Beyond Smart Distribution Systems: Beyond
Smart Distribution Systems:
Technical and Regulatory Opportunities and Challenges in Making Electricity Service
Challenges in Making Electricity Service
End‐to‐End Sustainable
© Marija Ilić
Electric Energy Systems Group http://www.eesg.ece.cmu.edu
Electrical and Computer Engineering
Carnegie Mellon University
6th CMU Annual Electricity Conference
1
Acknowledgment
Electric Energy Systems Group (EESG)
Electric Energy Systems Group (EESG) http://www.eesg.ece.cmu.edu
™ A multi‐disciplinary group of researchers from across Carnegie Mellon with common interest in electric energy.
™ Truly integrated education and research (> 20 PhD students)
™ Interests range across technical, policy, sensing, communications, computing and much more; ,
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;
emphasis on systems aspects of the changing industry, model‐based simulations and decision g/
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making/control for predictable performance.
End‐to‐End
End
to End Sustainable Services
Sustainable Services
™ Making “the most” out of what is available by effective technical implementations and according to the well‐defined regulatory rules which
‐align
align cost and value at the system level
cost and value at the system level
‐align sub‐objectives of stakeholders with the system‐wide objectives
system‐wide objectives
‐coordinate often conflicting attributes underlying sustainability
nderl ing s stainabilit
3
Multiple attributes of sustainability (
(context)
)
™ability for supply and demand to match during normal conditions (viability);
normal conditions (viability);
™ ability for supply and demand to match during abnormal conditions (reliability);
(
y);
™ short‐ and long‐term efficient energy utilization (efficiency);
™ low pollution (environmental sustainability); and,
™ impacts on technology providers and impacts on technology providers and
consumers (business sustainability and well being).
Some important observations (1)
p
( )
™ Sustainability is a long‐term performance metrics that can be achieved by interactive
metrics that can be achieved by interactive short‐ and long‐term decisions
™This req ires operations planning approach
™This requires operations planning approach under uncertainties (physical, financial and environmental)
™THIS REQUIRES ON‐LINE PREDICTIONS AND ADAPTATION
™Many other industries are moving in this direction (sustainable services)
5
Some important observations (2)
p
( )
™Technical challenge—rethink current operating and planning industry practices and assumptions in light of the sustainability challenge
™Regulatory challenge—rethink current g
y
g
y
regulatory rules in light of the sustainability challenge
™The answers are long and complex; not
™The answers are long and complex; not business as usual
™MUST PROCEED IN PHASES WITHOUT
™MUST PROCEED IN PHASES WITHOUT SHUTTING DOORS TO EVOLUTION
6
Today’s power grid
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Today’ss Industry Practice
Today
Industry Practice
™ T not coordinated with D. Knowledge of D power factors particularly poor.
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™ No on‐line voltage adaptation to manage load efficiently.
™ D systems are not managed on‐line. ™ As a result, large reserve needed in case something goes wrong. ™ Planning—deterministic for the peak hour.
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™ No innovative technologies considered, instead only planning based on proven technologies. What is Changing?
What is Changing?
™ Much more action at the D level due to:
(1) responsive demand; (2) variable distributed resources (DRs); (3) new security and environmental constraints. constraints.
™ Much harder to predict supply‐demand (SD) imbalance accurately by the control centers without
imbalance accurately by the control centers without self‐commitment by the DRs and LSEs (both short‐
term and long‐term).
™ Correlating diverse loads and DRs much harder than in the past (T level cannot assume D‐loads known.)
Key Smart Grid Functionalities
Key Smart Grid Functionalities ™ Just‐in‐Time (JIT) ‐‐predictions; dynamic look‐ahead decision making
( )
,
,
™ Just‐in‐Place (JIP) ‐‐distributed, interactive, multi‐
layered
( )
performance objectives p
j
™ Just‐in‐Context (JIC) ‐‐‐‐
function of organizational rules, rights, and responsibilities (3Rs) and system conditions.
™ Sample examples of improved performance—on‐
going work in EESG http://www.eesg.ece.cmu.edu
Planning Needs for the Changing Industry
d
™ The uncertainties are unacceptably high for the system operators to take the risk. ™ Instead distributed long‐term energy/capacity bids by th LSE
the LSEs and power plants, and the T&D providers. d
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d th T&D
id
™ LSEs offer what they need and WTP; suppliers offer what they need to build and provide in the future and at which
they need to build and provide in the future and at which price. ™ System operators clear these long‐term bids. System operators clear these long‐term bids
™ Distributed long‐term risks over all participants and at value. value.
