Making the Most out of Energy Technologies
at Value
Prof. Marija Ilić
milic@ece.cmu.edu
Electric Energy Systems Group (EESG)
http://www.eesg.ece.cmu.edu/, Director
SRC Smart Grid Research Center (SGRC)
http://www.src.org/program/eri/, Director
New Electricity Transmission Software Solutions (NETSS), Inc.
Founder
IEE-EPC Talk November 8 2012
Outline
Designing and operating low-cost green electric energy
systems –possible problem statement (Smart Grid and
Ostrom sustainable SES)
Making the most out of energy technologies at value [8]
-industry operations and planning: the way it was
(underlying assumptions and major hidden inefficiencies)
- estimates of possible efficiency enhancements (short- and
long-term)
-possible changes in operations and planning to enhance
efficiency (interactive planning; IT-enabled corrective
resource management; the need for next generation
software)
2
Possible problem statement - Coarse modeling of SocioEcological Systems (using SES interaction variables) [6]
“Smart Grid”  electric power grid
and IT for sustainable energy SES [2,5]
Energy SES
• Resource
system (RS)
• Generation
(RUs)
• Electric Energy
Users (Us)
Man-made Grid
• Physical network
connecting
energy
generation and
consumers
• Needed to
implement
interactions
Man-made ICT
•
•
•
•
Sensors
Communications
Operations
Decisions and
control
• Protection
Future Electric Energy Systems [1]
Energy Sources
Transmission Network
Electromechanical
Devices
(Generators)
Photo-voltaic
Device
Energy Sources
Load
(Converts
Electricity into
different forms of
work)
Electromechanical
Device
PHEVs
Demand
Respons
e
An illustrative future electric grid [2]
Transformational change in metrics of future energy systems [2]
Today’s Power Grid
(centralized objective subject to
many constraints (externalities)
``Smart Grid”
(multi-layered interactive
coordination of objectives)
Deliver supply to meet given demand
Deliver power to support supply and demand
schedules in which both supply and demand
have costs assigned
Deliver power assuming a predefined tariff
Deliver electricity at QoS determined by the
customers willingness to pay
Deliver power subject to predefined CO2
constraint
Deliver power defined by users’ willingness
to pay for CO2
Deliver supply and demand subject to
transmission congestion
Schedule supply, demand and transmission
capacity (supply, demand and transmission
costs assigned); transmission at value
Use storage to balance fast varying supply
and demand
Build storage according to customers
willingness to pay for being connected to a
stable grid
Build new transmission lines for forecast
demand
Build new transmission lines to serve
customers according to their ex ante (longerterm) contracts for service
Future: Multi-layered smart balancing authorities [3]
DYMONDS-enabled Physical Grid [2,5]
Making the most out of given
technologies-New technical problem
 At present the physical energy system, including its
communications and control, does not readily enable
choice and multi-participant information exchange and
processing for aligning often conflicting goals.
 It is essential to design intelligence for T&D operations
to align these goals and consequently to make the
most out of available resources while simultaneously
offering robust and affordable quality of service.
 New flexible energy processing equipment will also be
needed to handle increased variety and bandwidth of
many participant requests.
Building a stronger and smarter electric
energy infrastructure
IT-enabled methods for radical enhancements in
reliability and efficiency
Possible new operating framework in
future electric energy systems (Dynamic Monitoring
and Decision Systems (DYMONDS) [2,5]
Proof-of-concept using Azores Islands electric
power systems [4]
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Difficult Questions:
Systematic IT Design for Complex Energy Systems
 Establish sufficiently accurate (but not too complex)
modeling framework which captures interdependencies of energy Socio-Ecological Systems
(SES), physical grid, ICT and governance system
 The key objective: Match attributes of energy SES,
physical grid, ICT and governance system by designing
around a given energy SES
 Interaction variables: A means of going from very
coarse to granular and back
 IT design to manage interaction variables (temporal,
spatial and contextual)
 Interaction variables-based unifying framework for
relating engineering design, financial and
environmental objectives
The challenge of multi-temporal and multi-spatial
integration [2]
Today’s practice--- all information given to the
control center; very complex decision making
given many constraints
Future paradigm—distributed decision
making to internalize many externalities
(diverse inter-temporal dependencies,
uncertainties, congestion management)
Therefore… DYMONDS
13
Major challenges and opportunities [5]
Making ’’the most” out of available technologies
Under-estimated role of IT
Performance metrics for the changing industry
Managing resources at value:
- the key role of corrective actions (vs. preventive (N-1)
‘’reliability”);
-the key role of automation (distributed powerelectronically switched ‘’smart” devices)
Design of model-based quantifiable compliance
standards; must be easy to implement).
14
Efficiency metrics for systematic use by all? [5]
 Multiple values brought by different technologies
 Physical efficiency of stand-alone components (generation,
demand)
 Physical efficiency due to loss delivery savings
 Physical efficiency due to reduced reserve requirements (reliability
reserves-1) relaxed steady state limits; 2) relaxed stability limits.
