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WECC Planning Data Inventory Stan Holland

WECC Planning Data Inventory Stan Holland. DWG Web Meeting April 3, 2012. Outline. Data Inventory Use of Data WECC Planning Committees Data Overlap Recent Improvements Data collection issues Next Steps. Planning Data Inventory LRS – TEPPC – TSS ··· {VGS} What – Who - Why – Where - How.

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WECC Planning Data Inventory Stan Holland

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  1. WECC Planning Data InventoryStan Holland DWG Web Meeting April 3, 2012

  2. Outline • Data Inventory • Use of Data • WECC Planning Committees • Data Overlap • Recent Improvements • Data collection issues • Next Steps

  3. Planning Data Inventory LRS – TEPPC – TSS ··· {VGS} What – Who - Why – Where - How

  4. What are Data Needs?

  5. Who (Data Source)

  6. Some Key Outside Data Sources • Hydro generation is derived from aggregated historical data by HMTF volunteers • Transmission changes are provided via input to SCG • Mesoscale wind & solar profiles are provided by NREL; Renewable site selection is done using the WREZ tool • Load adjustments (EE/DR) are developed by LBNL • Fuel price forecasts use tools developed by NWPCC • Capital costs are calculated using a tool developed by E3 • Hourly reserves were provided by NREL (Jack King) • Cycling costs were provided by Intertek-APTECH • RPS requirements are provided by Tom Carr (WIEB)/DSIRE • Some historical data (generation output, transmission flows) are provided by RC’s • Historical transmission schedule data is provided by OATI • Emissions data is extracted from CEMS records

  7. Why (Used by)

  8. Use of Data • The data is used for the following activities • AC Power flow studies (TSS) • Long-term Reliability Assessment (LRS) • Seasonal Assessments (LRS) • Power Supply Assessment (LRS) • Production Cost Modeling (TEPPC) • WECC 10-Year Regional Transmission Plan and 20-Year Transmission Plan (TEPPC) • TEPPC Study Reports • Support for VGS studies

  9. Where is the data stored • The data is generally stored by the groups that collect and/or use the data. • The data is stored in various formats (i.e. excel workbooks, csv files, relational databases, etc.) • TSS data is used to build integrated base cases for the western interconnection • TEPPC data is used to develop study cases in Promod and generic formats.

  10. How is the data collected? • The TSS Area Coordinators work with BA and PA representatives to build several system study cases each year. WECC staff compiles the area cases into WECC cases. • LRS collects load and generation data at the beginning of each year. After the data is validated it is forwarded to NERC and used for LRS studies and reports. • TEPPC uses the data collected by TSS and LRS in their production cost datasets. Other data is collected from various sources and forums.

  11. WECC Planning Committees Similarities …………… ……… Differences

  12. Same Goal - Keep the power on

  13. Data Overlap & Differences • Transmission Network • TSS base, modified by TEPPC, simplified by LRS for zonal model • Bus loads not used by LRS • Loads • LRS loads are basis for LRS and TEPPC • TSS uses bus loads directly • Generation • Similar, granularity varies • Operating parameters needed for production cost model • More on next slide

  14. Generation Use

  15. Recent and Ongoing Improvements • TSS • BCCS • TEPPC high renewable case • LRS • Load (more details about EE/DSM/DR) • Generator additions (not interconnection queue) • Actual wind and solar output • TEPPC • Startup / Var. O&M costs • Hourly flexibility reserves • CHP / Cogeneration tuning

  16. Base Case Coordination System SCHENECTADY, N.Y., March 27, 2012 /PRNewswire/ -- Siemens Power Technologies International (Siemens PTI) today announced an agreement with the Western Electricity Coordinating Council (WECC) to deliver WECC's Base Case Coordination System (BCCS) planning tool. The BCCS solution, based on Siemens PTI's Model on Demand (MOD®) software product, will improve the collection and compilation of WECC data used to build their transmission system study models. With the project scheduled for completion in December 2012, the BCCS will enable WECC and its members to streamline data collection and the case building effort in the region. The BCCS provides a means to create a nearly infinite number of scenario cases and could save the average WECC member an estimated 12 to 15 weeks of labor per year. The centralized data base within the BCCS solution also ensures model data consistency, data accuracy and provides tracking and data logging to comply with North American Electric Reliability Corporation (NERC) standards.

