demand-side flexibility for reliable ancillary services
TRANSCRIPT
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Demand-Side Flexibility for ReliableAncillary Services in a Smart Grid:
Eliminating Risk to Consumers and the Grid
ANALYTIC RESEARCH FOUNDATIONSFOR THE NEXT-GENERATION ELECTRIC GRID
Irvine, California, Feb. 11–12, 2015
Sean P. Meyn
Florida Institute for Sustainable Energy
Department of Electrical and Computer Engineering — University of Florida
Thanks to my colleagues, Prabir Barooah and Ana Busicand to our sponsors NSF, AFOSR, DOE / TCIPG
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Eliminating Risk to Consumers and the GridOutline
1 Challenges of Renewable Energy Integration
2 FERC Pilots the Grid
3 Demand Dispatch
4 Buildings as Batteries
5 Intelligent Pools in Florida
6 Conclusions
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Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 GW (t) = Wind generation in BPA, Jan 2015
Scary ramps
Challenges
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Challenges of Renewable Energy Integration
Some of the Challenges
1 Large sunk cost (decreasing!)
2 Engineering uncertainty
3 Policy uncertainty
4 Volatility
Start at the bottom...
1 / 25
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Challenges of Renewable Energy Integration
Some of the Challenges
1 Large sunk cost (decreasing!)
2 Engineering uncertainty
3 Policy uncertainty
4 Volatility
Start at the bottom...
1 / 25
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Challenges of Renewable Energy Integration
Some of the Challenges
1 Large sunk cost (decreasing!)
2 Engineering uncertainty
3 Policy uncertainty
4 Volatility
Start at the bottom...
1 / 25
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Challenges of Renewable Energy Integration
Some of the Challenges
1 Large sunk cost (decreasing!)
2 Engineering uncertainty
3 Policy uncertainty
4 Volatility
Start at the bottom...
1 / 25
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Challenges of Renewable Energy Integration
Some of the ChallengesWhat’s so scary about volatility?
4 Volatility
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 GW (t) = Wind generation in BPA, Jan 2015
Scary ramps
2 / 25
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Challenges of Renewable Energy Integration
Some of the ChallengesWhat’s so scary about volatility?
4 Volatility =⇒ greater regulation needs
0
2
4
6
8
2
4
6
8
0
October 20-25 October 27 - November 1
Hydro
Gen
erat
ion
and
Laod
GW
GW Thermal
Wind
Load
Generation
Disturbance from Nature
0
2
4
6
8
2
4
6
8
0
0.8
-0.8
1
-1
0
0.8
-0.8
1
-1
0
Sun Mon Tue
October 20-25 October 27 - November 1
Hydro
Wed Thur FriSun Mon Tue Wed Thur Fri
Gen
erat
ion
and
Laod
GW
GW
GW
Reg
ulat
ion
GW
ThermalWind
Load
Generation
Regulation
Error Signal in Feedback Loop
3 / 25
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Challenges of Renewable Energy Integration
Some of the ChallengesWhat’s so scary about volatility?
4 Volatility =⇒ greater regulation needs
0
2
4
6
8
2
4
6
8
0
October 20-25 October 27 - November 1
Hydro
Gen
erat
ion
and
Laod
GW
GW Thermal
Wind
Load
Generation
Disturbance from Nature
0
2
4
6
8
2
4
6
8
0
0.8
-0.8
1
-1
0
0.8
-0.8
1
-1
0
Sun Mon Tue
October 20-25 October 27 - November 1
Hydro
Wed Thur FriSun Mon Tue Wed Thur Fri
Gen
erat
ion
and
Laod
GW
GW
GW
Reg
ulat
ion
GW
ThermalWind
Load
Generation
Regulation
Error Signal in Feedback Loop
3 / 25
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Challenges of Renewable Energy Integration
Comparison: Flight controlHow do we fly a plane through a storm?
Brains
Brawn
Brains
Brawn
What Good Are These?
4 / 25
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Challenges of Renewable Energy Integration
Comparison: Flight controlHow do we fly a plane through a storm?
Brains
Brawn
Brains
Brawn
What Good Are These?
4 / 25
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Challenges of Renewable Energy Integration
Comparison: Flight controlHow do we operate the grid in a storm?
Balancing Authority Ancillary Services Grid
Measurements: Voltage Frequency Phase
Σ
−
Brains
Brawn
What Good Are These?
5 / 25
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Challenges of Renewable Energy Integration
How do we operate the grid in a storm?Disturbance decomposition
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 GW (t) = Wind generation in BPA, Jan 2015
Scary ramps
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 G
Goal:
W (t) = Wind generation in BPA, Jan 2015
Scary ramps
GW (t) +Gr(t) ≡ 4GW
Sca
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 G
Goal:
W (t) = Wind generation in BPA, Jan 2015
Scary
Scary Scary
Scary
Scary GW (t) +Gr(t) ≡ 4GW
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Goal: GW (t) +Gr(t) ≡ 4GW
obtained from generation?
Gr(t)
Gr(t)
Scary
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
G3
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
G3 Where do we �nd these ailerons?
