Electricity Generation Mix by US Industrial Sectors Disaggregating Electricity Generation and...
-
Upload
moses-griffin -
Category
Documents
-
view
215 -
download
1
Transcript of Electricity Generation Mix by US Industrial Sectors Disaggregating Electricity Generation and...
Electricity Generation Electricity Generation Mix by US Industrial Mix by US Industrial
SectorsSectors
Disaggregating Electricity Disaggregating Electricity Generation and Modeling Generation and Modeling
Interstate TransfersInterstate Transfers
Joe Marriott, Carnegie Mellon UniversityJoe Marriott, Carnegie Mellon UniversityAdvisor Scott MatthewsAdvisor Scott Matthews
InLCA/LCM September 2003
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 22
Why Disaggregate?Why Disaggregate? Primary contributor to environmental impact is Primary contributor to environmental impact is
electricity generationelectricity generation
Impacts by generation type varies a lotImpacts by generation type varies a lot– e.g. Hydro versus coale.g. Hydro versus coal
This variation should be reflected in LCA This variation should be reflected in LCA resultsresults
For example: Aluminum manufacturingFor example: Aluminum manufacturing– Industry sector has emission numbers reduced due Industry sector has emission numbers reduced due
to plants in WA, which has 80% hydroelectric to plants in WA, which has 80% hydroelectric generationgeneration
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 33
Currently in LCACurrently in LCA
Use aggregate US generation mix to Use aggregate US generation mix to calculate emissions, GWP, etc.calculate emissions, GWP, etc.
Net Electricity Generation by Energy Source, 1999
SourceGeneration,
%
Coal 50.99%
Petroleum 3.22%
Gas 15.31%
Nuclear 19.72%
Hydroelectric
8.32%
Other 2.41%
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 44
Disaggregate ResultsDisaggregate Results Rather than a single sector:Rather than a single sector:
““Electric services (Utilities)”Electric services (Utilities)”
Have multiple electricity sectors:Have multiple electricity sectors:““Electric services (Utilities, Coal)”Electric services (Utilities, Coal)”““Electric services (Utilities, Nuclear)”Electric services (Utilities, Nuclear)”etc.etc.
Each industrial sector would receive a Each industrial sector would receive a specific mix of these disaggregated specific mix of these disaggregated electricity sectorselectricity sectors
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 55
Making It HappenMaking It Happen
Data is not readily availableData is not readily available– Need complete facility-level transaction data Need complete facility-level transaction data
for all US industrial sectorsfor all US industrial sectors– No central repository of this dataNo central repository of this data– Economic data not necessarily a good Economic data not necessarily a good
estimateestimate
Instead, assign a specific generation mix Instead, assign a specific generation mix to each industrial sector using:to each industrial sector using:
1.1. ““Locations” of industry sectors from BEALocations” of industry sectors from BEA
2.2. State generation mixes from DOEState generation mixes from DOE
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 66
Sector Allocation to StatesSector Allocation to States
Need percentage of each industry sector Need percentage of each industry sector located in each statelocated in each state– Available using a tool developed at Carnegie Available using a tool developed at Carnegie
