PREPA Renewable Energy Resources Integration Study San Juan February 19 th 2014

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1 PREPA Renewable Energy Resources Integration Study San Juan February 19 th 2014 Restricted Siemens Industry, Inc All rights reserved. Answers for infrastructure and cities.

2 Agenda Introduction System Studies Load Flow Short Term Stability Long Term Stability PROMOD Assessment Procedure and models to process the meteorological data. Models testing and assessment PROMOD set up. Scenario Definition Scenario I; Base Case Scenario II: Case with storage. Scenario III: Case with Combined Cycle Intra-Hour Modeling Conclusions Page 2

3 Introduction In this presentation we will cover the main results of the study carried out by Siemens PTI to determine the secure and economic levels that renewable generation can be incorporated in PREPA s system. The studies are organized in three main components: Load flow and short term dynamics Long Term Dynamics PROMOD / Production Costing evaluations. These sections correspond to the studies carried out to determine the impacts of renewable generation and determine solutions as presented next. Page 3

4 Issues to be considered when evaluating renewable Generation The table below describes the main issues associated with large penetration and the section of the study that addressed them Issues with typical Renewable Gen Impact Actions Timeframe Lack of Inertia Angular instability - Inertial Response/ storage 0 to 5 sec No governor and dispatched at max Large ramps / Contingencies Large frequency excursions Large frequency excursions - Governor like response - Increased reserves - Storage (as reserve) - Ramp Limitations - AGC adjustments - Reserve & storage 5 to 30 sec (tens of sec) Tens of sec to several minutes Primarily Evaluated in Short Term Dynamics Short Term Dynamics Long term dynamics Page 4 Large ramps / Contingencies Variability Uncertainty Reduced reserves / curtailment Increased Operating Costs Increased Operating Costs - Increase reg reserve. minutes - Increase reg reserve. - Cost assessment - Frequent ED minutes to hours - Improved forecasts Hours Un-dispatchability Curtailment - Limit the penetration long term PROMOD IV Intra-hour PROMOD IV Load following PROMOD IV Forecast Uncertainty PROMOD IV Optimal Penetration

5 PREPA Renewable Energy Resources Integration Study System Studies Restricted Siemens Industry, Inc All rights reserved. Answers for infrastructure and cities.

6 System Studies Initial Project Selection To start the system studies we need a feasible dispatch The dispatch was selected maximizing the renewable generation without consideration to economics, only the capability of the generating fleet at specific moments in time. This selection made sure that the system studies would be carried out with worst possible conditions Dispatch and Economic Studies (PROMOD) were expected to confirm or reduce these values. A model was created working with PREPA that accounted for these dispatch limitations and it was determined that that up to 895 MW of PV and Wind Turbine generation could be integrated. We show next an screen shot to the model for the critical light load day. Page 6

7 Initial Project Selection Light Load Day The critical time is noon time when renewable is expected to be maximum The light load has a load that has 90% probability of being exceeded at noon time. Checks on limits and reserves were carried out. Generation Characteristics Min Day Regulating Range Operating Range On Daytime Machine Machine Machine Nighttime Machine Machine Name Type Min Max Min Max Line Peak Primary R Regulating Reserve Peak Regulating Reserve MW MW MW MW I / O 12:00 PM PMax Margin Margin 12:00 PM Margin Margin Palo Seco 4 * Steam Not Reg Under Costa Sur 5 Steam Aguirre 1 Steam AgCCV 1 CCP Not Reg Not Reg No Reserve AgCCGT 1-1 CCP Not Reg AgCCGT 1-2 CCP Off Off Off AgCCGT 1-3 CCP Off Off Off AgCCGT 2-1 CCP Off Off Off AgCCGT 2-2 CCP Off Off Off AgCCGT 2-3 CCP Off Off Off SJ Rep Steam 1 CCP Not Reg Not Reg SJ Rep Gas 1 CCP Not Reg Not Reg AES1 Coal Not Reg Not Reg Not Reg 0.0 AES2 Coal Not Reg Not Reg Not Reg 0.0 ECOSTEA Cogen N/A ECOGT1 Cogen ECOGT2 Cogen PaloSecoGT11 GT Off Off PaloSecoGT12 GT Off Off PaloSecoGT21 GT Off Off PaloSecoGT22 GT Off Off PaloSecoGT31 GT Off Off PaloSecoGT32 GT Off Off VegaBajaGT11 GT Off Off VegaBajaGT12 GT Off Off AguirreGT21 GT Off Off AguirreGT22 GT Off Off JobosGT11 GT Off Off JobosGT12 GT Off Off DaguaoGT11 GT Off Off DaguaoGT12 GT Off Off YabucoaGT11 GT Off Off YabucoaGT12 GT Off Off SOUCOGT11 GT Off Off SOUCOGT12 GT Off Off MAY AERO #1 GT Off Off Page 7 TOTAL Conventional Total Renewable WTE Total Renewable PV & Wind Turbine Total Generation Dispatch Summary 0.0 Load MW Balance Check OK OK Regulating Reserve Regulating Reserve OK OK OK Regulating Reserve in Aguirre 1 & 2 + Costa Sur 5&6 (40 MW min) Regulating reserve OK in Ag & CS? OK OK Online Spining Reserve Spinning Reserve OK OK OK GT's Online (does not include Cambalache if dispatched) % GT's 0% 73% GT's Offline + Reserve (fast start) w/o Cambalache

8 Initial Project Selection Based on this and by inspection of PREPA s PPOA contracts we selected the projects indicated for a total of MW. This selection was done to have a representative sample to study that could be accommodated in the dispatch, but no inference to a particular contract should be made. Page 8 NUM Project Name Type Capacity 31 Pattern Wind Go Green PR (Punta Lima) Wind 26 1 AES Ilumina Solar Wind-59 Wind Wind-61 Wind Wind-2 Wind Solar-18 Solar 10 4 Solar-4 Solar Solar-30 Solar Solar-46 Solar 20 7 Solar-7 Solar 40 3 Solar-3 Solar Solar-36 Solar Solar-21 Solar Solar-62 Solar Solar-47 Solar Solar-42 Solar Solar-43 Solar Solar-15 Solar Solar-63 Solar Solar-17 Solar Solar-16 Solar Solar-54 Solar Solar-39 Solar Solar-40 Solar Solar-56 Solar Solar-57 Solar Solar-27 Solar Solar-23 Solar Solar-53 Solar Solar-44 Solar 20 8 Solar-8 Solar 10 9 Solar-9 Solar Solar-10 Solar 30 TOTAL: 884.2

