A model for estimating life cycle environmental impacts of offshore wind electricity considering specific characteristics of wind farms

Offshore wind electricity is becoming an important source of renewable energy due to its global warming potential (GWP). However, the GWP can vary significantly, depending on many factors, including the capacity of the installation, distance from the shore, supporting structure andmaintenance requirements. Currently, there is a lack of life cycle assessment (LCA) studies that take these specific conditions into account. As a consequence, developers and policy makers rely on average GWP values which could lead to inaccurate estimates of the GWP and other impacts. To address this gap, this paper presents a new model for estimating the life cycle impacts of offshore
wind electricity taking into account specific technical characteristics of individual installations and whole wind farms. Aimed at non-experts, the model provided freely with this paper is developed in Excel and follows the ISO 14040/44 LCA methodology. Supported by the built-in background LCA databases, it requires users to specify only a few key characteristics of an existing or proposed
installation, thus facilitating quick and yet robust estimations of impacts. Eleven impacts can be considered, including GWP, depletion of resources, human toxicity and eco-toxicities. The application of the model is illustrated by quantifying the LCA impacts of 20 offshore wind farms (OWF) operating in the UK. The results show that the impacts vary considerably with the specific
characteristics of OWF, including the age, type and size of wind turbines, their capacity and distance from the shore. For example, the GWP ranges by a factor of three (6.4-19.5 g CO2 eq./kWh) and the other impacts by a factor of 2.2-3.2. The developed model can be used by designers, developers and policy makers to customise the inputs for a specific OWF and estimate the impacts quickly and
cost-efficiently, without the need for prior expertise in LCA and extensive data collection.
Keywords: Energy planning; Energy modelling; Life cycle assessment; Wind electricity; Sustainable
energy