Mellon by Iavor Kostov and Scott MatthewsMellon by Iavor Kostov and Scott Matthews
Economic Census location data from BEA Economic Census location data from BEA used for placementused for placement– Then uses number of employees and Then uses number of employees and
shipments as measures of size and intensity to shipments as measures of size and intensity to weight various sectors in various statesweight various sectors in various states
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 77
Include Interstate TradingInclude Interstate Trading Lots of electricity transfer in the United States, Lots of electricity transfer in the United States,
especially following deregulation in 1996especially following deregulation in 1996
Currently, interstate electricity transfer Currently, interstate electricity transfer ignored, but it’s a big part of the marketignored, but it’s a big part of the market– 30% of California power is imported30% of California power is imported– West Virginia exports 60% of theirsWest Virginia exports 60% of theirs
These numbers have a significant impact, so These numbers have a significant impact, so new generation mixes are created for each new generation mixes are created for each statestate
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 88
Western US: Net Imports Western US: Net Imports (TWh)(TWh)
+2.7
-70.9
+2.8
+3.4 +14.6
-10.0
+10.7
+16.6 +10.8
-5.6
+26.9
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 99
Model: Linear OptimizationModel: Linear Optimization
Using 2 large matrices (23 x 28), find out Using 2 large matrices (23 x 28), find out where imports likely came from by where imports likely came from by minimizing distance (hops) traveledminimizing distance (hops) traveled
Classic transportation/distribution cost Classic transportation/distribution cost minimization problemminimization problem
This still isn’t what is This still isn’t what is actuallyactually happening happening on the grid, but it’s a pretty good estimateon the grid, but it’s a pretty good estimate
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 1010
State “Hops” for CaliforniaState “Hops” for California
1
0
1
23
2
2
12
3
3
3
3
4
4
4
4
1
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 1111
CA
CO
C T
DC
DE
FL
GA
IA
I D
I L
LA
MA
MD
ME
MI
MN
MO
MS
MX
NC
NJ
N V
NY
OH
OR
VA
V T
WI
AL 6 4 6 4 4 1 1 3 5 3 2 6 3 8 4 5 2 1 4 2 5 6 5 3 7 2 6 4
AR 4 2 6 4 5 3 2 2 4 2 1 6 4 8 4 3 1 1 2 2 6 4 5 3 2 3 6 3
AZ 1 1 9 6 7 6 5 3 2 4 3 9 6 11 5 4 3 4 1 5 8 1 8 5 3 5 9 4
CN 3 3 2 4 3 7 5 2 1 2 5 2 3 1 1 1 3 5 99 4 2 2 1 2 2 4 1 1
IN 7 4 4 3 3 4 3 2 5 1 4 4 3 6 1 3 2 3 5 3 3 6 3 1 6 2 4 2
KS 4 1 6 4 5 4 3 2 3 2 3 6 4 8 4 3 1 3 3 3 5 3 5 4 4 3 6 3
KY 6 3 4 2 3 3 2 2 5 1 3 4 2 6 2 3 1 2 4 2 3 5 3 1 6 1 4 2
MT 3 2 8 8 7 7 6 2 1 3 5 8 7 10 4 2 3 5 4 6 7 2 7 5 2 6 8 3
ND 4 3 8 8 7 7 6 2 2 3 6 8 7 10 3 1 3 5 5 6 6 3 6 4 3 6 8 2
NE 4 1 6 4 5 4 3 1 2 2 3 6 4 8 3 2 1 3 3 3 5 3 5 3 3 3 6 2
NH 12 8 2 5 4 9 7 7 9 6 8 1 4 1 5 7 6 7 8 6 3 10 2 4 11 5 1 6
NM 2 1 8 5 7 6 4 3 2 3 2 8 7 10 5 4 2 3 1 4 7 2 7 6 3 6 8 4
OK 3 3 6 5 6 4 3 2 3 2 2 6 5 8 4 3 1 2 2 3 7 3 5 3 4 4 6 3
PA 8 5 2 2 1 5 4 4 7 3 5 2 1 4 2 4 3 4 7 3 1 8 1 1 8 2 2 3
RI 11 8 1 5 4 9 8 7 9 6 8 1 4 3 5 7 6 8 10 6 3 10 2 4 10 5 2 7
SC 8 5 6 3 4 2 1 4 6 4 4 6 3 8 5 6 3 3 6 1 5 8 5 4 8 2 6 5
