Appendices. New York Control Area Installed Capacity Requirement. December 2, For the Period May 2017 To April 2018

Transcription

1 Appendices New York Control Area Installed Capacity Requirement For the Period May 2017 To April 2018 December 2, 2016 New York State Reliability Council, LLC Installed Capacity Subcommittee NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page i

2 Table of Contents A. Reliability Calculation Models and Assumptions... 7 A.1 GE MARS...9 A.2 Methodology A.3 Base Case Modeling Assumptions A.4 MARS Data Scrub B. Details for Study Results B.1 Sensitivity Results B.2 Impacts of Environmental Regulations B.3 Frequency of Implementing Emergency Operating Procedures C. ICAP to UCAP Translation C.1 NYCA and NYC and LI Locational Translations C.2 Transmission Districts ICAP to UCAP Translation C.3 Wind Resource Impact on the NYCA IRM and UCAP Markets D. Glossary NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page ii

3 Table of Tables & Figures Figure A.1 NYCA ICAP Modeling... 7 Table A.1 Modeling Details... 8 Equation A.1 Transition Rate Definition Equation A.2 Transition Rate Calculation Example Table A.2 State Transition Rate Example Table A.3 Load Model Table A Load Forecast Uncertainty Models Figure A.2 LFU Distributions Figure A.3 Per Unit Load Shapes Table A.5 Capacity Resources Table A.6 Wind Generation Figure A.4 NYCA Annual Zonal EFORds Figure A.5 Five-Year Zonal EFORds Figure A.6 NYCA Annual Availability by Fuel Figure A.7 NYCA Five-Year Availability by Fuel Figure A.8 NERC Annual Availability by Fuel Figure A.9 NERC Five-Year Availability by Fuel Figure A.10 Planned and Maintenance Outage Rates Figure A.11 Scheduled Maintenance Table A.7 Transmission System Model Table A.8 Interface Limits Updates Figure A IRM Topology Figure A PJM-SENY Interface Model Figure A.14 Full New England Representation Table A.9 External Area Representations Table A.10 Outside World Reserve Margins Table A.11 Assumptions for Emergency Operating Procedures Table A.12 Emergency Operating Procedures Values Table A.13 SCR Performance Table A.14 GE MARS Data Scrub Table A.15 NYISO MARS Data Scrub Table A.16 Transmission Owner Data Scrub Table B.1 Sensitivity Case Results Table B.2 Implementation of EOP steps Table C.1 Historical NYCA Capacity Parameters Table C.2 NYCA ICAP to UCAP Translation Table C.3 New York City ICAP to UCAP Translation Table C.4 Long Island ICAP to UCAP Translation Table C.5 GHIJ ICAP to UCAP Translation NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page iii

4 Table C.6 Central Hudson Gas & Electric ICAP to UCAP Translation Table C.7 Con Ed ICAP to UCAP Translation Table C.8 LIPA ICAP to UCAP Translation Table C.9 NGRID ICAP to UCAP Translation Table C.10 NYPA ICAP to UCAP Translation Table C.11 NYSEG ICAP to UCAP Translation Table C.12 O & R ICAP to UCAP Translation Table C.13 RGE ICAP to UCAP Translation NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page iv

5 Appendices NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 5

6 Appendix A NYCA Installed Capacity Requirement Reliability Calculation Models and Assumptions Description of the GE MARS Program: Load, Capacity, Transmission, Outside World Model, and Assumptions NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 6

7 A. Reliability Calculation Models and Assumptions The reliability calculation process for determining the NYCA IRM requirement utilizes a probabilistic approach. This technique calculates the probabilities of outages of generating units, in conjunction with load and transmission models, to determine the number of days per year of expected capacity shortages. The General Electric Multi-Area Reliability Simulation (GE-MARS) is the primary computer program used for this probabilistic analysis. The result of the calculation for Loss of Load Expectation (LOLE) provides a consistent measure of system reliability. The various models used in the NYCA IRM calculation process are depicted in Figure A.1 below. Table A.1 lists the study parameters, the source for the study assumptions, and where the assumptions are described in Appendix A. Finally, section A.3 compares the assumptions used in the 2016 and 2017 IRM reports. Figure A.1 NYCA ICAP Modeling NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 7

8 Table A.1 Modeling Details # Parameter Description Source Reference 1 GE MARS Internal NYCA Modeling General Electric Multi-Area Reliability Simulation Program 2 11 Zones Load Areas Fig A.1 3 Zone Capacity Models 4 Emergency Operating Procedures Generator models for each generating in Zone Generator availability Unit ratings Reduces load during emergency conditions to maintain operating reserves 5 Zone Load Models Hourly loads 6 7 Load Uncertainty Model Transmission Capacity Model Account for forecast uncertainty due to weather conditions Emergency transfer limits of transmission interfaces between Zones External Control Area Modeling GADS data 2016 Gold Book 1 NYISO NYCA load shape and peak forecasts Historical data NYISO Transmission Studies Section A.1 NYISO Accounting & Billing Manual Section A.3.2 Section A.3.5 Section A.3.1 Section A.3.1 Section A Ontario, Quebec, ISONE, PJM Control Area Parameters External Control Area Capacity models External Control Area Load Models External Control Area Load Uncertainty Models Interconnection Capacity Models See items 9-12 in this table Generator models in neighboring Control Areas Hourly loads Account for forecast uncertainty due to economic conditions Emergency transfer limits of transmission interfaces between control areas. Supplied by External Control Area Supplied by External Control Area Supplied by External Control Area Supplied by External Control Area Supplied by External Control Area Section A.3.4 Section A.3.4 Section A.3.4 Section A Load and Capacity Data Report, NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 8

