EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 16 (2025) EPJ Photovolt., 16 (2025) 33 References
Issue
EPJ Photovolt.
Volume 16, 2025
Special Issue on ‘EU PVSEC 2025: State of the Art and Developments in Photovoltaics', edited by Robert Kenny and Carlos del Cañizo
Article Number 33
Number of page(s) 20
DOI https://doi.org/10.1051/epjpv/2025018
Published online 09 January 2026
  1. S.A. Pvsyst, Pvsyst software. https://www.pvsyst.com/. Accessed: 2025-09–10. [Google Scholar]
  2. T. Huld, I. Pinedo Pascua, A. Gracia Amillo, in PVGIS 5: Internet tools for the assessment of solar resource and photovoltaic solar systems (2017) [Google Scholar]
  3. W. Holmgren, C. Hansen, M. Mikofski, pvlib python: a python package for modeling solar energy systems, J. Open Source Softw. 3, 884 (2018) [CrossRef] [Google Scholar]
  4. M. Theristis, N. Riedel-Lyngskær, J.S. Stein, L. Deville, L. Micheli, A. Driesse, W.B. Hobbs, S. Ovaitt, R. Daxini, D. Barrie, M. Campanelli, H. Hodges, J.R. Ledesma, I. Lokhat, B. McCormick, B. Meng, B. Miller, R. Motta, E. Noirault, M. Parker, J. Polo, D. Powell, R. Moreton, M. Prilliman, S. Ransome, M. Schneider, B. Schnierer, B. Tian, F. Warner, R. Williams, B. Wittmer, C. Zhao, Blind photovoltaic modeling intercomparison: a multidimensional data analysis and lessons learned, Prog. Photovolt.: Res. Appl. 31, 1144 (2023) [Google Scholar]
  5. D. Yang, B. Liu, H. Zhang et al., A second tutorial review of the solar power curve: applications in energy meteorology, Adv. Atmos. Sci. 42, 269 (2025) [Google Scholar]
  6. D. Yang, W. Wang, C.A. Gueymard, T. Hong, J. Kleissl, J. Huang, M.J. Perez, R. Perez, J.M. Bright, X. Xia, D. van der Meer, I.M. Peters, A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: towards carbon neutrality, Renew. Sustain. Energy Rev. 161, 112348 (2022) [Google Scholar]
  7. B.Y.H. Liu, R.C. Jordan, The interrelationship and characteristic distribution of direct, diffuse and total solar radiation, Sol. Energy 4, 1 (1960) [Google Scholar]
  8. R.E. Bird, C. Riordan, Simple solar spectral model for direct and diffuse irradiance on horizontal and tilted planes at the earth's surface for cloudless atmospheres, J. Appl. Meteorol. Climatol. 25, 87 (1986) [Google Scholar]
  9. J. Antonanzas, N. Osorio, R. Escobar, R. Urraca, F.J. Martinez de Pison, F. Antonanzas-Torres, Review of photovoltaic power forecasting, Sol. Energy 136, 78 (2016) [Google Scholar]
  10. M.J. Mayer, D. Yang, Pairing ensemble numerical weather prediction with ensemble physical model chain for probabilistic photovoltaic power forecasting, Renew. Sustain. Energy Rev. 175, 113171 (2023) [Google Scholar]
  11. M.J. Mayer, G. Gróf, Extensive comparison of physical models for photovoltaic power forecasting, Appl. Energy 283, 116239 (2021) [Google Scholar]
  12. A. Armstrong, R.R. Hernandez, G.A. Blackburn, G. Davies, M. Hunt, J.D. Whyatt, L. Guoqing, Local microclimatic impacts of utility scale photovoltaic solar parks, in 22nd EGU General Assembly (mar. 2020) [Google Scholar]
  13. J. Jiang, X. Gao, Q. Lv, Z. Li, P. Li, Observed impacts of utility-scale photovoltaic plant on local air temperature and energy partitioning in the barren areas, Renew. Energy 174, 157 (2021) [Google Scholar]
  14. C. Dupraz, H. Marrou, G. Talbot, L. Dufour, A. Nogier, Y. Ferard, Combining solar photovoltaic panels and food crops for optimising land use: towards new agrivoltaic schemes, Renew. Energy 36, 2725 (2011) [Google Scholar]
  15. G.A. Barron-Gafford, M.A. Pavao-Zuckerman, R.L. Minor, L.F. Sutter, I. Barnett-Moreno, D.T. Blackett, M. Thompson, K. Dimond, A.K. Gerlak, G.P. Nabhan et al., Agrivoltaics provide mutual benefits across the food-energy-water nexus in drylands, Nat. Sustain. 2, 09 (2019) [Google Scholar]
