https://www.newton.ac.uk/seminar/20190110160017001
Abstract: For decades power systems academics have proclaimed the need for real time prices to create a more efficient grid. The rationale is economics 101: proper price signals will lead to an efficient outcome. In this talk we will review a bit of economics 101; in particular, the definition of efficiency. We will see that the theory supports the real-time price paradigm, provided we impose a particular model of rationality. It is argued however that this standard model of consumer utility does not match reality: the products of interest to the various "agents" are complex functions of time. The product of interest to a typical consumer is only loosely related to electric power -- the quantity associated with price signals. There is good news: an efficient outcome is easy to describe, and we have the control technology to achieve it. We need supporting market designs that respect dynamics and the impact of fixed costs that are inherent in power systems engineering, recognizing that we need incentives on many time-scales. Most likely the needed economic theory will be based on an emerging theory of efficient and robust contract design.
HARDNESS, FRACTURE TOUGHNESS AND STRENGTH OF CERAMICS
Smart Grid Tutorial - January 2019
1. Irrational Agents and the Power Grid
January 10, 2019
Sean Meyn
Department of Electrical and Computer Engineering — University of Florida
Based in part on joint research with
In-Koo Cho, Matias Negrete-Pincetic, Gui Wang, Uday Shanbhag, Robert Moye
Prabir Barooah, Ana Buˇsi´c, Yue Chen, Neil Cammardella, Joel Mathias & Matthew Kiener
Thanks to to our sponsors: NSF, DOE, ARPA-E
2. Irrational Agents and the Power Grid
1 Goals of our Research
2 Genius of Control
3 Genius of the Market
4 Efficient Outcome
5 Conclusions
6 References
6. Genius of Control Happy grid
What does the Balancing Authority Want?
Ancillary services to match supply and demand:
• Balancing Reserves
Sun
0
1
-1
GW
AGC/Secondary control +
1 / 26
7. Genius of Control Happy grid
What does the Balancing Authority Want?
Ancillary services to match supply and demand:
• Balancing Reserves
• Peak shaving
1 / 26
8. Genius of Control Happy grid
What does the Balancing Authority Want?
Ancillary services to match supply and demand:
• Balancing Reserves
• Peak shaving
Modified Prices with Demand Dispatch
1 / 26
9. Genius of Control Happy grid
What does the Balancing Authority Want?
Ancillary services to match supply and demand:
• Balancing Reserves
• Peak shaving
• Ramp service
GW
Forecasted peak: 29,549Forecasted peak: 29,549
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
17
22
27
32
1 / 26
10. Genius of Control Happy grid
What does the Balancing Authority Want?
Ancillary services to match supply and demand:
• Balancing Reserves
• Peak shaving
• Ramp service
GW
Forecasted peak: 29,549Forecasted peak: 29,549
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
17
22
27
32
Modified Load with Demand Dispatch
1 / 26
11. Genius of Control Happy grid
What does the Balancing Authority Want?
Ancillary services to match supply and demand:
• Balancing Reserves
• Peak shaving
• Ramp service
• Contingency support
59.915 Hz
60.010 Hz Modified Load with Demand Dispatch
1 / 26
12. Genius of Control Happy consumers
What do the Consumers Want?
Rational agent wants a hot shower
http://www.onsetcomp.com/learning/application_stories/multi-channel-data-loggers-improve-forensic-analysis-complex-domestic-hot-water-complaints
Θ(t)
G(t)
Ambient
Temperature
Inlet Water
Temperature
3 kW
Water heater trajectories
Θ(t): Temperature
G(t): Power consumption
2 / 26
13. Genius of Control Happy consumers
What do the Consumers Want?
Rational agent wants a hot shower
http://www.onsetcomp.com/learning/application_stories/multi-channel-data-loggers-improve-forensic-analysis-complex-domestic-hot-water-complaints
Θ(t)
G(t)
Ambient
Temperature
Inlet Water
Temperature
3 kW
Mismatch:
G(t) (of interest to BA) Spike Train
Θ(t) (of interest to home) Smooth
Water heater trajectories
Θ(t): Temperature
G(t): Power consumption
2 / 26
14. Genius of Control Happy consumers
What do the Consumers Want?