9 A.1 GE MARS As the primary probabilistic analysis tool used for establishing NYCA IRM requirements, the GE-MARS program includes a detailed load, generation, and transmission representation for 11 NYCA Zones, as well as the four external Control Areas (Outside World Areas) interconnected to the NYCA (see Section A.3 for a description of these Zones and Outside World Areas). A sequential Monte Carlo simulation forms the basis for GE-MARS. The Monte Carlo method provides a fast, versatile, and easily expandable program that can be used to fully model many different types of generation, transmission, and demand-side options. GE-MARS calculates the standard reliability indices of daily and hourly LOLE (days/year and hours/year) and Loss of Energy Expectation (LOEE in MWh/year). The use of sequential Monte Carlo simulation allows for the calculation of timecorrelated measures such as frequency (outages/year) and duration (hours/outage). The program also calculates the need for initiating Emergency Operating Procedures (EOPs), expressed in days/year (see Section A.3.5). In addition to calculating the expected values for the reliability indices, GE-MARS also produces probability distributions that show the actual yearly variations in reliability that the NYCA could be expected to experience. In determining NYCA reliability, there are several types of randomly occurring events that must be taken into consideration. Among these are the forced outages of generating units and transmission capacity. Monte Carlo simulation models the effects of such random events. Deviations from the forecasted loads are captured using a load forecast uncertainty model. Monte Carlo simulation approaches can be categorized as non-sequential and sequential. A non-sequential simulation process does not move through time chronologically or sequentially, but rather considers each hour independent of every other hour. Because of this, non-sequential simulation cannot accurately model issues that involve time correlations, such as maintenance outages, and cannot be used to calculate time-related indices such as frequency and duration. Sequential Monte Carlo simulation (used by GE-MARS) steps through the year chronologically, recognizing the status of equipment is not independent of its status in adjacent hours. Equipment forced outages are modeled by taking the equipment out of service for contiguous hours, with the length of the outage period being NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 9

10 determined from the equipment s mean time to repair. Sequential simulation can model issues of concern that involve time correlations, and can be used to calculate indices such as frequency and duration. It also models transfer limitations between individual areas. Because the GE-MARS Program is based on a sequential Monte Carlo simulation, it uses state transition rates, rather than state probabilities, to describe the random forced outages of the thermal units. State probabilities give the probability of a unit being in a given capacity state at any particular time, and can be used if one assumes that the unit s capacity state for a given hour is independent of its state at any other hour. Sequential Monte Carlo simulation recognizes the fact that a unit s capacity state in any given hour is dependent on a given state in previous hours and influences its state in future hours. It thus requires additional information that is contained in the transition rate data. For each unit, a transition rate matrix is input that shows the transition rates to go from each capacity state to each other capacity state. The transition rate from state A to state B is defined as the number of transitions from A to B per unit of time in state A (Equation A.1). Transition (A to B) = Equation A.1 Transition Rate Definition Number of Transitions from A to B Total Time in State A Table A.2 shows the calculation of the state transition rates from historic data for one year. The Time-in-State Data shows the amount of time that the unit spent in each of the available capacity states during the year; the unit was on planned outage for the remaining 760 hours. The Transition Data shows the number of times that the unit transitioned from each state to each other state during the year. The State Transition Rates can be calculated from this data. For example, the transition rate from state 1 to state 2 equals the number of transitions from 1 to 2 divided by the total time spent in state 1 (Equation A.2). Equation A.2 Transition Rate Calculation Example Transition (1 to 2) = (10 Transitions) 5,000 Hours = NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 10

11 Table A.2 State Transition Rate Example Time in State Data Transition Data State MW Hours From To State To State To State State State Transition Rates From State To State 1 To State 2 To State From the state transition rates for a unit, the program calculates the two important quantities that are needed to model the random forced outages on the unit: the average time that the unit resides in each capacity state, and the probability of the unit transitioning from each state to each other state. Whenever a unit changes capacity states, two random numbers are generated. The first is used to calculate the amount of time that the unit will spend in the current state; it is assumed that the time in a state is exponentially distributed, with a mean as computed from the transition rates. This time in state is added to the current simulation time to calculate when the next random state change will occur. The second random number is combined with the state transition probabilities to determine the state to which the unit will transition when it leaves its current state. The program thus knows for every unit on the system, its current state, when it will be leaving that state, and the state to which it will go next. Each time a unit changes state, because of random state changes, the beginning or ending of planned outages, or mid-year installations or retirements, the total capacity available in the unit's area is updated to reflect the change in the unit's available capacity. This total capacity is then used in computing the area margins each hour. A.1.1 Error Analysis An important issue in using Monte Carlo simulation programs such as GE-MARS is the number of years of artificial history (or replications) that must be created to NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 11

