Review of Data & Modeling for Revenue Allocation

1. Review of Data & Modeling for Revenue Allocation 1st Workshop for Expected Parties to PG&E’s 2014 GRC Phase 2 Filing April 12th, 2012

2. Introductions and Overview • As part of the settlement, it was agreed that PG&E would hold two workshops for parties to its 2011 GRC Phase 2 application, prior to entering its 2014 filing Workshops’ Objectives • To agree on modeling requirements that will be used to develop positions for the 2014 GRC Phase 2 • To agree on methodologies and simplifications to increase the transparency of the 2014 Revenue Allocation and Rate Design model.

3. Workshop Agenda

4. Items for June workshop agenda • Rate Design component clean up of the RARD model • Customer Generation and its Impact on Diversity to the Distribution System • Provide copies of RARD model for party review

5. GRC Phase 2 Revenue Allocation & Rate Design (RARD) Model Enhancements Discussion topics: • Recap of model flow- flow diagram • Simplifying the revenue allocation model • Alternative allocation options for miscellaneous revenue and marginal costs • Class level capping mechanism • Alternate approaches to calculating the CARE discount

6. Revenue Allocation and Rate Design Model High-Level Data Flow Revenue Allocation Rate Design Residential-Non Care and CARE Agricultural Sm and Med Commercial Industrial Streetlights Standby Proposed Rates Proposed Rate Revenue Loads Marginal Costs Billing Determinants Present Rate Revenue Outputs Inputs Feedback loop required due to the iterative nature of the residential CARE rates calculation.

7. Simplifying the revenue allocation model PG&E has made significant progress to date toward simplifying the revenue allocation components of the RARD model for 2014 • Increasing uniformity of formats • Outputs from generation and distribution marginal cost files will match inputs from the RARD model • Distribution and Generation EPMC multipliers • Calculated on a separate tab in the revenue allocation file • Reduced number of separate files • Elimination of duplicate data

8. Simplifying the revenue allocation model • More work remains to be done • Consistent use of color coding will be applied • Further reduction in the number of separate files • Streetlighting files in particular • Further elimination of duplicate data

9. Reduced number of separate files Current Consolidated Loads_GRC Proposed Rate Revenue_GRC RD_RES_GRC Proposed Rate Revenue_GRC RD_RES_GRC MargCost_GRC MCRev_GRC RD_CARE Discs_GRC Summary_RARD_GRC MCRev_GRC RD_SM_GRC Res E6 2011 GRC Res E9 2011 GRC RD_RES_E6-E9_GRC RD_MED_GRC RD_CI_GRC Res E9 Off-Peak RD 2011 GRC RD_E19-E20_GRC RD_CARE_Discs_GRC Model also includes the following files which have not yet been consolidated: Model_Master.xls, RA_ Rev Alloc_GRC.xls, RD_AG_GRC.xls, RD_RES_GRC.xls, RD_STANDBY_GRC.XLS, Summary Prop Rates_GRC.xls, Streetlight files.

10. Alternative Allocation Options for Miscellaneous Revenue Revised Phase 2 model includes 7 pre-defined and 3-user defined allocators for miscellaneous distribution revenues* • Pre-defined allocators: • Equal percent distribution marginal cost revenue (default methodology) • Distribution system average percent (SAP) • Total revenue with DA/CCA imputed • Generation with DA/CCA imputed • Equal cents per kWh • 12 coincident peak • Top 100 hours • *Allocation methodologies may be selected from a drop-down box on the Dist_Alloctab of the Revenue Allocation file (RA_Rev Alloc_GRC.xls)

11. Current list of Miscellaneous Distribution Revenues from the 2012 AET

12. Alternative Allocation Options for Marginal Costs Two user-defined alternate marginal cost scenarios for each cost type* • Generation energy, generation capacity, distribution capacity and distribution customer marginal costs • Marginal distribution capacity cost includes NERA regression option • *If additional marginal cost methodologies are agreed upon in the marginal cost workshops, PG&E will incorporate those into the RARD model while retaining the user-defined options for each marginal cost type • **Marginal cost methodologies may be selected from a drop-down box on the Summary tab of the Marginal Cost Revenue file (MCRev_GRC.xls)

13. Class Level Capping Mechanism Principles underlying class level capping • In addition to a cap on revenue, there should be a floor • The cap/floor will be on the percentage change in class level bundled rate • Revenue shortfalls arising from capping will • be applied to distribution component of revenue, and • spread using the Distribution EPMC allocator • Before capping occurs, the currently applied "movement to marginal cost" adjustment will remain in effect