9 Load Flow Evaluation Verification of Interconnection Capability The 884 MW of renewable projects were added to PREPA s Load Flow models. The system was tested for N-1 contingency conditions No thermal or severe voltage violations associated with this integration were found resulting in an acceptable case. The importance of Renewable Projects to control voltages was observed (MTR compliance) Few high voltages were identified, but these were minor (at 38 kv) and do not compromise the assessment of stability. Note that 1.10 pu is usually accepted for contingency conditions. Page 9 Bus # Bus Name KV ContVolt BaseVolt Contingency Description Pre-Proj ContVolt Delta V Deviation (>0.5%) 49 MONACILLO MONACILLO CORRECCION #N/A #N/A 0.74% 59 MARTIN PE A MARTIN PE A DEPTOFAMILIA #N/A #N/A 0.65% 113 ACACIAS ACACIAS CABO ROJO NO #N/A #N/A 0.65% 123 BERWIND BERWIND LOS ANGELES #N/A #N/A 1.16% 215 BARRANQUITAS BARRANQUITAS OROCOVIS #N/A #N/A 0.62% 249 CABO ROJO NO CABO ROJO NO CABOROJOPRO #N/A #N/A 1.54% 276 COMERIO CIDRA SECC COMERIO #N/A #N/A 1.00% 300 S.GERMANTC S.GERMANTC LOCTITE #N/A #N/A 0.58% 361 COMERIO NO CIDRA SECC COMERIO #N/A #N/A 0.92% 456 PONCE CEMENT PONCE CEMENT CEMEX PONCE #N/A #N/A 1.32% 505 XTRA MAYAG ACACIAS CABO ROJO NO #N/A #N/A 0.50% 582 BAXT AIBONIT BAXT AIBONIT TO RICO #N/A #N/A 1.65% 584 LOCTITE SABANAGRANDE LOCTITE #N/A #N/A 0.55% 666 CORRECCION CIEN MEDICAS CORRECCION #N/A #N/A 1.25% 1051 TREN MARTIN PE A DEPTOFAMILIA #N/A #N/A 0.65% 1155 MARTINPE AB RECNATURALES MARTINPE AB #N/A #N/A 0.71% 1169 AAA TAP S.JOS AAA #N/A #N/A 2.34%

10 Short Term Stability Evaluation Verification of System Response A detailed dynamic stability model adding the dynamic models for the renewable generation and including storage to PREPA s model. The storage model was developed by us as well as new models for representing PREPA s load shedding schemes. Various contingencies including tripping of lines and generation were assessed. 3ph Faults at Generation Facilities, Normal Clearing - Unit Trip No Type Description kv FLT-01 3ph, normal clearing At Ecoelectrica 319, units at 858,859,869 are tripped 230 FLT-02 3ph, normal clearing At Aguirre 106, unit at 809 tripped 230 FLT-03 3ph, normal clearing At Costa Sur 96, unit at 805 tripped 230 FLT-04 3ph, normal clearing At AES 321, unit at 871 tripped 230 FLT-05 3ph, normal clearing At San Juan 88, units at 811 and 856 are tripped 115 FLT-06 3ph, normal clearing At Palo Seco 63, unit at 819 tripped 115 Page 10

11 Short Term Stability Evaluation Verification of System Response Also line trips and load rejection events were studied... 3ph Faults at Generation Facilities, Normal Clearing - Line Trip No Type Description kv FLT-20 3ph, normal clearing At Aguirre 106, trip line to Agubuenas FLT-21 3ph, normal clearing At Aguirre 107, trip line to Jobos FLT-22 3ph, normal clearing At Costa Sur 96, trip line to Manati FLT-23 3ph, normal clearing At Costa Sur 2, trip line to Canas FLT-24 3ph, normal clearing At AES 321, trip line to Yabucoa FLT-25 3ph, normal clearing At San Juan 88, trip line to Viaducto FLT-26 3ph, normal clearing At Palo Seco 63, trip line to Monacillo SLG Faults at Generation Facilities, Delayed Clearing - Line Trip No Type Description kv FLT-27 SLG, delayed clearing At Aguirre 106, trip line to Agubuenas FLT-28 SLG, delayed clearing At Aguirre 107, trip line to Jobos FLT-29 SLG, delayed clearing At Costa Sur 96, trip line to Manati FLT-30 SLG, delayed clearing At Costa Sur 2, trip line to Canas FLT-31 SLG, delayed clearing At AES 321, trip line to Yabucoa FLT-32 SLG, delayed clearing At San Juan 88, trip line to Viaducto FLT-33 SLG, delayed clearing At Palo Seco 63, trip line to Monacillo ph Fault at 230 kv S/S (Non-Generating), Normal Clearing - Line Trip No Type Description kv FLT-07 3ph, normal clearing At Bayamon 99, trip 3wnd TF to Bayamon /115 FLT-08 3ph, normal clearing At S.llana 120, trip line to Agubuenas FLT-09 3ph, normal clearing At Manati 196, trip line to Costa Sur FLT-10 3ph, normal clearing At Mayaguz 204, trip line to Maya TC FLT-11 3ph, normal clearing At Maya TC 232, trip line to Costa Sur FLT-12 3ph, normal clearing At Yabucoa 233, trip line to AES FLT-13 3ph, normal clearing At Mora 352, trip 3wnd TF to Moca /115 FLT-14 3ph, normal clearing At Ponce TC 363, trip line to Pon Bypass FLT-15 3ph, normal clearing At Camb GP 440, trip line to Dbocas FLT-16 3ph, normal clearing At Agubuenas 451, trip line to Aguirre FLT-17 3ph, normal clearing At Cacao 956, trip line to Yabucoa FLT-18 3ph, normal clearing At Pon Bypass 1077, trip line to Costa Sur FLT-19 3ph, normal clearing At Dbocas Fase 1110, trip line to Costa Sur ph Fault at Indicated Bus followed by opening of lines / buses No Type Description FLT-34 3ph fault at bus 38 Load Rejection , 177, 400, 555 FLT-35 3ph fault at bus 92 Load Rejection , 245, 248, 593, 594, 595, 599, 746, 747, 748, 990, 991, 1049, 1065, 1174 FLT-36 3ph fault at bus 98 Load Rejection , 220, 265, 285, 422, 435, 437, 438, 585, 617, 621, 641, 660, 783, 961, 965, 967, 1010, 1034, 1147 Page 11

