NewEnergyNews: Monday Study – Numbers Prove New Energy Transition Coming


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    Monday, November 01, 2021

    Monday Study – Numbers Prove New Energy Transition Coming

    Empirically grounded technology forecasts and the energy transition

    Rupert Way, Matthew Ives, Penny Mealy and J. Doyne Farmer, September 14, 2021 (Institute for New Economic Thinking)

    Rapidly decarbonising the global energy system is critical for addressing climate change, but concerns about costs have been a barrier to implementation. Most energy-economy models have historically underestimated deployment rates for renewable energy technologies and overestimated their costs1,2,3,4,5,6. The problems with these models have stimulated calls for better approaches7,8,9,10,11,12 and recent e↵orts have made progress in this direction13,14,15,16. Here we take a new approach based on probabilistic cost forecasting methods that made reliable predictions when they were empirically tested on more than 50 technologies17,18. We use these methods to estimate future energy system costs and find that, compared to continuing with a fossil-fuel-based system, a rapid green energy transition will likely result in overall net savings of many trillions of dollars - even without accounting for climate damages or co-benefits of climate policy. We show that if solar photovoltaics, wind, batteries and hydrogen electrolyzers continue to follow their current exponentially increasing deployment trends for another decade, we achieve a near-net-zero emissions energy system within twenty-five years. In contrast, a slower transition (which involves deployment growth trends that are lower than current rates) is more expensive and a nuclear driven transition is far more expensive. If non-energy sources of carbon emissions such as agriculture are brought under control, our analysis indicates that a rapid green energy transition would likely generate considerable economic savings while also meeting the 1.5 degrees Paris Agreement target.

    Future energy system costs will be determined by a combination of technologies that produce, store and distribute energy. Their costs and deployment will change with time due to innovation, economic competition, public policy, concerns about climate change and other factors. Figure 1 provides an historical perspective for how the energy landscape has evolved over the last 140 years. Panel (a) shows the historical costs of the principal energy technologies and panel (b) gives their deployment, both on a logarithmic scale. As we approach the present in panel (a), the diagram becomes more congested, making it clear that we are in a period of unprecedented energy diversity, with many technologies with global average costs around $100/MWh competing for dominance.

    The long term trends provide a clue as to how this competition may be resolved: The prices of fossil fuels such as coal, oil and gas are volatile, but after adjusting for inflation, prices now are very similar to what they were 140 years ago, and there is no obvious long range trend. In contrast, for several decades the costs of solar photovoltaics (PV), wind, and batteries have dropped (roughly) exponentially at a rate near 10% per year. The cost of solar PV has decreased by more than three orders of magnitude since its first commercial use in 1958.

    Figure 1(b) shows how the use of technologies in the global energy landscape has evolved since 1880. It documents the slow exponential rise in the production of oil and natural gas over a century, until they eventually replaced traditional biomass and equalled coal, as well as the rapid rise and plateauing of nuclear energy. But perhaps the most remarkable feature is the dramatic exponential rise in the deployment of solar PV, wind, batteries and electrolyzers over the last decades as they transitioned from niche applications to mass markets. Their rate of increase is similar to that of nuclear energy in the 70’s, but unlike nuclear energy, they have all consistently experienced exponentially decreasing costs. The combination of exponentially decreasing costs and rapid exponentially increasing deployment is di↵erent to anything observed in any other energy technologies in the past, and positions renewables to challenge the dominance of fossil fuels within a decade.

    Will clean energy technology costs continue to drop at the same rates in the future? What does this imply for the overall cost of the green energy transition? Is there a path forward that can get us there cheaply and quickly? We address these questions here.

    How good were past energy forecasts?

    Sound energy investments require reliable forecasts. As illustrated in Figure 2(a), past projections of present renewable energy costs by influential energy-economy models have consistently been much too high. (“Projections” are forecasts conditional on scenarios, so we use the terms interchangeably.) The inset of the figure gives a histogram of 2,905 projections by integrated assessment models, which are perhaps the most widely used type of global energy-economy models19,20,21,22, for the annual rate at which solar PV system investment costs would fall between 2010 and 202019. The mean value of these projected cost reductions was 2.6%, and all were less than 6%. In stark contrast, during this period solar PV costs actually fell by 15% per year. Such models have consistently failed to produce results in line with past trends3,23. Considering their central role in guiding energy investment decisions and climate policy, the consequences of such systematic bias in modelling projections are alarming. Failing to appreciate cost improvement trajectories of renewables relative to fossil fuels not only leads to under-investment in critical emission reduction technologies, it also locks in higher cost energy infrastructure for decades to come. In contrast, forecasts based on trend extrapolation consistently performed much better24,25,26,27.

    Some reasons for the poor performance of energy-economy models include their seemingly arbitrary assumptions regarding the maximum deployment and maximum growth rates of renewables, plus the imposition of “floor costs”, i.e. fixed levels that costs are assumed never to fall below28. As shown in Figure 2(b), past floor costs used in IAMs have repeatedly been violated. We know of no good empirical evidence supporting floor costs and do not impose them. (For a critique of other aspects of standard energy-economy models, see29,8,9).