6 / 25
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Challenges of Renewable Energy Integration
How do we operate the grid in a storm?Disturbance decomposition
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 GW (t) = Wind generation in BPA, Jan 2015
Scary ramps
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 G
Goal:
W (t) = Wind generation in BPA, Jan 2015
Scary ramps
GW (t) +Gr(t) ≡ 4GW
Sca
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 G
Goal:
W (t) = Wind generation in BPA, Jan 2015
Scary
Scary Scary
Scary
Scary GW (t) +Gr(t) ≡ 4GW
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Goal: GW (t) +Gr(t) ≡ 4GW
obtained from generation?
Gr(t)
Gr(t)
Scary
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
G3
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
G3 Where do we �nd these ailerons?
6 / 25
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Challenges of Renewable Energy Integration
How do we operate the grid in a storm?Disturbance decomposition
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 GW (t) = Wind generation in BPA, Jan 2015
Scary ramps
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 G
Goal:
W (t) = Wind generation in BPA, Jan 2015
Scary ramps
GW (t) +Gr(t) ≡ 4GW
Sca
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 G
Goal:
W (t) = Wind generation in BPA, Jan 2015
Scary
Scary Scary
Scary
Scary GW (t) +Gr(t) ≡ 4GW
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Goal: GW (t) +Gr(t) ≡ 4GW
obtained from generation?
Gr(t)
Gr(t)
Scary
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
G3
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
G3 Where do we �nd these ailerons?
6 / 25
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Challenges of Renewable Energy Integration
How do we operate the grid in a storm?Disturbance decomposition
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 GW (t) = Wind generation in BPA, Jan 2015
Scary ramps
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 G
Goal:
W (t) = Wind generation in BPA, Jan 2015
Scary ramps
GW (t) +Gr(t) ≡ 4GW
Sca
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 G
Goal:
W (t) = Wind generation in BPA, Jan 2015
Scary
Scary Scary
Scary
Scary GW (t) +Gr(t) ≡ 4GW
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Goal: GW (t) +Gr(t) ≡ 4GW
obtained from generation?
Gr(t)
Gr(t)
Scary
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
G3
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
G3 Where do we �nd these ailerons?
6 / 25
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Challenges of Renewable Energy Integration
How do we operate the grid in a storm?Disturbance decomposition
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 GW (t) = Wind generation in BPA, Jan 2015
Scary ramps
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 G
Goal:
W (t) = Wind generation in BPA, Jan 2015
Scary ramps
GW (t) +Gr(t) ≡ 4GW
Sca
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 G
Goal:
W (t) = Wind generation in BPA, Jan 2015
Scary
Scary Scary
Scary
Scary GW (t) +Gr(t) ≡ 4GW
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Goal: GW (t) +Gr(t) ≡ 4GW
obtained from generation?
Gr(t)
Gr(t)
Scary
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
G3
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
G3 Where do we �nd these ailerons?
6 / 25
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Challenges of Renewable Energy Integration
How do we operate the grid in a storm?Disturbance decomposition
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 GW (t) = Wind generation in BPA, Jan 2015
Scary ramps
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 G
Goal:
W (t) = Wind generation in BPA, Jan 2015
Scary ramps
GW (t) +Gr(t) ≡ 4GW
Sca
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 G
Goal:
W (t) = Wind generation in BPA, Jan 2015
Scary
Scary Scary
Scary
Scary GW (t) +Gr(t) ≡ 4GW
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Goal: GW (t) +Gr(t) ≡ 4GW
obtained from generation?
Gr(t)
Gr(t)
Scary
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
G3
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
G3 Where do we �nd these ailerons?
6 / 25
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Challenges of Renewable Energy Integration
How do we operate the grid in a storm?Disturbance decomposition
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 GW (t) = Wind generation in BPA, Jan 2015
Scary ramps
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 G
Goal:
W (t) = Wind generation in BPA, Jan 2015
Scary ramps
GW (t) +Gr(t) ≡ 4GW
Sca
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 G
Goal:
W (t) = Wind generation in BPA, Jan 2015
Scary
Scary Scary
Scary
Scary GW (t) +Gr(t) ≡ 4GW
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Goal: GW (t) +Gr(t) ≡ 4GW
obtained from generation?
Gr(t)
Gr(t)
Scary
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
G3
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
G3 Where do we �nd these ailerons?
6 / 25
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Challenges of Renewable Energy Integration
How do we operate the grid in a storm?Disturbance decomposition
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 GW (t) = Wind generation in BPA, Jan 2015
Scary ramps
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 G
Goal:
W (t) = Wind generation in BPA, Jan 2015
Scary ramps
GW (t) +Gr(t) ≡ 4GW
Sca
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4 G
Goal:
W (t) = Wind generation in BPA, Jan 2015
Scary
Scary Scary
Scary
Scary GW (t) +Gr(t) ≡ 4GW
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Goal: GW (t) +Gr(t) ≡ 4GW
obtained from generation?
Gr(t)
Gr(t)
Scary
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
G3
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
G3 Where do we �nd these ailerons?