12 achieve an acceptable level of statistical convergence in the expected value of the reliability index of interest. The degree of statistical convergence is measured by the standard deviation of the estimate of the reliability index that is calculated from the simulation data. The standard deviation has the same physical units (e.g., days/year) as the index being estimated, and thus its magnitude is a function of the type of index being estimated. Because the standard deviation can assume a wide range of values, the degree of convergence is often measured by the standard error, which is the standard deviation of the estimated mean expressed as a per unit of the mean. Convergence can also be expressed in terms of a confidence interval that defines the range in which you can state, with a given level of confidence that the actual value falls within the interval. For example, a range centered on the mean of two standard deviations in each direction (plus and minus) defines a confidence interval of 95%. For this analysis, the Base Case required 402 replications to converge to a standard error of 0.05 and required 1942 replications to converge to a standard error of For our cases, the model was run to 2000 replications at which point the daily LOLE of days/year for NYCA was met with a standard error of The confidence interval at this point ranges from 17.8% to 18.4%. It should be recognized that an 18.1% IRM is in full compliance with the NYSRC Resource Adequacy rules and criteria (see Base Case Study Results section). A.1.2 Conduct of the GE-MARS analysis The study was performed using Version 3.20 of the GE-MARS software program. This version has been benchmark tested by the NYISO. The current base case is the culmination of the individual changes made to last year s base case. Each change, however, is evaluated individually against last year s base case. The LOLE results of each of these pre-base case simulations are reviewed to confirm that the reliability impact of the change is reasonable and explainable. General Electric was asked to review the input data for errors. They have developed a program called Data Scrub which processes the input files and flags data that appears to be out of the ordinary. For example, it can identify a unit with a forced outage rate significantly higher than all the others in that size and type category. If NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 12

13 something is found, the ISO reviews the data and either confirms that it is correct as is, or institutes a correction. The results of this data scrub are shown in Section A.4. The top three summer peak loads of all Areas external to NYCA are aligned to be on the same days as that of NYCA, even though they may have historically occurred at different times. This is a conservative approach, using the assumption that peak conditions could be the result of a wide spread heat wave. This would result in reducing the amount of assistance that NYCA could receive from the other Areas. A.2 Methodology The 2017 IRM study continues to use the Unified Methodology that simultaneously provides a basis for the NYCA installed reserve requirements and the preliminary locational installed capacity requirements. The IRM/preliminary LCR characteristic consists of a curve function, a knee of the curve and straight line segments at the asymptotes. The curve function is represented by a quadratic (second order) curve which is the basis for the Tan 45 inflection point calculation. Inclusion of IRM/preliminary LCR point pairs remote to the knee of the curve may impact the calculation of the quadratic curve function used for the Tan 45 calculation. The procedure for determining the best fit curve function used for the calculation of the Tan 45 inflection point to define the base case requirement is based on the following methodology: 1) Start with all points on IRM/preliminary LCR Characteristic. 2) Develop regression curve equations for all different point to point segments consisting of at least four consecutive points. 3) Rank all the regression curve equations based on the following: Sort regression equations with highest R2. Ensure calculated IRM is within the selected point pair range, i.e., if the curve fit was developed between 14% and 18% and the calculated IRM is 13.9%, the calculation is invalid. In addition, there must be at least one statewide reserve margin point to the left and right of the calculated tan 45 point Ensure the calculated IRM and corresponding preliminary LCR do not violate the 0.1 LOLE criteria. Check results to ensure they are consistent with visual inspection methodology used in past years studies. This approach identifies the quadratic curve functions with highest R 2 correlations as the basis for the Tan 45 calculation. The final IRM is obtained by averaging the NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 13

14 Tan 45 IRM points of the NYC and LI curves. The Tan 45 points are determined by solving for the first derivatives of each of the best fit quadratic functions as a slope of -1. Lastly, the resulting preliminary LCR values are identified. A.3 Base Case Modeling Assumptions A.3.1 Load Model Table A.3 Load Model Parameter Peak Load Load Shape Model Load Uncertainty Model 2016 Study Assumption October 1, 2015 forecast NYCA: 33,377.6 MW NYC: 11,777 MW LI: 5,457 MW GHIJ: 16,375 Multiple Load Shapes Model using years 2002 (Bin 2), 2006 (Bin 1), and 2007 (Bin 3-7) Statewide and zonal model updated to reflect current data 2017 Study Assumption October 1, 2016 NYCA: 33,273 MW NYC: 11,670 MW LI: 5,450 MW G-J: 16,073 MW Multiple Load Shapes Model using years 2002 (Bin 2), 2006 (Bin 1), and 2007 (Bin 3-7) Statewide and zonal model updated to reflect current data Explanation Forecast based on examination of 2016 weather normalized peaks. Top three external Area peak days aligned with NYCA No Change No change (1) Peak Load Forecast Methodology The procedure for preparing the IRM forecast mirrors that detailed in the NYISO Load Forecasting Manual for the ICAP forecast. The NYISO's Load Forecasting Task Force had two meetings in September 2016 to review analyses prepared by the NYISO and Transmission Owners of the weather response during the summer. Regional load growth factors (RLGFs) for 2017 were updated by each Transmission Owner based on projections provided to the LFTF in August 2016 by Moody's Analytics. The 2017 forecast was produced by applying the RLGFs to each TO's weather-normalized peak for the summer of The results of the analysis are shown in Table A-4. The 2016 peak forecast was 33,359 MW. The actual peak of 31,996 MW (col. 2) occurred on August 11, After accounting for the impacts of weather and the demand response, the weather-adjusted peak load was determined to be 33,262 NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 14

