Decision Models for Bulk Energy Transportation Networks• LEAP (Long-range Energy Alternatives Planning) • MARKAL (MARKet ALlocation) • MESSAGE (Model for Energy Supply Strategy

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Decision Models for Bulk Energy Transportation Networks

Electrical Engineering Professor Jim McCalleyAna Quelhas (PhD-06)Esteban Gil (PhD-07)

Seshendra Vasireddy (MS-07)

EconomicsProfessor Leigh Tesfatsion

Junjie Sun (PhD-06)

SociologyProfessor Steven Sapp

Natalia Frishman (MA-07)

www.econ.iastate.edu/tesfatsi/nsfenergy2005.htm

Industrial EngineeringProfessor Sarah Ryan

Yan Wang (MS-07)

Computational modeling:• integrated fuel, electricity networks• environmental impacts• electricity commodity markets• behavior of market agents• uncertainty in demand & fuel price• participatory, repeated looping through fieldwork, model design, computational experiments.

Link infrastructure, decision-makingStudy interdependenciesAnswer energy-related questions:

national, regional, local significance

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National Scale (federal government, NERC):(1) What energy flow patterns would yield significantly improved energy

system performance? What operational production and/or transportation changes need to be made to realize these improvements?

(2) What infrastructure weaknesses exist? How do the effects of catastrophic events propagate through the network? What infrastructure enhancements would realize high performance benefit?

Regional Scale (regional independent system operator):(3) How well can we predict the influence of market design changes on

energy system performance?Local Scale (local electric utility company):(4) Can we reflect the influence of possible changes in raw fuel production

and transportation on a company’s return from investing in a specific type of plant at a specific location?

(5) How might buyers and sellers of energy respond to potential new policies designed to improve the transparency and ease of trade?

Objectives of Model

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Model Applications1. Investment:

Transportation: How does increased transportation capability influence wholesale electricity prices?• Build transmission between East-West, East-Texas, West-Texas?• Increase natural gas pipeline capacity from gulf to NE?

Production: How would major investment in a specific electricity national or regional generation portfolio affect electricity prices? • Build large mine-mouth generation at Powder River Basin• How much impact would 25% wind penetration have on price ?

2. Environmental:How would tight emission limits affect SO2 prices, compliance decisions?How would aggregate level of emissions and geographical distribution change if states imposed local standards/trading restrictions? What would be impacts on fuel and electricity markets?How do high natural gas prices drive emissions prices?How would CO2 regulations impact coal, gas, electricity, & SO2 markets?

3. Disruptions: How would a major disruption on the fuel side impact the generation mix? What are the vulnerabilities (Katrina, PRB rail lines); how to strengthen?

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Examples of Disruptions

Lightning strike

Labor strikesPekin, IL: 13 345kV transmission lines destroyed by a tornado in May 2003

El Paso, NM, 2000: Gas pipeline rupture

Ellet Valley, VA, 2003: Norfolk Southern coal train derailed

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Examples of Disruptions

Black Thunder, WY, 2005: Coal train derailment

1993 Flood Stops Barge Traffic

Disruption to Gulf Coast Gas Production from Katrina/Rita

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• Gas wells & pipelines• Coal mines & rail/barges• Storage• Electricity market

• Electric gen & trans• Costs, capacities, and η• SO2 constraints• Market decision agents

What is modeled

• spatial & temporal• energy flows• nodal prices (fuel & elec)• SO2, allowance price• total cost• electric network attributes

What is computed

Structural Model

Behavioral Model

Regional Electricity

market

Coal Piles Gas Storage

Gas Wells Coal Mines

… …

Primary Energy Supplies

Gas CoalRailroad, Barge

… …

Storage & Transportation Systems

… …

… …

Generation System

… … … … … …Electric Energy Demand

ElectricityElectric Transmission System

Electric TransmissionSystem

Nuclear

Renewables (hydro + others)

