Electromechanical Dynamics Of High Photovoltaic Power Grids
University of Tennessee, KnoxvilleTrace: Tennessee Research and CreativeExchangeDoctoral DissertationsGraduate School12-2017Electromechanical Dynamics of High PhotovoltaicPower GridsShutang YouUniversity of Tennessee, syou3@utk.eduRecommended CitationYou, Shutang, "Electromechanical Dynamics of High Photovoltaic Power Grids. " PhD diss., University of Tennessee, 2017.https://trace.tennessee.edu/utk graddiss/4847This Dissertation is brought to you for free and open access by the Graduate School at Trace: Tennessee Research and Creative Exchange. It has beenaccepted for inclusion in Doctoral Dissertations by an authorized administrator of Trace: Tennessee Research and Creative Exchange. For moreinformation, please contact trace@utk.edu.
To the Graduate Council:I am submitting herewith a dissertation written by Shutang You entitled "Electromechanical Dynamics ofHigh Photovoltaic Power Grids." I have examined the final electronic copy of this dissertation for formand content and recommend that it be accepted in partial fulfillment of the requirements for the degreeof Doctor of Philosophy, with a major in Electrical Engineering.Yilu Liu, Major ProfessorWe have read this dissertation and recommend its acceptance:Joshua S. Fu, Fangxing Li, Kai SunAccepted for the Council:Carolyn R. HodgesVice Provost and Dean of the Graduate School(Original signatures are on file with official student records.)
Electromechanical Dynamics of High PhotovoltaicPower GridsA Dissertation Presented for theDoctor of PhilosophyDegreeThe University of Tennessee, KnoxvilleShutang YouDecember 2017
Copyright 2017 by Shutang YouAll rights reserved.ii
ACKNOWLEDGEMENTSFirst and foremost, I would like to express my sincere thanks to my advisorProfessor Yilu Liu. I am deeply grateful for her encouragement and supportthroughout my PhD study. I cannot be luckier to have her be my advisor. Theimpact of her great personality and academic excellence on me is one of the fewvery best gifts I can have throughout my whole life.Special thanks to Professor Fangxing (Fran) Li, Professor Kai Sun, andProfessor Joshua S. Fu, for serving as my dissertation committee. I greatlyappreciate their insightful suggestions throughout the research and dissertationpreparation. It is impossible to complete this work without their tremendous help.Thanks to the faculties of the CURENT center and the EECS department at UTKfor providing encouraging environments and all great possibilities, especiallyProfessor Fei (Fred) Wang, Professor Kevin Tomsovic, Professor Leon Tolbert,and Dr. Chien-fei Chen.I would like to thank Dr. Stanton Hadley from Oak Ridge National Laboratory forhis guidance on my research. I would also like to thank Dr. Tassos Golnas, Dr.Rebecca Hott, and Dr. Guohui Yuan, from the U.S. Department of Energy SolarEnergy Technology Office for their support and advices on this dissertation study.With a special mention to all colleagues in the Power IT Lab. Working with themhas been the unforgettable experience in my life. I am especially thankful for thehelp from Dr. Yong Liu (Frank), Dr. Lin Zhu, Dr. Zhiyong Yuan (Alan), Dr. GefeiKou (Derek), Dr. Jiecheng Zhao, Dr. Wenxuan Yao, Dr. Jiahui Guo (Jason), Dr.Hesen Liu, Dr. Micah J. Till, Dr. Dao Zhou, Dr. Jidong Chai (Jack), Dr. ZhuohongPan, Dr. Yi Cui, Dr. Wenpeng Yu (Wayne), Dr. Yao Zhang, Dr. Jiaojiao Dong, Dr.iii
Fuhua Li, Dr. Yi Zhao, Dr. He Yin, Ellen Ling Wu, Mike Yu Su, Shirley XuemengZhang, Sundaresh Lakshmi, Weikang Wang, Cujie Zeng, Abigail Till, MelanieGonzalez, Zhihao Jiang, Summer Fabus, and many other colleagues.I would like to give my thank to my family, especially my parents Chungeng Youand Huaping Fang for their love and support.iv