  16. P. Yang, L.H.C. Chua, K.N. Irvine, J. Imberger, Radiation and energy budget dynamics associated with a floating photovoltaic system, Water Res. 206, 117745 (2021) [Google Scholar]
  17. N. Ali, Agrivoltaic system success: a review of parameters that matter, J. Renew. Sustain. Energy 16, 04 (2024) [Google Scholar]
  18. H. Marrou, L. Guilioni, L. Dufour, C. Dupraz, J. Wery, Microclimate under agrivoltaic systems: Is crop growth rate affected in the partial shade of solar panels?, Agric. Forest Meteorol. 177, 117 (2013) [Google Scholar]
  19. R. Nobre, S. Boulêtreau, F. Colas, F. Azemar, L. Tudesque, N. Parthuisot, P. Favriou, J. Cucherousset, Potential ecological impacts of floating photovoltaics on lake biodiversity and ecosystem functioning, Renew. Sustain. Energy Rev. 188, 113852 (2023) [Google Scholar]
  20. J. Chopard, A. Bisson, G. Lopez, S. Persello, C. Richert, D. Fumey, Development of a decision support system to evaluate crop performance under dynamic solar panels, AIP Conf. Proc. 2361, 050001 (2021) [Google Scholar]
  21. Y. Bellone, M. Croci, G. Impollonia, A. Nik Zad, M. Colauzzi, P.E. Campana, S. Amaducci, Simulation-based decision support for agrivoltaic systems, Appl. Energy 369, 123490 (2024) [Google Scholar]
  22. J. Chopard, G. Lopez, S. Persello, D. Fumey, Modelling canopy temperature of crops with heterogeneous canopies grown under solar panels. février 2024. Licence Creative Commons Attribution 4.0 International [Google Scholar]
  23. S. Edouard, D. Combes, M. Van Iseghem, M. Tin, A. Escobar-Gutiérrez, Increasing land productivity with agriphotovoltaics: application to an alfalfa field, Appl. Energy 329, 120207 (2023) [Google Scholar]
  24. V.S. Nysted, L.E.S. Stieng, M. Kumar, N. Roosloot, G. Otnes, T. Kjeldstad, J. Selj, Modelling wave-induced losses for floating photovoltaics: impact of design parameters and environmental conditions, Sol. Energy 293, 113439 (2025) [Google Scholar]
  25. H. Jin, X. Kong, C. Wang, D. Zhang, Y. Yao, A methodology for simulation of power generation characteristics and enhancement of mppt performance of offshore floating photovoltaic arrays, Appl. Energy 393, 126129 (2025) [Google Scholar]
  26. S. Yue, M. Guo, P. Zou, W. Wu, X. Zhou, Effects of photovoltaic panels on soil temperature and moisture in desert areas, Environ. Sci. Pollut. Res. 28, 17506 (2021) [Google Scholar]
  27. J. Sun, Y. He, X. Li, Z. Lu, X. Yang, Cfd simulations for layout optimal design for ground-mounted photovoltaic panel arrays, J. Wind Eng. Ind. Aerodyn. 242, 105558 (2023) [Google Scholar]
  28. V. Fthenakis, Y. Yu, Analysis of the potential for a heat island effect in large solar farms, in IEEE 39th Photovoltaic Specialists Conference (PVSC). (2013) [Google Scholar]
  29. D. Lindholm, J. Selj, T. Kjeldstad, H. Fjær, V. Nysted, CFD modelling to derive U-values for floating PV technologies with large water footprint, Sol. Energy 238, 238 (2022) [CrossRef] [Google Scholar]
  30. B. Berlioux, B. Amiot, J. Vernier, M. Ferrand, R. Le Berre, O.-L. Rhazi, R. Knikker, H. Pabiou, Modélisation des transferts de chaleur et de masse dans une centrale photovoltaïque flottante : utilisation de la méthode des frontières immergées, in Société Française de Thermique(Ed.), Actes du 33ème Congrès Français de Thermique (Le Bourget du Lac, France, June 2025, LOCIE), pp. 487–494 [Google Scholar]
