To continue reducing the costs of solar energy and other clean energy technologies,
scientists and engineers will likely need to focus, at least in part,
on improving technology features that are not based on hardware, according to MIT researchers.
They describe this finding and the mechanisms behind it in the journal Nature Energy.
While the cost of installing a solar energy system has dropped by more than
99 percent since 1980, this new analysis shows that “soft technology” features,
such as the codified permitting practices, supply chain management techniques, and system
design processes that go into deploying a solar energy plant, contributed only 10 to 15 percent
of total cost declines. Improvements to hardware features were responsible for the lion’s share.
But because soft technology is increasingly dominating the total costs of installing solar
energy systems, this trend threatens to slow future cost savings and hamper
the global transition to clean energy, says the study’s senior author, Jessika Trancik.
The team created a quantitative model to analyze the cost evolution of solar energy systems,
which captures the contributions of both hardware technology features and soft technology features.
The framework shows that soft technology hasn’t improved much over time — and that
soft technology features contributed even less to overall cost declines than previously estimated.
Their findings indicate that to reverse this trend and accelerate cost declines, engineers
could look at making solar energy systems less reliant on soft technology to begin with,
or they could tackle the problem directly by improving inefficient deployment processes.
Researchers have observed that the so-called “soft costs” of building a solar power plant — the costs
of designing and installing the plant — are becoming a much larger share of
total costs. In fact, the share of soft costs now typically ranges from 35 to 64 percent.
In the past, scientists have modeled the change in solar energy costs by dividing
total costs into additive components — hardware components and nonhardware
components — and then tracking how these components changed over time.
The researchers developed a quantitative approach that models the change in solar energy costs over
time by assigning contributions to the individual technology features,
including both hardware features and soft technology features.
For instance, their framework would capture how much of the decline in
system installation costs — a soft cost — is due to standardized practices of certified
installers — a soft technology feature. It would also capture how that same soft cost
is affected by increased photovoltaic module efficiency — a hardware technology feature.
With this approach, the researchers saw that improvements in hardware had
the greatest impacts on driving down soft costs in solar energy systems. For example,
the efficiency of photovoltaic modules doubled between 1980 and 2017,
reducing overall system costs by 17 percent. But about 40 percent of that overall decline
could be attributed to reductions in soft costs tied to improved module efficiency.
The framework shows that, while hardware technology features tend
to improve many cost components, soft technology features affect only a few.
The researchers used their model to study several countries,
since soft costs can vary widely around the world. For instance,
solar energy soft costs in Germany are about 50 percent less than those in the U.S.
The fact that hardware technology improvements are often shared globally
led to dramatic declines in costs over the past few decades across locations,
the analysis showed. Soft technology innovations typically aren’t shared across borders. Moreover,
the team found that countries with better soft technology performance 20 years ago still have
better performance today, while those with worse performance didn’t see much improvement.
This country-by-country difference could be driven by regulation and permitting processes,
cultural factors, or by market dynamics such as how firms interact with each other.
Their analysis points to two strategies for reducing soft costs. For one,
scientists could focus on developing hardware improvements that make soft costs more dependent
on hardware technology variables and less on soft technology variables, such as by creating simpler,
more standardized equipment that could reduce on-site installation time.
Or researchers could directly target soft technology features without changing hardware,
perhaps by creating more efficient
workflows for system installation or automated permitting platforms.
In practice, engineers will often pursue both approaches,
but separating the two in a formal model makes it easier to target innovation
efforts by leveraging specific relationships between technology characteristics and costs.
In the future, the researchers want to apply their quantitative model to study the soft costs related
to other technologies, such as electrical vehicle charging and nuclear fission. They
are also interested in better understanding the limits of soft technology improvement,
and how one could design better soft technology from the outset.