12 Short Term Stability Evaluation Verification of System Response All events analyzed resulted in stable behavior, but in some cases load shedding was required to obtain stability. In particular it was identified that it was important for the renewable to comply with the MTR and provide voltage support and frequency regulation. As a reference the figure below shows the trip of Ecoelectrica with compliance of the MTR (storage) to the left and without (no storage) to the right Fault 01 - Frequency at bus 50 MTR Compliance Time (seconds) 35 60,2 60, ,9 59,8 59,7 59,6 59,5 59,4 59,3 59,2 59, ,9 58,8 58,7 58,6 58,5 58,4 58,3 58,2 58, Fault 01: frequency at bus 50 No MTR Lower frequency for longer Time (seconds) 30 gfedcb 60*(1+A) : FLT01-3PH-peak_load gfedcb 60*(1+A) : FLT01-3PH-light_load gfedcb 60*(1+A) : FLT01-3PH-peak_load-no-storage gfedcb 60*(1+A) : FLT01-3PH-light_load-no-storage Page 12

13 Short Term Stability Evaluation Verification of System Response The tables below provide further details on the most critical contingencies evaluated that were associated with the trip of generation. Note the greater need for load shed if storage is not considered. Disturbance Peak Load Peak Load Renewable without BESS ID-Loss of Generation Generation Tripped [MW] Post-contingency Available Primary Reserve [MW] Lowest Frequency [Hz] Load Shedding [MW] Lowest Frequency [Hz] Load Shedding [MW] 01-Ecoelectrica Aguirre Costa Sur AES S J Repwr Palo Seco Disturbance Light Load Light Load Renewable without BESS ID-Loss of Generation Generation Tripped [MW] Post-contingency Available Primary Reserve [MW] Lowest Frequency [Hz] Load Shedding [MW] Lowest Frequency [Hz] Load Shedding [MW] 01-Ecoelectrica Aguirre Costa Sur AES S J Repwr Page Palo Seco

14 Long Term Dynamic Evaluation Verification of System Response The evaluation of long term dynamics (LTD) assesses how PREPA s conventional generation will respond in the multi-minute range to sudden events as the trip generation and generation changes in the renewable. A key component evaluated is the Automatic Generation Control (AGC) that should bring the frequency back to the nominal 60 Hz. In addition to the generation trips just presented the renewable generation changes include; Steep power change; changes of -3%/minute and 15 minutes duration on all PV s in the island (loss of 324 MW of PV power). Long duration power changes: changes of -2%/minute of 30 minutes duration affecting all PV s in the island (loss of 432 MW of PV power) Localized changes up to 50% / min without ramp control and 10%/min with ramp control. Page 14

15 60*(1+A) : FLT02-AGC-3PH-peak_load 21 - ACE : FLT02-AGC-3PH-peak_load Long Term Dynamic Evaluation Verification of System Response The analysis identified that with the initial dispatch considered there would be insufficient secondary reserve to recover the frequency to 60 Hz under certain contingencies. Disturbance Status after fault 60,5 FLT 02: trip Aguirre 01 f [Hz] & ACE [pu] 200 ID-Loss of Generation tripped [MW] Peak load Light load 01-Ecoelectrica 507 OK OK 02-Aguirre Insufficient reserve* OK 60, ,75 59, Costa Sur OK OK 04-AES OK OK 59, S J Rep 129 OK OK Palo Seco OK OK 58, , Time (seconds) gfedcb 60*(1+A) : FLT02-AGC-3PH-peak_load gfedcb 21 - ACE : FLT02-AGC-3PH-peak_load Page 15

16 60*(1+A) : RAMP-PV-02-peak_load 21 - ACE : RAMP-PV-02-peak_load 60*(1+A) : RAMP-PV-03-peak_load 21 - ACE : RAMP-PV-01-peak_load Long Term Dynamic Evaluation The analysis identified that with the initial dispatch considered there would be insufficient secondary reserve to recover the frequency to 60 Hz under certain RAMP 01: -3%/min x 15 min to all PV gen. f [Hz] & ACE [pu] contingencies (Cont). 60,075 60,05 60, Disturbance Status after fault ID Generation loss [MW] Peak load Light load 59,975 59, Ramp 01: -45% of all PV gen. 324 OK OK Ramp 02: -60% of all PV gen. 432 Insufficient reserve* OK 59,925 59,9 59, Ramp 03: -75% of north cluster PV gen. 213 OK OK Ramp 04: -90% of north cluster PV gen. 255 OK OK Ramp 05: -100% of all Wind gen. 164 OK OK Ramp 06: -100% of Site 21 PV gen. 87 OK OK 59,85 59, , Time (seconds) Ramp 02: -2%/min x 30 min to all PV gen. f [Hz] & ACE [pu] gfedcb 60*(1+A) : RAMP-PV-01-peak_load gfedcb 21 - ACE : RAMP-PV-01-peak_load Ramp 07: -100% of Site 21 PV gen. 87 OK OK Ramp 08: -100% of Wind gen at Pattern 76 OK OK 60, ,95 59,9 59,85 59, , , , , Time (seconds) Page 16 gfedcb 60*(1+A) : RAMP-PV-02-peak_load gfedcb 21 - ACE : RAMP-PV-02-peak_load

17 Long Term Dynamic Evaluation Adjustment From simulations, it was determined that if the amount of secondary reserve is equal or greater than 50% of the total renewable generation dispatch the response should be adequate. To meet these requirements, the peak load case was modified by a reduction of the generation at Ecoelectrica of 100 MW and increasing the generation at AES and Palo Seco 3 & 4 that were not regulating. This solved the situation as shown below for the previous events 60,5 FLT 02: trip Aguirre 01 unit - comparisson original and new dispatched case Ramp 02: MW PV variation - comparisson original and new dispatch case 60,05 60, ,75 59,5 59,95 59, ,9 58,75 58, Time (seconds) , Time (seconds) Page 17 gfedcb 60*(1+A) : FLT02-AGC-3PH-peak_load gfedcb 60*(1+A) : FLT02-AGC-3PH-peak_load-redispatched gfedcb 60*(1+A) : RAMP-PV-02-peak_load gfedcb 60*(1+A) : RAMP-PV-02-peak_load-redispatched

