A Support Process for Early-Stage Wind Farm Repowering Decisions Using Constrained Optimization Techniques to Address Uncertainty

As wind farms in North America near the end of their design life, different end-of-life options need to be considered. Common options include decommissioning, lifetime extension, and repowering. In this research, a methodology to support early-stage repowering decisions is presented. Performance decline and repowering forecasts are obtained by combining analysis of past performance data and preliminary site plans for new turbines with turbine performance models from windPRO software. Financial metrics are computed using a simple techno-economic model with parameters informed by historical financial records. Repowering decisions are often sensitive to assumptions on key parameters, such as capital cost of repowering, which are poorly defined at the beginning of the process and subject to change quickly. This makes it difficult to provide guidance that will remain relevant as more information is obtained during future project planning stages. In this work, constrained optimization methods are used to identify sets of the key inputs that lie on the break-even point at which repowering is more profitable than continuing operation. Using this approach, which is novel in this context, the client gains an intuition for the ‘envelope’ within which the recommended guidance still holds. This decision-making process is applied to a case study using performance data and cost ranges from a real, anonymous wind farm.