  31. J. Vernier, B. Berlioux, B. Amiot, S. Edouard, M. Ferrand, E. Dupont, C. Caruyer, V. Trotin, D. Combes, P. Massin, An innovative method based on cfd to simulate the influence of photovoltaic panels on the microclimate in agrivoltaic conditions, Sol. Energy 297, 113571 (2025) [Google Scholar]
  32. A. Glick, N. Ali, J. Bossuyt, M. Calaf, R. Bayoán Cal, Utility-scale solar PV performance enhancements through system-level modifications, Sci. Rep. 10, 1 (2020) [NASA ADS] [CrossRef] [Google Scholar]
  33. S.E. Smith, B. Viggiano, N. Ali, T.J. Silverman, M. Obligado, M. Calaf, R.B. Cal, Increased panel height enhances cooling for photovoltaic solar farms, Appl. Energy 325, 119819 (2022) [CrossRef] [Google Scholar]
  34. S.E. Smith, B.J. Stanislawski, B.K. Eng, N. Ali, T.J. Silverman, M. Calaf, R.B. Cal, Viewing convection as a solar farm phenomenon broadens modern power predictions for solar photovoltaics, J. Renew. Sustain. Energy. 14, 063502 (2022) [Google Scholar]
  35. M.P. van Soest, S.R. de Roode, R.A. Verzijlbergh, F.C. Vossepoel, H.J.J. Jonker, Improving solar radiation forecasts during stratocumulus conditions using large eddy simulations and an ensemble kalman filter, J. Adv. Model. Earth Syst. 17, e2024MS004759 (2025) [Google Scholar]
  36. W.C. Skamarock, J.B. Klemp, J. Dudhia, D.O. Gill, Z. Liu, J. Berner, W. Wang, J.G. Powers, M.G. Duda, D.M. Barker, X.-Y. Huang, A description of the advanced research wrf version 4. Tech. Note NCAR/TN-556+STR, NCAR, 2019 [Google Scholar]
  37. Y. Seity, P. Brousseau, S. Malardel, G. Hello, P. Bénard, F. Bouttier, C. Lac, V. Masson, The arome-france convective-scale operational model, Mon. Weather Rev. 139, 976 (2011) [Google Scholar]
  38. V. Masson, P. Le Moigne, E. Martin, S. Faroux, A. Alias, R. Alkama, S. Belamari, A. Barbu, A. Boone, F. Bouyssel, P. Brousseau, E. Brun, J.-C. Calvet, D. Carrer, B. Decharme, C. Delire, S. Donier, K. Essaouini, A.-L. Gibelin, H. Giordani, F. Habets, M. Jidane, G. Kerdraon, E. Kourzeneva, M. Lafaysse, S. Lafont, C. Lebeaupin Brossier, A. Lemonsu, J.-F. Mahfouf, P. Marguinaud, M. Mokhtari, S. Morin, G. Pigeon, R. Salgado, Y. Seity, F. Taillefer, G. Tanguy, P. Tulet, B. Vincendon, V. Vionnet, A. Voldoire, The surfexv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes, Geosci. Model Dev. 6, 929 (2013) [Google Scholar]
  39. R.A. Fisher, C.D. Koven, Perspectives on the future of land surface models and the challenges of representing complex terrestrial systems, J. Adv. Model. Earth Syst. 12, e2018MS001453 (2020) [Google Scholar]
  40. D.L. Evans, L.W. Florschuetz, Cost studies on terrestrial photovoltaic power systems with sunlight concentration, Sol. Energy 19, 255 (1977) [Google Scholar]
  41. V. Masson, M. Bonhomme, J.-L. Salagnac, X. Briottet, A. Lemonsu, Solar panels reduce both global warming and urban heat island, Front. Environ. Sci. 2, (2014) [Google Scholar]
  42. H. Taha, The potential for air-temperature impact from large-scale deployment of solar photovoltaic arrays in urban areas, Sol. Energy 91, 358 (2013) [Google Scholar]
  43. F. Salamanca, M. Georgescu, A. Mahalov, M. Moustaoui, A. Martilli, Citywide impacts of cool roof and rooftop solar photovoltaic deployment on near-surface air temperature and cooling energy demand, Bound. -Layer Meteorol. 161, 203 (2016) [Google Scholar]
  44. R. Chang, Y. Yan, Y. Luo, C. Xiao, C. Wu, J. Jiang, W. Shi, A coupled WRF-PV mesoscale model simulating the near-surface climate of utility-scale photovoltaic plants, Sol. Energy 245, 278 (2022) [Google Scholar]
  45. B. Amiot, R. Le Berre, S. Giroux-Julien, M. Ferrand, Boundary-layer parameterization for assessing temperature and evaporation in floating photovoltaics at the utility-scale. Available at SSRN: https://ssrn.com/abstract=5404845 or https://doi.org/10.2139/ssrn.540484 [Google Scholar]