Petroleum

E

A Decision Model for Bulk Energy Transportation

Networks

7

How is it different?• CIS (Critical Infrastructure Surety)• EMCAS (Electricity Markets Complex Adaptive Systems)• LEAP (Long-range Energy Alternatives Planning)• MARKAL (MARKet ALlocation)• MESSAGE (Model for Energy Supply Strategy

Alternatives & General Environmental Impacts)• Global Energy Decisions • NEMS (National Energy Modeling System)

Relative to NEMS, ours is unique in

• Ours is an optimization model; NEMS is an equilibrium model

• We enable analysis of bulk energy transportation substitutability

• We target “transient” analysis (2-3 years) instead of long-term (25 years)

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Coal System

Natural Gas System

Electricity System

9

Coal System

Natural Gas System

Electricity System

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In ters ta te P ip e lin esLegend

In tras ta te P ipe lin esT exas

O k lahom a

A rkansas

K en tucky

M ississipp i

A lab am a

Lou is ia na

D e law are

M ary land

C onnec ticu tN ew Je rseyP ennsylvan ia

R hode Is land

M assachuse tts

N ew H am pshire

V e rm ont

M aine

N ew Y o rk

K ansas

W yom ing

N ew M ex ico

F lo rida

S ou th D ako ta

IowaO h io

V irg in ia

N orth C a ro lina

G eorg ia

S outh C aro lina

T ennessee

M ich igan

In dianaIllino is

W iscons in

M inneso ta

C o lo radoM issou ri

A rizona

N e braska

N orth D ako taM on tana

Ida ho

C a lifo rn ia

N evada

W ash ing ton

O regon

U ta h

W es t V irg in ia

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• Energy movement, not power flow• Short tons coal, Mcf gas, MW-months, converted to MMBTU• Power plants differentiated by fuel type and prime mover:

Coal steam (no FGD, wet FGD, dry FGD), Gas steam, Combined cycle, Combustion turbine

• Other gen resources modeled as fixed inputs• Electric load is modeled fixed (inelastic)• Coal transportation is unconstrained but has cost• Gas production regions & aggregated pipelines represented• 4 gen, 1 demand node per subregion, with tie lines between• Computational approach is generalized network flow simplex

• 2002 data: 1290 nodes 3480 arcs, 1 year simulation

– monthly for gas, electricity

– yearly for coal

Some Model Attributes

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Title IV of the 1990 CAAA• Cap-and-trade: control SO2 emissions • 1 allowance = 1 ton of SO2 , Compliance period: 1 yr• Compliance strategies:

– Retrofit units with scrubbers – Build new power plants with low emission rates – Switch fuel – Trade allowances– Purchase power

• National annual emission limit: About 9000 tons• Emissions produced depends on fuel used, pollution

control devices installed, and amount of electricity generated

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Nodes and Arcs• Nodes

– Source node (dummy node)– Sink nodes (load centers)– Transshipment nodes (production facilities, storage

facilities, power plants), may also be sink nodes• Arcs

– Coal, natural gas, and electricity imports and exports– Coal and natural gas production– Coal transportation routes and natural gas pipeline

corridors– Storage injections and withdrawals, and inventories

carried over between two consecutive time periods– Electricity generation– Bulk electric power trade

• Arc Parameters– Lower bound, upper bound, cost, efficiency

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Modeling Issues: typical node

CANMEX

~~

Other transshipment

nodes

Storage node

Production nodes

Non-electric power sectors

demand

Electric generators demand

.

.

.

. . .

. ..

Natural gas transhipment

node

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Modeling Issues: typical arcs

i j(l, u, c, η)

i j(l, u, c, η)

(l, u, c, η)

Undirected Arcs

Nonzero Lower Bounds

i j(l, u, c, η)

i j(0, u–l, c, η)

bj+lbi–lbjbi

i i’i i’

(0, 20, 2.5, .40)

(0, 10, 5, .44)

(0, 10, 10, .42)

(lower bound, upper bound, IC, efficiency)

0 10 20 30 40

flow

200

100

0

totalcost

i. .

