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2011 California Statewide Critical Peak Pricing Evaluation Josh Bode, M.P.P. Sam Holmberg DRMEC Load Impact Workshop San Francisco, CA July 25-26, 2012. Presentation overview. Introduction and comparison of rates Evaluation methodology and validation Ex post results Ex ante results

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  1. 2011 California Statewide Critical Peak PricingEvaluationJosh Bode, M.P.P.Sam HolmbergDRMEC Load Impact WorkshopSan Francisco, CAJuly 25-26, 2012

  2. Presentation overview Introduction and comparison of rates Evaluation methodology and validation Ex post results Ex ante results Recommendations

  3. California is the only state with default Critical Peak Pricing (CPP) rates CPP is a rate in which the utility charges a higher price for consumption of electricity during peak hours on critical days in exchange for a reduction in non-peak energy charges, demand charges or both. Approximately 15,000 customers have been defaulted onto CPP and about 6,000 remain on the rate. Most of them are large customers (over 200 kW of max demand). All large customers defaulted onto CPP from TOU rates that already provided them an incentive to reduce or shift consumption from peak hours

  4. Although conceptually similar, many rate & deployment details vary across the utilities

  5. CPP discounts were mainly applied through reductions to demand charges *The rates are for illustrative purposes only. There are multiple rates that vary based on customer size and service voltage. In additions, rates are updated frequently to reflect changes in electricity production costs.

  6. The impact of CPP tariffs depends on the rates in place before default CPP - SCE example • All customers were already on TOU tariffs • The utilities have on-peak demand charges that provide an incentive to shift away from peak periods – in the graph, these have been converted to effective kWh charges • The TOU rates already provide strong incentives to shift or reduce electricity use during peak periods over summer months • Much of what customers could easily shift to off-peak periods may have been shifted in response to strong TOU prices, leaving less load reduction potential for CPP Effect of on-peak demand charge credit

  7. Several tests were conducted to validate the analysis • Do we get similar results if we apply an altogether different analysis approach − matched control groups? • How well do the regressions predict out-of-sample on event-like days? • Do the regressions confound event conditions with other factors? The primary method was individual customer regressions. A detailed discussion of each of these validation checks is included in the report.

  8. Predictive accuracy during event-like conditions was reasonably high • The better the electricity use pattern during event conditions is explained, the less likely it is that other factors will be confounded with impacts • Steps in assessment • Identify event-like days • Exclude those days from regression models • Predict load for excluded event-like days “out-of-sample” • Assess how well predicted values match up with actual values

  9. Ex post impact estimates with different methods produce similar results • Control groups are a useful cross check but are less reliable for sub segments due to the inherent variation in C&I loads • Estimates for the control group were calculated using a diff-in-diff calculation *The difference is likely greater for SDG&E because it called only one weekday event in 2011. There is more random error for single events.

  10. Event days are different across the three utilities • PG&E called 9 events • SCE called 12 events • SDG&E called 2 events • Each utility calls event days based on their system conditions • SDG&E’s events last from 11 AM to 6 PM while PG&E and SCE’s last from 2 to 6 PM • System load patterns across utilities are not always coincident, particularly for Northern and Southern California • Comparisons in impacts between the utilities should be made with caution

  11. PG&E’s average load reduction was 5.9%, or 27.8 MW, across the 9 events in 2011

  12. PG&E’s demand reductions were concentrated in two industries • Manufacturing and Wholesale & Transport accounted for 34% of the load and 74% of impacts • While the Offices, Hotels, Finance, Services sector had the most load, 34%, it accounted for only 3% of program impacts

  13. Not surprisingly, the largest customers account for a large share of the demand reductions • Customers with average demand over 500 kW make up 6% of the accounts, 28% of the reference load, and 34% of the impacts

  14. SCE’s average load reduction was 5.7%, or 35.0 MW, across the 12 event days in 2011

  15. For SCE, two industry groups account for 93% of the demand reductions • Manufacturing accounts for roughly 27% of customers and load but provides 68% of the reductions • Wholesale and transport accounts for 17% of the customers and load but provides 26% of the reductions

  16. At SCE, larger customers not only have more load but they reduce a larger share • Customers with average demand over 500 kW make up 4% of the accounts, 19% of the reference load, and 38% of the impacts

  17. Customers that voluntarily enrolled in CPP prior to the default accounted for 56% of aggregate impacts • Overall, 325 of the roughly 3,006 participants (11%) volunteered onto CPP prior to its implementation as the default rate • These customers comprise approximately 14% of the reference load on the average event day

  18. SDG&E’s load reduction on the single weekday event equaled 5.2%, or 18.6 MW • The August 27 event was called on a Saturday and can’t be directly compared to the September 7 event or most of SDG&E’s CPP events from prior years • The temperature profiles during SDG&E’s summer 2011 events were both rather unusual with consistent high temperatures throughout the day, but not peaky hours • There is less precision and more variability for single event days

  19. SDG&E had more customers and load in the Offices sector • Offices made up 44% of the load at SDG&E (versus 18% and 23% at PG&E and SDG&E). They also reduced demand more. • Manufacturing and Wholesale & Transport still performed the best and accounted for a substantial share of impacts.

  20. At SDG&E, larger customers also accounted for a large amount of the demand reductions • Customers with average demand over 500 kW make up 7% of the accounts, 31% of the reference load, and 48% of the impacts

  21. Customers that were dually enrolled or volunteered onto CPP provided larger impacts • Dually-enrolled customers were less than 1% of the participants and 1.2% of the reference load but accounted for 10% of the program impacts • Customers that were volunteered onto CPP accounted for 5.9% of the accounts, 7.5% of the reference load, and 19.6% of the program demand reductions

  22. In comparison to 2010, 2011 PG&E and SCE demand reductions were larger • Overall program loads decreased for PG&E and SCE but the percent load reductions of remaining customers increased

  23. Ex ante impacts are based on historical data and performance Ex ante impacts reflect the load reduction capability under a standard set of weather conditions that align with those that drive system planning

  24. The uncertainty for ex ante impacts varies by customer size category • For large customers we know: • how many of these customers tried out default CPP; • how much load reduction they provide during events; • what type of customers are more responsive; • how many remained on CPP after 1st bill protection expired; and • persistence of impacts. • For medium customers, we have far less empirical data • A small number of medium customers were defaulted (most are close to large) • Customers right above the official large/medium cutoff are similar • For small customers, we lack empirical data of response to default CPP

  25. Key enrollment and ex ante assumptions Limited change in large customer enrollment and impacts PG&E and SDG&E customers at the med/large threshold are similar enough to medium customers that their results can be used to estimate medium customer retention and impacts Data on small customer retention and response to default CPP is inadequate – zero impacts are assumed By 2016, 11% of SDG&E medium participants adopt enabling technology to automate the price response and deliver larger price response Medium default CPP is scheduled to be implemented at PG&E, SCE and SDG&E starting in 2014

  26. Statewide, ex ante impacts are projected to grow with the scheduled introduction of default CPP for medium customers

  27. Recommendations Reduce the uncertainty associated with defaulting small and medium C&I customers onto CPP by staging the deployment Target “best practice” information about the steps they can take to reduce load during CPP event days both at the time they defaulted and at the start of each summer If the weather conditions allow for it, SDG&E should call more CPP events in 2012

  28. For any questions, feel free to contact Josh Bode, M.P.P. or Sam Holmberg Freeman, Sullivan & Co. 101 Montgomery Street 15th Floor, San Francisco, CA 94104 joshbode@fscgroup.com 415.777.0707

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