Markets and Demand Management Coupling with
Renewable Energy Sources
Alberto J. Lamadrid
Eighth Annual Carnegie Mellon Conference on the Electricity Industry, 2012
CMU, Pittsburgh, March 14th 2012
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
1
Outline
1
Motivation
2
Theoretical Model
3
Model Calibration
4
Cases and results
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
2
Motivation
What is the cost of network congestion?
New York State
Congestion by Zone, NYISO
3000
2500
Nominal $M
2000
1500
1000
500
0
2007
2008
2009
2010
Year
West
Genesee
Central
North
Mohawk Valley
Hudson Valley
Millwood
Dunwoodie
NY City
Long Island
Capital
[nyiso(2011)]
2011 Congestion Assessment and Resource Integration Study (CARIS)
Four metrics used: Bid- Production Cost (BPC) as primary metric, then Load Payments
, Generator Payments, and Congestion Payments.
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
3
Motivation
Relevance
This research addresses a fundamental issue in the operation of the system
with storage resources
1
2
3
4
How to securely dispatch a set of previously committed generators
The use of inter-temporal resources for both time arbitrage and
uncertainty mitigation
Point of view of ISO (social planner), maximizing social welfare
Research combines engineering, economic models, and knowledge of
system constraints to identify solutions for better renewable
integration
Ongoing research at Cornell, Tim Mount, Dick Schuler, Bob Thomas, Ray
Zimmerman, Carlos E. Murillo-Sanchez, Lindsay Anderson, support
provided by PSERC
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
4
Theoretical Model
Where does this research stand?
Literature
Electricity Markets
[Harvey and Hogan(2002)]: bidding behavior in California crisis
[Kamat and Oren(2004)]: two settlement markets and contract formation
[Joskow and Tirole(2007)]: Model for demand management with
heterogeneous consumers
[Wolak(2007)]: complex bids, ramping costs
[Mansur(2008)]: Cournot competition and supply in PJM
Optimal ESS Management
[Pindyck(1991)]: Stochastic control
[Sioshansi and Denholm(2010)]: Ancillary services from PHEV’s
Capital Good Replacement
[Rust(1987)]: replacement of goods
[Shiau, Samaras, Hauffe, and Michalek(2009)]: deterioration of batteries
for V2G services
t A.J. Lamadrid (Cornell University)
Electricity Markets
Engineering Models
2012
57 / 64
Co-optimizing energy and
reserves → solve optimal
amounts
Use of Full AC Network
Economic management of
demand (deferrable)
Modeling of renewables
uncertainty
• t Network Model
[Carpentier(1962)]: optimal power flow formulation
[Outhred(1998)]: Australian Market design, ancillary services
[Zhang, Wang, and Luh(2000)]: dispatch of generators with ramping
constraints
[Chen, Mount, Thorp, and Thomas(2005)]: Co-optimization
[Condren, Gedra, and Damrongkulkamjorn(2006)]: management of
uncertainty (contingecies)
[Tuohy, Meibom, Denny, and O’Malley(2009)]: Montecarlo approach
Regulatory
[USCongress(2005)]: Electricity Modernization Act of 2005
[NERC(2011)]: set of reliability standards
Engineering and Economical
modeling of Energy Storage
Systems (ESS)
Back
A.J. Lamadrid (Cornell University)
Electricity Markets
2012
58 / 64
Full Literature
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
5
Theoretical Model
A Multiperiod, Security Constrained Optimal Power Flow
Determining the optimal power flows for operations and planning on an
AC network using Kirchhoff’s Laws.
Traditional Approach
Our Approach
Break into manageable
sub-problems.
Simultaneous co-optimization with explicit
contingencies and load following requirements
Sequential optimization
using proxy constraints
Combine into single mathematical programming
framework.