.

. .

.

. .

.Power Plant Representation

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Modeling Issues: Multiperiod operation

Period 1 for coal

Period 1for

electricity

Period 1 for gas

Period 2for

electricity

Period 3for

electricity

Period 4for

electricity

Period 2 for gas

… … … …

… … … …Period 2 for coal

Period 5for

electricity

Period 3 for gas

Period 6for

electricity

Period 7for

electricity

Period 8for

electricity

Period 4 for gas

… … … …

… … … …

……… …

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Generalized Network Flow Model

)()()( tbtete ji

ijijk

jk =−∑∑∀∀

η

max,min, )( ijijij etee ≤≤

∑ ∑∈ ∈

=Tt Mji

ijij tetcz),(

)()(

TtNj ∈∀∈∀ ,

TtMji ∈∀∈∀ ,),(

2)()1()(2),(

NSOtetSOTt Gji

ijii ≤⋅−⋅∑ ∑∈ ∈

α

ecz '=

maxmin eee ≤≤be =A

Minimize

Subject to

MinimizeSubject to

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Example

PVG1

GG1

GP1

PVC1

CG1

CP1

CA1

CS1

1 2

3

5

5’

4

4’

6

S

e11,1 e22,1

e23,1

e25,1e14,1

e44,1 e55,1

e35,1

e56,1e46,1

PVG2

GG2

GP2

PVC2

CG2

CP2

CA2

CS2

1 2

3

5

5’

4

4’

6

e23,2

e25,2e14,2

e44,2 e55,2

e35,2

e56,2e46,2

e33,1

e11,2 e22,2

-e6,1 -e6,2

-e33,2e33,0

Period 1 Period 2

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Example (cont.)e 11,1

e 11,1 e 22,1 e 14,1 e 33,1 e 23,1 e 25,1 e 35,1 e 44,1 e 55,1 e 46,1 e 56,1 e 11,2 e 22,2 e 14,2 e 23,2 e 25,2 e 35,2 e 44,2 e 55,2 e 46,2 e 56,2 e 22,1

e 14,1

1 -1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 e 33,1 02 0 -1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 e 23,1 0

3 0 0 0 1 -1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 e 25,1 e 33,0

4 0 0 -1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 e 35,1 04' 0 0 0 0 0 0 0 −η4 0 1 0 0 0 0 0 0 0 0 0 0 0 e 44,1 05 0 0 0 0 0 -1 -1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 e 55,1 05' 0 0 0 0 0 0 0 0 −η 5 0 1 0 0 0 0 0 0 0 0 0 0 e 46,1 0

6 0 0 0 0 0 0 0 0 0 −η 46 −η 56 0 0 0 0 0 0 0 0 0 0 e 56,1 -e 6,1

1 0 0 0 0 0 0 0 0 0 0 0 -1 0 1 0 0 0 0 0 0 0 e 11,2 02 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 1 1 0 0 0 0 0 e 22,2 0

3 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 -1 0 1 0 0 0 0 e 14,2 -e 33,2

4 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 1 0 0 0 e 23,2 04' 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 −η 4 0 1 0 e 25,2 05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 -1 0 1 0 0 e 35,2 05' 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 −η 5 0 1 e 44,2 0

6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 −η 46 −η 56 e 55,2 -e 6,2

e 46,2

e 56,2

=

Peri

od 2

Period 1 Period 2

Peri

od 1

X

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• Lagrangian functionNodal Prices

[ ] [ ]

⎥⎥⎦

⎤

⎢⎢⎣

⎡−⋅−⋅+

+−+−+

+⎥⎥⎦

⎤

⎢⎢⎣

⎡−−+=

∑ ∑

∑ ∑∑ ∑

∑∑ ∑∑∑ ∑

∈ ∈

∈ ∈∈ ∈

∈ ∈ ∀∀∈ ∈

2)()1()(2

)()()()(

)()()()()()(

),(

),(max.