6 / 25
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Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
G1
G2
G
Traditional generation
FERC Order 745
FERC Order 7553
Gr = G1 +G2 +G3
FERC Pilots the Grid
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FERC Pilots the Grid
FERC 745: Demand & Generation smooth the Grid
7 / 25
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FERC Pilots the Grid
Origin of FERC 745 – Incentives for Demand Response
FERC 745 is Born!
134 FERC ¶61,187UNITED STATES OF AMERICA
FEDERAL ENERGY REGULATORY COMMISSION18 CFR Part 35
[Docket No. RM10-17-000; Order No. 745]
Demand Response Compensation in Organized Wholesale Energy Markets(Issued March 15, 2011)
The Commission concludes that paying LMP can address the identifiedbarriers to potential demand response providers.
The outcome?
8 / 25
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FERC Pilots the Grid
Origin of FERC 745 – Incentives for Demand Response
FERC 745 is Born!
134 FERC ¶61,187UNITED STATES OF AMERICA
FEDERAL ENERGY REGULATORY COMMISSION18 CFR Part 35
[Docket No. RM10-17-000; Order No. 745]
Demand Response Compensation in Organized Wholesale Energy Markets(Issued March 15, 2011)
The Commission concludes that paying LMP can address the identifiedbarriers to potential demand response providers.
The outcome?
8 / 25
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FERC Pilots the Grid
Origin of FERC 745 – Incentives for Demand Response
FERC 745 is Born!
134 FERC ¶61,187UNITED STATES OF AMERICA
FEDERAL ENERGY REGULATORY COMMISSION18 CFR Part 35
[Docket No. RM10-17-000; Order No. 745]
Demand Response Compensation in Organized Wholesale Energy Markets(Issued March 15, 2011)
The Commission concludes that paying LMP can address the identifiedbarriers to potential demand response providers.
The outcome?
8 / 25
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FERC Pilots the Grid
FERC 745 Is Dead!
Conjecture: It had to die.
FERC 745 Nirvana:
Peaks shaved!
Contingencies resolved in seconds!!
Prices smoothed to Marginal Cost!
Contour Map: Real Time Market - Locational Marginal Pricing Help?
$10/MWh
Nirvana @ ERCOT
The problem: In this market, there is no opportunity without crisis
The paradox: In this nirvana,there is no business case for demand response
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FERC Pilots the Grid
FERC 745 Is Dead!
Conjecture: It had to die.
FERC 745 Nirvana:
Peaks shaved!
Contingencies resolved in seconds!!
Prices smoothed to Marginal Cost!
Contour Map: Real Time Market - Locational Marginal Pricing Help?
$10/MWh
Nirvana @ ERCOT
The problem: In this market, there is no opportunity without crisis
The paradox: In this nirvana,there is no business case for demand response
9 / 25
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FERC Pilots the Grid
FERC 745 Is Dead!
Conjecture: It had to die.
FERC 745 Nirvana:
Peaks shaved!
Contingencies resolved in seconds!!
Prices smoothed to Marginal Cost!
Contour Map: Real Time Market - Locational Marginal Pricing Help?
$10/MWh
Nirvana @ ERCOT
The problem: In this market, there is no opportunity without crisis
The paradox: In this nirvana,there is no business case for demand response
9 / 25
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FERC Pilots the Grid
FERC 745 Is Dead!
Conjecture: It had to die.
FERC 745 Nirvana:
Peaks shaved!
Contingencies resolved in seconds!!
Prices smoothed to Marginal Cost!
Contour Map: Real Time Market - Locational Marginal Pricing Help?
$10/MWh
Nirvana @ ERCOT
The problem: In this market, there is no opportunity without crisis
The paradox: In this nirvana,there is no business case for demand response
9 / 25
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FERC Pilots the Grid
FERC 745 Is Dead!
Conjecture: It had to die.
FERC 745 Nirvana:
Peaks shaved!
Contingencies resolved in seconds!!
Prices smoothed to Marginal Cost!
Contour Map: Real Time Market - Locational Marginal Pricing Help?
$10/MWh
Nirvana @ ERCOT
The problem: In this market, there is no opportunity without crisis
The paradox: In this nirvana,there is no business case for demand response
9 / 25
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FERC Pilots the Grid
FERC 745 Is Dead!
Conjecture: It had to die.
FERC 745 Nirvana:
Peaks shaved!
Contingencies resolved in seconds!!
Prices smoothed to Marginal Cost!
Contour Map: Real Time Market - Locational Marginal Pricing Help?
$10/MWh
Nirvana @ ERCOT
The problem: In this market, there is no opportunity without crisis
The paradox: In this nirvana,there is no business case for demand response
9 / 25
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FERC Pilots the Grid
FERC 745 Is Dead!
Conjecture: It had to die.
FERC 745 Nirvana:
Peaks shaved!
Contingencies resolved in seconds!!
Prices smoothed to Marginal Cost!
Contour Map: Real Time Market - Locational Marginal Pricing Help?
$10/MWh
Nirvana @ ERCOT
The problem: In this market, there is no opportunity without crisis
The paradox: In this nirvana,there is no business case for demand response
9 / 25
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FERC Pilots the Grid
FERC Order 755Pay-for-Performance
Time
Regulation Required @ MISO
0
200
400
600
-400
-200
Requires ISO/RTOs to pay regulation resourcesbased on actual amount of regulation service provided
(speed and accuracy).