DC approximations
AC Network and Dispatch coordination Scheme
Inter-temporal Constraints
in UC model
Explicit Inter-temporal constraints for generators
AND Energy Storage Systems (ESS)
misleading prices
more accurate prices
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
6
Theoretical Model
Overall Characteristics
Power (MW)
MW injections
pi3
upward
reserve
pi1
p+
i3
p+
i1
pi0
p+
i0
p−
ik
pik
2
physical
ramp down
limit
downward
reserve
pi2
1
pci
rP−i
p−
i2
0
physical
ramp up
limit
rP+i
3
k
central “high-probability” path
load following ramp up capacity
load following ramp down capacity
t
contingencies
t+1
t+2
t+3
t+4
time
Stored Energy MWh
st+4
+
smax
st+2
+
st+3
+
st+1
+
S1(1)
p11(1)
S1(2)
p12(1)
S2(1)
S2(2)
S4(1)
p14(1)
S1(3)
st+
P12(3)
p13(1)
S3(1)
P11(2)
S2(3)
S2(24)
S3(3)
S3(24)
S4(3)
S4(24)
P13(2)
S3(2)
S4(2)
A.J. Lamadrid (Cornell University)
P14(2)
st+1
−
st−
st+2
−
st+3
−
st+4
−
smin
t
t+1
Demand Management and Transmission
t+2
t+3
t+4
time
March 14 2012
7
Theoretical Model
Simplified objective function and eq. constraints
Objective:
XXX
min
Gitsk ,Ritsk ,LNSjtsk
πtsk
nXh
CGi (Gitsk )+
t∈T s∈S t k∈K
+
i∈I
−
+
Incits (Gitsk − Gitc )
X
+ Decits (Gitc − Gitsk )
VOLLj LNS(Gtsk , Rtsk )jtsk
+
i
o
+
j∈J
(1)
X X
ρt
t∈T
+
−
+
+
i∈I
− −
CL (Lit )+]
it
++
X
t∈T
−
+
[CR (Rit ) + CR (Rit ) + CL (Lit )+
it
it
it
+
Rpit (Gits2
− Gits )
1
+
ρt
X
X
X
s2 ∈S t s1 ∈S t−1 i∈I ts2 0
−
+ Rpit (Gits − Gits1 )
2
+
Subject to meeting demand and all network constraints (e.g. Active power flow
equations)
pit −
X
h
i
|vjt ||vit | Gijt cos(θi − θj ) + Bijt sin(θi − θj ) = 0, ∀i ∈ B, t ∈ T
j∈nB
A.J.
Lamadrid (Cornell
University)
Nomenclature
and Constraints
Demand Management and Transmission
March 14 2012
8
Model Calibration
Input Information
1
2
3
4
PCA on historical data to determine wind sites [NREL(2010)]
k-means clustering to specify the scenarios for the
day[Guojun Gan(2007)]
Data from New York and New England to calibrate load profile
[NYISO(2011)]
Network based on [Allen, Lang, and Ilic(2008)], heavily modified
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
9
Model Calibration
North East Test network
No changes in generation/load out of NY-NE
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
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





A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
10
Model Calibration
Geographical Location
Details Fleet
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
11
Cases and results
Cases studied
Main Cases Studied
1
Case 1: Base Case, no wind
2
Case 2: Wind added in 16 locations in NYNE
3
Case 3: Wind + Deferrable Demand (DD).
4
Case 4: Wind Collocated with Storage
Details Location
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
12
Cases and results
How Deferrable Demand is Calculated
Load Profile Shift, Millbury Bus (71797)
7,000
6,000
5,000
4,000
3,000
2,000
1,000
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Final Load
A.J. Lamadrid (Cornell University)
ESS contribution
Demand Management and Transmission
March 14 2012
13
Cases and results
Payments in the System
14
x 10
Composition of payments in the Wholesale Market
7
Operating Costs
Ramping Costs
Generators Net Revenue
Congestion Rents
12
Daily Cost ($)
10
8
6
4
2
0
1
2
3
4
Case
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
14
Cases and results
Expected Dispatch per Case
Cases 3 and 4
Expected Dispatch per Fuel Type
4
6
x 10
x 10
5
4
Nuclear
Hydro
Coal
NG
Oil
Wind
ESS
3
2
1
E[Dispatch] per fuel type, MW
E[Dispatch] per fuel type, MW
5
0
Expected Dispatch per Fuel Type
4
6
4
Nuclear
Hydro
Coal
NG
Oil
Wind
ESS
3
2
1
2
4
6
8
10
12
14
16
18
20
22
24
0
2
4
Hour
h=7
h = 19
6
8
10
12
14
16
18
20
22
24
Hour
h=6
h=7
h = 19
h=6
Dispatch 1 and 2
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
15
Cases and results
Wind Compensation
Income for All Wind Generators
5
x 10
7
Case 2
Case 3
Case 4
6
Pmt. to Wind for Pg $
5
4
3
2
1
2
8
4
10
6
12
8
14
10
16
12
Hour
14
20
16
18
22
20
22
24
2
4
6
24
Total Wind Compensation (USD Millions)
A.J. Lamadrid (Cornell University)
Case 2
Case 3
Case 4
10.112
12.262
12.094
Demand Management and Transmission
March 14 2012
16
Cases and results
Observed Congestion
Upgrades improves overall welfare in the system
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
