Environment General Courses (ENVIRON)

graduate level, taught in Durham

298.22 Understanding Energy Models and Modeling Syllabus

Timothy L. Johnson, Ph.D.
U.S. EPA, Office of Research and Development
johnson.tim@epa.gov
919-541-0575
“Office hours” before and after class, or at a mutually-agreeable time

Course Description and Learning Objectives

Energy models are widely used for forecasting, system design, and pedagogical purposes. The availability of cheap computing power has increased both the sophistication and accessibility of these models, providing the policy community with an increasingly broad range of studies as well as the ability to produce its own assessments. Such assessments can provide a reasonably transparent and objective foundation for studies of critical energy-related issues, including the need to mitigate global climate change, improve air quality, prepare for the peaking of petroleum and natural gas production, and ensure sufficient infrastructure capacity.

Like any modeling effort, however, energy models have their limitations and lend themselves to abuse and misinterpretation. Policy analysts and decision makers therefore need to acquire a basic literacy in energy modeling in order to understand the modeling process and critically evaluate the claims others derive from their analyses. What should one look for in order to determine if conclusions are credible? What happens inside the black box of an energy model? How elaborate does a model need to be, and at what point does technical sophistication take on a life of its own? In the end, are energy models actually useful?

This course aims to nurture a basic modeling literacy by focusing on a widely-used class of “bottom-up,” optimization-based, energy models commonly used for economic and environmental assessments. Students will acquire familiarity with the energy modeling literature, obtain a working knowledge of model mechanics and gain experience asking the type of questions needed to evaluate the quality of modeling results. Through class discussion, readings, and student projects, the course will cover the following topics:
• The history of energy modeling;
• Types of energy models and approaches to energy modeling;
• Commonly used energy models;
• What typically goes into an energy model, what comes out, and what happens in between;
• The critical need for sensitivity and uncertainty analyses;
• How modeling results are actually used in the policy process and different ways of interpreting conclusions; and
• Modeling pitfalls, both deliberate abuses and common misinterpretations.

All students with an interest in becoming good consumers of energy models, as well as those who wish to build the foundation needed to become an actual modeler, are welcome. Comfort with mathematical modeling would be beneficial, but prior experience with optimization techniques is not a prerequisite.

Expectations and Grading

Coursework will consist of in-class discussions of readings and two group-based assignments (see below). Most class sessions will adopt a seminar-style discussion format, structured around a combination of highlights from the day’s readings and student questions. Students should therefore come to class having read – and thought about – the related material. As the value and quality of energy modeling rest partly in the eye of the beholder, a critical, questioning attitude will be encouraged.

Final grades will be a combination of the two assignments (45% each) plus class participation (10%). The assignments will be graded on their thoroughness and integration of material discussed in class, and will depend on the quality of both the write-up and in-class presentation.

Assignments

The assignments are intended to familiarize students with the community of energy modeling users (developers, analysts, and decision makers) and its literature. In groups (size to be determined based on class enrollment), students will complete two analyses:
• The first assignment will draw on the material covered during the first three weeks and look at the history, basic structure, users, uses, and limitations of an existing energy model (e.g., AMIGA, LEAP, LIEF, MARKAL, MESSAGE, SAGE, etc.). Each group will write up their findings (5 to 10 pages) and present their analyses to the class during the fourth session.
• The second assignment will critique a published modeling-based energy study (one that applies the modeling framework analyzed in the first assignment) using the best practice principles discussed in class as a starting point. Again, each group will write up their findings (5 to 10 pages) and present their analyses to the class during the last (seventh) session.

While all group members will normally receive the same grade, please include a short statement with each assignment listing individual member contributions.