ABSTRACTThis dissertation study focuses on the impact of high PV penetration on powergrid electromechanical dynamics. Several major aspects of power gridelectromechanical dynamics are studied under high PV penetration, includingfrequency response and control, inter-area oscillations, transient rotor anglestability and electromechanical wave propagation.To obtain dynamic models that can reasonably represent future power systems,Chapter One studies the co-optimization of generation and transmission withlarge-scale wind and solar. The stochastic nature of renewables is considered inthe formulation of mixed-integer programming model. Chapter Two presents thedevelopment procedures of high PV model and investigates the impact of highPV penetration on frequency responses. Chapter Three studies the impact of PVpenetration on inter-area oscillations of the U.S. Eastern Interconnection system.Chapter Four presents the impacts of high PV on other electromechanicaldynamic issues, including transient rotor angle stability and electromechanicalwave propagation. Chapter Five investigates the frequency responseenhancement by conventional resources. Chapter Six explores system frequencyresponse improvement through real power control of wind and PV. For improvingsituation awareness and frequency control, Chapter Seven studies disturbancelocation determination based on electromechanical wave propagation. Inaddition, a new method is developed to generate the electromechanical wavepropagation speed map, which is useful to detect system inertia distributionchange. Chapter Eight provides a review on power grid data architectures formonitoring and controlling power grids. Challenges and essential elements ofdata architecture are analyzed to identify various requirements for operatinghigh-renewable power grids and a conceptual data architecture is proposed.Conclusions of this dissertation study are given in Chapter Nine.v
TABLE OF CONTENTSChapter One Co-optimizing Generation and Transmission Expansion withRenewables . 11.1. Introduction. 11.1.1. Literature review . 11.1.2. Aim and contributions . 41.2. Co-optimizing generation-transmission expansion . 51.3. Scenario creation for multi-region systems. 101.4. Implementation on the US EI system . 151.4.1 Dataset and case description. 151.4.2. Comparison and analysis on the expansion results . 171.4.3. Long-term (LT) and short-term (ST) simulation results comparison . 211.5. Conclusion. 23Chapter Two High PV Power Grid Model Development and Impact of PV onFrequency Response . 242.1. Introduction. 242.2 High PV power grid model development . 262.2.1. Base model overview . 262.2.2. Base model validation using synchrophasor frequency measurement. 272.2.3. Scenario development . 292.2.4. High PV power grid dynamic model development . 372.3. Impact of high PV on frequency response . 412.4. Conclusion. 46Chapter Three Impact of High PV Penetration on Inter-area Oscillations . 473.1. Introduction. 473.2. Current oscillation analysis in the EI using FNET/GridEye measurements. 483.3. Impact of high PV on EI oscillation modes . 503.3.1. Impact on frequency and damping ratios . 513.3.2. Impact on mode shape . 543.3.3. Impact of PV plant control strategies on inter-area oscillations. 563.3.4. Introduction of new inter-area oscillation modes under certain PV plantcontrol settings. 573.4. Conclusions . 61Chapter Four Impact of High PV on Transient Rotor Angle Stability andElectromechanical Wave Propagation . 624.1. Introduction. 624.2. Impact of high PV penetration on transient stability — case study of FRCCout-of-step stability . 624.2.1. Background. 62vi