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
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









A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
17
Cases and results
Congestion in Real System
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
18
Cases and results
Geographical Effects
Nodal Prices at low demand periods
75.21
76.33
55.73
76.36
76.35
76.41
75.45
76.23
76.12
75.99
76.15
76.15
55.73
76.36
76.15
55.72
55.73
76.33
76.15
55.72
55.73
75.17
76.18
76.7755.58
76.44
76.78
76.29
55.73
55.73
55.73
74.34
73.48
55.73
55.72
55.73
55.73
77.14
77.82
55.73
55.73
75.08
55.73
55.73
55.7355.58
55.73
55.73
55.73
55.73
74.46
A.J. Lamadrid (Cornell University)
55.72
55.72
79.06 66.58
73.66
71.28 81.3
88.15
85.07
88.15
72.92
55.72
55.73
55.72
55.82 55.69
55.76
55.7
57.17 88.15
55.58
66.62
55.73
Demand Management and Transmission
March 14 2012
19
Cases and results
Storage Management
Time Arbitrage versus Uncertainty Mitigation
Real Energy Available, ESS @ bus 4, Gen 123
Real Energy Available, ESS @ bus 4, Gen 124
4
3
x 10
15000
2
Expected Storage Level
Min Stor. L
Max Stor. L
S+
S−
1.5
1
Energy at end of period, MWh
Energy at end of period, MWh
2.5
10000
Expected Storage Level
Min Stor. L
Max Stor. L
S+
S−
5000
0.5
h=7
0
h = 19
5
10
h=6
15
20
h=7
0
Hour
A.J. Lamadrid (Cornell University)
h = 19
5
10
h=6
15
20
Hour
Demand Management and Transmission
March 14 2012
20
Cases and results
System Costs per Energy Delivered
Costs Total System
140
120
E[Cost], $/MWh−day
100
80
60
Operating Costs
Capital Costs
Environmental Costs
40
20
0
1
2
3
4
Case
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
21
Cases and results
Conclusions
Deferrable Demand both reduces capacity
requirements and weighted operating costs
Value of Storage lies in mechanisms for trading off
uncertainty and time arbitrage
Stochastic solution properly maintains system security
and adequacy.
Intelligent management of demand delivers higher
value than transmission upgrades
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
22
Cases and results
Allen, E., J. Lang, and M. Ilic. 2008. “A Combined
Equivalenced-Electric, Economic, and Market Representation of the
Northeastern Power Coordinating Council U.S. Electric Power
System.” Power Systems, IEEE Transactions on 23:896–907.
Carpentier, J. 1962. “Contribution a l’etude du dispatching
economique.” Bulletin de la Societe Francaise des Electriciens
3:431–447.
Chen, J., T.D. Mount, J.S. Thorp, and R.J. Thomas. 2005.
“Location-based scheduling and pricing for energy and reserves: a
responsive reserve market proposal.” Decis. Support Syst. 40:563–577.
Condren, J., T. Gedra, and P. Damrongkulkamjorn. 2006. “Optimal
power flow with expected security costs.” Power Systems, IEEE
Transactions on 21:541–547.
Guojun Gan, J.W., Chaoqun Ma. 2007. Data Clustering: Theory,
Algorithms, and Applications, Society for Industrial and Applied
Mathematics.
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
22
Cases and results
Harvey, S., and W. Hogan. 2002. “Market power and market
simulations.” Working paper, Center for Business and Government,
John F. Kennedy School of Government, July.
Joskow, P., and J. Tirole. 2007. “Reliability and Competitive
Electricity Markets.” The RAND Journal of Economics 38:pp. 60–84.
Kamat, R., and S. Oren. 2004. “Two-settlement Systems for
Electricity Markets under Network Uncertainty and Market Power.”
Journal of Regulatory Economics 25:5–37.
Mansur, E.T. 2008. “Measuring Welfare in Restructured Electricity
Markets.” The Review of Economics and Statistics 90:369–386.
NERC. 2011. Reliability Standards for the Bulk Electric Systems of
North America, NERC, ed. 116-390 Village Road, Princeton, NJ,
08540: North American Electric Reliability Corporation.
NREL. 2010. “Eastern wind integration and transmission study:
Executive Summary and Project Overview.” Working paper, EnerNex
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
22
Cases and results
Corporation, The National Renewable Energy Laboratory, 1617 Cole
Boulevard, Golden, Colorado 80401, January.
NYISO. 2011. “Power Trends 2011.” Working paper, New York
Independent System Operator.
nyiso, N. 2011. “2011 Congestion Assessment and Resource
Integration Study.” Working paper, New York Independent System
Operator.