15 MW (col. 6), 96 MW (0.3%) below the forecast. The Regional Load Growth Factors are shown in column 9. The 2017 forecast for the NYCA is 33,272 MW (col. 10c). The Locality forecasts are also reported in the second table below. The LFTF recommends this forecast to the NYSRC for its use in the 2017 IRM study. Table A Final NYCA Peak Load Forecast 2017 NYCA Coincident Peak Forecasts by Transmission District (1) (2) (3) (4) (5) (6) (7) (8) (9) (10a)=(8)*(9) (10b) (10c)=(10a)+(10b) Transmission District 2016 Actual MW 2016 Estimated SCR & Muni Self- Gen SCR/EDRP Estimate MW Weather Adjustment MW 2016 Weather Normalized MW Loss Reallocation MW 2016 WN MW, Adj for Losses Regional Load Growth Factors 2017 Forecast, Before Adjustments Other 2017 IRM Final Adjustments Forecast to Load Cen. Hudson 1, , , ,069 1,069 Con Ed 12, , , ,359 13,359 LIPA 5, , , ,409 5,409 NGrid 7, , , , ,097 NYPA NYSEG 3, , , ,277 3,277 O&R 1, , , ,167 1,167 RG&E 1, , , ,566 1,566 Grand Total 31, ,090 33, , , , Forecast from 2016 Gold Book 33,363 Change from Gold book Locality Peak Forecasts (Non-Coincident) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) 2016 SCR/EDR 2016 Regional 2017 Change Weather 2017 IRM 2016 Actual Estimate P Weather Load Forecast from Gold Locality Adjustment Final MW d Muni Estimate Normalized Growth from 2016 Book MW Forecast Self-Gen MW MW Factors Gold Book Forecast Zone J - NYC 11, , ,670 11, Zone K - LI 5, , ,450 5, Zone GHIJ 15, , ,073 16, NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 15

16 (2) Zonal Load Forecast Uncertainty, Due to cool summer weather in 2014 and 2015, the LFU models do not need to be updated for 2017 because there is no new information to model extreme weather conditions. Models for Zones H&I, J and K are provided by Con-Ed and LIPA. The NYISO developed models for Zones A through G and reviewed the models for the other Zones. The results of these models are presented in Table A.4. Each row represents the probability that a given range of load levels will occur, on a perunit basis, by Zone. As stated above, these results are unchanged from last year and presented graphically in Figure A.2. Table A Load Forecast Uncertainty Models 2017 Load Forecast Uncertainty Models Bin No. Probability A - E F&G H & I Zone J Zone K % 83.99% 79.97% 79.92% 85.43% 78.74% % 88.92% 86.70% 85.98% 90.02% 83.96% % 94.34% 93.47% 91.97% 94.40% 91.98% % % % 97.68% 98.42% % % % % % % % % % % % % % % % % % % % Low - Med 16.0% 20.0% % 13.0% 21.3% Hi-Med 14.9% 15.4% % 8.3% 14.0% Delta 30.9% 35.4% % 21.3% 35.3% NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 16

17 Figure A.2 LFU Distributions 2017 LFU Distributions 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 75% 80% 85% 90% 95% 100% 105% 110% 115% 120% A - E F & G H & I Zone J Zone K The Consolidated Edison models for Zones H, I & J are based on a peak demand with a 1-in-3 probability of occurrence (67th percentile). All other zones are designed at a 1-in-2 probability of occurrence of the peak demand (50th percentile). The methodology and results for determining the 2016 LFU models have been reviewed by the NYISO Load Forecasting Task Force. (3) Zonal Load Shape Models for Load Bins Beginning with the 2014 IRM Study, multiple load shapes were used in the load forecast uncertainty bins. Three historic years were selected from those available, as discussed in the NYISO s 2013 report, Modeling Multiple Load Shapes in Resource Adequacy Studies. The year 2007 was assigned to the first five bins (from cumulative probability 0% to 93.32%). The year 2002 was assigned to the next highest bin, with a probability of 6.06%. The year 2006 was assigned to the highest bin, with a probability of 0.62%. The three load shapes for the NYCA as a whole are shown on a per-unit basis for the highest one hundred hours in Figure A.3. The year 2007 represents the load duration pattern of a typical year. The year 2002 represents the load duration pattern of many hours at high load levels. The year 2006 represents the load duration pattern of a heat wave, with a small number of NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 17

