Implementing renewable energy generation and more sustainable consumption behaviour in the future inland shipping industry is necessary. Electrification of the inland shipping fleet leads to an increase in electrical energy demand on the consumption side. The generation of decentralized photovoltaic (PV) energy systems on board of general cargo vessels can be one solution to these challenges. To estimate the potential of these decentralized energy systems, accurate simulation models are needed. The objective of this research is to determine the photovoltaic potential of the Dutch general cargo inland shipping fleet.
A method is developed to simulate the energy yield of moving general cargo vessels. The estimated energy yield is based on hourly power calculations. A difference is made between container and bulk vessels. This simulation model consists of a skyline, a vessel, an irradiance, a PV module temperature, and an energy model. In the skyline model, skyline profiles for 3036 waterway points are generated using LIDAR AHN3 height data. The waterways skylines are corrected for every vessel individually. In the vessel model, AIS (automatic identification system) data is used to simulate the sailing behaviour of 2746 vessels. In the irradiance model, the diffused, direct and ground reflection irradiance received by the PV panel is simulated. The diffused irradiance is calculated according to the Perez model. The PV module temperature is estimated according to the fluid-dynamic model. The additional air caused by the forward movement of the vessel and the water temperature is taken into account. Finally, the PV energy yield is calculated by the received irradiance, the suitable PV surface and the corrected PV module efficiency.
An experiment is performed to validate the developed model, where a PV panel is installed on the vessel Harmonie. Equipment is installed to monitor Harmonie during one docking week and two sailing weeks. A co-variance linear regression model describes the relationship between the measured and the estimated PV power. According to the co-variance linear regression model, the P-value for the simulated PV power is below 0.05 and, therefore statistically significant. The outcome of the linear regression model is overestimated by 4 % with a 95 % confidence interval between 0.87 and 1.05.
740,958 PV panels can be installed on the general cargo fleet. Together, these panels have an installed peak power of 267 [MW] and an annual estimated PV potential of 230 [GWh]. The annual PV energy per unit area of a container vessel is 171 [kWh/m2 ] and for a bulk vessel 168 [kWh/m2 ]. The annual PV energy per installed power for a container vessel is 864 [Wh/Wp] and for a bulk vessel 852 [Wh/Wp]. When the outliers are removed from the annual specific power [Wh/Wp] dataset, the complete fleet can be modelled by a Weibull distribution. A t locationscale distribution is suggested when the outliers are not removed from the dataset. The average annual energy demand of a container vessel is 1440 [MWh], 7.17 % of this demand can be supplied by the PV panels installed on the vessel. Bulk vessels have an energy demand of 1350 [MWh] on average, of which the installed PV panels can cover 5.82 %.