Energy-Economic
Policy Modeling
Alan H. Sanstad
Lawrence
La
rence Berkele
Berkeley National Laborator
Laboratory
University of Chicago Computation Institute
APS Short Course: Physics of Sustainable Energy
June 17, 2016
Outline
 Terminology
 Economics in energy policy
 Theoretical foundations
 Examples of models and applications
 Uncertainty and other epistemological issues
Example of NEMS case (USEIA 2014b)
Terminology
 Energy policy model (energy model): A computational
model
d l based
b
d upon the
th economic
i principles
i i l off
optimization and equilibrium, representing:
• An energy system – e.g.,
e g the electric power system in
the Western U. S.
• An entire ((regional,
g
national, g
global)) economy
y with
emphasis – i.e., greater detail - on the energy system
 Integrated assessment (IA) model: A coupled modeling
system,
t
linking
li ki energy-economic
i with
ith ((reduced-form)
d
df
)
climate (and in some cases other environmental) models
 These models are dynamic, or intertemporal –
representing
ti th
the system
t
or economy over horizons
h i
ranging from hours (or less) up to decades (or more)
 This lecture will focus on those with multi
multi-decadal
decadal
horizons
 Why “policy”
policy models?
 Because they are normative - Used to numerically study
the hypothetical effects of potential energy and
environmental policies, and to identify better and worse
policy alternatives
Q: Why is economics important for
energy policy?
 A: Because
Behavior – choices by consumers and firms
 Prices
 Market dynamics and interactions
are first-order
first order – often if not always primary –
drivers of energy production and use, including
energy technology innovation, development, and
deployment

Why economics? Cont.
 Examples:

Physical limits of fossil fuel resources
• These necessarily exist but the estimated magnitudes keep
changing – increasing - as a result of exploration and discovery
efforts that are to a significant degree driven by economic factors
– E.g., the “oil crisis” of the 1970s, and its end in the 1980s

The “rebound
rebound effect”
effect in energy efficiency
• Indirect reductions in the costs of energy services resulting from
efficiency-promoting policies and programs, which offset energy
savings
i
estimated
ti t d b
by purely
l physics/engineering
h i /
i
i methods
th d (b
(based
d on
1st Law of Thermodynamics) (Gillingham et al. 2016)
Basic economic principles
 “Rational” behavior, represented mathematically as constrained
optimization: Utility or profit maximization, cost minimization (including
life-cycle
life
cycle cost minimization)

These assumptions are best interpreted as justifiable and extremely
useful approximations
 Market equilibrium: Supplies = demands

In the present context, this is more an empirical fact
 In practice
practice, these principles are very powerful and flexible tools for
building theories and models and for understanding the potential and
achieved effects of policies

They do not, as such, imply that government policies or regulations
are unjustifiable or unwarranted.

On the contrary, “standard” economic – neo-classical – theory
provides a rigorous way of understanding the circumstances in which
policies can improve market outcomes (welfare economics)
Underlying theory
 Energy/IA models are, with some exceptions, very large,
detailed, and complicated
 However, they are mostly based upon fairly
straightforward mathematical frameworks - generally
one of the following types (which incorporate the
behavioral and equilibrium assumptions)
 Nonlinear systems of algebraic equations:

Given F : R  R  R (with differentiability,
n
y, etc. assumptions)
p
) and   R m , find x  R
convexity,
such that
n
m
n
F  x,    0
 A variation on the previous: “Mixed, or Non-
linear, complementarity” models
 Linear programs (LPs):

Given
c  R n , b  R m , A  R mn ,
maxc x
s.t.
Ax  b
t
n
x

R
find
to
 Non-linear programs (NLPs):
n
f
:
R
R
 Given
to
x  Rn
and G : R  R , find
max f  x 
s.t.
G  x  0
n
m
 Optimal control:

Given
x0 ,

max
ut 
t 0
f  xt , ut 
1   
s.t.
xt 1  g  xt , ut 
t
Computational model types
 Relating these abstract schema to models in
practice: Major economic types are
Partial equilibrium: Represent systems of linked energy
markets but not the complete economy
markets,
• Non-linear systems of algebraic equations
 General equilibrium:
q
Represent
p
all energy
gy and other
markets in an economy as well as the optimizing
agents, and the interactions – in other words, the entire
economy
• “Computable General Equilibrium (CGE)”

