Estimation of Total of Non-Coincident Peak Demands CPUC NEM Cap Calculation Workshop Joint IOU Presentation June 25, 2012 Cyrus Sorooshian, SCE Josh Mondragon, SDG&E Zeynep Yücel, PG&E

Overview – Non-coincident Peak Demand For a given year, the total of non-coincident peak demands for all customers in each IOU’s service territory is defined as the sum of each customer’s maximum demand in that year. For each IOU, the value represents the maximum demand for the service territory that would occur if all customers use their maximum load at the same time. The total non-coincident peak demand value is an estimated value calculated in each IOU’s annual class load research studies where the calculations are based on load research samples for the rate classes which are not 100% metered.

Overview – Class Load Research Studies Class load research studies produce estimated load profiles by customer class that inform energy usage behavior of each IOU’s customers. These studies support a wide variety of applications such as revenue allocation and rate design for General Rate Case (GRC), CEC Load Data Delivery, and various other load research related data analyses. Standard statistical sampling techniques are used to design and select representative load research samples. Load research meters are installed to collect interval data from the samples. Class load research studies are designed to statistically estimate class load profiles based on the sample profiles of the customers that are in the load research samples.

Total of Non-Coincident Peak Demands in NEM Cap Calculation – IOU Approach In the NEM Cap calculation formula, for the total non-coincident peak value, the IOU’s approach is to use the value calculated in the annual class load studies. Even with AMI data availability, the class load studies will be based on samples, and load profiles will be estimated from those load research samples. The availability of more interval data will help enhance the sampling design and sample size to ensure that the estimated load profiles have the desired levels of precision. After a certain point, adding more samples does not significantly improve the precision levels.

Data - SCE We used 15-minute load data from approximately 29,000 customers representing all customer classes. 78.5% of the total of non-coincident customer peaks is estimated from samples (rate groups < 200kW). 21.1% represents accounts with individual load data (> 200 kW). 0.4% of the total is modeled (un-metered street lighting and traffic control). The most recent three years for which data are available are used in this computation. Three year averaging serves to account for weather and economic variability. Current value provided is 44,775 MW using the three year average of 2008-2010.

Methodology - SCE The total of non-coincident peak demands is a product of SCE’s annual rate group load studies1. This statistic is available by month, season, and Time of Use periods, in either 15-minute or 30-minute values. To estimate the total of annual non-coincident peak demands for the sampled rate groups: Calculate the annual peak of each account in the sample. Calculate each stratum mean. Calculate the weighted average of the stratum means. The total of annual non-coincident peak demands is the product of the number of customers and the weighted average. Balance sampled rate groups load profiles and total of annual non-coincident peak demands to recorded kWh sales. 1 http://www.sce.com/AboutSCE/Regulatory/loadprofiles/methodology.htm

Comparison of Load Research Estimate and Actual Billing Data – SCE Example To gauge how close our estimate of the total of non-coincident customer peak was to the value that would be computed from Smart Meter data; We examined the data from one of the sample groups whose demand meter records maximum demand values over a billing period. (i.e. GS-2 rate group which is for 20-200 kW commercial/industrial customers). The total of non-coincident customer peaks estimated from the load research data and the value calculated from the billing data varied by 0.2-0.8% depending on the year. However, after adjusting for the variations in the number of customers from the two data sources by expanding the average value per customer to an identical number of customers, the differences varied from 0.1-0.3%.

Data – SDG&E Source Result Non-Coincident Demands are products of SDG&E’s Annual Load Studies. Derived for 17 Commercial and Residential rate classes. DA and Bundled customers. 15-minute IDR data read through MV90 system. Combination of actual, estimated and calculated demands: Result 2007 2008 2009 3-Year Average 12,209 MW 12,124 MW 11,965 MW 12,099 MW Actual 11% Estimated 89% Calculated .23%

Methodology – SDG&E Actual Estimated Calculated C&I Customers GT 500 kW Stacked Highest Annual Demand Actual Representative Sample Stratified and Weighted Sample to Population Annual Consumption Ratio Applied Estimated Lighting Annual Energy divided by operating hours Flat Load Calculated

Data – PG&E Stratified random sampling design is used to select the load research samples that would produce class load estimates that have 10% precision at the hour of system peak with 90% confidence. Load research meters collect 15-min interval data. The load research studies system uses data with 30-min intervals collected over the year to estimate the half hourly load profiles by customer class. Estimated annual total non-coincident peak load from the class load research studies for the last three years are:

Methodology – PG&E Calculate the Class Diversified Peak Load (DPL) for each class. Class DPL is the maximum load for the class for the year. Calculate the Class Coincidence Factor (CF) for each class. The class CF provides insight into how that customer class spreads its peak over a period of time. A CF is always between 0 and 1. A value of 1 means that customers within the class peak at the same time. CF Calculation: Class CF = Max Average Class Load / Sum of Weighted Average Strata Peak Load Weighted Average Strata Peak Load = Average Strata Peak Load * (Strata Pop / Class Pop) Average Strata Peak Load = Sum of Sample Peak Loads in Strata / Number of Samples in Strata Calculate the class Non-Coincident Peak Load (NCPL) Class NCPL = Class DPL / Class CF Sum up all class NCPL’s to get the Total NCPL.