4.2.2. Approach . 634.2.3. Results and discussion . 654.3. Impact of high PV penetration on electromechanical wave propagationspeed . 664.3.1. Introduction . 664.3.2. Study approach and results . 684.3.3. A Study on the electromechanical wave propagation speed, inertia,and PV penetration using a simple ring system . 734.4. Conclusions . 76Chapter Five Frequency Response Enhancement by Conventional Resources . 785.1. Introduction. 785.2 EI system frequency response enhancement. 805.2.1. Adjust governor droop. 805.2.2. Adjust governor deadband . 815.2.3. Adjust governor ratio . 825.2.4. Summary of the EI system . 845.3. ERCOT frequency response enhancement . 865.3.1. Impact of high PV penetration on ERCOT UFLS . 875.3.2. Adjust governor droop. 885.3.3. Use fast load response . 905.3.4. Summary of the ERCOT system . 925.4. Conclusion. 93Chapter Six Study of Wind and PV Generation Control For Frequency Controland Oscillation Damping . 946.1. Introduction. 946.2. Renewable generation active power control . 966.2.1. Frequency response with renewable active power control. 966.2.2. Parameter sensitivity study and tuning . 1006.2.3. Electromechanical oscillation damping . 1036 .3. Conclusion . 104Chapter Seven Electromechanical Wave Propagation Based Event Location andInertia Distribution Detection in High PV Power Grids . 1067.1 Introduction. 1067.2. Overview of FNET/GridEye . 1107.2.1. The frequency disturbance recorder . 1107.2.2. The FNET/GridEye data center . 1137.3. Disturbance location determination based on electromechanical wavepropagation . 1147.3.1. Frequency measurement filtering, interpolation, and relative arrivaltime . 1167.3.2. Delaunay triangulation and bicubic 2D interpolation . 1207.3.3. Pinpointing event location and calculating the event start time . 122vii
7.3.4. Data validation . 1247.3.5. Application to a line trip disturbance using FNET/GridEyemeasurements . 1267.4. Non-invasive identification of inertia distribution change usingFNET/GridEye measurements . 1287.5. Conclusion. 130Chapter Eight Identify Challenges and Prototype Next-Generation Power GridData Architecture for High Renewable Power Grids . 1328.1. Introduction. 1328.2. Summary of existing studies on power grid data architecture . 1328.2.1. Data collection . 1338.2.2. Data transmission . 1348.2.3. Data service layer . 1358.2.4. Data utilization . 1378.2.5. Interoperability . 1378.2.6. Cyber security . 1398.3. Next-generation data architecture for high-renewable power grids . 1408.3.1. Data acquisition . 1418.3.2. Data condition and synchronization, data storage . 1438.3.3. Data middleware/API . 1438.3.4. Control middleware/API . 1448.4. Conclusions . 145Chapter Nine Conclusions . 148List of References . 150Appendices . 173Appendix A: Oscillation mode identification based on multivariate empiricalmode decomposition . 174A.1. Introduction. 174A.2. Background — empirical mode decomposition based oscillationidentification . 177A.3. Methodology — multivariate empirical mode decomposition basedambient oscillation mode identification . 179A.4. Case studies based on FNET/GridEye measurements . 182A.5. Conclusions . 186A.6. Tables and figures . 188Appendix B: Chapter One nomenclatures . 209Appendix C: Active power control diagram of renewable power plants . 212Appendix D: List of python scripts created in the dissertation study for PSS/esimulation . 217Vita. 218viii
LIST OF TABLESTable 1.1. Load scenario synchronization for multi-region power grids . 11Table 1.2. Description on the developed cases . 16Table 1.3. The expansion result summary of the five cases . 18Table 1.4. Expansion of gas and wind generation in Region PJM ROR andSPP N . 20Table 1.5. LT and ST simulation results in 2030 . 22Table 2.1. Basic Information of the EI Model . 27Table 2.2. Generation mix of high PV simulation scenarios in the EI . 29Table 2.3. PLEXOS model input data sources . 31Table 2.4. PV converter model ‘GEPVG’ generic parameters . 39Table 2.5. PV electrical controller ‘GEPVE’ generic parameters . 40Table 2.6. PV plants control strategies [85] . 41Table 2.7. EI frequency response metrics change due to renewable generation 42Table 3.1. Oscillation frequency and damping ratio . 