SD 4 2 7 6 6 6 5 1 2 2 5 7 6 9 3 1 2 4 4 5 6 3 6 4 3 5 7 3
TN 5 3 5 2 4 2 1 2 4 2 2 5 2 7 3 3 1 1 3 1 4 5 4 2 5 1 5 3
TX 3 2 7 7 6 4 4 3 4 3 1 7 4 9 5 4 2 2 1 3 6 3 7 5 4 3 7 4
UT 2 1 8 6 7 6 5 3 1 4 3 8 6 10 5 4 3 4 2 5 7 1 7 5 2 5 8 4
WA 2 3 10 8 8 7 7 4 1 5 5 10 8 12 6 4 4 6 3 7 8 2 9 6 1 7 10 5
WV 7 4 3 2 2 4 3 3 5 2 4 3 1 5 2 4 2 3 6 2 2 6 2 1 7 1 4 3
WY 3 1 8 6 6 6 5 2 1 3 4 8 5 10 4 2 2 4 3 5 6 2 6 4 2 4 8 3
Complete US Hop Count
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 1212
CA
CO
C T
DC
DE
FL
GA
IA
I D
I L
LA
MA
MD
ME
MI
MN
MO
MS
MX
NC
NJ
N V
NY
OH
OR
VA
V T
WI
AL 6 4 6 4 4 1 1 3 5 3 2 6 3 8 4 5 2 1 4 2 5 6 5 3 7 2 6 4
AR 4 2 6 4 5 3 2 2 4 2 1 6 4 8 4 3 1 1 2 2 6 4 5 3 2 3 6 3
AZ 1 1 9 6 7 6 5 3 2 4 3 9 6 11 5 4 3 4 1 5 8 1 8 5 3 5 9 4
CN 3 3 2 4 3 7 5 2 1 2 5 2 3 1 1 1 3 5 99 4 2 2 1 2 2 4 1 1
IN 7 4 4 3 3 4 3 2 5 1 4 4 3 6 1 3 2 3 5 3 3 6 3 1 6 2 4 2
KS 4 1 6 4 5 4 3 2 3 2 3 6 4 8 4 3 1 3 3 3 5 3 5 4 4 3 6 3
KY 6 3 4 2 3 3 2 2 5 1 3 4 2 6 2 3 1 2 4 2 3 5 3 1 6 1 4 2
MT 3 2 8 8 7 7 6 2 1 3 5 8 7 10 4 2 3 5 4 6 7 2 7 5 2 6 8 3
ND 4 3 8 8 7 7 6 2 2 3 6 8 7 10 3 1 3 5 5 6 6 3 6 4 3 6 8 2
NE 4 1 6 4 5 4 3 1 2 2 3 6 4 8 3 2 1 3 3 3 5 3 5 3 3 3 6 2
NH 12 8 2 5 4 9 7 7 9 6 8 1 4 1 5 7 6 7 8 6 3 10 2 4 11 5 1 6
NM 2 1 8 5 7 6 4 3 2 3 2 8 7 10 5 4 2 3 1 4 7 2 7 6 3 6 8 4
OK 3 3 6 5 6 4 3 2 3 2 2 6 5 8 4 3 1 2 2 3 7 3 5 3 4 4 6 3
PA 8 5 2 2 1 5 4 4 7 3 5 2 1 4 2 4 3 4 7 3 1 8 1 1 8 2 2 3
RI 11 8 1 5 4 9 8 7 9 6 8 1 4 3 5 7 6 8 10 6 3 10 2 4 10 5 2 7
SC 8 5 6 3 4 2 1 4 6 4 4 6 3 8 5 6 3 3 6 1 5 8 5 4 8 2 6 5
SD 4 2 7 6 6 6 5 1 2 2 5 7 6 9 3 1 2 4 4 5 6 3 6 4 3 5 7 3
TN 5 3 5 2 4 2 1 2 4 2 2 5 2 7 3 3 1 1 3 1 4 5 4 2 5 1 5 3
TX 3 2 7 7 6 4 4 3 4 3 1 7 4 9 5 4 2 2 1 3 6 3 7 5 4 3 7 4
UT 2 1 8 6 7 6 5 3 1 4 3 8 6 10 5 4 3 4 2 5 7 1 7 5 2 5 8 4
WA 2 3 10 8 8 7 7 4 1 5 5 10 8 12 6 4 4 6 3 7 8 2 9 6 1 7 10 5
WV 7 4 3 2 2 4 3 3 5 2 4 3 1 5 2 4 2 3 6 2 2 6 2 1 7 1 4 3
WY 3 1 8 6 6 6 5 2 1 3 4 8 5 10 4 2 2 4 3 5 6 2 6 4 2 4 8 3
Complete US Hop Count
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 1313
CA
CO
C T
DC
DE
F L
GA
IA
ID
IL
LA
MA
MD
ME
MI
MN
MO
MS
MX
NC
NJ
NV
NY
OH
OR
VA
VT
WI
AL 24 6 3 33
AR 4 4
AZ 16 16
CN 13 1 3 5 0 12 34
IN 5 1 8 14
KS 1 1 2
KY 2 0 7 9
MT 3 9 0 2 14
ND 17 2 19
NE 1 1 0 3
NH 5 5
NM 9 1 11
OK 1 2 3
PA 8 4 1 4 16
RI 0 0
SC 4 4
SD 3 3
TN 0 0
TX 1 0 1
UT 2 2
WA 17 17
WV 14 22 20 56
WY 23 3 26
70 5 13 10 4 24 6 5 9 5 1 8 14 3 1 17 2 10 1 5 4 0 5 30 2 27 0 14
Completed Optimization, Showing Electricity Transactions in TWh
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 1414
Generating the Generation Generating the Generation MixMix
Have the % of imports for each importing stateHave the % of imports for each importing state– Example: CA imports 30%Example: CA imports 30%
Have the % of that imported amount that came Have the % of that imported amount that came from each exporterfrom each exporter– AZ: 34%, MT: 3%, NM: 19%, UT: 4%, WA: 36%AZ: 34%, MT: 3%, NM: 19%, UT: 4%, WA: 36%
Know the % mix of the amount from each importerKnow the % mix of the amount from each importer– Arizona: 45% Coal, 10% Gas, 35% Nuclear, 10% HydroArizona: 45% Coal, 10% Gas, 35% Nuclear, 10% Hydro
Multiply these 3 sets of percentages, normalize Multiply these 3 sets of percentages, normalize with existing generation mix to get the new valueswith existing generation mix to get the new values
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 1515