Types, cont.
 CGE: Non-linear systems, non-linear
complementarity problems, non-linear programs
 Energy – especially, electric power system -
models: Linear and non-linear programs
Partial equilibrium
 Supply = Demand as a function of price:
General equilibrium schematic
Methodological aspects
 Most energy and IA models are deterministic and high-
dimensional

“Deterministic” means neither uncertainty on the part of the modeler
nor on the part of the agents in the model is explicitly represented
 With few exceptions
exceptions, they are calibrated and parameterized
by means other than statistical or econometric
 Their modal application is the projection of energy/
economic / environmental variables several decades or a
century (or longer) into the future, in part as a function of
policies, to analyze the potential consequences of the latter
 These deterministic projections typically take the form of
scenarios: Sets of assumptions on key inputs
Current modeling landscape
 Energy and IA modeling has become the predominant
analytical methodology in energy/GHG policy and regulation
 The models are now much more than “tools:”

They serve to define the universe of discourse, and to determine what
questions can be asked
asked, what form answers can take
take, and what
constitutes useful data – to serve as model inputs
 Used by

Federal agencies – Dept. of Energy, Environmental Protection Agency
– e.g., for new GHG rules - and others

State regulators – energy commissions and others

Operational authorities – e.g., “independent system operators” in the
electric power grid

Academic and national laboratory researchers

Advocacy groups
Example: NEMS
 The National Energy Modeling System (NEMS)
Coupled
C
l d partial
ti l equilibrium
ilib i
and
d lilinear programming
i models,
d l
with exogenous macroeconomic inputs
 Created and maintained by Energy Information
Ad i i t ti (EIA)
Administration

• Based in part on NRC recommendations (NRC 1992)
NEMS is essentially the official national energy model of the
U. S.
S ffederal government
 Projects U. S. energy supplies and demands (currently) to
2040, with time steps of 1 year
 Primary product is the Annual Energy Outlook report.
 Also used for specific analyses at request of Congress

Defining energy technology costs
 The basic behavioral assumption in NEMS is life-cycle cost
minimization
 How much does it cost to purchase and operate some unit of
energy-producing or energy-using equipment over a given period of
time?


Electric power plants
End-use energy devices such as appliances
 The fundamental metric is life-cycle cost
 The basic elements – let


CC be how much the unit costs – i.e., to build, purchase, install – the “capital
cost”
OCt be the operating cost during time-period t – typically, one-year
• This includes fuel costs – e.g.,
e g of coal or natural gas for a power plant
plant, or
electricity for an appliance
• Also maintenance and possibly labor costs
Life-cycle cost
Note that the operating cost is a function of hours of
operation and/or annual electricity output
 T be the lifetime of the unit
 r be a “discount rate” – consider this the rate p
paid on a
loan of amount CC to pay for the unit
 Then

T
LCC  CC  
t 1
OCt
1  r 
t
Structure of NEMS (USEIA 2014a)
 The AEO is organized around a Reference Case
 As a matter of policy, EIA includes in the Reference
Case only policies and regulations that are either already
in place
place, or have been approved/ authorized – e.g.,
e g by
Congress – and are certain to be implemented
 EIA also reports “Low
Low & High Economic Growth
Growth,” and
“Low & High Oil Price” variations on the Reference –
“Side Cases” with different, e.g., technology assumptions
Examples of NEMS cases (USEIA 2014b)
NEMS cases, cont.
NEMS cases, cont.
NEMS cases, cont.
Integrated assessment (IA) model
examples
l
 The Global Change Assessment Model (GCAM)
Integrated assessment type, built around partial
equilibrium global energy-economic model
 GCAM created, maintained, and run by Pacific Northwest
National Laboratory, Joint Global Change Research
Center (with U. of Maryland)
 Projects global energy, economic, climate, and
ecosystems to 2100 (or beyond),
beyond) in 5
5-year
year time steps

MiniCAM integrated assessment model, energyeconomic sub-model schematic (Kim et al. 2006)
Data
World
supply
Region
supply
get price
Resource
Sub-Resource
Supply Sector
Sub-Sector
Sub-Sector
input price
input
p price
p
input price
Grades
Demand Sector
Technology
Market:
Price
Supply
Demand
ED = 0
Technology
demand
GHG
Solution
Algorithm
GHG
demand
set price
Examples, cont.
 The Integrated Global System Model (IGSM)

Integrated assessment type, built around computable
general equilibrium global economic model:
• “Emissions Prediction and Policy Analysis (EPPA)”
• Based on Organisation for Economic Co-operation and
Development (OECD) “General Equilibrium Environmental
(GREEN)” CGE model, circa early 1990s
IGSM created, maintained and run by the MIT Joint
Program on the Science and Policy of Global Change
 Projects global energy, economic, climate, and
ecosystems to 2100, in 5-year time steps