50Table 4.1. Increased tie-line flows. 64Table 4.2. FRCC OOS test results with increased tie-line flows . 65Table 5.1. Impact of mitigation tactics on EI frequency response metrics (20%renewable scenario) . 85Table 5.2. Impact of mitigation tactics on EI frequency response metrics (80%renewable scenario) . 85Table 5.3. Generation mix of high PV simulation scenarios in the ERCOT . 86Table 5.4. FFR and UFLS amounts in each ERCOT high PV scenario . 88Table 5.5. Impact of mitigation tactics on ERCOT frequency response metrics(20%&80% renewable scenario). 92Table 7.1. Relative arrival time of some FDRs . 119Table 8.1. Challenges in power grid data architecture . 133Table 8.2. Overview on power grid architecture research . 146ix
LIST OF FIGURESFigure 1.1. The sub-scenario creation procedure . 13Figure 1.2. Regions of the U.S. EI system (EI includes all east regions) [55] . 16Figure 1.3. Transmission expansion over the planning horizon in the five cases(Refer to Figure 1.2 for the labels) . 18Figure 1.4. Annual energy flow of Case 20-Scn and 160-Scn-Sync (Linewidth isproportional to interface annual energy flow) . 19Figure 1.5. Annual energy flow of the not-co-optimized case (Linewidth isproportional to interface annual energy flow) . 21Figure 2.1. Frequency response model validation in EI . 28Figure 2.2. 15% wind power distribution in EI . 32Figure 2.3. PV regional distribution in 20% renewable (5% PV 15%WT) . 33Figure 2.4. PV regional distribution in 40% renewable (25% PV 15%WT) . 33Figure 2.5. PV regional distribution in 60% renewable (45% PV 15%WT) . 33Figure 2.6. PV regional distribution in 80% renewable (65% PV 15%WT) . 34Figure 2.7. PV distribution in different scenarios (5%PV 15%WT) . 34Figure 2.8. PV distribution in different scenarios (25% PV 15%WT) . 34Figure 2.9. PV distribution in different scenarios (45%PV 15%WT) . 35Figure 2.10. PV distribution in different scenarios (65%PV 15%WT) . 35Figure 2.11. 20% renewable geographical distribution in EI . 35Figure 2.12. 40% renewable geographical distribution in EI . 36Figure 2.13. 60% renewable geographical distribution in EI . 36Figure 2.14. 80% renewable geographical distribution in EI . 36Figure 2.15. Flowchart of developing high PV dynamic models . 37Figure 2.16. PV dynamic model connectivity [85] . 38Figure 2.17. EI frequency response change due to renewable integration (1.128GW generation loss) . 42Figure 2.18. PV penetration’s effects on local frequency and UFLS . 44Figure 2.19. PV plants control mode’s impact on system frequency response. . 45Figure 2.20. Voltage profile of Bus 514889 using different PV control mode. . 46Figure 3.1. Dominant frequency distribution of inter-area oscillations [114] . 49Figure 3.2. Damping ratio distribution of inter-area oscillations [114]. 50Figure 3.3 Observation locations in the U.S. EI . 51Figure 3.4 Oscillation frequency change as PV penetration increases . 52Figure 3.5 Oscillation damping ratio change as PV penetration increases . 52Figure 3.6 Oscillation frequency change as PV penetration increases (CT) . 53Figure 3.7 Local oscillation changes as PV penetration increases (TN) . 54Figure 3.8 Mode shape change with PV penetration . 55Figure 3.9 Oscillation frequency in CT under different PV power plant controlstrategies (65% PV) . 56Figure 3.10 Oscillation mode shape at 65% PV (Control Strategy 2). 57x
