Marija Ilić

Transcription

1 The Role of Sensing, Estimation and Communications in the Emerging Electric Energy Systems Marija Ilić Electric Energy Systems Group (EESG) Director Invited keynote talk IEEESmartGridComm2014 Venice, Italy November 2014

2 Outline Basic vision for the emerging energy systems; new challenge Today s use of sensors, estimation and communications Lessons learned from recent pilot experiments Remaining major open problems/challenges: --Need for new operating paradigm --Bridge physics-based modeling and automation design to the architecture design of communication networks Toward new paradigms --Standards/protocols for operating the emerging power grids; and/or --Business models in support of innovative technologies at value Some open problems to support performance of new paradigms CMU on-going work: DyMonDS and simulations test beds

3 Basic Vision for Smart Grids: Massive Systems Integration Opportunity and Challenge [1,2] Multi-national scale integration of coordinated complex systems: - energy system (power grid, power electronics, energy resources) - communication system (hardware and protocols) - control system (algorithms and massive computation) - economic and policy system (regulation and stimulus) Why? On-line IT enables: - 20% increase in economic efficiency (FERC estimate) - cost-effective integration of renewables and reduction of emissions - differentiated Quality of Service without sacrificing basic reliability - seamless prevention of blackouts - expanding the infrastructure (generation, T&D, demand side) for maximum benefit and minimum intrusion Who? - Huge intellectual challenge must be university led - industrial partners include leading technologists in all four systems - government partners

4 Key R&D Challenge CPS Design of Future Electric Energy Systems New technical problem:at present the physical energy system, including its communications and control, does not readily enable choice and multiparticipant 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.

5 Key R&D Challenge CPS Design of Future Electric Energy Systems (2) New social science problem: The culture of managing vital services in a robust way by assigning responsibilities and expectations, in addition to technical and institutional design. New institutional problem: Integration can be achieved through different institutional design which will have qualitatively different impacts. Options include: -Fully regulated monopolies and centralized planning and operations. -Complete, carefully designed markets. -Common set of interface standards and protocols. -Common regulator level and/or lose cooperation of distributedregulators. The biggest challenge is to capture the interdependence of the technical, social and institutional problems so as to reconcile the need for choice, and affordable and robust electric energy services. 5

6 Basic cyber system today backbone SCADA

7 The Role of State Estimation Measurements Static: State Standard Deviation Visualize System Operating Point on Monitor. Power System Measurements AC State Estimation Estimated Measurements On-line DC OPF Input System State to Security Analysis Programs. 7

8 8 Today s state estimation (SE) Assumption: Power System Changes slowly Solution: Previous State Estimate Newton s method Current Global Optimum 8

9 Physical and Information Network Graphs Today Network graph of the physical system Load serving entities (LSEs) Information graph of today s SCADA Local serving entities (LSEs) Local Distribution Network (Radio Network) PQ Dies el PQ Wind PQ PQ Predicted P L,Q L Information flow: MISO State information exchange Backbone Power Grid and its Local Networks (LSEs) LS E LS E LS E LS E LS E LS E LS E LS E LS E LS E Backbone LS E LS E LS E LS E LS E LS E LS E LS E LS E LS E LS E Redundant measurement sent to Control center (hub) LS E

10 Potential of Measurements, Communications and Control DLR Constrained Line Line-to-Ground Clearance Transfer Capacity in Real Time PMU Control 10

11 Real Power Load P(p.u.) Real Power Load P (p.u.) From preventive to corrective management of future electric energy systems System Load Curve Every 10 min Real Time Load of NYISO in Jan 23, 2010 Forecasted Load Actual Load with Disturbance min Real Time Load of NYISO in Jan 23, Time (hours) Time (min) Upper Bound Lower Bound

