Researchers Mark Bolinger, Ryan Wiser, and Eric O'Shaughnessy gathered information on renewables from 908 wind farms and 822 solar businesses in the United States, all of which had a capacity of more than five megawatts.
They gathered data on wind farms from the present to 1982, when modern, utility-scale wind farms first appeared in the United States. The data for solar dates back to 2007, when the United States developed its first utility-scale solar photovoltaic plants greater than five megawatts. The study was just published in the journal iScience.
Cost of Renewable Energy
Photo by Andreas Rentz/Getty Images
The researchers discovered that workers who operate renewable energy sources such as solar and wind farms learn to do it more effectively, decreasing the levelized cost of power (LCOE). According to Mark Bolinger, the same is true for funding those renewable energy plants, a research scientist at Lawrence Berkeley National Laboratory.
He claims that because a fledgling sector is regarded as dangerous, the cost of borrowing is high. However, when lenders and investors gain expertise in the market, they become more comfortable with the assets and are prepared to give more competitive rates, resulting in lower costs. "Learning can help with more than simply the upfront capital costs. Instead, all five or six LCOE inputs might benefit from time spent learning by doing. All of these can help to reduce expenditures."
LCOE, according to Bolinger, is made up of various components. The most critical factor is the plant's initial installation cost. The capacity factor, which measures how much energy the plant can generate each year, comes next. Operating expenses, government tax rates, finance costs, and estimated plant useful lives are all considered in the LCOE. He explains, "[LCOE] is effectively spreading expenses throughout the whole life of those plants."
The researchers discovered that learning rates based on LCOE were 15% for wind and 24% for solar. "You can use those past learning rates to forward-looking deployment forecasts after calculating them," Bolinger explains. For example, doubling the incremental deployment of wind by 2030 would result in a 30% reduction in the LCOE. "Learning curves are all about looking at previous correlations and extending them."
According to Bolinger, the previous study was mostly centered on capital costs, which solely focused on the upfront installment cost, perhaps because empirical data on installment prices is simpler. In contrast, data on the other expenses included in LCOE is more challenging to come by.
"If you want to accomplish this based on LCOE, your data intensity will skyrocket." However, when you consider it, LCOE is the proper measure to employ here because capital cost is merely one of five or six inputs into the LCOE calculation. And, rather than a restricted emphasis on capital expenditures, the industry has generally concentrated on optimizing or decreasing LCOE."
Previous Cost Reduction
The researchers focus on previous cost reductions in renewables in their report. From an average of $440 per MWh in the industry's early years (1982-1984) to an average of $32 per MWh in 2020 dollars, the LCOE for wind has dropped by 93 percent. From more than $230 per MWh during the first few years of the market (2007-2010) to $34 per MWh in 2020, the utility scale solar LCOE dropped by 85 percent in a significantly shorter time.
"While we depict these LCOE decreases over time, it is important to note that incremental deployment, not time itself, drives the cost reduction under a learning curve framework. In other words, a decrease in LCOE over time is a reflection of learning rather than a straight assessment of it, according to the researchers.
According to Mark Bolinger, there is a lot of literature on learning rate and learning curve theory. Moore's Law, for example, states that the number of transistors per silicon chip will double every year. According to Bolinger, the learning rate - a measure of how much cost decreases for each doubling of cumulative output - is identical.
This study adds some well calculated predictability to the future development of renewable energy. The more confident lenders are in their investments' safety, the more money will be available.
