A hierarchical cost learning model for developing wind energy infrastructures

Renewable energy has been increasingly promoted and used to substitute non-renewable fossil-fuels, which cause negative effects on the environment. The Taiwan Statute for Renewable Energy Development has regulated and promoted renewable energy since 2009. A feed-in tariff (FIT) for renewable energy is one of the incentives that the government uses to promote the installation of green power generation facilities. The price of the electricity feed-in tariff is based on the current and future costs of renewable energy generation. When analyzing cost trends for renewable energy installation, many researchers use a single factor cost learning curve model. However, past studies indicate that there are multiple factors affecting the overall cost of installing renewable energy. Hence, this research develops a hierarchical installation cost learning model which considers multiple factors to accurately model and forecast wind energy development. This research uses wind power development data from Taiwan as a case study. We identify the cost factors, evaluate the learning effects, and compare the hierarchical learning curve model to the basic (non-hierarchical) learning curve model. The research results show an improved fit between the hierarchical model and the actual data when compared to the basic learning model. The study also provides new insights between the wind power learning progression of Taiwan and three countries in Europe. •Develops a superior hierarchical cost learning model for wind power installations.•The Taiwan renewable energy policy and its feed-in tariff plan are described.•Demonstrates a generic hierarchical cost learning model for the energy industry.