toward’a’unified’approach’to’sustainable’and’ … · • communications •...
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Toward a Unified Approach to Sustainable and Resilient Electric Energy Systems-‐-‐ Modeling, Control and Testbeds
Marija Ilic [email protected]; [email protected]
Carnegie Mellon University/M.I.T.-‐LL Invited dis>nguished lecture INECS-‐ID September 23,2016
On leave at MIT-‐LL; the presenta3on based on CMU work.
Cyber-‐physical electric energy systems (CPEES) *
2 *Ilic Marija, “Unified Modeling for Sustainable and Resilient Electric Energy Systems NOW, 2016 (to appear) Founda>ons and Trends in Electric Energy Systems..
Hindsight view
v Innova>on in power systems hard and slow v Outdated assump>ons in the new environment v No simulators to emulate >me evolu>on of complex event driven
states v Fundamental need for more user-‐friendly innova>on/technology
transfer v General simulators (architecture, data driven) vs. power systems
simula>ons (physics-‐based, specific phenomena separately) v Missing modeling for provable control design v Difficult to define performance objec>ves at different industry
layers; coordina>on of interac>ons between the layers for system-‐wide reliability and efficiency ; tradeoff between complexity and performance
v Challenge of managing mul>ple performance objec>ves 3
v EESG Ilic group h[p://www.eesg.ece.cmu.edu/ v Dynamic Monitoring and Decision Systems (DyMonDS)
framework for enabling smart SCADA; direct link with sustainability (enabler of clean, reliable and efficient integra>on of new resources); main role of interac>ve physics –based modeling for IT/cyber
v Coopera>ve effort with Na>onal Ins>tute of Standards (NIST) for building Smart Grid in a Room Simulator (SGRS)
v ***Recent new unifying modeling in support of DyMonDS*** v Early version of DyMonDS simulator (precedural; centralized) v Data on Azores Islands power grids by EdA; many early
concepts shown in the monograph under CMU-‐MIT-‐Portugal programs
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Acknowledgements
Outline v Technological and social drivers in the electric energy systems; basic landscape
v Socio-‐ecological systems (SES) view v Systems view of mul>-‐layered electric energy systems
v New SCADA for aligning diverse objec>ves (cyber) v Unified mul>-‐layered modeling v Mul>-‐layered control v Smart Grid in a Room Simulator (SGRS) testbed
Technological and social drivers in the electric energy systems v Mul>ple objec>ves (reliability/resiliency, efficiency and environmental)
v Porcolia of non-‐u>lity-‐owned resources v Renewable resources and demand response v Technology drivers: Cost-‐effec>ve IT; GPS synchronized wide-‐area measurement systems (WAMS)
v Emergence of electricity markets v Technologies for plug-‐and-‐play deployment
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An illustraHve future electric grid
ConvenHonal Power System
Electro-mechanical Devices (Generators)
Energy Sources
Load (Converts Electricity into different forms of work)
Transmission Line
The next four slides drawn by Andrew Hsu.
More Complex Power System
Future Power Systems
Electro-mechanical Devices (Generators)
Energy Sources
Load (Converts Electricity into different forms of work)
Transmission Network
Electro-mechanical
Device
Photo-voltaic Device
Energy Sources
Demand Respons
e PHEVs
PotenHal Use of Real-‐Time Measurements for Data-‐Driven Control and Decision-‐Making (new)
v GPS synchronized measurements (synchrophasors ; power measurements at the customer side.
v The key role of off-‐line and on-‐line compu>ng. Too complex to manage relevant interac>ons using models and sodware currently used for planning and opera>ons.
v Our proposed design: Dynamic Monitoring and Decision Systems (DYMONDS)
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Hybrid Open Access Electric Energy System
Fully distributed small-‐scale systems
Re-‐think modeling v Mathema>cal formula>ons of market objec>ves un-‐aligned with technical objec>ves of physical controllers
v Can one have a modular mul>-‐layered modeling which supports interac>ve informa>on exchange in terms of variables common to physical and market processes?
