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VGS Study Benefits of Leveraging Diversity across the Western Interconnection

VGS Study Benefits of Leveraging Diversity across the Western Interconnection

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VGS Study Benefits of Leveraging Diversity across the Western Interconnection

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  1. Project team N. Samaan, Y. V. Makarov, T. Nguyen, C. Jin, R. Hafen, R. Diao, X. Guo, and M. Elizondo 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) R. Guttromson Sandia National Laboratory VGS StudyBenefits of Leveraging Diversity across the Western Interconnection Presented at WECC VGS Subcommittee Meeting Salt Lake City, Utah December 2, 2010

  2. 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

  3. 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

  4. 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

  5. Basic Components of the Analysis • Use PROMOD to determine production costs for a defined scheduling period • Scheduling period can be 1 hour or 10 minutes • 10 minute scheduling period using PROMOD required ‘tricks’ • Day ahead forecasts and actual data are needed 5

  6. Simulation Details for Each Scenario 6

  7. Determination of Regulation Requirements Regulation requirements Load Following requirements • Generate hour-ahead and 10-minute ahead forecast errors for load, wind and solar • Use actual and forecast data to extract the minute by minute load following and regulation requirements for each BA • Monthly Maximum up and down regulation capacity requirements for each BA will be used as a constraint in PROMOD 7

  8. Example Savings in Load Following Due to a Consolidated BA (CBA) 8

  9. 2009 Load and Net Interchange Data Processing

  10. Combined the intra-hour variability characteristics of 2009 load data with 2006 1-hour average load Error scaled based on maximum load Error= Generated-Interpolated

  11. Generated Load Curve for CAISO in 2006

  12. Day-Ahead Load Forecast ErrorsBackground 12 • We wish to better understand the dependence of load forecast errors on the time of day • Studied 3 months of day-ahead forecast errors • Forecast values given on an hourly basis • The last ten minutes of one hour’s forecast value is ramped up into the first ten minutes of the next hour’s forecast value • The actual load is observed at minute resolution

  13. Day-Ahead Forecast and Actual Load 13

  14. Day-Ahead Forecast and Actual Load 14 • From the sample just shown, a bias in forecast error is apparent: • The forecast value is typically lower than the actual load in the afternoon • The forecast value is typically higher than the actual load in the early morning

  15. Load vs. Temperature Forecast ErrorsBackground 15 • As load is dependent on temperature, we investigated the dependence of load forecast errors on temperature forecast errors • Used 2007 hourly load data for 3 BAs • Temperature forecasts obtained from government data at airports in the BA regions • Actual temperature obtained from scraping the web

  16. Actual Temperature vs. Actual Load

  17. Load vs. Temperature Forecast Errors 1

  18. Load vs. Temperature Forecast Errors 2

  19. Load vs. Temperature Forecast Errors • Just as we saw with actual load vs. actual temperature: • Load forecast errors are negatively correlated with temperature forecast errors at lower temperatures • Load forecast errors are positively correlated with temperature forecast errors at higher temperatures • The correlation is not extremely strong, but is significant • For this particular BA, a one degree change in temperature forecast error corresponds to up to a 5 megawatt change in load forecast error

  20. Disaggregation of TEPCC Wind Profiles from Profiles to Bus Level

  21. 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):

  22. Assumptions for regulation database • For the regulation 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: • Nuclear, coal and steam units • Units without “ramp rate up” data • Units that have an hourly profile (such as run-of-river hydro)

  23. 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) 23 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

  24. Scenario 1A • 32 areas, 32 pools • Tariff is $0/MWh • Full transmission • Hydro is scheduled to benefit area where the unit belongs • Summary

  25. Effect of transmission tariff ($/MWh 0,5,10,50,1000)

  26. CAISO

  27. BPA

  28. How to Identify the Most Realistic Transmission Tariff between BAs to Reflect Current Situation (Scenario 1 of the Study BA

  29. How to Identify the Most Realistic Transmission Tariff between BAs to Reflect Current Situation in Scenario 1 of the Study (2)

  30. The Match is not good for all BAs

  31. Scenarios 2&3 • 1 pools, 2 areas • Transmission is considered in Scenario 3

  32. Cost Components 32 • Thermal Production Cost • Bus LMP price (energy consumption cost) • That will reflect transmission Tariff and consequently cost of congestion

  33. Next Steps 33 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 Transmission Tariff assumptions PNNL will perform PROMOD simulation for scenarios 1,2 and 3 using TEPPC 2020 case

  34. Questions? QUESTIONS?