),(min.

),(

NSOtetSO

etetteet

tbtetettetc

Tt Gjiijii

Tt Mjiijijij

Tt Mjiijijij

Tt Njj

iijij

kjkj

Tt Mjiijij

αγ

μδ

ηλL

• (i,j) does not represent electricity generation

• (i,j) represents electricity generation

Env. constraintis the only binding =>constraint

0)()()()()()(

=+−−+=∂∂ tttttc

te ijijijjiijij

μδηλλL

0)1)((2)()()()()()(

=−++−−+=∂∂

iiijijijjiijij

tSOtttttcte

αγμδηλλL

)1)((2)()( iiij tSOtt αγλλ −+=

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Data Sources

Petroleum

Natural Gas

Coal

Emissions Electric Power Generation

Import/Export

Transmission End Use

EIA Forms 7A, 176, 191, 857, 895

MSHA Form 7000-2

FERC Forms 423, 549B, 580

DOE, NMA DOT/FRA, OFE, API

DOE/EIA

EPA (eGRID)

DOE/

EIA Form 767, 860, 906

FERC Form 423

ISOs

FERC Form 715EIA Form 412

NERC, ISOs

DOE

EIA Form 826, 861 FERC Form 714

NERC, ISOs

NEBCDOE/OFPISOs

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Coal Characteristics

23

AZNM

NWPPMAPP

MAINECAR

PRB

The Coal Dog….Powder River Basin Coal

Movement

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Three Simulation Cases• Case A:

– 2002 actual generation, demand, emissions const

– Optimized coal and natural gas flows

• Case B: – Optimized coal, gas, generation

without emissions constraint• Case C:

– Optimized coal, gas, generation, with emissions constraint

Production

Fuel Xport

Generation

Transmission

Load

Comparison to Case A gives potential savings.

Comparison to Case B gives cost of emissions constraints.

Comparison to actual data gives validation.

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Comparison

Result Actual Case A Case B Case C Coal deliveries (million tons) 976 953 1,054 1,048 Natural gas deliveries (million

Mcf) 5,398 5,125 3,615 3,615

Electricity generation from coal (thousand GWh) 1,910 2,117 2,116

Electricity generation from natural gas (thousand GWh) 607 414 414

Net electric power trade (thousand GWh) 205 382 367

Allowance price ($) 130 98 ------ 359 Total costs (billion $) 101.42 96.89 96.96

Gen const;

Emconst

Gen not const;

Emconst

Gen not const; Em not const

NSF budget is 5.4 billion, Non-Iraq defense is $500 billion.

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Energy FlowsGAS COAL

ELECTRIC

Case A: actual generation

Case B: optimized, no emissions constraint

Case C: optimized, with emissions constraint

PRB at max for optimal gen

Emissions constraint causes more Cent. App, less North App

Texas gas decreases Red represents

congestion

Regions w/coal gen increase exports; Cal & Fla increase imports

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Emissions

Nodal Prices

ActualOptimized, w/o em constOptimized, w/ em const

Emissions>allowances (white) due to banking & trading

Emissions are very high w/o em const (dark one)

Blue and yellow have same em const but optimal gen causes redistribution to achieve lower costs.

Eastern interconnection sees big drop in prices.

Except in congested areas.

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Variation with Time

0

10,000

20,000

30,000

40,000

50,000

60,000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

GW

h

Case A Case B Case C

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

GW

h

Case A Case B Case C

Coal Gas

Nodal prices for optimized cases are lower.

Congestion occurs between MAAC & NYISO.

