Upload
others
View
0
Download
0
Embed Size (px)
Citation preview
1
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
2
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
3
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?
4
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
5
Examples of Disruptions
Black Thunder, WY, 2005: Coal train derailment
1993 Flood Stops Barge Traffic
Disruption to Gulf Coast Gas Production from Katrina/Rita
6
• 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)
8
Coal System
Natural Gas System
Electricity System
9
Coal System
Natural Gas System
Electricity System
10
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
11
• 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
12
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
13
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
14
Modeling Issues: typical node
CANMEX
~~
Other transshipment
nodes
Storage node
Production nodes
Non-electric power sectors
demand
Electric generators demand
.
.
.
. . .
. ..
Natural gas transhipment
node
15
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
16
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
… … … …
… … … …
……… …
17
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
18
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
19
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
20
• 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 −+=
21
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
22
Coal Characteristics
23
AZNM
NWPPMAPP
MAINECAR
PRB
The Coal Dog….Powder River Basin Coal
Movement
24
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.
25
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 $500 billion.
26
Energy FlowsGAS COAL
ELECTRIC
Case A: actual generation
Case B: optimized, no emissions constraint
Case C: optimized, with emissions constraint
PRB at max for optimal gen
Emissions constraint causes more Cent. App, less North App
Texas gas decreases Red represents
congestion
Regions w/coal gen increase exports; Cal & Fla increase imports
27
Emissions
Nodal Prices
ActualOptimized, w/o em constOptimized, w/ em const
Emissions>allowances (white) due to banking & trading
Emissions are very high w/o em const (dark one)
Blue and yellow have same em const but optimal gen causes redistribution to achieve lower costs.
Eastern interconnection sees big drop in prices.
Except in congested areas.
28
Variation with Time
0
10,000
20,000
30,000
40,000
50,000
60,000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
GW
h
Case A Case B Case C
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
GW
h
Case A Case B Case C
Coal Gas
Nodal prices for optimized cases are lower.
Congestion occurs between MAAC & NYISO.
Summer peak
ECAR Coal-fired Generation ECAR Gas-fired generation
29
93.50Increased US pipeline capacity based on 2010 EIA estimates
2010
97.47Simulated effects of Katrina on natural gas production and transportation
Katrina
92.604000 MW transmission increase on paths between NERC regions that are congested
4000mw increase
96.96Optimized coal, gas, generation with emissions constraint
C, 2002
96.90Optimized coal, gas, generation without emissions constraint
B
101.422002 actual generation. Optimized coal, gas, with emissions constraint.
A
Total Cost ($billion)
DescriptionCaseID
All simulations performed using 2002 year data.
30
4000 MW Transfer Capability increase between regions of Transmission Congestion
31
Nodal PricesNodal Pr i ces
0102030405060
NWPP CPAAZNMRMPAMAPP SPPERCOT MAI N
ECAR EES TVAVACAR SOCO
FRCCMAACNYI SOI SONE
Regi ons
$/Mw
hr 20024000MW I ncr ease
Some winners ☺…. and some losers
32
93.50Increased US pipeline capacity based on 2010 EIA estimates
2010
97.47Simulated effects of Katrina on natural gas production and transportation
Katrina
92.604000 MW transmission increase on paths between NERC regions that are congested
4000mw increase
96.96Optimized coal, gas, generation with emissions constraint
C, 2002
96.90Optimized coal, gas, generation without emissions constraint
B
101.422002 actual generation. Optimized coal, gas, with emissions constraint.
A
Total Cost ($billion)
DescriptionCaseID
All simulations performed using 2002 year data.
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
$/Mw
hr 20022010
Everybody wins ☺. Why?
Texas gas incurs less expensive production and transportation costs than Canadian gas.
35
93.50Increased US pipeline capacity based on 2010 EIA estimates
2010
97.47Simulated effects of Katrina on natural gas production and transportation
Katrina
92.604000 MW transmission increase on paths between NERC regions that are congested
4000mw increase
96.96Optimized coal, gas, generation with emissions constraint
C, 2002
96.90Optimized coal, gas, generation without emissions constraint
B
101.422002 actual generation. Optimized coal, gas, with emissions constraint.
A
Total Cost ($billion)
DescriptionCaseID
All simulations performed using 2002 year data.
36
")")
")
")
") ")
")
") ")
")
")
")
#I#I
#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
37
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.
38
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