1. 1 RETI 2.0 Plenary Group Meeting 3/18/2016 DRAFT 2016 RPS Portfolios

2. What is the RPS Calculator? An Excel-based forecasting and planning tool Forecasts plausible types, general locations, and amounts of renewable resources that will be procured to meet RPS goals Resource/Tx Selection Meet Yearly RPS Goal Select Using Value Dynamic Valuation Based on Portfolio Future Porfolio of Resources Type General Location Capacity & Energy Cost MODEL LOGICOUTPUT Load Forecast Existing Resources INPUTS Resource Cost & Potential Tx Capability & Costs 2

3. RPS Calculator Outputs Used as Inputs in Variety of Planning Processes Examples: RPS Calculator Portfolios LTTP: Procurement Planning CAISO TPP: Transmission Planning CARB/CEC: GHG Planning WECC: Transmission Planning IOUs: Bid Evaluation * *RPS Calc outputs inform TPP and TPP outputs are used by IOUs in their own modeling work 3

4. Dynamic Valuation is a Key Feature of RPS Calculator 6.0+ Cost & value of renewable generation changes over time due to: – Technological innovation – Financing and tax policies – Portfolio saturation effects – Avoided costs of energy & capacity Unlike prior versions of the RPS Calculator, v.6.0+ considers how each of these factors will impact the supply curve of renewables over time 4 Charts are generic and are shown for illustrative purposes 0%10%20%30%40%50% RPS Penetration (%) Capacity Value 0%10%20%30%40%50% RPS Penetration (%) Energy Value 0%10%20%30%40%50% RPS Penetration (%) Curtailment Cost

5. CPUC Energy Division Recently Released Draft 2016 RPS Portfolios for LTPP/TPP Six portfolios were specified in “Assumptions and Scenarios” Document issued in CPUC’s draft Long Term Procurement Plan Proceeding Additional portfolios were studied as “sensitivities” to provide transparency on how different assumptions influence portfolio outcomes Background information available on RPS Calculator home page under “2016 RPS Portfolios” http://www.cpuc.ca.gov/RPS_Calculator/ 5

6. Sensitivity Analysis: Case Descriptions Land Use: – “Environmental Baseline” assumption in RPS Calculator reflects no new generation land classified as RETI Category 1 or Category 2 – “DRECP/SJVP” assumption reflects increased procurement in specific areas (DFAs in DRECP, least-conflict in SJVP) In-State Wind: – Forces in 1,300 MW of highest-quality in-state wind Geothermal: – Reduces geothermal cost assumption and forces in 1,700 MW of geothermal resources High BEV: – Increases load and shifts load shape to account for charging (consistent with PATHWAYS project) Exports: – Allows 5,000 MW of energy to exported out of CAISO 6

7. Quantifying Sensitivity Impacts All portfolios were developed for the year 2030 (including default) ‒More divergence between portfolios occurs by 2030 than by 2026 ‒Allows clearer examination of difference between sensitivity and default Impact on each of five key portfolio metrics was calculated Total new generic generation (MW) Total transmission network upgrades (DNU) (MW) PV Ratio (PVR) (PV GWh as % of total renewable GWh)* Curtailment (% of RPS generation) Revenue Requirement ($) Average Rate (¢/kWh) 7 *PV GWh = RPS-eligible PV energy + behind the meter (BTM) PV energy; total renewable energy (RN) = all RPS-eligible energy + BTM rooftop PV energy

8. 2016 RPS Portfolio Sensitivities Comparison (Total Generation in Year 2030) 8

9. 2016 RPS Portfolio Sensitivities Results (Year 2030) Sensitivity Total Generic Buildout (MW) Total DNU (MW) PV Ratio (PV GWh/ RN GWh) Curtailmt (% RPS energy) Rev Reqmt ($MM) Avg Rate (¢/kWh) Default 5,4953,2600.497.9%37,53030.8 Env Baseline 5,6894,0000.508.9%37,68630.9 DRECP/SJVP 5,5804,5000.498.2%37,74531.0 In-State Wind 4,3481,5000.476.1%37,469 * 30.6 * Geotherm. 2 2,7857,5000.405.4%37,632 *† 30.7 *† High BEV 6,9524,2600.525.9%37,69328.7 Exports 3,5211,5000.480.5%37,40230.7 Storage 4,1512,5000.482.6%38,78831.8 *costs are not comparable with other portfolios because of forced-in resources †without assuming lower geothermal capital costs, rev. req. is $37,632 MM, avg. rate is 30.9¢/kWh 9

10. Conclusions from Individual Sensitivities Land Use: – More restrictive land use assumptions may increase curtailment by eliminating high quality in-state wind In-State Wind: – In-state wind connecting as energy-only resources may reduce overall portfolio costs if prioritized for available transmission capacity Geothermal: – Assuming a significantly lower geothermal costs, including in the Salton Sea area, reduces the amount of PV on the system by 2026 Electric Vehicles: – Battery electric vehicle adoption tends to increase solar PV selection and reduce curtailment Exports: – Exports can greatly reduce solar PV curtailment Storage: – Storage can greatly reduce solar PV curtailment, but at a higher cost than exports 10

11. Summary of Assumptions that Have Large Influence Within Tested Ranges 11 Sensitivity Total Generic Buildout (MW) Total DNU (MW) PV Ratio (PV GWh/ RN GWh) Curtailmt (% RPS energy) Rev Reqmt ($MM) Avg Rate (¢/kWh) Load +++ --- BTM PV +++++++++ Energy-Only -- --- OOS Wind +------- In-State Wind ---- Geothermal -++----- Env Baseline ++++ DRECP/SJVP +++ BEV Vehicles ++++- --- Exports ------- Storage ----+++++

12. Over Time, Declining Capacity Value and Increasing Curtailment Favor Wind 12