Need for Smart Regulation—The New Problem ™Need to revisit the performance metrics in the changing industry (cost vs. benefits; cost allocation vs value based services)
allocation vs. value‐based services)
™The cost of managing uncertainties –very different depending on the context
different depending on the context
™The value of high technologies (DYMONDS) –
very different depending on the context ™Heterogeneous performance metrics ( reliability, short term‐, long term‐efficiency; environmental impacts; cyber security)
environmental impacts; cyber security)
™Who takes the risks for what and at which price?
Smart Grid Initiatives in the US
Smart Grid Initiatives in the US
™EPA 2007, Article 13 requires utilities to ,
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demonstrate significant reduced demand by 2015
™Many technological advances (renewable DERs
™Many technological advances (renewable DERs, AMIs, PMUs, NIST Inter‐operability platform designs)
™Uncertain environmental regulation; subsidies for clean power; difficult to integrate new resources in the existing system
resources in the existing system
™Stimulus funding for demonstrating smart grids
Need for 3Rs in support of Smart Grids
d
™ Uncertainties huge ‐‐‐need for distributed dynamic g
y
risk management over time, geography and industry participants
™ Value of prediction; forward contracts/commitments V l
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and their valuation
™ Need to differentiate value of technologies w.r.t. Need to differentiate value of technologies w.r.t.
their rate of response and availability, and time horizon over which they are committed (value of long term contracts);
long‐term contracts); ™ Incentives to build just enough storage; Adaptive Load Management (self‐regulation);
Load Management (self
regulation); sustainable LSEs
sustainable LSEs
Unbundling and Smart Grids
Unbundling and Smart Grids
™ Unlikely to have a sustainable penetration of smart grid technologies (despite targeted pilot deployments) at value without designing rules, rights and responsibilities (3Rs) to support it g
p
( )
pp
™ Asymmetric risk not managed at value
™ No incentives to implement both differentiated reliability of service and enhanced short and long‐
reliability of service and enhanced short‐
and long
term efficiency
™ No regulatory mechanisms for sustainable i
investment in technologies that are key to reconciling i
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reliability efficiency and impacts on the environment (JIT, JIP and JIC technologies)
Approach taken by the EESG at CMU
Approach taken by the EESG at CMU
™ Theoretical work in support of a simulator (and an emulator) of future sustainable energy systems
™ Demonstrate pros and cons of various policies on performance
™ Demonstrate the role and value of facilitating the integration of existing and new technologies
™ Impact transformation of SCADA into next generation I
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ti
f SCADA i t
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SCADA
™ Work with industry and government to introduce and y
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disseminate sustainable technical and policy solutions Future Smart Grid (Physical system)
Key results
™Not possible to come up with sustainable p
top‐down solutions for assumed characteristics of distributed (groups of) users connected to today’s electric power y
p
grid
™Not possible to design for the worst case
™Not possible to design for the worst case scenario
™Instead IT enabled implementation of 3Rs
™Instead, IT‐enabled implementation of 3Rs ™Technical solutions a means of implementing 3R th
3Rs; the role of IT fundamental
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Key open research questions
™What information should be exchanged ™Wh
ti f
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h ld b
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d
between different (groups of) power system users to induce sustainable services?
t i d
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i ?
™This is where
‐T&D operators and planners MUST interact on‐
line
‐groups of users within D must interact with the D operator
‐groups of D’s within T must interact with the T operator
‐T operators must interact within the region (pool, T
i
i hi h
i (
l
19
ISO) …etc
Toward SBAs, SCAs for End‐to‐End Stabilization, Economic Dispatch and Regulation –
Economic Dispatch and Regulation –
INTERACTION VARIABLES KEY 20
Future Energy Systems: Regulation and Stabilization
d
bl
™Issue 1: Who is responsible for balancing the ssue :
o s espo s b e o ba a c g t e
interconnection?
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y
™Issue 2: Modeling of inter‐area dynamics and instabilities for efficient controls
™Issue 3: Uneconomical regulation requirement for control areas as per today’s reliability criteria
™I
™Issue 4: Model dependent on measurements 4 M d ld
d t
t
through sensing devices (PMUs)
21
Inter‐area
Inter
area Dynamics
Dynamics—An
An Example
Example
Generator Real Power Output in 5 Bus System
3.5
Pg1
3
Pg2
Pg3
2.5
Pg (p.u.)