Rational agent wants a hot shower
http://www.onsetcomp.com/learning/application_stories/multi-channel-data-loggers-improve-forensic-analysis-complex-domestic-hot-water-complaints
Θ(t)
G(t)
Ambient
Temperature
Inlet Water
Temperature
3 kW
Mismatch:
G(t) (of interest to BA) Spike Train
Θ(t) (of interest to home) Smooth
Water heater trajectories
Θ(t): Temperature
G(t): Power consumption
Mismatch = gift to the control engineer
2 / 26
15. Genius of Control Distributed control
Gift to the control engineer
Rational agent wants a hot shower
Power GridControl Flywheels
Batteries
Coal
GasTurbine
BP
BP
BP C
BP
BP
Voltage
Frequency
Phase
HC
Σ
−
Actuator feedback loop
A
LOAD
Mismatch:
G(t) (of interest to BA) Spike Train
Θ(t) (of interest to home) Smooth
Mismatch = gift to the control engineer
3 / 26
16. Genius of Control Distributed control
Tracking with 100,000 Water Heaters
80
100
120
140
0 5 10 15 200 5 10 15 20
80
100
120
140
80
100
120
140
0
50
100
-50
0
50
MWMWMW
-10
0
10
Nominal power consumption
Tracking
Tracking
Typical Load Response
temp(F)temp(F)temp(F)
rt≡0Noreg:|rt|≤40MW|rt|≤10MW
LoadOnLoadOnLoadOn
(hrs)t (hrs)t
BPA Reference:
Power Deviation
rt
Tracking results with 100,000 water heaters, and the behavior of a single
water heater in three cases, distinguished by the reference signal [12]a
Theoretical power capacity is approx 8 MW (with no flow)
a
Buˇsi´c & M. – summary of six year program – see Newton Institute 2013
4 / 26
17. Genius of Control Distributed control
Tracking with 100,000 Water Heaters
Energy Limits – Ramps and Contingencies
-8
-6
-4
-2
0
2
4
6
8
Powerdeviation(MW)
-6
-5
-4
-3
-2
-1
0
1
2
0 5 10 15 20
hours
ζ
ζ
Every water heater OFF
ReferencePower Deviation
Powerdeviation(MW)
-8
-6
-4
-2
0
2
4
6
8
-6
-4
-2
0
2
0 5 10 15 20
hours
ζ
ζ
Tracking a sawtooth wave with 100,000 water heaters:
Average power consumption = 8MW
Quality of Service = temperature limits
By design, QoS violation is not possible
See [12]
5 / 26
18. Genius of Control Distributed control
Tracking with 100,000 Water Heaters
Energy Limits – Ramps and Contingencies
-8
-6
-4
-2
0
2
4
6
8
Powerdeviation(MW)
-6
-5
-4
-3
-2
-1
0
1
2
0 5 10 15 20
hours
ζ
ζ
Every water heater OFF
ReferencePower Deviation
Powerdeviation(MW)
-8
-6
-4
-2
0
2
4
6
8
-6
-4
-2
0
2
0 5 10 15 20
hours
ζ
ζ
Tracking a sawtooth wave with 100,000 water heaters:
Average power consumption = 8MW
Quality of Service = temperature limits
By design, QoS violation is not possible
See [12]
... and research at Berkeley, Michigan, Imperial College, Vermont, ...
5 / 26
19. Purchase Price $/MWh
Previous week
Spinning reserve prices PX prices $/MWh
100
150
0
50
200
250
10
20
30
40
50
60
70
Texas: February2,2011
California: July2000Illinois:July1998
Ontario: November,2005
0
1000
2000
3000
4000
5000
Mon Tues Weds Thurs Fri Mon Tues WedsWeds Thurs Fri Sat Sun
Tues Weds Thurs
Time3 6 9 12 15 18 213 6 9 12 15 18 213 6 9 12 15 18 21
Demand in MW Last Updated 11:00 AM Predispatch 1975.11 Dispatch 19683.5
Hourly Ontario Energy Price $/MWh Last Updated 11:00 AM Predispatch 72.79 Dispatch 90.82
2000
21000
18000
15000
1500
1000
500
0
ForecastPricesForecastDemand
5am 10am 3pm 8pm
−500
0
1000
2000
3000
$/MWh
Average price
is usually $30
$/MWh
Genius of the Market
20. Genius of the Market Real time markets
RTM Model
The dream
"The active participation of final demand in the wholesale market is essential to managing
the greater intermittency of renewable resources and in limiting the ability of wholesale
electricity suppliers to exercise unilateral market power. A demand that is able to reduce its
consumption in real-time in response to higher prices limits the ability of suppliers
to exercise unilateral market power in a formal wholesale market such as the California ISO"
(http://www.stanford.edu/group/fwolak/cgi-
bin/sites/default/files/files/little_hoover_testimony_wolak_sept_2011.pdf) -F. Wolak
Low-cost information and communications technologies
and advanced metering
enable more cost-reflective prices and charges for
electricity services that can finally animate the“demand
side”of the power system and align myriad decisions
"Virtually all economists agree that the outcome [of the California crisis] was exacerbated by the inability of the demand side of the
market to respond to real or artificial supply shortages. This realization prompted my research stream on." real-time electricity pricing.”
