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8 EXHIBIT I

9 Final Report Prepared For: Southwest Power Pool 415 North McKinley, #140 Plaza West Little Rock, AR SPP WITF Wind Integration Study Prepared By: 200 Clarendon Street T-33 Boston, Massachusetts Date: CRA Project No. D14422

10 Disclaimer (CRA) and its authors make no representation or warranty as to the accuracy or completeness of the material contained in this document and shall have, and accept, no liability for any statements, opinions, information or matters (expressed or implied) arising out of, contained in or derived from this document or any omissions from this document, or any other written or oral communication transmitted or made available to any other party in relation to the subject matter of this document. CRA Project Team Project Manager: T. Bruce Tsuchida Engineering Lead: Pablo A. Ruiz Officer in Charge: Aleksandr Rudkevich Team Members: Peter W. Sauer Germán G. Lorenzón Rodney Yeu Richard Baxter Jesse Cooper Daniel Cross-Call Scott L. Englander John Goldis Final Report Page i

11 Acknowledgments CRA would like to thank the dedicated SPP and WITF members for providing the opportunity to study the complex challenges addressed in this report, as well as for their invaluable guidance throughout the process, insightful feedback on drafts of this report, and suggestions for its improvement. It has truly been a pleasure to work with all of the stakeholders. CRA would also like to express appreciation to the SPP engineers, not only for providing the many gigabytes of data essential to this analysis, but also for the hours spent providing their many keen insights on system planning and operation, without which the results of this study would be much less rich. Finally, the CRA Project Team extends its gratitude to the support staff at CRA, especially software engineering for their timely handling of the programming requests, and ITS, whose tireless efforts kept all servers in superb condition, greatly facilitating this simulation-intensive analysis. Final Report Page ii

12 TABLE OF CONTENTS 1. EXECUTIVE SUMMARY OVERVIEW STUDY APPROACH MAJOR FINDINGS AND RECOMMENDATIONS INTRODUCTION BACKGROUND AND OBJECTIVES OF THE STUDY THE SOUTHWEST POWER POOL WIND GENERATION POTENTIAL IN SPP AND NEIGHBORING REGIONS KEY CHALLENGES OF WIND INTEGRATION SPP WIND RESOURCES: CASES ANALYZED AND THEIR CHARACTERISTICS WIND PENETRATION CASES FOR SPP AND NEIGHBORING REGIONS WIND PROFILE STATISTICAL CHARACTERISTICS SPATIAL AND TEMPORAL DIVERSITY OF SPP WIND RESOURCES IMPACTS OF WIND INTEGRATION ON THE SPP TRANSMISSION SYSTEM OVERVIEW OF POWER FLOW CASES AND METHODOLOGY BASE CASE ANALYSIS % CASE ANALYSIS Generation Dispatch in Power Flow Cases and Transmission Expansion AC Contingency Analysis and Transmission Expansion Transmission Expansion Summary % CASE ANALYSIS Generation Dispatch in Power Flow Cases and Transmission Expansion AC Contingency Analysis and Transmission Expansion Transmission Expansion Summary VOLTAGE STABILITY ANALYSIS PV Analysis / Transfer Characteristics dv/dq Sensitivity Analysis VQ Analysis / Reactive Reserves Final Report Page iii

13 4.6. TRANSIENT STABILITY ANALYSIS Single Line Fault Transient Stability Analysis GGS Flowgate Limit Transient Stability Analysis Critical Clearing Time Transient Stability Conclusions ADDITIONAL SPP FLOWGATES FOR COMMITMENT AND DISPATCH WIND POWER DELIVERABILITY % Case % Case AVAILABLE TRANSFER CAPABILITY IMPACTS OF INCREASED WIND PENETRATION IMPACTS OF WIND INTEGRATION ON SPP OPERATIONS OVERVIEW OF SPP OPERATIONS BY TIMEFRAME WIND INTEGRATION AND RESERVE SERVICES NEEDS Regulation-Up and Regulation-Down Requirements Updated Regulation Needs Wind Variability in the Regulation Timeframe Load-Following Needs Net Load Variability: Ramping Capability Needs Net-Load Uncertainty: Load-Following Reserve Needs Contingency Reserve Requirements MULTI-HOUR TIMEFRAME: WIND INTEGRATION IMPACTS ON UNIT COMMITMENT Effects of Wind Integration on Power Flows within SPP and Interchanges with Neighboring Systems The Impact of Wind Penetration on Power Flows Between SPP and Neighboring Systems The Impact of Wind Penetration on Power Flows within SPP Transmission Congestion and Wind Curtailments New Operational Patterns of Generating Units % Case Results % Case Results Impacts of Wind Forecast Uncertainty Analysis Methods Overview Results - Number of Starts and Capacity Factor While Up by Unit Type Results Wind Curtailment SUB-HOUR TIMEFRAME: WIND INTEGRATION IMPACTS ON GENERATION DISPATCH AND PROVISION OF ANCILLARY SERVICES % Case Final Report Page iv

14 Impcts of Minimum-Generation Constraints Impacts of Reserve Requirements Impacts of Transmission Congestion Impacts of Forecasting Errors % Case IMPACTS OF WIND INTEGRATION ON SPP MARKETS IMPLICATIONS OF WIND INTEGRATION FOR SPP DAY 2 MARKET DESIGN AND OPERATIONS SPP and the Day 2 Market Benefits of Being a Single Balancing Authority Change in Market Clearing Price Increase in Net Load Forecast Error PROPOSAL FOR ADDRESSING WIND AND LOAD FORECAST ERRORS Comprehensive and Consistent Wind and Load Forecasts for Operations Enforcement of Load-Following Reserve Requirements Stochastic Methods for Unit Commitment OVERVIEW OF BEST PRACTICES OF WIND INTEGRATION IN THE UNITED STATES AND ABROAD POLICY RECOMMENDATIONS FOR SPP Interconnection Requests Transmission Regulation Requirements Wind Forecasting Unit Commitment and Dispatch Market Updates Additional Recommendations METHODOLOGY OVERVIEW OF THE STAKEHOLDER PROCESS MAJOR ANALYTICAL TASKS AND THEIR ROLES IN THE STUDY DEVELOPMENT OF WIND PROFILES POWER FLOW ANALYSIS Contingency Analysis and Transmission Expansion PV Analysis Methodology ANALYSIS OF REGULATING RESERVE REQUIREMENTS UNIT COMMITMENT ANALYSIS Final Report Page v

15 Analytical Approach Input Assumptions and Data Sources Software Tools REAL-TIME SIMULATIONS OF ECONOMIC DISPATCH Input Assumptions and Data Sources Software Tools OVERVIEW OF WIND INTEGRATION PRACTICES IN THE U.S. AND ABROAD GLOSSARY REFERENCES APPENDIX A: TRANSMISSION ANALYSIS... A-1 APPENDIX B: SEASONAL DATA FROM SECTION 5.3: PRODUCTION SIMULATION... B-1 APPENDIX C: OVERVIEW OF WIND INTEGRATION PACTICES IN THE U.S. AND INTERNATIONALLY... C-1 APPENDIX D: ENERGY STORAGE AND WIND... D-1 APPENDIX E: SPP FLOWGATES... E-1 APPENDIX F: LOAD DATA - INTRA-HOUR AND FORECAST PROFILES... F-1 APPENDIX G: GE MAPS...G-1 APPENDIX H: POWERWORLD SIMULATOR... H-1 Final Report Page vi

16 LIST OF FIGURES 1. EXECUTIVE SUMMARY 2. INTRODUCTION 2.2-1: SPP footprint : Wind potential in the United States : Wind turbine power curve SPP WIND RESOURCES: CASES ANALYZED AND THEIR CHARACTERISTICS 3.1-1: Wind plant sites and clusters : Base Case wind nameplate capacity distribution by cluster : 10% Case wind nameplate capacity distribution by cluster : 20% Case wind nameplate capacity distribution by cluster : Wind power output: histograms by case : Wind power output: autocorrelation by case : Hourly change in available wind power output: histograms by case : Hourly change in available wind power output: autocorrelations by case : Histograms of deviations from day-ahead wind forecasts by case : Base Case average daily wind output by season : Base Case range of daily wind output : Wind output cross-correlation between wind penetration cases : Wind output cross-correlation between clusters : Standard deviation of hourly available wind power increments by cluster : Correlation factor between NREL site 394 and the remaining NREL sites in SPP : Maximum correlation factor between NREL site 394 and the remaining NREL sites in SPP : Time-shift for maximum correlation factor between NREL site 394 and the remaining NREL sites in SPP: 3-year time series : Time-shift for maximum correlation factor between NREL site 394 and the remaining NREL sites in SPP: 16 March 2004 (high ramp down event) IMPACTS OF WIND INTEGRATION ON THE SPP TRANSMISSION SYSTEM 4.2-1: Base Case transmission topology above 200 kv Final Report Page vii

17 4.2-2: Base Case generation dispatch in the four seasonal power flow cases : 10% Case generation dispatch in the four seasonal power flow cases : 10% Case transmission expansions above 200 kv included to eliminate pre-contingency violations : Initial 10% Case transmission topology above 200 kv : Contingencies that cause violations in the Spearville - Wichita corridor in the 10% Case spring power flow : Contingencies that caused violations in the Spearville - Wichita corridor in the 10% Case fall power flow : Contingencies that caused violations in the SPS - OKGE corridor in the 10% Case spring power flow : Contingencies that caused violations in the SPS - OKGE corridor in the 10% Case fall power flow : Final 10% Case transmission topology above 200 kv : 20% Case generation dispatch in the four seasonal power flow cases : 20% Case transmission expansions above 200 kv included to eliminate pre-contingency violations : 20% Case transmission topology above 200 kv : 20% Case contingency analysis of the E Manhattan - Concordia region : 20% Case contingency analysis of northeast Nebraska : 20% Case contingency analysis of west Kansas : 20% Case contingency analysis of the Wichita area : 20% Case contingency analysis of Roman Nose - El Reno : 20% Case contingency analysis of west Oklahoma City : 20% Case contingency analysis of SPS : Final 20% Case transmission topology above 200 kv : Voltage contour for the maximum post-contingency transfer from SPS to SPP; Tuco-OKU line outaged, fall 10% Case : Voltage contour for the SPS to SPP pre-contingency transfer and minimum voltage of 0.9 p.u.; fall 20% Case : VQ curve for the Riley 161 kv bus in the 10% Case summer power flow, pre-contingency case : Bus voltage contour for the maximum wind power injection in the summer 20% Case : ATC for selected transfers by case : Net export for each seller for selected transfers by case Final Report Page viii

18 4.9-3: Net import for each buyer for selected transfers by case IMPACTS OF WIND INTEGRATION ON SPP OPERATIONS 5.1-1: Operations timeframes for SPP Day 2 market design : Recommended SPP operations timeframe : Winter regulation requirements as a function of the total wind nameplate capacity for different daily load levels : Spring regulation requirements as a function of the total wind nameplate capacity for different daily load levels : Summer regulation requirements as a function of the total wind nameplate capacity for different daily load levels : Fall regulation requirements as a function of the total wind nameplate capacity for different daily load levels : Short-term wind variability as a function of the total nameplate wind capacity : Wind variability down as a function of the total nameplate wind capacity by season : Wind variability up as a function of the total nameplate wind capacity by season : Wind variability down as a function of the total wind nameplate capacity by day-ahead wind forecast level : Wind variability up as a function of the total wind nameplate capacity by day-ahead wind forecast level : Wind variability down as a function of the day-ahead wind forecast level by wind penetration level : Wind variability up as a function of the day-ahead wind forecast level by wind penetration level : Net load profiles for increased wind penetration levels (April 14, 2010, based on 2004 historical profiles) : Net load 4-hour-ahead forecast uncertainty by wind penetration case : Net load ramps as a function of time for 6-7 am in the Base Case : Net load ramps as a function of time for 6-7 am: a) upperbound, b) lower-bound : Upper-bound net load ramps as a function of time for each hour of the day : Lower-bound net load ramps as a function of time for each hour of the day : Net load hourly ramp range as a function of the hour of the day : Net load hourly ramp as a function of the installed wind capacity Final Report Page ix

19 : Net load hourly ramp range as a function of the hour of the day in winter : Net load hourly ramp range as a function of the hour of the day in spring : Net load hourly ramp range as a function of the hour of the day in summer : Net load hourly ramp range as a function of the hour of the day in fall : Net load hourly ramp range as a function of the hour of the day: high positive wind ramp forecast : Net load hourly ramp range as a function of the hour of the day: high negative wind ramp forecast : Net load hourly ramp range as a function of the hour of the day: low wind ramp forecast : Day-ahead wind and load forecast uncertainty up: evolution with time : Day-ahead wind and load forecast uncertainty down: evolution with time : Day-ahead net load forecast uncertainty: evolution with time : Changes between day-ahead and 4-hour-ahead load and wind forecasts : Changes between day-ahead and 4-hour-ahead load and wind forecasts as a function of the time of day : Changes between day-ahead and 4-hour-ahead net load forecasts as a function of the time of day : 4-hour-ahead load and wind forecast uncertainty up: evolution with time : 4-hour-ahead load and wind forecast uncertainty down: evolution with time : 4-hour-ahead net load forecast uncertainty by hour of the day : 4-hour-ahead net load forecast uncertainty up: time evolution : 4-hour-ahead net load forecast uncertainty down: time evolution : Evolution of 10-minute-ahead persistence wind forecast uncertainty (range of deviation from 10-minute-ahead wind forecasts as a function of time) : Persistence wind forecast uncertainty as a function of the forecast lead time : SPP hourly load for Wednesdays during summer : Base Case annual average RTC SPP import/export flows, in MW Final Report Page x

20 : 10% Case annual average RTC SPP import/export flows, in MW : 20% Case annual average RTC SPP import/export flows, in MW : Base Case annual average (RTC) intra-spp flows, in MW : 10% Case annual average (RTC) intra-spp flows, in MW : 20% Case annual average (RTC) intra-spp flows, in MW : 10% Case summer binding constraints : 10% Case fall binding constraints : 10% Case winter binding constraints : 10% Case spring binding constraints : Wind sites 52 and : Wind sites 52 and 64 (enlarged) : Wind site 52 and NF8 constraint : 20% Case summer binding constraints : 20% Case fall binding constraints : 20% Case winter binding constraints : 20% Case spring binding constraints : Wind sites curtailed in excess of 2% : NF8 constraint and wind site : FG6140 constraint and wind site : Non-wind generation by unit type (annual total) : Average hours running per start for non-wind generation by unit type (annual average) : Non-wind generation by unit type (annual total) : Average hours running per start for non-wind generation by unit type : Spring 2004 Case : Fall 2005 Case : Fall 2006 Case : Forecast direction and wind curtailment for all 20% Cases : 4-hour-ahead forecast direction and wind curtailment for Spring % Case : Day-ahead forecast direction and wind curtailment for Spring % Case : 4-hour-ahead forecast direction and wind curtailment for Fall % Case Final Report Page xi

21 : Day-ahead forecast direction and wind curtailment for Fall % Case : 4-hour-ahead forecast direction and wind curtailment for Fall % Case : Day-ahead forecast direction and wind curtailment for Fall % Case : Wind curtailment as a function of the forecast error : Intra-hour profiles with perfect foresight, no transmission congestion, and no reserve requirements for Nov 1, 2010 (based on 2006), 20% Case : Intra-hour profiles with perfect foresight and no transmission congestion with reserve requirements enforced for Nov 1, 2010 (based on 2006), 20% Case : Wind power dispatch with perfect foresight, compared to the case with no transmission congestion for Nov 1, 2010 (based on 2006), 20% Case : Wind power dispatch with perfect foresight and fixed hourly unit commitment, compared to the case with updated intra-hourly commitment for fast-start units; Nov 1, 2010 (based on 2006), 20% Case : Wind forecasts and available wind power for Nov 1, 2010 (based on 2006), 20% Case : Wind power dispatch with commitment performed using dayahead and 4-hour-ahead wind forecasts, compared to the case with perfect foresight for Nov 1, 2010 (based on 2006), 20% Case : Wind power dispatch with perfect foresight with and without reserve requirements, compared to the available wind power for Nov 1, 2010 (based on 2006), 10% Case MARKET IMPACT STUDY : SPP generation stack with 1,000 MW of wind : SPP generation stack with 2,500 MW of wind : SPP generation stack with 5,000 MW of wind : Standard deviation of hourly wind generation increments : Operational timeframe for load following reserves : Load-following reserves example : Scenarios for stochastic unit commitments: high wind cut-out example : Scenarios for stochastic unit commitments: 20% - 80% forecast example Final Report Page xii

22 7. METHODOLOGY 7.2-1: Flow of CRA tasks for the Wind Integration Study : Wind deviations from the wind short-term forecast versus load deviations from the load short-term forecast : Frequency of occurrences of joint load and wind deviations for the six days analyzed Final Report Page xiii

23 LIST OF TABLES 1. EXECUTIVE SUMMARY 2. INTRODUCTION 3. SPP WIND RESOURCES: CASES ANALYZED AND THEIR CHARACTERISTICS 3.1-1: Wind generation capacity by penetration level : Base Case wind from SPP GI queue as of February : 10% Case wind from SPP GI queue as of February : 20% Case wind from SPP GI queue as of February : 40% Case wind from SPP GI queue as of February : Summary of the wind power statistics by case IMPACTS OF WIND INTEGRATION ON THE SPP TRANSMISSION SYSTEM 4.1-1: SPP load and wind power dispatch in the power flow cases : Wind power dispatch in the power flow cases by power flow area, in MW : Transmission lines added to the 10% Case : Transmission lines added to the 20% Case : PV study results for SPS to SPP transfers in the 10% Case : PV study results for SPS to SPP transfers in the 20% Case : Nodes with VQ characteristics analyzed : Single line fault transient stability results for Base Case fall : Single line fault transient stability results for Base Case spring : Single line fault transient stability results for Base Case summer : Single line fault transient stability results for Base Case winter : Single line fault transient stability results for 10% Case fall : Single line fault transient stability results for 10% Case spring : Single line fault transient stability results for 10% Case summer : Single line fault transient stability results for 10% Case winter : Single line fault transient stability results for 20% Case fall : Single line fault transient stability results for 20% Case spring : Single line fault transient stability results for 20% Case summer : Single line fault transient stability results for 20% Case winter Final Report Page xiv

24 : Base Case GGS flowgate limit : 10% Case GGS flowgate limit : 20% Case GGS flowgate limit : Summary of single-line fault results : Newly identified constraints for the 10% Case (Part 1 of 2) : Newly identified constraints for the 10% Case (Part 2 of 2) : Newly identified constraint limits for the 10% Case (Part 1 of 2) : Newly identified constraint limits for the 10% Case (Part 2 of 2) : Newly identified constraints for the 20% Case (Part 1 of 2) : Newly identified constraints for the 20% Case (Part 2 of 2) : Newly identified constraint limits for the 20% Case (Part 1 of 3) : Newly identified constraint limits for the 20% Case (Part 2 of 3) : Newly identified constraint limits for the 20% Case (Part 3 of 3) : Critical constraints for wind power output increases in the summer 10% Case : Critical constraints for wind power output decreases in the summer 10% Case : Critical constraints for wind power output increases in the summer 20% Case (Part 1 of 2) : Critical constraints for wind power output increases in the summer 20% Case (Part 2 of 2) : Critical constraints for wind power output decreases in the summer 20% Case IMPACTS OF WIND INTEGRATION ON SPP OPERATIONS : Total regulation requirements for seasonal peak loads, in MW : Day-ahead load and wind forecast uncertainty : Day-ahead net load forecast uncertainty : 4-hour-ahead load and wind forecast uncertainty : 4-hour-ahead net load forecast uncertainty : 4-hour-ahead net load forecast error by season, in MW : Seasonal definitions : SPP regions for flow pattern analysis : SPP and neighboring areas annual average flows (RTC), in MW : SPP and neighboring areas annual average flows (on-peak), in MW Final Report Page xv

25 : SPP and neighboring areas annual average flows (off-peak), in MW : SPP area annual average flows (RTC), in MW : SPP area annual average flows (on-peak), in MW : SPP area annual average flows (off-peak), in MW : Binding constraints and hours binding by season : Average wind curtailment for the 10% Case : Wind curtailment by season and profile year for the 10% Case : Binding constraints and hours binding by season 20% Case : Average wind curtailment for the 20% Case : Wind curtailment by season and profile year for the 20% Case : Curtailment for wind site : Curtailment for wind site : Curtailment for wind site : Curtailment for wind site : Curtailment for wind site : Non-wind generation (GWh) by unit type (annual total) : Average hours running per start for non-wind generation by unit type : Minimum average hours running per start for non-wind generation by unit : Generation (GWh) by SPP area : Generation difference (GWh) between Base Case and 10% Case : Capacity factors while up for the Base Case and 10% Case : Number of starts for the Base Case and 10% Case for summer : Non-wind generation (GWh) by unit type : Average hours running per start for non-wind generation by unit type : Minimum average hours running per start for non-wind generation by unit : Generation (GWh) by SPP area : Generation difference (GWh) between Base Case and 20% Case : Capacity factors while up for the Base Case and 20% Case : Number of starts for the Base Case and 20% Case : SPP load for selected weeks : MAPS runs performed for forecast error analysis Final Report Page xvi

26 : Number of starts by unit type for 20% Cases : Capacity factors while up by unit type for 20% Cases : Number of starts by unit type for 10% Cases : Capacity factors while up by unit type for 10% Cases : Direction of forecast errors for 20% Cases : Forecast direction and wind curtailment for all 20% Cases : Scenarios analyzed for the 20% Case intra-hour simulations : 10-minute deviations from hourly wind averages and wind forecasts for the 20% Case for Nov 1, 2010 (based on 2006), in MW MARKET IMPACT STUDY : Count of marginal hours by unit type : Ratio of marginal hours by unit type : Market clearing price using assumptions from table : Fuel price and heat rate assumptions METHODOLOGY 7.5-1: Load and wind deviations from their short-term forecasts for representative days, in MW : 36 MAPS model simulations Final Report Page xvii