P
2
1.5
1
™Work in Progress: How to use PMUs to ™Work
in Progress: How to use PMUs to
0.5
stabilize the inter‐area variables
0
-0.5
-1
0
10
20
30
40
50
60
Time (sec)
70
80
90
100
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Fast Measurements for Frequency, Flow and V l
Voltage Regulation
R
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P
P
P
23
P
Fast Measurements for Frequency, Flow and V l
Voltage Regulation
R
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™System Demand Curve [New York]
Every 10 min Real Time Load of NYISO in Jan 23, 2010
220
Forecasted Load
Actual Load with Disturbance
Upper Bound
Lower Bound
200
160
10 min Real Time Load of NYISO in Jan 23, 2010
140
210
Real Power Load P(p.u.)
Real Power Load P
P(p.u.)
180
120
100
200
190
180
170
160
0
0
5
Upper Bound
10
Lower Bound
2
15
Time (hours)
4
6
Time (min)
8
10
20
24
25
Fast Measurements for Frequency, Flow and V l
Voltage Regulation
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™Robust AVC Illustration in NPCC System [GM, NPCC]
™Robust AVC Illustration in NPCC System [GM, NPCC]
™ Limited System Observability
™ Pilot Point: Bus 76663 il
i
6663
Automatic Voltage Control for ONE Pilot Point Control Case
System Worst V
Voltage Deviation
n (p.u.)
0.25
No Control
One Pilot Point Control
5% Reliability
y Criteria
02
0.2
0.15
0.1
0.05
0
0
20
40
60
Time (sec)
80
100
25
Fast Measurements for Frequency, Flow and V l
Voltage Regulation
R
l i
™Robust AVC Illustration in NPCC System
™Robust AVC Illustration in NPCC System
™ Full System Observability
Automatic Voltage Control for Unlimited Information Control Case
System Worstt Voltage Deviatio
on (p.u.)
0.25
0.2
No Control
Full Information Control
5% Reliability Criteria
0.15
0.1
0
0.05
0
0
10
20
30
40
50
60
Time (sec)
70
80
90
100
26
Fast Measurements for Frequency, Flow and V l
Voltage Regulation
R
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™Robust AFC Illustration in NPCC System [7, 9]
™Robust AFC Illustration in NPCC System [7, 9]
™ Limited System Observability
™ Pilot Point: Bus 75403 il
i
03
Automatic Flow Control for ONE Pilot Point Control Case
System Worst Flow Deviation (p.u.)
0.7
0.6
No Control
One Pilot Point Control
5% Reliability Criteria
0.5
0.4
03
0.3
0.2
0.1
0
0
10
20
30
40
50
60
Time (sec)
70
80
90
100
27
Fast Measurements for Frequency, Flow and V l
Voltage Regulation
R
l i
™Robust AFC Illustration in NPCC System
™Robust AFC Illustration in NPCC System
™ Full System Observability
Automatic Flow Control for Unlimited Information Control Case
System Wo
orst Flow Deviattion (p.u.)
0.7
0.6
No Control
Full Information Control
5% Reliability Criteria
0.5
0.4
0.3
0.2
0.1
0
0
10
20
30
40
50
60
Time (sec)
70
80
90
100
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Economics of regulation and control—
i
importance of rate‐of‐response capabilities
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Economics of regulation and control—importance of rate‐of‐response capabilities
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™Case I: Uncoordinated Regulation
™Case I: Uncoordinated Regulation
Uneconomical Uneconomical
Solution!
Flywheel
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Economics of regulation and control—importance of rate‐of‐response capabilities
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™Case II: Coordinated Regulation
™Case II: Coordinated Regulation
Economical Economical
Solution!
Flywheel
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Revisiting Ancillary Services
Revisiting Ancillary Services
™System Load Curve [New York]
Every 10 min Real Time Load of NYISO in Jan 23, 2010
220
Forecasted Load
Actual Load with Disturbance
Upper Bound
Lower Bound
200
160
10 min Real Time Load of NYISO in Jan 23, 2010
140
210
Real Power Load P(p.u.)
Real Power Load P
P(p.u.)