12 Load Reactive Power Evolution (p.u.) Load Reactive Power Evolution (p.u.) 12 Predictable load and the disturbance NYISO August 2006 Load Data for 3 Hours, Power Factor = 0.8 NYISO August 2006 Load Reactive Power Data for 3 Hours, Measurement Frequency = 0.50 Hz,Power Factor = Pre-planed Load Value 23 Real Load Evolution, with 0.5Hz Sampling Time (mins) Time (mins) 12

13 Issues with SCADA today Quite inaccurate transmission/sub-transmission SE (mix of algorithmic, sensing and communications problems); bad data, topology status and computing intertwined. Time synchronization problems (inconsistency of planning and EMS data; mapping of models with breakers into models w/o breakers) Disconnect between T&D data management Fast relays and primary controllers local; only some experiments with SPS/coordinated RAS 13

14 Implications on system performance Hard to implement corrective actions for enhanced efficiency and reliability Poor voltage/reactive power data prevent optimization of voltage support Disconnect of transmission/sub-transmission and distribution SEs disables use of demand response in operations The trade-off between local enhanced control/relays and faster communications 14

15 Utilities major concern: Reliability Dynamical problems Types of Component Synchronous generators Wind generators Small signal instab. Transienti nstab. SS R SSCI Freq. instab. Volt. Instab. Power flow imbalance?????????????? Solar plants??????? FACTS??????? Storage??????? Table 1. 15

16 Recent pilot experiments Industry-government(-academia) collaborations on hardware for smart grids University campuses (``micro-grids ) UCSD, IIT Chicago Utilities deploying AMIs, synchrophasors (PMUs) Lessons learned Familiarity with new smart hardware The remaining challenge (protocols for systematic integration of scalable technologies at value) 16

17 Lessons learned from pilot experiments familiarity with new hardware (AMIs, PMUs, PHEVs, EVs, microgrids) Energy Sources Transmission Network Electromechanical Devices (Generators) Load (Converts Electricity into different forms of work) Photo-voltaic Device Electromechanical Device PHEVs Demand Respons e Energy Sources

18 Future Smart Grid (Physical system)

19 Contextual complexity FERC ISO Market Makers Power Traders Scheduling Generator Generator Generator XC Transmission Operator Supply Aggregators PUC Distribution Operator Some Utilities Are all Three Customer Customer Customer Demand Aggregators

20 Critical: Transform SCADA From single top-down coordinating management to 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 tools for self-commitment and clearing such commitments. \

21 New SCADA

22 DYMONDS-enabled Physical Grid

23 Aligning technical and social interactions- Making the most out of the naturally available resources? [4]

24 Smart Grid electric power grid and ICT for sustainable energy systems Core Energy Variables 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

25 Typical challenge: From old to new paradigm Flores Island Power System, Portugal [11] 26

26 Controllable components today s operations (very little dynamic control, sensing) H Hydro D Diesel W Wind *Sketch by Milos Cvetkovic

27 Two Bus Equivalent of the Flores Island Power System Generator Diesel Transmission line From Diesel to Load bus R[pu] L[pu] Base values S b = 10MVA V b = 15KV State Equilibriu m AVR Diesel K A [pu] 400 T A [s] 0.02 K E pu 1.3 T E [s] 1 S E [pu] K F [pu] 0.03 T F [s] 1 Governor k t [pu] 40 Diesel T g [s] 0.6 r pu 1/0.03 T t [s] Base values S b = 10MVA, V b = 0.4KV

28 Information exchange in the case of Flores---new (lots of dynamic control and sensing) Transmission grid PMU DSO DSO DSO Module PMU SVC Power-electronics Module Phasor Measurement Units Dynamic Purpose Communication Market and Equipment Status Communication

29 Smart Grid: More Local Optima Problems Open problems Smart Grid Components: Distributed Generator Plants Vehicle Charging/Discharging Various Demand Management Software Feature: More Uncertainties State Changes Fast Previous Estimate Current Global Optimum More Local Optimums