27 1. EXECUTIVE SUMMARY 1.1. OVERVIEW The Southwest Power Pool (SPP) selected (CRA) in early 2009 to conduct a study to determine the operational and reliability impact of integrating wind generation into the SPP transmission system and energy markets. The study required an intensive effort to perform a detailed engineering analysis and then interpret the findings and associated policy implications in the context of a regional market. To achieve the objectives, the study assessed the impacts of wind generation on three different aspects of SPP: transmission, operation, and markets STUDY APPROACH The study was performed for the year 2010 with the assumption that SPP operates as a single balancing authority (BA) with a co-optimized energy and ancillary service market (Day 2 Market). Three wind penetration levels were studied and each was compared to the current system conditions (Base Case, with approximately 4% wind penetration). The three penetration levels were 10%, 20%, and 40% by annual energy (10% Case, 20% Case, and 40% Case, respectively). Detailed studies were performed on the 10% and 20% Cases; the 40% Case was examined in those portions of the study that related to wind characteristics. The goal of the study was to identify the challenges of integrating high levels of wind penetration into the SPP transmission system. In order to meet that objective, it was necessary to identify transmission upgrades needed to accommodate the studied wind power additions with minimal curtailment. This was not an economics study; no economic optimization, such as an analysis of the tradeoff between building transmission upgrades and curtailing wind, was performed. Furthermore, the transmission upgrades implemented in the study were based on the assumed wind plant locations and sizes. To begin the study, SPP selected a set of wind plants from the SPP Generation Interconnection (GI) queue as of February 2009 to encompass the full range of wind capacity needed for the Base Case through the 40% Case. 1 CRA then analyzed the characteristics of the selected wind plants, including probabilistic output and geographic correlation characteristics, as discussed in Section 3. 2 Next, CRA assessed the power flow models 1 It is important to note that this study was not intended to address the impact of any individual wind plant and therefore the results should not be tied to, or used to evaluate, any individual wind plant or its operations. 2 The study results may have been different if future wind generation sites were based on wind resource assessment that showed greater geographical diversity, rather than the GI queue. Greater geographical diversity helps mitigating the variability of aggregate wind generation. Final Report Page 1-1

28 provided by SPP representing four seasons for the Base, 10%, and 20% Cases. This assessment led to the identification of transmission upgrades needed to accommodate the wind plant additions associated with each penetration level. 3 The transmission upgrades were studied using several different approaches, including voltage analysis, dynamic stability analysis, and available transfer capability (ATC) analysis, as discussed in Section 4. The results of the wind characteristics analysis (Section 3) and transmission analysis (Section 4) were then used to analyze the impact of wind power on ancillary services (reserves in particular), as well as their impact on the dynamic system operations via a production simulation. The production simulation analyzed the effects of increased wind power on congestion patterns, unit commitment and dispatch decisions, and forecasting errors. Additionally, intra-hour simulations were performed for a selected day to address the challenges of wind variability. The implications of these simulation results are discussed in Section 5. Finally, Section 6 synthesizes the findings of Section 3 through Section 5 in order to examine the implications of wind power for the SPP market. It also provides recommendations ranging from methods for addressing forecast errors to policy reform. Methodological details are provided in Section MAJOR FINDINGS AND RECOMMENDATIONS SPP wind generation resources are primarily located in the western portion of the SPP footprint, mostly in transmission-constrained locations away from load and non-wind generation centers. For this reason, increase in the wind penetration level causes changes in the power flow patterns, requiring upgrades and/or reconfigurations to the transmission system. In particular, the power flows from western SPP to eastern SPP increase significantly. To accommodate the increased west-to-east flows while meeting the reliability standards of the SPP Criteria, a number of transmission expansions were required. These included new transmission lines totaling 1,260 miles of 345 kv and 40 miles of 230 kv lines for the 10% Case, and an additional 485 miles of 765 kv, 766 miles of 345 kv, 205 miles of 230 kv, and 25 miles of 115 kv lines for the 20% Case. With the aforementioned transmission expansions, current voltage and thermal transfer limitations in the areas of greatest wind expansion were eased. For example, voltage-driven transfer limitations from SPS to SPP were increased from 529 MW in the Base Case to 1,200 MW and 3,090 MW in the 10% and 20% Cases, respectively. The contingency analysis that CRA performed on this expanded grid identified new flowgates that should be monitored during operations. No voltage stability issues were found, mainly due to the transmission expansions. 3 Transmission upgrades were selected based on their projected impact on SPP transmission system capabilities; no consideration was made of the estimated cost of any of the upgrades. Final Report Page 1-2

29 No transient stability issues were found for the 10% or 20% Cases, supporting the conclusion that increasing wind penetration levels, along with appropriate transmission expansions, does not adversely affect the transient stability of the power system. Based on the findings of this study, several transmission-related recommendations are made. First, major transmission reinforcements are needed to accommodate increased wind penetration levels, starting as low as 10%. Considering that the lead times of transmission projects are longer than those of wind plant projects, it is recommended that SPP take definitive steps to reinforce its transmission network, especially west to east, and that economic analyses of the needed transmission expansions be undertaken. Second, the addition of high voltage lines requires the installation of voltage control devices to prevent over-voltages under low-flow conditions due to contingencies or low wind power availability. Third, dynamic voltage support becomes increasingly important for higher wind penetration levels, in which several conventional generators may become displaced in the dispatch order by wind generators. Therefore, it is recommended that new reactive capability of the same nature as that provided by the displaced thermal units (i.e., continuously and instantaneously controllable) be added as wind penetration increases. 4 The study found that, with all needed transmission upgrades in place, integrating the levels of wind studied in the 10% and 20% Cases could be attained without adversely impacting SPP system reliability. Although localized voltage issues and transmission congestion were observed, wind curtailment levels, on average, were around 1% for both the 10% Case and the 20% Case. Even with the transmission upgrades, however, operational complexity would increase and lead to economic challenges. Consolidating SPP into a single BA, as is planned, should reduce overall needs for reserves and flexible resources. To accommodate higher wind penetration levels, however, more operational flexibility (more start-ups and cycling of units) is required. The need for flexible units increases as the forecast error increases. A robust transmission system could reduce local generation requirements. Additionally, as the operational needs for non-wind units change, resulting changes in the commitment and dispatch bring about new flow patterns. Coordinated planning between wind and transmission is therefore essential. SPP should proceed with the cluster approach for generation interconnection evaluations, and explicitly account for the diversity and correlation of wind resources in generation interconnection and planning studies. If possible, SPP should explore ways to increase diversity in the wind resource base by encouraging wind power investment in areas without significant wind development. Ancillary service requirements depend on the wind penetration level. The increase of wind power leads to a need for increased regulation capability. The regulation requirement increase accelerates as the wind penetration level increases. Wind regulation needs are time- 4 The quantity of voltage support provided by individual units was not assessed in this study. Final Report Page 1-3

30 varying and can be reduced by improvements in forecast accuracy. The study findings show that regulation-up and regulation-down requirements are not symmetric and could differ significantly from one another. Furthermore, wind may be able to provide regulation down during high-wind periods. Therefore, CRA recommends that these two ancillary services be separated. A new type of ancillary service, such as load-following reserves, which are not currently defined or required in SPP, may become highly beneficial as the net load forecast variability increases with higher wind penetration levels. 5 As with regulation, the need for these reserves is also time-dependent and not symmetric in each direction. The study found that forecast errors increase startups of flexible units and reduce generation of less flexible units, which typically have lower marginal costs. Forecast errors were observed to have different impacts depending on whether the deviation from the forecast was positive or negative. Wind under-forecasts tend to exacerbate wind curtailments and small forecast errors lead to increased curtailment levels. Wind over-forecasts have a much smaller impact on curtailment but could lead to reliability issues if not enough non-wind resources are committed. Primary causes for wind curtailments observed in the study include minimum generation requirements of committed thermal units, the need to dispatch units capable of providing reserves (especially regulation down), and transmission congestion. Minimum generation challenges arise when minimum generation is higher than net load (which decreases as the wind penetration level increases). Given the high correlation in output observed among wind sites within SPP, the implementation of a centralized forecasting system would be advantageous. It is recommended that specific-purpose forecasts be procured for difficult operational situations, such as high magnitude ramps. The needs for operational flexibility, enhanced ancillary services, and accommodation of forecast error all lead to the conclusion that unit commitment capabilities are key to wind integration. Efficient wind integration requires a sophisticated unit commitment process that explicitly addresses the uncertainty associated with forecast errors. Based on the operational impacts observed and the wind characteristics analyzed, it is recommended that the dayahead unit commitment be supplemented with an intra-day unit commitment (e.g., 4-hourahead). 6 The study identified four major implications of wind integration under the Day 2 Market, which will operate as a single BA. First, the Day 2 Market will lead to different unit commitment and dispatch decisions than are currently observed, even without high wind penetration level and additional transmission. Second, a single BA would greatly facilitate the integration of wind. Third, higher penetration levels of wind do not immediately guarantee a lower market clearing price for energy. Fourth, error in the net load forecast will increase. 5 Net load is defined as load minus wind generation. 6 The Day 2 Market is assumed to have a day-ahead commitment schedule. Final Report Page 1-4

31 This study contains an overview of existing policies pertaining to wind integration in other markets, both in North America and Europe. CRA has reviewed these policies in conjunction with the findings of the analyses performed and has provided recommendations for policy implementation. These recommendations are presented and discussed in Section 6. The analytical results of the study show that there are no significant technical barriers to integrating wind generation to a 20% penetration level into the SPP system, provided that sufficient transmission is built to support it. The study, however, did not include an optimization of the level of transmission expansion required to support wind integration. The findings of this study could be used as the basis of such an optimization, which along with further analyses using actual SPP wind plant operating data (when available), is recommended as a follow-up study. Final Report Page 1-5

32 2. INTRODUCTION 2.1. BACKGROUND AND OBJECTIVES OF THE STUDY The Southwest Power Pool (SPP) selected (CRA) in early 2009 to conduct a study to determine the operational and reliability impact of integrating wind generation into the SPP transmission system and energy markets. The study required an intensive effort to perform a detailed engineering analysis correctly and then interpret the findings and associated policy implications in the context of a broad regional market. To achieve the overall objective, the study assessed the impacts of wind generation on three different aspects of SPP: transmission, operation, and markets. The three aspects and the analyses associated with each are summarized below. Transmission Impact Study Steady-state thermal and voltage analysis: pre- and post-contingency Voltage stability analysis: PV, VQ, and dv/dq analyses Transmission expansion requirements to facilitate the wind projects including transient stability, voltage stability, and VAR requirements Impacts of wind generation on ATC Recommendations on generation interconnection (GI) analysis, procedures, and requirements for wind generation Operational Impact Study Impacts on both operations and markets in the regulation, load-following, and unit commitment timeframes Time-synchronized load and wind data Impacts on ancillary services Market Impact Study Impact of the additional wind generation on the Energy Imbalance Market and identification of potential policy changes required to accommodate the additional wind generation Review of the practices of other markets that have successfully integrated large amounts of wind generation and identification of best practices Propose method to incorporate day-ahead wind and load forecasting error in the unit commitment and dispatch processes Final Report Page 2-1

33 The study was performed for the year 2010 with the assumption that SPP operates as a single balancing authority (BA) with a co-optimized energy and ancillary service market (Day 2 Market). The goal was to identify the challenges of high wind penetration levels and the infrastructure required to accommodate the integration of wind into the SPP transmission system. One of the objectives was to identify the transmission upgrades needed to accommodate wind power without curtailment. This was not an economics study and therefore economic optimization, such as determining an appropriate balance between building transmission upgrades and curtailing wind was not performed. Another important note is that the study was not intended to address the impact of any individual wind plant and therefore the analyses results should not be tied to, or used to evaluate, any individual wind plant and its operations. The analyses were performed in the following order. First, SPP selected the set of wind plants to be included in the study. Analysis of these select wind plants characteristics was performed. This is discussed in Section 3. Then, transmission expansions needed to accommodate these wind plants were identified band added to the current system. These needs were based on the application of the SPP Criteria to a set of power flows with increased wind penetration without any consideration for cost. Transmission related analysis was performed for the upgraded system and discussed in Section 4. The results from the wind characteristic analysis (Section 3) and transmission analysis (Section 4) were then used to analyze the impact of wind on ancillary services, reserves in particular, and on system operation over time by running a production simulation. The production simulation analyzed additional congestion not observed in the transmission analysis in Section 4, unit commitment and dispatch issues, and the impact of forecast errors. These impacts to operations are all discussed in Section 5. Finally Section 6 translates findings from Section 3 through Section 5 to market impacts, and provides policy recommendations including methods to address forecast errors. Details of the methodologies used for this study are described in Section THE SOUTHWEST POWER POOL SPP, originally founded in 1941, is a Regional Transmission Organization (RTO) approved by the Federal Energy Regulatory Commission (FERC). As an RTO, SPP ensures reliable supply of power, adequate transmission infrastructure, and competitive wholesale pricing of electricity. SPP currently serves parts or all of eight states (Arkansas, Kansas, Louisiana, Missouri, Nebraska, New Mexico, Oklahoma, and Texas) and has members in nine states (Mississippi in addition to the eight states listed above), with over five million customers. It covers a footprint of over 370,000 square miles with 47,000 miles of transmission lines (nearly enough to circle the earth twice). SPP is interconnected heavily with Entergy, Associated Electric Cooperative Incorporated (AECI), the Midwest Independent System Operator (MISO), Mid-Continent Area Power Pool (MAPP), and through limited DC ties with Electric Reliability Council of Texas (ERCOT) and Western Electricity Coordinating Council (WECC). SPP is also a North American Electric Reliability Corporation (NERC) Regional Final Report Page 2-2

34 Entity, overseeing compliance enforcement and reliability standards development. Figure [52] shows the geographic footprint of SPP. Figure 2.2-1: SPP footprint As of December 2008, the forecasted 2009 peak demand for SPP was approximately 50 GW and the annual energy consumption was 240 TWh. As of December 2008, SPP had nearly 66 Final Report Page 2-3

35 GW of installed capacity with a generation portfolio of approximately 40% coal, 42% gas, 4% nuclear, 4% hydro, 2% wind, and 8% other generation technologies. The wind power component (nameplate) of the portfolio was approximately 1.8 GW in December 2008, and approaching 3 GW in December Since 2007, SPP has been operating an Energy Imbalance Service Market in which participants can buy and sell wholesale electricity in real time. This market is currently operated under 13 BAs. The definition of SPP used for the purpose of this study is limited to the SPP members that are part of the SPP market. These members are American Electric Power West (AEPW), Empire District Electric Company (EMDE), Grand River Dam Authority (GRDA), City of Independence, Missouri (INDN), Kansas City Board of Public Utilities (KACY), Kansas City Power and Light (KACP), Westar (WERE), Lincoln Electric System (LESY), Midwest Energy Incorporated (MIDW), Missouri Public Service (MIPU), 1 Nebraska Public Power (NPPD), Oklahoma Gas and Electric Company (OKGE), Omaha Public Power District (OPPD), Southwestern Public Service Company (SPS), Sunflower Electric Power Corporation (SUNC), and Western Farmers Electric Cooperative (WFEC). SPP plans to implement the Day-Ahead Market and Ancillary Services Market by December 2013 under a single BA (Day-2 Market). For this study, it is assumed that these markets were already in service WIND GENERATION POTENTIAL IN SPP AND NEIGHBORING REGIONS Figure [55] shows the wind potential of the entire United States, as provided by 3Tier Inc. As seen in Figure 2.3-1, high wind potential exists on either side of the Rocky Mountains, including a significant portion of the SPP footprint (most of Nebraska and Kansas, the western half of Oklahoma, and the portions of New Mexico and Texas in SPP), along with the upper Midwest region of the Eastern Interconnection (EIC), including MAPP, Western Area Power Administration (WAPA), and MISO. Significant penetration of wind has an enormous potential to displace generation from existing thermal units, leading to reduced emissions (NO x, SO 2, and CO 2 ) and to reduce overall energy production costs. As of December 2009, the SPP GI queue contains approximately 48,000 MW of wind generation (nearly 80% of all generation projects in the queue). This high wind potential region is sure to play a key role in evolving national energy policy. Even in the absence of a national requirement, many states are considering or requiring Renewable Energy Standards (RES), mandating that utilities generation portfolios must 1 Missouri Public Service (MIPU) is now KCP&L Greater Missouri Operations Company (GMOC). However it will be referred to as Missouri Public Service or MIPU throughout this report. This is to avoid unnecessary confusion as the data received from SPP, including the power flow cases, historical load data, and load forecast data, were all using the MIPU nomenclature. Final Report Page 2-4

36 contain a minimum amount of renewable sources. Of the states within the SPP footprint, Missouri, New Mexico, and Texas already require RES, and Kansas 2 has recently established renewable portfolio standard although the rules and regulations that administer this portfolio standard has not yet been established at the time of preparing this report. Of the renewable resources available, wind is in the most advanced stage of development in the SPP region. Figure 2.3-1: Wind potential in the United States Under pending federal and state policies, states that lack renewable resources may need to seek imported power generated by renewable resources. SPP has the potential to become a large exporter of wind power to neighboring states with little wind potential. As discussed in Section 2.4, however, SPP faces significant operational considerations at all levels. 2 House Bill 2369 that established the renewable portfolio standard for Kansas was enacted in May The Kansas Corporation Commission is now given 12 months to establish rules and regulations to administer the portfolio standard, which requires state s investor owned utilities and larger corporative utilities to generate or purchase certain amount of generation capacity from eligible renewable resources. Final Report Page 2-5

37 2.4. KEY CHALLENGES OF WIND INTEGRATION Integrating wind power into an existing portfolio does not pose significant operational challenges when the wind penetration level is low, especially in portfolios with abundant flexible resources with high response rates. As the wind penetration level increases, however, challenges arise due to the unique characteristics of wind. The most apparent challenge is that wind is a variable resource and cannot be controlled in the same manner as traditional generators. The variability leads to a greater need for reserves; disturbances of the generation-to-load balance due to high ramp events require supplementation by responsive resources, including generation and demand. Additional reserves have additional costs and increase operational challenges, especially in a market like that of SPP, in which the generation is primarily thermal with few hydro resources. Furthermore, the peak hours for wind generation usually occur in the early morning, just before sunrise, and do not coincide with the peak hours for load, which typically occur mid- to late-afternoon. 3 As a result, net load (load minus wind generation) exhibits more significant fluctuations between off-peak and on-peak periods. This leads to more operational challenges in controlling non-wind generators serving the net load. For a primarily thermal generation portfolio like that of SPP, this means that additional challenges arise during the off-peak hours because of minimum generation requirements. The variation of wind output and forecast errors have a significant impact on non-wind unit commitment. Under-forecasting wind generation leads to over-commitment of non-wind generation and over-forecasting wind generation leads to under-commitment. Overcommitment can result in a suboptimal economic dispatch and high uplift costs as well as wind generator curtailment. Under-commitment can result in shortage of supply, a reliability concern. In order to avoid these commitment problems, the uncertainty introduced by wind power in the unit commitment timeframe must be minimized and explicitly modeled in the unit commitment decisions, especially with high wind power penetration levels. Another factor affecting wind integration is the output characteristic of a wind plant power curve. Figure [53] shows a representative power curve. The power curve is unique to each turbine type and location and represents the relationship between wind speed and electric power output for a given unit at a given site. As seen in Figure 2.4-1, the forecast error can vary greatly depending on the wind speed. If the wind speed is in the range shown as S2 (in between the two red lines), a small forecast error in wind speed leads to a large error in wind generation output. If wind speed is in the S3 range but near the border of the S3 and S4 range, there is a risk of a cutoff event, which shuts down the wind turbine to avoid mechanical failure. These differing potential forecasting errors will lead to different reserves needs. 3 This is typically not true for off-shore wind, but there are no planned off-shore wind additions for SPP. Final Report Page 2-6

38 S1 S2 S3 S4 Figure 2.4-1: Wind turbine power curve Apart from the unique characteristics of wind power, it is important to note that wind generation is usually located far from load centers; therefore, advanced transmission planning is required. The topology and technology of the transmission overlay is critical to integrating high wind power penetration levels. This is especially true for SPP because the areas of highest wind potential are in the west, but the load centers are in the east. Another transmission-related concern is that SPP has limited DC connections with ERCOT (to the south) and WECC (to the west). Therefore, if SPP were to export wind power, the best option would be to export to neighboring areas east and southeast of SPP, requiring additional transmission expansion. Finally, the geographic characteristics of the SPP footprint lead to another challenge. Because the SPP footprint is primarily flat terrain, the correlation of output among wind plants is relatively high compared to that of regions like PJM, where most wind plants are sited on ridge tops. Therefore, as this report will illustrate, the stochastic properties of wind and load time series become critical. Final Report Page 2-7

39 3. SPP WIND RESOURCES: CASES ANALYZED AND THEIR CHARACTERISTICS This section describes the wind penetration cases modeled in this study, including a specification of the wind plants considered in each case, and analyzes their statistical characteristics. The focus of the analysis in this section is on the characteristics of wind resources that have large impacts on operations, such as average wind profiles and the deviations from those averages, and spatial and temporal wind profile diversity. The insights gained in this part of the study were used to shape and inform the transmission analysis described in Section 4 and the operations analysis described in Section 5. Four wind penetration cases defined by SPP were analyzed: the power system as it currently exists (approximately 4% wind penetration), and three levels of higher wind penetration 10%, 20%, and 40% (10% Case, 20% Case, and 40% Case, respectively). The level of penetration in each scenario signifies the percentage of annual energy generated by wind in SPP. Most wind plants in each case are concentrated in the western part of SPP. The statistical analysis performed by CRA and described in this section revealed high variability in the available wind power outputs. In fact, the maximum and minimum observed available output for the aggregate wind generating resources in SPP ranges from less than 1% to 92% of the nameplate wind capacity. On average, available wind generation depends on the season and time of day, among others factors. The overall capacity factor 1 of the wind plants is about 39%. SPP wind resources exhibit geographic diversity in their profiles, so for this reason, consolidating BAs in SPP should reduce overall needs for reserves and flexible resources. For the wind cases analyzed, however, increasing wind penetration yielded little gain in diversity, because the geographic spread of wind plants in the higher wind penetration cases is similar to that of the existing wind plants. Greater geographic diversity mitigates the variability of aggregate wind generation. The study results might have been different, therefore, had different assumptions for wind generation siting been used, e.g., based on wind resource assessments instead of the current generation interconnection queue. Based on the findings in this section, CRA makes the following recommendations to enable high levels of wind integration in the SPP power system: Proceed with the consolidation of SPP into a single BA. Proceed with the cluster approach for GI evaluations. 1 Capacity factor is the average output as a percentage of the total nameplate capacity. Final Report Page 3-1

40 Explicitly account for the diversity and correlation of wind resources in planning and interconnection studies, e.g., through the use of historical wind data in the determination of wind power dispatch for the cluster studies mentioned above. Explore ways to increase diversity in the wind resource base by encouraging wind power investment in areas without significant development. 2 The remainder of this section is organized into three portions. Section 3.1 describes the wind penetration cases modeled in the study; Section 3.2 provides the statistical characteristics of wind profiles; and Section 3.3 discusses the spatial and temporal diversity of wind resources in SPP WIND PENETRATION CASES FOR SPP AND NEIGHBORING REGIONS For the purposes of this study, four scenarios defined by SPP were analyzed: the power system as it currently exists (Base Case, 4% wind penetration), and three levels of wind penetration 10%, 20%, and 40%. The level of penetration in each scenario signifies the percentage of annual energy generated by wind in SPP; because the capacity factor of wind generation is below the fleet average, the wind generation proportions of total installed nameplate capacity for each scenario were higher than the corresponding penetration levels. Detailed analysis was done for the Base, 10%, and 20% Cases, while partial analysis was done for the 40% Case. Table shows the wind generation capacity for each wind penetration level. 3 Table 3.1-1: Wind generation capacity by penetration level Penetration Scenario Base Case 10% Case 20% Case 40% Case Number of farms Installed Nameplate Wind Capacity (MW) Wind/Non-Wind Nameplate Capacity 2,877 6,840 13,674 25, Western Nebraska is one example of a region in SPP with good wind potential and low wind profile correlation with the areas where most of the wind sites in this study are located. 3 Wind/Non-Wind Nameplate Capacity shows the ratio of wind generation capacity to non-wind generation capacity. It does not represent the percentage of total generation comprised of wind power. Since this study defined wind penetration level by annual energy provided by wind plants, both the wind/non-wind ratio will differ from the penetration level (as would the percentage of wind to total nameplace capacity) based on the assumed capacity factor for wind plants. Final Report Page 3-2