- S. Borenstein
6 / 26
21. Genius of the Market Real time markets
RTM Model
The dream
"The active participation of final demand in the wholesale market is essential to managing
the greater intermittency of renewable resources and in limiting the ability of wholesale
electricity suppliers to exercise unilateral market power. A demand that is able to reduce its
consumption in real-time in response to higher prices limits the ability of suppliers
to exercise unilateral market power in a formal wholesale market such as the California ISO"
(http://www.stanford.edu/group/fwolak/cgi-
bin/sites/default/files/files/little_hoover_testimony_wolak_sept_2011.pdf) -F. Wolak
Low-cost information and communications technologies
and advanced metering
enable more cost-reflective prices and charges for
electricity services that can finally animate the“demand
side”of the power system and align myriad decisions
"Virtually all economists agree that the outcome [of the California crisis] was exacerbated by the inability of the demand side of the
market to respond to real or artificial supply shortages. This realization prompted my research stream on." real-time electricity pricing.”
- S. Borenstein
reduce its
"The active participation of final demand in the wholesale market is essential to managing
the greater intermittency of renewable resources and in limiting the ability of wholesale
electricity suppliers to exercise unilateral market power. A demand that is able to reduce its
consumption in real-time in response to higher prices limits the ability of suppliers
to exercise unilateral market power in a formal wholesale market such as the California ISO"
(http://www.stanford.edu/group/fwolak/cgi-
bin/sites/default/files/files/little_hoover_testimony_wolak_sept_2011.pdf) -F. Wolak
higher prices
reduce
consumption in real-time
6 / 26
22. Genius of the Market Real time markets
RTM Model
The dream
"The active participation of final demand in the wholesale market is essential to managing
the greater intermittency of renewable resources and in limiting the ability of wholesale
electricity suppliers to exercise unilateral market power. A demand that is able to reduce its
consumption in real-time in response to higher prices limits the ability of suppliers
to exercise unilateral market power in a formal wholesale market such as the California ISO"
(http://www.stanford.edu/group/fwolak/cgi-
bin/sites/default/files/files/little_hoover_testimony_wolak_sept_2011.pdf) -F. Wolak
Low-cost information and communications technologies
and advanced metering
enable more cost-reflective prices and charges for
electricity services that can finally animate the“demand
side”of the power system and align myriad decisions
"Virtually all economists agree that the outcome [of the California crisis] was exacerbated by the inability of the demand side of the
market to respond to real or artificial supply shortages. This realization prompted my research stream on." real-time electricity pricing.”
- S. Borenstein
"Virtuallyalleconomistsagreethattheoutcome[oftheCaliforniacrisis]wasexacerbatedbytheinabilityofthedemandsideofthe
markettorespondtorealorartificialsupplyshortages.Thisrealizationpromptedmyresearchstreamon." real-timeelectricitypricing.”
-S.Borenstein
demand side
respond
real-time electricity pricing
6 / 26
23. Genius of the Market Real time markets
Electricity Markets Today
Two coupled markets review of Newton Institute 2010
Day-ahead market (DAM):
Cleared one day prior to the production and delivery of energy: The ISO
generates a schedule of generators to supply specific levels of power for
each hour over the next 24 hour period.
7 / 26
24. Genius of the Market Real time markets
Electricity Markets Today
Two coupled markets review of Newton Institute 2010
Day-ahead market (DAM):
Cleared one day prior to the production and delivery of energy: The ISO
generates a schedule of generators to supply specific levels of power for
each hour over the next 24 hour period.