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FERC Pilots the Grid
FERC Order 755Pay-for-Performance
Time
Regulation Required @ MISO
0
200
400
600
-400
-200
Requires ISO/RTOs to pay regulation resourcesbased on actual amount of regulation service provided
(speed and accuracy).
10 / 25
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FERC Pilots the Grid
FERC Order 755Pay-for-Performance
Time
Regulation Required @ MISO
0
200
400
600
-400
-200
Two part settlement:
1 Uniform price for frequency regulation capacity
2 Performance payment for the provision of frequency regulationservice, reflecting a resource’s accuracy of performance
Performance? Now interpreted as mileage:
Payment ∝∫ T
0
∣∣ ddtG(t)| dt (or discrete-time equivalent)
Not perfect, but it is creating incentives
Question: What is the cost/value of regulation?
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FERC Pilots the Grid
FERC Order 755Pay-for-Performance
Time
Regulation Required @ MISO
0
200
400
600
-400
-200
Two part settlement:
1 Uniform price for frequency regulation capacity
2 Performance payment for the provision of frequency regulationservice, reflecting a resource’s accuracy of performance
Performance?
Now interpreted as mileage:
Payment ∝∫ T
0
∣∣ ddtG(t)| dt (or discrete-time equivalent)
Not perfect, but it is creating incentives
Question: What is the cost/value of regulation?
11 / 25
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FERC Pilots the Grid
FERC Order 755Pay-for-Performance
Time
Regulation Required @ MISO
0
200
400
600
-400
-200
Two part settlement:
1 Uniform price for frequency regulation capacity
2 Performance payment for the provision of frequency regulationservice, reflecting a resource’s accuracy of performance
Performance? Now interpreted as mileage:
Payment ∝∫ T
0
∣∣ ddtG(t)| dt (or discrete-time equivalent)
Not perfect, but it is creating incentives
Question: What is the cost/value of regulation?
11 / 25
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FERC Pilots the Grid
FERC Order 755Pay-for-Performance
Time
Regulation Required @ MISO
0
200
400
600
-400
-200
Two part settlement:
1 Uniform price for frequency regulation capacity
2 Performance payment for the provision of frequency regulationservice, reflecting a resource’s accuracy of performance
Performance? Now interpreted as mileage:
Payment ∝∫ T
0
∣∣ ddtG(t)| dt (or discrete-time equivalent)
Not perfect, but it is creating incentives
Question: What is the cost/value of regulation?
11 / 25
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Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
Gr = G1 +G2 +G3
G1
G2
G3 Where do we �nd these ailerons?
Demand Dispatch
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Demand Dispatch
We want: Responsive RegulationDemand Dispatch the Answer?
A partial list of the needs of the grid operator, and the consumer:
High quality AS?
Reliable?
Cost effective?
Is the incentive to the consumer reliable?
Customer QoS constraints satisfied?
Perhaps demand dispatch can do all of this?
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Demand Dispatch
We want: Responsive RegulationDemand Dispatch the Answer?
A partial list of the needs of the grid operator, and the consumer:
High quality AS? (Ancillary Service)Does the deviation in power consumption accurately track the desireddeviation target?
Reliable?
Cost effective?
Is the incentive to the consumer reliable?
Customer QoS constraints satisfied?
Perhaps demand dispatch can do all of this?
12 / 25
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Demand Dispatch
We want: Responsive RegulationDemand Dispatch the Answer?
A partial list of the needs of the grid operator, and the consumer:
High quality AS? (Ancillary Service)
-15
-10
-5
0
5
10
15
20
25
30
35
6:00 7:00 8:00 9:00 6:00 7:00 8:00 9:00
Reg
ulat
ion
(MW
)
Gen 'A' Actual Regulation
Gen 'A' Requested Regulation
-20
-15
-10
-5
0
5
10
15
20
:
Reg
ulat
ion
(MW
)
Gen 'B' Actual Regulation
Gen 'B' Requested Regulation
Fig. 10. Coal-�red generators do not follow regulation signals precisely.... Some do better than others
Regulation service from generators is not perfectFrequency Regulation Basics and Trends — Brendan J. Kirby, December 2004
Reliable?Cost effective?Is the incentive to the consumer reliable?Customer QoS constraints satisfied?
Perhaps demand dispatch can do all of this?
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Demand Dispatch
We want: Responsive RegulationDemand Dispatch the Answer?
A partial list of the needs of the grid operator, and the consumer:
High quality AS?
Reliable?Will AS be available each day?It may vary with time, but capacity must be predictable.
Cost effective?
Is the incentive to the consumer reliable?
Customer QoS constraints satisfied?
Perhaps demand dispatch can do all of this?
12 / 25
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Demand Dispatch
We want: Responsive RegulationDemand Dispatch the Answer?
A partial list of the needs of the grid operator, and the consumer:
High quality AS?
Reliable?
Cost effective?This includes installation cost, communication cost, maintenance,and environmental.
Is the incentive to the consumer reliable?
Customer QoS constraints satisfied?
