Cost and uncertainty in the design of offshore wind farms

Abstract

Offshore wind cost modelling seeks to understand and quantify how different project specifications, technology choices and market trends contribute to the overall project finances; linking extensive financial valuation to the engineering design and supporting investment decisions at the initial stages of development. This offers a basis for objective communication and decision-making; allowing for a greater number of cases to be analysed; and when considering new ideas, offering the option to assess the economic feasibility and potential. Cost modelling involves a heavy reliance on models. As models become more realistic, they also become more complex and difficult to understand; especially where model inputs are subjected to sources of uncertainty. The goal of the Engineering Doctorate is to develop new methodologies to optimise the design of offshore wind farms subject to uncertainty, simultaneously considering cost and risk aspects. This thesis describes the methodology of a cost modelling tool to evaluate the financial performance of offshore wind assets, as well as the development, validation and deployment of a framework for quantitative uncertainty management with several applications relevant to the offshore wind industry. This framework is key for risk analysis, producing metrics for the spread of the project performance. However, due to model complexity and input uncertainty, modellers find it difficult to grasp the response of the risk metric to variation in cost drivers based, solely, on intuition. For this reason, global sensitivity analysis is used to identify key cost drivers and neglect the contribution of those that are not relevant. To accomplish this, a toolbox is built to benchmark two techniques: the variance-based and distribution-based method against a set of well-known test functions. This comparison provides new insights on the applicability of the methods. In addition, the application of the framework to the cost modelling tool highlights: key parameters when building financial models for offshore wind farms, guidance on additional efforts towards reducing their uncertainties and recommendations when choosing among global sensitivity analysis techniques. The application of this framework to offshore wind cost modelling equips management with a method to arrive at optimal solutions to complex decision-making problems. For example, it provides a competitive advantage when performing strategic and competitive tender analysis, comparative evaluation of multiple sites, detailed evaluation of specific project layouts and sensitivity studies on both design/technology choices and cost variations. Finally, two techno-economic applications are dealt with in this thesis. While the first one provides a framework to answer the question: does the deployment of additional advanced sensing technology, which presumably reduces wind speed uncertainty, compensate for the incurred development expenditure? The second aims at answering the question: given the fact that most of the time the wind farm is not generating at full power, is there any economic benefit to install additional wind turbines for a given export capacity?