    Predicting future technology costs

    The diversity of historical cost improvement rates seen in Figure 1(a) applies to technologies in general30,25,17,18. For the vast majority of technologies, inflation-adjusted costs remain roughly constant through time. In contrast, for some technologies, such as optical fibers, solar PV or transistors, costs drop roughly exponentially, at rates ranging from over 50% per year to a few percent per year31 (SN8.1). Once a track record is established, the rates of improvement tend to remain constant. While there are occasionally breaks in the trend, this is rare.

    In contrast to the energy-economy models mentioned above, during the past decade simple time series models have been shown to make reliable forecasts of technology costs32,25,33,18. In this study we apply these methods to key energy technologies and use them to make probabilistic estimates for the cost of providing energy services under several different scenarios.

    For renewable technologies we use a stochastic generalization of Wright’s law, which predicts that costs drop as a power law of cumulative production. This relationship is also called an experience curve or learning curve, and cumulative production is also called experience. Experience does not directly cause costs to drop, but is believed to be correlated with other factors that do, such as level of e↵ort and R&D, and has the essential advantage of being relatively easy to measure34,35. Forecasting using this model requires estimating two parameters for each technology, corresponding to a progress rate and a volatility (see Methods). In addition, there is an autocorrelation parameter that is common to all technologies. For a discussion of challenges and caveats concerning Wright’s law see SN8.2.

    Successful technologies tend to follow an “S-curve” for deployment, starting with a long phase of exponential growth in production that eventually tapers o↵ due to market saturation36. Under Wright’s law, during the exponential growth phase, costs drop exponentially in time according to a generalized form of Moore’s law, which is consistent with the historical behavior of renewable energy technologies. When growth eventually slows, under Wright’s law, improvement slows down. (For a discussion of causality see SN8.2.1.)

    Wright’s law is already widely used in energy system models37,38,39, though to the best of our knowledge, only deterministic implementations have been used so far. Our key contribution here takes advantage of new results that extend Wright’s law to provide an estimate of the probability distribution of future technology costs, thus providing an estimate of forecasting uncertainty. This method was carefully tested by making forecasts at reference dates in the past, using only the data available at the time, and making predictions over all time horizons up to 20 years into the future with respect to each reference date. This was done using historical data for 50 di↵erent technologies, for a total of roughly 6,000 forecasts. The forecasting accuracy closely matched a priori derived estimates on all time horizons17,18.

    Because fossil fuel costs have not changed in the long run, they require a di↵erent time series forecasting model. Since costs have not dropped with experience40, the stochastic form of Wright’s law that we use here reduces to a geometric random walk without drift. This is a common model for financial time series, including tradeable commodities such as oil or gas, and can be justified based on the ecient markets hypothesis. On short timescales (say ten years or less) this is a reasonable approximation, but over longer timescales it predicts too much volatility in comparison to the historical record. Fossil fuel prices show mean reversion on longer timescales and are better captured by an AR(1) autoregressive process41.

    We thus use a univariate AR(1) model to forecast coal, oil, and gas (see Methods), SN5.1 and SN6.1-6.3). While coal-fired electricity and gas-fired electricity showed significant drops in cost for some of the twentieth century, in the long run their costs are increasingly dominated by fuel costs42, so we use the AR(1) model for these as well (SN6.4-6.5). The technologies for which we use Wright’s law to generate probabilistic cost forecasts are: solar PV, wind, batteries, electrolyzers, nuclear power, biopower and hydropower. While the first four of these technologies have strong historical progress trends, the latter three have either flat or rising costs, so have less potential to play a significant role in energy transition, and hence are less important in this analysis (SN6.6-6.12).

    Figure 3 shows probabilistic forecasts for seven key energy technologies under a rapid energy transition scenario that we will define in a moment. Each renewable technology initially follows its current trend of exponential decreasing costs, which slows when it becomes dominant and its rate of deployment drops. We also show a selection of cost projections reported by IAM and IEA studies. We show only their most optimistic projections, i.e. low cost projections that correspond to high technological progress scenarios. Consistent with the historical behavior of these models illustrated in Figure 2, these projections are high relative to historical trends. Although viewed as highly optimistic, they are all higher than our median forecasts, and except for wind, substantially higher.

    The stochastic version of Wright’s law we use here captures the historical volatility of past performance and the resulting estimation error, and projects this uncertainty forward in future cost distributions. It thus provides cost ranges that are supported by empirical evidence, as opposed to the ad hoc ranges that are often used43. The insets show costs vs. experience and emphasize that median costs develop identically as a function of experience in all scenarios. The side panels of Figure 3 illustrate that under Wright’s law forecasts depend on the scenario; as a result, under a rapid transition, we reach lower costs sooner.

    From single technologies to a full system model…How much will each scenario cost?...Discussion…


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