Outhred, H. 1998. “A review of electricity industry restructuring in
Australia.” Electric Power Systems Research 44:15 – 25.
Pindyck, R.S. 1991. “Irreversibility, Uncertainty, and Investment.”
Journal of Economic Literature 39:1110–1148.
Rust, J. 1987. “Optimal Replacement of GMC Bus Engines: An
Empirical Model of Harold Zurcher.” Econometrica 55:pp. 999–1033.
Shiau, C.S.N., C. Samaras, R. Hauffe, and J.J. Michalek. 2009.
“Impact of battery weight and charging patterns on the economic and
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
22
Cases and results
environmental benefits of plug-in hybrid vehicles.” Energy Policy
37:2653 – 2663.
Sioshansi, R., and P. Denholm. 2010. “The Value of Plug-In Hybrid
Electric Vehicles as Grid Resources.” The Energy Journal 0.
Tuohy, A., P. Meibom, E. Denny, and M. O’Malley. 2009. “Unit
Commitment for Systems With Significant Wind Penetration.” Power
Systems, IEEE Transactions on 24:592 –601.
USCongress. 2005. Energy Policy Act, P. L. 109-58, ed. 109th
Congress of the United States of America.
Wolak, F.A. 2007. “Quantifying the supply-side benefits from forward
contracting in wholesale electricity markets.” Journal of Applied
Econometrics 22:1179–1209.
Zhang, D., Y. Wang, and P. Luh. 2000. “Optimization based bidding
strategies in the deregulated market.” Power Systems, IEEE
Transactions on 15:981 –986.
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
23
Cases and results
Thank you
ajl259@cornell.edu
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
23
Cases and results
Sensitivity to Wind and Network Specification
Consider the following two cases:
1
2
Assume Wind is perfectly forecastable, and its output is at the
expected level over the day (Case E[W])
Assume the network is not constrained, (Case Up)
How does this affect the following four metrics?
1
Operating Costs
2
Wind Dispatched
3
Generation Capacity Needed
4
Compensation to Wind Owners
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
24
Cases and results
Costs, Dispatches and Wind Compensation
Sensitivity of Formulation
45,000
160,000
40,000
158,000
156,000
35,000
154,000
152,000
25,000
150,000
20,000
MWh
Thousand $/day
30,000
148,000
15,000
146,000
10,000
144,000
5,000
142,000
140,000
Case 3
Case 4
Case 3 E[W]
Case 4 E[W]
Case 3 Up
Case 4 Up
Case
Wind Compensation
A.J. Lamadrid (Cornell University)
Cost of the System (USD)
Wind Dispatched (MWh)
Demand Management and Transmission
March 14 2012
25
Cases and results
How the Available Wind is Dispatched
Cases 3 and 3E
Real Total Wind Output
Real Total Wind Output
18000
18000
16000
16000
14000
14000
12000
E[dispatch]
E[available]
Min[dispatch]
Max[dispatch]
Min[available]
Max[available]
10000
8000
Power, MW
Power, MW
12000
8000
6000
6000
4000
4000
2000
2000
h=7
0
E[dispatch]
E[available]
Min[dispatch]
Max[dispatch]
Min[available]
Max[available]
10000
2
h = 19
4
6
8
10
12
14
h=6
16
18
20
22
24
h=7
0
2
Hour
h = 19
4
6
8
10
12
14
h=6
16
18
20
22
24
Hour
Prices system
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
26
Cases and results
Capacity Needed for Adequacy
Generation Capacity Needed
70,000
Capacity (MW)
65,000
60,000
55,000
50,000
45,000
Case 3
Case 4
Case 3 E[W]
Case 4 E[W]
Case 3 Up
Case 4 Up
Case
Generation Capacity Needed
A.J. Lamadrid (Cornell University)
Generation Capacity Needed (No ESS)
Demand Management and Transmission
March 14 2012
27
Cases and results
Literature
Electricity Markets
[Harvey and Hogan(2002)]: bidding behavior in California crisis
[Kamat and Oren(2004)]: two settlement markets and contract formation
[Joskow and Tirole(2007)]: Model for demand management with
heterogeneous consumers
[Wolak(2007)]: complex bids, ramping costs
[Mansur(2008)]: Cournot competition and supply in PJM
Optimal ESS Management
[Pindyck(1991)]: Stochastic control
[Sioshansi and Denholm(2010)]: Ancillary services from PHEV’s
Capital Good Replacement
[Rust(1987)]: replacement of goods
[Shiau et al.(2009)Shiau, Samaras, Hauffe, and Michalek]: deterioration of
batteries for V2G services
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
28
Cases and results
Engineering Models
Network Model
[Carpentier(1962)]: optimal power flow formulation
[Outhred(1998)]: Australian Market design, ancillary services
[Zhang, Wang, and Luh(2000)]: dispatch of generators with ramping
constraints
[Chen et al.(2005)Chen, Mount, Thorp, and Thomas]: Co-optimization
[Condren, Gedra, and Damrongkulkamjorn(2006)]: management of
uncertainty (contingecies)
[Tuohy et al.(2009)Tuohy, Meibom, Denny, and O’Malley]: Montecarlo
approach
Regulatory
[USCongress(2005)]: Electricity Modernization Act of 2005
[NERC(2011)]: set of reliability standards
Back
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
29
Cases and results
Some of the Constraints...