Real-time market (RTM):
As supply and demand are not perfectly predictable, the RTM plays the
role of fine-tuning this resource allocation process
RTM is the focus here
7 / 26
25. Genius of the Market Competitive equilibrium
RTM Model
Dynamic model for reserves
Simplest model of Cho & M:
R(t) = Available power − Demand = G(t) − D(t)
D(t) = Actual demand − Forecast
G(t): Deviation in on-line capacity from day-ahead market
8 / 26
26. Genius of the Market Competitive equilibrium
RTM Model
Dynamic model for reserves
Simplest model of Cho & M:
R(t) = Available power − Demand = G(t) − D(t)
D(t) = Actual demand − Forecast
G(t): Deviation in on-line capacity from day-ahead market
Dynamic model
Generation cannot increase instantaneously:
For all t ≥ 0 and t > t,
G(t ) − G(t)
t − t
≤ ζ
8 / 26
27. Genius of the Market Competitive equilibrium
RTM Model
Dynamic model for reserves
Simplest model of Cho & M:
R(t) = Available power − Demand = G(t) − D(t)
D(t) = Actual demand − Forecast
G(t): Deviation in on-line capacity from day-ahead market
Dynamic model
Generation cannot increase instantaneously:
For all t ≥ 0 and t > t,
G(t ) − G(t)
t − t
≤ ζ
Later work: lower bounds on generation, as well as network constraints [9]
8 / 26
28. Genius of the Market Competitive equilibrium
Market Analysis
Second Welfare Theorem
Self-Interested Agents
max
GS
E e−γt
WS(t) dt max
GD
E e−γt
WD(t) dt
9 / 26
29. Genius of the Market Competitive equilibrium
Market Analysis
Second Welfare Theorem
Self-Interested Agents
max
GS
E e−γt
WS(t) dt max
GD
E e−γt
WD(t) dt
Welfare functions defined with a nominal price function P(t):
WS(t) = P(t)GS(t) − cS(GS(t))
WD(t) = wD(GD(t)) − P(t)GD(t)
9 / 26
30. Genius of the Market Competitive equilibrium
Market Analysis
Second Welfare Theorem
Self-Interested Agents
max
GS
E e−γt
WS(t) dt max
GD
E e−γt
WD(t) dt
Welfare functions defined with a nominal price function P(t):
WS(t) = P(t)GS(t) − cS(GS(t))
WD(t) = wD(GD(t)) − P(t)GD(t)
Key component of equilibrium theory:
Perfect competition
The price of power P(t) in the RTM is assumed to be exogenous;
prices do not depend on the decisions of the market agents.
9 / 26
31. Genius of the Market Competitive equilibrium
Market Analysis
Second Welfare Theorem
Self-Interested Agents
max
GS
E e−γt
WS(t) dt max
GD
E e−γt
WD(t) dt
Welfare functions defined with a nominal price function P(t):
WS(t) = P(t)GS(t) − cS(GS(t))
WD(t) = wD(GD(t)) − P(t)GD(t)
Key component of equilibrium theory:
Perfect competition
The price of power P(t) in the RTM is assumed to be exogenous;
prices do not depend on the decisions of the market agents.
“price-taking assumption”
9 / 26
32. Genius of the Market Competitive equilibrium
Market Analysis
Second Welfare Theorem
Efficient Equilibrium
max
GS
E e−γt
WS(t) dt max
GD
E e−γt
WD(t) dt
The market is efficient if G∗
S
= G∗
D
Key component of equilibrium theory:
Perfect competition
The price of power P(t) in the RTM is assumed to be exogenous (it
does not depend on the decisions of the market agents).
