Abstract
Tidal energy has the potential to form an important part of renewable energy production, helping to reach global targets, such as those outlined in the Paris Agreement. The predictable nature of tides offers a reliable supply of renewable energy, making tidal energy converters (TECs) a desirable source of electrical generation. However, the drive for sustainable development and the legislative protection of species and habitats means that, in most countries, the environmental impacts of TECs must be quantified before consent for an installation is granted. There are several environmental issues raised regarding TECs, however, one major concern is the risk of an animal fatally colliding with the moving parts of a TEC i.e. collision risk. Collision risk was identified as an issue before the first grid-connected TEC was installed in 2008. Now, over 10 years later, it is still a major barrier in the consenting process. To address collision risk, modelling is used to predict the likely number of animals to collide with a TEC in a given time period, and the results from these models are presented to allow informed licencing decisions by regulators. Several collision risk models have been used in previous environmental assessments, however, they all have limitations. Firstly, they cannot estimate risk for novel device designs such as a tidal kite or crossflow devices. Secondly, they are limited in their ability to incorporate additional information, such as through altering input parameters or post-processing of results. Previously, a simulation-based approach to estimating collision risk was developed for a tidal kite using open-source 3D modelling software. By simulating the 3D shapes of a TEC, an animal, and their movement, the approach allows for all aspects of the model to be controlled and therefore has the potential to be developed to address many different aspects of collision risk modelling. Here, the development of a simulation-based approach to collision risk was undertaken with the aim of producing robust collision risk estimates between animals and tidal energy convertors. Developments such as (i) producing collision risk probabilities for any device design, (ii) altering various animal parameters, such as angle of approach and different body size, and (iii) extracting the speed of collisions and where they occur on the body of the animal to estimate mortality were undertaken. Also, (iv) streamlining of the model was performed to reduce computational time and (v) results from the simulation-based approach were compared against two collision risk models previously used in environmental impact assessment, reproducing the results of a collision risk assessment. This work has resulted in the development of collision risk model using a simulation-based approach that can incorporate any type of TEC using the free open-source software Blender. I also demonstrated how variations in input parameters (animal size, speed, angle of approach) can be incorporated into the model with relative ease and how it can be used to post-process results from the simulations to incorporate additional information on animal behaviour. I provided an example of how mortality estimates can be obtained from the model, based on collision speed and where on the animal the collision occurs. I also highlighted the complexities of collision risk modelling which identified that some relationships between parameters are non-linear. The efficiency of the model was also improved by decreasing the computational time needed for simulations to run. Finally, I demonstrated that the model produces similar results to two previously used collision risk models, the encounter rate and Band model, for a simple scenario when all conditions are matched, which provided reassurance in the validity of the approach. Overall, I have developed a model using a simulation-based approach that can be adapted and expanded to the needs of the user and therefore provides a state-of-the-art tool for producing collision risk estimates. The work presented in this thesis significantly advances the modelling of collision risk between TECs and animals. The flexibility of the simulation-based approach allows for the best available data to be incorporated to produce robust and transparent collision risk estimates. Ultimately, this collision risk model is a step forward that was needed, as collision risk continues to be a barrier to the growth of the tidal energy industry.