EPPA schematic (Paltsev et al. 2005)
IGSM schematic (Paltsev et al. 2005)
DICE model
 Dynamic Integrated model of Climate &
Economyy
 Created by W. Nordhaus of Yale University; first
version circa 1990
 Underlying theory: Optimal control
 The single most influential
influential, and used or adapted
- as well as criticized - IA model
Publicly available documentation and code; relatively
simple structure – esp., not too big (defined by ~20
equations)
 Straightforward to change assumptions/ parameters

Equations of DICE model (Nordhaus 2008)
DICE,, cont.: Emissions
DICE,, cont.: Climate
The Energy Modeling Forum (EMF) at
Stanford University
 Created in the 1970s
 Pioneered multi-model inter-comparison and
structured scenario analysis
 Very influential - In effect, the global intellectual
center for energy and IA modeling
 Recently completed study #27, on technology and
GHG emissions abatement
37
Energy Modeling Forum (EMF) 27: “Global
Technology and Climate Policy Strategies”
 Overall aim: Understand how technology characteristics –
cost, availability, carbon output, etc. – affect achievement of
global CO2 reduction goals
 Subject of special issue of Climatic Change, April 2014
 Both partial equilibrium (PE) and general equilibrium (GE)
models (modelers) participated
 Policies analyzed included:
550 ppm CO2 concentration
 450 ppm CO2 concentration

38
 The models incorporate representations of a range of
energy technologies –

Fossil fuel based, including carbon capture and sequestration (CCS)

Wind and solar

N l
Nuclear

Biomass
Plus exogenous energy intensity-reducing technological change –
decline in ratio of economic output to energy input through unspecified
mechanisms
 Basic design for scenarios: Examine the feasibility and cost
implications of limiting low-carbon technologies
Scenario feasibility under different
assumptions (Krey et al
al. 2014)
40
EMF 27 cost estimates (Kriegler et al. 2014)
EMF 27: Global abatement costs –
5% discount rate (Kriegler et al
al. 2014)
42
Interpreting and evaluating the models
and the analyses

“Epistemology” (from Oxford English Dictionary):
“The
The theory of knowledge and understanding, esp. with
regard to its methods, validity, and scope, and the
distinction between justified belief and opinion; [and/or]
a particular theory of knowledge and understanding
understanding.”
43
EIA view on NEMS epistemology
 “Projections by EIA are not statements of what will happen
but of what might happen, given the assumptions used for
any particular scenario…energy models are simplified
representations of [the energy system]
system]. Projections are
highly dependent on the data, methodologies, model
structures, and assumptions used in their development.
Behavioral characteristics are indicative of real-world
tendencies rather than representations of specific
outcomes [These] projections are subject to much
outcomes…[These]
uncertainty [some of which is addressed via side cases]”
((USEIA 2014b).
)
The meaning
g of scenarios
 “Scenarios are alternative images of how the future
might unfold and are an appropriate tool with which to
analyze how driving forces may influence future
emissions outcomes and to assess the associated
uncertainties…The possibility that any single emissions
path will occur as described in the scenarios is highly
uncertain” (IPCC 2000).
uncertain
A synopsis
 In this modeling domain, there are no established or
commonly applied theoretical, empirical, or computational
methods or procedures for evaluating models and defining
and determining model validity, verisimilitude, or “quality”

The EIA, by law, conducts retrospective reviews of NEMS forecast
accuracy, but this is an exception
 Model credibility
y is claimed on other g
grounds,, including:
g
Internal consistency, and plausibility of results
 Usefulness for g
generating
g insights…
g

 “The
The most dramatic contributions of models are found in the isolation
of counterintuitive results, which may arise because of overlooked
factors or complex system interactions that cannot be assimilated
easily ” (Hogan 1979; see also Peace and Weyant 2008)
easily…
 Although not always stated explicitly, there is a revealed
epistemology of increasing levels of model detail being
identified with increasing validity, verisimilitude, and/or
usefulness for policy applications
Calibrationist philosophy
 [Modelers view] the widespread use of a particular structure
in the theoretical literature [as] an indication of its worth, so
that they seek less to test or validate models and more to
explore the numerical implications of a particular model
model,
conditional on having chosen it…[their] focus is to generate
g
about the effects of p
policy
y or other changes
g
insights
conditional on a particular theoretical structure…” (Dawkins
et al. 2001).
Intra-model uncertainty
 The deterministic energy and IA models embody a very high
degree of “Knightian” uncertainty:

Uncertainty that cannot be represented in terms of probability
distributions
 This uncertainty is generally not formally recognized or
addressed as such
 The scenario methodology can be interpreted as a strategy
for in effect side-stepping this problem

Only
y an extremely
y small subset of p
possible equally
q
yp
plausible sets of
inputs and parameters are actually analyzed

The models, and their outputs, can be seen abstractly as single points
y high-dimensional
g
space
p
in a very
Inter-model uncertainty
 Example: Modeling the potential impacts of the Kyoto
Protocol on the U. S. economy
 Energy Modeling Forum conducted a structured multi-model
scenario study of this topic (Weyant and Hill 1999)
EMF-16 Model Predictions of Marginal Abatement Costs for the United
States Derived from the “No Trade” and Annex 1 Trading Scenarios
for Implementing
p
g the Kyoto
y
Protocol
Concluding remarks
 Energy policy modeling both reveals and reflects a rather
profound dilemma: While models of this type (although not
necessarily any particular model or type of model) are
indispensable for developing, analyzing, and implementing
policies this modeling is unavoidably subject to
policies,
fundamental, irreducible uncertainties.
 This is ultimately a problem for policy
policy, not for modeling:
Notwithstanding widespread belief to the contrary, our
capacity to accurately predict the effects of energy policies –
including those directed at CO2 emissions – is extremely
limited.
Thank you.
you
[email protected]
References
 Dawkins, Christina, and T. N. Srinivasan, John Whalley. 2001.
“Calibration.” Chapter 58 in J. J. Heckman and E. Leamer, Eds.,
Handbook of Econometrics, Volume 5. Elsevier Science B. V.
 Hogan, William W. 1979. “Energy Modeling: Building Understanding for
Better Use.” In: Proceedings of the Second Lawrence Symposium on
Systems and Decision Sciences
Sciences, Berkeley,
Berkeley California
California, October
October.
 IPCC (Intergovernmental Panel on Climate Change). 2000. IPCC
Special Report [on] Emissions Scenarios – Summary for Policymakers.
A Special Report of IPCC Working Group III.
 Gillingham, K., and D. S. Rapson, G. Wagner. 2016. “The Rebound
Effect and Energy Efficiency Policy
Policy.” Review of Environmental
Economics and Policy 10 (1), Winter: 68-88.
 Kim, Son H., and Jae Edmonds, Josh Lurz, Steven J. Smith, Marshall
Wise. 2006. “The ObjECTS Framework for Integrated Assessment:
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Transportation ” In JJ-C
C Hourcade,
Hourcade M.
M Jaccard,
Jaccard C.
C
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Modeling of Energy-Environmental Policies: Reconciling Bottom-Up and
T D
Top-Down,
pp. 63-92.
63 92
 Krey, Volker, and Gunnar Luderer, Leon Clarke, Elmar Kriegler. 2014.
“Getting
g from here to there – energy
gy technology
gy transformation
pathways in the EMF27 scenarios.” Climatic Change 123: 369-382.
 Kriegler, Elmar, et al. 2014. “The role of technology for achieving climate
policy objectives: overview of the EMF 27 study on global technology
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 National Research Council (NRC). 1992. The National Energy Modeling System.
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Sciences and Education. Washington, DC: National Academy Press.
 Nordhaus, William. 2008. A Question of Balance – Weighing the Options on
Global Warming
g Policies. Yale Universityy Press.
 Paltsev, S. et al., The MIT Emissions Prediction and Policy Analysis (EPPA)
Model: Version 4, Report No. 125, MIT Joint Program on the Science and Policy
of Global Change
Change, August 2005
2005. USEIA (U
(U. S
S. Energy Information
Administration).
 USEIA (U. S. Energy Information Administration). 2014a. Integrating Module of
the National Energy Modeling System: Model Documentation 2014
2014, U.
U S
S.
Energy Information Administration (EIA), July.
 USEIA (U. S. Energy Information Administration). 2014b. Annual Energy Outlook
2014 – with
ith projections
j ti
to
t 2040.
2040 Publication
P bli ti DOE/EIA
DOE/EIA-0383
0383 (2014)
(2014), A
Aprilil
 Weyant, John P., and Jennifer N. Hill. 1999. “Introduction and Overview.” The
Energy Journal, Kyoto Special Issue: vii-xiiv.