30 Need for new operating paradigm Different cyber support for different evolving power grids (islands, micro-grids, distribution grids, transmission grids) Remaining major open problems/challenges: --Need for new operating paradigm --Bridge physics-based modeling and automation design to the architecture design of communication networks Toward new paradigms --Standards/protocols for operating the emerging power grids --Business models in support of innovative technologies at value 31

31 Our proposal: TCP/IP like standards Given specified disturbances and range of operating conditions within a known system: - specified with e.g voltage, power - similar to LVRT curves for wind turbines - with specified duration All components (synchronous gens, wind gens) should guarantee that they would not create any of the problems in Table 1. (Clear objectives goals for components, assigned responsibility for system reliability) Two key questions: Q1-- Why does it matter? Q2)--- Can this be technically done? Not one way to achieve these! 32

32 A1: Examples of ibas it matters for ensuring both reliable and efficient operations iba 1 iba 3 Storage iba 2 33

33 Possible to create ibas for meeting transient stability distributed standard [13] Given disturbance Tripping of generator iba

34 Q2: Can we have a unifying theoretically sound approach to TCP/IP like standards for smart grids?

35 Basic functionalities Simple transparent TCP/IP like functionalities Transparency based on a unifying modular modeling of network system dynamics Provable performance-difficult Proposal use interaction variables to specify family of standards sufficient to avoid operating problems Measure of how well modules balance themselves in steady state Measure of rate of exchange of stored energy between a module and the rest of the system over different time horizons 36

36 Value of dynamic system-wide coordination Minimal coordination by using an aggregation-based notion of ``dynamic interactions variable Zoom-in Zoom-out 38

37 Interaction variable-based two-layer model [12] 39

38 Physics/Model Based Spatial Scaling Up CONFLICTING OBJECTIVES COMPLEXITY AND COST OF COMMUNICATIONS VS. COMPLEXITY AND COST OF SENSORS,CONTROL -SBA: Smart Balancing Authorities (Generalization of Control Area) -IR: Inter-Region -R: Region -T: Tertiary -D: Distribution -S: Smart Component -The actual number of layers depends on needs/technologies available/electrical characteristics of the grid ``SMART BALANCING AUTHORITY CREATED IN A BOTTOM-UP WAY (AGREGATION)--DyMonDS; --COMPARE WITH CONVENTIONAL TOP-DOWN DECOMPOSITION 40

39 Market solutions Fundamentally require alignment of technical and financial/economic/policy interaction variables Primal/dual decompositions define interaction variables in the generalized statespace IT essential to support Examples --- market-based integration of renewables and demand response at value 41

40 Information flow within- and among modules Based on the temporal decomposition of exogenous inputs and control Dynamic Monitoring and Decision Systems (DyMonDS)-basis for algorithms Working on computer platform Video demonstrating SGRS; send us request for more info. NIST funded activity. Will be Webex demos for industry, academia and government 42

41 Must simplify! Utilities are having hard time adding all these new components and their smarts for simulating system-wide dynamics Is there a ``smarter way to model and define modular functionalities so that the interconnected system meets system-level performance (Table 1)? 80% of each solution is modeling (PetarKokotovic, Chalenges in Control Theory, Santa Clara, circa 1982) 43

42 Must proceed carefully The very real danger of new complexity. Technical problems at various time scales lend themselves to the fundamentally different specifications for on-line data No longer possible to separate measurements, communications and control specifications Major open question: WHAT CAN BE DONE IN A DISTRIBUTED WAY AND WHAT MUST HAVE FAST COMMUNICATIONS

43 Thank you! Questions? 45

44 Smart Grid in a Room Simulator (SGRS) [] First demos of end-to-end SGRS Physics-based modeling of diverse power system components and their interactions over time Software embedded into modules Information exchange (learned/communicated); the rest private/secure *Huge* R&D what information to exchange for what purpose? Ilic, M. DyMonDS Computer Platform for Smart Grids, PSCC

