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VGS Study Benefits of Leveraging Diversity across the Western Interconnection

VGS Study Benefits of Leveraging Diversity across the Western Interconnection. Project team N. Samaan, R. Guttromson, Y. V. Makarov, T. Nguyen, C. Jin, M. Elizondo, and X. Guo Pacific Northwest National Laboratory (PNNL) M. Milligan, and K. Orwig National Renewable Energy Laboratory (NREL)

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VGS Study Benefits of Leveraging Diversity across the Western Interconnection

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  1. VGS StudyBenefits of Leveraging Diversity across the Western Interconnection Project team N. Samaan, R. Guttromson, Y. V. Makarov, T. Nguyen, C. Jin, M. Elizondo, and X. Guo Pacific Northwest National Laboratory (PNNL) M. Milligan, and K. Orwig National Renewable Energy Laboratory (NREL) B. Nickel, M. Mizumori, M. Hunsaker, and H. Pacini Western Electricity Coordinating Council (WECC) Presented at WECC VGS Subcommittee Meeting Salt Lake City, Utah October 27, 2010

  2. Study Objectives • To determine the saving in operating costs (production and balancing costs) due to WECC BAs consolidation as an upper bound for BAs cooperation benefits • To examine the effect of congestion on potential benefits • To evaluate the benefits of intra-hour (10-min) scheduling • The analysis will be performed for three different scenarios of VG penetration 15%, 20%, and 30% as percentage of WECC energy demand for one year 2

  3. Study Scenarios • Scenario 1: BA structure like today’s: BAs are separate, congestion exists • Scenario 2: Full consolidation, copper sheet no congestion • Scenario 3: Full consolidation, congestion exists • Scenario 4: Separate BAs with 10-min intra hour scheduling

  4. Base Simulation Case • TEPPC 2020 Case will be the base case for the study • The case will be in PROMOD format with the full WECC nodal model • The 2006 load shapes are scaled up to represent 2020 estimated values • Wind and Solar production time series will be generated based on 2006 weather • Hydro production is based on an average year (which is representative for hydro production in 2006) • Proportional Load Following (PLF) model • Hydro Thermal Coordination (HTC) (used for 21 hydro plants in the model ) 4

  5. Progress in the Study WECC provided 2009 1-minute resolution load and net interchange data for each BA PNNL combined the intra-hour variability characteristics of 2009 load data with 2006 1-hour average load to create a representative 1-min time series for 2006 load WECC provided 1-hour resolution aggregated wind profiles NREL provided 1-hour resolution solar profiles PNNL performed trial simulations for scenarios 1, 2 and 3 using TEPPC 2019 case

  6. Progress in the Study (2) PNNL developed a forecast simulation model to generate load, wind and solar forecast errors at different time frames taking into consideration the correlation between the errors Work is in progress to put assumptions for AGC capabilities of generating units in the model Flow gates were added to PRMOD model to monitor hourly interchanged power between individual BAs Analysis is in progress to better understand hydro-scheduling in PRMOD

  7. Data Processing-Original Data • 2009 1-min resolution load of WECC 32 BAs (no load for the 5 generation-only BAs) • Outliers & missing data Outlier Missing Data

  8. Data Processing-First Step • Load should be highly persistent locally in time around a changing mean • We highlight deviations by modeling this changing mean and observing the magnitude of deviations from the mean • For robustness, mean is modeled using a 15-minute running median • We set rules for anomalous values and also inspect visually • Our rule was to flag values greater than 6 standard deviations from the mean • For visual inspection, it is impossible to view the entire year with 1-minute resolution data, so we look at the data on a daily basis 8

  9. Data Processing-First Step (2)

  10. Data Processing-First Step • Gray dots are the load values, black line is the running median, red dots are flagged as anomalous • Should the jump before 8:00 be deemed as anomalous by visual inspection?

  11. Data Processing-Second Step • Replace missing data by the data in previous week plus random noise • To identify outliers, a threshold is set at 3 standard deviations from the mean • Mean is obtained by dividing a yearly data set into 10 smaller sets • Use spline interpolation to correct outlying data based on prior and subsequent “good” data points • Further correct data that deviates more than ±5% of the mean value in 1min by changing the current value to the previous value ±5%

  12. Data Processing-Second Step

  13. Combined the intra-hour variability characteristics of 2009 load data with 2006 1-hour average load 1. Compute hourly average load data for all BAs in 2009 2. Apply nonlinear interpolation method to smooth the step hourly change 3. Calculate the difference between actual and interpolated values 4. Scale these differences based on the maximum load level during the whole year 5. Take the hourly load data in 2006 and interpolate these data to obtain interpolated load data 6. Apply the 2009 differences to the interpolated 2006 load and obtain the final load curves in 2006, with 1 minute resolution

  14. Example Error scaled based on maximum load Error= Generated-Interpolated

  15. Generated Load Curve for CAISO in 2006

  16. Generated 2006 Load Curves with 1-minute resolution for all BAs

  17. Trial Simulations with TEPPC 2019 case to determine Pools and BAs Structure for each Scenario • Current TEPPC model has 7 pools. Each pool has a set of consolidated BAs (Scenario 0) 17 The objective of the first scenario of the study is to model current situation as realistic as possible but in 2020 http://www.nerc.com/fileUploads/File/AboutNERC/maps/NERC_Regions_BA.jpg

  18. Scenario 0-TEPPC 2019 Structure 39 areas, 7 pools • Full transmission • Hydro is scheduled to benefit pools • Results summary

  19. Scenario 1A -25 Areas, 25 Pools • 25 is the maximum number of Pools allowed in PROMOD • Full transmission • Hydro is scheduled to benefit pool where the unit belongs • Results summary IPC PACE • Huge dump and emergency energy because • The optimization is done over pools • Some pools don’t have enough capacity while the others have too much to serve their own load.