And reactive power flow equations
qit −
X
h
i
|vjt ||vit | Gijt sin(θi − θj ) − Bijt cos(θi − θj ) = 0,
(2)
j∈nB
∀i ∈ B, t ∈ T
And inequalities, e.g.,
(3)
PHYS−
PHYS+
t
−RPi
≤ pit − pi,t−1
≤ RPi
, ∀i ∈ G , t ∈ T
P
t∈T
eit · t = 0, ∀i ∈ E
(4)
Back
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
30
Cases and results
Characteristics of the generation fleet, 36-Bus system
Summary of Generation Capacity and Load, NPCC system
Capacity per Fuel Type (MW)
RTO
Total Cap.
Load
coal
ng
oil
hydro
nuclear
wind
refuse
(GW)
(GW)
isone
marit.
nyiso
ont.
pjm
quebec
1,840
2,424
4,557
5,287
14,453
0
9,219
1,072
18,185
3,594
14,611
0
4,327
22
5,265
0
8,915
0
1,878
641
7,345
779
2,604
800
5,698
641
4,714
12,249
12,500
0
0
0
30
0
0
0
0
0
55
0
0
0
22.9
4.8
40.1
21.9
53.1
800
23.8
3.5
38.2
21.1
51,6
0
Total
Total NYNE
28,562
6,397
46,681
27,404
18,530
9,592
14,048
9,223
35,802
10,412
30
30
55
55
143.7
63
138.4
62
30
10
10
60
60
0
60
Rp.C.
Back
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
31
Cases and results
Wind and Storage Locations
Area
Bus
Place
NE
NE
NE
NE
NE
NE
NE
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
70002
71786
71797
72926
73106
73110
73171
74316
74327
75050
77950
78701
79578
79581
79583
79584
79800
Orrington
Sandy Pond
Millbury
Northfield
Southington
Millstone
Norwalk Harbor
Dunwodie
Farragut (NYC)
Newbridge
9M. Point
Leeds
Massena
Gilboa
Marcy
Niagara
Rochester
Total
Wind (MW)
ESS (MWh)
1725
3375
1560
2157
1145
1478
2560
241
7,332
14,345
6,630
9,168
4,867
6,282
10,881
1,024
142
1922
1327
3000
1705
1373
3672
4616
604
8,169
5,640
12,751
7,247
5,836
15,607
19,619
31,998
136,000
DL (MWh)
29,000
15,000
24,000
52,000
16,000
136,000
Back
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
32
Cases and results
Expected Dispatch per Case
Cases 1 and 2
Expected Dispatch per Fuel Type
4
6
x 10
x 10
5
4
Nuclear
Hydro
Coal
NG
Oil
Wind
ESS
3
2
1
E[Dispatch] per fuel type, MW
E[Dispatch] per fuel type, MW
5
0
Expected Dispatch per Fuel Type
4
6
4
Nuclear
Hydro
Coal
NG
Oil
Wind
ESS
3
2
1
2
4
6
8
10
12
14
16
18
20
22
24
0
2
4
Hour
h=7
h = 19
6
8
10
12
14
16
18
20
22
24
Hour
h=6
h=7
h = 19
h=6
Back
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
33
Cases and results
Final Nodal Prices
Cases 3 and 3E
Nodal Prices observed
Nodal Prices observed
200
Price, $/MW
150
100
50
0
−50
h=7
−100
2
h=6
h = 19
4
6
8
10
12
14
16
Hour
18
20
22
24
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
250
200
150
Price, $/MW
250
100
50
0
−50
h=7
−100
2
h=6
h = 19
4
6
8
10
12
14
16
18
20
22
24
Hour
Back
A.J. Lamadrid (Cornell University)
Demand Management and Transmission
March 14 2012
34