The power output of a photovoltaic system is dependent on the operating temperature of the solar cells. For floating PV (FPV), increased wind speeds can result in increased yield due to lowered operating temperatures, which has long been stated as a key advantage for FPV. So far, this effect has not been included in commercial software packages for yield estimation. Typically, only standard settings are provided, taking into account the mounting type (PVsyst) or mounting and module type (Sandia). This may result in an underestimation of the yield, and consequently, the estimated Levelized Cost of Electricity (LCOE) of the FPV project. In this study, a linkage between recorded module temperatures from FPV systems located in The Netherlands and Sri Lanka and the prevalent models employed within PVsyst and Sandia software for estimating module temperatures are established. Our findings reveal that the models within PVsyst and Sandia tend to overestimate module temperatures by 2.4% and 3%, respectively, for each 1 m/s increment in wind speed. We present two methods for determining the single heat loss coefficient, or U-value, tailored to specific sites accounting for local wind conditions. The first method computes the U-value based on the average monthly wind speed, whereas the second employs the irradiance-weighted average monthly wind speed. The latter method can be advantageous for locations characterized by significant fluctuations in wind speeds between night and day. Through a statistical residual analysis comparing measured and modeled module temperatures, we demonstrate that our proposed methods offer a more accurate representation of module temperature compared to the PVsyst and Sandia models when default settings are used. When we subsequently compute the specific yield using both measured and modeled temperatures, we observe that the approach using irradiance-weighted average wind speed shows a higher yield of up to 2% compared to the traditional methods.

Accurately calculating the annual yield of floating PV (FPV) systems necessitates incorporating appropriate FPV-specific heat loss coefficients into the calculation, including both wind-dependent and wind-independent factors. The thermal behavior of several FPV systems has been investigated within this study, through the analysis of heat loss coefficients across various system sizes and configurations. Over a one-year period, data were collected from two measurement sites with three distinct systems: two ~50 kWp demonstrator-scale setups of Solarisfloat (azimuthal tracking) and Solar Float (East-West orientation) and a 2 MWp commercial-scale East–West system by Groenleven. The Solarisfloat demonstrator revealed a wind-dependent heat loss coefficient of 3.2 W/m³Ks. In comparison, the Solar Float demonstrator system displayed elevated wind-dependent heat loss coefficients, measuring 4.0 W/m³Ks for the east-facing module and 5.1 W/m³Ks for the west-facing module. The Groenleven system, which shares design similarities with Solar Float, showed lower wind-dependent heat loss coefficients of 2.7 W/m³Ks for the east-facing module and 2.8 W/m³Ks for the west-facing module. A notable discrepancy in the wind-dependent coefficients, particularly evident under a north wind direction, indicates a more efficient convective cooling effect by the wind on the demonstrator scale system of Solar Float. This could possibly be attributed to improved wind flow beneath its PV modules, setting it apart from the Groenleven system. Additionally, a thermal model founded on a ‘balance-of-energy’ methodology, integrating water temperature as a variable was introduced. The heat loss coefficient, dependent on the surface water temperature, fluctuated around zero, depending on whether the water temperature surpassed or fell below the ambient air temperature. It can be concluded that it is not of added value to introduce this floating specific heat loss coefficient parameter, as this parameter can be integrated in the wind speed independent U_c parameter.

Floating solar technology has gained significant attention as part of the global expansion of renewable energy due to its potential for installation in underutilized water bodies. Several countries, including the Netherlands, have initiated efforts to extend this technology from inland freshwater applications to open offshore environments, particularly within offshore wind farm areas. This development is motivated by the synergistic benefits of increasing site energy density and leveraging the existing offshore grid infrastructure. The deployment of offshore floating photovoltaic (OFPV) systems involves assembling multiple modular units in a marine environment, introducing operational risks that may give rise to safety concerns. To mitigate these risks, weather windows must be considered prior to the task execution to ensure continuity between weather-sensitive activities, which can also lead to additional time delays and increased costs. Consequently, optimizing marine logistics becomes crucial to achieving the cost reductions necessary for making OFPV technology economically viable. This study employs a simulation-based approach to estimate the installation duration of a 5 MWp OFPV plant at a Dutch offshore wind farm site, started in different months and under three distinct risk management scenarios. Based on 20 years of hindcast wave data, the results reveal the impacts of campaign start months and risk management policies on installation duration. Across all the scenarios, the installation duration during the autumn and winter period is 160% longer than the one in the spring and summer period. The average installation durations, based on results from 12 campaign start months, are 70, 80, and 130 days for the three risk management policies analyzed. The result variation highlights the additional time required to mitigate operational risks arising from potential discontinuity between highly interdependent tasks (e.g., offshore platform assembly and mooring). Additionally, it is found that the weather-induced delays are mainly associated with the campaigns of pre-laying anchors and platform and mooring line installation compared with the other campaigns. In conclusion, this study presents a logistics modeling methodology for OFPV systems, demonstrated through a representative case study based on a state-of-the-art truss-type design. The primary contribution lies in providing a framework to quantify the performance of OFPV installation strategies at an early design stage. The findings of this case study further highlight that marine installation logistics are highly sensitive to local marine conditions and the chosen installation strategy, and should be integrated early in the OFPV design process to help reduce the levelized cost of electricity.