45 General Module Definitions 47

46 Information Exchange Between Modules 48

47 General Module Structure 49

48 Module Structure Generator Example 50

49 51 Open questions Interactive on-line practical software to support system operator decisions and his interactions with the grid users Measurable software benefits (in terms of reliability, efficiency and environmental impacts) ***Academic challenge: Multi-physics verifiable emulator of real-world power systems***

50 Conclusions Our proposal: Interaction variable-based Standards/protocols for interactive ibas can set the basis for plug-and-play in smart grids bounds on stored energy change and on rate of change of stored energy for T of interest Standards need to define transparent protocols for all dynamic components Complexity of smart grids can be managed this way At the same time system performance is guaranteed With current NERC standards system performance cannot be mapped into responsibilities of different components 52

51 References [1] Ilic, M., Toward a Multi-Layered Architecture of Large-Scale Complex Systems for Reliable and Efficient Man-Made Infrastructures, Proc. of the MIT/ESD Symposium, Cambridge, MA, March 29-31, [2] Marija Ilic, IT-Enabled Electricity Services: The Missing Piece of the Environmental Puzzle, Issues in Technology and Policy, 2011 IAP Seminar Series, MIT, Jan.,19,2011 [3] Yang Weng, Statistical and Inter-temporal Methods Using Embeddings for Nonlinear AC Power System State Estimation, PhD thesis, CMU, ECE, August 2014 [4] ] ElinorOstrom, et al, A General Framework for Analyzing Sustainability of Socio- Ecological Systems, Science 325, 419 (2009). [5] Ilic, M., E. Allen, J. Chapman, C. King, J. Lang, and E. Litvinov. Preventing Future Blackouts by Means of Enhanced Electric Power Systems Control: From Complexity to Order., Proc. of the IEEE, vol. 93, no. 11, pp , November [6] Ilic, Marija and Liu, Zhijian, A New Method for Selecting Best Locations of PMUs for Robust Automatic Voltage Control (AVC) and Automatic Flow Control (AFC), IEEE PES 2010, Minneapolis, MN, July 25-29, [7]MilosCvetkovic, Power-Electronics-Enabled Transient Stabilization of Power Systems. PhD thesis, CMU. ECE Department, May 2014 [8] Kevin D. Bachovchin, Electromechanical Design and Applications in Power Grids of Flywheel Energy Storage Systems, PhD thesis, CMU, ECE Dept. May

52 [9] M. Prica and M. Ilic, Maximizing Reliable Service by Coordinated Islanding and Load Management in Distribution Systems under Stress Proc. of the 16th Power Systems Computation Conference, July 14-18, 2008, Glasgow, Scotland. [10] SiriphaJunlakarn, Differentiated Reliability Options Using Distributed Generation and System Reconfiguration, PhD thesis, CMU. EPP Department, May [11] Ilic, M. D, Le, X., Liu, Q. (co-eds) Engineering IT-Enabled Sustainable Electricity Services: The Tale of Two Low-Cost Green Azores Islands, Springer August 2013 [12] Ilic, et al, CDC 2014 tutorial. [13]S.Baros, M.Ilic intelligent Balancing Authorities (ibas) for Transient Stabilization of Large Power Systems IEEE PES General Meeting 2014 [14] Smart Grids and Future Electric Energy Systems, , CMU, ECE, Fall [15] Marija Ilic, Integration of Heterogeneous Small Test Beds for Emulating Large Scale Smart Power Grids: The Emphasis on Cyber Architectures, MSCPES2014Workshop, Invited keynote Berlin, CPS week, April 14,2014 [16] Ilic, Marija; Xie, Le; Joo, Jhi-Young, Efficient Coordination of Wind Power and Price-Responsive Demand Part I: Theoretical Foundations, IEEE Trans. on Power Systems. [17] Marija Ilic, Jhi-Young Jho, Le Xie, Marija Prica, NiklasRotering, A Decision-Making Framework and Simulator for Sustainable Energy Services, IEEE Transactions on Sustainable Energy, vol. 2, No. 1, January 2011, pp