Transcript Equilibrium Modeling of Combined Heat and Power Deployment Anand Govindarajan Seth Blumsack Pennsylvania State University USAEE Conference, Anchorage, 29 July 2013

Equilibrium Modeling of Combined
Heat and Power Deployment
Anand Govindarajan
Seth Blumsack
Pennsylvania State University
USAEE Conference, Anchorage, 29 July 2013
1
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
2
Some Motivation
Current CHP capacity
• U.S. utilization of CHP
is low but technical
potential is vast
• Utilization pathway for
shale-gas supplies
Technical potential for additional CHP
3
Basic CHP Economics
• Increased efficiency
of heat + electricity
(adsorptive chiller
can add cooling)
• Avoided electricity
purchases
• Other benefits :
reduced emissions,
reliability benefits
4
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 potential
Short run Marginal cost($/MWh)
450
400
350
300
250
Oil
200
150
Gas
100
Coal
50
0
0
50
100
150
PJM Demand (GW)
200
5
Equilibrium CHP Modeling
Increase in number of CHP units
Decrease in zonal electricty
demand
Decrease in wholesale electricity
prices
Marginal Savings from avoided
electrcity purchase costs decreases
Marginal NPV decreases
6
Philadelphia Case Study
• We use Philadelphia,
PA as a case study
for our equilibrium
modeling
• High technical
potential, high
electricity prices
• Transmission
constrained
7
Supply curve modeling
(Sahraei-Ardakani et al 2012)
We want to identify:
1. Thresholds where the marginal
technology changes;
2. The slope of each portion of the
locational dispatch curve.
8
CHP Load Profiles
Rank*
Building Type
Number of
buildings
1
Hospital
50
2
Large Hotel
74
3
Restaurant
29
4
Large Of ice
284
5
Supermarket
51
6
School**
63
7
Small Hotel/Motel
22
8
Warehouse
439
• Building-integrated
CHP (BCHP) tool
used to generate
profiles for eight
building types
• Electric loadfollowing (FEL) and
thermal loadfollowing (FTL)
9
Method
10
Energy Savings from Incremental
CHP Investment in Philadelphia
5
6
x 10
5
Marginal Savings ($)
FTL
FEL
Assumes $4/mmBTU
natural gas price
4
3
2
1
0
100
200
300
400 500 600
# CHP units
700
800
900
1000
11
Energy Savings from Incremental
CHP Investment in Philadelphia
5
9
x 10
FTL
FEL
Assumes $8/mmBTU
natural gas price
8
Marginal Savings ($)
7
6
5
4
3
2
1
0
100
200
300
400 500 600
# CHP units
700
800
900 1000
12
NPV of Incremental CHP ($4 gas)
6
3
x 10
FTL
FEL
2.5
Marginal NPV ($))
2
1.5
1
0.5
0
-0.5
100
200
300
400 500 600
# CHP units
700
800
900 1000
13
NPV of Incremental CHP ($8 gas)
5
18
x 10
FTL
FEL
16
Marginal NPV ($))
14
12
10
8
6
4
2
0
100
200
300
400 500 600
# CHP units
700
800
900
1000
14
Conclusion: Are High Gas Prices
Good for CHP?
6
3
x 10
FTL
FEL
$4/mmBTU Gas
2.5
Marginal NPV ($))
2
1.5
1
0.5
0
-0.5
100
200
300
5
18
x 10
16
700
800
900
1000
FTL
FEL
$8/mmBTU Gas
14
Marginal NPV ($))
400
500
600
# CHP units
12
10
8
6
4
2
0
100
200
300
400
500
600
# CHP units
700
800
900
1000
• 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
15
Thank You!
Anand Govindarajan
[email protected]
16
Locational Marginal Cost Curves
17
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 Procedure
We want to minimize the SSE of:
J
pik = å M ji (qik , qTk , f ji , pikF )SFji (qik , qTk , w ji , p Fjik ) + eik
j=1
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.
CMA-ES
Classification
parameters
Generation i
OLS
Regression
Regression Parameters / SSE
Generation i-1
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Marginal Fuel Results
Low gas price scenario
High gas price scenario
Coal
Natural Gas
Oil
Coal
Natural Gas
Oil
No Tax Tax No Tax Tax No Tax Tax No Tax Tax No Tax Tax No Tax Tax
APS
45.72 55.47 54.04 44.53 0.25 0.00 47.37 55.66 52.36 44.30 0.27 0.05
AEP
58.66 61.45 41.34 38.55 0.00 0.00 65.61 70.97 34.39 29.03 0.00 0.00
AECO
35.64 49.16 63.10 50.78 1.26 0.06 29.98 43.54 68.37 55.86 1.65 0.61
BGE
42.68 49.77 56.34 50.23 0.97 0.00 41.50 46.71 57.53 53.17 0.97 0.11
COMED
42.66 53.21 57.34 46.79 0.00 0.00 41.53 51.88 58.47 48.12 0.00 0.00
DPL
36.47 48.11 61.23 51.64 2.30 0.25 31.64 42.52 65.80 56.31 2.57 1.17
DUQ
60.22 62.75 39.78 37.25 0.00 0.00 70.24 72.82 29.76 27.18 0.00 0.00
JCPL
33.38 48.66 65.13 51.32 1.49 0.02 27.45 41.00 71.05 58.73 1.49 0.27
METED
52.32 55.85 47.68 44.15 0.00 0.00 55.81 60.54 44.19 39.46 0.00 0.00
PECO
43.87 54.65 54.00 45.19 2.13 0.17 43.59 53.92 53.96 45.06 2.45 1.02
PPL
45.41 55.61 52.45 44.26 2.14 0.13 46.17 55.65 51.49 43.51 2.35 0.84
PENELEC 54.41 58.47 45.59 41.53 0.00 0.00 61.17 65.36 38.83 34.64 0.00 0.00
PEPCO
29.79 45.77 69.19 54.23 1.02 0.00 23.91 37.13 74.87 62.57 1.22 0.30
PSEG
35.03 48.82 63.32 51.12 1.66 0.06 29.57 42.63 68.38 56.61 2.05 0.77
RECO
31.74 47.30 66.52 52.62 1.75 0.08 25.88 39.40 72.14 59.93 1.97 0.66
DAY
64.73 61.87 35.27 38.13 0.00 0.00 80.98 76.28 19.02 23.72 0.00 0.00
DOM
34.48 46.37 65.46 53.63 0.06 0.00 35.04 41.68 64.89 58.32 0.06 0.00
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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
22
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):
¶Fl
PTDF(l, k) =
(L1,..., Ln )
¶Gk
G2
G1
k
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Piecewise Supply Curve Estimation
J
pik = å M ji (qik , qTk , f ji , pikF )SFji (qik , qTk , w ji , p Fjik ) + eik
j=1
Threshold indicator
function
Slope of the
relevant portion of
the supply curve
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