Presentation on theme: "Defaulting Customers onto CPP – Lessons from Actual Experience, 2009 Defaulting Customers onto CPP – Lessons from Actual Experience Josh Bode, FSC July."— Presentation transcript:
Defaulting Customers onto CPP – Lessons from Actual Experience, 2009 Defaulting Customers onto CPP – Lessons from Actual Experience Josh Bode, FSC July 14, 2009
Page 1 Key Questions We Will Address Why does default dynamic pricing matter from a national perspective? How did SDG&E transfer customers onto default dynamic pricing? Did customer actively decide or were their decisions passive? How do customer decisions regarding opt-out CPP vary? Do customers who win or lose without changing their behavior make different decisions? How did customers respond to demand reservation options? Can account representatives influence customer decisions?
Page 2 Why do default or opt-out dynamic rates matter? As the FERC National Assessment of DR potential shows, the enrollment approach used for dynamic pricing fundamentally affects the potential for load reduction Under opt-in pricing, the national DR potential is 9 percent of peak demand Under opt-out (not mandatory) dynamic pricing, the DR potential is as much as 14 percent of peak Under mandatory dynamic pricing, the DR potential is as much as 20 percent of peak California is making TOU/CPP the default rate for C&I customer and many states are deciding whether or not to do the same Importantly, default dynamic pricing allows customers to choose and can provide them the opportunity to test new rates if 1st year bill protection is offered The TOU/CPP rates adopted reflect the not only wholesale market costs, but the value of capacity - $1.06/kwh or $1060/MWh The SDG&E results provide data based on customers actual choices The analysis and subsequent tools developed allows utilities to estimate 1st year participation under opt-out pricing based on utility specific rates, customer mix, and load shapes
Page 3 How did SDG&E transfer customers onto default dynamic pricing? Bill protection was offered for the first year – customers could test the rate without any risk By default, customers who did not opt out had 50% of their summer maximum demand insured, but could adjust the value up or down, if desired Customers were given 45 days from the default date to opt out (Customers default dates rate varied depending on their bill cycle) In addition, SDG&E made a CPP Analysis Tool available to customers who registered online that allowed them to assess bill impact under a variety of user defined scenarios Customers were told to expect an average of 9 events per year with a maximum of 18 events during a season The event period is from 11 to 6 p.m. and can only occur Monday trough Saturday during summer months (May 1st–Sept. 30th)SDG&E filed to allow events to be called year round
Page 4 All customers with remotely read 15 minute interval meters whose electric demand exceeded 20 kW, with a few exceptions Approximately 400 Customers below 200 kW were defaulted onto CPP in 2008, allowing estimation of the response by medium (20-200 kW) customers Exceptions Direct access customers Participants in the several, but not all, existing DR programs Who Was Defaulted Onto CPP? Size Category Number of Customers% Average of Max Summer On- Peak Demand 100 kW or below1759.9%48.2 100 to 200 kW22512.7%157.3 200 to 500 kW87149.3%315.3 500 kW and above38621.8%1,003.3 Unclassified1106.2%~400.0 Total1,767100.0%399.4
Page 5 Did customer actively decide or were their decisions passive? At minimum, 64 percent of customers made active decisions Of the customers that accepted the default TOU/CPP tariff and insurance levels, some unknown share of them made an active decision About 75 percent of the smallest and the biggest of customers made active decisions
Page 6 Most Customers Remained on Default CPP Except for hotels, acceptance rates exceeded 70% across industries Acceptance rates vary depending on expected structural wins, industry, the share of the annual consumption that occurs during CPP hours, and direct access to billing analysis tools
Page 7 The Higher the Structural Wins, the Higher the Likelihood of Customers Remaining on Default CPP Structural wins vary by industry because of differences in the load shapes The share of total consumption that occurs during high price CPP or TOU hours is closely related to structural wins without insurance Models that relied on the share of consumption during CPP or TOU hours (heuristics) were stronger predictors than those based strictly on structural wins and losses
Page 8 Approximately 50% of remaining customers accepted the default insurance levels Almost all customers who declined the default capacity reservation value chose no capacity reservation – they preferred to face the risk rather than pay for the insurance The acceptance pattern across industries is different for default capacity reservation than it is for default CPP
Page 9 Opt Out Decisions Varied Across Account Reps Some, but not all, of the variation is due to differences in account rep assignments Account reps influence the decision even after controlling for industry, size, and customer load shapes Includes account reps with more than 20 assigned accounts. Most have over 100 assigned accounts.