  46. L. Rapella, N. Viovy, J. Polcher, D. Faranda, J. Badosa, P. Drobinski, Simulating generic agrivoltaic systems with orchidee: model development and multi-case study insights, Agric. Forest Meteorol. 371, 110589 (2025) [Google Scholar]
  47. R. Chang, Y. Shen, Y. Luo, B. Wang, Z. Yang, P. Guo, Observed surface radiation and temperature impacts from the large-scale deployment of photovoltaics in the barren area of gonghe, China, Renew. Energy 118, 131 (2018) [Google Scholar]
  48. A. Almgren, A. Lattanzi, R. Haque, P. Jha, B. Kosovic, J. Mirocha, B. Perry, E. Quon, M. Sanders, D. Wiersema, D. Willcox, X. Yuan, W. Zhang, Erf: Energy research and forecasting, J. Open Source Softw. 8, 5202 (2023) [Google Scholar]
  49. P.A. Jimenez, J.P. Hacker, J. Dudhia, S.E. Haupt, J.A. Ruiz-Arias, C.A. Gueymard, G. Thompson, T. Eidhammer, A. Deng, Wrf-solar: Description and clear-sky assessment of an augmented nwp model for solar power prediction, Bull. Am. Meteorol. Soc. 97, 1249 (2016) [Google Scholar]
  50. J. Thaker, R. Höller, Evaluation of high resolution wrf solar, Energies 16, 3518 (2023) [Google Scholar]
  51. S. Wang, T. Dai, C. Li, Y. Cheng, G. Huang, G. Shi, Improving clear-sky solar power prediction over china by assimilating himawari-8 aerosol optical depth with wrf-chem-solar, Remote Sens. 14, 4990 (2022) [Google Scholar]
  52. K. Perini, A. Chokhachian, S. Dong, T. Auer, Modeling and simulating urban outdoor comfort: coupling envi-met and trnsys by grasshopper, Energy Build. 152, 373 (2017) [Google Scholar]
  53. N. Luo, X. Luo, M. Mortezazadeh, M. Albettar, W. Zhang, D. Zhan, L. (Leon) Wang, T. Hong, A data schema for exchanging information between urban building energy models and urban microclimate models in coupled simulations, J. Build. Perform. Simul. 18, 333 (2025) [Google Scholar]
  54. T. Vermeulen, J.H. Kämpf, B. Beckers et al., Urban form optimization for the energy performance of buildings using citysim, in Proceedings of CISBAT (2013), pp. 915–920 [Google Scholar]
  55. Modelica Association, Functional mock-up interface for model exchange and co-simulation, 2022. Accessed on 18 May 2025 [Google Scholar]
  56. S. Hong, H. Hwang, D. Kim, S. Cui, I. Joe, Real driving cycle-based state of charge prediction for ev batteries using deep learning methods, Appl. Sci. (Switzerland) 11, 11285 (2021) [Google Scholar]
  57. H. Andersson, P. Nordin, T. Borrvall, K. Simonsson, D. Hilding, M. Schill, P. Krus, D. Leidermark, A co-simulation method for system-level simulation of fluid-structure couplings in hydraulic percussion units, Eng. Comput. 33, 317 (2017) [Google Scholar]
  58. W.S. Dols, S.J. Emmerich, B.J. Polidoro, Coupling the multizone airflow and contaminant transport software contam with energyplus using co-simulation, Build. Simul. 9, 469 (2016) [Google Scholar]
  59. T.L. Nguyen, Q.T. Tran, R. Caire, Y. Besanger, T. The Hoang, V.H. Nguyen, Fmi compliant approach to investigate the impact of communication to islanded microgrid secondary control, arXiv:1712.00888, 2017 [Google Scholar]
  60. P.J. Hueros-Barrios, F.J. Rodriguez-Sánchez, P. Martin-.Sánchez, M. Tradacete-Ágreda, C. Santos-Perez, Digital twin design framework for photovoltaic generation systems using fmu and modelica, in 2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON) (IEEE, 2024), pp. 461–466 [Google Scholar]
  61. G.M. Ottino, D. Manzione, Buildings indoor environment monitoring: a hybrid approach for coupling real and virtual sensors, in Roomvent 2020 (2020). DOFWARE S.r.l., Turin, Italy) [Google Scholar]
