAR)
  • Global tilted irradiance (GTI) also known as Plane of Array (POA) irradiance.

In addition to high quality radiative measurements, the development and evaluation of methods for the estimation of solar resource on horizontal and tilted planes are other objectives of this activity. Data input can be in situ measurements, nearby stations measurements, geostationary satellite observation or numerical weather predictions.

Wind measurements exist in different kinds at SIRTA: Sodar, UHF radar, wind lidar, sonic anemometer and cup anemometers are installed in the main SIRTA’s zone. This allows vertical wind profile measurements for up to a few km up in the atmosphere and with a spatial resolution that depends on the device. Sonic anemometer measurements exist at different altitudes.

BSRN
Publications
  • Migan-Dubois, A., Badosa J., Bourdin V., Torres Aguilar M. I., Bonnassieux Y. (2021) Estimation of the uncertainty due to each step of simulating the photovoltaic conversion under real operating conditions. Submitted to the Special Issue “Modeling and Forecasting for Energy Production of Photovoltaic (PV) Systems”
Nuages
Outdoor PV modules
© IPSL

2. Outdoor PV modules characterization and modelling

Contact: Anne Migan-Dubois, Jordi Badosa, Vincent Bourdin

Photovoltaic production is strongly dependent on the conditions to which the panels are exposed in terms of lighting, temperature, dirt, shading, humidity (rain, snow, dew, frost) and environment. In this context, test benches for photovoltaic panels in real conditions were installed on the SIRTA main platform. The provision of measurements allows the development of mathematical models of the operation of photovoltaic panels. The test benches are both used for research and training purposes. Three configurations exist: fix tilt PV modules, PV+reflectors and PV modules on sun tracker.

Publications
  • Li, B., Migan-Dubois, A., Delpha, C., & Diallo, D. (2021). Evaluation and improvement of IEC 60891 correction methods for IV curves of defective photovoltaic panels. Solar Energy, 216, 225-237.
  • Nour, C. A., Migan-Dubois, A., Badosa, J., Bourdin, V., Marchand, C., & Akiki, T. (2020). Geometrical optimization for a photovoltaic installation equipped with flat reflectors based on plane of array estimations. EPJ Photovoltaics, 11, 1.
  • Lindsay, N., Libois, Q., Badosa, J., Migan-Dubois, A., & Bourdin, V. (2020). Errors in PV power modelling due to the lack of spectral and angular details of solar irradiance inputs. Solar Energy, 197, 266-278.
  • Fadhel, S., Diallo, D., Delpha, C., Migan, A., Bahri, I., Trabelsi, M., & Mimouni, M. F. (2020). Maximum power point analysis for partial shading detection and identification in photovoltaic systems. Energy Conversion and Management, 224, 113374.
  • Li, B., Migan-Dubois, A., Delpha, C., & Diallo, D. (2020). ANALYSIS OF THE PERFORMANCE OF IV CURVE CORRECTION METHODS IN THE PRESENCE OF DEFECTS. Group, 2(1000), 10-80
Nuages
View of the Nanogrid Research and Teaching Lab (NRLAB)
© IPSL

3. Microgrid energy management

Contact: Fausto Calderon, Jordi Badosa

The development of smart grids is expected to come from the aggregation of basic low voltage power supply networks, known as microgrids, which associate local energy production with storage capacities and energy consumers. Microgrids restricted to a single building are often called “nanogrids” and are also getting attention as the building block of a microgrid.  Such nanogrids are vulnerable to both sudden changes of power generation and load demand because of their small size, especially when operating in an island mode. Thus, managing uncertainty becomes essential when searching for an optimal nanogrid operation. In this context, a laboratory-scale nanogrid (hereafter referred to NRLab for Nanogrid research lab) is being developed at SIRTA to serve as experimental test bench for research and pedagogical purposes in the frame of the Energy4Climate (E4C) Interdisciplinary Centre.

The NRLAB consists of a photovoltaic panel, a wind turbine, a secondary energy source and two types of batteries. The power consumption of the system is fixed with a programmable charge that reads and imposes in real time the power consumption of the Drahi-X Novation Center building on a scale of 1:100

Publications
  • Calderon-Obaldia, F., Badosa, J., Migan-Dubois, A., & Bourdin, V. (2020). A two-step energy management method guided by day-ahead quantile solar forecasts: cross-impacts on four services for smart-buildings. Energies, 13(22), 5882.
  • Levent, T., Bonnassieux, Y., Preux, P., Cordier, P., Henri, G., & Alami, R. (2020). The challenge of controlling microgrids in the presence of rare events with Deep Reinforcement Learning. IET Smart Grid.
  • Dridi, A., Moungla, H., Afifi, H., Badosa, J., Ossart, F., & Kamal, A. E. (2020, June). Machine Learning Application to Priority Scheduling in Smart Microgrids. In 2020 International Wireless Communications and Mobile Computing (IWCMC) (pp. 1695-1700). IEEE.
Nuages
Cloud motion vectors example retrieved by comparing consecutive satellite images
© IPSL

4. Rewable energy forecasting

Contact: Sylvain Cros, Riwal Plougonven, Jordi Badosa

In 2003, SIRTA joint the Baseline Surface Radiation Network (BSRN) with a roof-top radiative station (station PAL, for Palaiseau),  equipped to measure shortwave (Global, direct and diffuse) and longwave downwelling irradiance. All radiometers are class A following the standards of the ISO 9060:2018. The radiometers are manned and strict quality control is applied to measurements before submission to BSRN.

Associated to these measurements, estimations of sky cover and clear-sky irradiance are applied following the Radiative Flux Analyses methodology developed by the Chuck Long’s team (Long and Ackerman (2000), Long and Gaustad (2004), Long et al., (2006)).

BSRN
Publications
  • Paletta, Q., Arbod, G., & Lasenby, J. (2021). Benchmarking of Deep Learning Irradiance Forecasting Models from Sky Images–an in-depth Analysis. arXiv preprint arXiv:2102.00721.
  • Paletta, Q., & Lasenby, J. (2020). A Temporally Consistent Image-based Sun Tracking Algorithm for Solar Energy Forecasting Applications. arXiv preprint arXiv:2012.01059.
  • Cros, S., Badosa, J., Szantaï, A., & Haeffelin, M. (2020). Reliability predictors for solar irradiance satellite-based forecast. Energies, 13(21), 5566.
  • La Salle, J. L. G., Badosa, J., David, M., Pinson, P., & Lauret, P. (2020). Added-value of ensemble prediction system on the quality of solar irradiance probabilistic forecasts. Renewable Energy, 162, 1321-1339.
  • Dupré, A., Drobinski, P., Alonzo, B., Badosa, J., Briard, C., & Plougonven, R. (2020). Sub-hourly forecasting of wind speed and wind Energy. Renewable Energy, 145, 2373-2379.
  • Dupré, A., Drobinski, P., Alonzo, B., Badosa, J., Briard, C., & Plougonven, R. (2020). Sub-hourly forecasting of wind speed and wind Energy. Renewable Energy, 145, 2373-2379.

Table of Contents