Learning a Better Way To Forecast Wind and Solar Energy Costs

Past learning curve studies have often focused on the upfront installed cost of wind and solar. But installed cost is just one of a handful of inputs—including operating costs, financing cost, and annual energy production—that affect the levelized cost of energy (LCOE) generated, and each of these cost components can benefit from learning.

In a new study published in the journal iScience, Lawrence Berkeley National Laboratory researchers Mark Bolinger, Ryan Wiser, and Eric O’Shaughnessy improve upon past learning curves for utility-scale wind and solar through a combination of approaches. First, drawing on Berkeley Lab’s extensive utility-scale wind and solar plant databases, they calculate plant-level LCOE estimates over time, and then use LCOE, rather than installed costs, to assess historical learning curves. Second, they normalize LCOE to control for, or remove, external influences that are unrelated to learning. Finally, they employ techniques to identify how learning has varied over time.

Figure 1 shows the raw and normalized LCOE history of both wind and solar. These curves represent annual averages of LCOE for the majority of wind and solar plants built in the United States through 2020.