41 Attributes of the wind plants considered in this study, including location, size, and operating characteristics, were obtained from the SPP GI queue as of February The Base Case includes wind generators in commercial operation as of February To allocate queue capacity as new entry in the 10%, 20%, and 40% Cases, SPP prioritized the interconnection requests in the following order: Interconnection Agreement fully executed or on-schedule, Facility Study completed, Facility Study in progress, Impact Study completed, Impact Study in progress, Feasibility Study completed, Feasibility Study in progress. The wind plants for the 10%, 20% and 40% Cases were selected from the queue using this order, so that each case approximately meets its wind penetration level. 4 The Base Case wind generation is included in the 10% Case, as is the 10% Case generation in the 20% Case, and the 20% Case generation in the 40% Case. These wind plants were clustered by their geographic location and point of connection to the transmission system. Table through Table show the 142 wind plants modeled for this study, including their GI queue ID, capacity, county and state, and the CRA-defined cluster. Figure shows the geographic location of these wind plants and their clusters, as modeled by CRA. 4 Wind plants for each case were selected assuming a 40% capacity factor for wind plants and projected 2010 annual energy sales for SPP. The 40% capacity factor is consistent with the findings of the NREL wind profiles analyses discussed in Table Final Report Page 3-3

42 Table 3.1-2: Base Case wind from SPP GI queue as of February 2009 Site # GI REQUEST CAPACITY (MW) AREA COUNTY STATE Case Cluster 1 GEN WFEC HARPER OK Base C OK 2 GEN WFEC COMANCHE OK Base C OK 3 GEN SPS CHAVES NM Base SP/NM 4 GEN SPS QUAY NM Base SP/NM 5 GEN OKGE WOODWARD OK Base C OK 6 GEN M 110 WERE WICHITA KS Base Wichita 7 GEN EMDE BUTLER KS Base Wichita 8 GEN EMDE BUTLER KS Base Wichita 9 GEN WFEC ROGER MILLSOK Base C OK 10* GEN SPS HANSFORD TX Base Hitchland 11 GEN SPS HANSFORD TX Base Hitchland 12 GEN SPS OLDHAM TX Base Amarillo 14 GEN A 101 KACP FORD KS Base Spearville 16 GEN WFEC CADDO OK Base C OK 17 GEN A 110 WERE CLOUD KS Base NE KS 18 GEN A 110 EMDE CLOUD KS Base NE KS 19 GEN MIDW ELLSWORTH KS Base NE KS 20 GEN MIDW ELLSWORTH KS Base NE KS 21 GEN SPS CARSON TX Base Amarillo 23 GEN AEPW CUSTER OK Base C OK 24 GEN AEPW CUSTER OK Base C OK 25 GEN WFEC CADDO OK Base C OK 26 GEN WFEC CADDO/KIOWOK Base C OK 27 GEN OKGE HARPER OK Base C OK 28 GEN WERE BARBER KS Base Wichita 29 GEN S 19 WFEC HARPER OK Base C OK 30 MONTE 110 MKEC MONTEZUMA KS Base Spearville 31 LLANOEST 80 SPS CARSON TX Base Amarillo 32 SPSDIST 10 SPS MOORE TX Base Hitchland 33 SPSDIST 10 SPS MOORE TX Base Hitchland 34 SPSDIST 10 SPS MOORE TX Base Hitchland 35 SPSDIST 10 SPS MOORE TX Base Hitchland 36 SPSDIST 10 SPS SHERMAN TX Base Hitchland 37 SPSDIST 10 SPS SHERMAN TX Base Hitchland 38 SPSDIST 10 SPS HANSFORD TX Base Hitchland 39 SPSDIST 10 SPS TEXAS OK Base Hitchland 40 SPSDIST 10 SPS TEXAS OK Base Hitchland 41 AINSWRTH 60 NPPD BROWN NE Base E NE 42 ELKHRNRDG 81 NPPD KNOX NE Base E NE 43 CRFTNHLS 42 NPPD KNOX NE Base E NE * Wind site 10 has 160 MW of wind power in the Base Case and 240 MW of wind power in the 10% Case and above. Final Report Page 3-4

43 Table 3.1-3: 10% Case wind from SPP GI queue as of February 2009 Site # GI REQUEST CAPACITY (MW) AREA COUNTY STATE Case Cluster 10* GEN SPS HANSFORD TX 10% Hitchland 13 GEN SPS OLDHAM TX 10% Amarillo 15 GEN A 50 KACP FORD KS 10% Spearville 22 GEN SPS CARSON TX 10% Amarillo 44 GEN SPS TEXAS OK 10% Hitchland 45 GEN WFEC CADDO OK 10% C OK 46 GEN SUNC FORD KS 10% Spearville 47 GEN WERE ELK KS 10% Wichita 48 GEN MKEC FORD KS 10% Spearville 49 GEN S 20 SPS HANSFORD TX 10% Hitchland 50 GEN SUNC SHERMAN KS 10% NW KS 51 GEN SUNC THOMAS KS 10% NW KS 52 GEN AEPW BECKHAM OK 10% C OK 53 GEN SPS HANSFORD TX 10% Hitchland 54 GEN SPS HANSFORD TX 10% Hitchland 55 GEN SPS HANSFORD TX 10% Hitchland 56 GEN SPS RANDALL TX 10% Amarillo 57 GEN OKGE DEWEY OK 10% C OK 58 GEN SPS RANDALL TX 10% Amarillo 59 GEN SPS EDDY NM 10% SP/NM 60 GEN SPS SEWARD KS 10% Hitchland 61 GEN SPS SEWARD KS 10% Hitchland 62 GEN SPS SEWARD KS 10% Hitchland 63 GEN SPS TERRY TX 10% SP/NM 64 GEN SPS HUTCHINSONTX 10% Hitchland 65 MADISONCO 120 NPPD MADISON NE 10% E NE 66 BOYDCO1 125 WAPA BOYD NE 10% E NE 67 ANTELOPECO 111 NPPD ANTELOPE NE 10% E NE 68 COOKERCO 108 NPPD HOOKER NE 10% W NE 69 BUTLERCO 105 NPPD BUTLER NE 10% E NE * Wind site 10 has 160 MW of wind power in the Base Case and 240 MW of wind power in the 10% Case and above. Final Report Page 3-5

44 Table 3.1-4: 20% Case wind from SPP GI queue as of February 2009 Site # GI REQUEST CAPACITY (MW) AREA COUNTY STATE Case Cluster 70 GEN OKGE BLAINE OK 20% C OK 71 GEN SPS GRAY TX 20% Amarillo 72 GEN SPS CASTRO TX 20% Amarillo 73 GEN SUNC HAMILTON KS 20% Spearville 74 GEN SUNC RAWLINS KS 20% NW KS 75 GEN SUNC WICHITA KS 20% Wichita 76 GEN WERE NEMAHA KS 20% NE KS 77 GEN MIPU NODAWAY MO 20% NE KS 78 GEN SPS HAMILTON KS 20% Spearville 79 GEN SPS HAMILTON KS 20% Spearville 80 GEN OKGE DEWEY OK 20% C OK 81 GEN WERE BARBER KS 20% Wichita 82 GEN SPS DEAF SMITH TX 20% Amarillo 83 GEN SPS CURRY NM 20% SP/NM 84 GEN MKEC CLOUD KS 20% NE KS 85 GEN SPS GRAY TX 20% Amarillo 86 GEN WFEC CUSTER OK 20% C OK 87 GEN SPS HUTCHINSONTX 20% Hitchland 88 GEN SPS HUTCHINSONTX 20% Hitchland 89 GEN SPS ROOSEVELT NM 20% SP/NM 90 GEN SUNC FORD KS 20% Spearville 91 GEN SUNC FORD KS 20% Spearville 92 GEN SUNC FORD KS 20% Spearville 93 GEN SUNC GRAY KS 20% Spearville 94 GEN SPS TEXAS OK 20% Hitchland 95 GEN SPS HANSFORD TX 20% Hitchland 96 GEN OKGE GRADY OK 20% C OK 97 GEN OKGE BLAINE OK 20% C OK 98 PIERCECO 171 NPPD PIERCE NE 20% E NE 99 CHERRYCO 272 NPPD CHERRY NE 20% E NE 100 HAMILTONCO 301 NPPD HAMILTON NE 20% E NE Final Report Page 3-6

45 Table 3.1-5: 40% Case wind from SPP GI queue as of February 2009 Site # GI REQUEST CAPACITY (MW) AREA COUNTY STATE Case Cluster 101 GEN SPS CARSON TX 40% Amarillo 102 GEN SPS TEXAS OK 40% Hitchland 103 GEN SUNC THOMAS KS 40% NW KS 104 GEN SPS RANDALL TX 40% Amarillo 105 GEN WFEC BECKHAM OK 40% C OK 106 GEN OKGE WOODWARD OK 40% C OK 107 GEN WFEC WOODWARD OK 40% C OK 108 GEN MIPU NODAWAY MO 40% NE KS 109 GEN SPS LEA NM 40% SP/NM 110 GEN SPS CIMARRON TX 40% Hitchland 111 GEN SPS MOORE TX 40% Hitchland 112 GEN OKGE BLAINE OK 40% C OK 113 GEN OKGE ELLIS OK 40% C OK 114 GEN OKGE HARPER OK 40% C OK 115 GEN OKGE HARPER OK 40% C OK 116 GEN OKGE HARPER OK 40% C OK 117 GEN OKGE HARPER OK 40% C OK 118 GEN MIDW ELLIS OK 40% NW KS 119 GEN OKGE WOODWARD OK 40% C OK 120 GEN SPS GARZA TX 40% SP/NM 121 GEN SPS GARZA TX 40% SP/NM 122 GEN SPS CHAVES NM 40% SP/NM 123 GEN SUNC FINNEY KS 40% Spearville 124 GEN EMDE BENTON AR 40% NE KS 125 GEN OKGE GARFIELD OK 40% C OK 126 GEN SPS FLOYD TX 40% SP/NM 127 GEN SPS FLOYD TX 40% SP/NM 128 GEN SPS LYNN TX 40% SP/NM 129 GEN SUNC SCOTT KS 40% NW KS 130 BOYDCO2 491 WAPA BOYD NE 40% E NE 131 CHEYENNECO 468 WAPA CHEYENNE NE 40% W NE 132 BOOKERCO 408 NPPD HOOKER NE 40% W NE 133 GEN SUNC FINNEY KS 40% Spearville 134 GEN OKGE DEWEY OK 40% C OK 135 GEN SPS CHAVES NM 40% SP/NM 136 GEN AEPW WASHITA OK 40% C OK 137 GEN ,001 SUNC CLARK KS 40% Wichita 138 GEN SUNC SHERMAN KS 40% NW KS 139 GEN SUNC THOMAS KS 40% NW KS 140 GEN WERE MORRIS KS 40% NE KS 141 GEN SPS TEXAS OK 40% Hitchland 142 GEN OKGE ELLIS OK 40% C OK Final Report Page 3-7

46 East Nebraska West Nebraska Northwest Kansas Northeast Kansas Spearville Wichita Hitchland Amarillo Central Oklahoma South Panhandle / New Mexico Base Case 10% Case 20% Case 40% Case 40 MW 200 MW 500 MW 900 MW 230 kv 345 kv 500 kv Figure 3.1-1: Wind plant sites and clusters Figure through Figure show the wind power distribution among the clusters for the Base Case, 10% Case, and 20% Case. Note that most wind plants are concentrated in north Texas, west Oklahoma and southwest Kansas. In fact, the Spearville, Wichita, Central Oklahoma, Amarillo and Hitchland clusters comprise 71%, 72% and 73% of the total wind capacity in SPP in the Base, 10% and 20% Cases, respectively. Final Report Page 3-8

47 Wichita 13% Amarillo 11% Spearville 7% South Panhandle/ New Mexico 7% Base Case 2,877 MW Central Oklahoma 30% Northeast Kansas 16% Hitchland 10% East Nebraska 6% Figure 3.1-2: Base Case wind nameplate capacity distribution by cluster Spearville 10% Northwest Kansas 3% Wichita 8% West Nebraska 2% Amarillo 14% South Panhandle/ New Mexico 7% Northeast Kansas 7% 10% Case 6,840 MW Hitchland 23% Central Oklahoma 17% East Nebraska 9% Figure 3.1-3: 10% Case wind nameplate capacity distribution by cluster Final Report Page 3-9

48 Wichita 7% Northwest Kansas 4% West Nebraska 1% Amarillo 13% Spearville 17% South Panhandle/ New Mexico 5% Northeast Kansas 7% 20% Case 13,674 MW Hitchland 19% Central Oklahoma 17% East Nebraska 10% Figure 3.1-4: 20% Case wind nameplate capacity distribution by cluster For areas with high wind potential that neighbor SPP (WAPA, MAPP, and the wind-rich western side of MISO, as seen in Figure 2.3-1), a comparable wind penetration level was assumed in each case. For example, for the 10% Case, these areas were also assumed to have a 10% wind penetration level. Unlike the wind generators within SPP, which were modeled individually at specific locations, wind units outside of SPP were modeled as aggregates WIND PROFILE STATISTICAL CHARACTERISTICS The analysis of the statistical characteristics of the wind profiles for each wind penetration level is presented in this section. Because the analysis contained in this section was performed on the entire portfolio of wind generation in SPP, the results are not reflective of the statistical characteristics of individual wind plants or clusters. The wind profiles used for this study are based on the profiles developed by AWS Truewind for the National Renewable Energy Laboratory (NREL) Eastern Wind Integration and Transmission Study (EWITS).[21] Section 7.3 provides a detailed explanation of the method employed to assign NREL profiles to the wind plants in each of the cases. Final Report Page 3-10

49 Table provides a summary of the statistics for the aggregate wind penetration cases. The average capacity factor of the profiles analyzed was just below 40%. Actual available wind output 5 varies greatly around the capacity factor, with maximum and minimum observed values at approximately 92% and less than 1% of the nameplate wind capacity. The 95 th and 5 th percentile outputs are at about 80% and 8% of the wind capacity, i.e., 90% of the time, the available wind power output is between these two values. 6 Table 3.2-1: Summary of the wind power statistics by case Penetration Scenario Base Case 10% Case 20% Case 40% Case Number of farms Installed Nameplate Wind 2,877 6,840 13,674 25,003 Capacity (MW) Wind/Non-Wind Capacity Factor 39.5% 38.8% 39.0% 39.2% Maximum Output 2,663 6,265 12,556 23,072 Percentile 95 Output 2,336 5,342 10,756 19,622 Mean Output 1,135 2,653 5,334 9,804 Aggregate Output Median Output 1,021 2,418 4,846 8,941 Statistics (MW) Percentile 5 Output ,096 2,067 Minimum Output Standard Deviation 670 1,524 3,043 5,542 Max Hourly Increase 687 1,538 3,276 6,428 Max Hourly Decrease 626 1,382 2,977 5,134 Standard Deviation ,136 2,092 4-Hour-Ahead Forecast Maximum 934 2,387 4,753 9,178 Error Statistics (MW) Minimum -1,025-2,492-4,564-8,139 Standard Deviation ,260 2,334 Day-Ahead Forecast Maximum 1,258 2,871 5,576 10,338 Error Statistics (MW) Minimum -1,071-2,319-4,574-7,916 5 Available wind power output is the maximum power that can be extracted from the wind plants at a given moment. 6 NERC requirements establish the average ACE limit for 90% of the 10-minute intervals. Therefore, the 5th and 95th percentiles of the 10-minute average deviations from forecasts were used as metrics for regulation needs (in Section 5). Doing so ensures that that the range (95th percentile 5th percentile) covers 90% of the cases. Final Report Page 3-11

50 The histograms in Figure indicate the likelihood of different values of total available wind output for each of the four wind penetration cases. Likelihood in the histograms is measured using the average numbers of hours observed to have the total available wind power within a small range (bin). The histograms have shapes that are different from Gaussian bells. Rather, the histograms exhibit typical truncated Weibull shapes, in which the number of hours increases rapidly as the available wind goes from 0 MW to 10% of the wind nameplate capacity, then increases more gradually until reaching the most likely available wind at 20% of nameplate capacity. The number of hours decreases gradually for further increases in total available wind until reaching 80% of the wind nameplate capacity, where the reductions are more abrupt, dropping to zero observed hours at about 91% of the wind nameplate capacity. In the histograms, the most frequent available wind outputs, or modes, are well below the median available wind output (indicated with dotted lines), and these are in turn below the mean available wind output. The mode, median and mean are approximately 20%, 35% and 39% of the wind nameplate capacity, respectively. Base Case 10% Case Number of hours per year Number of hours per year ,000 1,500 2,000 2,500 Wind output (MW) 0 0 1,000 2,000 3,000 4,000 5,000 6,000 Wind output (MW) 20% Case 40% Case Number of hours per year Number of hours per year ,000 4,000 6,000 8,000 10,000 12,000 Wind output (MW) 0 5,000 10,000 15,000 20,000 Wind output (MW) Figure 3.2-1: Wind power output: histograms by case Final Report Page 3-12

51 Figure shows the autocorrelation factors for the total available wind output for the four wind penetration cases as a function of the time lag in hours. The correlation factor between two random variables shows the degree to which there is a linear dependency between the two variables. A correlation factor of 1 indicates an exact, positive linear relation between the random variables, -1 indicates an exact negative linear relation, and 0 indicates no linear relation between the variables. The autocorrelation for a time lag of one hour indicates the level of linear relation between the available wind power in consecutive hours. Note that, in the diagrams, the autocorrelation is high (above 0.8) for lags up to four hours. For lags between 12 and 24 hours, the autocorrelation is low (below 0.4) and relatively constant, indicating that total available wind power output would not be a good basis for predicting the available wind power 12 to 24 hours into the future. All wind penetration levels show similar autocorrelation structures. This is because the aggregate wind output profiles for each of the wind penetration levels are highly correlated with each other, as can be seen in the next Section (Figure 3.3-1). 7 1 Base Case 1 10% Case Autocorrelation factor Autocorrelation factor Lag (hour) Lag (hour) 1 20% Case 1 40% Case Autocorrelation factor Autocorrelation factor Lag (hour) Lag (hour) Figure 3.2-2: Wind power output: autocorrelation by case 7 High autocorrelations alone do not mean that a single forecast covering the entire SPP footprint would be sufficient. Forecasts should be done on a plant by plant basis, even if prepared by a centralized forecaster. Final Report Page 3-13

52 The histograms in Figure describe the probability distribution of the hourly change in available wind power output by wind penetration case. As expected from the previous autocorrelation diagrams, most hourly changes are small, although there are some that are quite large. That is, the distribution of hourly changes is super Gaussian, meaning that, as compared with the Gaussian distribution, 8 the likelihood of small hourly changes is higher (higher peak) and the likelihood of large hourly changes is also higher (thicker tails), while the likelihood of intermediate hourly changes is lower. 9 Approximately half of the hourly changes are within 2.5% of the nameplate wind capacity, 90% are within 7.5% of nameplate capacity, and 98% are within 12% of nameplate wind capacity. In terms of load, 98% of the hourly wind changes are within 1.3%, 3.2% and 6.4% of the annual average SPP load for the Base, 10% and 20% Cases, respectively. The corresponding figure for the hourly load changes is 8.1%, indicating that load variability is about 25% higher than wind variability in the 20% Case. Base Case 10% Case 1,000 1,000 Number of hours per year Number of hours per year Wind output hourly change (MW) 0 1, ,000 1,500 Wind output hourly change (MW) 1,000 20% Case 1,000 40% Case Number of hours per year Number of hours per year ,000 2,000 1, ,000 2,000 3,000 Wind output hourly change (MW) 0 4,000 2, ,000 4,000 6,000 Wind output hourly change (MW) Figure 3.2-3: Hourly change in available wind power output: histograms by case 8 A Gaussian distribution is synonymous with a normal distribution of a random variable. 9 The excess kurtosis for the hourly wind power changes increases from 1.22 for the Base Case to 1.44 for the 40% Case. (A high kurtosis distribution has a sharper peak and longer, wider tails, while a low kurtosis distribution has a more rounded peak and shorter, thinner tails.) Final Report Page 3-14

53 The autocorrelation factors for the hourly wind changes are shown in Figure by case. These decrease relatively rapidly, reaching 0 at about 5 hours, reaching a minimum intra-day value of -0.2 to at a twelve-hour lag, and increasing for the remainder of the day. This means that on average, if power increased from the previous hour to a given hour, it will increase for four more hours and will then decrease about half a day from the given hour, showing some level of daily periodicity in the hourly increments (in a probabilistic sense). Note that the autocorrelation values are low, however, pointing to the difficulty of estimating hourly wind power changes from previous hourly changes. 1 Base Case 1 10% Case Autocorrelation factor Autocorrelation factor Lag (hour) Lag (hour) 1 20% Case 1 40% Case Autocorrelation factor Autocorrelation factor Lag (hour) Lag (hour) Figure 3.2-4: Hourly change in available wind power output: autocorrelations by case Final Report Page 3-15

54 Figure shows the histograms of wind power deviations from the day-ahead forecasts. The deviations were defined as actual minus forecast, such that positive deviations indicated that the forecast was less than the actual, i.e., an under-forecast. The mean and median deviations were nearly 0. The distributions were not symmetric, however, due to the algorithms employed in producing the day-ahead forecasts. The mode, or most frequent deviation, is about -7% of the nameplate wind capacity in each case, i.e., 7% over-forecast. The likelihood of over-forecasts dropped quickly for higher over-forecasts. For underforecasts, or positive deviations, the likelihood decreased more gradually as the underforecast magnitude increased. Approximately half of the forecast deviations were within 7% of the nameplate wind capacity, 90% were within 15% of nameplate wind capacity, and 98% were within 20% of nameplate wind capacity. Wind deviations from day-ahead wind forecasts in the 20% Case were similar to the load deviations from day-ahead load forecasts, as will be discussed in Section Base Case 10% Case Number of hours per year Number of hours per year , , ,000 1, ,000 2,000 Forecast error (MW) Forecast error (MW) 20% Case 40% Case Number of hours per year Number of hours per year ,000 2, ,000 4,000 Forecast error (MW) 0 5, ,000 10,000 Forecast error (MW) Figure 3.2-5: Histograms of deviations from day-ahead wind forecasts by case Final Report Page 3-16

