The SOWFIA project is a three year project funded by Intelligent Energy Europe (IEE) that brings together ten partners from across Europe who share an interest in planning for wave farm developments.
Wave energy is an innovative and developing technology that could contribute to meeting EU green energy goals and climate change mitigation targets. However, technological and administrative hurdles still need to be overcome in order to establish wave energy as a viable and reliable energy source. A particular issue experienced in the development of the technology across Europe is the lack of knowledge of the potential impacts that Wave Energy Converters (WEC) may have on the environment. The EU union has developed Directives to protect biodiversity, habitats and endangered species. National governments require that the potential effects on the environment be addressed before granting permission for wave converter deployment.
Wave developers must comply with Environmental Impact Assessment (EIA) legislation and consequently are required to supply large amounts of environmental information to facilitate informed decision making.
The EIA legislation was not designed with the wave energy industry in mind; national requirements can vary across Europe. As a nascent industry, environmental effects of wave energy projects are largely unknown at this time. Similarly, the socio-economic impacts of wave energy developments are scarcely addressed in existing consenting processes and are also largely unknown.
The aim of SOWFIA is to investigate the current European wave energy industry and provide recommendations for the streamlining of approval processes and impact assessment requirements for wave energy developments in Europe. This should ensure the protection of marine ecosystems while simultaneously encouraging the development of renewable energy.
In order to achieve this goal, the SOWFIA project is working in collaboration with six European wave energy test centres located in six different EU countries to gather and share environmental and socio-economic data and information. The work carried out by the SOWFIA project is divided in three main streams:
- Analysis of non-technological barriers and accellerators for wave energy development;
- Review of the protocol and methodology used for monitoring of environmental receptors including assessing the likelihood of impacts and creating a data repository for environmental data across the various test centres; and
- Review of the consenting processes in the different EU countries and administrative hurdles that may hinder the development of wave energy.
One of the key goals of the SOWFIA project is the creation of a Data Management Platform (DMP) containing monitoring data from the six test centres and serving as a data repository.
The DMP is an interactive tool designed to assist the decision making process by providing information on different wave energy technologies, monitoring activities at the different test centres and allowing direct visualization and downloading of the data.
The Data Management Platform is publicly available on the SOWFIA website and can be accessed at http://sowfia.hidromod.com/PivotMapViewer/.
The DMP is structured into three main components:
- A Project tab, highlighting the different WECs developed and tested in EU waters;
- A Sites tab, providing information on the environmental monitoring carried out at each test site. This allows for comparison of monitoring techniques and information among the test centres participating in the SOWFIA project; and
- A Map tab, based on Google® Maps technology, which provides a user interface for visualizing and downloading data. Each test centre is indicated on the map, and from here the user can access the different types of data available for that site, view time series data, visualise shape files and become familiar with the monitoring requirements for each activity undertaken at a specific location.
The integration of datasets from the seven test centres contributing to the DMP creates a data repository of environmental information for different factors and increases the ability to undertake more powerful statistical analyses of impacts across sites.