Learning Outcomes:

On successful completion of the course, the students shall be able to:

• Compare different energy technologies in terms of their cost and performance.

• Apply different methods to forecast costs of energy technologies.

• Evaluate different forecasting methods and results in terms of their uncertainty.

Contents:

Historical trends in energy technology costs; fundamentals of drivers of learning and cost reduction in energy technologies; levelized cost of energy (service) calculation; introduction to probabilistic forecasting methods; model-based approaches to technology forecasting; learning curves; Wright’s law; Moore’s law; two-factor learning curves; uncertainty in forecasting methods; Monte-Carlo methods; component-based learning curves incorporating global and local learning rates; expert elicitation-based methods for forecasting; aggregation, interpolation and extrapolation of expert inputs; bottom-up modelling of manufacturing cost and reduction potential, technology differences and heuristics for cost forecasting; pros and cons of different forecasting methods.

Target Audience:

Managers in energy companies looking to forecast and assess the competitiveness of different technological options in the energy sector.