Using Energy Economy Models to

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7 Νοε 2013 (πριν από 3 χρόνια και 7 μήνες)

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Using Energy Economy Models to
Deliver Policy
-
Relevant Insight

Joe
DeCarolis

Assistant Professor

Dept of Civil, Construction, and Environmental Engineering

NC State University

1

SAMSI Workshop

20 September 2011

Talk Outline

1.
Motivation and description of EEO models

2.
Problems with model development and application

3.
Introduce the TEMOA project

4.
Describe our approach to uncertainty analysis

5.
Conclude


2

1. Motivation and description

of EEO models


3

4

Based on WRE carbon
-
cycle model. For details see:

Hoffert

et al (1998). “Energy implications of future stabilization of atmospheric CO
2

content.”

Nature
, 395: 881
-
884.


Energy Implications of Climate Change

0
5
10
15
20
25
30
35
40
45
50
750
ppm (~
3
.
6
°
C)
550
ppm (~
3
.
1
°
C)
450
ppm (~
2
.
6
°
C)
Primary Power (
10
12
Watts)
Stabilization Target for Year
2100
Gas
Oil
Coal
Carbon
-
Free
Total Primary Power Required by
2100
2005
Total
Primary
Power
2005
Carbon
-
Free Primary
Power
Energy Economy Optimization (EEO) Models

Energy economy optimization models refer to partial or general
equilibrium models that
minimize cost or maximize utility

by, at least
in part, optimizing the energy system over multiple decades


Examine the competition among energy technologies,
including
renewables


Expansive system boundaries and multi
-
decadal timescales


Encoded with a set of structured, self
-
consistent assumptions and
decision rules


Such models have emerged as a key tool for the analysis






5

Model Types

Computable General Equilibrium:
Adjusts all prices until
all supplies and demands in all markets are balanced
simultaneously. Technologies often represented
stylistically by production functions (output as a function
of capital, labor, land, resources)


Technology Explicit, Partial Equilibrium:

Detailed
technology representation using engineering
-
economics;
demand fixed or elastic


6

Modeling Objectives

Three broadly defined objectives for such models:


1.
Prediction of future quantities

“The price of oil in 2030 will be …”


U
nder a federal climate policy,
t
he installed capacity of Technology x in 2050 will be …”


2.
Prescriptive analysis for planning purposes

“The following mechanisms should be included in federal climate policy …”

“The following technologies should be deployed to minimize system
-
wide costs …”


3.
Generation of insight

“Increased use of natural gas for vehicle H
2

could lead to poor air quality”

“The
widescale

deployment of plug
-
in hybrid vehicles could lead to high coal consumption”





7

2
. Problems with model

development and application


8

Problems with energy modeling

Inability to
validate model
results

Increasing
availability of
data

Moore’s Law

+

+

Increasing
model
complexity

Lack of
openness

+

Inability to
verify model
results

Uncertainty
analysis is
difficult

9

Lack of model validation

No practical way to validate energy
-
economy models →

cannot be validated in the same way as models of physical
processes

Three validation options:

1.
Wait

2.
Backcast

3.
Compare results with other models

Little to guide the modeler and reign in efforts that do not improve
model performance

10

Inability to
validate model
results
Increasing
availability of
data
Moore’s Law
+
+
Increasing
model
complexity
Lack of
openness
+
Inability to
verify model
results
Uncertainty
analysis is
difficult
Lack of openness

Most EEO models and datasets remain closed source. Why?


protection of intellectual property


fear of misuse by uninformed end users


inability to control or limit model analyses


implicit commitment to provide support to users


overhead associated with maintenance


unease about subjecting code and data to public scrutiny


11

Inability to
validate model
results
Increasing
availability of
data
Moore’s Law
+
+
Increasing
model
complexity
Lack of
openness
+
Inability to
verify model
results
Uncertainty
analysis is
difficult
Inability to verify model

results

With a couple exceptions,

energy
-
economy models are not open source


Descriptive detail provided in model documentation
and peer
-
reviewed journals is insufficient to reproduce
a specific set of published results


Reproducibility of results is fundamental to science



Replication and verification of large scientific models
can’t be achieved without source code and input data








12

Inability to
validate model
results
Increasing
availability of
data
Moore’s Law
+
+
Increasing
model
complexity
Lack of
openness
+
Inability to
verify model
results
Uncertainty
analysis is
difficult
Critique of scenario analysis

Stretch one’s thinking about how the future may unfold


Shell Group (2005):


They are not forecasts, projections or predictions of what is to come. Nor are they
preferred views of the future. Rather, they are plausible alternative futures: they
provide reasonable and consistent answers to the ‘what if?’ questions relevant to
business.