14. BREAK

15. Alternative Approaches to CARE Discount Calculation • Enhancements made • CARE discount and funding of CARE discount shown separately on Run Model tab of Model_Master.xls file • All components necessary to balance model consolidated on one page • Balancing of model now automated • Responsible for content: • Dan Pease • Nancy Breckenridge • Patricia Gideon • Hallam Stanton • Items currently for inclusion: • Workshop to focus on a review of PG&E and Edison’s approach. • Discussion to be invited as to the core issues and possible solutions. • Consideration to be given as to using Goal Seek within the PG&E model. • Pictorial representation of CARE rates calculation to be developed.

16. 2. Using Smart Meter (SM) Data in Class Load Research Discussion topics: • PG&E’s class load research studies. • PG&E’s current capabilities in making use of SM data in enhancing class load research samples - Load research software (LRS). • The impact of sample enhancement on the precision levels of the estimated load profiles – Example: Enhancing AGA class load research sample by including SM data.

17. PG&E’s class load research studies PG&E carries out annual class load studies that produce hourly load profiles by customer class at the system level (CLASSKW Report), and also hourly load profiles by customer class at the DPA (Distribution Planning Area) and Division level (AREALOAD Report). • The load profiles by customer class are estimated based on the profiles of the customers in the load research sample for that class. • For 2010 CLASSKW study, there were 5,955 customers in the load research sample, where the sample data are collected from MV90 meters. 2010 CLASSKW study included load research samples that converted from MV90 meters to Smart Meters.

18. PG&E’s class load research studies Example CLASSKW Profile – Average Residential, July 2009

19. PG&E’s class load research studies Example AREALOAD Profile – DPA E1, July 2009

20. PG&E’s class load research studies The load profiles are estimated from a random sample of customers where the sample size is determined to ensure desired precision levels. After a certain point, increasing the sample size does not significantly improve the precision levels.

21. PG&E’s current capabilities in making use of SM data in enhancing class load research samples. PG&E has started making use of SM data in enhancing load research samples in estimating class load profiles within the current systems. PG&E is also using a Load Research System (LRS) software for effective sampling and load profile estimation in carrying out future class load studies . • Some of the benefits of SM data and LRS software in carrying out effective class load studies are: • LRS software allows us to quickly assess the sample precision levels, calculate how many samples are needed to achieve desired precision levels, select customers to the sample, and estimate the load profiles. • The availability of SM data enables us to effectively select samples eliminating the dependency to the process of MV90 meter installation • LRS software allows flexibility in producing load profiles by desired stratification.

22. Load Research System (LRS) Software The Load Research Life Cycle Preliminary Plan Sample Design Analysis Data Collection • LRS is a comprehensive SAS based software system for sample design and analysis in load research and program evaluation. • LRS is designed around the load research life cycle. The system integrates preliminary project planning, detailed sample design and analysis. • Visualize IT, a companion to LRS, a graphical tool, enables quick validation of load data, examination of the the load profiles from LRS, presenting results in graphical and tabular forms.

23. Impact of sample enhancement on the precision levels of the estimated load profiles For 2011 class load studies, PG&E is using the Load Research System (LRS) software to review the current sampling designs, to assess the precision levels with current load research samples, and to enhance the sample sizes with SM data to achieve desired precision levels at the strata cell and class levels. Example: AGA Sample Enhancement for Class Load Studies

24. Impact of sample enhancement on the precision levels of the estimated load profiles Example: AGA Sample Enhancement for Class Load Studies • There are currently 247 available MV90 samples with an expected relative precision of ±17% at the class level. • Enhancing the AGA class load research sample with SM data improves the current sample size from 247 to 1,996 resulting in an expected relative precision of ± 6% at the class level. • In the future, with the availability of more SM data, our desire is to further enhance the AGA class sample size to 5,700 as determined by the LRS software to achieve the desired precision levels at strata cell level.

25. Impact of sample enhancement on the precision levels of the estimated load profiles Example: Sample Enhancement for AG5A and AG1A

26. Using Smart Meter (SM) Data in Class Load Research In Summary: • For 2011 class load studies, PG&E is reviewing the current sampling designs by customer classes, and enhancing the load research samples with the SM data to achieve the desired precision levels in estimating class load profiles within the current systems. • PG&E is also using a Load Research System (LRS) software for effective sampling and load profile estimation in carrying out future class load studies.

27. Wrap up • Set next workshop date • Next steps • Other?