v Key to managing a stratum of opera>onally implementable market deriva>ves (energy, ancillary services)
-‐internalizing externali>es -‐synthe>c reserves -‐incen>ves across temporal/spa>al spectrum 14
Main claims v Not a radical concept, natural evolu>on from today’s engineering & market prac>ces
-‐view physical system as a dynamical system with lots of structure
-‐treat all components as dynamical components (resources, consumers, wires)
-‐pose the problem as a control design problem—decision makers define performance objec>ves, physical models define feasible trajectories
v Protocols for managing market and physical dynamics (DyMonDS)
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Basic Backbone SCADA-‐Today
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Simple protocols that may work? Dynamic Monitoring and Decision Systems (DyMonDS)—new SCADA
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Toward unified modeling…
v Establish sufficiently accurate (but not too complex) modeling framework which captures inter-‐dependencies of energy Socio-‐Ecological Systems (SES), physical grid, IT and governance system
v The key objec>ve: Match a[ributes of energy SES, physical grid, ICT and governance system by designing around a given energy SES
v Interac>on variables: A means of going from very coarse to granular and back
v IT design to manage interac>on variables (temporal, spa>al and contextual)
v Interac>on variables-‐based unifying framework for rela>ng engineering design, financial and environmental objec>ves
Vast temporal and spaHal scales-‐engineering view
Vast temporal and spaHal inter-‐dependencies (deeper-‐level)
InteracHon variables within a physical system
v Interac>on variables -‐-‐-‐ variables associated with sub-‐systems which can only be affected by interac>ons with the other sub-‐systems and not by the ac>ons taken at the sub-‐system level
v Dynamics of physical interac>on variables zero when the system is disconnected from other sub-‐systems
v Existence –consequence of general power conserva>on laws
Coarse modeling of Socio-‐Ecological Systems (using SES interacHon variables) [16]
“Smart Grid” !" electric power grid and IT for sustainable energy SES [14]
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 • Needed to align
interactions
IT Design for New Architectures v Measuring, communicaHng and controlling (physical) grid interacHon variables to shape the deeper-‐level interacHon variables of SES systems to induce sustainable performance
v The crea>on of “smart grids” is the applica>on of informa>on technology to the power system while coupling this with an understanding of the business and regulatory environment
v Cri>cal to the crea>on of “smart grids” is; § development of models of the power system § development of control sodware § incorpora>on of security, communica>ons, and safety systems
§ BEFORE hardware is deployed! § Our Main Approach-‐-‐Dynamic Monitoring and Decision Systems (DYMONDS) []
Is there a more general simple paradigm? General structure of electric energy systems
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-‐SBA: Smart Balancing Authori>es (Generaliza>on of Control Area) -‐IR: Inter-‐Region -‐R: Region -‐T: Ter>ary -‐D: Distribu>on -‐S: Smart Component
Ilic, M., “Dynamic Monitoring and Decision Systems for Enabling Sustainable Energy Services”, Network Engineering for Meeting the Energy and Environmental Dream, Scanning the Issue, Proc. of the IEEE,2011.
• Note: SBAs renamed to iBAs (suggestion by a PSERC member)
-general idea---rethink physical dynamics in terms of interaction variables
General structure in operating interconnected electric energy systems
• All about balancing power at the right temporal and spatial granularity
• But, the models used are not explicitly posed this way
• New modeling to capture this fact • Use to support interactive Dynamic Monitoring
and Decision Systems- DyMonDS • Much room for generalizing today’s hierarchical
control • Much room for making use of nonlinear control
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Interactive MPC-driven market dynamics • General result---there exists interaction dynamics as
sent and reflected power travels between the components (scattering, positive system formulations needed) [CMU provisional patent, Ilic]
• Yet, to shape this dynamics no internal details about the technology-specific processes are necessary!!! (MAJOR)
• Zoomed out interactive model/architecture in terms of z(t) only. Transparent market in terms of common physically meaningful variables.
• The only derivatives traded –incremental energy over time T (market clock) E(t), instantaneous power p(t) and rate of change of power dp(t)/dt.