180
120
100
200
190
180
170
160
0
0
5
Upper Bound
10
Lower Bound
2
15
Time (hours)
4
6
Time (min)
8
10
20
32
25
Revisiting Ancillary Services
Revisiting Ancillary Services
™Bidding for Regulation
™Bidding for Regulation
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Ancillary Services : Effect of Location on Regulation
™5 Bus System Example
™5 Bus System Example
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Ancillary Services : Effect of Wind Scheduling on Regulation
y
g
™5 Bus System : Wind as
Negative Load
Exhausting R
Regulation l i
Limits
™Work in Progress
ƒ EEconomics of Regulation and Control: Incentive i
f R l ti
dC t l I
ti
for Rate‐of‐Response
35
Wrong Control Design ‐‐Circulating Var flow— OLTC Case n>1
™ n=Vp/Vs
n<1
Ye/n
™ n=Vp/Vs
Ye/n
PL
(1‐n)Ye/n2
(n‐1)Ye/n
™ Capacitor generates reactive p
g
power, inductors consume reactive power
™ “Circulating” reactive flow created
t d
™ Consequence: Less power (active and reactive) can be transferred to the load
to the load
™ PLmax≈Ye/n
PL
(1‐n)Ye/n2
((n‐1)Ye/n
)
™ More
More efficient way
efficient way
™ No reactive power circulation
™ Consequence: More power can b
be transferred to the load
f
d
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™ PLmax≈Ye/n
36
Circulating real power flow—Conjecture based on small system examples: PAR Case ll
l PAR C
Injection into bus j
Injection into bus i
Sis
Sjs
™ Analogy with OLTC
™ Complex power circulation
C
l
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™ Because of circulation, less power can be given to the load
Sis
™
™
™
™
Sjs
Analogy with OLTC
More efficient way
More efficient way
No complex power circulation
More complex power can be transferred to the load
f
d
h l d
37
Critical: Transform SCADA
Critical: Transform SCADA
™ From single top‐down coordinating management to g
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the multi‐directional multi‐layered interactive IT exchange. ™ At CMU we call such transformed SCADA Dynamic Monitoring and Decision Systems (DYMONDS) and have formed a Center to work with industry and government on: (1) new models to define what is the type and rate of key IT exchange; (2) new decision
type and rate of key IT exchange; (2) new decision tools for self‐commitment and clearing such commitments. \http:www.eesg.ece.cmu.edu.
New SCADA
Smart users
DYMONDS‐enabled Physical Grid
T&D as an Enabler
T&D as an Enabler
™ New dispatch of self‐regulating resources together p
g
g
g
will make it possible to fit different pieces of the puzzle together. ™ Much more reliance on distributed sensing, actuation and coordinated management of these resources. Real time awareness of D flows. l
f fl
™ No models, no simulations, no decision tools. ,
,
Without these, it will be much more costly to proceed. R&D ahead of us.
Wind prediction, look‐ahead management using storage Compare the outcome of ED from both the centralized and distributed MPC approaches.
Coal Unit 2 (Expensive) Generation
Coal Unit 2 Generation: Zoomed In
150
150
Conventional Dispatch
Centralized Predictive Dispatch
Distributed Predictive Dispatch
Conventional Dispatch
Centralized Predictive Dispatch
Distributed Predictive Dispatch
MW
100
MW
100
50
0
0
50
50
100
150
200
250
Time Steps (10 minutes interval)
300
0
50
60
70
80
90
Time Steps (10 minutes interval)
BOTH EFFICIENCY AND RELIABILITY MET
100
Adaptive
Load Management (Joo, 2009)
Adaptive Load Management (Joo, 2009)
Tertiary level
Bid function
y(λ)
Market price
λ
Secondary level
Load aggregator I
Demand function
x(λI)
Load aggregator II
…
Load aggregator III
End‐user rate
λI
…
Primary level
End‐user
45
Optimal Control of Plug‐in‐Electric Vehicles: Fast vs. Smart (Rotering)
h l
(
)
46
Information flow—Fantastic Use of Multi‐layered Dynamic Programming
l l
d
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Integrating >50% Wind [1]
Integrating >50% Wind [1] Potential Savings with Self ‐
Committing Dispatch h
ALM not Direct Load Control
ALM not Direct Load Control
Closing remarks
Closing remarks
™ There exists now a highly unusual window of opportunity to i t d
introduce modern electric energy research and education d
l ti
h d d ti
programs.
™ Obvious societal needs
™ We will waste this opportunity without a full understanding of
ƒ the potential of embedded IT‐enabled intelligence in the new reso rces and
resources, and ƒ the role of multi‐layered multi‐directional coordination within the complex novel network architectures.
™ It is essential to pose the design and operation of new electric energy systems as the problem of multiple performance‐driven cyber‐physical systems over various contextual, temporal and spatial phenomena.
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