From Theory to Smart Grid Simulator for Assessing and Demonstrating The for Assessing and Demonstrating The Potentials of New Technologies Jhi‐Young (J.Y.) Joo, Zhijian Liu and Marija Ilić Electric Energy Systems Group gy y p Electrical and Computer Engineering Carnegie Mellon University Acknowledgements Le Xie e e Former PhD student, Assistant Professor at Texas A&M University Marija Prica PhD student, EESG, ECE CMU h d Niklas Rotering Former visiting Master’s student, PhD student at RWTH Aachen, Germany , y 2 DYMONDS — Future electric energy system DYMONDS Future electric energy system 3 Dynamic Monitoring and Decision‐ making System (DYMONDS) k ( ) [1] Dynamic Monitoring Decision‐making System (DYMONDS) Distributed decision making system Computationally feasible p y Individual decisions submitted to ISO (as supply/demand bids) Individual inter‐temporal constraints internalized p The pieces we’ve got SSystem operator t t Wind generation, price‐responsive demand, PHEVs, planning and PMU(phasor measurement unit)s, and more 3 Concepts of DYMONDS Simulator Concepts of DYMONDS Simulator 5 IEEE 24‐bus Reliability Test System (RTS) in GIPSYS [2] Jovan Ilić 11 generator buses (including a slack bus) (including a slack bus) Nuclear, coal, oil, natural gas Inelastic demand 4 Current electric power systems Current electric power systems 5 DYMONDS Simulator Scenario 1: + Wind generation d [3,4] Le Xie 20% / 50% penetration to penetration to the system 6 DYMONDS Simulator Scenario 1: + Wind generation d 7 DYMONDS Simulator i i i d d [3‐5] Scenario 2: + Price‐responsive demand $ kWh J.Y. Joo Elastic demand Elastic demand that responds to time‐varying y g prices 8 DYMONDS Simulator i i i d d Scenario 2: + Price‐responsive demand 9 DYMONDS Simulator Scenario 3: + Electric vehicles i l i hi l [6] Niklas Rotering Interchange supply / demand supply / demand mode by time‐ varying prices y gp 10 DYMONDS Simulator Scenario 3: + Electric vehicles i l i hi l [4] 11 DYMONDS Simulator SScenario 4: + long‐run decision making i 4 l d ii ki [4] ? Marija Prica j Long‐run planning of new generation capacity installation Long‐run marginal bids For the next 10 years 14 DYMONDS Simulator Scenario 4: + long‐run decision making l d k Long‐term load forecast g Coal power plant p p 15 DYMONDS Simulator Scenario 4: + long‐run decision making l d k Natural gas power plant g p p Long‐run bidding functions g g 16 DYMONDS Simulator S Scenario 5: + PMU‐Based Robust Control i 5 PMU B d R b C l [7] P Zhijian Liu Automated Voltage C t l (AVC) d Control (AVC) and Automated Flow Control (AFC) P P Design Design Best Locations Best Locations of PMUs Design Feedback Control Gains P 17 PMU‐based Automatic Voltage and Flow Regulation System Demand Curve [8] y [ ] 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 25 18 PMU‐based Automatic Voltage and Flow R Regulation l i Robust AVC Illustration in NPCC System [7, 9] Robust AVC Illustration in NPCC System [7, 9] 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 19 PMU‐based Automatic Voltage and Flow Regulation Robust AVC Illustration in NPCC System Robust AVC Illustration in NPCC System Full System Observability A t Automatic ti Voltage V lt Control C t l for f Unlimited U li it d Information I f ti Control C t l Case C System Wors st Voltage Deviattion (p.u.) 0.25 0.2 No Control Full Information Control 5% Reliability Criteria 0.15 0.1 0.05 0 0 10 20 30 40 50 60 Time (sec) 70 80 90 100 20 PMU‐based Automatic Voltage and Flow Regulation 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 21 PMU‐based Automatic Voltage and Flow Regulation 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 Deviation (p.u.) 0.7 0.6 No Control Full Information Control 5% Reliability Criteria 05 0.5 0.4 0.3 0.2 0.1 0 0 10 20 30 40 50 60 Time (sec) 70 80 90 100 22 Concluding remarks Concluding remarks Current power systems simulator Cu e t po e syste s s u ato Centralized optimization Information and decision‐making concentrated on ISO Future energy systems simulator (DYMONDS) Distributed optimization Æ modularized components and decision‐makings Appropriate information exchange between the Appropriate information exchange between the components Balance of the system by ISO 23 References 1. 2. 3. 4. 5. 6. 7. 8. 9. Marija Ilić, Dynamic Monitoring and Decision Systems (DYMONDS) and Smart Grids: One and The Same, EESG Working Paper R WP 21 2009 EESG Working Paper R‐WP‐21, 2009 Jovan Ilić, Interactive Power Systems Simulator, Carnegie Mellon University, https://www.ece.cmu.edu/~nsf‐education/software.html Marija Ilić, Le Xie and Jhi‐Young Joo, Efficient Coordination of Wind Power and Price‐Responsive Demand Part I: Theoretical Foundations Part II: Case Studies IEEE Transactions on Power Systems Demand Part I: Theoretical Foundations, Part II: Case Studies, IEEE Transactions on Power Systems, under review, Jan 2010 Marija Ilić, Jhi‐Young Joo, Le Xie, Marija Prica and Niklas Rotering, A Decision Making Framework and Simulator for Sustainable Electric Energy Systems, under review in Feb 2010 g and Marija j Ilić, A Multi‐Layered Adaptive Load Management (ALM) System: Information , y p g ( ) y Jhi‐Young Joo exchange between market participants for efficient and reliable energy use, 2010 IEEE PES Transmission & Distribution Conference, April 2010, accepted Niklas Rotering and Marija Ilić, Optimal Charge Control of Plug‐In Hybrid Electric Vehicles In Deregulated Electricity Markets, IEEE Transactions on Power Systems, accepted in Aug 2009 Zhijian Liu and Marija Ilić, Toward PMU‐Based Robust Automatic Voltage Control (AVC) and Automatic Flow Control (AFC), 2010 IEEE PES General Meeting, July 2010, accepted in Feb 2010 and to be presented New York ISO Website Data available at http://www.nyiso.com/ E. Allen, J. Lang, and M. Ilic, “A Combined Equivalenced‐Electric, Economic, and Market Representation of the Northeastern Power Coordinating Council U.S. Electric Power System,” IEEE Transactions On f h h d l l ” Power Systems, vol. 23, no. 3, pp. 896‐907, Aug 2008. 24