Perhaps demand dispatch can do all of this?
12 / 25
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Demand Dispatch
We want: Responsive RegulationDemand Dispatch the Answer?
A partial list of the needs of the grid operator, and the consumer:
High quality AS?
Reliable?
Cost effective?
Is the incentive to the consumer reliable?If a consumer receives a $50 payment for one month, and only $1 thenext, will there be an explanation that is clear to the consumer?
Customer QoS constraints satisfied?
Perhaps demand dispatch can do all of this?
12 / 25
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Demand Dispatch
We want: Responsive RegulationDemand Dispatch the Answer?
A partial list of the needs of the grid operator, and the consumer:
High quality AS?
Reliable?
Cost effective?
Is the incentive to the consumer reliable?
Customer QoS constraints satisfied?The pool must be clean, fresh fish stays cold, building climate issubject to strict bounds, farm irrigation is subject to strict constraints,data centers require sufficient power to perform their tasks.
Perhaps demand dispatch can do all of this?
12 / 25
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Demand Dispatch
We want: Responsive RegulationDemand Dispatch the Answer?
A partial list of the needs of the grid operator, and the consumer:
High quality AS?
Reliable?
Cost effective?
Is the incentive to the consumer reliable?
Customer QoS constraints satisfied?
Perhaps demand dispatch can do all of this?
12 / 25
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Demand Dispatch
Control ArchitectureFrequency Decomposition for Demand Dispatch
Power GridControl FlywheelsBatteries
CoalGas Turbine
BP
BP
BP C
BP
BP
Voltage Frequency Phase
HCΣ
−
Actuator feedback loop
A
LOAD
Today: PJM decomposes regulation signal based on bandwidth,R = RegA + · · · + RegD
Proposal: Each class of DR (and other) resources will have its ownbandwidth of service, based on QoS constraints and costs.
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Demand Dispatch
Control ArchitectureFrequency Decomposition for Demand Dispatch
Power GridControl FlywheelsBatteries
CoalGas Turbine
BP
BP
BP C
BP
BP
Voltage Frequency Phase
HCΣ
−
Actuator feedback loop
A
LOAD
Today: PJM decomposes regulation signal based on bandwidth,R = RegA + · · · + RegD
Proposal: Each class of DR (and other) resources will have its ownbandwidth of service, based on QoS constraints and costs.
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Demand Dispatch
Control ArchitectureFrequency Decomposition for Demand Dispatch
Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06
GW
0
1
2
3
4
Gr(t)
G1
G2
G
Traditional generation
DD: Chillers & Pool Pumps
DD: HVAC Fans3
Gr = G1 +G2 +G3
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Demand Dispatch
Control ArchitectureFrequency Decomposition for Demand Dispatch
Balancing Reserves from Bonneville Power Authority:
−800−600
-1000
−400−200
0200400600800
BPA Reg signal(one week)
MW
= HVAC + Pool Pumps
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Demand Dispatch
Control ArchitectureFrequency Decomposition for Demand Dispatch
Balancing Reserves from Bonneville Power Authority:
−800−600
-1000
−400−200
0200400600800
BPA Reg signal(one week)
MW
0
−800−600
-1000
−400−200
200400600800
MW
−800−600
-1000
−400−200
0200400600800
= +
= HVAC + Pool Pumps
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Demand Dispatch
Control ArchitectureFrequency Decomposition for Demand Dispatch
Balancing Reserves from Bonneville Power Authority:
−800−600
-1000
−400−200
0200400600800
BPA Reg signal(one week)
MW
0
−800−600
-1000
−400−200
200400600800
MW
−800−600
-1000
−400−200
0200400600800
= +
= HVAC + Pool Pumps
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0
−800−600
-1000
−400−200
200400600800
MW
=
Buildings as Batteries
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Buildings as Batteries
Buildings as BatteriesHVAC flexibility to provide additional ancillary service
◦ Buildings consume 70% of electricity in the USHVAC contributes to 40% of the consumption.
◦ Buildings have large thermal capacity
◦ Modern buildings have fast-responding equipment:VFDs (variable frequency drive)
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Buildings as Batteries
Buildings as BatteriesHVAC flexibility to provide additional ancillary service
◦ Buildings consume 70% of electricity in the USHVAC contributes to 40% of the consumption.
◦ Buildings have large thermal capacity
◦ Modern buildings have fast-responding equipment:VFDs (variable frequency drive)
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Buildings as Batteries
Buildings as BatteriesHVAC flexibility to provide additional ancillary service
◦ Buildings consume 70% of electricity in the USHVAC contributes to 40% of the consumption.
◦ Buildings have large thermal capacity
◦ Modern buildings have fast-responding equipment:VFDs (variable frequency drive)
16 / 25
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Buildings as Batteries
Buildings as BatteriesHVAC flexibility to provide additional ancillary service
◦ Buildings consume 70% of electricity in the USHVAC contributes to 40% of the consumption.