55 Figure shows the average daily available wind profile by season for the Base Case. The average profiles are different for different seasons, although the fall and winter profiles are quite similar. All seasons have the highest average available wind in the morning, with the spring, summer, and fall average peaks occurring around 6 am, while the winter peak occurs around 10 am. The season with the steepest average hourly ramps is summer, followed by spring, fall, and winter. Figure 3.2-6: Base Case average daily wind output by season Final Report Page 3-17

56 Figure shows the range of available wind power by hour and season as compared to the average profiles. Note that even though average profiles are time-dependent, the range does not appear to be as time-dependent. In other words, for a given season and day, the maximum observed wind output can occur in any hour, not necessarily in the morning, although morning peaks are more frequent. The distribution between the maximum and minimum observed values in each hour has a similar shape to those in the histograms of Figure Figure 3.2-7: Base Case range of daily wind output 3.3. SPATIAL AND TEMPORAL DIVERSITY OF SPP WIND RESOURCES Figure shows the correlation coefficients of the available wind power in the different wind penetration cases. Note that these are extremely high: 0.95 and higher. In other words, the higher wind penetration cases show little gain from the spatial diversity of the wind resources as compared to the Base Case. This is due to the composition of the cases rather than to a lack of diversity in the SPP wind resources. The additional wind plants in the higher wind penetration cases are spread over an area similar to those for the Base Case, thus the high cross-correlations. As a result, the wind profile in each subsequent case is close to a linear scale-up of the preceding one, and the variability and uncertainty in the wind profiles for each case grows almost linearly with nameplate wind capacity, as will be presented in Section and Section Final Report Page 3-18

57 Figure 3.3-1: Wind output cross-correlation between wind penetration cases The different wind plant clusters, shown in Figure 3.1-1, exhibit higher wind profile diversity than do the different wind penetration cases. Figure displays the cross-correlations between clusters (40% Case) in different shades of red, with darker shades indicating higher cross-correlations. All cross-correlations are positive, and tend to be higher than 0.4 except for the pairs involving Nebraska, which tend to be lower due to distance. In fact, the relative position between a pair of clusters plays a key role in their cross-correlation, as can be inferred from the Figure The four clusters with the highest nameplate wind capacity in the 20% Case, i.e., Central Oklahoma, Spearville, Amarillo, and Hitchland, have crosscorrelations that are higher than 0.6. Figure 3.3-2: Wind output cross-correlation between clusters Wind profile diversity can also be studied by analyzing the impacts of aggregating wind plants across different regions. The top ten bars in Figure indicate the standard deviations of Final Report Page 3-19

58 hourly changes in available wind power of each wind plant cluster. The bar in the bottom, labeled as SPP Separate, shows the sum of the standard deviations in each cluster, 1,740 MW. This sum can be interpreted as the standard deviation of the sum of the wind profiles in each cluster if there were no diversity in the profiles, i.e., perfect correlation between the profiles of the different clusters. The second bar from the bottom, in brown, shows the actual standard deviation of the aggregate wind profile (i.e., the sum of the profiles for each cluster), labeled as SPP Aggregate. Note that the standard deviation of the aggregate wind profile, 1,070 MW, is 38.5% lower than the sum of the standard deviations of individual profiles, indicating that there is a good degree of diversity in the wind profiles, consistent with the correlations shown in Figure Figure 3.3-3: Standard deviation of hourly available wind power increments by cluster Standard deviations of hourly wind increments give an indication of the flexible non-wind resources needed to accommodate the wind variability. The total flexible resources needed for the case where each cluster performs dispatch functions independently, akin to maintaining separate BAs in SPP, is proportional to the sum of the standard deviations for each cluster, 1,740 MW. If there is a single BA, the flexible resources needed are proportional to 1,070 MW. This large reduction indicates the advantages of aggregating wind resources into a single BA in terms of reduced need for flexible non-wind resources. To further investigate the geographic diversity of SPP wind resources, both correlation and cross correlation (i.e., with a time lag applied) between the wind profile for each NREL site in SPP and the wind profile for a large NREL site in central Kansas were performed. The Final Report Page 3-20

59 selected reference site is NREL site The correlation results are displayed in Figure In the figure, darker shades of red indicate higher correlations. Note that the correlations are a function of the relative position between the two sites. Correlations decrease fastest with distance along northwest southeast directions and slowest along northeast southwest directions, making iso-correlation curves similar to ellipses centered on site 394, and with major axis in the SW NE direction. This figure is consistent with Figure in that sites in western Nebraska are poorly correlated with sites in northeast Kansas. Site 394 Figure 3.3-4: Correlation factor between NREL site 394 and the remaining NREL sites in SPP Figure shows the simultaneous correlations between the different wind profile pairs. Sometimes, cross-correlations (i.e., with the comparison profiles shifted in time) are higher than the correlations for the same pair, especially for pairs with significant northwest to southeast separation. For example, if a storm front comes from the north to the south, reaching the northern Nebraska border at 5 am and the southern Kansas border at 11 am, then for that day the cross-correlations between the profiles of a wind plant in northern Nebraska and another wind plant in southern Kansas will be highest if a six-hour delay is considered. 10 NREL site 394 has a capacity of 1,165 MW in the EWITS study. This site was selected as a reference due to its location in central Kansas, and its large size. Because these profiles were built as a sum of profiles for smaller wind site cells [8], the profile for this large site does not exhibit high intra-hour variability, making it appropriate for average comparisons with other wind sites. Final Report Page 3-21

60 Figure displays the maximum correlations between the profile for NREL site 394 and the profile for each other NREL site in SPP s footprint, allowing for time shifts between profiles. Note that although there are some differences with Figure 3.3-4, these are not major. The plot of the time shifts to attain maximum cross-correlation, shown in Figure 3.3-6, is more instructive. In this figure, blue circles indicate that meteorological phenomena tend to occur before than at site 394 on average, while red circles indicate the opposite. From the figure, it is evident that a) the predominant weather patterns in SPP develop from northwest to southeast, and b) on average it takes about 10 hours for the phenomena to reach southeast Oklahoma from northwest Nebraska. These results are only for average conditions; particular weather patterns may exhibit different directions and speeds. For example, Figure shows the shift for maximum correlation for a two-day period around March 16, This day had a high ramp-down event which progressed west to east rather than northwest to southeast. Site 394 Figure 3.3-5: Maximum correlation factor between NREL site 394 and the remaining NREL sites in SPP Final Report Page 3-22

61 Site 394 Figure 3.3-6: Time-shift for maximum correlation factor between NREL site 394 and the remaining NREL sites in SPP: 3-year time series The shifts for maximum correlations in Figure and Figure show that there is temporal diversity in the wind profiles. This diversity can be measured by the time it takes for events to cross the area with the largest wind penetration in SPP, i.e., northern Texas, southwest Kansas and western Oklahoma. These times are usually a few hours, giving operators equipped with appropriate wind forecasts ample time to plan operations to accommodate the variable wind power phenomena. Final Report Page 3-23

62 Site 394 Figure 3.3-7: Time-shift for maximum correlation factor between NREL site 394 and the remaining NREL sites in SPP: Mar 16, 2004 (high ramp down event) Final Report Page 3-24

63 4. IMPACTS OF WIND INTEGRATION ON THE SPP TRANSMISSION SYSTEM SPP wind generation resources are located predominantly in the western portion of the SPP footprint in locations with limited transfer capability to load centers. Additional wind penetration requires transmission upgrades to enable the delivery of wind power while maintaining acceptable reliability levels. This section of the report identifies transmission expansion necessary to accommodate increased wind penetration in SPP, relying primarily on results of previous SPP transmission studies. Two wind penetration cases were analyzed, the 10% Case and 20% Case, with the Base Case used as a reference. The composition of these wind penetration cases is described in Section 3. A comprehensive transmission analysis of the SPP system was performed for each case with the specified transmission expansion. The findings of the studies in this section are summarized as follows: Increased wind penetration causes significant increases in power flows from western SPP to eastern SPP. To accommodate the increased west-to-east flows while meeting the reliability standards in the SPP Criteria, a number of transmission expansions were required. These included new transmission lines totaling 1,260 miles of 345 kv lines and 40 miles of 230 kv lines for the 10% Case and an additional 485 miles of 765 kv lines, 766 miles of 345 kv lines, 205 miles of 230 kv lines, and 25 miles of 115 kv lines for the 20% Case. These transmission reinforcements eliminate voltage and thermal issues for transfers between SPS North and South. Voltage-driven transfer limitations from SPS to SPP remain, although the maximum transfer is increased from 529 MW in the Base Case to 1,200 MW and 3,090 MW in the 10% and 20% Cases, respectively. Contingency analyses performed by CRA identified new flowgates. These flowgates should be monitored in SPP operations given wind additions and identified transmission upgrades. No voltage stability issues were found for voltages above 0.85 p.u, mainly due to the transmission expansions. However, some voltage violations were seen in the wind-rich areas, mainly in high-wind conditions due to lack of voltage support in some wind plants, or near new 345 kv and 765 kv lines under low flow conditions due to contingencies or low availability of wind. No transient stability issues were found for the 10% and 20% Cases, confirming that increasing wind capacity with appropriate transmission expansion does not adversely affect the transient stability of the power system. Final Report Page 4-1

64 Based on the findings in this chapter, the following recommendations are made to enable high levels of wind integration in the SPP transmission system: Major transmission reinforcements are needed to accommodate high levels of wind penetration, starting as low as 10%. Considering that the lead times of transmission projects are longer than those of wind plant projects, it is recommended that SPP take definitive steps to reinforce its transmission network, especially west to east, and that economic analyses of the needed transmission expansions be undertaken. The addition of high voltage lines requires the installation of voltage control, such as switchable reactors or FACTS devices, to prevent over-voltages under low flow conditions due to contingencies or low availability of wind. Dynamic voltage support becomes increasingly important for the higher wind penetration cases, in which several conventional generators may become displaced in the dispatch order by wind generators. Therefore, it is recommended that new wind plants be required to provide reactive support of the same type and quantity of the displaced thermal units, i.e., continuously and instantaneously controllable reactive support. The remainder of this section is organized in nine subsections. Section 4.1 provides an overview of the power flow cases and the methodology employed in the analyses. Section 4.2 contains a brief description of the Base Case and the analyses performed. Section 4.3 and Section 4.4 discuss the transmission expansion analysis for the 10% and 20% Cases, respectively. The remaining sections analyze the transmission impacts of the increased wind penetration cases with the transmission expansion from Section 4.3 and Section 4.4. Section 4.5 and Section 4.6 report on the voltage and transient stability studies. Section 4.7 presents additional flowgates to model transmission limitations in the 10% and 20% Cases. Section 4.8 and Section 4.9 discuss the results of deliverability and transfer capability analyses that consider the flowgates from Section OVERVIEW OF POWER FLOW CASES AND METHODOLOGY For the transmission analysis, SPP provided four power flow models for each case, 12 power flow cases in total. These power flow cases represent the 2010 peak load conditions for summer, winter and fall, and minimum load conditions for spring. The wind plant dispatch in the power flow cases was based on wind profiles for from the NREL EWITS dataset (Section 7.3) and historical load profiles for the same period provided by SPP. For the summer and winter peak power flow cases, the wind plants in the corresponding case were dispatched to the average of the wind output available for the hour with highest load in the given season over the period ( ). In the fall and spring power flow cases, the wind plants were dispatched at the average of the wind output available for the hour with the highest wind in the season of each year. Thus, the fall power flow case represented a peak Final Report Page 4-2

65 load, peak wind condition, while the spring power flow case represented a minimum net load 1 condition. Table provides the total SPP load 2 compared to the total wind power dispatch for each of the power flow cases analyzed. The average wind dispatch was about 91% of the installed wind capacity for the spring and fall power flow cases and about 42% of the installed wind capacity for the summer and winter power flow cases. Table 4.1-1: SPP load and wind power dispatch in the power flow cases Power Flow Description SPP Load Total Wind Generation (MW) (MW) Base Case 10% Case 20% Case Spring minimum Min load, max wind 19,198 2,619 6,195 12,413 Summer peak Peak load, corresp wind 48,005 1,267 3,015 6,040 Fall peak Peak load, max wind 35,604 2,604 6,171 12,356 Winter peak Peak load, corresp wind 35,754 1,258 2,858 5,926 Installed Nameplate Wind 2,877 6,840 13,674 Capacity Wind / Non-Wind Table shows the dispatch by power flow case area. The distribution of the wind generation by area indicates that most of the generation is in the western part of the SPP footprint. As such, without transmission expansion the insufficient transfer capability between the western and eastern parts of SPP would lead to severe overloads and voltage violations in the 10% and 20% Cases. Each power flow case provided by SPP contained transmission expansions needed to meet the thermal and voltage standards of the SPP Criteria [41] in the normal, pre-contingency state. In general, the expansions were: new facilities from the Balanced Portfolio upgrades [42], new facilities from the Priority Project list [40], facilities assigned to specific GI requests, or re-rating of existing facilities. These expansions included to avoid pre-contingency overloads are specified in the appropriate sections below. 1 Net load is load minus wind power. 2 The SPP load here does not include SWPA, CELE, LAFA, and LEPA because these are not SPP market participants. Final Report Page 4-3

66 Table 4.1-2: Wind power dispatch in the power flow cases by power flow area, in MW Season Spring Summer Winter Fall Case Base 10% 20% Base 10% 20% Base 10% 20% Base 10% 20% WFEC SPS 731 2,734 5, ,240 2, ,394 2, ,753 5,093 OKGE , ,247 WERE EMDE KACP MIDW AEPW MKEC NPPD , ,217 SUNC , , ,891 WAPA MIPU Total 2,619 6,195 12,413 1,267 3,015 6,040 1,258 2,858 5,926 2,604 6,171 12,356 CRA performed an AC contingency analysis on each power flow case provided by SPP. Whenever a violation of the post-contingency SPP Criteria was found, CRA proposed solutions to these violations to the SPP Wind Integration Task Force (WITF). Sometimes, e.g., when line overloads were above 5% and could not be avoided by a generation redispatch, the proposed solutions were transmission upgrades. The WITF considered each of the proposed solutions in consultation with SPP engineers, occasionally leading to alternative solutions that align with SPP expansion plans. The final expansions needed to meet postcontingency reliability criteria are discussed in the sections below. Each new facility included in the 10% Case was also included in the 20% Case. Moreover, if a new facility was required to meet the SPP post-contingency Criteria in one of the seasonal power flow cases for a given wind penetration level, that facility was included in all four seasonal power flow cases. The resulting power flow cases were used to perform voltage stability, transient stability, available transfer capability (ATC), and deliverability studies, all of which are presented in the following sections. They were also used to define the transmission topology and to generate new sets of transmission constraints for production simulation (Section 4.7). The assumptions and methodology for the studies in this section are provided in Section 7.4. The remainder of the section discusses the transmission study results and their implications. Final Report Page 4-4

67 4.2. BASE CASE ANALYSIS The Base Case did not require any transmission upgrade to meet the SPP Criteria for the pre-contingency state, beyond those included in the 2010 power flow cases published in the 2009 SPP Transmission Expansion Plan (STEP). The AC contingency analysis performed by CRA yielded post-contingency violations of the SPP Criteria. These Base Case violations, specified in Appendix A.8, were not addressed by transmission expansion and were ignored in the contingency analysis for the 10% and 20% Cases because SPP engineers and SPP WITF members determined that they were due either to model issues or to SPP Criteria violations in the current system, and as such not specifically due to wind power injections. The Base Case transmission topology for lines above 200 kv is shown in Figure Figure shows the power flow dispatch in the Base Case seasonal power flow cases. The green and blue circles represent generation from wind and non-wind units, respectively. The area of the circles is proportional to the generation dispatch. The non-wind generators tend to be near load centers, mainly located in the eastern portion of SPP. The wind units tend to be located on the western portion of SPP where the load is relatively low. Figure 4.2-1: Base Case transmission topology above 200 kv Final Report Page 4-5

68 Spring Minimum Summer Peak 750 MW 400 MW 100 MW Non-Wind Wind 750 MW 400 MW 100 MW Non-Wind Wind Fall Peak Winter Peak 750 MW 400 MW 100 MW Non-Wind Wind 750 MW 400 MW 100 MW Non-Wind Wind Figure 4.2-2: Base Case generation dispatch in the four seasonal power flow cases % CASE ANALYSIS Generation Dispatch in Power Flow Cases and Transmission Expansion Wind generation in the 10% Case is more than double that in the Base Case, with the majority of the additions in the western portion of SPP (Figure ). The spring and fall power flow cases had wind power dispatched at almost 30% and 16% of the power flow case load, respectively, while summer and winter wind generation were between 6% and 8% of the SPP load. Due to the significant level of wind power and the limited transfer capability between west and east SPP in the Base Case, the 10% Case required a number of upgrades. Final Report Page 4-6

69 Spring Minimum Summer Peak 750 MW 400 MW 100 MW Non-Wind Wind 750 MW 400 MW 100 MW Non-Wind Wind Fall Peak Winter Peak 750 MW 400 MW 100 MW Non-Wind Wind 750 MW 400 MW 100 MW Non-Wind Wind Figure : 10% Case generation dispatch in the four seasonal power flow cases To enable power flow convergence and meet the SPP Criteria for the normal, precontingency state without reducing wind generation, SPP engineers included in the 10% Case the following transmission upgrades: All Balanced Portfolio upgrades to eliminate violations due to high north-south flows in the SPS and SUNC areas Hitchland Woodward 345 kv line assigned to the GEN interconnection request; essential to solve the power flow cases once it connects western and eastern parts of the SPP footprint; located at a strategic point of the system based on the generation/load distribution Spearville Comanche Wichita 345 kv line to eliminate voltage violations and line overloads in the 230 kv system in the area Final Report Page 4-7

70 Line and transformer re-ratings to eliminate low overloads near wind plants. In some cases, the re-rating was done to match the winter rating, but most cases required line reconductoring. Additional parallel facilities due to overload in the existing facilities The upgrades above 200 kv are shown in Figure , and the resulting network with voltage above 200 kv is in Figure Note the reinforcements between the western and eastern portions of SPP. The list of all upgrades included by SPP to meet the precontingency SPP Criteria is shown in Appendix A.1. Figure : 10% Case transmission expansions above 200 kv included to eliminate precontingency violations Final Report Page 4-8

71 Figure : Initial 10% Case transmission topology above 200 kv AC Contingency Analysis and Transmission Expansion CRA performed AC contingency analyses on the four seasonal power flow cases with the transmission expansion described in the previous section. The contingencies used include SPP-defined multi-element contingencies, as well as single contingencies of lines, transformers, and generators in the SPP footprint and surrounding areas. The methodology and assumptions employed and the contingencies implemented are detailed in Section The contingencies that cause post-contingency violations were clustered according to geographic and electrical proximity. CRA identified two clusters that require transmission reinforcements: 1. Spearville Wichita corridor 2. SPS OKGE corridor The remaining violations were either solved by re-dispatching thermal generation or were minor violations (lower than 5%) captured by new transmission constraints (Section 4.7). The violations in the clusters are discussed next. Final Report Page 4-9

72 Spearville Wichita Corridor AC Contingency Analysis Contingencies in this cluster led to violations in the spring and fall power flow cases. The contingencies that led to violations in the spring were: OPEN LINE (Flat Ridge Harper 138 kv) SPP-WERE-34 (Gill Clearwater Milan Harper 138 kv) OPEN LINE (Harper Milan 138 kv) OPEN LINE (Clearwater Gill 138 kv) OPEN LINE (Sawyer Medicine Lodge 138 kv) OPEN LINE (Sawyer Pratt 138 kv) OPEN LINE (Comanche G07-25T 345 kv) OPEN NEW LINE 345 kv (Comanche Spearville 345 kv) OPEN LINE (G07-25T Wichita 345 kv) OPEN LINE (Mullergren Spearville 230 kv) OPEN LINE (Spearville Nor-Jud 138 kv) OPEN 3WXFormer (Spearville 345/230 kv) These contingencies are shown in the one-line diagram of Figure using thick traces. In this area, there were 420 MW flowing to Spearville from the Hitchland area, three wind plants injected 551 MW at Spearville, a wind plant injected 98 MW at Flat Ridge, and 232 MW were injected at Smoky Hills, between Knoll and Summit, just north of the area of interest. The load center was Wichita and there were therefore strong flows from west to east on the 345 kv line Spearville Comanche Wichita, on the 230 kv line Spearville Mullergren Circle, and on the 138 kv lines going through Medicine Lodge to Wichita. The postcontingency violations were: The outage of the referred 345 kv lines caused significant overloads (up to 32%) on the 138 kv lines east of Flat Ridge and light overloads (4%) on the 230 kv line Smoky Hills Summit. The outage of the 230 kv line Spearville Mullergren overloaded the 345/230 kv transformer at Spearville by 20%, since the outage re-routed power from the 230 kv network to the 345 kv network. Final Report Page 4-10

73 The outage of the 115 kv line Nor-Jud Spearville re-routed power from the 230 kv and 345 kv lines to the 138 kv and 115 kv lines towards Wichita. The outage of the 115 kv lines north of Medicine Lodge re-routed power that was flowing north (to Mullergren) directly east to Wichita. These outages overloaded (up to 22%) the 138 kv lines east of Medicine Lodge, which were heavily loaded in the pre-contingency case. The outage of the lines east of Flat Ridge overload the 138/115 kv Medicine Lodge transformer by 62% and the lines north of Medicine Lodge by 21%. 420 MW 551 MW 98 MW Figure : Contingencies that cause violations in the Spearville Wichita corridor in the 10% Case spring power flow The contingencies that led to violations in the fall power flow case were: OPEN LINE (Flat Ridge Harper 138 kv) OPEN NEW LINE 345 kv (Spearville Comanche 345 kv) OPEN LINE (Comanche G07-25T 345 kv) OPEN LINE (G07-25T Wichita 345 kv) OPEN 3WXFormer (Flat Ridge 138/34.5 kv) Final Report Page 4-11

74 OPEN LINE (Flat Ridge 138 kv) OPEN Gen (Flat Ridge) OPEN LINE (Greensburg Jud Lrg 138 kv) OPEN LINE (Circle Mullergren 230 kv) OPEN LINE (Greensburg Suncity 138 kv) OPEN LINE (E McPherson Refinery 115 kv) SPP-WERE-34 OPEN LINE (Harper Milan 138 kv) OPEN LINE (Sawyer Medicine Lodge 138 kv) OPEN LINE (Medicine Lodge Suncity 138 kv) OPEN LINE (Sawyer Pratt 138 kv) OPEN LINE (E Manhattan Elm Creek 230 kv) OPEN LINE (Mullergren Spearville 230 kv) This list, illustrated in Figure , includes most contingencies in the spring case and some new contingencies. The additional contingencies were due to the higher load in the fall power flow case, almost twice that in the spring power flow case. The main differences were: The outage of the 345 kv line Comanche Wichita caused 13% overloads on the 230 kv line Smoky Hills Summit The outage of the 230 kv line Mullergren Circle overloaded the 115 kv lines south of Mullergren by 9% The outage of the Elm Creek East Manhattan caused light overloads on the 115 kv lines north of Elm Creek The following transmission upgrades solved violations in the Spearville Wichita corridor and were included based on consultation with the SPP WITF: Summit - Knoll 345 kv line: This line from west to east SPP improved all overloads and eliminated the overload on the 230 kv line Smoky Hills Summit. The line was already included in the 20% Case power flow cases. Final Report Page 4-12