18 60*(1+A) : RAMP-PV-07-peak_load 21 - ACE : RAMP-PV-06-light_load 21 - ACE : RAMP-PV-07-peak_load Long Term Dynamic Evaluation Localized Events Renewable simulation and field observations indicate that sharp changes in renewable generation are possible. We evaluated a 50%/min (43 MW/min) change and found that the frequency could dip under 59.9 Hz and require 400 seconds to recover. Considering that this could happen several times during the day and could be compounded with other conditions in the system, from a planning perspective we are of the opinion that would be unacceptable. With 10%/min the frequency dips close to HZ, which is quite acceptable. 60,35 60,3 60,25 60,2 RAMP 06: -50%/min x 2 min at Site 21 PV gen. f [Hz] & ACE [pu] ,03 60,025 60,02 60,015 60,01 Ramp 07: -10%/min x 10 min at Site 21 PV gen. f [Hz] & ACE [pu] , , ,1 60, ,95 59, ,995 59,99 59,985 59,98 59, , Time (seconds) Page 18 gfedcb 60*(1+A) : RAMP-PV-06-light_load gfedcb 21 - ACE : RAMP-PV-06-light_load 59, gfedcb Time (seconds) 60*(1+A) : RAMP-PV-07-peak_load gfedcb 21 - ACE : RAMP-PV-07-peak_load

19 System Assessment Conclusions The study confirms that with 884 MW of renewable generation PREPA s grid should be able to ride through the set of severe contingencies and generation changes. No voltage collapse occurred. Primary and secondary reserve can be secured It is recommended that PREPA keeps resources available with secondary reserve of least 50% of the renewable generation dispatched. The importance of 100% compliance with the MTR s was observed; voltage, frequency and ramp controls. Given the significant contribution that the renewable generation can provide from BESS; It is recommended that PREPA revises its MTR to require that renewable generation projects do not inhibit the contribution from the BESS even when dispatched at contractual maximum. Page 19

20 PREPA Renewable Energy Resources Integration Study PROMOD EVALUATION Restricted Siemens Industry, Inc All rights reserved. Answers for infrastructure and cities.

21 Introduction This part of the presentation will enter into the details and results of the Production costing evaluations using PROMOD. PROMOD is widely used program by the industry for the formulation of a Security Constraint Unit Commitment and Security Constrained Economic Dispatch The PROMOD simulations achieve the objective of evaluating the performance of the generation fleet in the presence of the renewable generation. Page 21

22 Topics Procedure and models to process the meteorological data to create the Renewable models used for PROMOD. Models testing and assessment Set up the PROMOD evaluations considering the renewable. Scenario Definition Scenario I; Base Case Scenario II: Case with storage. Scenario III: Case with Combined Cycle Intra-Hour Modeling Page 22

23 PREPA Renewable Energy Resources Integration Study Procedure and models to process the meteorological data to create the Renewable models used for PROMOD Restricted Siemens Industry, Inc All rights reserved. Answers for infrastructure and cities.

24 Renewable Generation Models AWS Truepower (AWS) Mesoscale Models Renewable Models Start From Meteorological Data AWS developed a Mesoscale Numerical Prediction Model for Puerto Rico. The model provides simulated wind and solar data for Puerto Rico and captures in a realistic fashion both the temporal and spatial variability of the wind and solar resource The model was developed taking into consideration the conditions during 2011, corrected for the effect of Hurricane Irene. The model provides all data required for accurate representation of the renewable resource Parameters for 23 sites were produced. Page 24

25 Evaluated Renewable Projects Solar (PV) Projects 720 MW Solar PV projects were modeled. The project capacity and corresponding AWS site is a key element in the modeling. NUM Name Location Capacity MW AWS SITE 1 AES Ilumina, LLC Guayama Photovoltaic #1 Barceloneta Photovoltaic #2 Salinas Photovoltaic #3 Humacao Photovoltaic #4 Lajas Photovoltaic #5 Naguabo Photovoltaic #6 San Lorenzo Photovoltaic #7 Hatillo Photovoltaic #8 Guayama Photovoltaic #9 Hatillo Photovoltaic #10 Salinas Photovoltaic #11 Morovis Photovoltaic #12 Juncos Photovoltaic #13 Juncos Photovoltaic #14 Aguadilla Photovoltaic #15 Vega Baja Photovoltaic #16 Barceloneta Photovoltaic #17 Fajardo Photovoltaic #18 Añasco Photovoltaic #19 Quebradillas Photovoltaic #20 Añasco Photovoltaic #21 Loíza Photovoltaic #22 San German Photovoltaic #23 Vega Baja Photovoltaic #24 Yabucoa Photovoltaic #25 Dorado-Toa Baja Photovoltaic #26 Yauco-Guayanilla Photovoltaic #27 Ponce Photovoltaic #28 Yabucoa Total Page 25

26 Evaluated Renewable Projects Wind (WTG) Projects WTG Projects added to 164 MW installed. As before the project s capacity and corresponding AWS site is a key element in the modeling. NUM Name Location Capacity MW AWS SITE 2 Wind Generation # 1 Santa Isabel Pattern Santa Isabel, LLC Santa Isabel Punta Lima (Go Green PR) Naguabo Wind Generation # 2 Guayanilla Wind Generation # 3 Guayanilla Total These projects with the 720 MW PV add to the 884 MW considered during the stability evaluations Page 26

27 Renewable Generation Models Grouping of Projects 8 -Mayaguez Regional effects are important to be evaluated so the projects were grouped in increasing level: AWS Site / Project All projects move in unison. Area Some diversity between the Network Areas. Cluster Further diversity within Areas in Cluster Island Level Maximum diversity between Clusters. 7 Arecibo Bayamon 2 Bayamon 1 San Juan West Page 27 North South 6 Ponce OE 5 Ponce ES East 3 Carolina 4 Caguas Site Area Name Cluster PV WTG Total 1 7 ARECIBO North ARECIBO North ARECIBO North ARECIBO North BAYAMON North BAYAMON North BAYAMON North CAROLINA East CAROLINA East CAROLINA East CAGUAS East CAGUAS East PONCE ES South PONCE ES South PONCE ES South PONCE OE South PONCE OE South MAYAGUEZ West MAYAGUEZ West MAYAGUEZ West CAGUAS East CAROLINA East CAROLINA East Total

28 Power per MW installed PV Model Development Solar Models The program PVSyst was used to estimate production at each AWS Site based on the Global Horizontal Irradiance (GHI) and the Diffuse Horizontal Irradiance (DHI) as provided by AWS in 10 minutes intervals New Design Original Global Effective Incidence W/m2 Page 28