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Required information exchange for distributed power dispatch—DyMonDS (Xie)
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Typical supply-demand –diverse technologies (result of distributed MPC)
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General observation: Prices --adjoint variables for DAM/RTM energy constraints
Missing prices—adjoint variables for LTM, power, rate of power change
DYMONDS Simulator IEEE RTS with Wind Power
• 20% / 50% penetration to the system [2]
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Conventional cost over 1 year *
Proposed cost over the year
Difference Relative Saving
$ 129.74 Million $ 119.62 Million $ 10.12 Million
7.8%
*: load data from New York Independent System Operator, available online at h[p://www.nyiso.com/public/market_data/load_data.jsp
0 50 100 150 200 250 3000
50
100
150Coal Unit 2 (Expensive) Generation
Time Steps (10 minutes interval)
MW
50 60 70 80 90 1000
50
100
150Coal Unit 2 Generation: Zoomed In
Time Steps (10 minutes interval)
MW
Conventional DispatchCentralized Predictive DispatchDistributed Predictive Dispatch
Conventional DispatchCentralized Predictive DispatchDistributed Predictive Dispatch
BOTH EFFICIENCY AND RELIABILITY MET
DYMONDS Simulator Impact of price-responsive demand
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• Elastic demand that responds to time-varying prices
kWh
$
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DYMONDS Simulator Impact of Electric vehicles
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• Interchange supply / demand mode by time-varying prices
Optimal Control of Plug-in-Electric Vehicles: Fast vs. Smart
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Unaligned TE and system dynamics
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Market command destabilizes wind generator
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General problem -----Not all adjoint variables exchanged! (missing prices)
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Flexible technologies for risk management —missing price for reliability/resiliency
Stochastic DP (Donadee)?
General CMU-NIST simulator
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General SGRS Module Structure
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Information Exchange Between Modules in SGRS
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Dynamics of interaction variables between the areas—Sao Miguel System
[2] M. Ilic “The Tale of Two Green Islands in the Azores Archipelago,” Chapter 2 of Engineering IT-Enabled Sustainable Electricity Services : The Tale of Two Low-Cost Green Azores Islands.
Key notion of interaction variable dynamics and their control
• Interactions variables of area-1 and area-2
• Controlled IntV v.s. uncontrolled IntV
A multi-layered frequency stabilization and regulation
• Control objective - Stabilization of the interconnected system - Eigenvalues of the closed-loop system negative real parts
• Multi-layered control approach - Component-level: distributed control with limited coordination - Subsystem-level: distributed control with limited coordination - Interconnected system-level: coordinated control
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Continuous real power load fluctuations around predictable load --continuously varying non-zero mean disturbances
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Time response of the interaction variables
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Time response of continuous frequency deviations
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Control efforts provided by the generators
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Example 3--Market for power electronics automation? (Cvetkovic)
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Interaction variable choice 1:
Interaction variable choice 2:
“Primary frequency reserve” (BAAL3)—how much? Non-linear control for transient stabilization
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Linear PI power controller[2]
Nonlinear Lyapunov controller[3]
No controller on TCSC
Fault: - a short circuit at Bus 3 - created at 𝑡=0.1𝑠 - cleared at 𝑡=0.43𝑠 Critical clearing time: 𝑇↓𝐶𝐶𝑇 =0.25𝑠
Islanded microgrid dynamics? (Rupamathi Jaddivada)
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Feasibility issues when islanded! Gen-set droops invalid for assessing instabilities in systems with gen-sets and PVs Potentially unstable very fast electro-magnetic phenomena) not typical of bulk electric systems Similar problem to off-shore islanded wind farms
References v [1] Private correspondence with Dale Osborn, MISO. v [2] Talaat, Nermeen and Marija D. Ilic. "ANNs Based on Subrac>ve Cluster Feature for Classifying
Power Quality Disturbances." 2008 North American Power Symposium (NAPS 2008), September 28-‐30, 2008. Calgary, Canada.
v [3] Allen, E.H., J.W. Chapman and M.D. Ilic, "Effects of Torsional Dynamics on Nonlinear Generator Control," IEEE Transac>ons on Control Systems Technology, 4, 125-‐140, March 1996.
v [4] IEEE SSR Task Force of the Dynamic System Performance WG: First benchmark model for computer simula>ons of SSR, IEEE Trans. 1977, pp. 1565-‐1572.
v [5] M.D. Ilic and J.W. Chapman, "Decentralized Excita>on Control for an Electrical Power U>lity System," U.S. patent number 5 483 147, 1996.
v [6] M.D. Ilic and S.X. Liu, "Direct Control of Inter-‐area Dynamics in Large Power Systems Using Flexible AC Transmission Systems (FACTS) Technology," U.S. patent 5 517 422, 1996.
v [7] Cvetkovic, Milos, and Marija Ilic, Nonlinear Control for Stabilizing Power Systems During Major Disturbances, IFAC World Congress, Milano, August 2011.
v [8] Cvetkovic, Milos, Bachovchin, Kevin and Marija Ilic, Transient Stabiliza>on in Systems With Wind Power Using Fast Power-‐Electronically-‐Switched Storage, Chapter 19 in Ilic, M., Xie, Le and Liu, Qixing (editors), Engineering IT-‐Enabled Sustainable Electricity Services : The Tale of Two Low-‐Cost Green Azores Island, Springer, 2012 (to appear) .