75 Comanche Woodward 345 kv line: This line provided an additional low impedance path towards the west, especially useful in post-contingency situations for the outage of any portion of the 345 kv line Spearville Comanche Wichita. 345/138 kv transformer at Medicine Lodge, connecting Medicine Lodge with a tap in the 345 kv line Comanche Wichita: This transformer reduced overloads in the 138 kv lines near Medicine Lodge. The transformer was disconnected for the outage of the eastern section of the 345 kv line to avoid overloads in the 138 kv lines east of Medicine Lodge. 345/230 kv transformer at Spearville: CRA replaced the existing transformer with a higher capacity transformer to eliminate low overloads for the outage of the 230 kv line Spearville Mullergren. 138/115 kv transformer at Medicine Lodge: CRA replaced the existing transformer with a higher-capacity transformer to eliminate overloads for the outage of the 138 kv line lines east of Flat Ridge. 232 MW Figure : Contingencies that caused violations in the Spearville Wichita corridor in the 10% Case fall power flow Final Report Page 4-13

76 SPS to OKGE Corridor AC Contingency Analysis Contingencies in this cluster led to violations in all four seasonal power flow cases; the most significant violations occurred in the spring and fall power flow cases. The contingencies that led to violations in the spring power flow cases were: OPEN LINE (Northwest Tatonga 345 kv) OPEN LINE (Woodward Tatonga 345 kv) OPEN NEW LINE 345 kv (Woodward Hitchland 345 kv) OPEN LINE (Swisher Tuco 230 kv) OPEN LINE (Ama South G07-48T 230 kv) OPEN LINE (Swisher G07-48T 230 kv) OPEN LINE (PlantX G06-45T 230 kv) OPEN LINE (PlantX Sundown 230 kv) OPEN LINE (Roosevelt PNM-DC6 230 kv) OPEN LINE (Tolk Tuco 230 kv) OPEN 2WXFormer (Pringle 230/115 kv) OPEN LINE (Pringle G07-33T 230 kv) OPEN LINE (G07-33T Harrington 230 kv) OPEN Gen (Tolk) These contingencies are shown in the one-line diagram of Figure using thick traces. In this area, there were 665 MW injected by wind plants in the Hitchland area, 308 MW in the Woodward area, and 970 MW near to and south of Amarillo, TX. The flows west to east toward Oklahoma City were strong on all tie lines. The post-contingency violations were: The outage of the 345 kv lines Hitchland Woodward, Woodward Tatonga or Tatonga Northwest caused overloads in parallel 138 kv lines (up to 21%), in lines south of Hitchland (up to 9%), and in the 138/115 kv corridor Spearville Wichita (up to 5%). The outage of the 230 kv lines south of Amarillo caused overloads in parallel 230 kv (up to 18%) and 115 kv lines (up to 29%). Final Report Page 4-14

77 665 MW 308 MW 970 MW Figure : Contingencies that caused violations in the SPS OKGE corridor in the 10% Case spring power flow The contingencies that led to violations in the fall power flow case were: OPEN LINE (Finney Holcomb 345 kv) OPEN LINE (Finney G06-49T 345 kv) OPEN NEW LINE 345 kv (Woodward Hitchland 345 kv) OPEN LINE (Woodward Tatonga 345 kv) OPEN LINE (Northwest Tatonga 345 kv) OPEN LINE (OKU G08-14T 345 kv) OPEN LINE (OKU LES 345 kv) OPEN 3WXFormer (Tuco 345/230 kv) OPEN LINE (McClellan Kirby 115 kv) OPEN LINE (McClellan McLean 115 kv) Final Report Page 4-15

78 OPEN LINE (Sham McLean 115 kv) OPEN 3WXFormer (Sham 115/69 kv) OPEN LINE (Well 4WT Sham 4WT 138 kv) OPEN 3WXFormer (Sham 115/69 kv) OPEN LINE (Yarnell Conway 115 kv) OPEN LINE (Nichols Yarnell 115 kv) OPEN LINE (Clinton AF Elk City 138 kv) OPEN LINE (Grapevine Wheeler 230 kv) OPEN LINE (Wheeler Beckham Co 230 kv) OPEN LINE (Elk City Beckham Co 230 kv) OPEN 3WXFormer (Elk City 230/138 kv) OPEN LINE (Clinton AF Hobart Junc 138 kv) OPEN LINE (Roosevelt PNM DC6 230 kv) OPEN Gen (PNM DC6) OPEN 2WXFormer (Washita Wind plant 138/34.5 kv) OPEN LINE (Harring Mid Harring E 230 kv) OPEN LINE (South Rd Romnose 138 kv) SPP-SWPS-01 SPP-SWPS-02 SPP-SWPS-02b SPP-SWPS-03a SPP-SWPS-03b This list of contingencies, shown in Figure by thick traces, is exclusively composed by outages of portions of SPS tie lines or by generator outages. This is different from the Final Report Page 4-16

79 contingency list for the spring power flow case. In general, the outage of any of the listed portions of tie lines created overloads on other tie lines. In particular: The outage of the 345 kv lines Hitchland Woodward, Woodward Tatonga or Tatonga Northwest caused overloads in parallel 138 kv lines (up to 63%), in lines south of Hitchland (up to 9%), and in the 138/115 kv corridor Spearville Wichita (up to 5%). The outage of the 345 kv lines Finney Holcomb or Finney G06-49T caused 14% overloads in the 345/230 kv transformer at Tuco and the 230/138 kv transformer at Elk City, as well as 8% overloads in 138 kv lines in the Woodward area. The outage of portions of the 345 kv line Tuco OKU LES or the 345/230 kv transformer at Tuco caused overloads up to 32% in the 230/138 kv transformer at Elk City, overloads up to 15% in the 138/115 kv transformer at Shamrock, and overloads up to 9% in the 230 kv tie between SPS and AEPW. The outage of portions of the 230 kv line Grapevine Elk City or the 230/138 kv transformer at Elk City overloaded the 345/230 kv transformer at Tuco by up to 9% and the 138/115 kv tie between SPS and AEPW by up to 6%. The outage of the 138 kv lines Clinton AF Elk City or Clinton AF Hobart Jct caused 10% overloads on the 138 kv line Elk City Clinton Jct. The outage of portions of the 138/115 kv tie between SPS and AEPW caused overloads up to 14% on parallel facilities. The contingencies that caused violations in the summer power flow case were: OPEN LINE (Sundown Wolfforth 230 kv) OPEN LINE (Amoco G07-04T 230 kv) OPEN LINE (Nichols Ama South 230 kv) OPEN LINE (Tolk Tuco 230 kv) Final Report Page 4-17

80 Figure : Contingencies that caused violations in the SPS OKGE corridor in the 10% Case fall power flow The corresponding contingencies for the winter power flow case were: OPEN LINE (Sundown Wolfforth 230 kv) OPEN LINE (Tolk Tuco 230 kv) OPEN LINE (Amoco G07-04T 230 kv) OPEN LINE (Lubbock Wolfforth 230 kv) OPEN LINE (G07-45T Yoakum 230 kv) Final Report Page 4-18

81 The following transmission upgrades solved violations in the SPS OKGE corridor and were agreed upon with the SPP WITF to be included: Potter Co Stateline 345 kv line: This line from Potter Co to a substation near Beckham Co in the 345 kv line Tuco Woodward reduced the pre- and postcontingency flows on the parallel 345 kv, 230 kv, and 115 kv lines, improving voltage levels and overloads. The line is in the Priority Project list. Tuco Tolk Roosevelt Potter Co 345 kv line: This line eliminated the overloads caused by the outage of 230 kv lines south of Amarillo, and, together with the Potter Co Stateline line, reduced the flows on and the overloads caused by the outage of Hitchland Woodward and Tuco OKU 345 kv lines. The line is also in the Priority Project list. Comanche Woodward 345 kv line: This line provided an additional low impedance path toward the west, which was especially useful in post-contingency situations for the outage of any portion of the 345 kv line Hitchland Finney. Hitchland Pringle 230 kv line: This line helped relieve overloads caused by the outage of the 230 kv line Pringle G07-33T and the 345 kv line Hitchland Woodward. AC Contingency Analysis with Transmission Expansion When the transmission expansion detailed above for each cluster was included, the remaining post-contingency line overloads either could be solved by a thermal generation redispatch or were lower than 5%. The overloads that were not solved by generation redispatch are covered by the discussion of new transmission constraints (Section 4.7). Several buses had post-contingency voltages that did not meet the SPP Criteria, i.e., were below 0.90 p.u. or above 1.05 p.u. The contingencies and buses can be found in Appendix A.4.1. These buses were either in the proximity of a wind plant or were part of the 345kV, 230 kv or 115/138 kv SPS SPP ties. The causes for the voltage violations were: In some occasions, high over-voltages were due to the capacitive nature of 345 kv lines with low flows as a result of the contingency. To resolve these violations, switchable reactors must be added on the new high voltage lines. The switchable reactors would also improve the pre-contingency voltage profiles in wind-rich areas during hours when available wind power is low, resulting in low power flows on the line. Other violations were due to some transformers not having tap changing capabilities (or having it disabled in the power flow models). Such was the case of the transformers at Shamrock and Tuco. Final Report Page 4-19

82 The remaining violations were due to wind plants not having the ability to control voltage, (e.g., G03-006A) or having a limited reactive support capability for the area in which they were located (e.g., G06-43 connected to the 230 kv tie between SPS and AEPW) Transmission Expansion Summary Table summarizes the line additions to the 10% Case from the Base Case transmission topology, needed to solve either pre- or post-contingency violations of the SPP Criteria. 1,260 miles of 345 kv lines and 40 miles of 230 kv lines were added. Table : Transmission lines added to the 10% Case Line Violation Type Length (miles) Cleveland Sooner 345 kv Pre 30 Iatan Nashua 345 kv Pre 30 Muskogee Seminole 345 kv Pre 95 Knoll Axtell 345 kv Pre 115 Spearville Knoll 345 kv Pre 80 Tuco Woodward 345 kv Pre 225 Hitchland Woodward 345 kv Pre 105 Spearville Comache Wichita 345 kv Pre 155 2nd Line Groton Groton 115 kv Pre 0.3 Tuco Tolk Roosevelt Potter Co 345 kv Post 170 Potter Co Stateline 345 kv Post 100 Summit Knoll 345 kv Post 95 Comanche Woodward 345 kv Post 60 Hitchland Pringle 230 kv Post 40 The resulting overlay of 200 kv or above lines for the 10% Case is shown in Figure The thick traces indicate transmission expansion from the Base Case, either to solve precontingency violations or post-contingency violations. This overlay was used for the remaining studies on the 10% Case, including voltage and transient stability and production simulation. Final Report Page 4-20

83 Figure : Final 10% Case transmission topology above 200 kv % CASE ANALYSIS Generation Dispatch in Power Flow Cases and Transmission Expansion Wind generation capacity in the 20% Case is roughly double that in the 10% Case, with the majority of the additions in the western portion of SPP (Figure ). The spring and fall power flow cases had wind power dispatched at almost 60% and 32% of the power flow load, respectively, while summer and winter wind generation was between 11% and 15% of the load. Due to the significant levels of wind power and the still-limited transfer capability between west and east SPP in the 10% Case, the 20% Case required a number of additional upgrades. Final Report Page 4-21

84 Spring Minimum Summer Peak Fall Peak Winter Peak Figure : 20% Case generation dispatch in the four seasonal power flow cases To allow for the power flow convergence and to meet the SPP Criteria for the pre-contingency state, SPP engineers included the following transmission upgrades in the 20% Case: 765 kv facilities: During the study, 765 kv lines were adopted linking the areas with the highest concentration of wind plants because at least three 345 kv circuits were needed in some areas to dispatch nearly 13 GW of wind generation in the SPP footprint. A loop configuration was selected to prevent post-contingency voltage stability issues. The lines were Hitchland Woodward, Spearville Comanche Medicine Lodge Wichita, Comanche Woodward, and Spearville Holcomb Hitchland. Mingo Knoll 345 kv line created an additional path for the power flow in the Red Willow Setab 345KV line Finney Holcomb 345 kv second circuit assigned to GEN interconnection customer for possible 2010 in-service based on the Facility Study posted in November 2008 Final Report Page 4-22

85 Woodward Tatonga 345 kv second circuit needed to avoid overloads in the first circuit of the Woodward Tatonga 345 kv line Woodward Woodring 345 kv line and Kingfisher Co tap and tie on Tatonga -Northwest and Woodring Cimarron 345 kv solved overloads and voltage problems in the Woodward/Northwest area Valliant Hugo Sunnyside 345 kv line assigned to Aggregate Study AG Customers for 2011 in-service: The line prevented overloads in the 138 kv system in that area. LES Seminole and Pittsburg - Ft. Smith 345 kv lines solved overloads due to the power flow coming from the west part of the SPP footprint Rose Hill - Sooner 345 kv line to be built by WERE/OKGE for 2010 in-service: This line prevented overload and voltage problems in the area. Ft. Randall - Valentine 230 kv line needed to connect the wind plants (about 270 MW) located in the Valentine area (Nebraska) because the existing 115 kv lines in the area could not support the power output from the wind plants Hitchland - Moore 230 kv line needed to solve voltage and overload problems in the Hitchland area due to the large amount of generation in this area Line and transformer re-ratings eliminated low overloads near wind plants: Most cases required line re-conductoring. Addition of parallel facilities at 345 kv and 115 kv due to overloads in the existing facilities The upgrades above 200 kv are shown in Figure and the resulting network with voltages above 200 kv is shown in Figure Note the reinforcements between the western and eastern portions of SPP. The list of all upgrades included by SPP to meet the pre-contingency SPP Criteria is in Appendix A.2. Final Report Page 4-23

86 Figure : 20% Case transmission expansions above 200 kv included to eliminate precontingency violations Figure : 20% Case transmission topology above 200 kv Final Report Page 4-24

87 AC Contingency Analysis and Transmission Expansion CRA performed AC contingency analyses on the four seasonal power flow cases with the transmission expansion from the previous section. The contingencies used in these analyses consist of all contingencies used for the 10% Case contingency analyses and the single outage of all facilities added to the 20% Case. The contingencies that caused post-contingency violations were clustered according to geographic and electrical proximity. Seven clusters were identified requiring transmission reinforcements: 1. East Manhattan Concordia 2. Valentine Ft Randall 3. Central Place City Service Setab 4. Wichita area 5. SPS OKGE corridor 6. SPS area 7. Sunnyside The remaining violations were either solved by re-dispatching thermal generation or were minor violations (lower than 5%) captured by new transmission constraints (Section 4.7). The violations in the clusters are discussed next. East Manhattan Concordia Cluster Contingencies in this cluster led to violations in all four power flow cases. The contingencies that led to violations were: OPEN LINE (Elm Creek E Manhattan 230 kv) OPEN LINE (Elm Creek Concordia 230 kv) OPEN TRANSFORMER (Concordia 230/115 kv) These contingencies are shown by thick traces in Figure In the 20% Case, there were two wind plants (G07-28 and G03-006A) with a combined capacity of 400 MW connected to Elm Creek 230 kv. If the 230 kv line Elm Creek East Manhattan was outaged, there were significant overloads (up to 90 MW, 100% overload) on the 115 kv lines from Concordia going east and on the Concordia 230/115 kv transformer. The outage of the Elm Final Report Page 4-25

88 Creek Concordia 230 kv line or the Concordia transformer overloaded the Elm Creek E Manhattan 230 kv line up to 20%. The solution implemented, shown by a dashed thick line in Figure , was: Summit Elm Creek 230 kv line: This line relieved overloads caused by any of the contingencies enumerated for this cluster and was assigned to the GEN interconnection request. Figure : 20% Case contingency analysis of the E Manhattan Concordia region Final Report Page 4-26

89 Valentine Ft Randall Contingency The outage of the new 230 kv line Valentine Ft. Randall caused overloads on the 115 kv lines going north of Valentine by up to 70% in the fall and spring power flow cases. This contingency is illustrated by a thick trace in Figure In the 20% Case, there was a 272 MW wind plant connected to Valentine 230 kv. The solution implemented was: Re-rate the 115 kv line Valentine Harmony St. Francis Mission from 80/80 MVA to 160/160 MVA (equivalent to adding a parallel line) Figure : 20% Case contingency analysis of northeast Nebraska Final Report Page 4-27

90 Central Place City Service Setab Contingency This contingency led to a violation in the fall and spring power flow cases: OPEN LINE CENTRAL PLACE CITY SERVICE SETAB 115 KV This line is shown by a thick trace in Figure In the 20% Case, there was a 105 MW wind plant connected to Central Place 115 kv and a 99 MW farm at Selkirk 115 kv. The outage of the 115 kv line Central Place City Service Setab overloaded the parallel 115 kv line by up to 32%. The solution implemented was: Re-rate the 115 kv lines Central Place City Service Setab from 120/143 MVA to 180/210 MVA (equivalent to adding a third parallel line) Figure : 20% Case contingency analysis of west Kansas Wichita Area Contingencies in this cluster led to violations in the fall, spring and winter power flow cases. The post-contingency violations in the winter power flow case overloaded the 345/138 kv transformers at Wichita and were related to the thermal generation dispatch in the Wichita area. They could be solved by re-dispatching thermal units. The contingencies that led to violations in the fall power flow case were: OPEN LINE (Comanche G kv) OPEN LINE (G07-25 Wichita 345 kv) OPEN LINE (Wichita Benton 345 kv) Final Report Page 4-28

91 OPEN NEW 345 kv LINE - WOODWARD TO WOODRING The contingencies causing violations are indicated by thick traces in Figure This area had five wind plants connected to Spearville, injecting almost 1,500 MW in the fall power flow case. The flows towards Wichita from the west were strong (approximately 1,150 MW on the 765 kv line and 900 MW on the 345 kv line). In pre-contingency conditions, the 138 kv lines east of Flat Ridge going toward Wichita were overloaded and the outage of any other line carrying flow to Wichita worsened the overload. There was one 765/345 kv transformer at Wichita loaded at 93% of rated capacity. The outage of the Woodward Woodring 345 kv line overloaded this transformer by 30%. The outage of the Wichita Benton 345 kv line redirected flow to the 138 kv network, overloading the 345/138 kv transformers at Wichita by 26%. Figure : 20% Case contingency analysis of the Wichita area The contingencies that led to violations in the spring power flow case were: OPEN LINE (Comanche G kv) OPEN LINE (G07-25 Wichita 345 kv) Final Report Page 4-29

92 OPEN LINE (Wichita Benton 345 kv) OPEN NEW WICHITA 765/345 KV TRANSFORMER OPEN NEW 765 kv LINE - COMANCHE TO WICHITA The main difference from the fall power flow case is that the Flat Ridge Harper 138 kv line was not overloaded under pre-contingency conditions, but became overloaded by 10% for the outage of the 765 kv line Comanche Wichita. In consultation with the SPP WITF, the following solutions to the pre- and post-contingency violations were implemented for the remainder of the study: Additional 765/345 kv transformer at Wichita to eliminate overloads on the first 765/345 kv transformer at Wichita Curtail the output of the Flat Ridge wind plant to eliminate overloads on the 138 kv lines east of Flat Ridge 3 SPS to OKGE Corridor Contingencies in this cluster led to violations in the summer and winter power flow cases, with the most significant ones occurring in the spring and fall power flow cases. The contingencies that led to violations in the fall were: OPEN LINE (Roman Nose El Reno 138 kv) OPEN LINE (Yarnell Conway 115 kv) OPEN LINE (Nichols Yarnell 115 kv) In addition to these three contingencies, there were 29 single contingencies of 115 or 138 kv lines or 115/69 or 138/69 kv transformers that overloaded one of the following transformers by more than 5% and up to 43%: Taloga 138/69 kv Elk City 138/69 kv Stillwater 138/69 kv Jericho 115/69 kv 3 Wind curtailments at Flat Ridge were an existing issue and are expressed in an operating guide. For that reason WITF members determined that Flat Ridge output could be curtailed under the high wind conditions in the analysis. Final Report Page 4-30

93 Figure illustrates the Roman Nose El Reno area. In the fall 20% Case, there was a wind plant (G07-06) injecting 180 MW at Roman Nose and another wind plant (G06-46) injecting 118 MW at Dewey. The pre-contingency state had the 138 kv line Roman Nose El Reno overloaded by 71%, due to these injections. Moreover, the outage of the Roman Nose El Reno line overloaded the 138 kv lines north of Roman Nose by 30%. Figure : 20% Case contingency analysis of Roman Nose El Reno The outage of the Nichols Yarnell Conway 115 kv line in SPS going towards Oklahoma caused 8% overloads on the Grapevine Kirby 115 kv line. In this area, the pre-contingency voltage at Shamrock 115 kv was 0.93 p.u. (below the acceptable level of 0.95 p.u.). Also, the Shamrock 138/115 kv transformer was loaded at 90% of its rating in the normal state. The contingencies causing violations in the spring power flow case were: OPEN LINE (Roman Nose El Reno 138 kv) OPEN LINE (El Reno Cimarron 138 kv) OPEN LINE (Washita Southwestern 138 kv) OPEN LINE (Washita Anadarko 138 kv) OPEN LINE (Weatherford 138 kv) OPEN LINE (Weatherford Hinton 138 kv) OPEN LINE (Hinton Can Gas 138 kv) Final Report Page 4-31

94 OPEN LINE (Can Gas Jensen 138 kv) These contingencies are illustrated in Figure with thick traces. The load center was Oklahoma City, so flows were predominantly west to east in the figure. As in the fall power flow case, the 138 kv line Roman Nose El Reno was overloaded (62%) in the precontingency state, and its outage caused the same overloads as in the fall power flow case. The outage of the line Weatherford Hinton Jensen overloaded the line Clinton Clinton Jct. by 17%. The outage of El Reno Cimarron redirected flows through El Reno Jensen Cimarron, overloading this line by 12%. The outage of Washita Southwestern or Washita Anadarko overloaded the other line by 10%. 139 MW 133 MW 178 MW 206 MW Figure : 20% Case contingency analysis of west Oklahoma City In consultation with the SPP WITF, the following solutions to the pre- and post-contingency violations were implemented: Stateline Elk City Anadarko 345 kv line to solve overloads and undervoltages in the region s 115 and 138 kv network and relieve the 138/69 kv Elk City and Taloga transformers Final Report Page 4-32

