Marine renewable energy (MRE) is an emerging industry hampered by high monitoring costs and extended timelines associated with consenting/permitting (hereafter consenting). Through interactions with US regulators and the international MRE community, OES-Environmental (formerly known as Annex IV) has developed a pathway to risk retirement to inform a set of solutions that could allow regulators to consent and license MRE devices more readily than is currently available.
A key aspect of using the risk retirement process is ensuring that datasets from consented MRE projects are readily available and catalogued so that the two projects (an already consented project, and a project subject to consenting and licensing permission) can be compared in terms of the stressor-receptor interactions, the size and technologies involved in the projects, and the methodologies used to collect data. This process, termed “data transferability”, consists of four components, shown graphically in Figure 1:
- Data transferability framework ‒ brings together datasets in an organized fashion, compares the applicability of each dataset for use in other locations, and guides the process of data transfer.
- Data collection consistency table ‒ provides preferred measurement methods or processes, reporting units, and the most common methods of analysis or interpretation/use of data.
- Monitoring datasets discoverability matrix ‒ allows a practitioner to discover datasets based on the approach presented in the Framework.
- Best management practices ‒ consists of four best management practices (BMPs) that help guide data transferability and collection consistency.
Figure 1. Relationship of four of the components of the data transferability process.
OES-Environmental has engaged with regulators, technical experts, and other stakeholders through surveys, workshops, and direct interactions to receive feedback throughout the development of the data transferability process. More information, including links to relevant workshop presentations, recordings, and reports can be found below in the Outreach & Engagement section.
More information on the components of the data transferability process can also be found in the Data Transferability Short Science Summary.
Data Transferability Framework
Under OES-Environmental, a data transferability framework has been developed that brings together datasets from already consented projects in an organized fashion, compares the applicability of each dataset for use in consenting future projects, assures data collection consistency through preferred measurement methods or processes, and guides the process for data transfer.
The framework can be used to develop a common understanding of data types and parameters to determine and address potential effects, to engage regulators to test the framework, to create a set of BMPs for data transferability and collection consistency, and to set limits and considerations for how BMPs can be applied to assist with effective and efficient siting, consenting, post-installation monitoring, and mitigation.
By implementing the data transferability framework , the siting and consenting processes for installation of single MRE devices and MRE arrays may be shortened and scarce funding resources may be directed toward environmental interactions that remain most uncertain.
Data Collection Consistency
Inherent in the effort to enable the application of monitoring data from already consented projects in one jurisdiction to projects in another jurisdiction is the need to understand how similar the data might be. Ensuring that data from an already consented project are compatible with the needs of future projects, and that multiple datasets from one or more projects can be pooled or aggregated, requires that data are collected consistently and able to be compared. To date, few efforts have prescribed or compared collection methods, instrumentation, or analyses.
MRE is an international industry with consenting processes and research norms that differ among countries, regions, and research and commercial data collection efforts. It would be extremely difficult to enforce the use of specific protocols or instruments to collect all data for pre- or post-installation monitoring. However, encouraging the use of consistent data collection processes and reporting units for monitoring data could increase confidence in the transfer of data from already consented/permitted projects to future projects. Below, the data collection consistency table below provides preferred measurement methods or processes, reporting units, and the most common methods of analysis or interpretation/use of data.
|Stressor||Process or Measurement Tool||Reporting Unit||Analysis or Interpretation|
||Number of collisions or close interactions of animals with turbines to validate collision risk models|
|Sound outputs compared to regulatory action levels. Generally reported as broadband noise.|
|Electromagnetic Fields (EMF)||
||Measured EMF levels to validate existing EMF models around cables|
||Compare potential changes in habitat to maps of rare and important habitats to determine if they are likely to be harmed.|
Population estimates by:
|Changes in Physical Systems||
||Data collected around arrays to validate models.|
Table 1. Data Collection Consistency Table, listing methods for data collection, reporting units, and analysis.
Growing from the OES-Environmental data transferability and risk retirement research, Pacific Northwest National Laboratory’s Triton project is investigating the application of data collection consistency for monitoring around MRE devices under a task known as TFit (Triton Field Trials). As TFit carries out field tests and identifies a suite of methods and instruments that are preferred for measuring key stressor-receptor interactions, OES-Environmental will assess those outputs for potential inclusion in the pathway to risk retirement.
Monitoring Datasets Discoverability Matrix
The monitoring datasets discoverability matrix ("matrix) classifies monitoring datasets from already consented projects for six stressors (collision, underwater noise, electromagnetic fields, habitat change, displacement/barrier effects, changes to physical systems). The matrix will allow regulators and/or developers to discover datasets and evaluate the consistency of information from an already consented project that will allow for the transfer of data to future projects. By doing so, the goal is to increase the efficiency of consenting processes and decrease the need for new monitoring when applicable data already exists.
The matrix is currently under development and will be made available in early 2020.
Best Management Practices
Best management practices (BMPs) were developed to help guide the data transferability process . The BMPs are practical steps for implementation of the data transferability process and address the use of data from consented projects to delineate stressor-receptor interactions as MRE development progresses.
|Best Management Practices||Purpose||Interested Parties|
|BMP 1: Meet the necessary minimum requirements to be considered for data transfer.||Ensure minimum thresholds including using same interactions, are met for transferring data.||Regulators, as well as MRE device developers and consultants.|
|BMP 2: Determine likely datasets that meet data consistency needs and quality assurance requirements.||Ensure methods used to collect/analyze data are compatible and will help to determine the validity of comparing the datasets.||Regulators, as well as MRE device developers and consultants.|
|BMP 3: Use models in conjunction with and/or in place of datasets.||Encourages the use of numerical models to simulate interactions.||Researchers, consultants, regulators.|
|BMP 4: Provide context and perspective for datasets to be transferred.||Encourages the use of available and pertinent
datasets to enhance the interpretation of data and information.
|Device developers, consultants, regulators, researchers.|
Table 2. Best management practices for data transferability, including the purpose of each BMP and the interested parties who would benefit from their use.
Outreach & Engagement
The activities and timeline for developing and sharing the data transferability process with MRE regulators, developers, researchers, and stakeholders are shown here.
- February 2020 Data Transferability Workshops for US and UK Regulators previewing the Monitoring Datasets Discoverability Matrix
- May 2019 Risk Retirement and Data Transferability Workshops for US Regulators
- April 2019 Data Transferability Workshops for US Regulators
- The PNNL team held a series of webinars to engage US regulators and the broader MRE community to gather feedback on the data transferability process and best management practices.
- August 2018 Data Transferability Process for MRE Webinar for US Regulators
- September 2018 Optimizing Permitting for MRE through Data Transferability Webinar
- International Conference on Ocean Energy (ICOE) 2018 Workshop: Based on the findings of the 2018 regulator workshops, a workshop was held at ICOE 2018 in France that brought together regulators, developers, consultants, and researchers to gather additional feedback on the data transferability framework, to detail lessons learned from the focus groups, and to work towards implementation.
- Data Transferability Regulator Workshops: Regulator workshops were held with US regulators in-person and online to present data and information on environmental effects of MRE developments, to understand regulators’ willingness to transfer data, and to gather feedback on the data transferability framework.
- Data Transferability Literature Review and White Paper: The PNNL team completed a literature review on data transferability and put together a white paper that detailed the literature review and a framework based on the findings.
- US Regulator Webinars: The PNNL team held two consecutive webinars to discuss environmental effects of consenting MRE developments with regulators and conducted a survey with US regulators to better understand challenges for consenting MRE developments.