◦ Buildings have large thermal capacity
◦ Modern buildings have fast-responding equipment:VFDs (variable frequency drive)
16 / 25
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Buildings as Batteries
Buildings as BatteriesTracking RegD at Pugh Hall — ignore the measurement noise
In one sentence:
Ramp up and down power consumption, just 10%, totrack regulation signal.Result:
PJM RegD Measured
Power
(kW
)
0
1
-1
0 10 20 30 40Time (minute)
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Buildings as Batteries
Buildings as BatteriesTracking RegD at Pugh Hall — ignore the measurement noise
In one sentence: Ramp up and down power consumption, just 10%, totrack regulation signal.
Result:
PJM RegD Measured
Power
(kW
)
0
1
-1
0 10 20 30 40Time (minute)
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Buildings as Batteries
Buildings as BatteriesTracking RegD at Pugh Hall — ignore the measurement noise
In one sentence: Ramp up and down power consumption, just 10%, totrack regulation signal.Result:
PJM RegD Measured
Power
(kW
)
0
1
-1
0 10 20 30 40Time (minute)
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Buildings as Batteries
Buildings as BatteriesTracking RegD at Pugh Hall — ignore the measurement noise
In one sentence: Ramp up and down power consumption, just 10%, totrack regulation signal.Result:
PJM RegD Measured
Power
(kW
)
0
1
-1
0 10 20 30 40Time (minute)
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Buildings as Batteries
Pugh Hall @ UFHow much?
−4
−2
0
2
4
−10
0
10
0 500 1000 1500 2000 2500 3000 3500−0.2
0
0.2
Time (s)
Fan Power Deviation (kW )Regulation Signal (kW )
Input (%)
Temperature Deviation (◦C)
. One AHU fan with 25 kW motor:> 3 kW of regulation reserve
. Pugh Hall (40k sq ft, 3 AHUs):> 10 kW
Indoor air quality is not affected
. 100 buildings:> 1 MW
That’s just using the fans!
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Buildings as Batteries
Pugh Hall @ UFHow much?
−4
−2
0
2
4
−10
0
10
0 500 1000 1500 2000 2500 3000 3500−0.2
0
0.2
Time (s)
Fan Power Deviation (kW )Regulation Signal (kW )
Input (%)
Temperature Deviation (◦C)
. One AHU fan with 25 kW motor:> 3 kW of regulation reserve
. Pugh Hall (40k sq ft, 3 AHUs):> 10 kW
Indoor air quality is not affected
. 100 buildings:> 1 MW
That’s just using the fans!
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Buildings as Batteries
Pugh Hall @ UFHow much?
−4
−2
0
2
4
−10
0
10
0 500 1000 1500 2000 2500 3000 3500−0.2
0
0.2
Time (s)
Fan Power Deviation (kW )Regulation Signal (kW )
Input (%)
Temperature Deviation (◦C)
. One AHU fan with 25 kW motor:> 3 kW of regulation reserve
. Pugh Hall (40k sq ft, 3 AHUs):> 10 kW
Indoor air quality is not affected
. 100 buildings:> 1 MW
That’s just using the fans!
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Buildings as Batteries
Buildings as BatteriesWhat do you think?
Questions:
Capacity?
Tens of Gigawatts from commercial buildings in the US
Can we obtain a resource as effective as today’s spinning reserves?
Yes!! Buildings are well-suited to balancing reserves,and other high-frequency regulation resources
much better than any generator
How to compute baselines?
Who cares? The utility or aggregator is responsible for the equipment –the consumer cannot ‘play games’ in a real time market!
What open issues do you see?
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Buildings as Batteries
Buildings as BatteriesWhat do you think?
Questions:
Capacity? Tens of Gigawatts from commercial buildings in the US
Can we obtain a resource as effective as today’s spinning reserves?
Yes!! Buildings are well-suited to balancing reserves,and other high-frequency regulation resources
much better than any generator
How to compute baselines?
Who cares? The utility or aggregator is responsible for the equipment –the consumer cannot ‘play games’ in a real time market!
What open issues do you see?
19 / 25
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Buildings as Batteries
Buildings as BatteriesWhat do you think?
Questions:
Capacity? Tens of Gigawatts from commercial buildings in the US
Can we obtain a resource as effective as today’s spinning reserves?
Yes!! Buildings are well-suited to balancing reserves,and other high-frequency regulation resources
much better than any generator
How to compute baselines?
Who cares? The utility or aggregator is responsible for the equipment –the consumer cannot ‘play games’ in a real time market!
What open issues do you see?
19 / 25
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Buildings as Batteries
Buildings as BatteriesWhat do you think?
Questions:
Capacity? Tens of Gigawatts from commercial buildings in the US
Can we obtain a resource as effective as today’s spinning reserves?
Yes!! Buildings are well-suited to balancing reserves,and other high-frequency regulation resources
much better than any generator
How to compute baselines?
Who cares? The utility or aggregator is responsible for the equipment –the consumer cannot ‘play games’ in a real time market!
What open issues do you see?
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Buildings as Batteries
Buildings as BatteriesWhat do you think?
Questions:
Capacity? Tens of Gigawatts from commercial buildings in the US
Can we obtain a resource as effective as today’s spinning reserves?
Yes!! Buildings are well-suited to balancing reserves,and other high-frequency regulation resources
much better than any generator
How to compute baselines?