 Physical efficiency due to temporal shifts during normal operations
(peak load shaving)
 Economic efficiency in electricity markets (spot and capacity)
 These are not additive. System-dependent.
 Tradeoffs defined by the system users, not by the hard constraints.
 Need systematic framework for modeling, and IT for implementing.
15
The key role of corrective actions [6,7]
 reduction of spilled wind power (380GWh spilled
in ERCOT in 2002);
 economic efficiency increase measured in terms of
annual generation cost savings (25% , estimated by
FERC; $600M-$900M NY only);
 increased power transfer into large load centers
(300MW to 1GWW into NYC; 1 GW into SW CT.
 increased power transfers across key transmission
corridors.
Much higher equipment utilization; should study
16
the effect on reducing the estimated $200B
The key role of PE-enabled automation:
Power system with flywheel control [8]
17
The key role of ‘’smart” automation [8]
18
Potential stabilization by means of fast storage
–nonlinear control [8]
19
Challenges to IT
Software for complex bids (distributed optimization
under uncertainties)
Software for corrective actions during non-time
critical contingencies
Software for ensuring dynamic stability and
observability of the system
Primary multi-modal nonlinear control design for fast
automation, with minimal coordination
Integration of these functionalities in a single
platform
Proof-of-concept for small islands [6]; need to
20
pursue for continental power grids
Three qualitatively different paradigms for
standardization of dynamics in future smart grids [15,16]
 Plug-and-play standards for dynamics, with no
requirements for on-line communications. Much
stricter standards at the component level will be
needed for this to work.
 System-level technical standards based on
minimal coordination of interactions of
decentralized component-level standards.
Interactive protocols for ensuring technical
performance according to choice and at value—
dynamic monitoring and decision systems
(DYMONDS) [3].
Major differences
 Plug-and-play standards for dynamics –enhanced
decentralized control for internalizing effects of interactions
and canceling them. Lots of advanced local control.
 Standards based on minimal coordinated control of
interaction variables for given nested architecture of future
electric energy systems. Technical specifications at the
decentralized level, economic and technical specifications at
the system level. Minimal exchange of technical signals.
 Interactive protocols in terms of interaction variables
evolving dynamically over time and space according to
system users’ preferences. Both economic and technical
specifications at all levels. Minimal exchange of technical
and economic signals. (markets)
IT-enabled markets to harvest hidden efficiencies [18-20]
Key role of aggregators to account for the effects of DERs
in distributions systems on whole-sale markets Adaptive Load Management (ALM)
Long-term contract
Load serving entity
LSE
LSE
End-user
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Potential Use of Real-Time Measurements for
Data-Driven Control and Decision-Making (new)
 GPS synchronized measurements
(synchrophasors ; power measurements at
the customer side.
The key role of off-line and on-line
computing. Too complex to manage relevant
interactions using models and software
currently used for planning and operations.
Our proposed design: Dynamic Monitoring
and Decision Systems (DYMONDS)
24
Proof-of-Concept for Low-Cost Green Flores
and Sao Miguel [4]
 Collected data and used to derive dynamic models (linear and
non-linear; with wind power dynamics, flywheels and powerelectronics-control included)
-equilibrium solutions (power flow); predictive models for
wind power and demand power
-demonstrate the use of DYMONDS decision-making
algorithms (distributed, MPC-based) for enabling efficient
integration of wind power; efficient integration of Adaptive
Load Management (ALM); efficient integration of electric
vehicles (EVs)
- demonstrate new methods for automated load following, EAGC and E-AVC for balancing hard-to-predict small wind
power fluctuations
25
AZORES ISLAND: ELECTRICAL CHARACTERISTICS[3], [4, CH. 3]
FLORES ISLAND
Radial 15 kV distribution network
Total demand : ~2 MW
Diesel generator with total capacity: 2.5 MW
Hydro power generator with total capacity: 1.3
MW (reservoir)
Wind turbine with total capacity: 0.6 MW
SAO MIGUEL ISLAND
Ring 60 kV and 30 kV distribution network
Total demand - ~70 MW
Two large diesel generators with total
capacity: 97 MW
Two large geothermal plants with total
capacity: 27 MW
7 small hydro power generator with total
capacity: 5 MW
M. Honarvar Nazari and M. Ilić, “Electrical Networks of Azores Archipelago”, Chapter 3, Engineering
IT-Enabled Electricity Services, Springer 2012.
Overall educational challenge and opportunity [8]
Important to educate ourselves how more efficient
services can be provided w/o creating operating
problems
The opportunity is presented itself as IT is deployed
The challenge: How to manage the infrastructure
instead of simply deploying more hardware
Need for new software for interactive planning,
corrective on-line resource management and
automation; non-transmission solutions
States should take the lead in educating themselves
and the industry (NARUC); design regulation to
27
support this [10]
Educational Challenge to the US Universities [9]
 An important educational challenge: How to pose the problem,
and how to design sensing, communications, automated control
and decision-making computer algorithms using well-understood
concepts from basic disciplines?