29 Wind Turbine Model Development WTG were created based on the results of the climate data and a Siemens SWT wind turbine generator Power Curve model. This turbine was selected as representative of the wind turbines available in the market for medium to low speed sites. Note it s better performance at low speeds than the SWT 3.0 The power curve correlates the wind speed at the hub with power output was used to determine a curve fitting equation. pu Wind Turbine Power Output SWT vs SWT The wind speed at the hub is affected by Shading: impact of one turbine on another or external obstacles The speed was forecasted at the right height Wind Speed m/sec SWT Power (pu) SWT Power (pu) Page 29

30 Wind Turbine Model Development Model Development Shading Factors were selected considering that the impact of shading being about 5% of the energy output due to other turbines and that bodies outside the fence can reduce the energy output further by another 5% or more. This was verified for PREPA considering the actual reported production for existing projects. A model (analogous to PVSyst) was created to determine power starting from the AWS forecasted wind speed by site,. Page 30

31 Model Development Post Processing A post processing model was developed for Produce 10 minute output based on PVSyst hourly production and irradiance data from AWS. WTG production was already in 10 minutes Verification maximum output and capacity factors. Aggregation of results by Area, Cluster and Island, to evaluate impacts at this level. of capacity Site Number Area Area 6 Cluster PONCE OE Site Adjustment Factor: Max Ramp %/min Contracted Capacity (CA) % Max MW output at site per MW gross Time Max MW output per MW gross - Selected /10/11 3:20 PM Base Power Maximum value Days reached 38% 38% Capacity Factor 33.76% 33.76% Time Base Power 0 D 2/25/11 6:20 AM Check Active Total PONCE OE Longest 30 min Ramp Increase MW & %/min % Duration mn & %/min Time 5/13/11 1:30 AM Power per MW Power output gross MW %CA/min - 10min Index MW/min 10Site Number Total Island wide Area AWS Site Data Cluster % % 1.0 Site Adjustment Factor: Max Ramp %/min Longest 30 min+ Ramp % Contracted Capacity (CA) % 7.32% -2.0 Increase MW & %/min 10Max MW output at site per MW gross % Time Max MW output per MW gross - Selected /30/ % 7:40 AM % % 3.0 Base Power Maximum value Duration mn & %/min % Days reached % % 0.15% Capacity Factor % % 0.13% Time 6.0 Time 0 D 1/26/11 10:00 AM 7/14/11 4:20 PM Active Total Island wide Page 31 Power per MW gross Power output MW %CA/min - 10min Index MW/min % % % % % 1.0

32 PV Model Development 60 Minutes Models PROMOD requires hourly values so two models were created to produce these inputs to the program. Code 2 Hour SITE 32 SITE 02 SITE 31 SITE 59 SITE 61 max ouput Capacity Factor 28.22% 32.18% 37.90% 27.46% 27.46% Installed PPA AVG Max Min Hour SITE 32 SITE 02 SITE 31 SITE 59 SITE Page 32

33 PREPA Renewable Energy Resources Integration Study Models testing and assessment Restricted Siemens Industry, Inc All rights reserved. Answers for infrastructure and cities.

34 Model Testing and Assessment PV Results Cluster or Island-wide Cluster Installed PV Maximum value Capacity Factor Max Ramp MW Max Ramp %/min Long Ramp MW Long Ramp %/Min Time for max ramp Time for long ramp North % % % 5/1/11 12:00 PM 6/16/11 10:50 AM East % % % 5/15/11 11:00 AM 5/1/11 12:20 PM South % % % 8/13/11 1:20 PM 7/7/11 12:50 PM West % % % 6/30/11 12:20 PM 9/19/11 1:50 PM Island Wide % % % 5/1/11 12:00 PM 5/1/11 12:20 PM All projects can be operating close to their maximum output simultaneously. 5% of the time the combined PV output is over 650 MW (90% of installed capacity) and 20% of the time we would have production over 600 MW at noontime The aggregated capacity factor for each of the clusters is as expected in the order of 21%. Page 34

35 . Model Testing and Assessment PV Cluster or Island-wide The maximum aggregated generation changes are in the range of 4.34% to 6.37% at the cluster and 2.64% at the Island-Wide level The figure shows the maximum change over 10 minutes; 123 MW at the cluster level and 190 MW Island-Wide on May 1st at noon. The long steep changes of 257 MW Island-Wide over 20 minutes were observed (1.8% / minute ). Page 35

36 Model Testing and Assessment PV at the Area Level Area Area Name Installed PV Capacity Factor Maximum value Max Ramp MW The max changes (6.01% average) and the long changes (2.96% average) are higher than the corresponding results for the Clusters and Island-Wide values. This is expected earlier because as we reduced the size of the area under consideration, local effects have greater impact. Max Ramp %/min Long Ramp MW Long Ramp %/Min Time for max ramp Time for long ramp 1 S.JUAN 0 2 BAYAMON % % % 5/1/11 1:00 PM 5/1/11 1:00 PM 3 CAROLINA % % % 7/4/11 11:50 AM 9/25/11 10:50 AM 4 CAGUAS % % % 5/15/11 11:00 AM 8/10/11 11:30 AM 5 PONCE ES % % % 9/6/11 10:40 AM 5/14/11 1:20 PM 6 PONCE OE % % % 3/21/11 12:20 PM 7/16/11 2:20 PM 7 ARECIBO % % % 5/1/11 12:00 PM 5/31/11 2:00 PM 8 MAYAGUEZ % % % 6/30/11 12:20 PM 7/27/11 12:00 PM Total or avg % 2.96% Page 36

37 Model Testing and Assessment Sites Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9 Site 10 Installed MW Max MW Capacity Factor 20% 21% 21% 21% 20% 21% 21% 0% 19% 20% Max Ramp% 8.2% 8.0% 8.3% 8.4% 8.3% 7.8% 8.3% 0.0% 8.3% 7.1% Max MW change Site 11 Site 12 Site 13 Site 14 Site 16 Site 17 Site 18 Site 19 Site 20 Site 21 Site 22 TOTAL Installed MW Max MW Capacity Factor 20% 20% 21% 21% 21% 21% 21% 19% 20% 20% 21% Ramp% 8.2% 8.4% 7.0% 7.3% 8.2% 7.2% 7.7% 8.3% 8.2% 8.1% 8.0% 7.6% Max MW change We observe that all sites have capacity factors in the expected range and of course all of them reach their maximum value. Sites have the greatest volatility with maximum aggregated production changes as high as 8.4%/min observed. Note that as the meteorological data was in 10 minutes interval changes of 10%/min or greater cannot be observed with this information. Page 37

