Offshore wind energy technology has developed rapidly over the last decade. It is expected to significantly contribute to the further increase of renewable energy in the global energy production in the future. However, even with floating wind turbines, only a fraction of the global offshore wind energy potential can be harvested because grid-connection, moorings, installation and maintenance costs increase tremendously as the distance to shore and the water depth increase. Thus, new technologies enabling harvesting the far offshore wind energy resource are required. To tackle this challenge, mobile energy ship concepts have been proposed. In those concepts, electricity is produced by a water turbine attached underneath the hull of a ship propelled by the wind using sails. It includes an on-board energy storage system since energy ships are not grid-connected. Thus, the ships route schedules could be dynamically optimized taking into account weather forecast in order to maximize their capacity factors (CF). The aim of this study is to investigate how high the capacity factors of energy ships could be when using weather-routing and compare them to that of stationary wind turbines that would be deployed in the same areas. To that end, a modified version of the weather-routing software QtVlm was used. Velocity and power production polar plots of an energy ship that was designed at LHEEA were used as input to QtVlm. Results show that capacity factors over 80% can be achieved with energy ships and stationary offshore wind turbines deployed in the North Atlantic Ocean.

The energy ship concept has been proposed as an alternative wind power conversion system to harvest offshore wind energy. Energy ships are ships propelled by the wind and which generate electricity by means of water turbines attached underneath their hull, The generated electricity is stored on-board (batteries, hydrogen, etc.) It has been shown that energy ships deployed far-offshore in the North Atlantic Ocean may achieve capacity factors over 80% using weather-routing. The present paper complements this research by investigating the capacity factors of energy ships harvesting wind power in the near-shore. Two case studies are considered: the French islands of Saint-Pierre et-Miquelon, near Canada, and Ile de Sein, near metropolitan France. The methodology is as follows. First, the design of the energy ship considered in this study is presented. It was developed using an in-house Velocity, and Power Performance Program (VPPP) developed at LHEEA. The velocity and power production polar plots of the ship were used as input to a modified version of the weather-routing software QtVlm. This software was then used for capacity factor optimization using 10m altitude wind data analysis which was extracted from the ERA-Interim dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three years (2015, 2016, and 2017) data are considered. The results show that average capacity factors of approximately 40% and 40% can be achieved at Ile de Sein and Saint-Pierre-et-Miquelon with considered energy ship design.

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