1. California Energy Commission www.energy.ca.gov Draft Rate Scenario Model Methodology DAWG Forecasting Subgroup Sept. 11, 2014 Lynn Marshall Supply Analysis Office Assessments Division Lynn.marshall@energy.ca.gov / 916-654-4767

2. Why we are here CEC staff is developing a model for developing retail electric rate scenarios First application will be to support 2015 IEPR demand/supply analysis cycle Have not had internal tool where we could understand what’s in the rate or driving the rate. DAWG comments requested on o Proposed model structure o Methodology incorporating price effects o Interactions with other proceedings? o Data sources we should use? California Energy Commission www.energy.ca.gov

3. Background on Ratemaking Revenue Requirement = Rate Base*Rate of Return + Operating & program costs + depreciation expense +utility incentives + taxes Rate Base = Capital Stock– Accumulated Depreciation+ working cash +inventory Operating costs include generation, transmission, distribution o ERRA Procurement and program costs are “passed through” o GRC costs are embedded and not trued up. Rate of Return set by CPUC – varies with economic conditions Class Average Rate = Class Cost Allocation Share* Revenue Requirement / Sales Cost allocation shares based on marginal cost of serving the class for various elements of cost of service (energy, capacity, distribution, customer service, transmission) Marginal cost varies by TOU period California Energy Commission www.energy.ca.gov

4. High Level Perspective on IOU Revenue Requirements Example using PG&E 2015 Annual Electric True-Up and GRC Decision Tables California Energy Commission www.energy.ca.gov Source: PG&E Advice 4484-E and 2014 CPUC General Rate Case D.1408032 Decision Tables Revision 2, Appendix C.

5. Staff criteria: what do we want in a model? The ability to assess interactive effects of energy usage, operating costs, cost allocation, rate design, and revenue requirements. The ability to model California market characteristics, policies, plans and scenarios (LTTP, RPS, TPP, AB 32, RA) Transparency: o Model individual components of rates to allow easier evaluation. o POU v. IOU; bundled v. ESP rates. Flexibility to adapt to evolving rate or market design and allow stochastic analyses Leverage analysis from, and interface efficiently with, other state and CEC tools and processes including the LTPP, RPS calculator, TPP, Cost of Generation model, PLEXOS, and NamGas. Generation by technology type Some costs and rates by TOU/LOLP time periods California Energy Commission www.energy.ca.gov

6. CEC Demand Forecast Modeling Interactions o Support and be consistent with the state demand forecast development. Needed output:  annual average rates by sector and utility forecast area, so need to translate from classes to sectors.  IOU rates need to be compiled with public utility rates.  support distributed resource and transportation demand analysis. o Incorporate future analysis on load shape trends. o Account for effects of TOU rates. o Model common econ/demo and other scenario assumptions California Energy Commission www.energy.ca.gov

7. Modeling Approach California Energy Commission www.energy.ca.gov Dynamic, simultaneous equation simulation o Annual time step, with TOU detail for some elements. Structural equations define sales, capital additions, operating costs and other elements of revenue requirement solved using the SAS Proc Model procedure. o Allows estimation, simulation, and forecasting of nonlinear simultaneous equation models. o Variance structure from internal or externally estimated variables can be incorporated for stochastic analysis. Treat major long run investments (LTPP, TPP) as fixed per planning decisions, and load-related short run operational costs (such as energy purchases, portions of distribution operations) as endogenous Post process to translate to real, sector rates, NEM 2.0, and iterate with self gen model

8. High Level Equation Summary California Energy Commission www.energy.ca.gov

9. Incorporating CEC Econometric models California Energy Commission www.energy.ca.gov Demand Office estimates econometric models for residential, commercial, industrial, resource, and TCU as a function of average annual rates and economic and demographic drivers. Incorporate parameter estimates, including price elasticity, and calibrate to recorded utility distribution area usage. Econometric models are estimated statewide by sector; need to adapt to utility area and class; account for direct access How to capture residential TOU rate effects on sales and usage?

10. Residential Usage and TOU Rates Apply constant elasticity of substitution (CES) approach (as in statewide pricing pilot) Disaggregate the annual forecast to summer and winter Decompose seasonal usage into TOU periods and apply substitution elasticity The substitution equation models the ratio of peak to off-peak quantities as a function of the ratio of peak to off-peak prices and other factors. The seasonal energy usage modeled as a function of average price and other factors. But assumptions and effects depend heavily on program design and effectiveness. External analysis on TOU design could be incorporated Other considerations Fixed charges could lower marginal rates Marginal cost-based revenue allocations could change with o Load shapes o Hourly Market prices, technology mix California Energy Commission www.energy.ca.gov

11. Residential Usage and TOU Rates California Energy Commission www.energy.ca.gov

12. ERRA Procurement Costs Existing contracts: Use CEC Plexos output to estimate output trends of conventional resources by resource or resource type; reconcile with utilty-level historic data and IEPR supply forms Current Renewable: Combine RPS procurement report data on forecasted energy production and costs by generation technology with LTPP RPS scenarios to fill net short. Forecasting : Estimate new authorizations – LTPP scenarios; COG costs Forecast future renewables costs using CEC cost of generation model Residual market and capacity purchases: Use standard GRC/Avoided Costs methodologies for pricing market energy and capacity purchases; CEC COG, NamGas, Plexos inputs Track “New Gen/Local Gen” costs for cost allocation to direct access Fuel costs: CEC NamGas forecast GHG costs: CEC GHG forecast o indirect cost impact on market prices o Direct costs from natural gas generation, CHP, imports California Energy Commission www.energy.ca.gov

13. ERRA Procurement Costs California Energy Commission www.energy.ca.gov

14. Distribution Costs Major drivers of distribution capital and operation costs: Customer growth - housing starts, floorspace Design demand – noncoincident peak Interconnections Ongoing reliability upgrades & maintenance –forecast in line with historic trends But… Expanded Distributed Energy Resources? (DER) Forthcoming work to develop distribution cost scenarios: CEC pilot study on cost-effective strategies for integrating DER CEC tech support to develop base scenario assumptions and methods CPUC proceeding on IOU Distribution Resource Plan Proposals in 2015 Other resources or suggestions for modeling? California Energy Commission www.energy.ca.gov

15. FERC Transmission Major projects known well in advance from CAISO TPP Renewables integration Reliability additions are ongoing CAISO publishes TAC area charge forecast model with forecast of projects in current transmission plan and total revenue requirements The model makes some simplified escalation assumptions Low voltage planned and in progress IOU transmission projects are reported in each PTO’s five year forecast on FERC Form 730. CEC will undertake further analysis on capital additions, costs and scenario assumptions as part of tech support work. California Energy Commission www.energy.ca.gov

16. Generation GRC Generation O&M Utility Owned Resources (SCE peakers, PG&E Hydro, solar) Energy production based on common scenarios and CEC Plexos output (shape) Estimate costs based on forecasted profile and cost trends. Generation Capital Expenditures Assume no new IOU-owned generation unless specifically authorized Upgrades, maintenance on existing resources continue in line with historic trends California Energy Commission www.energy.ca.gov

17. Next Steps Further comments and suggestions requested to Lynn Demand Forecast “input” workshop in February with preliminary results. 2015 IEPR scenarios will incorporate current econometric analysis, common scenario assumptions, CPUC decisions, data from utility supply and demand forms for 2015 IEPR Draft rate scenarios in April 2015 California Energy Commission www.energy.ca.gov