38 7:00 AM 7:30 AM 8:00 AM 8:30 AM 9:00 AM 9:30 AM 10:00 AM 10:30 AM 11:00 AM 11:30 AM 12:00 PM 12:30 PM 1:00 PM 1:30 PM 2:00 PM 2:30 PM 3:00 PM 3:30 PM 4:00 PM 4:30 PM 5:00 PM 5:30 PM 6:00 PM 7:00 AM 7:30 AM 8:00 AM 8:30 AM 9:00 AM 9:30 AM 10:00 AM 10:30 AM 11:00 AM 11:30 AM 12:00 PM 12:30 PM 1:00 PM 1:30 PM 2:00 PM 2:30 PM 3:00 PM 3:30 PM 4:00 PM 4:30 PM 5:00 PM 5:30 PM 6:00 PM Model Testing and Assessment PV Cluster or Island-wide - Peaks Finally although the model produced contains the plots for all sites, here we will present two groups of sites for the typical day in July were we note the difference in volatility across sites MW MW MW MW Site 1 Site 2 Site 3 Site 4 Site 6 Site 7 Site 10 Site 11 Site 12 Site 19 Site 20 Site 21 Total Site 5 Site Site 13 Site Site 16 Site Site 18 Site Total Page 38

39 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0% 10.0% 11.0% 12.0% 13.0% 14.0% 15.0% 16.0% 17.0% Probablity that the value will be less Model Testing and Assessment PV Effect of time intervals on Ramps Timeframe has an important impact on observed ramps. Considering 5 minutes intervals we identified change was in the order of 10% to 13% and it can reach values as high as 16% using one weather station data At the site level; PREPA has observed changes as high as 75 %/minute; see below. Changes need time to propagate, but steep ramps can happen at the local level and can have potential impacts in the frequency and voltages, as shown earlier % % 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 5 Minutes 15 minutes 30.00% 20.00% 10.00% 0.00% Ramp Rate %/minute Page 39

40 7:00 AM 7:40 AM 8:20 AM 9:00 AM 9:40 AM 10:20 AM 11:00 AM 11:40 AM 12:20 PM 1:00 PM 1:40 PM 2:20 PM 3:00 PM 3:40 PM 4:20 PM 5:00 PM 5:40 PM 6:20 PM 7:00 PM 7:40 PM 8:20 PM 9:00 PM 9:40 PM 10:20 PM 11:00 PM 11:40 PM 12:20 AM 1:00 AM 1:40 AM 2:20 AM 3:00 AM 3:40 AM 4:20 AM 5:00 AM 5:40 AM 6:20 AM Model Testing and Assessment WTC Cluster or Island-wide Cluster Installed WTG Maximum value Capacity Factor Max Ramp MW The peaks can be long duration; Max Ramp %/min Long Ramp MW Long Ramp %/Min Time for max ramp Time for long ramp North East % % % 4/30/11 12:20 AM 5/2/11 12:30 PM South % % % 7/30/11 7:40 AM 7/30/11 7:40 AM West Island Wide % % % 7/30/11 7:40 AM 7/14/11 4:20 PM The all projects reach their maximum output simultaneously and in multiple days. WTG reached its maximum output simultaneously 8% of the days MW South East Total Page 40

41 05:02 AM 05:45 AM 06:28 AM 07:12 AM 07:55 AM 08:38 AM 09:21 AM 10:04 AM 10:48 AM 11:31 AM 12:14 PM 12:57 PM 01:40 PM 02:24 PM 03:07 PM 03:50 PM 04:33 PM 05:16 PM 06:00 PM 06:43 PM 07:26 PM 08:09 PM Model Testing and Assessment Cluster or Island-wide When PV production is considered in addition to the WTG we find that on April the maximum renewable simultaneous production of 876-MW was reached. This represents 99% of the installed capacity It demonstrates that this event can and will happen MW WTG PV TOTAL Page 41

42 7:00 AM 7:40 AM 8:20 AM 9:00 AM 9:40 AM 10:20 AM 11:00 AM 11:40 AM 12:20 PM 1:00 PM 1:40 PM 2:20 PM 3:00 PM 3:40 PM 4:20 PM 5:00 PM 5:40 PM 6:20 PM 7:00 PM 7:40 PM 8:20 PM 9:00 PM 9:40 PM 10:20 PM 11:00 PM 11:40 PM 12:20 AM 1:00 AM 1:40 AM 2:20 AM 3:00 AM 3:40 AM 4:20 AM 5:00 AM 5:40 AM 6:20 AM 7:00 AM 7:40 AM 8:20 AM 9:00 AM 9:40 AM 10:20 AM 11:00 AM 11:40 AM 12:20 PM 1:00 PM 1:40 PM 2:20 PM 3:00 PM 3:40 PM 4:20 PM 5:00 PM 5:40 PM 6:20 PM 7:00 PM 7:40 PM 8:20 PM 9:00 PM 9:40 PM 10:20 PM 11:00 PM 11:40 PM 12:20 AM 1:00 AM 1:40 AM 2:20 AM 3:00 AM 3:40 AM 4:20 AM 5:00 AM 5:40 AM 6:20 AM Model Testing and Assessment WTG Cluster or Island-wide Given it concentrated location in the south, the WTG generation experiences larger aggregated changes. The maximum observed changes are 7.32%/min for PREPA and 8.8%/min for the South Cluster. At the project level we observed ramps of 10%/min, which is the maximum that could be observed for 10 min intervals. The maximum observed Long Changes in the case were % for a 134 MW reduction. This occurred on July 14 at 4:20 pm and is shown in the lower figure. At the local level meteorological data and PREPA s measurements indicate that14%/min ramps are possible and values higher than 19%/min are possible MW South East Total MW South East Total Page 42

43 Renewable Modeling Conclusion and Observations Renewable generation does peak at the same time on certain days of the year. PV can present important aggregated changes at the island level 2.64%/minute over 10 minutes; total change 190 MW 1.8%/minute and a total change of 257 MW over 20 minutes. The North Cluster also shows important changes: 4.34%/min for 10 min as well as the long ramp of 2.2/min % for 20 minutes. PV can experience localized changes 37%/min and up to 65% per minute. For WTG 10%/min for 10 minutes can happen at the island level 20%/min in 5 minutes at the project level. Page 43

44 PREPA Renewable Energy Resources Integration Study PROMOD Setup Considering Renewable Resources Restricted Siemens Industry, Inc All rights reserved. Answers for infrastructure and cities.

