equilibrium modeling of combined heat and power deployment
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Equilibrium Modeling of Combined Heat and Power Deployment. Anand Govindarajan Seth Blumsack Pennsylvania State University USAEE Conference, Anchorage, 29 July 2013. Problem Statement. - PowerPoint PPT PresentationTRANSCRIPT
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Equilibrium Modeling of Combined Heat and Power Deployment
Anand GovindarajanSeth Blumsack
Pennsylvania State University
USAEE Conference, Anchorage, 29 July 2013
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Problem Statement
• Assess the economic potential for Combined Heat and Power (CHP) in electricity-market equilibrium framework, accounting for the impact that CHP adoption will have on electricity prices
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Some Motivation
• U.S. utilization of CHP is low but technical potential is vast
• Utilization pathway for shale-gas suppliesCurrent CHP capacity
Technical potential for additional CHP
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Basic CHP Economics
• Increased efficiency of heat + electricity (adsorptive chiller can add cooling)
• Avoided electricity purchases
• Other benefits : reduced emissions, reliability benefits
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Technical vs Economic potential
• CHP investment reduces demand for grid provided power, lowering market price
• At some point, incremental CHP units become uneconomical
• The economic potential maybe different(less) than the technical potential0 50000 100000 150000 200000
0
50
100
150
200
250
300
350
400
450
PJM Demand (GW)
Shor
t run
Mar
gina
l cos
t($/
MW
h)
Oil
GasCoal
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Equilibrium CHP Modeling
Increase in number of CHP units
Decrease in zonal electricty demand
Decrease in wholesale electricity prices
Marginal Savings from avoided electrcity purchase costs decreases
Marginal NPV decreases
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Philadelphia Case Study
• We use Philadelphia, PA as a case study for our equilibrium modeling
• High technical potential, high electricity prices
• Transmission constrained
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Supply curve modeling (Sahraei-Ardakani et al 2012)
We want to identify:
1. Thresholds where the marginal technology changes;
2. The slope of each portion of the locational dispatch curve.
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CHP Load Profiles
• Building-integrated CHP (BCHP) tool used to generate profiles for eight building types
• Electric load-following (FEL) and thermal load-following (FTL)
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Method
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100 200 300 400 500 600 700 800 900 10000
1
2
3
4
5
6x 10
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# CHP units
Ma
rgin
al
Savin
gs
($)
FTLFEL
Energy Savings from Incremental CHP Investment in Philadelphia
Assumes $4/mmBTU natural gas price
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100 200 300 400 500 600 700 800 900 10000
1
2
3
4
5
6
7
8
9x 10
5
# CHP units
Mar
gin
al S
avin
gs (
$)
FTLFEL
Energy Savings from Incremental CHP Investment in Philadelphia
Assumes $8/mmBTU natural gas price
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100 200 300 400 500 600 700 800 900 1000-0.5
0
0.5
1
1.5
2
2.5
3x 10
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# CHP units
Ma
rgin
al
NP
V (
$))
FTLFEL
NPV of Incremental CHP ($4 gas)
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100 200 300 400 500 600 700 800 900 10000
2
4
6
8
10
12
14
16
18x 10
5
# CHP units
Marg
ina
l N
PV
($))
FTLFEL
NPV of Incremental CHP ($8 gas)
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Conclusion: Are High Gas Prices Good for CHP?
100 200 300 400 500 600 700 800 900 1000-0.5
0
0.5
1
1.5
2
2.5
3x 10
6
# CHP units
Ma
rg
ina
l N
PV
($
))
FTLFEL
100 200 300 400 500 600 700 800 900 10000
2
4
6
8
10
12
14
16
18x 10
5
# CHP units
Margin
al
NP
V (
$))
FTLFEL
$4/mmBTU Gas
$8/mmBTU Gas
• Higher gas prices may mean more economic opportunities for CHP, otherwise economic potential is perhaps 1/3 of technical potential.
• Disproportionate impacts on electricity prices relative to operational costs
• FTL maybe a more economical operational strategy when fuel prices are low
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Locational Marginal Cost Curves
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Life is Heaven When Gas is $7
Price separation between fuels (on $/MBTU basis) means that thresholds are easy to identify.
Note: Other fuel prices – Coal $2/mmBTU; Oil $20/mmBTU
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Life Ain’t a Breeze When Gas is $3
When relative fuel price differences are small, a mix of fuels/technologies can effectively be “on the margin.”
Note: Other fuel prices – Coal $2/mmBTU; Oil $20/mmBTU
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Estimation ProcedureWe want to minimize the SSE of:
CMA-ESOLS
Regression
Regression Parameters / SSEGeneration i-1
Classification parametersGeneration i
1. Choose initial parameters φ 2. Find associated slope
parameters ω using least squares
3. Given estimates for ω and the regression SSE, choose a new set of threshold parameters φ*
4. Repeat until convergence.
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Marginal Fuel Results
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Estimating Threshold Functions
Thresholds are estimated using a fuzzy logic approach to capture multiple marginal fuels:1. Relative fuel price
threshold for having the fuzzy gap
2. Fuzzy gap width coefficient
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Example Result
• Wide band where gas/coal are jointly setting prices.
• More defined threshold between gas and oil.
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Supply Curve Modeling
• Philadelphia is transmission-constrained, so the available capacity of a generator is not relevant – only the amount of electricity that is deliverable to a location in the network.
• Power Transfer Distribution Factor (PTDF):
G1
k
G2
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Piecewise Supply Curve Estimation
Threshold indicator function
Slope of the relevant portion of the supply curve