“price-taking assumption”
10 / 26
33. Genius of the Market Competitive equilibrium
Market Analysis
Second Welfare Theorem
Social Planner’s Problem
An efficient equilibrium is optimal for the social planner:
max K(G) = E e−γt
WS(t) + WD(t) dt
s.t. GS(t) = GD(t) for all t
Welfare functions defined with a nominal price function P(t)
11 / 26
34. Genius of the Market Competitive equilibrium
Market Analysis
Second Welfare Theorem
Social Planner’s Problem
An efficient equilibrium is optimal for the social planner:
max K(G) = E e−γt
WS(t) + WD(t) dt
s.t. GS(t) = GD(t) for all t
Welfare functions defined with a nominal price function P(t)
Price is irrelevant when GS(t) = GD(t):
WS(t) = P(t)GS(t) − cS(GS(t))
WD(t) = wD(GD(t)) − P(t)GD(t)
11 / 26
35. Genius of the Market Competitive equilibrium
Market Analysis
Second Welfare Theorem
Second Welfare Theorem ⇐⇒ Lagrangian Decomposition
max K(G) = E e−γt
WS(t) + WD(t)
+ λ(t) GS(t) − GD(t) dt
12 / 26
36. Genius of the Market Competitive equilibrium
Market Analysis
Second Welfare Theorem
Second Welfare Theorem ⇐⇒ Lagrangian Decomposition
max K(G) = max
GS
E e−γt
WS(t) + λ(t)GS(t) dt
+ max
GD
E e−γt
WD(t) − λ(t)GD(t) dt
12 / 26
37. Genius of the Market Competitive equilibrium
Market Analysis
Second Welfare Theorem
Second Welfare Theorem ⇐⇒ Lagrangian Decomposition
max K(G) = max
GS
E e−γt
WS(t) + λ(t)GS(t) dt
+ max
GD
E e−γt
WD(t) − λ(t)GD(t) dt
Assume: Social planner’s problem has a solution,
and there is no duality gap.
12 / 26
38. Genius of the Market Competitive equilibrium
Market Analysis
Second Welfare Theorem
Second Welfare Theorem ⇐⇒ Lagrangian Decomposition
max K(G) = max
GS
E e−γt
WS(t) + λ(t)GS(t) dt
+ max
GD
E e−γt
WD(t) − λ(t)GD(t) dt
Assume: Social planner’s problem has a solution,
and there is no duality gap.
Then
P∗(t) = P(t) + λ∗(t) provides an efficient equilibrium.
Price is marginal value: P∗
(t) = wD(G∗
D(t))
12 / 26
39. Genius of the Market Competitive equilibrium
Market Analysis
Second Welfare Theorem
P∗(t) = P(t) + λ∗(t) provides an efficient equilibrium.
Price is marginal value: P∗
(t) = wD(G∗
D(t))
13 / 26
40. Genius of the Market Competitive equilibrium
Market Analysis
Second Welfare Theorem
P∗(t) = P(t) + λ∗(t) provides an efficient equilibrium.
Price is marginal value: P∗
(t) = wD(G∗
D(t))
Average price is average marginal cost:
E e−γt
P∗
(t) dt = E e−γt
cS(G∗
D(t)) dt
Economist Nirvana!
13 / 26
41. Genius of the Market Competitive equilibrium
Market Analysis
Second Welfare Theorem
P∗(t) = P(t) + λ∗(t) provides an efficient equilibrium.
Price is marginal value: P∗
(t) = wD(G∗
D(t))
Average price is average marginal cost:
E e−γt
P∗
(t) dt = E e−γt
cS(G∗
D(t)) dt
Economist Nirvana!
With transmission constraints, equilibrium prices are nodal: they
can be negative, or above marginal value [8, 9]
See bibliography: [8, 3, 2, 9]
13 / 26
42. Genius of the Market Competitive equilibrium
Real-world price dynamics
Marginal value? Obviously not marginal cost
Purchase Price $/MWh
Previous week
Spinning reserve prices PX prices $/MWh
100
150
0
50
200
250
10
20
30
40
50
60
70
Texas: February2,2011
California: July2000Illinois:July1998
Ontario: November,2005
0
1000
2000
3000
4000
5000
Mon Tues Weds Thurs Fri Mon Tues WedsWeds Thurs Fri Sat Sun
Tues Weds Thurs
Time3 6 9 12 15 18 213 6 9 12 15 18 213 6 9 12 15 18 21
Demand in MW Last Updated 11:00 AM Predispatch 1975.11 Dispatch 19683.5
Hourly Ontario Energy Price $/MWh Last Updated 11:00 AM Predispatch 72.79 Dispatch 90.82
2000
21000
18000
15000
1500
1000
500
0
ForecastPricesForecastDemand
5am 10am 3pm 8pm
−500
0
1000
2000
3000
$/MWh
Average price
is usually $30
$/MWh
14 / 26
43. Genius of the Market Rational agents?
But where are the rational agents?