Class Schedule

Date Topic(s) Reading(s)
2/9 Introduction – The world of energy models and modeling
Uses: Why model?
A systems view of the US energy economy
Approaches to energy modeling Craig, et al. (2002)
Hogan (2002)
Sterman (1991)
2/16 Energy Modeling 101
Energy modeling approaches and frameworks
What to look for in an energy model
The importance of technology change
Scenario analysis Edmonds, et al. (2000)
Grubler, et al. (1999)
Hansen, et al. (2004)

2/23 A look at the U.S. Department of Energy’s National Energy Modeling System (NEMS): Its structure and uses EIA (2003)

Skim over and familiarize yourself with the following:
EIA (2005a)
EIA (2005b)
3/9 Assignment 1 presentations
Assignment 1 write-ups due
3/23 Interpretation of model results
The need for sensitivity and uncertainty analyses
Best practices for energy modeling Koomey (2002)
Morgan and Henrion (1990)
Munson (2004)
3/30 Energy models in the policy process
Critique of energy modeling DeCanio (2003)
Laitner, et al. (2003)
Smil (2003)
Worrell, et al. (2004)
4/13 Assignment 2 presentations
Assignment 2 write-ups due

Note that there will be no class March 2, March 16, and April 6.
Readings

Craig, P.P., Gadgil, A., and Koomey, J.G. (2002). “What can history teach us? A retrospective examination of long-term energy forecasts for the United States.” Annu. Rev. Energy Environ. 27:83-118.

DeCanio, S. (2003). “The Forecasting Performance of Energy-Economic Models.” Chapter 5 from Economic Models of Climate Change: A Critique. NY: Palgrave Macmillan, pp. 126-152.

Grubler, A., Nakicenovic, N, and Victor, D.G. (1999). “Dynamics of energy technologies and global change.” Energy Policy 27:247-280.

Edmonds, J., Roop, J.M., and Scott, M.J. (2000). “Technology and the economics of climate change policy.” Washington, DC: Pew Center on Global Climate Change. Accessed from http://www.pewclimate.org/global-warming-in-depth/economics/reports.

EIA (Energy Information Administration), Office of Integrated Analysis and Forecasting, US Department of Energy (2003). The National Energy Modeling System: An Overview 2003. DOE/EIA-0581(2003). Washington, DC: US Government Printing Office. Accessed from http://www.eia.doe.gov/oiaf/aeo/overview/index.html.

EIA (Energy Information Administration), Office of Integrated Analysis and Forecasting, US Department of Energy (2005a). Annual Energy Outlook 2005 With Projections to 2025. DOE/EIA-0383(2005). Washington, DC: US Government Printing Office. Accessed from http://www.eia.doe.gov/oiaf/aeo/index.html. Supplemental tables available from http://www.eia.doe.gov/oiaf/aeo/supplement/index.html.

EIA (Energy Information Administration), Office of Energy Markets and End Use, US Department of Energy (2005b). Annual Energy Review 2004. DOE/EIA-0384(2004). Washington, DC: US Government Printing Office. Accessed from http://www.eia.doe.gov/emeu/aer/contents.html.

Hansen, D.A., Mintzer, I., Laitner, J.A., and Leonard, J.A. (2004). Engines of Growth: Energy Challenges, Opportunities, and Uncertainties In the 21st Century, Argonne, IL: Argonne National Laboratory, Decision and Information Sciences Division. Available from amiga.dis.anl.gov/Engines_of_GrowthJan17-04_rev161.pdf.

Hogan, W.W. (2002). “Energy modeling for policy studies.” Operations Research 50(1): 89-95.

Koomey, J. (2002). “Avoiding ‘The Big Mistake’ in forecasting technology adoption.” Technology Forecasting & Social Change 69:511-518.

Laitner, J.A., DeCanio, S.J., Koomey, J.G., and Sanstad, A.H. (2003). “Room for improvement: increasing the value of energy modeling for policy analysis.” Utilities Policy 11:87-94.

Morgan, M.G., and Henrion, M. (1990). “Large and complex models.” Chapter 11 from Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis. Cambridge, MA: Cambridge University Press, pp. 289-306.

Munson, R. (2004). “Improving prediction of energy futures.” Issues in Science and Technology Spring: 26-29.

Smil, V. (2003). “Against forecasting.” Chapter 3 from Energy at the Crossroads: Global Perspectives and Uncertainties. Cambridge, MA: The MIT Press, pp. 121-180.

Sterman, J.D. (1991). “A skeptic's guide to computer models.” In Barney, G. O. et al. (eds.), Managing a Nation: The Microcomputer Software Catalog. Boulder, CO: Westview Press, 209-229.

Worrell, E., Ramesohl, S., and Boyd, G. (2004). “Advances in energy forecasting models based on engineering economics.” Annu. Rev. Environ. Resour. 29:345-81.