Down the learning Curve with Emerging Technologies
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Figure 1. Experience curve for photovoltaic modules, 19761992 (William
and Terzian 1993).



Promising new
technologies that are still very expensive will become less costly if
they are manufactured in large numbers. The exponential reduction in cost
with increased production can be specified by an “experience curve”. However,
linear programming models of energy systems do not properly account for
this nonlinearity. Initial steps with a nonlinear, nonconvex model, GENIE,
that explicitly contains experience curves for emerging technologies,
suggest that photovoltaics and fuel cells could come to dominate electricity
generation if a long enough and broad enough view is taken, especially
if future carbon dioxide emissions must be restricted.
Proponents of photovoltaic technology argue that expected reductions in
its cost will make it more and more competitive as time goes on. This
expectation is based on the observation that the unit costs of a manufactured
product progressively decrease as more units are manufactured. The pattern
of this reduction among many different types of technology has been remarkably
consistent, following an exponential reduction variously named a learning
curve, progress curve, or experience curve.
The rate of reduction in an experience curve can be defined by the percen
tage reduction in the unit cost with successive doubling of the quantity
manufactured. For photovoltaics, for example, an 82 percent experience
curve has been postulated based on early experience (Figure
1). The cost of the hundredth unit will be 82 percent of the fiftieth,
the costs of the twohundredth 82 percent of the hundredth, etc. While
the reduction on an experience curve would theoretically continue indefinitely,
the amount of the reduction between successive units eventually becomes
minuscule, and at some point the unit cost can be considered constant.
Experience curves as an empirical fact must be ignored in linear programming
formulations for the future development of the technologies comprising
a national energy system, because they constitute a nonlinearity. Linearity
requires that successive units each cost the same. This is an important
problem when new technologies, far from the point where unit costs have
levelled out, are to be considered.
In a dynamic linear programming model in which successive time periods
are represented, it has been the practice to assume successively lower
unit costs in future time periods for technologies initially high on the
experience curve. The solution space remains bounded by a convex curve
consisting of linear elements, with optimization at the intersection of
two such elements assured. However, the solution may omit the early expensive
applications of the technology, ignoring the fact that the initial investment
is necessary to bring down the subsequent cost. It is not a matter of
time, but of continued production.
“Some have suggested that technical progress is a factor that may justify
deferring carbon dioxide emission abatement”, notes Niclas Mattsson of
Sweden’s Chalmers University of Technology. “Autonomous energy efficiency
improvement over time is usually explicit in topdown models, and it is
implicit in bottomup models. However, new technologies do not appear
automatically. Both technology push  such as publicly funded R&D  and
market pull are needed for the dissemination of a new technology. The
significance of market pull is that it enables learning by doing to take
place. Unless the high initial costs of introducing emerging technologies
are paid, they may be locked out before they can contribute to future
carbon dioxide emission abatement”.
With the costs of individual technologies characterized by experience
curves, determining the best mix of energy technologies to meet future
requirements becomes a nonlinear, nonconvex optimizing problem. To address
this problem, Mattsson and ClasOtto Wene developed the dynamic, nonlinear
model named GENIE (Global ENergy system with Internalized Experience curves).
GENIE models longterm development of the global electricity system, spanning
the years 19952075 with eight tenyear time periods. The objective of
GENIE is to minimize the present value of the total cost of the global
electric system, assuming perfect foresight. The main purpose of GENIE
is to provide qualitative insights into the dynamics of technological
development in the energy system. It is not intended as a complete tool
for general energy policy analysis.
There are two conceptual ways to address the nonlinear, nonconvex optimization
problem:
By keeping the continuous experience curve and solving the resulting nonconvex
problem directly, using modern algorithms for global optimization.
By breaking
the experience curve into successive discrete units, assuring the sequential
order, and solving the problem using mixedinteger programming.
Mattsson and Wene began with the first approach, starting with an ad hoc
optimization procedure. This alternative is very simple to implement and
solves rapidly to a local optimum, according to Mattsson, but the global
optimum cannot be proved. Many model runs from different starting points
are therefore necessary to satisfy the user that the global optimum has
indeed been found.
In subsequent work reported in his thesis for the degree of licentiate
of engineering at Chalmers, Mattsson used GENIE in the second approach.
The great advantage of this method is the guarantee of finding the global
optimum. However, the implementation is more complicated, and solution
times are several orders of magnitude larger.
The model consists of four world regions; North, South, East and West.
The nonconventional electricity generation technologies for producing
electricity are advanced coal power, e.g. pressurized fluidized bed combustion
or integrated gasification combinedcycle gas turbines (CCGT), wind power,
fuel cells using natural gas, photovoltaics (PV), and photovoltaic hydrogen
production (PVH2). PV generates electricity intermittently, when the
sun is shining. PVH2, on the other hand, provides power on demand as
it uses PV electricity to electrolyze water, stores the resulting hydrogen
and oxygen, and when required recombines them in fuel cells.
To minimize computational difficulty, only technologies with a large potential
for experiencebased cost reduction are treated in the model by experience
curves. The modular technologies PV, PVH2 and fuel cells were assumed
to have the steepest progress ratios (0.82, 0.85 and 0.85, respectively),
the smallscale technologies CCGT and wind power slightly less steep ratios
(both 0.88), while the largescale technologies display little (advanced
coal, 0.95) or no experiencebased learning (all others). The reduction
in cost is approximated by linear segments in the cumulative investment
cost curve, i.e., the integral of the experience curve. The maximum allowed
growth rate for all technologies was taken as 30 percent per year.
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