18 hours at high load levels followed by a sharper decrease in per-unit values than the other two profiles. With GE-MARS version 3.18, the logic to calculate the daily LOLE index was enhanced. Previously, the index was calculated using the base load shape s daily peak hours for all bins. The enhanced version (3.18) calculates the LOLE index using the daily peak hour for each load shape in each bin. This is the GE-MARS default setting. Figure A.3 Per Unit Load Shapes Per-Unit Loads Shapes for Top 100 Load Hours A.3.2 Capacity Model The capacity model includes all NYCA generating units, including new and planned units, as well as units that are physically outside New York State, that have met specific criteria to offer capacity in the New York Control Area. The 2016 Load and Capacity Data Report is the primary data source for these resources. Table A.5 provides a summary of the capacity resource assumptions in the 2017 IRM study. NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 18

19 Table A.5 Capacity Resources Parameter 2016 Study Assumption 2017 Study Assumption Explanation 2015 Gold Book values. Use 2016 Gold Book values. Use 2015 Gold Book Generating Unit min (DMNC vs. CRIS) capacity min (DMNC vs. CRIS) capacity Capacities publication value value Planned Generator Units MW of new non- wind resources 0 MW of new non- wind resources MW of project related re-ratings Unit rerate Wind Resources Wind Capacity MWs. Same units as One unit rated 2 MWs lower MW of Wind Capacity additions totaling MW of qualifying wind Total Wind Modeled Wind Shape Actual hourly plant output of the 2013 calendar year. Summer Peak Hour availability of 14% Actual hourly plant output over the period New units will use zonal hourly averages or nearby units. New functionality of the GE MARS program to randomly select a wind shape from multiple years of production data Solar Resources Solar Capacity of 31.5 MW per 2014 production data with a summer capacity factor of 38.8% MW Solar Capacity. Model chooses from 4 years of production data covering the period GE MARS program will randomly select a solar shape from multiple years of production data. Retirements and Mothballed units 0 MW retirements or mothballs reported MW retirements or mothballs reported or Units in Ineligible Forced Outage (IIFO) and Inactive Reserve (IR) Updated Policy 5 guidelines on retirement or mothball disposition in IRM studies. NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 19

20 Parameter 2016 Study Assumption 2017 Study Assumption Explanation Five-year ( ) GADS Five-year ( ) GADS data for each unit data for each unit represented. Those units represented. Those units with less than five years use with less than five years use representative data. representative data. Forced Outage Rates Transition Rates representing the Equivalent Forced Outage Rates (EFORd) during demand periods over the most recent five-year period ( ) Planned Outages Based on schedules received by the NYISO and adjusted for history Based on schedules received by the NYISO and adjusted for history Updated schedules Summer Maintenance Nominal 50 MWs divided equally between upstate and downstate Nominal 50 MWs divided equally between Zones J & K Review of most recent data Gas Turbine Ambient Derate Small Hydro Derate Large Hydro Derate based on provided temperature correction curves. Derate based on provided temperature correction curves. 46% derate 46% derate Probabilistic Model based on 5 years of GADS data Probabilistic Model based on 5 years of GADS data Operational history indicates derates in line with manufacturer s curves Review of five years of unit production data over the years 2011 to 2015 Historical data submitted via GADS (1) Generating Unit Capacities The capacity rating for each thermal generating unit is based on its Dependable Maximum Net Capability (DMNC). The source of DMNC ratings are seasonal tests required by procedures in the NYISO Installed Capacity Manual. Additionally, each generating resource has an associated capacity CRIS (Capacity Resource NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 20

21 Interconnection Service) value. When the associated CRIS value is less than the DMNC rating, the CRIS value is modeled. (2) Wind units are rated at the lower of their CRIS value or their nameplate value in the model. The 2016 NYCA Load and Capacity Report, issued by the NYISO, is the source of those generating units and their ratings included on the capacity model. Planned Generator Units There were no new planned generator units scheduled to come on-line during the IRM 2017 study period. Two existing wind units, however, have sought CRIS rights in the currently open class year process, which is expected to be complete before the start of the 2017 capability year. The increase in capacity totals 221 MW. (3) Wind Modeling Wind generators are modeled as hourly load modifiers. The GE MARS program will randomly select a wind shape from multiple years of production data. The output of each unit varies between 0 MW and the nameplate value based actual hourly plant output over the period New units will use zonal hourly averages or nearby units. Characteristics of this data indicate a capacity factor of approximately 14% during the summer peak hours. A total of MW of installed capacity associated with wind generators is included in this study. Table A.6 Wind Generation Wind Resouce B3 - Wind Resources Zone In Service Date CRIS (MW) Summer Capability (MW) MARS Model ICAP Participating Wind Units Altona Wind Power D 09/23/ Bliss Wind Power A 03/20/ Canandaigua Wind Power C 12/05/ Chateaugay Wind Power D 10/07/ Clinton Wind Power D 04/09/ Ellenburg Wind Power D 03/31/ Hardscrabble Wind E 02/01/ High Sheldon Wind Farm C 02/01/ Howard Wind C 12/01/ Madison Wind Power E 09/01/ Maple Ridge Wind 1 E 01/01/ Maple Ridge Wind 2 E 12/01/ Munnsville Wind Power E 08/20/ Orangeville Wind Farm C 12/01/ Steel Wind A 01/23/ Wethersfield Wind Power C 12/11/ Totals Non - ICAP Participating Wind Units Erie Wind 02/01/ Fenner Wind Farm 12/01/ Western NY Wind Power 10/01/ Totals Proposed IRM Study Wind Units Marble River D 7/1/2012* Orangeville re-rate C 6/1/2017* Totals Total Wind Resources Totals * Study assumes 2015 Class Year will be complete in 2016, awarding Marble River Wind 215 MW and Orangeville Wind 5.9 MW of additional CRIS rights NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 21