95 345/138 kv transformer at Elk City to solve post-contingency overloads in the Elk City 138/69 kv transformer 345/138 kv transformer at Tatonga/Taloga to solve post-contingency overloads in the Taloga 138/69 kv transformer Wind plant G07-06 (160 MW) connected to Tatonga 345 kv: This wind plant was connected to the 138 kv Roman Nose bus and created significant overloads in preand post-contingency situations. SPS Area Contingencies in this cluster led to violations in spring power flow case. The contingencies that led to violations in the fall were: OPEN LINE (G06-45 Plant X 230 kv) OPEN TRANSFORMER (Pringle 230/115 kv) OPEN TRANSFORMER (Hitchland 230/345 kv) These contingencies are illustrated in Figure A summary of the overloads follows: Two wind plants injected 74 MW at Hansford, north of Pringle, and 182 MW at Pringle. The outage of the Pringle 230/115 kv or the Hitchland 345/230 kv transformers redirected flow south towards Riverview, overloading the Pringle Riverview 115 kv line by 6%. There were approximately 750 MW of wind power injected at G06-45T, south of Amarillo, creating a 161 MW flow south on the G06-45T Plant X 230 kv line. The outage of this line redirected flow through Deafsmith, overloading the line G06-45 Deafsmith by 7%. The SPP WITF agreed to implement the following solutions to the pre- and post-contingency violations for the remainder of the study: Re-rate Pringle Riverview 115 kv line from 85/96 MVA to 100/110 MVA Wind plant G06-45 (240 MW) connected to a tap in the Roosevelt Potter Co 345 kv line: This wind plant was previously connected to the 230 kv node G06-45T together with other wind plants and this created significant post-contingency overloads on the G06-45T Deafsmith 230 kv line. Final Report Page 4-33

96 Figure : 20% Case contingency analysis of SPS AC Contingency Analysis with Transmission Expansion Once the transmission expansions detailed above for each cluster were included, the postcontingency line overloads that remained could either be solved by a thermal generation redispatch or were lower than 5%. The overloads that were not solved by generation redispatch are captured by the discussion of new transmission constraints (Section 4.7). A few buses had post-contingency voltages that did not meet the SPP Criteria (were below 0.90 p.u. or above 1.05 p.u.). The contingencies and buses are detailed in Appendix A.4.1. The number and range of these violations were lower than in the 10% Case in spite of the higher wind penetration. This was due to two factors: a stronger extra-high voltage overlay and the use of switchable reactors that controlled the voltage of 345 kv and 765 kv nodes in high wind penetration areas. Final Report Page 4-34

97 Transmission Expansion Summary Table summarizes the line additions to the 20% Case from the 10% Case transmission topology needed to solve either pre- or post-contingency violations of the SPP Criteria. The transmission line additions total 485 miles in 765 kv, 766 miles in 345 kv, 205 miles in 230 kv, and 25 miles in 115 kv. The resulting overlay of 200 kv or above lines for the 20% Case is shown in Figure The thick traces indicate transmission expansion from the 10% Case, either to solve precontingency violations or post-contingency violations. This overlay was used for the remaining studies of the 20% Case, including voltage and transient stability and production simulation. Figure : Final 20% Case transmission topology above 200 kv Final Report Page 4-35

98 Table : Transmission lines added to the 20% Case Line Violation Type Length (miles) Comanche Wichita 765 kv Pre 100 Hitchland Woodward 765 kv Pre 105 Woodward Comanche 765 kv Pre 60 Comanche Spearville 765 kv Pre 55 Spearville Holcomb 765 kv Pre 60 Holcomb Hitchland 765 kv Pre 105 Mingo Knoll 345 kv Pre 85 Finney Holcomb Ckt2 345 kv Pre 1 Woodward Tatonga Ckt2 345 kv Pre 35 Tatonga Kingfisher 345 kv Pre 75 Kingfisher Northwest 345 kv Pre 10 Woodward Woodring 345 kv Pre 80 Hugo Sunnyside 345 kv Pre 105 Hugo Valliant 345 kv Pre 10 LES Seminole 345 kv Pre 90 Pittsburg Ft. Smith 345 kv Pre 90 Rose Hill Sooner 345 kv Pre 80 Ft. Randall Valentin 230 kv Pre 100 Selkirk Leoti Cntrlplns Ckt2 115 kv Pre 15 Cntrlplns Ctyserv Setab Ckt2 115 kv Pre 10 Hitchland Moore 230 kv Pre 45 Stateline Elk City Anadarko 345 kv Post 105 Summit Elm Creek 230 kv Post VOLTAGE STABILITY ANALYSIS The following approach for analyzing the voltage stability of the SPP transmission system was established based on discussions with SPP engineers and consideration of the existing voltage-related transmission limitations: Perform PV studies for all seasonal power flow cases for the following transfers: Final Report Page 4-36

99 - SPS to SPP for the loss of SPS SPP ties and major generators in SPS - SPS North to South and South to North for loss of ties and major nearby generators Monitor the dv/dq sensitivity of all buses in SPP with voltage higher than 100 kv in the AC contingency analysis for all seasonal power flow cases Study the VQ characteristics of major load buses in Kansas City for loss of nearby lines and generators for the summer power flow case PV Analysis / Transfer Characteristics Two sets of PV analyses were studied: SPS to SPP and SPS North to South and South to North. The results of these studies were used in the re-definition of the SPSSPPTIES and SPSNORTH_STH flowgates (Appendix E) for the 10% and 20% Cases. 10% Case SPS to SPP Transfers The SPS to SPP transfers were limited by either: Low voltage levels, usually at the Shamrock 115 kv bus due to low voltages at the Grapevine, Wheeler, and Beckham 230 kv nodes caused by high flows from west to east, as illustrated in Figure Available committed maximum generation in SPS No voltage instability situation was encountered before attaining a 0.9 p.u. post-contingency voltage level for a monitored bus. Voltage instability was only found for voltage levels of monitored buses of 0.85 p.u. and below. For example, if voltages were allowed to drop to 0.85 p.u., the tip of the PV curve was reached in the spring power flow case for the outage of Finney Holcomb 345 kv. Table summarizes the PV analysis for transfers from SPS to SPP. The table shows the maximum incremental transfers over the transfer in the power flow case and the corresponding total transfer for each seasonal power flow case under different contingency conditions. The max value indicates that the maximum transfer was limited by the available committed generation capacity in SPS. The first row indicates the level of transfers in the power flow case from SPS to SPP before the generation in the injection groups is varied. Rows 2 through 4 refer to pre-contingency situations. The second and third rows indicate the Final Report Page 4-37

100 maximum transfer that leads to a minimum nodal voltage of 0.95 and 0.90 p.u., respectively. 4 The fourth row indicates the minimum transfer level that overloads at least one monitored branch. Note that the values in the fourth row are higher than those in the second row for all seasons, indicating that minimum nodal voltages, rather than line overloads, were the limiting factor for normal state transfers. The minimum transfer that led to a violation was 1,197 MW for the summer power flow case (the value for the winter power flow case, 1,205 MW, was similar). Post-contingency transfer limits are shown for each contingency in the subsequent rows. The minimum post-contingency limit was 1,478 MW and occurred for the outage of Tuco OKU 345 kv in the fall power flow case. Note that generator outages did not lead to violations. Figure : Voltage contour for the maximum post-contingency transfer from SPS to SPP; Tuco-OKU line outaged, fall 10% Case 4 The minimum acceptable voltage levels under the SPP Criteria for normal- and contingency-state operation are 0.95 and 0.90 p.u., respectively. Final Report Page 4-38

101 Table : PV study results for SPS to SPP transfers in the 10% Case Pre- Contingency Contingency Tuco - OKU 345 kv Finney - Holcomb 345 kv Wheeler - Beckham 230 kv Kirby - Jericho 115 kv Hitchland - Woodward 345 kv Stateline - Woodward 345 kv Texas Co 115 kv PAR Mc Clellan - Shamrock 115 kv Generator Outages Maximum Transfer (MW) Winter Spring Summer Fall Inc. Total Inc. Total Inc. Total Inc. Total Initial , ,478 Min V 0.95 pu 312 1, , , ,483 Min V 0.9 pu 1,542 2,369 max max 1,656 2,303 max max No Overloads max max 1,050 2,638 1,500 2, , , ,159 1,169 1, ,478 1,206 2, ,095 1,218 1, ,543 1,261 2, ,441 1,256 1, ,635 1,144 1, ,177 1,300 1, ,635 1,119 1, ,334 1,306 1, ,556 1,238 2, ,421 1,381 2, ,643 either max or higher than pre-contingency min V 0.9 pu The results shown in Table establish an updated SPSSPPTIES flowgate for the 10% Case. The definition for this updated flowgate needed to account for the transmission expansion in the 10% Case from the Base Case. That is, two 345 kv lines were added to the interface: Woodward Hitchland and Woodward Stateline. Rounding the figures in Table (as shown in bold), the updated limits were 1,200 MW for the pre-contingency state and 1,475 MW for the contingency state. The limiting factor for these two limits was the voltage at 115 kv and 230 kv tie nodes. Therefore, the following options could help to increase the limits if needed: Taps on the 345 kv line Potter Co Stateline, with step down transformer(s) connected to the 230 kv bus(es) and special protection schemes that would open the transformers for the outage of the 345 kv line 345 kv line Stateline - Anadarko (in addition to the previous) 138 kv line Shamrock Stateline with 345/138 kv transformer Phase shifters at Wheeler Beckham and McClellan Shamrock Capacitor banks at both Wheeler and Shamrock Open the 115 kv tie Given that the seasonal power flow cases modeled substantially different power flow conditions, some of which may occur in more than just the season in which they were modeled, the results in the table should not be used to establish seasonal limits for the SPSSPPTIES flowgate. Further studies are required to determine seasonal limits. Final Report Page 4-39

102 SPS South to North and North to South Transfers For all contingencies and all seasonal power flow cases, the transfer between SPS North and South was limited by the available generation in the SPS North area. That is, no voltage issues or line overloads were found. This is not surprising, given the addition of two 345 kv tie lines in the 10% Case: Tuco Stateline and Roosevelt Potter Co. Given these results, the flowgate SPSNORTH_STH is not enforced in any subsequent analysis of the 10% Case. 20% Case Given that, in the 10% Case, no violations were found for transfers between SPS South and North, and that the 20% Case network is stronger in the area with the addition of several lines, only the SPS to SPP transfers were studied for the 20% Case. The SPS to SPP transfers in the 20% Case were limited by either: Low voltage levels, usually at the Shamrock 115 kv bus due to low voltages at the Grapevine, Wheeler, and Beckham 230 kv nodes caused by high flows from west to east, as illustrated in Figure , or, for a few transfers in the winter power flow case, at the OKU 345 kv bus Available committed maximum generation in SPS Voltage instability was not encountered before attaining a 0.9 p.u. post-contingency voltage level for a monitored bus. Table summarizes the PV analysis for transfers from SPS to SPP. The table shows the maximum incremental transfers over the transfer in the power flow case and the corresponding total transfer for each seasonal power flow case under different contingency conditions. The minimum transfer that led to a pre-contingency violation was 3,088 MW for the summer power flow case, and the minimum post-contingency transfer was 3,621 MW and occured for the outage of Stateline Elk City 345 kv, also in the summer power flow case. As in the 10% Case, generator outages did not lead to violations. Final Report Page 4-40

103 Figure : Voltage contour for the SPS to SPP pre-contingency transfer and minimum voltage of 0.9 p.u.; fall 20% Case Table : PV study results for SPS to SPP transfers in the 20% Case Pre- Contingency Contingency Stateline - Elk City 345 kv Kirby - Jericho 115 kv Tuco - OKU 345 kv Hitchland - Woodward 765 kv Hitchland - Woodward 345 kv Stateline - Woodward 345 kv Wheeler - Beckham 230 kv Hitchland - Holcomb 765 kv Finney - Holcomb 345 kv Texas Co 115 kv PAR Mc Clellan - Shamrock 115 kv Generator Outages Maximum Transfer (MW) Winter Spring Summer Fall Inc. Total Inc. Total Inc. Total Inc. Total Initial 0 2, , , ,575 Min V 0.95 pu 707 3,354 1,406 4, , ,586 Min V 0.9 pu 2,281 4,824 max max 1,000 3,190 1,125 4,658 No Overloads 1,750 4,334 max max 2,431 4, ,957 1,256 3,871 1,450 4,485 1,450 3, ,903 1,236 3,852 max max 1,650 3, ,814 1,494 4,095 1,497 4,531 2,011 4, ,214 1,894 4,467 max max 2,036 4, ,281 2,168 4,720 max max 2,312 4, ,513 1,938 4,508 max max 2,319 4, ,500 2,031 4,594 max max 2,087 4,225 1,000 4,538 2,081 4,640 max max 2,331 4,454 1,019 4,556 either max or higher than pre-contingency min V 0.9 pu Final Report Page 4-41

104 The results in Table establish an updated SPSSPPTIES flowgate for the 20% Case. The definition for this updated flowgate included four lines in addition to those lines in the 10% Case flowgate definition. These new lines were: Woodward Hitchland 765 kv, Holcomb Hitchland 765 kv, Holcomb Finney 345 kv Ckt 2, and Stateline Elk City 345 kv. Rounding the figures in Table (as shown in bold), the updated limits were 3,090 MW for the pre-contingency state and 3,620 MW for the contingency state. Note the significant increase over the 10% Case limits (1,200 MW pre-contingency and 1,475 MW post-contingency) due to the 20% Case transmission expansion. The limiting factor for these two limits is the voltage at 115 kv and 230 kv tie nodes. Given that the seasonal power flow cases modeled substantially different power flow conditions, some of which may occur in more than just the season in which they were modeled, the results in the table should not be used to establish seasonal limits for the SPSSPPTIES flowgate. Further studies are required to determine seasonal limits dv/dq Sensitivity Analysis CRA monitored the sensitivity of the nodal voltage with respect to changes in the reactive power injection at the same node for all buses in SPP with nominal voltage higher than 100 kv. A negative value for this sensitivity is an indication of voltage instability. Also, a large change of this sensitivity, such as a change by one order of magnitude, between the normal and a post-contingency state indicates the need for further investigation. For all monitored buses and all contingencies analyzed, this sensitivity remained positive. Moreover, only in a few cases did the post-contingency value of the sensitivities change by more than a factor of 10 from the pre-contingency sensitivity value; these instances are detailed in Appendix A.5. Upon further investigation, CRA found that these large changes in dv/dq often occured on voltage-regulated buses and were due to the outages of lines or generators injecting reactive power at the node. Because the post-contingency voltage level for each of these nodes was acceptable, and nearby buses had smaller changes, the large changes did not indicate voltage stability problems. In summary, no voltage stability issues were found in the dv/dq sensitivity analysis VQ Analysis / Reactive Reserves VQ analyses were performed for high load nodes in Kansas City for the 10% Case summer power flow case. The nodes analyzed, specified in Table , were among the 10 nodes with the highest loads in Kansas City. The contingencies used were the single outages of: 345/161 kv transformers at West Gardner, Iatan, Nashua, Craig, Hawthorn, and Stillwell Five of the ten generators with the largest dispatch in the area: Montrosse 1, Bull Creek 3, La Cygne 2, Hawthorn 5, and Iatan 2 Final Report Page 4-42

105 The 161 kv line connected to each node with the largest power injection: GLADSTN5 SHOLCRK5, NEAST 5 CROSTWN5, KNLWRTH5 REEDER 5, SWITZER5 RILEY 5, and TROOST 5 MIDTOWN5 Table : Nodes with VQ characteristics analyzed Bus Number Bus Name Load Area Load Zone ID Bus Nom kv MIDTOWN5 KACP METRO RILEY 5 KACP JOCO KNLWRTH5 KACP JOCO CROSTWN5 KACP DWNTWN GLADSTN5 KACP NORTH For each simulated contingency condition, if there was a 161 kv node in KACY or KACP with lower voltage or dv/dq sensitivity lower than that of the five selected nodes in Table , the VQ characteristic of that node was also studied. Both nodal voltages and dv/dq sensitivities were monitored for the selected nodes. The VQ analysis connects a fictitious controllable condenser at the node of interest. This condenser is set to control its nodal voltage to a specified value that is varied in 0.05 p.u. steps from 1.05 p.u. to 0.40 p.u. or until there is no power flow solution (this voltage is denoted by V min ). The reactive power output of the condenser is monitored and recorded and a voltage versus reactive power (VQ) curve, such as the one shown in Figure is prepared. The vertical axis shows the reactive power output of the fictitious condenser and the nodal voltage is shown on the horizontal axis. The quantity Q min represents the increase in reactive power load at the bus that leads to voltage instability, and is interpreted as the reactive reserve available at the bus. Reactive reserves were observed for all monitored buses and all contingency conditions analyzed in the range of 744-1,441 MVAr, with the lowest reactive reserve value occurring at the Riley 161 kv node for the outage of the 161 kv line Riley Switzer. Thus, the nodal reactive reserves are of the same order of magnitude as the peak reactive load at KACP (807 MVAr) and KACY (125 MVAr) and higher than their sum for most nodes and contingencies. The voltage at Q min was between 0.75 p.u. and 0.52 p.u., significantly lower than the minimum voltage allowed in the SPP Criteria (0.90 p.u.). Therefore, ample reactive reserve margins were found for the 10% Case in the Kansas City area. Consequently, the reactive reserves were not analyzed for the 20% Case, because the sources of reactive power available in the Kansas City area did not change substantially between the 10% and the 20% Cases. Final Report Page 4-43

106 Q at Vmax Reactive Power Injection (MVAr) Q at Vmin Qmin Vmin Unstable Stable Voltage (pu) Figure : VQ curve for the Riley 161 kv bus in the 10% Case summer power flow, precontingency case 4.6. TRANSIENT STABILITY ANALYSIS Single Line Fault Transient Stability Analysis Methodology Transient stability analysis was performed for the Base, 10%, and 20% Cases for the four seasonal models provided by SPP: fall, spring, summer and winter. The dynamic models were provided by SPP. The entire EIC was modeled with detailed representation of the machines in the SPP footprint. In particular, the power system models were comprised of wind generator models provided by their respective vendors. As commonly practiced by SPP for transient stability studies, the real power loads were converted to constant current loads, and reactive power loads were converted to constant impedance loads. Transient stability analyses were conducted using PSS E. 5 5 Details of PSS E can be found at Final Report Page 4-44

107 A majority of the wind plants are on the west side of the imaginary line drawn from Oklahoma City to Wichita (Figure 3.1-1). For this reason, the stability analysis focused on the lines in this area for voltage levels of 345 kv and 765 kv. In practice, [1], [13], [14] transient stability has been shown to be more suspect for lightly loaded cases. This is because there is less rotational inertia to absorb the disturbance. The excitation levels of the generators are also lower in lightly loaded cases and there is therefore less magnetic field strength coupling the stator and the rotor. For these reasons, more line faults were studied for the light-load spring case. The outage of heavily loaded lines in the spring cases was studied while the outage of heavily loaded lines that transferred power from west to east toward the imaginary line from Oklahoma City to Wichita was analyzed for the fall, summer, and winter cases. Additionally, the outage of each line added to the 10% and 20% Cases was studied. Transient stability was analyzed for 3-phase branch faults that were cleared after five cycles with line disconnection. The five-cycle clearing time was chosen in consultation with SPP because it provided a good margin from the actual breaker clearing times. If it was found that the system was not stable for a five-cycle clearing time, then a 4.5-cycle clearing time was tested. Stability was determined by looking at angles and speeds of all machines in the SPP footprint. The power outputs of the generators in the SPP footprint, as well as the voltage magnitudes of 345 kv and above buses, were also monitored. Results In general, the results of the analysis show that as long as transmission is expanded sufficiently to facilitate increasing wind capacity, the increase in wind capacity does not negatively affect the transient stability of the power system. Table through Table show the transient stability analysis results for all cases studied (the observations shown with asterisks are discussed in Appendix A.7). The results show that for all cases except for the winter Base Case, the system was stable for a fivecycle clearing time. There were two line outages in the winter Base Case, GENTLMN3 to SWEET W3 and GENTLMN3 to REDWILO3, that were unstable for a five cycle clearing time. The outage of these lines in all other seasonal models and wind penetration cases did not cause instabilities. These lines have the highest power transfers in the winter Base Case, however, and the transfers were too big for the system to recover with a five cycle clearing time. The system was stable with a 4.5-cycle clearing time in both instances. Since the actual clearing times at the breakers is four cycles, transient stability should not be an issue, but the margin for error is only 0.5 cycles. Final Report Page 4-45

108 Table : Single line fault transient stability results for Base Case fall From Bus # From Bus Name To Bus # To Bus Name CKT ID 5 Cycle CLR Stable WWRDEHV TATONGA 1 Yes WWRDEHV HITCHLAND 7 1 Yes SPERVIL KNOLL EHV 1 Yes GENTLMN SWEET W3 1 Yes GENTLMN REDWILO3 1 Yes Table : Single line fault transient stability results for Base Case spring From Bus # From Bus Name To Bus # To Bus Name CKT ID 5 Cycle CLR Stable O.K.U L.E.S.-7 1 Yes WWRDEHV TATONGA 1 Yes WWRDEHV HITCHLAND 7 1 Yes HITCHLAND CONESTOGA 1 Yes FINNEY CONESTOGA 1 Yes FINNEY HOLCOMB7 1 Yes* GEN_2007_ HOLCOMB7 1 Yes SPERVIL COMANCHE 1 Yes SPERVIL KNOLL EHV 1 Yes HOLCOMB SETAB 7 1 Yes MINGO G Yes AXTELL SWEET W3 1 Yes GENTLMN SWEET W3 1 Yes GENTLMN REDWILO3 1 Yes *Machine at bus SIDRCH 2 unstable Table : Single line fault transient stability results for Base Case summer From Bus # From Bus Name To Bus # To Bus Name CKT ID 5 Cycle CLR Stable O.K.U L.E.S.-7 1 Yes WWRDEHV TATONGA 1 Yes WWRDEHV HITCHLAND 7 1 Yes GEN_2007_ HOLCOMB7 1 Yes SPERVIL COMANCHE 1 Yes GENTLMN SWEET W3 1 Yes GENTLMN REDWILO3 1 Yes Final Report Page 4-46

109 Table : Single line fault transient stability results for Base Case winter From Bus # From Bus Name To Bus # To Bus Name CKT ID 5 Cycle CLR Stable O.K.U L.E.S.-7 1 Yes WWRDEHV TATONGA 1 Yes WWRDEHV HITCHLAND 7 1 Yes FINNEY CONESTOGA 1 Yes SPERVIL COMANCHE 1 Yes GENTLMN SWEET W3 1 No (4.5 Cycle Stable) GENTLMN REDWILO3 1 No (4.5 Cycle Stable) Table : Single line fault transient stability results for 10% Case fall From Bus # From Bus Name To Bus # To Bus Name CKT ID 5 Cycle CLR Stable KNOLL EHV SUMMIT 7 1 Yes** WWRDEHV COMANCHE 1 Yes** WWRDEHV TATONGA 1 Yes** WWRDEHV HITCHLAND 7 1 Yes SPERVIL KNOLL EHV 1 Yes GENTLMN SWEET W3 1 Yes GENTLMN REDWILO3 1 Yes STATELINE POTTER_CO 7 1 Yes POTTER_CO ROOSEVELT2 1 Yes ROOSEVELT TOLK 7 1 Yes TOLK TUCO 1 Yes **Wind generation goes offline Final Report Page 4-47