An imperfect but reasonable RTM model:
WS(t) = P(t)GS(t) − cS(GS(t))
15 / 26
44. Genius of the Market Rational agents?
But where are the rational agents?
An imperfect but reasonable RTM model:
WS(t) = P(t)GS(t) − cS(GS(t))
What about this?
WD(t) = wD(GD(t)) − P(t)GD(t)
15 / 26
45. Genius of the Market Rational agents?
But where are the rational agents?
An imperfect but reasonable RTM model:
WS(t) = P(t)GS(t) − cS(GS(t))
What about this?
WD(t) = wD(GD(t)) − P(t)GD(t)
Irrational Agents
15 / 26
46. Genius of the Market Rational agents?
But where are the rational agents?
An imperfect but reasonable RTM model:
WS(t) = P(t)GS(t) − cS(GS(t))
What about this?
WD(t) = wD(GD(t)) − P(t)GD(t)
What is the “value of power” to consumers?
Irrational Agents
15 / 26
47. Genius of the Market Rational agents?
But where are the rational agents?
An imperfect but reasonable RTM model:
WS(t) = P(t)GS(t) − cS(GS(t))
What about this?
WD(t) = wD(GD(t)) − P(t)GD(t)
What is the “value of power” to consumers?
Irrational Agents
Power is NOT the commodity of interest!
15 / 26
48. Genius of the Market Rational agents?
What do the Consumers Want?
Rational agent wants a hot shower
http://www.onsetcomp.com/learning/application_stories/multi-channel-data-loggers-improve-forensic-analysis-complex-domestic-hot-water-complaints
Θ(t)
G(t)
Ambient
Temperature
Inlet Water
Temperature
3 kW
Water heater trajectories
Θ(t): Temperature
G(t): Power consumption
Irrational Agents
Power NOT commodity of interest!
16 / 26
50. Efficient Outcome Price signals
Control and Price Signals
TotalPower(GW)
0
5
10
15
20
hrs
P nominal
P desired
P delivered
1 2 3 4 5
Example: Aggregator has contracts with consumers
7 million residential ACs
700,000 water heaters
700,000 commercial water heaters
17 million refrigerators
All the pools in California
Promises strict bounds on QoS for each customer
17 / 26
51. Efficient Outcome Price signals
Control and Price Signals
TotalPower(GW)
Temperature,cycling,energy
0
5
10
15
20
hrs
P nominal
P desired
P delivered
1 2 3 4 5
hrs1 2 3 4 5
Example: Aggregator has contracts with consumers
Promises strict bounds on QoS for each customer
ACs
Small WHs
Commercial WHs
Refrigerators
Pools
QoS
17 / 26
52. Efficient Outcome Price signals
Control and Price Signals
TotalPower(GW)
0
5
10
15
20
hrs
P nominal
P desired
P delivered
1 2 3 4 5
Example: Aggregator has contracts with consumers
Balancing authority desires power reduction over 2 hours
Sends PRICE SIGNAL: 10% increase
Aggregator optimizes subject to QoS constraints
Promises strict bounds on QoS for each customer
17 / 26
53. Efficient Outcome Price signals
Control and Price Signals
TotalPower(GW)Power(GW)
0
5
10
15
20
hrs
0
2
4
6
P nominal
P desired
P delivered
ACs FWHs
SWHs Fridges
Pools
1 2 3 4 5
Price event: 10% increase
17 / 26
54. Efficient Outcome Price signals
Control and Price Signals
TotalPower(GW)Power(GW)
0
5
10
15
20
hrs
0
2
4
6
P nominal
P desired
P delivered
ACs FWHs
SWHs Fridges
Pools
1 2 3 4 5
Price event: 10% increase
Promises strict bounds on QoS for each customer
17 / 26
55. Efficient Outcome Price signals
Control and Price Signals
TotalPower(GW)Power(GW)
0
5
10
15
20
hrs
0
2
4
6
P nominal
P desired
P delivered
ACs FWHs
SWHs Fridges
Pools
1 2 3 4 5
Price event: 10% increase
No QoS promises to Balancing Authority!