  20. Full transmission • Hydro is scheduled to benefit area where the unit belongs • Results summary Scenario 1B: 20 pools, 20 areas • There still exists some amount of dump and emergency energy, but they are much smaller compared to the scenario 1A.

  21. Scenario 2: Consolidate BA(1 pool, 2 areas), no transmission constraint • Maximum area load in PROMOD is 100 GW, so we have to divide the consolidated WECC into two areas • Simulation runs gave errors message and final results are not realistic • Further investigation is in progress with PRMOD support

  22. Scenario 3: Consolidate BA (1 pool, 2 areas) with transmission constraint • Full transmission • Hydro is scheduled to benefit area where the unit belongs • Results summary

  23. Generation and Cost Comparison 3 3 • About 4 billion dollars in savings comparing scenario 1B and scenario 3

  24. Regulation in PROMOD Pmax 3) Ramp rate [MW/min] 2) Regulation Maximum range P [MW] Reg. Capacity (= Ramp rate * Response time) Desired operating point 1) Regulation Minimum range t [min] Pmin 4) Response time [min] 5) Regulation up bid [$/MW], and 6) Regulation down [bid $/MW] TEPPC cases do not have regulation data Input data needed (underlined):

  25. Regulation in PROMOD • Regulation requirement by Area and by subperiod • Subperiods: weekday (1), weekday night (2), weekend (3) • These values are changed monthly

  26. Regulation in PROMOD (cont…) • PROMOD optimizes energy and regulation either sequentially (commits ancillary services only after committing energy) or simultaneously (co-optimizes ancillary service and energy during unit commitment and dispatch). • Clearing price for regulation: • Determined by most expensive accepted bid if regulation bids are provided • Determined as incremental cost of generation whose dispatch has to be changed to provide regulation if regulation bids are NOT provided

  27. Assumptions for regulation database • For the ramp rate, we used the “ramp rate up” existing in the PROMOD data base (corresponds to power changes from one hour to the next) • Generators that do not provide regulation: • Units without “ramp rate up” data • Units that have an hourly profile (such as run-of-river hydro) • Units built after or retired before the study year

  28. Assumptions (cont…) [1] Makarov, Y.V., et al., Assessing the Value of Regulation Resources Based on Their Time Response Characteristics. 2008, Pacific Northwest National Laboratory. Technologies with regulation capabilities: CC, Coal, CT, Hydro, Steam

  29. Assumptions (cont…) [1] Makarov, Y.V., et al., Assessing the Value of Regulation Resources Based on Their Time Response Characteristics. 2008, Pacific Northwest National Laboratory.

  30. Assumptions (cont…)

  31. Summary of regulation units obtained from assumptions [1] Makarov, Y.V., et al., Assessing the Value of Regulation Resources Based on Their Time Response Characteristics. 2008, Pacific Northwest National Laboratory. e.g. California: 120 units connected AGC in CAISO (SCE, SDGE, PG&E_VLY, PG&E_BAY) in 2007 [1]. From the table CAISO has 292 units

  32. Explore Hydro Operation in PROMOD • In 2019 TEPPC datasets, most of hydro plants are using Proportional Load Following (PLF) model. Two questions need to be answered: • Which load schedule does the hydro plant follow, the native load, load-wind or load-ContractParticipationEnergy? • Wind impacts on hydro schedule; • How does the value of PLF factor affect the hydro schedule?

  33. What Does Hydro Follow?

  34. What Does Hydro Follow? (continue…) • Hydro is dispatched to follow net load, that is NativeLoad-ContractParticipaptionEnergy (including wind, solar, negative load, conventional hydro (which set as must-runs), and geothermal). Several evidences lead us to this conclusion: • The hydro is not dispatched to follow native load; • The hydro is not dispatched to follow NativeLoad-Wind.

  35. Hydro Schedules of cases with and w/o wind Wind production is part of the contract participation energy, so it affects the net load and ultimately affects the hydro schedule.

  36. Load following Factor Increase the proportional load following constant K makes more hydro energy is generated during peak time and less is generated during off-peak time.

  37. Next Steps WECC will finalize TEPPC 2020 case NREL will provide 1-minute resolution solar profiles PNNL will generate day-ahead, hour-ahead and real time forecast data PNNL and NREL will perform load following and regulation analysis for each scenario WECC will send a request for proposal to perform scenario 4 (intra-hour scheduling) Finalize AGC and Pool structure assumptions PNNL will perform PROMOD simulation for scenarios 1,2 and 3 using TEPPC 2020 case

  38. Questions? QUESTIONS?

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