110 Table : Single line fault transient stability results for 10% Case spring From Bus # From Bus Name To Bus # To Bus Name CKT ID 5 Cycle CLR Stable KNOLL EHV SUMMIT 7 1 Yes WWRDEHV COMANCHE 1 Yes O.K.U L.E.S.-7 1 Yes WWRDEHV TATONGA 1 Yes WWRDEHV HITCHLAND 7 1 Yes HITCHLAND CONESTOGA 1 Yes FINNEY CONESTOGA 1 Yes FINNEY HOLCOMB7 1 Yes GEN_2007_ HOLCOMB7 1 Yes SPERVIL COMANCHE 1 Yes SPERVIL KNOLL EHV 1 Yes HOLCOMB SETAB 7 1 Yes MINGO G Yes AXTELL SWEET W3 1 Yes GENTLMN SWEET W3 1 Yes GENTLMN REDWILO3 1 Yes STATELINE POTTER_CO 7 1 Yes POTTER_CO ROOSEVELT2 1 Yes ROOSEVELT TOLK 7 1 Yes TOLK TUCO 1 Yes Table : Single line fault transient stability results for 10% Case summer From Bus # From Bus Name To Bus # To Bus Name CKT ID 5 Cycle CLR Stable KNOLL EHV SUMMIT 7 1 Yes WWRDEHV COMANCHE 1 Yes O.K.U L.E.S.-7 1 Yes WWRDEHV TATONGA 1 Yes WWRDEHV HITCHLAND 7 1 Yes GEN_2007_ HOLCOMB7 1 Yes SPERVIL COMANCHE 1 Yes GENTLMN SWEET W3 1 Yes GENTLMN REDWILO3 1 Yes STATELINE POTTER_CO 7 1 Yes POTTER_CO ROOSEVELT2 1 Yes ROOSEVELT TOLK 7 1 Yes TOLK TUCO 1 Yes Final Report Page 4-48

111 Table : Single line fault transient stability results for 10% Case winter From Bus # From Bus Name To Bus # To Bus Name CKT ID 5 Cycle CLR Stable KNOLL EHV SUMMIT 7 1 Yes WWRDEHV COMANCHE 1 Yes O.K.U L.E.S.-7 1 Yes WWRDEHV TATONGA 1 Yes WWRDEHV HITCHLAND 7 1 Yes FINNEY CONESTOGA 1 Yes SPERVIL COMANCHE 1 Yes GENTLMN SWEET W3 1 Yes GENTLMN REDWILO3 1 Yes STATELINE POTTER_CO 7 1 Yes POTTER_CO ROOSEVELT2 1 Yes ROOSEVELT TOLK 7 1 Yes TOLK TUCO 1 Yes Table : Single line fault transient stability results for 20% Case fall From Bus # From Bus Name To Bus # To Bus Name CKT ID 5 Cycle CLR Stable KNOLL EHV SUMMIT 7 1 Yes WWRDEHV COMANCHE 1 Yes WWRDEHV TATONGA 1 Yes WWRDEHV HITCHLAND 7 1 Yes SPERVIL KNOLL EHV 1 Yes GENTLMN SWEET W3 1 Yes GENTLMN REDWILO3 1 Yes STATELINE POTTER_CO 7 1 Yes POTTER_CO ROOSEVELT2 1 Yes ROOSEVELT TOLK 7 1 Yes TOLK TUCO 1 Yes WWRDEHV WOODRNG7 1 Yes MINGO KNOLL EHV 1 Yes L.E.S SEMINOL7 1 Yes SOONER ROSEHIL7 1 Yes COMANCHE WICHITA 1 Yes HITCHLAND WOODWARD 1 Yes WOODWARD COMANCHE 1 Yes COMANCHE SPEARVILLE 1 Yes SPEARVILLE HOLCOMB 1 Yes HOLCOMB HITCHLAND 1 Yes Final Report Page 4-49

112 Table : Single line fault transient stability results for 20% Case spring From Bus # From Bus Name To Bus # To Bus Name CKT ID 5 Cycle CLR Stable KNOLL EHV SUMMIT 7 1 Yes WWRDEHV COMANCHE 1 Yes O.K.U L.E.S.-7 1 Yes WWRDEHV TATONGA 1 Yes WWRDEHV HITCHLAND 7 1 Yes HITCHLAND CONESTOGA 1 Yes FINNEY CONESTOGA 1 Yes FINNEY HOLCOMB7 1 Yes GEN_2007_ HOLCOMB7 1 Yes SPERVIL COMANCHE 1 Yes SPERVIL KNOLL EHV 1 Yes HOLCOMB SETAB 7 1 Yes MINGO G Yes AXTELL SWEET W3 1 Yes GENTLMN SWEET W3 1 Yes GENTLMN REDWILO3 1 Yes STATELINE POTTER_CO 7 1 Yes POTTER_CO ROOSEVELT2 1 Yes ROOSEVELT TOLK 7 1 Yes TOLK TUCO 1 Yes WWRDEHV WOODRNG7 1 Yes MINGO KNOLL EHV 1 Yes L.E.S SEMINOL7 1 Yes SOONER ROSEHIL7 1 Yes COMANCHE WICHITA 1 Yes HITCHLAND WOODWARD 1 Yes WOODWARD COMANCHE 1 Yes COMANCHE SPEARVILLE 1 Yes SPEARVILLE HOLCOMB 1 Yes HOLCOMB HITCHLAND 1 Yes Final Report Page 4-50

113 Table : Single line fault transient stability results for 20% Case summer From Bus # From Bus Name To Bus # To Bus Name CKT ID 5 Cycle CLR Stable KNOLL EHV SUMMIT 7 1 Yes WWRDEHV COMANCHE 1 Yes O.K.U L.E.S.-7 1 Yes WWRDEHV TATONGA 1 Yes WWRDEHV HITCHLAND 7 1 Yes GEN_2007_ HOLCOMB7 1 Yes SPERVIL COMANCHE 1 Yes GENTLMN SWEET W3 1 Yes GENTLMN REDWILO3 1 Yes STATELINE POTTER_CO 7 1 Yes POTTER_CO ROOSEVELT2 1 Yes ROOSEVELT TOLK 7 1 Yes TOLK TUCO 1 Yes WWRDEHV WOODRNG7 1 Yes MINGO KNOLL EHV 1 Yes L.E.S SEMINOL7 1 Yes SOONER ROSEHIL7 1 Yes COMANCHE WICHITA 1 Yes HITCHLAND WOODWARD 1 Yes WOODWARD COMANCHE 1 Yes COMANCHE SPEARVILLE 1 Yes SPEARVILLE HOLCOMB 1 Yes HOLCOMB HITCHLAND 1 Yes Final Report Page 4-51

114 Table : Single line fault transient stability results for 20% Case winter From Bus # From Bus Name To Bus # To Bus Name CKT ID 5 Cycle CLR Stable KNOLL EHV SUMMIT 7 1 Yes WWRDEHV COMANCHE 1 Yes O.K.U L.E.S.-7 1 Yes WWRDEHV TATONGA 1 Yes WWRDEHV HITCHLAND 7 1 Yes FINNEY CONESTOGA 1 Yes SPERVIL COMANCHE 1 Yes GENTLMN SWEET W3 1 Yes GENTLMN REDWILO3 1 Yes STATELINE POTTER_CO 7 1 Yes POTTER_CO ROOSEVELT2 1 Yes ROOSEVELT TOLK 7 1 Yes TOLK TUCO 1 Yes WWRDEHV WOODRNG7 1 Yes MINGO KNOLL EHV 1 Yes L.E.S SEMINOL7 1 Yes SOONER ROSEHIL7 1 Yes COMANCHE WICHITA 1 Yes HITCHLAND WOODWARD 1 Yes WOODWARD COMANCHE 1 Yes COMANCHE SPEARVILLE 1 Yes SPEARVILLE HOLCOMB 1 Yes HOLCOMB HITCHLAND 1 Yes GGS Flowgate Limit Transient Stability Analysis Methodology The Gentleman Generating Station (GGS) flowgate limit was tested for transient stability for outages of 345 kv lines. This was performed by first increasing the power transfer such that transfer over the flowgate lines was at the flow gate limit. Then either the GENTLMN3 to SWEET W3 or the GENTLMN3 to REDWILO3 line was faulted and cleared and the transient stability of the system was determined. The power transfer needed to reach the limit was created by increasing the generation at buses GENTLM1G and GENTLM2G with a corresponding increase of real power load at MOORE 3 (640277) for the GENTLMN3 to SWEET W3 line study and MINGO 7 (531451) for the GENTLMN3 to REDWILO3 line study. For all cases, each line was faulted and cleared with line disconnection after five cycles. As in the single line fault analysis, CRA chose a five-cycle clearing time because it provided an appropriate margin from the real breaker timing. If a case was unstable with clearing after five Final Report Page 4-52

115 cycles, it was studied with 4.5-cycle clearing time. The machine angle and speed of all the machines in the SPP area was monitored to determine transient stability. Results The results of the GGS flowgate transient stability study are shown in Table through Table Only the fall Base, 10%, and 20% Cases are stable for a five-cycle clearing time. The spring Base, 10%, and 20% cases are not stable with a 4.5 cycle clearing time. The summer 10% and 20% Cases are stable for a 4.5 cycle clearing time, but the summer Base Case is not. All winter cases are unstable for a 4.5 cycle clearing time except the GENTLMN3 to SWEET W3 line in the winter 20% Case. Based on the summer and winter cases, additional wind generation with transmission expansion improves the transient stability of the power system. The simulations showed that some cases were unstable at the current flowgate limit of 1,625 MW. Following consultation with SPP, it was concluded that these instabilities resulted from modeling errors and the flowgate limit should remain at the current limit. Table : Base Case GGS flowgate limit From Bus # From Bus Name To Bus # To Bus Name CKT ID Case Power Transfer 5 Cycle CLR Stable 4.5 Cycle CLR Stable GENTLMN REDWILO3 1 Fall Base 185 Yes GENTLMN SWEET W3 1 Fall Base 185 Yes GENTLMN REDWILO3 1 Spring Base 960 No No GENTLMN SWEET W3 1 Spring Base 960 No No GENTLMN REDWILO3 1 Summer Base 220 No No GENTLMN SWEET W3 1 Summer Base 220 No No GENTLMN REDWILO3 1 Winter Base 130 No No GENTLMN SWEET W3 1 Winter Base 130 No No Final Report Page 4-53

116 Table : 10% Case GGS flowgate limit From Bus # From Bus Name To Bus # To Bus Name CKT ID Case Power Transfer 5 Cycle CLR Stable 4.5 Cycle CLR Stable GENTLMN REDWILO3 1 Fall 10% 340 Yes GENTLMN SWEET W3 1 Fall 10% 340 Yes GENTLMN REDWILO3 1 Spring 10% 1066 No No GENTLMN SWEET W3 1 Spring 10% 1066 No No GENTLMN REDWILO3 1 Summer 10% 190 No Yes GENTLMN SWEET W3 1 Summer 10% 190 No Yes GENTLMN REDWILO3 1 Winter 10% 120 No No GENTLMN SWEET W3 1 Winter 10% 120 No No Table : 20% Case GGS flowgate limit From Bus # From Bus Name To Bus # To Bus Name CKT ID Case Power Transfer 5 Cycle CLR Stable 4.5 Cycle CLR Stable GENTLMN REDWILO3 1 Fall 20% 605 Yes GENTLMN SWEET W3 1 Fall 20% 605 Yes GENTLMN REDWILO3 1 Spring 20% 1130 No No GENTLMN SWEET W3 1 Spring 20% 1130 No No GENTLMN REDWILO3 1 Summer 20% 205 No Yes GENTLMN SWEET W3 1 Summer 20% 205 No Yes GENTLMN REDWILO3 1 Winter 20% 145 No No GENTLMN SWEET W3 1 Winter 20% 145 No Yes Critical Clearing Time Critical clearing time is the longest fault duration for which the power system is stable. The critical clearing time was obtained for the spring Base, 10%, and 20% Cases to test the robustness of the transmission system and to further examine the effect of wind penetration on transient stability. The spring models were chosen because they had the lightest load and were therefore more prone to transient instability. The same spring faults considered in the previous section were analyzed. The fault durations tested ranged from 5.5 cycles to 11 cycles in 0.5-cycle intervals. The fault duration was limited to 11 cycles, which is more than double the typical 5-cycle clearing time tested by SPP. Therefore, if a case was stable with a Final Report Page 4-54

117 fault duration of 11 cycles, it could only be determined that the critical clearing time was greater than or equal to 11 cycles. The critical clearing time for each simulated fault in the spring Base, 10%, and 20% Cases had a critical clearing time greater than or equal to 11 cycles with one exception. The exception was the fault of the ROOSEVELT2 to TOLK kv line for the spring 10% Case, which has a critical clearing time of 10 cycles (double the typical clearing time tested by SPP). These results show that the system is robust even with 10% and 20% wind penetration levels and reaffirms the fact that increasing wind penetration with accompanying transmission expansion does not adversely affect the transient stability of the power system Transient Stability Conclusions A summary of the results for the single-line fault five-cycle clearing simulations is shown in Table Only two cases within the winter Base Case (GENTLMN3 to SWEET W3 and GENTLMN3 to REDWILO3) were not stable for a five-cycle clearing time. However, these lines were stable for a 4.5-cycle clearing time. Since the actual clearing times at these lines are 4 cycles, these cases should not cause any problems, but there is only a 0.5 cycle margin. Table : Summary of single-line fault results Stable for 5 Cycle Clearing Case Season Base 10% 20% Spring Yes Yes Yes Summer Yes Yes Yes Fall Yes Yes Yes Winter No Yes Yes Analysis of the critical clearing times on the light-load spring models, which are typically the most prone to voltage instability, showed that the system was stable for all simulated faults with clearing times of 10 cycles, indicating ample transient stability margins. It can be seen from the studies conducted that increasing wind capacity with commensurate transmission expansion does not adversely affect the transient stability of the power system, and may, in fact, improve it. Final Report Page 4-55

118 4.7. ADDITIONAL SPP FLOWGATES FOR COMMITMENT AND DISPATCH The addition of wind plants and transmission facilities in the 10% and 20% Cases changes the power flow patterns in the SPP transmission system. Therefore, a set of new transmission constraints was developed for the 10% and 20% Cases for enforcement as flowgates in the production simulation in Section 5.3. The new flowgates are in addition to the current list of SPP flowgates. Moreover, the new flowgates determined for the 20% Case are in addition to the flowgates for the 10% Case. The extra transmission constraints were determined by conducting AC contingency analyses on all four seasonal power flow cases. These contingency analyses monitored all branches in the SPP footprint with voltage higher than 100 kv. A new flowgate was created if a monitored branch had a pre- or post-contingency flow above 90% of its appropriate rating and the flowgate condition was not captured by an existing flowgate. The 10% Case had 40 such new flowgates, detailed in Table through Table Of these, eight monitor the flow on the 138 kv line Flat Ridge Harper for the loss of facilities that connect west and east SPP; the remainder monitor the contingent flows on lines in windrich areas. The 20% Case had 67 flowgates in addition to those for the 10% Case. These flowgates are detailed in Table through Table Although a few of these are due to specific wind plants, most of them are due to the increased flows from west to east SPP. Table 4.7-1: Newly identified constraints for the 10% Case (Part 1 of 2) Constraint Name Monitored Element Contingent Element NF1 MOORLND4-GLASMTN4 fl WWR MOORLND4 138-GLASMTN4 138 WWRDEHV7 345-TATONG NF2 SOUTHRD4-ROMNOSE4 fl WWR SOUTHRD4 138-ROMNOSE4 138 WWRDEHV7 345-TATONG NF3 FLATRDG3-HARPER 4 fl WWR FLATRDG3 138-HARPER WWRDEHV7 345-TATONG NF4 FLATRDG3-HARPER 4 fl O.K FLATRDG3 138-HARPER O.K.U L.E.S NF5 ELKCITY6-ELKCTSTR fl WWR ELKCITY6 230-ELKCTSTR 230 WWRDEHV7 345-HITCHL NF6 MOORLND4-GLASMTN4 fl WIC MOORLND4 138-GLASMTN4 138 WICHITA7 345-G07-25T 345 NF7 FLATRDG3-HARPER 4 fl WIC FLATRDG3 138-HARPER WICHITA7 345-G07-25T 345 NF8 ELKCTY-4-ELKCTSTR fl CL- ELKCTY ELKCTSTR 138 CL-AFTP4 138-ELKCTY NF9 CLIFTON3-GRNLEAF3 fl EMA CLIFTON3 115-GRNLEAF3 115 EMANHAT6 230-ELMCREK6 230 NF10 TALOGA 4-TALOGSTR fl DE TALOGA TALOGSTR 138 DEWEY SOUTHRD4 138 NF11 FLATRDG3-HARPER 4 fl SA FLATRDG3 138-HARPER SAWYER MED-LDG3 115 NF12 HARRNG_EST6-POTTER_CO 6 HARRNG POTTER HARRNG HARRNG NF13 TALOGA 4-TALOGSTR fl CE TALOGA TALOGSTR 138 CEDRDAL4 138-MOORLND4 138 NF14 SPEARVL6-SPEARSTR fl MU SPEARVL6 230-SPEARSTR 345 MULGREN6 230-SPEARVL6 230 NF15 WICHITA7-WICHISTR fl WI WICHITA7 345-WICHISTR 345 WICHITA7 345-WICHISTR 345 NF16 FLATRDG3-HARPER 4 fl WI FLATRDG3 138-HARPER WICHITA7 345-WICHISTR 345 NF17 EMCPHER3-MANVILE3 fl EM EMCPHER3 115-MANVILE3 115 EMCPHER3 115-REFINRY3 115 NF18 FLATRDG3-HARPER 4 fl EV FLATRDG3 138-HARPER EVANS S4 138-LAKERDG4 138 NF19 SPEARVL6-MULGREN6 fl SP SPEARVL6 230-MULGREN6 230 SPEARVL6 230-SPEARSTR 345 NF20 GRAPEVINE 3-KIRBY3 fl N GRAPEV KIRBY3 115 NICHOL YARNELL3 115 Final Report Page 4-56

119 Table 4.7-2: Newly identified constraints for the 10% Case (Part 2 of 2) Constraint Name Monitored Element Contingent Element NF21 FLATRDG3-HARPER 4 fl BE FLATRDG3 138-HARPER BENTON WICHITA7 345 NF22 PRINGLE 3-RIVERVIEW 3 f PRINGL RIVERV PRINGL PRINGL NF23 FLATRDG3-HARPER 4 fl SP FLATRDG3 138-HARPER SPEARVL7 345-KNOLL NF24 G06-45T-DEAFSMITH 6 fl G06-45T 230-DEAFSM HITCHL POTTER POTTERSTR 345 NF25 PRINGLE 3-RIVERVIEW 3 f PRINGL RIVERV HITCHL HITCHSTR 345 NF26 FLATRDG3-MED-LDG4 fl FL FLATRDG3 138-MED-LDG4 138 FLATRDG3 138-HARPER NF27 CANADAY7-LXNGTN 7 fl C. CANADAY7 115-LXNGTN C.CREEK4 230-RIVERDL4 230 NF28 SIBLEYPL-ECKLES-161 fl SIBLEYPL 161-ECKLES BRUNSWK5 161-SALSBRY5 161 NF29 SIBLEYPL-ECKLES-161 fl SIBLEYPL 161-ECKLES BLSPE DUNCAN NF30 TERRYC3-WOLFFORTH 3 fl TERRYC3 115-WOLFFO SUNDOW WOLFFO NF31 COWSKIN4-CENTENN4 fl EV COWSKIN4 138-CENTENN4 138 EVANS S4 138-LAKERDG4 138 NF32 CLIFTON3-GRNLEAF3 fl HO CLIFTON3 115-GRNLEAF3 115 HOYT JEC N NF33 RIVERDL4-RIVERDL7 fl AX RIVERDL4 230-RIVERDL7 115 AXTELL AXTELSTR 345 NF34 HASTCTY7-E7THST 7 fl AX HASTCTY7 115-E7THST AXTELL AXTELSTR 345 NF35 OGALALA7-ROSCOE 7 fl MA OGALALA7 115-ROSCOE MALONEY7 115-N.PLATT7 115 NF36 ROOSEVELT 3-CURRY 3 fl ROOSEV CURRY OASIS SW_4K NF37 FLATRDG3 to HARPER 4 FLATRDG3 138-HARPER NF38 SIBLEYPL to ECKLES-161 SIBLEYPL 161-ECKLES NF39 TURNER 5 to BELTONS5 TURNER BELTONS5 161 NF40 EMANHAT6 to EMANHSTR EMANHAT6 230-EMANHSTR 230 Table 4.7-3: Newly identified constraint limits for the 10% Case (Part 1 of 2) Constraint Name Summer Limit Winter Limit Fall Limit Spring Limit (MW) (MW) (MW) (MW) NF1 MOORLND4-GLASMTN4 fl WWR NF2 SOUTHRD4-ROMNOSE4 fl WWR NF3 FLATRDG3-HARPER 4 fl WWR NF4 FLATRDG3-HARPER 4 fl O.K NF5 ELKCITY6-ELKCTSTR fl WWR NF6 MOORLND4-GLASMTN4 fl WIC NF7 FLATRDG3-HARPER 4 fl WIC NF8 ELKCTY-4-ELKCTSTR fl CL NF9 CLIFTON3-GRNLEAF3 fl EMA NF10 TALOGA 4-TALOGSTR fl DE NF11 FLATRDG3-HARPER 4 fl SA NF12 HARRNG_EST6-POTTER_CO NF13 TALOGA 4-TALOGSTR fl CE NF14 SPEARVL6-SPEARSTR fl MU NF15 WICHITA7-WICHISTR fl WI NF16 FLATRDG3-HARPER 4 fl WI NF17 EMCPHER3-MANVILE3 fl EM NF18 FLATRDG3-HARPER 4 fl EV NF19 SPEARVL6-MULGREN6 fl SP NF20 GRAPEVINE 3-KIRBY3 fl N Final Report Page 4-57