17 / 26
56. Efficient Outcome Price signals
Problem with Price Signals
Automatic
Generation Control
Real-time
Market
Day Ahead
Market
Desired behavior
Desired behavior
Predictions
GRID
Millions of Residential
and commercial electric loads
Generators with their own
local control loops (DROOP)
Distributed generation,
possibly not grid-friendly
Dynamics of transmission
Brains
Brawn
Disturbance Voltage,
Frequency,
Phase
Conjecture: We could create a price signal P∗(t) that would induce the
behavior we want.
18 / 26
57. Efficient Outcome Price signals
Problem with Price Signals
Automatic
Generation Control
Real-time
Market
Day Ahead
Market
Desired behavior
Desired behavior
Predictions
GRID
Millions of Residential
and commercial electric loads
Generators with their own
local control loops (DROOP)
Distributed generation,
possibly not grid-friendly
Dynamics of transmission
Brains
Brawn
Disturbance Voltage,
Frequency,
Phase
Conjecture: We could create a price signal P∗(t) that would induce the
behavior we want.
The price is necessarily non-causal and device-dependent:
functional of the nonlinear dynamics of each collection of loads
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58. Efficient Outcome Price signals
Problem with Price Signals
Automatic
Generation Control
Real-time
Market
Day Ahead
Market
Desired behavior
Desired behavior
Predictions
GRID
Millions of Residential
and commercial electric loads
Generators with their own
local control loops (DROOP)
Distributed generation,
possibly not grid-friendly
Dynamics of transmission
Brains
Brawn
Disturbance Voltage,
Frequency,
Phase
Conjecture: We could create a price signal P∗(t) that would induce the
behavior we want.
The price is necessarily non-causal and device-dependent:
functional of the nonlinear dynamics of each collection of loads
This intuition can be justified based on
Lagrangian decomposition / solution to Euler-Lagrange equations.
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59. Efficient Outcome Distributed control
Efficient Outcome
Efficient Outcome 2018
Example: Aggregator has contract with consumers, and with BA.
Promises QoS constraints to all parties
Aggregator’s optimization problem: Demand Dispatch
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60. Efficient Outcome Distributed control
Efficient Outcome
Efficient Outcome 2018
Example: Aggregator has contract with consumers, and with BA.
Promises QoS constraints to all parties
Aggregator’s optimization problem: Demand Dispatch
Formulate as a convex program [11]
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66. Conclusions Summary and To-Do List for the Spring
Conclusions
Markets are awesome
Rational agent in Cambridge wants a hot shower
http://www.onsetcomp.com/learning/application_stories/multi-channel-data-loggers-improve-forensic-analysis-complex-domestic-hot-water-complaints
Θ(t)
G(t)
Ambient
Temperature
Inlet Water
Temperature
3 kW
Typical water heater trajectories
Θ(t): Temperature
G(t): Power consumption
Not-so rational agent: max
G
T
0
U(G(t)) − p(t)G(t) dt
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67. Conclusions Summary and To-Do List for the Spring
Conclusions
Markets are awesome
Rational agent in Cambridge wants a hot shower
http://www.onsetcomp.com/learning/application_stories/multi-channel-data-loggers-improve-forensic-analysis-complex-domestic-hot-water-complaints
Θ(t)
G(t)
Ambient
Temperature
Inlet Water
Temperature
3 kW
Typical water heater trajectories
Θ(t): Temperature
G(t): Power consumption
Not-so rational agent: max
G
T
0
U(G(t)) − p(t)G(t) dt
Markets are awesome
Control is cool
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68. Conclusions Summary and To-Do List for the Spring
Conclusions
Markets are awesome
Rational agent in Cambridge wants a hot shower
http://www.onsetcomp.com/learning/application_stories/multi-channel-data-loggers-improve-forensic-analysis-complex-domestic-hot-water-complaints
Θ(t)
G(t)
Ambient
Temperature
Inlet Water
Temperature
3 kW
Typical water heater trajectories
Θ(t): Temperature
G(t): Power consumption
Not-so rational agent: max
G
T
0
U(G(t)) − p(t)G(t) dt
Markets are awesome
Control is cool
Real time prices are irrational
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69. Conclusions Summary and To-Do List for the Spring
Conclusions
Questions to answer this semester
History
How did we get here?
Why are spot prices seen as the control solution?
Can someone find an economic justification?
