Authors
Phillip Tseng, US Department of Energy (DOE)
John Lee, Brookhaven National Laboratory (BNL)
Leonardo Barreto and
Socrates Kypreos, Paul Scherrer Institute (PSI),
Energy Modeling Group, General Energy Research Department, CH-5232, Villigen, Switzerland
Contact Phillip Tseng for the full report or other details
PSI and ECN have worked on endogenous technology learning since 1998.
An overview of ETSAP information and publications related to technology
learning is available on this site.
Introduction and Summary
The Kyoto Protocol requires the United States and other major developed countries to reduce greenhouse gas emissions from the Kyoto baseline, which is the 1990 emission level for CO2, CH4, N2O, and the 1995 level for SF6, HFCs, and PFCs. For the United States, the required emission reductions between 2008 and 2012 will be 7 percent below the 1990 level. The United States insisted that the Protocol include where and when emission must be reduced. International permit trading and the Clean Development Mechanism are the resulting products of such negotiation; they form a framework in which all the countries can reduce greenhouse gas emissions at the least cost. This framework also provides time and incentives for countries to adopt energy efficient technologies and carbon free technologies. Many of these technologies, renewable energy-based ones in particular, are still in their infancy and need to be improved further in costs and performances before they can compete in the market place. In the long run, the potential contributions of these technologies to reducing of carbon emissions could be very significant if correct policies are implemented to accelerate their penetration into the market.
Empirical evidence shows that the decline in the unit cost of a technology is closely related to its cumulative production and use. Knowledge and experience accumulated from manufacturing, installing, and using a technology help to improve its performance and reduce cost (1). Technology learning, therefore, is a crucial process for to bringing about the large-scale commercialization of a new technology. Without of technology learning, currently more expensive, renewable energy technologies will have no opportunity to move down the learning curve and compete with the matured fossil fuel technologies. Recognizing the potential benefits that renewable technologies could bring to the environment, and the need to reduce their cost in the long point to the fact that governments must play an active role in encouraging technology learning.
This raises several important questions. What technologies to support? How, and at what level to support them? What benefits will they bring with their increased market share? These questions can best be answered within an integrated modeling framework in which endogenous learning processes are explicitly simulated.
In this study, we used two different modeling approaches to analyze the effects of technology learning. First, the U.S. MARKAL-MACRO model calibrated to the 1998-Annual Energy Outlook (AEO98)(2) was used to study the impact of technology learning on four renewable technologies for generating electricity (wind, photovoltaics, solar thermal, and biomass). The second approach presents the results of MARKAL for a simplified global electricity system using the Mixed Integer Programming (MIP) formulation of endogenous learning. In both models, technology learning is limited to the relationship between reduction in investment cost and the cumulative installed capacity. The key parameter characterizing this relationship is the progress ratio. In the U.S. case, the learning curve for each technology is generated based on the estimated cumulative capacity for the planning horizon and an increasing progress ratio over time. The learning curves were made time-dependent and simulated in the dynamic structure of MARKAL-MACRO. These curves allow the study on the effect of accelerated or delayed learning and the buildup of manufacturing capacity to meet the market demand implied in the installed capacity. Under this structure, the effects of endogenously learning were evaluated as the change in electricity generation mix, the cost of the total energy system, environmental emissions, and in the marginal cost of reduction in carbon emission. The level of governmental support (e.g., subsidies) required for earlier adoption of these technologies was derived.
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DISCLAIMER
This report was prepared as an account of work sponsored by an agency of the United States Government and the Paul Scherrer Institute, Switzerland. Neither the United States Government nor any agency thereof, nor any of their employees, nor any of their contractors, subcontractors, or their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency, contractor, or subcontractor thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency, contractor or subcontractor thereof.