Figure 3.11 Frequency profile in IL under Control Strategy 1 (Volt/Var control withSolarControl) (65% PV) . 58Figure 3.12 Frequency profile in IL under Control Strategy 1 with fast powerfactor control (65% PV) . 58Figure 3.13 Mode shape of the 1.2 Hz Inter-area mode (65% PV) . 59Figure 3.14 Oscillation frequency change with PV penetration (1.2 Hz mode) . 60Figure 3.15 Damping ratio change with PV penetration (1.2 Hz mode) . 60Figure 4.1. Two 500kV tie lines between FRCC and main EI . 63Figure 4.2. DUVAL-HATCH 500kV tie-line voltage phase angle difference afterthe loss of 2.0 GW generation in FL . 64Figure 4.3. DUVAL-HATCH 500kV tie-line voltage phase angle difference afterthe loss of 3.5 GW generation in FL . 64Figure 4.4. Wave propagation without PV (left) and with 80% PV (right) . 69Figure 4.5. Electromechanical wave propagation speed distribution in the 5% PVpenetration scenario . 70Figure 4.6. Electromechanical wave propagation speed distribution in the 25%PV penetration scenario . 71Figure 4.7. Electromechanical wave propagation speed distribution in the 45%PV penetration scenario . 71Figure 4.8. Electromechanical wave propagation speed distribution in the 65%PV penetration scenario . 72Figure 4.9. Interconnection-scale average electromechanical wave propagationspeed in different PV penetration scenarios of EI . 72Figure 4.10. 64-generator ring system . 74Figure 4.11. Composition of each unit in the ring system . 74Figure 4.12. Relation between wave propagation speed and inertia reduction . 75Figure 4.13. High PV penetration scenarios are developed by replacingconvention generators by PV at equidistant intervals . 75Figure 4.14. Relation between wave propagation speed and PV penetration. 76Figure 5.1. EI frequency response under different PV penetration level (4.5 GWgeneration loss) . 80Figure 5.2. EI frequency responses with different governor droop settings . 81Figure 5.3. EI frequency responses with different governor deadbands (20%renewable) . 82Figure 5.4. EI frequency responses with different governor ratios . 83Figure 5.5. ERCOT frequency response under various PV penetration scenarios(2.7 GW generation loss, UFLS disabled) . 87Figure 5.6. ERCOT frequency after enabling FFR and UFLS (2.75 GWgeneration loss) . 88Figure 5.7. ERCOT frequency responses with different governor droops (20%renewable) . 89xi
Figure 5.8. ERCOT frequency responses with different governor droops (80%renewable) . 90Figure 5.9. ERCOT frequency responses with fast load response (20%renewable) . 91Figure 5.10. ERCOT frequency responses with fast load response (80%renewable) . 91Figure 6.1. Active power output of a wind farm in EI . 97Figure 6.2. Wind turbine speed change of a wind farm in EI . 97Figure 6.3. Active power output of a PV power plant in EI . 98Figure 6.4. EI frequency response improvement with wind and PV controls . 98Figure 6.5. TI frequency response improvement with wind and PV controls. 100Figure 6.6. Kwi value change impact on frequency response . 101Figure 6.7. Kwg value change impact on frequency response . 101Figure 6.8. EI generator inertia value distribution . 102Figure 6.9. EI governor droop value (R) distribution . 102Figure 6.10. Frequency of a 500kV Bus in EI due to frequency control andoscillation damping . 104Figure 7.1. Generation-II FDR . 111Figure 7.2. The FDR location map in North America . 111Figure 7.3. Worldwide FDR deployment and the frequency map . 112Figure 7.4. The FNET/GridEye data center structure. 112Figure 7.5. A flow chart of the disturbance location method . 115Figure 7.6. Filtered frequency (5-point median) of the detected disturbance . 117Figure 7.7. Relative arrival time calculation . 119Figure 7.8. Relative arrival time of FDRs at different locations . 120Figure 7.9. Delaunay triangulation of FDR locations in the U.S. EasternInterconnection . 121Figure 7.10. The contour map of time of ROCOF passing a threshold for all FDRs.
Trace: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 12-2017 Electromechanical Dynamics of High Photovoltaic Power Grids Shutang You University of Tennessee, syou3@utk.edu This Dissertation is brought to you for free and open access by the Graduate School at Trace: Tennessee Research and Creative Exchange. It .
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