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70. Conclusions Summary and To-Do List for the Spring
Conclusions
Questions to answer this semester
History
How did we get here?
Why are spot prices seen as the control solution?
Can someone find an economic justification?
Can we validate the claims that PJM FP&L?
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71. Conclusions Summary and To-Do List for the Spring
Conclusions
Questions to answer this semester
History
How did we get here?
Why are spot prices seen as the control solution?
Can someone find an economic justification?
Can we validate the claims that PJM FP&L?
Market design: Let’s create a theoretical foundation for zero marginal
cost resources such as batteries, wind, and Demand Dispatch
A working solution requires a CEO model, combined with stable public
policy to enable long-term planning
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72. Conclusions Summary and To-Do List for the Spring
Conclusions
Questions to answer this semester
History
How did we get here?
Why are spot prices seen as the control solution?
Can someone find an economic justification?
Can we validate the claims that PJM FP&L?
Market design: Let’s create a theoretical foundation for zero marginal
cost resources such as batteries, wind, and Demand Dispatch
A working solution requires a CEO model, combined with stable public
policy to enable long-term planning
Control architectures
If our goal is smoothing net-load and congestion control, what is
essentially different between bits vs. watts?
Our work and research@Vermont suggests the gap isn’t always wide
What questions arise when we look seriously at distribution along with
transmission?
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75. References
Control Techniques
FOR
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
References
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76. References
Selected References I
[1] M. Chen, I.-K. Cho, and S. Meyn. Reliability by design in a distributed power transmission
network. Automatica, 42:1267–1281, August 2006. (invited).
[2] I. K. Cho and S. Meyn. Dynamics of ancillary service prices in power distribution systems.
In Proc. of the 42nd IEEE CDC, volume 3, 2003.
[3] I.-K. Cho and S. P. Meyn. Efficiency and marginal cost pricing in dynamic competitive
markets with friction. Theoretical Economics, 5(2), 2010.
[4] S. Robinson. Math model explains volatile prices in power markets. SIAM News, Oct.
2005.
[5] R. Moye and S. Meyn. Redesign of U.S. electricity capacity markets. In IMA volume on
the control of energy markets and grids. Springer, 2018.
[6] R. Moye and S. Meyn. The use of marginal energy costs in the design of U.S. capacity
markets. In Proc. 51st Annual Hawaii International Conference on System Sciences
(HICSS), 2018.
[7] R. Moye and S. Meyn. Scarcity pricing in U.S. wholesale electricity markets. In Proc. 52nd
Annual Hawaii International Conference on System Sciences (HICSS) (submitted), 2018.
[8] M. Negrete-Pincetic. Intelligence by design in an entropic power grid. PhD thesis, UIUC,
Urbana, IL, 2012.
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77. References
Selected References II
[9] G. Wang, M. Negrete-Pincetic, A. Kowli, E. Shafieepoorfard, S. Meyn, and U. V.
Shanbhag. Dynamic competitive equilibria in electricity markets. In A. Chakrabortty and
M. Illic, editors, Control and Optimization Methods for Electric Smart Grids, pages
35–62. Springer, 2012.
[10] R. A¨ıd, D. Possama¨ı, and N. Touzi. Electricity demand response and optimal contract
theory. SIAM News, 2017.
[11] N. Cammardella, J. Mathias, M. Kiener, A. Buˇsi´c, and S. Meyn. Balancing California’s
grid without batteries. IEEE Conf. on Decision and Control (submitted), Dec 2018.
[12] Y. Chen, U. Hashmi, J. Mathias, A. Buˇsi´c, and S. Meyn. Distributed Control Design for
Balancing the Grid Using Flexible Loads. In IMA volume on the control of energy markets
and grids Springer, 2018.
[13] J. Mathias, A. Buˇsi´c, and S. Meyn. Demand dispatch with heterogeneous intelligent
loads. In Proc. 50th Annual Hawaii International Conference on System Sciences, 2017.
[14] S. Meyn, P. Barooah, A. Buˇsi´c, Y. Chen, and J. Ehren. Ancillary service to the grid using
intelligent deferrable loads. IEEE Trans. Automat. Control, 60(11):2847–2862, Nov 2015.
[15] Coase, R.H. The marginal cost controversy. Econometrica 13(51), 169–182 (1946)
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