Summer peak

ECAR Coal-fired Generation ECAR Gas-fired generation

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93.50Increased US pipeline capacity based on 2010 EIA estimates

2010

97.47Simulated effects of Katrina on natural gas production and transportation

Katrina

92.604000 MW transmission increase on paths between NERC regions that are congested

4000mw increase

96.96Optimized coal, gas, generation with emissions constraint

C, 2002

96.90Optimized coal, gas, generation without emissions constraint

B

101.422002 actual generation. Optimized coal, gas, with emissions constraint.

A

Total Cost ($billion)

DescriptionCaseID

All simulations performed using 2002 year data.

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4000 MW Transfer Capability increase between regions of Transmission Congestion

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Nodal PricesNodal Pr i ces

0102030405060

NWPP CPAAZNMRMPAMAPP SPPERCOT MAI N

ECAR EES TVAVACAR SOCO

FRCCMAACNYI SOI SONE

Regi ons

$/Mw

hr 20024000MW I ncr ease

Some winners ☺…. and some losers

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2010


Katrina


4000mw increase


C, 2002


B


A


DescriptionCaseID


33

34

Nodal Prices

Nodal Pr i ces

0

10

20

30

40

50

60

NWPP CPA AZNM RMPA MAPP SPP ERCOT MAI N ECAR EES TVA VACAR SOCO FRCC MAAC NYI SOI SONE

Regi ons

$/Mw

hr 20022010

Everybody wins ☺. Why?

Texas gas incurs less expensive production and transportation costs than Canadian gas.

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2010


Katrina


4000mw increase


C, 2002


B


A


DescriptionCaseID


36

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#I

#I

#I #I

!H!H

!H

!H

!H

!H

!H

!H!H

!H

!H

!H

!H!H

Texas

Kansas

Midwest

Oklahoma

MS and AL

Northeast

AR and LANew Mexico

California

Other Central

Other Western

Gulf of Mexico

Other Southeast

Rocky Mountains

GA03CGA02W

GT02CGT01W

GA05NE

GA04MW

GT06SEGT05SW

GT04NE

GT03MW

GE06MEX

GA01CAN

Effect of a Katrina-like event in NG cost in Central

00,5

11,5

22,5

33,5

44,5

5

1 2 3 4 5 6 7 8 9 10 11 12

Effect of a Katrina-like event in NG cost in the West

44,24,44,64,8

55,25,45,6

1 2 3 4 5 6 7 8 9 10 11 12

Month

Effect of a Katrina-like event in NG cost in the NorthEast

5,4

5,6

5,8

6

6,2

6,4

6,6

6,8

1 2 3 4 5 6 7 8 9 10 11 12

Month

Effect of a Katrina-like event in NG cost in the SouthWest

00,5

11,5

22,5

33,5

44,5

1 2 3 4 5 6 7 8 9 10 11 12

Month

Effect of a Katrina-like event in NG cost in the SouthEast

0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10 11 12

Month

Effect of a Katrina-like event in NG cost in the MidWest

0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10 11 12

Month

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Electricity prices at the NY ISO

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12

Month

Simulation marginalcostNYISO reference nodalprice

WHY THE DIFFERENCE?

1. 2002 data used; 2005 system more stressed. 2. Significant increase in nodal prices starting in months before Katrina, for unknown reasons, not modeled.3. Simulations done by reducing Gulf gas production. Failures in other subsystems not included.4. Market behavior not modeled.

General Analysis Approach:

1. Compare simulation to actual data

2. Hypothesize reasons for differences

3. Investigate the model & the system

4. Modify and re-simulate.

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What we are working on now….More simulation studies

• Continue with Katrina

• 25% wind portfolio

• Cost of emission constraints

Enhance model:• Transmission model

• Represent electricity market

• Represent hydro-systems

• Represent learning

Establish price-based reliability metrics

Study a larger bulk energy market

Documents

Decision Models for Bulk Energy Transportation Networks• LEAP (Long-range Energy Alternatives Planning) • MARKAL (MARKet ALlocation) • MESSAGE (Model for Energy Supply Strategy