45 PROMOD Set Up Renewable Generation Renewable Generation was modeled as Must Run with hourly profiles. Page 45

46 PROMOD Set Up Conventional Generation PROMOD Generator Capability & Capacity Segments The operating limits, ramping rates, O&M costs, start up costs, capacity segments, maintenance and heat rate curves of the conventional generation was reviewed and updated as necessary. Also minimum down time and run time was reviewed (more on this later) Fuels Specific fuels were added to the database in the years when expected to be available Natural Gas Ecoelectrica (from first year 2015 in the model) Natural Gas Norte (from 2017 in the model) Natural Gas Costa Sur (from first year 2015 in the model) Natural Gas Aguirre (from 2017 in the model) The fuel prices are adjusted by month and year and are deemed conservative. Page 46

47 PROMOD Renewable Models Fuel Costs Forecast ($/MMBtu) FuelForecast Jan-15 Feb-15 Mar-15 Apr-15 May-15 Jun-15 Jul-15 Aug-15 Sep-15 Oct-15 Nov-15 Dec-15 Average Bunker- Aguirre Bunker-PSSP Bunker-SJSP Bunker-Souco COAL-AES Light D.-AGUIRRE Light D.-Camabalac Light D.-PREPA Light D.-SJUAN Natural Gas - Aguirre Natural Gas - Contract Natural Gas EcoElectrica Natural Gas Norte FuelForecast Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Jul-17 Aug-17 Sep-17 Oct-17 Nov-17 Dec-17 Average Bunker- Aguirre Bunker-PSSP Bunker-SJSP Bunker-Souco COAL-AES Light D.-AGUIRRE Light D.-Camabalac Light D.-PREPA Light D.-SJUAN Natural Gas - Aguirre Natural Gas - Contract Natural Gas EcoElectrica Natural Gas Norte Page 47

48 PROMOD Set Up Requirements FLOWGATES; PROMOD takes into consideration the transmission limitations an in particular the limits indicated below: 230 kv Line name Line Number Bus From Bus To Ckt# Thermal Limit MVA Loading Limit % of Thermal Costa Sur Manati COSTA SUR230_96 MANATI 230_ % 300 Aguirre Aguas Buenas AGUIRRE 230_106 AGUBUENAS230_ % 416 Aguirre Aguas Buenas AGUIRRE 230_106 AGUBUENAS230_ % 416 Costa Sur Mayagüez COSTA SUR230_96 MAYA TC 230_ % 300 Aguirre Costa Sur COSTA SUR230_96 AGUIRRE 230_ % 139 Actual Limit MVA RETIREMENTS: Given the working period ( ) a number of units were noted as retired. These were initially set and then revised by PREPA later the in evaluation process. The following Units were modeled as retired for PSSP 1& 2 SJSP 7&8 SOUCO 1 to 4 Several GTs Page 48

49 PROMOD Renewable Models Thermal Modeling Impacts Heat Rates PREPA provided updates for almost all units Later adjustments were performed as some units did not perform as expected to reflect the onsite conditions. Fixed and Variable O&M Fixed O&M prices were adjusted for all units (per PREPA) Variable O&M prices were adjusted for all units (per PREPA) Maintenance Maintenance schedules were updated (per PREPA) Page 49

50 PREPA Renewable Energy Resources Integration Study Scenario Definition Restricted Siemens Industry, Inc All rights reserved. Answers for infrastructure and cities.

51 Scenario I Expected Short Term Conditions Scenario I corresponds to the expected conditions in the system by 2015 and it is considered the Base Scenario. The scenario assumes the load as observed in PREPA during 2011 and it assumes that there is a recovery of the island industrial and economic activity. Natural Gas availability is considered to be limited to Costa Sur, hence in addition to Ecoelectrica, the generating units Costa Sur 5&6 are expected to be generating using natural gas (80% natural gas / 20% bunker). The scenario considers the renewable generation indicated previously (884.2 MW installed) and 64 MW of Net-metering. Page 51

52 Scenario II Evaluation of Storage This scenario corresponds to the expected conditions in the system by 2017 or 2018 and they are considered medium term sensitivities. The load is assumed to be the same as 2015 (Scenario I). Natural gas is expected to become available in Aguirre and in the north of the island, hence the following units are expected to be converted to natural gas: Name Short Name Unit Number Category Startup Fuel AGUIRRE COMBINED CYCLE 1 C.CYCLE 1 STEAM Natural Gas - Aguirre AGUIRRE COMBINED CYCLE 2 C.CYCLE 2 STEAM Natural Gas - Aguirre AGUIRRE STEAM 1 AGUIRRE 1 STEAM Natural Gas - Aguirre AGUIRRE STEAM 2 AGUIRRE 2 STEAM Natural Gas - Aguirre PSSP 3 PSSP 3 STEAM Natural Gas Norte PSSP 4 PSSP 4 STEAM Natural Gas Norte SAN JUAN COMBINED CYCLE 5 CC SJUAN 1 STEAM Natural Gas Norte SAN JUAN COMBINED CYCLE 6 CC SJUAN 2 STEAM Natural Gas Norte SJSP 10 SJSP 10 STEAM Natural Gas Norte SJSP 9 SJSP 9 STEAM Natural Gas Norte Page 52

53 Scenario II Evaluation of Storage 12% Penetration is required and more projects need to be added, largely solar. The following table shows the determination of the additional PV required (note Net-metering not considered only PPOA) Current Target Total PV + WTG 1,890, ,317, Less net-metering 126, , PPOA 1,763, ,190, Penetration 9.66% 12.00% Additional Required 426, In order to produce the 427 GWh from PV, approximately 235 MW of PV generation with a profile similar to the average PV production in Puerto Rico. This generation was added in a strong bus in the north for which we selected Bayamon 230 kv Net-Metering is expected to increase to 100 MW in this scenario Page 53

54 Scenario III Modeling Scenario III is identical to Scenario II but in order to accommodate the additional generation and achieve 12% energy penetration new combined cycles were added to the system. A coincident peak 1158 MW of renewable generation occurs when PV as necessary for reaching the 12% penetration is added to the system Coincident Max Energy MWh MW PPOA 1,763, Net Metering 126, Total 1,890, Additional PV 426, Total with new PV 2,317, ,158 With this we found that a feasible dispatch during Low Demand Day can be obtained with two combined cycles that had a) peak capacity of at least 320 MW each (640 MW total), b) that could be turned on and off daily c) a minimum operating value 128 MW (40% of max). Page 54

55 Scenario III Modeling It is important to note that the only gives indication that PROMOD would be able to find an acceptable solution integrating this level of renewable. The next step was to select the combined cycle plant (CCP) the following were considered and the SGT800 5x1 adopted due its flexibility and efficiency Similar machines can be obtained from various vendors. Page 55

56 PREPA Renewable Energy Resources Integration Study PROMOD Analysis: Scenario I Restricted Siemens Industry, Inc All rights reserved. Answers for infrastructure and cities.