The Next Generation (Mix) The Next Generation (Mix) in CAin CA
0%
10%
20%
30%
40%
Coal Oil Gas Nuclear Hydro Other
w/ o Imports w/ Imports
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 1616
Electricity Allocation to Electricity Allocation to SectorsSectors
Now, apply each state’s generation mix to Now, apply each state’s generation mix to the percentage of all the industrial sectors the percentage of all the industrial sectors in the statein the state– 20% of all widgets are manufactured in CA, so 20% of all widgets are manufactured in CA, so
1/5 of the widget sector will have CA’s 1/5 of the widget sector will have CA’s generation mixgeneration mix
Then sum the generation types across all Then sum the generation types across all sectors and statessectors and states– Each sector now includes part of the generation Each sector now includes part of the generation
mix for each state it’s located inmix for each state it’s located in
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 1717
Results of ModificationResults of Modification
DescriptionCoal,
%Gas,
%Oil,
%Hydro,
%Nuclear,
%
Household laundry equipment manufacturing 83 2 0 2 13
Oil and gas field machinery and equipment 41 44 1 2 10
Jewelry and silverware manufacturing 23 34 14 9 16
Aircraft manufacturing 30 16 3 31 16
Cellulosic organic fiber manufacturing 49 3 2 2 41
Industries with the highest specific electricity generation mix values (by energy source) in the U.S. economy
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 1818
Aircraft ManufacturingAircraft Manufacturing
0%
10%
20%
30%
40%
50%
60%
Coal Oil Gas Nuclear Hydro Other
US Average Aircraft Manufacturing
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 1919
Dirty Laundry? And How!Dirty Laundry? And How!
0%
20%
40%
60%
80%
100%
Coal Oil Gas Nuclear Hydro Other
US Average Household Laundry Equipment
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 2020
United States: Well OiledUnited States: Well Oiled
0%
10%
20%
30%
40%
50%
60%
Coal Oil Gas Nuclear Hydro Other
US Average Oil Change & Lube Shops
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 2121
Results Trend Towards Results Trend Towards AverageAverage
0%
20%
40%
60%
80%
100%
1% 6% 11% 16% 21% 26% 31%
% Difference from US Average
% o
f U
S I
ndust
rial Sect
ors
September 25, 2003September 25, 2003 Joe Marriott - Carnegie Mellon UniversityJoe Marriott - Carnegie Mellon University 2222
ContributionsContributions Disaggregating adds accuracy to a critical sector in terms of Disaggregating adds accuracy to a critical sector in terms of
environmental impactenvironmental impact
Industrial sector generation mixes answer some interesting Industrial sector generation mixes answer some interesting questionsquestions– Which sectors are vulnerable to shifts in fuel price or Which sectors are vulnerable to shifts in fuel price or
technology change?technology change?– What is the potential impact of carbon taxes on the US What is the potential impact of carbon taxes on the US
economy?economy?
Import-export estimate an intuitive substitute for complete Import-export estimate an intuitive substitute for complete transmission grid analysistransmission grid analysis
Most industrial sectors have a mix close to the US Most industrial sectors have a mix close to the US average mixaverage mix– Some interesting sectors have significant differencesSome interesting sectors have significant differences