Toward’a’Unified’Approach’to’Sustainable’and’ … · • Communications •...
Transcript of 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.
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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..
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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on in power systems hard and slow v Outdated assump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
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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
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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
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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
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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.
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More Complex Power System
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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
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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
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Fully distributed small-‐scale systems
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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
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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
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Vast temporal and spaHal scales-‐engineering view
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Vast temporal and spaHal inter-‐dependencies (deeper-‐level)
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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
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Coarse modeling of Socio-‐Ecological Systems (using SES interacHon variables) [16]
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“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
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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) []
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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
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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
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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
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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
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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
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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)?
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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.
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Key notion of interaction variable dynamics and their control
• Interactions variables of area-1 and area-2
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• Controlled IntV v.s. uncontrolled IntV
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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:
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“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
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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
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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.
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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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