57 PROMOD Analysis Overview The PROMOD analysis was conducted for the various scenarios to capture the impacts of the renewable resources on existing thermal generation. Several key aspects are derived from the analysis are: Energy curtailment (dump energy) Unit cycling Impact of renewable generation variability Impact of errors in the renewable generation forecast Overall generation costs. Before we enter into the details of Scenario I it is important to discuss some of these key aspects. Page 57

58 PROMOD Analysis Energy Curtailment & Emergency Power PROMOD utilizes the term Dump (Curtailment)& Emergency Power to allow for successful solutions. Dump reflects a condition where more power than load is dispatched due to restrictions in the case, Curtailment of generation is required; curtailing renewable generation and/or reducing power plants to their emergency lower limits. Emergency Power reflects the condition where not enough generation is dispatched, therefore more generation dispatch is required; or load shedding in practice. This occurs because of the inflexibilities or insufficiencies build into the models. It should be noted that typically when there is only conventional generation, the inequalities above are rare. Page 58

59 PROMOD Analysis Energy Curtailment & Emergency Power The PREPA model produced conditions where Energy Curtailment is needed to balance the generation to load. The PREPA model produced these conditions because of two factors: Renewable generation is modeled as Must Run according to the hourly profiles. Conventional generation can be dispatched but is set up to maintain required levels of reserve, running times and ramping. These conditions do occur in real time, however, operators resort to curtailing generation as needed to maintain the system balance, stability and security. Page 59

60 PROMOD Analysis Energy Curtailment & Emergency Power Curtailment of Renewable Generation in the Industry This is a common occurrence in the industry ERCOT, SPS, PSCo, NSP, MISO, BPA and PJM have all experienced renewable curtailment There is a clear trend to within the industry to reduce curtailment of renewable resources which we expect to stabilize in the order of 2% to 3%. Page 60

61 PROMOD Analysis Energy Curtailment & Emergency Power If PREPA curtails renewable energy it has to pay for it, based on the energy that would have been produced based on the meteorological conditions during the time of the curtailment and the curtailed project production characteristics. This result is an increase in the price of the energy that is actually taken A 2% curtailment implies an increase in price of 2.04% or about 3.5 $/MWh. PREPA s Operation is committed to minimize this value, but as will be seen for larger levels of penetration it cannot be made zero. Page 61

62 PROMOD Analysis Unit Cycling Renewable Resources has high injection levels during the day and zero or very low injection levels at night This introduces the risk that the conventional generation fleet will exhibit a large number of starts and shutdowns (Cycling.) This is particularly noted in the steam generators in the fleet in this analysis This condition creates wear-and-tear, high operating costs and eventually outages on the steam generation; thus it is not a desired operating practice. Actions were taken to prevent excessive number of starts and stops on the steam generators in PREPA (discussed later) Page 62

63 PROMOD Analysis Renewable Variability Renewable variability introduces additional operations costs by forcing conventional units to deviate from optimal dispatches. To define how this impacts the system, the renewable resources were modeled as block generation across the entire year while maintaining the energy produced at each renewable site With the renewable resources defined as block sources the conventional generation is able to dispatch optimally The cost difference of the conventional generation between the renewable variable and block models provides an estimation of the cost of variability Impact of Variability of Renewable Resources Increased in production cost $36,078,136 Increased curtailment 4.22% Increased in average production cost $/MWH $1.60 More GT's on line hours Page 63

64 PROMOD Analysis Renewable Generation Forecast Error Impacts Generators are required to make day-ahead plans so that system security and economics can be maintained; Security Constrained Unit Commitment. This includes projections of renewable generators. Renewable forecasting errors increase with the forecasting horizon, having maximum impact on the day-ahead unit commitment. Large Forecasting errors result in Increased production costs due to deviation from optimal unit commitment and/or Increased curtailment; generation cannot be taken. Our study estimated the impact of forecast error using different methodologies to show how error may impact production costs and curtailment Page 64

65 PROMOD Analysis Scenario I The results of Scenario I and the required adjustments to the case are presented next. As will be seen two types of adjustments were required; to increase the minimum run time for the steam units to avoid excessive cycling and a reduction of the renewable generation to obtain reasonable levels of curtailment. These adjustments resulted in variations of the Scenario I which are presented and discussed. Page 65

66 PROMOD Analysis Scenario 1-0 This scenario corresponds to the initial run of Scenario I, without any adjustments. The case resulted in an excessive amount of cycling for economic reasons. As can be seen below where the Base Case only has the existing renewable projects, there was a large jump in the number of starts. Total Base Case I Scenario 1-O Forced / Sch Starts Economic Starts Total Forced / Sch Starts Economic Starts EcoElect AES CC SJUAN AGUIRRE 1& SOCO 5 & Aguirre CCP PSSP 3& SJSP 9& Cambalache Mayaguez GT's Hydro New CCP Page 66

67 PROMOD Analysis Scenario 1-1-0; Overview This scenario corresponds to the same conditions in previous scenario, but to achieve acceptable cycling, the minimum run time was increased to 720 hours for all the steam units. As will be shown next this change resulted in correct cycling of the steam generation, but there was important energy dump and the need to curtail renewable generation Page 67

68 PROMOD Analysis Scenario 1-1-0; Overview The tables below show that to match the generation with the load approximately 106 GWh have to be curtailed, most of it out of renewable generation, representing 5.63% of the energy and quite high. We also note that there is an small amount of thermal curtailment, but this can be absorbed using the emergency minimum of generators. Page 68

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