v [9] Q. Liu, M. Cvetkovic, and M. Ilic “Toward Stabilizing Linearized System Dynamics in Future Electric Energy Systems by Means of Enhanced Voltage Control,” Chapter 16, ibid.
v [10] Ilic, M and Liu, Qixing, Toward Sensing, Control and Communica>ons for Frequency Regula>on in Systems with Highly Variable Resources, in Control and Op>miza>on Methods for Smart Grids, Springer 2012, Chapter 1.
v [11] Popli, Nipun and Ilic, M, Chapter 14 in Engineering IT-‐Enabled Sustainable Electricity Services : The Tale of Two Low-‐Cost Green Azores Islands, Springer, 2012 (to appear).
v [12] Ilic, M., E. Allen, J. Chapman, C. King, J. Lang, and E. Litvinov. “Preven>ng Future Blackouts by Means of Enhanced Electric Power Systems Control: From Complexity to Order.” IEEE Proceedings, November 2005.
v [13] Ilic, Marija and Liu, Zhijian, ``A New Method for Selec>ng Best Loca>ons of PMUs for Robust Automa>c Voltage Control (AVC) and Automa>c Flow Control (AFC)”, IEEE PES 2010, Minneapolis, MN, July 25-‐29, 2010.
v [14] Ilic, M., Dynamic Monitoring and Decision Systems for Sustainable Electric Energy, Proc of the IEEE, Jan 2011.
v [15] Ilic, M., Smart Grid and Future Electric Energy Systems, Lecture Notes, 18-‐618, Carnegie Mellon Univ, ECE, Spring 2012.
v [16] ] Elinor Ostrom, et al, A General Framework for Analyzing Sustainability of social-‐Ecological Systems, Science 325, 419 (2009).
v [17] Ilic, M, Standards for Dynamics, PSERC White Posi>on Paper, June 22, 2012. v [18] Elizondo, Marcelo, Marija Ilic, and Pedro Marcado. “Determining the Cost of Dynamic
Control Capacity for Improving System Efficiency.” Proceedings of the IEEE General Power Mee>ng, Montreal CA, June 2006, paper # PESGM2006-‐000839.
v [19] Ilic, M., F. D. Galiana and L. Fink (eds.) Electric Power Systems Restructuring: Engineering and Economics, Kluwer Academic Publishers, 1998, Chapter 2.
v [20]Ilic, M., Lang., J., Litvinov, E., Luo, X., Tong, J., Fardanesh, B., Stefopoulos, G., Toward Coordinated-‐Voltage-‐Control-‐Enabled HV Smart Grids, ISGT Europe 2011, Manchester, Dec.2011.
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Reference [21] 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. [22]Lavei, J., Low, S.H., “Zero duality gap in op>mal power flow problem”, IEEE Trans Power
Systems, in press. [23] Zhang, B., Tse, D., “Geometry of feasible injec>on region of power networks”, Poroc of
Allerton, 2011. [24] Chiang, M., Low, S.H., Calderbank, A.R., Doyle, J.C., “Layering as op>miza>on decomposi>on:
A mathema>cal theory of network architectures”, Proc. IEEE, vol.95, Jan 2007. [25] Varaiya, P., Wu, F.F., Bialek, J.W., “Smart opera>on of smart grid: Risk-‐limi>ng dispatch”,
Proc. IEEE. Jan. 2011. [26] Jabr, R.A., “Radial distribu>on load flow using conic programming”, IEEE Trans. Power Syst.,
vol. 21, 2006. [27] Ilic, Marija. “From Hierarchical to Open Access Electric Power Systems.” IEEE Special Issue on
“Modeling, Iden>fica>on, and Control of Large-‐Scale Dynamical Systems,” Simon Haykin and Eric Mouines, Guest Editors. Vol. 95, No. 5, May 2007.
[28] Ilic, M., Xie, L, Jo, J-‐Y.,” Efficient Coordina>on of Wind Power and Price Responsive Demand: Parts I and II”, IEEE Trans. On Power Systems, Nov. 2011.
[29] Bo[erud, Audun, Krisitansen, Tarjei, Ilic, Marija, “The Rela>onship Between Spot and Future Prices in the Nord Pool Electricity Market”, Energy Economics Journal, h[p://dx.doi.org/10.1016/j.eneco.2009.11.009.
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