Offshore areas in Norwegian waters with negative correlation between wind and wave energy resources have been identified. This finding is of great importance, as it suggests that combined wind and wave energy deployment sites may be particularly efficient, as they provide a prolonged effective period of use with high energy absorption which would otherwise not be possible. This comes with the additional economic advantage of concurrent location in terms of installation and access for maintenance. In the present study, co-located wind and wave farms are considered at different sites with varying wind-wave correlations. Metrics such as the Annual Energy Production (AEP) and Mean Annual Energy Production (MAEP), and their variation during a year are analyzed. Different wind/wave energy converter designs with published power matrices are used for estimating the absorbed power, as well as other relevant metrics, throughout the entire period covered by the ERA5 database. A methodology is proposed for easily assessing the potential of new marine wind and wave renewable devices at early stages of development, covering areas of the Norwegian coastal zone, and using real climate data.


Following Cozzi et al. (2022), electricity use based on renewable energy grew to 26.2 % (2019), bringing the share of renewables in global electricity consumption to the fifth place of Total Final Energy Consumption (TFEC) in 2019. Hydropower remains by far the largest source of renewable electricity globally, followed by wind and solar PV.

Lately, the combination of offshore wind and wave energy has attracted the interest of both academic and industrial world; see, e.g., Pérez-Collazo et al. (2015); McTiernan and Sharman (2020).

There are various synergies in this combination that could potentially lead to benefits both in the technological domain (common grid infrastructure, shared logistics, common mooring or foundation systems, shadow effects) and the generation process (enhanced energy harvest, smoother power output,reduction in sudden grid disconnections, reduced number of hours of non-activity, more accurate output forecast) (Rosa-Santos et al., 2022).

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