Data-driven Infrastructure Planning for Offshore Wind Farms

The UK's share of offshore wind energy has been steadily increasing in recent years; there is now 10.4 GW of installed capacity offshore (reference: UK Wind Energy Database online: (https://www.renewableuk.com/page/UKWEDhome/Wind-Energy-Statistics.htm). There are already plans for developing large-scale offshore wind farms with capacities exceeding 1 GW.

There remains however a degree of uncertainty over how to best develop, maintain and operate the wind farms and their underlying connection system to achieve cost competitiveness compared to conventional generation technologies. For example, larger turbines located farther offshore are more difficult to access.

Further, accessibility of offshore assets depends on weather conditions, which can have a significant impact on income and expenditure.

Consequently, the operator does not always possess sufficient information to make the most cost-effective decisions relating to planning and maintaining their assets.

Similar problems arise from uncertainty over long-term decisions for investment and integration of offshore wind assets.

Existing modelling methods make simplifying assumptions which unfortunately lead to suboptimal solutions, especially in larger wind farms where many factors are at play.

For instance, existing tools typically use two state Markov chains for failure and repair, meaning that failure and repair times are exponentially distributed.

Whilst this can be a reasonable approximation for failure, repair is rarely exponentially distributed due to sometimes large and random lead times to reach the assets depending on weather conditions.

Durham has experience with more accurately representing failure and repair processes and handling modelling assumptions, especially under severe uncertainty in the planning stage.

The aim of this project is to develop new methods for modelling and optimising decisions involving planning and operation for larger offshore wind farms, especially when facing uncertainties in the available actionable information.

We also aim to link failure and repair to the environmental conditions of the wind farm, and operational conditions of individual turbines.

We expect this to improve the predictive capability of failures and repairs, including the consideration of turbine accessibility, thereby reducing costs.

The outcome of this project will inform decision-making processes for years to come for efficient integration and operation of larger offshore wind farms paving the way for future developments in a more robust and cost-effective manner.