22 (4) Solar Modeling Solar generators are modeled as hourly load modifiers. The GE MARS program will randomly select a solar shape from multiple years of production data. The output of each unit varies between 0 MW and the nameplate MW value based on 4 years of production data covering the period A total of 31.5 MW of solar capacity was modeled in Zone K. (5) Retirements A total of MW of retirements are being modeled in the 2017 base case. The retirements consist primarily of 10 gas turbines ranging in size between 15 and 25 MW that are being retired in Zone J. There is also a 50 MW bio-gen plant in Zone A that is retiring and two ROS units totaling 75 MW that are leaving the capacity market. (6) Forced Outages Performance data for thermal generating units in the model includes forced and partial outages, which are modeled by inputting a multi-state outage model that is representative of the equivalent demand forced outage rate (EFORd) for each unit represented. Generation owners provide outage data to the NYISO using Generating Availability Data System (GADS) data in accordance with the NYISO Installed Capacity Manual. The NYSRC is continuing to use a five-year historical period for the 2017IRM Study. Figure A.4 shows the trend of EFORd for various regions within NYCA. Figure A.5 shows a rolling 5-year average of the same data. Figures A.6 and A.7 show the availability trends of the NYCA broken out by fuel type. The multi-state model for each unit is derived from five years of historic events if it is available. For units with less than five years of historic events, the available years of event data for the unit is used if it appears to be reasonable. For the remaining years, the unit NERC class-average data is used. The unit forced outage states for the most of the NYCA units were obtained from the five-year NERC.GADS outage data collected by the NYISO for the years 2011 NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 22

23 through This hourly data represents the availability of the units for all hours. From this, full and partial outage states and the frequency of occurrence were calculated and put in the required format for input to the GE-MARS program. Where the NYISO had suspect data for a unit that could not be resolved prior to this study, NERC class average data was substituted for the year(s) of suspect data. Figures A.8 and A.9 show the unit availabilities of the entire NERC fleet on an annual and 5-year historical basis. NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 23

24 Figure A.4 NYCA Annual Zonal EFORds New York Annual Zonal EFORds Weighted Values for Thermal and Large Hydro Units 16.00% 14.00% 12.00% 10.00% K J 8.00% 6.00% NYCA 4.00% A-E 2.00% F-I 0.00% NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 24

25 Figure A.5 Five-Year Zonal EFORds NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 25

26 PERCENT Figure A.6 NYCA Annual Availability by Fuel NYCA EQUIVALENT AVAILABILITY BASED ON NERC-GADS DATA FROM ANNUAL WEIGHTED AVERAGES FOR NUCLEAR, COAL, OIL & GAS, AND COMBUSTION TURBINES YEARS COAL NUCLEAR OIL & GAS COMBUSTION TURBINES NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 26

27 PERCENT Figure A.7 NYCA Five-Year Availability by Fuel NYCA EQUIVALENT AVAILABILITY BASED ON NERC-GADS DATA FROM FIVE YEAR WEIGHTED AVERAGE NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page YEAR ENDING COAL NUCLEAR OIL & GAS COMBUSTION TURBINES

28 PERCENT Figure A.8 NERC Annual Availability by Fuel NERC EQUIVALENT AVAILABILITY BASED ON NERC-GADS DATA FROM ANNUAL WEIGHTED AVERAGES FOR NUCLEAR, COAL, OIL, GAS, AND COMBUSTION TURBINES YEARS COAL NUCLEAR OIL GAS COMBUSTION TURBINES NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 28

29 PERCENT Figure A.9 NERC Five-Year Availability by Fuel NERC EQUIVALENT AVAILABILITY BASED ON NERC-GADS DATA FROM FIVE YEAR WEIGHTED AVERAGE YEAR ENDING COAL NUCLEAR OIL GAS COMBUSTION TURBINES NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 29