Without subjective probabilities
p
(
X
|
e
), scenarios of little value



13

Inability to
validate model
results
Increasing
availability of
data
Moore’s Law
+
+
Increasing
model
complexity
Lack of
openness
+
Inability to
verify model
results
Uncertainty
analysis is
difficult
Source:
IPCC Fourth Assessment Report,
Synthesis Report
,

Chapter 3.

Cognitive heuristics play a role and can lead to misinterpretation of
results.


Availability heuristic:

Probabilities of a future event or outcome assessed on the basis of
how easily an individual can remember or imagine examples


Anchoring and adjustment:

People start with an initial value or “anchor” and then modify their
judgment as they consider factors relevant to the specifics


often
insufficient adjustment




A few highly detailed scenarios can create cognitively compelling
storylines.



Critique of scenario analysis (continued)

Drawn from: Morgan G, Keith D. Improving the way we think about projecting future
energy use and emissions of carbon dioxide
. Climatic Change

2008; 90; 189
-
215.


14

3
. The TEMOA Project


15

(Tools for Energy Model Optimization and Analysis)

The TEMOA Project

Goal:
Create a set of community
-
driven energy economy
optimization models

Our Approach:



Open source code (GNU Public License)



Open source data (GNU Public License)



No commercial software dependencies



Input and output data managed directly with a relational DB



Data and code stored in a web accessible electronic repository



A version control system



Programming environment with links to linear, mixed integer,
and non
-
linear solvers



Built
-
in capability for sensitivity and uncertainty analysis





Tools for Energy Model

Optimization and Analysis

16

Version control with Subversion

We are using a version control system called Subversion (SVN)


http://subversion.apache.org/


http://svnbook.red
-
bean.com/

Why? Ensure the integrity, sustainability and traceability of changes
during the entire software lifecycle
.


SVN enables:


Multiple developers to work simultaneously on software
components; automatic integration of non
-
conflicting changes


Display the modifications to model source code


Create software snapshots (releases) that represent well
-
tested and
clearly defined milestones


Utilize the release mechanism to take snapshots of the model code
and data used to produce research publications.


Public access to snapshots of the code and data


Works on all major (Unix, Windows,
MacOS
) platforms

17

Python Optimization Modeling Objects

We're developing the model against the
Pyomo

API


Why
Pyomo
?


Uses a full
-
featured modern programming language


Rich set of Python libraries that cover nearly every task


Active development; linkages between
Pyomo

and
custom solvers are being developed within the
COmmon

Optimization Python Repository (COOPR)

Pyomo

developed at Sandia National Laboratories:

https://software.sandia.gov/trac/coopr/wiki/Pyomo

18

COmmon

Optimization Python Repository

Pyomo

is part of the COOPR package, which is in turn part of A
Common Repository for Optimizers (ACRO)

Two
-
language approach: high
-
level language for model
formulation and efficient low
-
level languages for numerical
computations (e.g., C, C++, Fortran)

ACRO includes both libraries developed at Sandia and publicly
available third
-
party libraries (e.g., GLPK and COIN
-
OR)

Gives us the capability to formulate linear, mixed integer, and
non
-
linear model formulations without commercial solvers

Active collaboration with Discrete Math and Complex Systems
Department at Sandia National Laboratories

19

Model Structure

Coal
Resource Commodities
Energy Technologies
Demand Technologies
End
-
Use Demands
Coal
-
fired
power
Coal
gasification
Refinery
Gas
turbine
CH
4
-
H
2
O
reforming
Electric
water heater
H
2
fuel
cell vehicle
Gasoline
vehicle
Gas water
heater
P
Q
P
Q
Oil
P
Q
Nat Gas
Electricity
P
Q
P
Q
H
2
P
Q
Gasoline
P
Q
P
Q
Hot Water
Vehicle Miles
Intermediate Commodities
20

Basic TEMOA Model Formulation

Minimize

present cost of energy


Such that


Supply ≥ Demand


Commodity_Into_Process


Commodity_Outof_Process


Commodity_Produced


Commodity_Consumed


Capacity
×

Capacity_Factor

≥ Activity

MARKAL ‘Utopia’ System Diagram

Diagram generated using
Graphviz
:
http://www.graphviz.org/


22

Calibration to Utopia

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1990
2000
2010
Capacity (GW)
Year
E01
E01
E31
E31
E51
E51
E70
E70
SRE
SRE
MARKAL: Dark Colors; TEMOA: Light Colors
MARKAL Objective value: 36,821

TEMOA Objective value: 38,502

Installed Capacity of Process Technologies:

23

Calibration to Utopia (continued)

0
10
20
30
40
50
60
70
80
90
100
1990
2000
2010
Capacity (PJ/yr)
Year
RHO
RHO
RL1
RL1
TXD
TXD
TXG
TXG
MARKAL: Dark Colors; TEMOA: Light Colors
Installed Capacity of Demand Technologies:

24

Questions to address with TEMOA


How does uncertainty in technology
-
specific
characteristics (e.g., capital cost of solar PV) affect
outcomes of interest (e.g., fuel prices, fossil fuel
consumption, air emissions)?


