My father worked for IBM in the 1960s, and one time someone showed him a megabyte of memory. The machine filled a whole room. It reportedly cost $1MM, and it was a big deal. Checking on Amazon, you can buy a 64GB thumb drive for $13. Unpacking this, we can adjust $1MM in, say, 1965 to $8MM in 2018 dollars using the consumer price index. So, in 2018 dollars, this early megabyte of memory apparently cost $8Bil./GB. Today, it costs $0.20/GB, which is nearly an 11 order-of-magnitude difference! Could they have predicted in the 1960s that memory would become ubiquitous and near free?
What about solar power? According to LBNL’s 2018 Utility Scale Solar report, solar energy now makes up about 2% of the electricity used in the U.S.1 It is beginning to achieve market penetration but is still frequently grouped into the “Other” category in power generation pie charts. Will costs of solar power fall substantially, and will solar power become ubiquitous? There may be reasons besides cost that holds back the solar industry, but lowering cost always helps to expand a market.
There are good cost data since about 2010 for utility-scale solar plants, defined by LBNL as plants larger than 5MW, so cost trends can be developed. LBNL’s data, replotted in Figure 1, indeed show a precipitous decline in the price of solar power as the total solar capacity increases.
One way to look at cost trends is with an experience curve, sometimes called a learning curve. Developed by BCG in the 1960s, the idea is that there may be an empirical correlation between the cost of producing something and the total amount of that thing that has been produced.2 As a group or an industry makes more of something, it somehow finds ways to reduce cost. A power law curve fit can be applied, and one might talk about a reduction in cost, say 20%, for every doubling of total goods produced.3 So, as shown below, Cn is the cost of the nth unit produced. C1 is the hypothetical cost of a first unit (really just a curve fit parameter). n is the total number of units produced. Finding the exponent a can be used to show the cost reduction percentage for each doubling of the number produced.
% Reduction for Each Doubling = 1 – b
Back to LBNL’s solar data, the price of solar versus total solar capacity (utility-scale plants only) can be fit reasonably well to a power law curve, as in Figure 2. This curve is saying that for each doubling of capacity, the PPA price fell by roughly 20% (And, you can tell your nerd friends you saw log base 2 used for something.).
The trick now is to learn something from this. This empirical curve is saying that the U.S. solar industry is getting better and better at offering low-cost solar power as it gets more experience and, without saying why, that the rate of improvement follows a trend. What this curve does not show are the conditions required for the curve to continue.
The rate of cost reduction could slow or stop if various factors changed. First, if utility customers stopped asking for lower prices, power plant developers would happily stop lowering them. This might happen because customers instead asked for performance. Adding batteries to solar plants would deliver more value at higher cost, for example. The government can affect things. For example, the Federal Investment Tax Credit is set to step down and expire, and this may result in a temporary pause in cost reduction. Increased regulations could also halt cost reduction. Another implicit assumption in this plot is that PPA price is shown, and solar power does not have to be sold in a power purchase agreement paradigm. In this system, an agreement is made before the plant is build that the off-taker agrees to buy power for many years at a set price. Solar plants could be built with no contract price and could instead sell power at market prices. This probably would result in higher average solar power prices since it forces the seller to assume more risk.
Assume though that confounding factors do not ruin the trend. With a 10X increase in deployed solar capacity, this curve extrapolates the PPA price to under $15/MWh, as in Figure 3.
One might wonder that it will take a long time for an additional 350 GW of solar plants to be built in the U.S. when only 6 GW were built in 2017. (The math comes out to 58 years).
One factor that may increase demand for solar power plants is the relative cost compared with other options. The “team to beat” in power generation these days is the combined cycle natural gas plant, fueled by low-cost shale gas. The price of natural gas has bounced around $3/MMBtu for a while.4 Combined cycle power plants perhaps do 50% efficiency on a higher heating value basis. So, the fuel expense for natural gas electricity comes out to $20.50/MWh. So, the extrapolated solar power PPA price with a 10X scale-up of the solar plant fleet is 25% lower than the current fuel OPEX for efficient natural gas plants. This comparison should be motivating for utilities if these numbers in fact come to pass.
Of course, this extrapolated conclusion is riddled with assumptions. It costs more than just fuel to run a natural gas plant. The cost of natural gas is not static. There is a wide range of costs for solar plants, not just a single number. Lots of things can change the cost reduction curve for solar. Nevertheless, there is a general trend.
Will solar power become ubiquitous and cheap? It seems to be on that path for the foreseeable future.
References
- Bollinger, M. and Seel, J., “Utility-Scale Solar: Empirical Trends in Project Technology, Cost, Performance, and PPA Pricing in the United States – 2018 Edition, Lawrence Berkeley National Laboratory, September, 2018. https://emp.lbl.gov/utility-scale-solar
- “The experience curve,” The Economist, Sep. 14, 2009, https://www.economist.com/news/2009/09/14/the-experience-curve
- “Experience Curve Effects,” Wikipedia, https://en.wikipedia.org/wiki/Experience_curve_effects
- “Natural Gas: U.S. Average Natural Gas Price,” NASDAQ, March 2, 2019, https://www.nasdaq.com/markets/natural-gas.aspx?timeframe=2y
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