Ya Brigitte Assoa

Achieving the EU 2050 net-zero target requires rapid PV deployment, yet ground-mounted PV systems raise land-use concerns. Ecovoltaics (EPV) aim to co-optimize PV electrical production and ecosystem functions, but soil and especially microbial communities under PV modules remain under-characterized. In this study, PV-induced shading was hypothesised to modify plant development and the soil parameters that would structure bacterial communities. An EPV experiment was conducted with the hyperaccumulator Noccaea caerulescens cultivated in culture trays placed under three PV module technologies and different sun exposures (shade, sun and shade) and in unshaded controls. Soil physicochemical parameters, plant parameters and microbial enzyme activities were measured. The structure and diversity of the bacterial community was also characterized. In this study, plant parameters were affected by growth conditions: plant biomass and leaf C and N contents were higher under PV modules than without PV module shade, while N. caerulescens hyperaccumulation of Cd, Ni and Zn across all culture trays was maintained. Arylsulfatase, β-glucosidase, and alkaline phosphatase activities were lower under full sun conditions. Bacterial alpha-diversity was higher under both shaded and, sun and shade conditions than under unshaded controls. Furthermore, soil parameters were the primary drivers of soil bacterial community variation, with plant parameters being secondary drivers. Moreover, RDA analysis showed that soil Mn content, CEC, plant biomass, and plant C content contributed significantly to total bacterial community variation. These results suggest that PV shading has a direct effect on plant development and an indirect effect on the soil physicochemical parameters and bacterial communities.

Understanding the operating temperature of photovoltaic (PV) modules is crucial for assessing their electrical performance, yet most agrivoltaic models focus on crop yield rather than PV thermal behaviour. This study addresses this gap by proposing a one-dimensional dynamic thermal model based on a nodal approach to simulate the PV modules’ temperatures. The model was validated using PV back-surface temperatures measured during a two-year experiment and integrate soil and vegetation surface temperatures and meteorological parameters. This model (Model 3) was evaluated against two simplified thermal models: the Ross thermal model (Model 1) and the Normal Operating Cell Temperature model (Model 2). Model 3 showed the greatest precision with a Root Mean Square Error (RMSE) down to 1.6°C and a Nash-Sutcliffe Efficiency coefficient (NSE) > 92 %, outperforming Models 1 and 2 by 20 % in NSE and 50-80 % in RMSE reduction. Model 1 performed better under cloudy conditions (RMSE 3.6-4.5°C; NSE 72-80 %), while Model 2 slightly improved sunny-day predictions (NSE up to 82 %). Model 3 consistently provided the best results (NSE > 92 %). Comparing simulated and measured electrical energy production of an opaque module, Models 1 and 2 overestimated output (+2 % to +3 %, more under sunny conditions), while Model 3 exhibited the closest agreement (deviation < 1 %). Parametric simulations with Model 3 also highlighted the potential of brownfield sites in southern France for ecovoltaic deployment. This work provides a robust thermal modelling framework for ecovoltaic systems, improving performance predictions and supporting sustainable energy integration.