Which technologies and fuels appear to be robust
options given uncertainty in future climate policy and
rates of technology learning?


How much flexibility exists in energy system design
and at what cost?


25

4
. Approach to uncertainty analysis


26

Approach to uncertainty analysis

Use the following techniques in series:


Sensitivity analysis and Monte Carlo simulation

→ Determine key sensitivities


Multi
-
stage stochastic optimization

→ Develop a hedging strategy


Explore near
-
optimal, feasible region (MGA)

→ Test robustness of hedging strategy

Uncertainties associated with
renewables

Easy


Capital costs


Marginal and fixed operational costs


Performance characteristics


Hard


Dispatch (of intermittent
renewables
)


Representation of forecasting error



28

Residual load duration curve

29

Taken from:

Ueckerdt

F,
Brecha

R,

Luderer

G,

Sullivan P,

Schmid

E, Bauer

N,
Böttger

D. (2011) “Variable
Renewable

Energy

in

Modeling

Climate Change

Mitigation

Scenarios

.”
International Energy
Workshop
, Stanford, CA


Implemented in REMIND‐D,
a hybrid energy‐economy

model

of

Germany


30

Stochastic Optimization

Decision
-
makers need to make choices before uncertainty is

resolved → requires an “act then learn” approach


Need to make short
-
term choices that hedge against future risk


→ Sequential decision
-
making process that allows recourse


Stochastic optimization


Build a scenario tree


Assign subjective probabilities to future outcomes


Optimize over all possibilities



Stochastic optimization of energy
m
odels

Desirable features for energy models:


Multi
-
stage (greater than 2)


Multi
-
objective (e.g., cost, risk, emissions)


Mixed integer (esp. endogenous tech learning)


Potential stochastic parameters:


Fuel prices (esp. crude oil, natural gas, coal)


Policy targets (e.g., CO
2

constraints, subsidies)


Technology performance (e.g., capital cost, thermal
eff
)


End
-
use demand projections (e.g., heating, cooling)




31

32

Simple example of stochastic optimization

Suppose we have two technologies, A and B. Let
x
and

y
represent
the installed capacity in Stages

1 and 2, respectively
.



t
1

t
2

s
1

s
2

Stage 1 Decision

Variables:



Stage 2 Decision Variables:



,
A B
x x
1 1
2 2
,,
,,
,
,
A s B s
A s B s
y y
y y
1
p
2
p
1
Minimize: c
N
T T
s s s
s
x p d y

  

Subject To:
1,...,
0
0 1,...,
s s s s
s
Ax b
T x W y h for s N
x
y for s N

  

 
Scenario 1: s
1

Scenario 2: s
2

Stochastic optimization with
PySP

Python
-
based Stochastic Programming (
PySP
) is part of the
COOPR package.


To perform stochastic optimization, specify a
Pyomo

reference model
and a scenario tree


PySP

offers two options:

1.
runef
: builds and solves the extensive form of the model.

“Curse of dimensionality” → memory problems

2.
runph
: builds and solves using a scenario
-
based decomposition
solver (i.e., “Progressive Hedging) based on
Rockafellar

and Wets
(1991).


Can be implemented in a compute cluster environment; more
complex scenario trees possible.




R.T.
Rockafellar

and R. J
-
B. Wets. Scenarios and policy aggregation in optimization under
uncertainty.
Mathematics of Operations Research
, pages 119

147, 1991.


33

34

A Test Case of the US Electric Sector

Time periods: 2010
-
2040, 5
-
year increments

2030 and after, 2 possible CO
2

emissions
levels, 3 possible natural gas prices

Electric sector CO
2

emissions in 2010: 2340 MmtCO
2

BAU CO
2
: 0.6% annual increase,
CO
2

Constrained
: 4.7% annual decrease

[
-
50% to +20% change in CO
2

emissions in 2040 relative to 2010]

Natural gas prices in 2010: 4.45 $/GJ

Low
: 1.1% annual decrease,
Constant
,
High
: 8.4% annual increase

[Price ranges from 3.8 to 15 $GJ in 2040]






2010

2015

2020

2025

2030

BAU CO
2
, Low Gas Price

Constrained CO
2
, Low Gas Price

BAU CO
2
, Constant Gas Price

Constrained CO
2
, Constant Gas Price

BAU CO
2
, Low Gas Price

Constrained CO
2
, Low Gas Price

6
3

= 216 scenarios

6
0
+6
1
+6
2
+6
3

= 259 nodes

BAU CO
2
, natural gas price constant

Uniform Probabilities

Technology
a

Capital
Cost

($/kW)

Fixed
O&M

($/kW∙yr)

Variable
O&M

($/kWh)

Efficiency

(%)

