A modelling approach for offshore wind farm feasibility with respect to ecosystem-based marine spatial planning

Last modified: 
December 13, 2019 - 1:38pm
Type: Journal Article
Year of publication: 2019
Date published: 06/2019
Authors: Kemal Pınarbaşı, Ibon Galparsoro, Daniel Depellegrin, Juan Bald, Germán Pérez-Morán, Angel Borja
Journal title: Science of The Total Environment
ISSN: 00489697

Demand for renewable energy is increasing steadily and regulated by national and international policies. Offshore wind energy sector has been clearly the fastest in its development among other options, and development of new wind farms requires large ocean space. Therefore, there is a need of efficient spatial planning process, including the site selection constrained by technical (wind resource, coastal distance, seafloor) and environmental (impacts) factors and competence of uses. We present a novel approach, using Bayesian Belief Networks (BBN), for an integrated spatially explicit site feasibility identification for offshore wind farms. Our objectives are to: (i) develop a spatially explicit model that integrates the technical, economic, environmental and social dimensions; (ii) operationalize the BBN model; (iii) implement the model at local (Basque Country) and regional (North East Atlantic and Western Mediterranean), and (iv) develop and analyse future scenarios for wind farm installation in a local case study. Results demonstrated a total of 1% (23 km2) of moderate feasibility areas in local scaled analysis, compared to 4% of (21,600 km2) very high, and 5% (30,000 km2) of high feasibility in larger scale analysis. The main challenges were data availability and discretization when trying to expand the model from local to regional level. The use of BBN models to determine the feasibility of offshore wind farm areas has been demonstrated adequate and possible, both at local and regional scales, allowing managers to take management decisions regarding marine spatial planning when including different activities, environmental problems and technological constraints.

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