 The boundaries between electric energy processing and other
types of energy processing (mechanical to electrical in generators;
chemical/wind/hydro, diesel, nuclear into mechanical and/or
electrical) becoming more gray than in the past as new energy
resources are used
 One possible unifying path– model the electric energy systems as
dynamical systems and use systematic control design to pose the
design objectives, and data-driven feedback and decision making
for adaptation (18-618, Spring 2012, Carnegie Mellon University
[9])
28
Conclusions and Next Steps
IT has key role to play in operating future electric energy
systems with renewable resources.
Systematic modeling of electric energy systems as complex
dynamic systems needed.
Not possible to manage all as a single problem.
The challenge is what IT to embed into hardware, what
information needs to be exchanged and why.
 Possible to be both reliable and efficient green with
carefully designed IT. Many examples of this not being
possible with today’s IT in electric power systems.
Need to communicate to NARUC/US Congress
Demonstrate using simulations for continental systems 29
(``Smart Grid in a Room”?)
References
[1] Ilic, M., Smart Grid and Future Electric Energy Systems, Lecture Notes, 18-618, Carnegie
Mellon Univ, ECE, Spring 2012.
[2] ] Ilic, M, et al, A Decision Making Framework and Simulator for Sustainable Electric Energy
Systems, The IEEE Trans. On Sustainable Energy, TSTE-00011-2010, January 2011.
[3] ] Ilic, M., Dynamic Monitoring and Decision Systems for Sustainable Electric Energy, Proc of
the IEEE, Jan 2011.
[4] Engineering IT-Enabled Sustainable Electricity Services: The Case of Low-Cost Azores Islands
(co-editors, M. Ilic and Le Xie), Springer Monograph (2012, to appear)
[5] Ilic, M., Dynamic Monitoring and Decision Systems for Sustainable Electric Energy Systems,
Proc. of the IEEE, January 2011.
[6] Elinor Ostrom, et al, A General Framework for Analyzing Sustainability of social-Ecological
Systems, Science 325, 419 (2009).
[7] MIT Portugal, Universidade dos Açores, ”Characterization of the Azorean Residential Building
Stock”, Report, 2010.
[8] Ilic, M. , ``Making the Most out of Energy Technologies at Value”, Ilic, White paper, April 17,
2012; under revision with FERC comments, November 2012.
[9] Ilic, M., ``Critical Needs for Multi-Disciplinary Approach to Teaching Electric Energy Systems”,
IEEE Transactions on Education (Special Issue, invited), November 2012.
[10] Ilic, Marija, “3Rs for Power and Demand”, Public Utilities Fortnightly Magazine, December
2009.
30
References
[11 ] Ilic, Marija. “From Hierarchical to Open Access Electric Power Systems.” IEEE Special Issue on “Modeling,
Identification, and Control of Large-Scale Dynamical Systems,” Simon Haykin and Eric Mouines, Guest Editors. Vol.
95, No. 5, May 2007.
[12] M. Ilic. Toward reliable and efficient on-line resource management: A ramp rate-limited ac optimal power flow for
integrating renewable resources and responsive demand. FERC Staff Technical Conference on Increasing Real-Time
and Day-Ahead Market Efficiency through Improved Software, Docket No. AD10-12-003, Washington DC, June 2527, 2012.
[13] M. Ilic. Voltage dispatch and pricing in support of efficient real power dispatch. Final Report, NYSERDA Funded
NETSS Project #10476, November 20, 2011.
[14] M.Ilic, ‘’Toward Standards for Dynamics in Future Electric Energy Systems”, PSERC White Paper, June 2012.
[15] M. Ilic,’’ Toward IT-enabled power systems: Large-scale distributed control for tomorrow's electricity grid”,Semiplenary talk, American Control Conference, Montreal, CA, June 2012.
[16] Ilic, M., Liu, Q., ``Toward standards for dynamics in future electric energy systems”, APSIPA, Dec. 2012.
[17] ] Computing
Research for Sustainability, NRC of the National Academies of Science,
http://www.nap.edu/catalog.php?record_id=13415 , 2012.
[18] J.-Y. Joo and M.D. Ilić, A multi-layered adaptive load management system: information
exchange between market participants for efficient and reliable energy use, IEEE PES
Transmission and Distribution Conference, Apr 2010
[19] J.-Y. Joo and M. Ilić, “Distributed Multi-Temporal Risk Management Approach To
Designing Dynamic Pricing”, IEEE Power and Energy Society General Meeting, July 2012
[20] J.-Y. Joo and M. Ilić, Multi-Temporal Risk Minimization Of Adaptive Load Management In
Electricity Spot Markets, IEEE PES Innovative Smart Grid Technologies, Europe, Dec 2011
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