Capacity
Factor

(%)

Average
Cost

($/kWh)

Baseload

/
Shoulder

/
Peak

(B/
S/
P)

Capacity
Constrain
t
b

(GW)

Pulverized
Coal

2058

27.5

0.0459

39

95

0.043

B


IGCC

2378

38.7

0.0292

46

90

0.045

B


IGCC
-
CCS

3496

46.1

0.0444

41

90

0.066

B


GTCC
-
CCS

1890

19.9

0.0294

46

90

0.086

B


Nuclear

3318

90.0

0.0049

33

95

0.054

B


Geothermal

1711

165

0.00

11

90

0.044

B

23

GTCC

948

11.7

0.0200

54

95

0.062

Any


GT

634

10.5

0.0317

40

95

0.076

Any


Hydro

2242

13.6

0.0243

34

65

0.047

Any

2

Wind
-
Onshore

1923

30.3

0.00

34

35

0.076

S

8000

Wind
-
Offshore

3851

89.5

0.00

34

40

0.14

S

800

Solar Thermal

5021

56.8

0.00

34

40

0.17

S

100

Solar PV

6038

11.7

0.00

34

30

0.25

S



Technology Cost and Performance Characteristics

Source:
EIA (US Energy Information Administration), Office of Integrated Analysis and Forecasting, US
Department of Energy.
Assumptions to the
Annual Energy Outlook 2009
. DOE/EIA
-
0554(2009);
Washington DC; US Government Printing Office; 2009b.


Annual growth in electricity demand of 0.6% based on the reference case in the
Annual
Energy Outlook 2009
.

35

Natural Gas Price in 2040 vs. Total Cost

9.85E+06
9.90E+06
9.95E+06
1.00E+07
1.01E+07
1.01E+07
1.02E+07
1.02E+07
1.03E+07
1.03E+07
1.04E+07
0
2
4
6
8
10
12
14
16
System Cost ($)
Natural Gas Price in 2040 ($/GJ)
36

0
100
200
300
400
500
600
2010
2015
2020
2025
2030
2035
2040
Electricity Generation (GWyr)
Year
geothermal
nuclear
hydro
cc gas turbine
sc gas turbine
coal
37

Constant Nat Gas Prices, Increasing CO
2

CO
2

emissions allowed to grow 0.6% annually

Natural gas prices remain constant at 4.5 $/GJ

0
100
200
300
400
500
600
2010
2015
2020
2025
2030
2035
2040
Electricity Generation (GWyr)
Year
onshore wind
geothermal
nuclear
hydro
cc gas turbine
sc gas turbine
coal
38

High Nat Gas Prices, Decreasing CO
2

CO
2

emissions decrease 4.6% annually from 2030
-
2040

Natural gas prices increase 8.3% annually from 2030
-
2040

Modeling to Generate Alternatives

39

Need a method to test the robustness of a hedging strategy →
“Modeling to Generate Alternatives”



MGA generates alternative solutions that are
maximally different
in decision space

but perform well with respect to modeled
objectives


The resultant MGA solutions provide modelers and decision
-
makers with a set of alternatives for further evaluation




Brill (1979), Brill et al. (1982), Brill et al. (1990)

Hop
-
Skip
-
Jump (HSJ) MGA

40

Steps:

1.
Obtain an initial optimal solution by any method

2.
Add a user
-
specified amount of slack to the value of the
objective function

3.
Encode the adjusted objection function value as an
additional upper bound constraint

4.
Formulate a new objective function that minimizes the
decision variables that appeared in the previous solutions

5.
Iterate the re
-
formulated optimization

6.
Terminate the MGA procedure when no significant changes
to decision variables are observed in the solutions

Brill et al. (1982)

HSJ MGA

41

Mathematical formulation



X
j
T
x
f
x
p
j
j
K
k
k






x


)
(

s.t.

min
where:



K

represents the set of indices of decision
variables with nonzero values in the
previous
s
olutions



is
the
j
th

objective
function


T
j

is the target specified for the
j
th

modeled
objective


X

is the set of feasible solution vectors



x
f
j
Conclusions

Most EEO models and model
-
based analyses are opaque to
external parties


The TEMOA project represents a new, transparent modeling
framework designed for rigorous uncertainty analysis


Combine sensitivity analysis, stochastic optimization, and
modeling
-
to
-
generate
-
alternatives to identify robust hedging
strategies


Critical to analyze the deployment of
renewables

in a systems
context

42

Acknowledgments


Kevin Hunter, MS student, Civil Engineering, NCSU


Sarat

Sreepathi
, PhD student, Computer Science, NCSU


Jean
-
Paul Watson and Bill Hart, Sandia National
Laboratory



This work would made possible through the generous
support of the National Science Foundation. CAREER:
Modeling for Insights with an Open Source Energy
Economy Optimization Model
. Award #1055622