Who cares? The utility or aggregator is responsible for the equipment –the consumer cannot ‘play games’ in a real time market!
What open issues do you see?
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Buildings as Batteries
Buildings as BatteriesWhat do you think?
Questions:
Capacity? Tens of Gigawatts from commercial buildings in the US
Can we obtain a resource as effective as today’s spinning reserves?
Yes!! Buildings are well-suited to balancing reserves,and other high-frequency regulation resources
much better than any generator
How to compute baselines?
Who cares? The utility or aggregator is responsible for the equipment –the consumer cannot ‘play games’ in a real time market!
What open issues do you see?
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Buildings as Batteries
Buildings as BatteriesWhat do you think?
Questions:
Capacity? Tens of Gigawatts from commercial buildings in the US
Can we obtain a resource as effective as today’s spinning reserves?
Yes!! Buildings are well-suited to balancing reserves,and other high-frequency regulation resources
much better than any generator
How to compute baselines?
Who cares? The utility or aggregator is responsible for the equipment –the consumer cannot ‘play games’ in a real time market!
What open issues do you see?
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Buildings as Batteries
Buildings as BatteriesWhat do you think?
Questions:
Capacity? Tens of Gigawatts from commercial buildings in the US
Can we obtain a resource as effective as today’s spinning reserves?
Yes!! Buildings are well-suited to balancing reserves,and other high-frequency regulation resources
much better than any generator
How to compute baselines?
Who cares? The utility or aggregator is responsible for the equipment –the consumer cannot ‘play games’ in a real time market!
What open issues do you see?
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−600−400−200
0200400600
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
MW
Intelligent Pools in Florida
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−600−400−200
0200400600
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
MW
Intelligent Pools in Florida
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Intelligent Pools in Florida
Example: One Million Pools in FloridaHow Pools Can Help Regulate The Grid
1,5KW 400V
Needs of a single pool
. Filtration system circulates and cleans: Average pool pump uses1.3kW and runs 6-12 hours per day, 7 days per week
. Pool owners are oblivious, until they see frogs and algae
. Pool owners do not trust anyone: Privacy is a big concern
Randomized control strategy is needed.
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Intelligent Pools in Florida
Example: One Million Pools in FloridaHow Pools Can Help Regulate The Grid
1,5KW 400V
Needs of a single pool
. Filtration system circulates and cleans: Average pool pump uses1.3kW and runs 6-12 hours per day, 7 days per week
. Pool owners are oblivious, until they see frogs and algae
. Pool owners do not trust anyone: Privacy is a big concern
Randomized control strategy is needed.
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Intelligent Pools in Florida
Example: One Million Pools in FloridaHow Pools Can Help Regulate The Grid
1,5KW 400V
Needs of a single pool
. Filtration system circulates and cleans: Average pool pump uses1.3kW and runs 6-12 hours per day, 7 days per week
. Pool owners are oblivious, until they see frogs and algae
. Pool owners do not trust anyone: Privacy is a big concern
Randomized control strategy is needed.
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Intelligent Pools in Florida
Example: One Million Pools in FloridaHow Pools Can Help Regulate The Grid
1,5KW 400V
Needs of a single pool
. Filtration system circulates and cleans: Average pool pump uses1.3kW and runs 6-12 hours per day, 7 days per week
. Pool owners are oblivious, until they see frogs and algae
. Pool owners do not trust anyone: Privacy is a big concern
Randomized control strategy is needed.
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Intelligent Pools in Florida
Pools in Florida Supply G2 – BPA regulation signal∗Stochastic simulation using N = 105 pools
Reference Output deviation (MW)
−300
−200
−100
0
100
200
300
0 20 40 60 80 100 120 140 160t/hour
0 20 40 60 80 100 120 140 160
∗transmission.bpa.gov/Business/Operations/Wind/reserves.aspx
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Intelligent Pools in Florida
Pools in Florida Supply G2 – BPA regulation signal∗Stochastic simulation using N = 105 pools
Reference Output deviation (MW)
−300
−200
−100
0
100
200
300
0 20 40 60 80 100 120 140 160t/hour
0 20 40 60 80 100 120 140 160
Each pool pump turns on/off with probability depending on1) its internal state, and 2) the BPA reg signal
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Intelligent Pools in Florida
Pools in Florida Supply G2 – BPA regulation signal∗Stochastic simulation using N = 105 pools
Reference Output deviation (MW)
−300
−200
−100
0
100
200
300
0 20 40 60 80 100 120 140 160t/hour
0 20 40 60 80 100 120 140 160
Mean-field model: Input-output system stable? Passive?
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Power GridControl Water PumpBatteries
CoalGas Turbine
BP
BP
BP C
BP
BP
Voltage Frequency Phase
HCΣ
−
Actuator feedback loop
A
LOAD
Conclusions
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Conclusions
ConclusionsAnswers ... and Questions
1 Volatility
This appears to be manageable! Demand Dispatch can be designedto work cooperatively with generation and other resources.
Open questions on many spatial and temporal scales. Most loadscould provide synthetic inertia and governor response1. Is this wise?
