School of Computing, Engineering and Technology

Integration of Strategic Zones into Offshore Windfarm Arrays using Explainable Evolutionary Methods

girl on a mountain looking at the sun rise

As offshore wind energy development expands, conflicts with other marine users and critical ecological areas important to the marine environment are becoming more frequent. This PhD aims to address these challenges by using Evolutionary-Computing inspired methods of offshore wind farm layout design that balance energy production with ecological and marine user needs.

Award

MRes | MPhil | PhD | MSc by Research

Start Date

Usually February and October - at individual School's discretion

Research Opportunities

By integrating open spaces and employing explainable AI (XAI) for transparent decision-making, the research aims to create configurations that accommodate vessel traffic, fishing, and conservation efforts without compromising energy efficiency and ensuring accessibility to a broad range of end-users.

Summary

This PhD will use methods of wind farm layout generation to balance renewable energy production with the needs of various stakeholders, including vessel traffic, fishers and the protection of important environmental features. An important aspect of this research is the use of explainable artificial intelligence (XAI) to enhance transparency in the planning process, ensuring that all stakeholders understand the trade-offs and reasoning behind specific layout decisions.

Early stages of this project will focus on identifying suitable open areas within planned wind arrays, using vessel tracking data and environmental information to determine zones where open spaces might be strategically introduced. These open spaces could accommodate vessel routes, enable sustainable fishing practices, and preserve ecological features, providing a multi-functional layout that addresses key stakeholder concerns. Objective optimization using Evolutionary Computing techniques will be used to evaluate a range of configurations, balancing the competing needs of marine users and ecological interests without significantly impacting energy production.

Building on research conducted under the D4NZ initiative, the project will further refine turbine and cable layouts within designated open areas, using evolutionary computation to optimize layouts according to a range of Key Performance Indicators (KPIs). These KPIs include energy efficiency, ecological conservation, compatibility with fishing and marine traffic, and stakeholder satisfaction.

To support this, the candidate will employ a range of XAI techniques to the layouts generated, providing a means for stakeholders to understand the complex trade-offs in layout design. Offshore wind farm planning involves a diverse audience, from technical experts and fishers to policymakers and the public. By exploiting such XAI techniques, this will allow the project’s findings to be presented in an accessible manner, demystifying how optimization algorithms prioritize certain factors and highlighting the trade-offs necessary to balance competing interests. By making these trade-offs clear and understandable, trust in the layout generation process is gained, supporting informed decision-making.

Expected outcomes of this project may include a comprehensive literature review of relevant topics, the development of a methodology and decision support framework for creating strategic zones and the selection of such configurations given a range of KPIs.

Candidates should have previous experience with programming, using languages such as C#, Java, or Python, and be proficient in data analysis to an MSc level or equivalent experience. Prior experience in marine, environmental, or ecological modelling is advantageous but not essential. Familiarity with evolutionary algorithms, optimization techniques, fisheries data analysis, and spatial analysis would also be beneficial; however, training will be provided in these areas to support skill development.