Page 10 Conclusions The regression models developed can help customize estimates of default CPP acceptance for utilities with different tariff designs, business mix, and load shapes The models cannot cannot, however, control for differences in the process employed in defaulting customers onto dynamic prices, nor do they predict customer decision after they have tried dynamic pricing So far, we have learned that A substantial share of customer actively engage in the decision of whether to accept default dynamic rates and demand insurance levels The majority of them choose to remain on default CPP A large proportion of customers preferred to face the risk rather than pay for insurance that reduces bill volatility Accounts representatives can influence customer decisions - but unless a clear recommendation or approach is adopted, the influence may not be uniform Much more will be learned over the next several years concerning customer decisions associated with fundamental shifts in pricing strategy, including:
Page 11 For questions, feel free to contact Josh Bode, M.P.P. Freeman, Sullivan & Co. 101 Montgomery Street 15 th Floor, San Francisco, CA 94104 firstname.lastname@example.org 415.777.0707
Page 12 Regression results and key drivers of customer decisions How And Why Choice Regression Models are Useful Graphs and tables do not disentangle the relationships between potential drivers/predictors Regressions disentangle those relationships and assign weights to the factors affecting the decision Regressions can help guide policy decisions, particularly about expected enrollment and where to focus efforts in transitioning customers What is the effect of structural wins and losses on decision to stay or opt out? What share of customers will accept default CPP and provide load reduction potential? How much do customers value insurance against the price spikes in CPP? Regressions can customize estimates of opt out rates in other jurisdictions, with some limitations Regressions can be used by SDG&E to predict likely opt out rates for new customers defaulted onto the rate Key factors in assessing and shaping the model: Does it make sense? Are the estimates unbiased? Do the results change substantially if we add or exclude other potential drivers? How does it perform when compared with the actual decisions?
Page 13 Summary of Regression Analysis Conducted Regression for Share of CPP Period Consumption Consumption by TOU block % Structural wins or losses Acceptance of Default CPP Based on heuristic decision-making % of kWh subject to CPP charges is highly correlated with structural wins/losses Can draw conclusions w/o full bill analysis Designed to assess influence of account reps after controlling for other factors Allows calculation of opt out and capacity reservation decisions based on TOU billing data – does not require manipulating interval data Sacrifices some precision and accuracy for usability Predicts opt out and CRC decision as a function of wins/losses with CPP, both with and without a capacity reservation charge % of kWh subject to CPP dominates the structural win/loss variables when included Acceptance of default Capacity Reservation (Given acceptance of default CPP)
Page 14 Regression results and key drivers of customer decisions Key Drivers of Default CPP Acceptance Key Drivers of Default CPP Acceptance Relative Structural Wins: For each 1% increase in relative structural wins, the probability of accepting default CPP changes by approximately 0.02 ( 3%) Industry Type: Hotels are more likely to reject default CPP and wholesale and transportation are more likely to accept it Ease of Access to Billing Analysis Tools: Providing direct access to tools decreases the probability of acceptance by approximately 0.056 (7.5%) NOTE: For choice models, impacts are not linear Factors that Did Not Influence Acceptance Customer Size: There were differences, but they were explained by other factors (e.g. load shape) Load Factor: There were differences, but they are explained by other factors The load factor does not necessarily reflect the coincidence of high customer load with high system load Climate Zone: There were no statistically significant differences based on SDG&E climate zone There is also little variation in SDG&E and much of the climate zone differences are captured in the structural wins or loses.
Page 15 Key Drivers of Default Capacity Reservation Level Acceptance (Given CPP Acceptance) Key Drivers of Acceptance Ease of access to billing analysis tools and structural wins w/o CR % of CPP hours where demand exceeds the default capacity reservation Volatility of load during CPP-like periods Customer size Industry type NOTE: For choice models, impacts are not linear Factors that Did Not Influence Acceptance Peak to off-peak average demand ratio: Most of the effect is already captured by structural wins Load Factor: Again, there are differences, but they are explained by other factors Climate Zone: The conclusion applies to SDG&E, but may not apply to IOUs because of SDG&Es limited variation in weather However, weather is related to load shapes and structural wins.
Page 16 Overview of rate and deployment process Comparison of CPP-D, Opt Out, and Pre-default Tariffs An on-peak demand charge made the opt out tariff less appealing Pre-default T&D charges were the same for CPP-D and the opt out tariff An on-peak demand charge made the opt out tariff less appealing T&D charges were the same for CPP- D and the opt out tariff