2 Engineering uncertaintyThis is real We don’t know why the grid is so reliable today.Need for better understanding of grid2/distribution/social dynamics.
3 Policy uncertaintyScary!
Need for Research in Engineering and Economics1Scweppe et. al. 19802Thorpe et. al. 2004
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Conclusions
ConclusionsAnswers ... and Questions
1 VolatilityThis appears to be manageable! Demand Dispatch can be designedto work cooperatively with generation and other resources.
Open questions on many spatial and temporal scales. Most loadscould provide synthetic inertia and governor response1. Is this wise?
2 Engineering uncertaintyThis is real We don’t know why the grid is so reliable today.Need for better understanding of grid2/distribution/social dynamics.
3 Policy uncertaintyScary!
Need for Research in Engineering and Economics
1Scweppe et. al. 1980
2Thorpe et. al. 2004
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Conclusions
ConclusionsAnswers ... and Questions
1 VolatilityThis appears to be manageable! Demand Dispatch can be designedto work cooperatively with generation and other resources.
Open questions on many spatial and temporal scales. Most loadscould provide synthetic inertia and governor response1. Is this wise?
2 Engineering uncertaintyThis is real We don’t know why the grid is so reliable today.
Need for better understanding of grid2/distribution/social dynamics.
3 Policy uncertaintyScary!
Need for Research in Engineering and Economics
1Scweppe et. al. 1980
2Thorpe et. al. 2004
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Conclusions
ConclusionsAnswers ... and Questions
1 VolatilityThis appears to be manageable! Demand Dispatch can be designedto work cooperatively with generation and other resources.
Open questions on many spatial and temporal scales. Most loadscould provide synthetic inertia and governor response1. Is this wise?
2 Engineering uncertaintyThis is real We don’t know why the grid is so reliable today.Need for better understanding of grid2/distribution/social dynamics.
3 Policy uncertaintyScary!
Need for Research in Engineering and Economics
1Scweppe et. al. 19802Thorpe et. al. 2004
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Conclusions
ConclusionsAnswers ... and Questions
1 VolatilityThis appears to be manageable! Demand Dispatch can be designedto work cooperatively with generation and other resources.
Open questions on many spatial and temporal scales. Most loadscould provide synthetic inertia and governor response1. Is this wise?
2 Engineering uncertaintyThis is real We don’t know why the grid is so reliable today.Need for better understanding of grid2/distribution/social dynamics.
3 Policy uncertaintyScary!
Need for Research in Engineering and Economics
1Scweppe et. al. 19802Thorpe et. al. 2004
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Conclusions
ConclusionsAnswers ... and Questions
1 VolatilityThis appears to be manageable! Demand Dispatch can be designedto work cooperatively with generation and other resources.
Open questions on many spatial and temporal scales. Most loadscould provide synthetic inertia and governor response1. Is this wise?
2 Engineering uncertaintyThis is real We don’t know why the grid is so reliable today.Need for better understanding of grid2/distribution/social dynamics.
3 Policy uncertaintyScary!
Need for Research in Engineering and Economics1Scweppe et. al. 19802Thorpe et. al. 2004
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Conclusions
Conclusions
Thank You!
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Conclusions
Control TechniquesFOR
Complex Networks
Sean Meyn
Pre-publication version for on-line viewing. Monograph available for purchase at your favorite retailer More information available at http://www.cambridge.org/us/catalogue/catalogue.asp?isbn=9780521884419
Markov Chainsand
Stochastic Stability
S. P. Meyn and R. L. Tweedie
August 2008 Pre-publication version for on-line viewing. Monograph to appear Februrary 2009
π(f)<
∞
∆V (x) ≤ −f(x) + bIC(x)
‖Pn(x, · )− π‖f → 0
sup
CEx [S
τC(f)]<
∞
References
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Conclusions
Selected ReferencesMore at www.meyn.ece.ufl.edu
A. Brooks, E. Lu, D. Reicher, C. Spirakis, and B. Weihl. Demand dispatch. IEEE Powerand Energy Magazine, 8(3):20–29, May 2010.
H. Hao, T. Middelkoop, P. Barooah, and S. Meyn. How demand response from commercialbuildings will provide the regulation needs of the grid. In 50th Allerton Conference onCommunication, Control, and Computing, pages 1908–1913, 2012.
H. Hao, Y. Lin, A. Kowli, P. Barooah, and S. Meyn. Ancillary service to the grid throughcontrol of fans in commercial building HVAC systems. IEEE Trans. on Smart Grid,5(4):2066–2074, July 2014.
S. Meyn, P. Barooah, A. Busic, Y. Chen, and J. Ehren. Ancillary service to the grid usingintelligent deferrable loads. ArXiv e-prints: arXiv:1402.4600 and to appear, IEEE Trans. onAuto. Control, 2014.
D. Callaway and I. Hiskens, Achieving controllability of electric loads. Proceedings of theIEEE, vol. 99, no. 1, pp. 184–199, 2011.
M. Parashar, J. Thorp, and C. Seyler. Continuum modeling of electromechanical dynamicsin large-scale power systems. IEEE Trans. on Circuits and Systems I, 51(9):1848–1858,2004.
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