120 Table 4.7-4: Newly identified constraint limits for the 10% Case (Part 2 of 2) Constraint Name Summer Limit Winter Limit Fall Limit Spring Limit (MW) (MW) (MW) (MW) NF21 FLATRDG3-HARPER 4 fl BE NF22 PRINGLE 3-RIVERVIEW 3 f NF23 FLATRDG3-HARPER 4 fl SP NF24 G06-45T-DEAFSMITH 6 fl NF25 PRINGLE 3-RIVERVIEW 3 f NF26 FLATRDG3-MED-LDG4 fl FL NF27 CANADAY7-LXNGTN 7 fl C NF28 SIBLEYPL-ECKLES-161 fl NF29 SIBLEYPL-ECKLES-161 fl NF30 TERRYC3-WOLFFORTH 3 fl NF31 COWSKIN4-CENTENN4 fl EV NF32 CLIFTON3-GRNLEAF3 fl HO NF33 RIVERDL4-RIVERDL7 fl AX NF34 HASTCTY7-E7THST 7 fl AX NF35 OGALALA7-ROSCOE 7 fl MA NF36 ROOSEVELT 3-CURRY 3 fl NF37 FLATRDG3 to HARPER NF38 SIBLEYPL to ECKLES NF39 TURNER 5 to BELTONS NF40 EMANHAT6 to EMANHSTR Table 4.7-5: Newly identified constraints for the 20% Case (Part 1 of 2) Constraint Name Monitored Element Contingent Element NF201 AUBURN TRF fl HOYT 7 AUBURSTR AUBURN HOYT JEC N NF202 AUBURN TRF fl AUBURN 6 AUBURSTR AUBURN AUBURN SWISVAL6 230 NF203 BELTONS5-TURNER 5 fl P TURNER BELTONS5 161 PECULR PHILL NF204 BUFBEAR2-BUFFALO2 fl F BUFFALO BUFBEAR2 69 FTSUPLY IODINE NF205 BUSHLAND3-COULTER3 fl COULTER BUSHLA POTTER BUSHLA NF206 BUTLER4-ALTOONA4 fl NE ALTOONA BUTLER NEOSHO NEOSHSTR 138 NF207 CANADAY4-CANADAY7 fl C CANADAY CANADAY4 230 C.CREEK RIVERDL4 230 NF208 CIMARON4-HAYMAKR4 fl C HAYMAKR CIMARON4 138 CZECHAL CIMARON4 138 NF209 CIMARON-DRAPER fl CLEV DRAPER CIMARON7 345 CLEVLND SOONER NF210 CIMARON-DRAPER fl SEMI DRAPER CIMARON7 345 SEMINOL L.E.S NF211 CIMARON-DRAPER fl NORT DRAPER CIMARON7 345 NORTWST ARCADIA7 345 NF212 CLIFTN-GRNLEAF fl HOYT GRNLEAF CLIFTON3 115 HOYT JEC N NF213 CLIFTN-GRNLEAF fl SUMM GRNLEAF CLIFTON3 115 SUMMIT ELMCREK6 230 NF214 CONCORD-CLIFTN fl EMAN CLIFTON CONCORD3 115 EMANHAT ELMCREK6 230 NF215 CONCORD-CLIFTON fl HOY CLIFTON CONCORD3 115 HOYT JEC N NF216 DVISION4-LAKESID4 fl M LAKESID DVISION4 138 MEMRIAL TENESTP4 138 NF217 ELKCITY TRF fl STATELI ELKCTY ELKCTSTR 230 STATEL ELKCITY7 345 NF218 EMANHAT TRF fl HOYT EMANHSTR EMANHAT6 230 HOYT JEC N NF219 EMCPHER3-MANVILE3 fl E MANVILE EMCPHER3 115 EMCPHER REFINRY3 115 NF220 EVANS N4-MAIZE 4 fl B MAIZE EVANS N4 138 BENTON WICHITA7 345 NF221 EVANS S4-LAKERDG4 fl C LAKERDG EVANS S4 138 CENTENN COWSKIN4 138 NF222 EVANS S4-LAKERDG4 fl E LAKERDG EVANS S4 138 EVANS N MAIZE NF223 FLATRDG-HARPER fl BENT HARPER FLATRDG3 138 BENTON WICHITA7 345 NF224 G07-25T-WICHITA7 fl CO WICHITA G07-25T 345 COMANCHE WICHITA 765 NF225 G07-32T-CLINTJC4 fl G0 CLINTJC G07-32T 138 G07-32T CLINTON4 138 NF226 G07-32T-CLINTON4 fl WT CLINTON G07-32T 138 WTH_JCT WTH_SE NF227 G07-33T-HARRNG_EST6 fl HARRNG G07-33T 230 HITCHL HITCHSTR 345 NF228 GILL STR-GILL W 2 fl G GILL W GILL STR 138 GILL S GILL STR 138 NF229 HARPER 4-MILANTP4 fl C MILANTP HARPER COMANCHE WICHITA 765 Final Report Page 4-58

121 Table 4.7-6: Newly identified constraints for the 20% Case (Part 2 of 2) Constraint Name Monitored Element Contingent Element NF230 HITCHLAND 7-HITCHLAND HITCHL HITCHL HITCHL HITCHL NF231 HOLCOMB3-PLYMELL3 fl H PLYMELL HOLCOMB3 115 HOLCOMB HOLCOSTR 345 NF232 HOYT-MERI fl HOYT 7-54&MERI HOYT HOYT STRANGR7 345 NF233 JEC N 7-HOYT 7 fl AUBU HOYT JEC N AUBURN JEC NF234 JEC N 7-HOYT 7 fl JEC HOYT JEC N JEC N MORRIS NF235 JERICHO3-JERICSTR fl M JERICSTR JERICHO3 115 MCCLEL KIRBY3 115 NF236 LAWHILL6-LAWHISTR fl L LAWHISTR LAWHILL6 230 LAWHILL MIDLAND6 230 NF237 MAXWELS7-CALAWAY7 fl C CALAWAY MAXWELS7 115 C.CREEK C.CREEK7 115 NF238 MCELROY4-KINZE 4 fl C KINZE MCELROY4 138 CLEVLND SOONER NF239 MIDLAND6-MIDLASTR fl L MIDLASTR MIDLAND6 230 LAWHILL LAWHISTR 230 NF240 MOORLND4-GLASMTN4 fl W GLASMTN MOORLND4 138 WOODRNG WOODRSTR 345 NF241 NASHUA 7-NASHUSTR fl N NASHUSTR NASHUA NASHUA HAWTH NF242 NORTWST TRF fl NORTWST NORTWSTR NORTWST7 345 NORTWST NORTWSTR 345 NF243 PAOLI TRF fl WYNNTAP4- PALIOSTR PAOLI WYNNTAP PAOLI NF244 SMOKYHLLS6-SUMMIT 6 fl SUMMIT SMOKYH KNOLL SUMMIT NF245 SUMMIT 3-NORTHVW3 fl P NORTHVW SUMMIT PHILIPS SCHILNG3 115 NF246 SUNNYSD4-UNIROY 4 fl S UNIROY SUNNYSD4 138 SUNNYSD ROCKYPT4 138 NF247 SW 5TAP4-CLASSEN4 fl M CLASSEN SW 5TAP4 138 MUSTANG COUNCIL4 138 NF248 SWISVAL7-W.GRDNR7 fl H W.GRDNR SWISVAL7 345 HOYT STRANGR7 345 NF249 TECHILE3-STULL T3 fl H STULL T TECHILE3 115 HOYT STRANGR7 345 NF250 TOLK_E-TUCO fl TOLK TUCO_I TOLK_E TOLK TUCO_I NF251 W.GRDNR7-STILWEL7 fl L STILWEL W.GRDNR7 345 LACYGNE STILWEL7 345 NF252 WASHITA4-S.W.S.-4 fl A S.W.S WASHITA4 138 ANADARK WASHITA4 138 NF253 WICHISTR-EVANS N4 fl B EVANS N WICHISTR 345 BENTON WICHITA7 345 NF254 WICHITA7-BENTON 7 fl S BENTON WICHITA7 345 SWISVAL W.GRDNR7 345 NF255 WICHITA 765 TRF fl BEN WICHISTR WICHITA7 345 BENTON WICHITA7 345 NF256 WILLIAM-FLETCHR fl SYR FLETCHR WILLIAM3 115 SYRACUS TRIBUNE3 115 NF257 WILLIAM-FLETCHR fl KAN FLETCHR WILLIAM3 115 KANARAD G06-34T 115 NF258 CLIFTON3 to GRNLEAF3 CLIFTON GRNLEAF3 115 NF259 FIXCT4 to MAUD 4 FIXCT MAUD NF260 YUMA_INTG 3 to WOLFFOR YUMA_I WOLFFO NF261 WILKES 7 to WILKESTR WILKES WILKESTR 345 NF262 EVANS S4 to LAKERDG4 EVANS S LAKERDG4 138 NF263 S.W.S.-4 to WASHITA4 S.W.S WASHITA4 138 NF264 FLETCHR3 to WILLIAM3 FLETCHR WILLIAM3 115 NF265 HIGHLANDTP3 to PANTEX_ HIGHLA PANTEX NF266 MRWYP16 to MRWYPSTR MRWYP MRWYPSTR 34.5 NF267 MRWYP26 to MRWYPSTR MRWYP MRWYPSTR 34.5 Table 4.7-7: Newly identified constraint limits for the 20% Case (Part 1 of 3) Constraint Name Summer Limit Winter Limit Fall Limit Spring Limit (MW) (MW) (MW) (MW) NF201 AUBURN TRF fl HOYT NF202 AUBURN TRF fl AUBURN NF203 BELTONS5-TURNER 5 fl P NF204 BUFBEAR2-BUFFALO2 fl F NF205 BUSHLAND3-COULTER3 fl NF206 BUTLER4-ALTOONA4 fl NE NF207 CANADAY4-CANADAY7 fl C NF208 CIMARON4-HAYMAKR4 fl C NF209 CIMARON-DRAPER fl CLEV NF210 CIMARON-DRAPER fl SEMI NF211 CIMARON-DRAPER fl NORT NF212 CLIFTN-GRNLEAF fl HOYT NF213 CLIFTN-GRNLEAF fl SUMM Final Report Page 4-59

122 Table 4.7-8: Newly identified constraint limits for the 20% Case (Part 2 of 3) Constraint Name Summer Limit Winter Limit Fall Limit Spring Limit (MW) (MW) (MW) (MW) NF214 CONCORD-CLIFTN fl EMAN NF215 CONCORD-CLIFTON fl HOY NF216 DVISION4-LAKESID4 fl M NF217 ELKCITY TRF fl STATELI NF218 EMANHAT TRF fl HOYT NF219 EMCPHER3-MANVILE3 fl E NF220 EVANS N4-MAIZE 4 fl B NF221 EVANS S4-LAKERDG4 fl C NF222 EVANS S4-LAKERDG4 fl E NF223 FLATRDG-HARPER fl BENT NF224 G07-25T-WICHITA7 fl CO 1,440 1,440 1,440 1,440 NF225 G07-32T-CLINTJC4 fl G NF226 G07-32T-CLINTON4 fl WT NF227 G07-33T-HARRNG_EST6 fl NF228 GILL STR-GILL W 2 fl G NF229 HARPER 4-MILANTP4 fl C NF230 HITCHLAND 7-HITCHLAND 1,250 1,250 1,250 1,250 NF231 HOLCOMB3-PLYMELL3 fl H NF232 HOYT-MERI fl HOYT NF233 JEC N 7-HOYT 7 fl AUBU 1,076 1,076 1,076 1,076 NF234 JEC N 7-HOYT 7 fl JEC 1,076 1,076 1,076 1,076 NF235 JERICHO3-JERICSTR fl M NF236 LAWHILL6-LAWHISTR fl L NF237 MAXWELS7-CALAWAY7 fl C NF238 MCELROY4-KINZE 4 fl C NF239 MIDLAND6-MIDLASTR fl L NF240 MOORLND4-GLASMTN4 fl W NF241 NASHUA 7-NASHUSTR fl N NF242 NORTWST TRF fl NORTWST NF243 PAOLI TRF fl WYNNTAP NF244 SMOKYHLLS6-SUMMIT 6 fl NF245 SUMMIT 3-NORTHVW3 fl P NF246 SUNNYSD4-UNIROY 4 fl S NF247 SW 5TAP4-CLASSEN4 fl M NF248 SWISVAL7-W.GRDNR7 fl H NF249 TECHILE3-STULL T3 fl H NF250 TOLK_E-TUCO fl TOLK NF251 W.GRDNR7-STILWEL7 fl L NF252 WASHITA4-S.W.S.-4 fl A NF253 WICHISTR-EVANS N4 fl B NF254 WICHITA7-BENTON 7 fl S NF255 WICHITA 765 TRF fl BEN NF256 WILLIAM-FLETCHR fl SYR NF257 WILLIAM-FLETCHR fl KAN NF258 CLIFTON3 to GRNLEAF NF259 FIXCT4 to MAUD NF260 YUMA_INTG 3 to WOLFFOR NF261 WILKES 7 to WILKESTR NF262 EVANS S4 to LAKERDG Final Report Page 4-60

123 Table 4.7-9: Newly identified constraint limits for the 20% Case (Part 3 of 3) Constraint Name Summer Limit Winter Limit Fall Limit Spring Limit (MW) (MW) (MW) (MW) NF263 S.W.S.-4 to WASHITA NF264 FLETCHR3 to WILLIAM NF265 HIGHLANDTP3 to PANTEX_ NF266 MRWYP16 to MRWYPSTR NF267 MRWYP26 to MRWYPSTR WIND POWER DELIVERABILITY The deliverability of wind power was analyzed by performing two series of studies: PV analyses for increments/reductions in wind generation against reductions/increments in non-wind generators, and Production simulations These studies are complementary, and focus on different aspects of deliverability. The PV studies use an AC model of the transmission network to indicate whether there are any voltage concerns. In these studies, variations in generation dispatch, both for wind and nonwind units, are based on participation factors and the unit commitment is fixed. Production simulations, on the other hand, employ a DC model of the transmission network, but model variations in dispatch and commitment in detail. This section discusses the results of the PV analyses; production simulations are presented in Section 5.3. The summer peak power flow cases for the 10% and 20% Cases were used for the PV analysis. Two injection groups were defined. The non-wind injection group included all nonwind generators in SPP that were connected in the power flow cases. The wind injection group included all wind plants in the corresponding case. The total output of the wind injection group was varied from a 20% capacity factor to a 100% capacity factor in 100 MW increments, with the variations compensated by the non-wind injection group. The changes in generation in the wind injection group were proportional to the un-dispatched generation capacities of the wind plants, with the changes in the non-wind injection groups proportional to the participation factors in the power flow model, while abiding by each generator s maximum and minimum capacity requirement. The study monitored the flows on all current and new SPP flowgates (Section 4.7 and Appendix E) and on all SPP branches with nominal voltage higher than 100 kv, as well as the voltages of all SPP nodes with nominal voltage higher than 100 kv. Final Report Page 4-61

124 % Case The results are summarized in Table and Table The violations for the maximum wind power injection, with all wind plants dispatched at their nameplate capacity, are in Appendix A.6.2. The minimum voltage was 0.91 p.u. at the Shamrock 115 kv bus. There were 19 buses with voltage below 0.95 p.u. and only four of them were below 0.94 p.u. The eight buses with the lowest voltages were on the 115/138 kv and 230 kv ties between SPS and SPP. Seven buses had voltages above 1.05 p.u. There were two lines lightly overloaded (below 2.5%). Most violations were in terms of overloaded flowgates, with the NewSPSSPPTIES flowgate overloaded by 70%, indicating that this scenario with all wind plants dispatched at 100% is not realistic. Considering that a) the dispatch and commitment were not optimized for this high wind power injection level, b) the voltage issues were minor, and were for the most part captured by the new SPSSPPTIES flowgate, and c) most violations were in terms of flowgates, the results indicate that the production simulations for the 10% Case were expected to give an accurate description for the deliverability of the wind plants. Final Report Page 4-62

125 Table : Critical constraints for wind power output increases in the summer 10% Case Wind Power Increment (MW) Wind Power (MW) Non-Wind Power (MW) Newly Critical Constraint Flowgate GIBRAMWEBRIC; Flowgate FLCXFRFLCXFR Flowgate NF32 CLIFTON3-GRNLEAF3 fl HO Flowgate NF9 CLIFTON3-GRNLEAF3 fl EMA Inadequate Voltage at Bus SHAM 3WT (512107) Flowgate NF7 FLATRDG3-HARPER 4 fl WIC Flowgate NewSPSToSPPTies Flowgate NF10 TALOGA 4-TALOGSTR fl DE Flowgate NF1 MOORLND4-GLASMTN4 fl WWR; Flowgate SHAXFROKULES; Flowgate NF8 ELKCTY-4-ELKCTSTR fl CL Flowgate NF20 GRAPEVINE 3-KIRBY3 fl N Flowgate SUNXFRPITSEM Flowgate NF6 MOORLND4-GLASMTN4 fl WIC; Flowgate NF13 TALOGA 4- TALOGSTR fl CE Flowgate SHAXFRTUCOKU Overload on Greenleaf to Clifton 115 kv line Flowgate SHAXFRELKGRP; Flowgate NF18 FLATRDG3-HARPER 4 fl EV Overload on Mooreland To Glass Mountain 138 kv line; Flowgate SHAXFRFINHOL Flowgate NF26 FLATRDG3-MED-LDG4 fl FL Max Wind Generation Final Report Page 4-63

126 Table : Critical constraints for wind power output decreases in the summer 10% Case Wind Power Decrement (MW) Wind Power (MW) Non-Wind Power (MW) Newly Critical Constraint Flowgate EUFXFRWELXFR Flowgate DANMAGANOFTS Flowgate FLCXFRFLCXFR % Max Wind Generation; Inadequate bus voltage at 115 kv bus MINDEN-3 (507778) Final Report Page 4-64

127 % Case The results are summarized in Table through Table Table : Critical constraints for wind power output increases in the summer 20% Case (Part 1 of 2) Wind Power Increment (MW) Wind Power (MW) Non-Wind Power (MW) Newly Critical Constraint Flowgate NF247 SW 5TAP4-CLASSEN4 fl M Flowgate NF220 EVANS N4-MAIZE 4 fl B Flowgate NF31 COWSKIN4-CENTENN4 fl EV Flowgate HSBIS_ELDXF Flowgate NF30 TERRYC3-WOLFFORTH 3 fl; Flowgate NF213 CLIFTN-GRNLEAF fl SUMM; Flowgate NF216 DVISION4-LAKESID4 fl M Flowgate NF7 FLATRDG3-HARPER 4 fl WIC Flowgate NF8 ELKCTY-4-ELKCTSTR fl CL-; Flowgate NF232 HOYT-MERI fl HOYT Flowgate NF234 JEC N 7-HOYT 7 fl JEC Flowgate NF15 WICHITA7-WICHISTR fl WI Flowgates NF248 SWISVAL7-W.GRDNR7 fl H; Flowgate NF233 JEC N 7-HOYT 7 fl AUBU Inadequate Voltage at buses SHAM 3WT (512107), PAWNEE 3 (530621), and RIVERDL4 (640330) 0.950; Flowgate NF9 CLIFTON3-GRNLEAF3 fl EMA; Flowgate STIMCRSPRNOR Flowgate NF255 WICHITA 765 TRF fl BEN; Flowgate NF253 WICHISTR-EVANS N4 fl B Flowgate NF223 FLATRDG-HARPER fl BENT; Flowgate NF21 FLATRDG3-HARPER 4 fl BE; Flowgate NF240 MOORLND4-GLASMTN4 fl W; Flowgate NF20 GRAPEVINE 3-KIRBY3 fl N Flowgate NF18 FLATRDG3-HARPER 4 fl EV; Flowgate NF238 MCELROY4-KINZE 4 fl C Flowgate NewSPStoSPPTies Flowgate NF249 TECHILE3-STULL T3 fl H; 115 kv line Clifton to Greenleaf Flowgate NF16 FLATRDG3-HARPER 4 fl WI; Flowgate NF237 MAXWELS7-CALAWAY7 fl C Flowgate BRKSPRSWPSWD Flowgate NF11 FLATRDG3-HARPER 4 fl SA; Flowgate NF235 JERICHO3-JERICSTR fl M; Flowgate NF251 W.GRDNR7-STILWEL7 fl L Flowgate NF254 WICHITA7-BENTON 7 fl S Final Report Page 4-65

128 Table : Critical constraints for wind power output increases in the summer 20% Case (Part 2 of 2) Wind Power Increment (MW) Wind Power (MW) Non-Wind Power (MW) Newly Critical Constraint Overload on 138 kv line Flat Ridge to Harper Flowgate NF230 HITCHLAND 7-HITCHLAND; Flowgate NF250 TOLK_E-TUCO fl TOLK; Overload on 138/69 kv transformer Elk City Flowgate NF23 FLATRDG3-HARPER 4 fl SP; Flowgate NF4 FLATRDG3-HARPER 4 fl O.K; Flowgate NF245 SUMMIT 3-NORTHVW3 fl P Flowgate SHAXFROKULES Flowgate NF206 BUTLER4-ALTOONA4 fl NE; Flowgate NF229 HARPER 4-MILANTP4 fl C; Flowgate NF3 FLATRDG3-HARPER 4 fl WWR Flowgate NF217 ELKCITY TRF fl STATELI; Flowgate NF13 TALOGA 4-TALOGSTR fl CE Flowgate NF205 BUSHLAND3-COULTER3 fl; Flowgate NF6 MOORLND4-GLASMTN4 fl WIC; Overload on 138 kv line Elk City to Clinton Flowgate SHAXFRTUCOKU; Overload on 345 kv line Wichita to Benton Overload on 138 kv line S.W.S. to Washita; Overload on 138 kv line Milan to Harper Flowgate NF1 MOORLND4-GLASMTN4 fl WWR Flowgate NF224 G07-25T-WICHITA7 fl CO Overload on 138 kv line Glass Mountain to Mooreland Overload on 138 kv line Clinton to Hobart; Overload on 345 kv line Hoyt to JEC Flowgate NF10 TALOGA 4-TALOGSTR fl DE; Overload on 230 kv line G06-45 to Deafsmith Flowgate NF226 G07-32T-CLINTON4 fl WT Flowgate NF225 G07-32T-CLINTJC4 fl G Flowgate NF26 FLATRDG3-MED-LDG4 fl FL Flowgate NF204 BUFBEAR2-BUFFALO2 fl F Flowgate NF239 MIDLAND6-MIDLASTR fl L Max Wind Generation; Overload on 138 kv line Haymark to Cimaron; Overload on 138 kv line Cimaron to Czech Hall; Overload on 138 kv line Kinze to Mc Elroy Final Report Page 4-66

129 Table : Critical constraints of wind power output decreases in the summer 20% Case Wind Power Decrement (MW) Wind Power (MW) Non-Wind Power (MW) Newly Critical Constraint Flowgate DANMAGANOFTS Flowgate RUSDARANOFTS % Max Wind Generation Figure : Bus voltage contour for the maximum wind power injection in the summer 20% Case The violations for the maximum wind power injection, with all wind plants dispatched at their nameplate capacity, are in Appendix A.6.2. There were 171 buses with voltage below 0.95 p.u. The minimum voltage was p.u. at the Shamrock 115 kv bus, the remaining voltages were above 0.91 p.u. and occurred mainly in buses in AEPW and OKGE, which had a large number of wind plants, and across all voltage levels, including 345 kv nodes. This can be seen in the voltage contour of Figure The low voltages were due to several wind Final Report Page 4-67