  17. BCCS Schedule

  18. Data Collection Issues Issues Discussion Examples

  19. Data Collection Issues • Limited coordination between WECC committees • Reported data not consistent • Data varies between committees • Desire for additional data that is not currently being collected

  20. Limited Coordination • Independent data collection processes • Independent data reporting groups • Independent collection schedules • Can processes be merged? • What coordination is reasonable?

  21. Inconsistent Data • Data is often not consistent within same committee • Confidentiality views vary • Internal procedures and tools vary

  22. Data Varies between Committees • Data can serve different purposes • Granularity can vary • Definitions can vary • Collection schedules can vary

  23. Desire for additional data • New policies and regulations can create need for additional data • Benefits must support costs • New tasks must be prioritized

  24. Example – Load Adjustments • LRS and TEPPC use different assumptions regarding load targets • LRS models an expected load • TEPPC models an optimistic load • Different energy efficiency assumptions • TEPPC more optimistic about effects of EE standards • Reported data does not support TEPPC needs

  25. LRS Load Reporting Instructions The peak demand load forecasts should include the effects of such factors as economic, demographic, weather effects , and customer trends; conservation, improvements in the efficiency of electrical energy use, and other changes in the end uses of electricity. The peak demand load forecast for each month should have a 50% probability of not being exceeded (expected peak demand). This load forecast is commonly referred to as the 1-in-2 monthly peak load forecast.

  26. Review of LBNL Adjustments Next slides extracted from Galen Barbose’s CC assumptions presentation See full presentation for other details

  27. Energy Efficiency AdjustmentsSchematic of Approach

  28. Estimating the “Embedded” Savings in Balancing Authority Load Forecasts • Two types of policies of primary interest: • Customer-funded (aka “ratepayer-funded”) EE programs • Federal appliance, lighting, and equipment standards • Most balancing authorities provided WECC with a projection of savings from customer-funded EE programs incorporated into their load forecast • Each balancing authority was subsequently contacted in order to: • Clarify any inconsistencies in their projection of savings from customer-funded programs • Determine how/whether their load forecast accounts for savings from federal standards

  29. Adjusting Load Forecasts to Account for Accelerated Savings from Federal Standards • Savings from federal standards over the next decade expected to accumulate at faster than the historical rate, due to standards adopted over the 2009-2013 timeframe Assume that savings from 2009-2013 standards are not captured in pure econometric forecasts lacking an end-use model or adjustment For other balancing authorities, assume that forecast captures all or a portion of savings from 2009-2013 standards Source: Chart constructed by LBNL based on data in ACEEE/ASAP “KaBOOM” report

  30. Adjustments to Initial BA Load Forecasts Annual Energy Percentage Reduction to LRS Load Forecast (Annual Energy)

  31. Adjustments to Initial BA Load Forecasts Non-Coincident Annual Peak Demand Percentage Reduction to LRS Load Forecast (Annual Peak Demand)

  32. Options • Request that reporting entities submit EE data that supports both LRS and TEPPC • Help reporting entities to standardize EE reporting • Other suggestions?

  33. Another example - Generation • Past problems with LRS data • Project names withheld (confidential) • Interconnection queues submitted • Difficult to match with other sources • Moving target on additions and retirements • Recent Improvements • Names required • Bus locations requested

  34. Generation in TEPPC 2022 CC Should TEPPC and LRS values match? Should TEPPC and TSS values match? Should LRS and TSS values match?

  35. Future Improvements • Data collection efficiencies • BCCS – LRS – TEPPC • LRS Load Forecasts • Addition of 1-in-10 or 1-in-20 forecast • Other data • Develop capabilities within WECC • Historical data from RC’s • Other sources

  36. Next Steps • Get input on data inventory • Identify issues and data gaps • Existing issues • New and anticipated issues • Coordinate with other committees • Identify data synergies and efficiencies • Understanding of other processes and policies • Develop plans that address problems

  37. Final Questions & Comments

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