Wind farm capital cost regression model for accurate life cycle cost estimation

Various studies over the last decade have attempted to forecast capital cost of wind power. The main assumption underpinning these models is that cost reductions will accrue indefinitely from technological learning over time. In this paper a regression model is proposed for wind farm capital cost which is based on commodities price and water depth rather than technological learning. With greater simplicity and certainty in the theoretical foundations of such a model, it is possible to gain realistic estimates of wind turbine capital cost. Such pragmatic and well-reasoned output is needed so that wind farm developers can understand their future risk exposure to price fluctuations in capital cost of plant.