  62. T. Guilbaud, C. Fiorina, S. Lorenzi, A. Scolaro, F. Carminati, D. Maire, A. Pautz, Investigating the functional mock-up interface as a coupling framework for the multi-fidelity analysis of nuclear reactors, Prog. Nucl. Energy 169, 105022 (2024) [Google Scholar]
  63. The MathWorks Inc. Simulink version: 24.2 (r2024b), 2022 [Google Scholar]
  64. P. Fritzson, A. Pop, K. Abdelhak, A. Ashgar, B. Bachmann, W. Braun, D. Bouskela, R. Braun, L. Buffoni, F. Casella, R. Castro, R. Franke, D. Fritzson, M. Gebremedhin, A. Heuermann, B. Lie, A. Mengist, L. Mikelsons, K. Moudgalya, L. Ochel, A. Palanisamy, V. Ruge, W. Schamai, M. Sjölund, B. Thiele, J. Tinnerholm, P. Östlund, The OpenModelica integrated environment for modeling, simulation, and model-based development, Model. Identif. Control 41, 241 (2020) [Google Scholar]
  65. C. Andersson, J. Åkesson, C. Führer, Pyfmi: A python package for simulation of coupled dynamic models with the functional mock-up interface. Technical Report in Mathematical Sciences 2, Centre for Mathematical Sciences, Lund University, 2016 [Google Scholar]
  66. T. Thummerer, L. Mikelsons, J. Kircher, Neuralfmu: towards structural integration of fmus into neural networks, in Proceedings of 14th Modelica Conference 2021 (Linköping, Sweden, September 20-24 2021) [Google Scholar]
  67. J. Brkic, M. Ceran, M. Elmoghazy, R. Kavlak, A. Haumer, C. Kral, Open source PhotoVoltaics library for systemic investigations, in Linköping Electronic Conference Proceedings (Linköping University Electronic Press, feb 2019), pp. 40–50 [Google Scholar]
  68. B. Amiot, H. Pabiou, R. Le Berre, S. Giroux-Julien, An innovative method for measuring the convective cooling of photovoltaic modules, Sol. Energy 274, 112531 (2024) [Google Scholar]
  69. EDF SA. code_saturne homepage., 2025. https://www.code-saturne.org/cms/web/ [Google Scholar]
  70. M. Milliez, B. Carissimo, Numerical simulations of pollutant dispersion in an idealized urban area, for different meteorological conditions, Bound. -Layer Meteorol. 122, 321 (2007) [Google Scholar]
  71. L. Al Asmar, L. Musson-Genon, E. Dupont, J.-C. Dupont, K. Sartelet, Improvement of solar irradiance modelling during cloudy-sky days using measurements, Sol. Energy 230, 1175 (2021) [Google Scholar]
  72. H. Amino, C. Flageul, B. Carissimo, M. Ferrand, CFD study of PM10 dispersion in a sports stadium using a mesh based on geometry obtained from a 3-D cloud of laser points, in: HARMO24 (Pärnu, Estonia, June 2024) [Google Scholar]
  73. V. Guimet, D. Laurence, A linearised turbulent production in the k-ϵ model for engineering applications, in Engineering Turbulence Modelling and Experiments 5, Elsevier, 2002, pp. 157–166 [Google Scholar]
  74. A.A. Lacis, J. Hansen, A parameterization for the absorption of solar radiation in the earth's atmosphere, J. Atmos. Sci. 31, 118 (1974) [Google Scholar]
  75. K. Sartelet, C. Legorgeu, L. Lugon, Y. Maanane, L. Musson-Genon, Representation of aerosol optical properties using a chemistry transport model to improve solar irradiance modelling, Sol. Energy (2018) [Google Scholar]
  76. Y. Seity, P. Brousseau, S. Malardel, G. Hello, P. Bénard, F. Bouttier, C. Lac, V. Masson, The arome-france convective-scale operational model, Mon. Weather Rev. 139, 976 (2011) [Google Scholar]
  77. Meteo-France, Open-source data services of meteo-france (arome forecasts), 2025. https://www.data.gouv.fr/fr/dataservices/api-modele-arome/ [Google Scholar]
  78. E. Skoplaki, J.A. Palyvos, Operating temperature of photovoltaic modules: A survey of pertinent correlations, Renew. Energy 34, 23 (2009) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.