30 (7) Outages and Summer Maintenance A second performance parameter to be modeled for each unit is scheduled maintenance. This parameter includes both planned and maintenance outage components. The planned outage (PO) component is obtained from the generator owners, and where necessary, extended so that the scheduled maintenance outage (MO) period equals the historic average using the same five-year period used to determine EFORd averages. Figure A.10 provides a graph of scheduled outage trends over the 1992 through 2015 period for the NYCA generators. Typically, generator owners do not schedule maintenance during the summer peak period. However, it is highly probable that some units will need to schedule maintenance during this period. Each year, the previous summer capability period is reviewed to determine the scheduled maintenance MW during the previous peak period. An assumption is determined as to how much to model in the current study. For the 2017 IRM Study, a nominal 50 MW of summer maintenance is modeled. The amount is equally divided between Zone J and Zone K. Figure A.11 shows the weekly scheduled maintenance for the 2016 IRM Study compared to this study. (8) Gas Turbine Ambient Derate Operation of combustion turbine units at temperatures above DMNC test temperature results in reduction in output. These reductions in gas turbine and combined cycle capacity output are captured in the GE-MARS model using deratings based on ambient temperature correction curves. Based on its review of historical data, the NYISO staff has concluded that the existing combined cycle temperature correction curves are still valid and appropriate. These temperature corrections curves, provided by the Market Monitoring Unit of the NYISO, show unit output versus ambient temperature conditions over a range starting at 60 degrees F to over 100 degrees F. Because generating units are required to report their DMNC output at peak or design conditions (an average of temperatures obtained at the time of the transmission district previous four like capability period load peaks), the temperature correction for the combustion turbine units is derived for and applied to temperatures above transmission district peak loads. The derate does not affect all units because there are units capable of generating up to 88 or 94 MW but are limited by permit to 79.9 MW, so these units are not NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 30

31 Percent Outage impacted by the temperature derating in obtaining an output of 79.9 MW. About one quarter of the existing 3,700 MW of simple cycle Combustion Turbines fall into this category. The accuracy of temperature corrections for all combustion turbines will continue to be evaluated as operational data becomes available. (9) Large Hydro Derates Hydroelectric projects are modeled as are thermal units, with a probability capacity model based on five years of unit performance. See Capacity Models item 6 above. Figure A.10 Planned and Maintenance Outage Rates 30% New York Control Area Planned and Maintenance Outage Rates (Over the period ) 25% 20% 15% 10% 5% 0% Year MO PO Total NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 31

32 MW Figure A.11 Scheduled Maintenance Scheduled Maintenance For NYCA Generation (IRM Studies) 11,000 10,000 9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1, Week 2017 Study Year 2016 Study Year A.3.3 Transmission System Model A detailed transmission system model is represented in the GE-MARS topology. The transmission system topology, which includes eleven NYCA Zones and four External Control Areas, along with transfer limits, is shown in Figure A.13. The transfer limits employed for the 2017 IRM Study were developed from emergency transfer limit analyses included in various studies performed by the NYISO, based upon input from Transmission Owners and neighboring regions. The transfer limits are further refined by assessments conducted for this IRM study. The assumptions for the transmission model included in the 2017 IRM Study are listed in Table A.7. Forced transmission outages are included in the GE-MARS model for the underground cables that connect New York City and Long Island to surrounding Zones. The GE-MARS model uses transition rates between operating states for each interface, which were calculated based on the probability of occurrence from the historic failure rates and the time to repair. Transition rates into the different operating states for each interface are calculated based on the circuits comprising each interface, including failure rates and repair times for the individual cables, and for any transformer and/or phase angle regulator associated with that cable. The TOs provided updated transition rates. NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 32

33 The interface transfer limits were updated for the 2017 IRM Study model based on transfer limit analysis performed for the 2016 Reliability Needs Assessment. Table A.7 Transmission System Model Parameter Interface Limits Cable Forced Outage Rates 2016 Model Assumptions All changes reviewed and commented on by TPAS All existing Cable EFORs updated for NYC and LI to reflect most recent fiveyear history 2017 Model Assumptions Recommended All changes reviewed and commented on by TPAS All existing Cable EFORs will be updated for NYC and LI to reflect most recent five-year history New UDRs No new UDRs No new UDR projects Basis for Recommendation Based on 2016 Operating Study, 2016 Operations Engineering Voltage Studies, 2016 Reliability Planning Process, and additional analysis including interregional planning initiatives Based on TO analysis Existing UDR elections are made by August 1 st and were incorporated into the model Figure A.13 shows the transmission system representation for this year s study. Figure A.13 shows a more detailed representation of the interconnections surrounding the PJM/NYCA downstate interface. NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 33

34 As can be seen from the figures, the changes made to interface limits are as follows: Table A.8 Interface Limits Updates Delta Interface Forward Reverse Forward Reverse Forward Reverse Dysinger East Zone A Group UPNY-SENY UPNY-Con Ed I to J & K J to K /461/ /390/236-5/-71/-49 LI Sum /202/ /91/ /-111/-96 I to K LI West J3 to PJME J to J /700/500/ G to RECO As a result of the Con Edison notice to terminate the PSEG wheel: G-PJME New interface made up of G J2 Dummy Bubble removed to J2 and J2 to PJME J to PJME New interface of J to J2 and Represents A, B, and C lines parts of J to J3 and J3 to PJME G to PJM New interface Grouping of G to RECO and G to PJME LIPA has revised its methodology used to calculate its facility ratings. This resulted in reductions in the ratings of the limiting facilities, which impacted the J to K, LI Sum, I to K, and LI West interface limits compared to the 2016 IRM Study. These updated limits on the Long Island related interfaces were incorporated in the 2017 IRM Study. There is a reduction in the UPNY-SENY limit for this study. This is caused by the change in how the Con Ed/PSEG wheel schedule is modeled. For the 2016 IRM Study, 1000 MW was modeled flowing to PJM on the S. Mahwah to Waldwick ties and 1000 MW was modeled flowing to NY on the A, B and C ties. For the 2017 IRM Study, because of the noticed cancellation of the agreement to wheel that power, 0 MW is modeled on all of these ties. The modeling change resulted in a 100 MW decrease in the UPNY-SENY limit. The change in how the Con Ed/PSEG wheel is modeled also resulted in an increase in the UPNY-Con Ed and the I to J & K interface limits. Not modeling the 1000 MW withdrawal of power from Zone G to supply the wheel reduces the reactive power losses in SENY and NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 34

35 increases voltage constrained transfer limits in that area. The reduction in load growth also impacts these transfer limits. The MARS topology between SENY and PJM was updated to reflect the impact of the noticed cancellation of the PSEG/Con Ed wheeling agreement. The topology was simplified by removing Dummy Zone J2 from the model and connecting the tie lines from Zone G and Zone J directly to the PJM East area. The A, B and C tie lines were combined into a single tie line. Reductions in the PJM-SENY group limit and the A, B and C Lines interface in both directions were made to limit the emergency assistance available. NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 35

36 Figure A IRM Topology 2017 IRM Topology Summer Ratings Includes changes to SENY 11/1/ A PJM West Dysinger East Zone A Group IESO (Ontario) 1700 B 1850 PJM Central PJM South West NYCA zonal interfaces NYCA zonal connections External Connections Standard Grouping Grouping used for monitoring West Central PJM - RTO X X C Hydro Quebec (HQ) 300 Cedars 1999 PJM East 1500 Dynamic internal transfer limits 1500 NYCA internal transfer limits 1500 External transfer limits NYCA zone Dummy zone for analysis 0 DOM_VEPC Volney East Moses South E D Marcy South /4925/4840 /4685/ CE Group See G to J Detail 0 Millwood South Connecticut New York Independent System Operator, Inc. All Rights Reserved /3050/2990/ 2885/2770 I 1999 Dunwoodie South F G UPNY-CE H 1999 City & East J F to G 800 Athens- Gilboa UPNY-SENY 5500 LI Sum /91/ LI West 99, /390/ Highgate 0 NY / NE K Vermont Western MA New England NorwalkCT / Phase Central MA Neptune Controllable Line 330 Cross Sound Controllable Line NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 36

37 Figure A PJM-SENY Interface Model PJM-SENY MARS Model 2017 IRM Topology Summer Ratings Includes changes to SENY 6/22/2016 RECO 5018 Line 1000 PJM NYCA J&K Line PJM to G Grouping G H I 1000 A&B&C Line 200 J PJM East Dummy Zone (J3) Dummy Zone (J4) 0 VFT 660 HTP Neptune Controllable Line 660 K [(PJM East to RECO) + (PJM East to J) + (PJM East to J3) + (PJM East to J4) + (PJM East to G)] Grouping Limited to 2,000 MW New York Independent System Operator, Inc. All Rights Reserved. 2 NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 37

38 Figure A.14 Full New England Representation NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 38

39 A.3.4 External Area Representations NYCA reliability largely depends on emergency assistance from its interconnected Control Area neighbors (New England, Ontario, Quebec and PJM) based on reserve sharing agreements with these external Control Areas. Load and capacity models of these Areas are therefore represented in the GE-MARS analyses with data received directly from the Areas and through NPCC sources. The primary consideration for developing the final load and capacity models for the external Control Areas is to avoid over-dependence on the external Control Areas for emergency capacity support. For this purpose, rules are applied whereby; 1) an external Control Area s LOLE cannot be lower than its LOLE criteria, 2) its isolated LOLE cannot be lower than that of the NYCA, 3) its Reserve Margin can be no higher than its minimum requirement. If the Area s reserve margin is lower than its requirement and its LOLE is higher than its criterion, pre-emergency Demand Response can be represented. In other words, the neighboring Areas are assumed to be equally or less reliable than NYCA. Another consideration for developing models for the external Control Areas is to recognize internal transmission constraints within the external Control Areas that may limit emergency assistance to the NYCA. This recognition is considered implicitly for those Areas that have not supplied internal transmission constraint data. Additionally, EOPs are removed from the external Control Area models. In order to avoid over-dependence from emergency assistance, the three highest summer load peak days of the external Control Areas are modeled to match the same load sequence as NYCA. For this study, both New England and PJM continue to be represented as multi-area models, based on data provided by these Control Areas. Ontario and Quebec are represented as single area models. The load forecast uncertainty model for the outside world model was supplied from the external Control Areas. Modeling of the neighboring Control Areas in the base case in accordance with Policy 5-10 is as follows: NYSRC: NYCA Installed Capacity Requirement for the Period May 2017 through April 2018 Page 39