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Forecasting Workshop supporting the DPL IRP. Economics and Forecasting Group Pepco Holdings Inc. . September 15, 2009. Summary of Suggestions from Respondents to DPL’s DE IRP. Stakeholder Recommendations. Staff
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Forecasting Workshopsupporting theDPL IRP Economics and Forecasting Group Pepco Holdings Inc. September 15, 2009
Stakeholder Recommendations • Staff • Update load and EE forecast to account for the current economic downturn and to inform specific supply procurement needs. • DPA • DPL should develop an in-house long-term energy and demand requirements forecast for its various customer classes in-house and on an annual basis. • DPL should develop full documentation of the forecast expressly noting all assumptions and key inputs and providing the forecasted outputs. • DPL should conduct a more detailed analysis of key factors impacting energy and demand. As part of its forecasting process DPL should consider economic factors other than employment for forecasting energy consumption. In addition, DPL should project trends in customer migration and support forecasted migration rates as part of the assumptions to the forecast. • DPL should test whether using different weather data for the southern portions of the state (perhaps using Dover or Georgetown data) would better predict load for that portion of its customers.
Required Features of the Forecast • Both historical data and future estimates. • Both winter and summer peak demand for total Delmarva Delaware load and Delmarva Delaware SOS load by customer class. • Weather adjustments, including consideration of climate change potential. • Five (5) year historical loads, current year-end estimate and ten (10) year weather adjusted forecast showing individually and aggregated Delmarva Delaware and Delmarva Delaware SOS load, and both Delmarva Delaware and Delmarva Delaware SOS load disaggregated by customer classes, including both capacity (MW) and energy requirements (MWh). • Analyses of how existing and forecast Conservation, DR, DSM, Customer-Sited Generation, various economic and demographic factors, including the price of electricity, will affect the consumption of electric services, and how customer choice under Retail Competition of utility service may affect future loads. • Description of the process the Company used to develop these forecasts. Forecasts shall include the probability of occurrence. Within the forecasting modeling descriptions the Company shall demonstrate how well its model predicted past load data for the prior five (5) years.
DPL’s Forecasting Activities • Forecasts are updated on a specified schedule. • Each September-December the benchmark Budget and Planning Forecast is prepared and approved. • Any additional forecasting work must be consistent. • On-going monitoring for material changes. • To support DE IRP modeling, IRP forecasts must be available by January 1, 2010.
DPL Forecast Methodology • Forecasting at DPL must also address the needs of the 1.7 million customers that PHI serves in six jurisdictions in three states and the District of Columbia. • The forecasts we prepare must be carefully crafted to avoid real or apparent contradictions. • They must also meet a very high technical standard because of regulatory scrutiny (and the corporate need for a high quality forecast). • While we don’t have time to write long narratives as part of a routine, we do insure that our forecasting work is documented, archived, reproducible and auditable.
Forecast Consistency • With such a wide range of service areas, zones and planning needs, it is of the utmost importance that the forecast “hang together.” Forecast components must be “mutually confirming.” • Growth rates will compare reasonably between history and forecast, between jurisdictions, and against our experience. • Implied weather normalization factors, price elasticities, income elasticities, residential usage per customer and other metrics must compare reasonably across our jurisdictions, with what little literature exists on the subject, with the experience reported by other utilities and against our own experience elsewhere.
Checks and Balances • For the Delmarva zone we prepare independent forecasts of DPL Gross Retail Output, zonal Net Sendout (NSO), and zonal MW demand. • We expect that Billed Sales will: • Exhibit reasonable implied price and income elasticities. • Exhibit reasonable growth rates. • Produce reasonable usage per customer numbers. • The ratio of RES sales to total residential customers will be well behaved. • The ratio of residential to commercial customers will stay within a reasonable range. • We expect that sales, GRO and NSO will imply reasonable losses, consistent with our prior experience • Define and discuss GRO, NSO, NEL and MW. • We expect that NSO and MW demand will imply reasonable load factors, consistent with our prior experience. • As new information becomes available, it serves to either confirm our outlook or cause us to re-examine our forecast.
Factors to Consider In Forecasting • DPL considers many other factors besides employment and income. • As a rule of thumb, employment growth determines new customer formation (breadth of market), while real income per household and the real price of electricity determines customer usage (depth of market).
Other Types Of Data • It often seems reasonable to include many other kinds of data. • We look at everything – distribute Housing Survey. • Candidate data concepts must not contain large measurement errors. • Candidate data concepts must be forecastable, and someone has to actually forecast them. • Candidate data concepts must not contribute unduly to forecast risk.
Models Don’t Forecast … • The model doesn’t do the forecast, it’s the forecaster. • Good forecasters are always very aware of all of these different pieces of information, and consider them carefully. • Example – DE Residential Usage. • Discuss recent patterns of customer usage, referring back to usage slides (p. 9). • Discuss how difficult they are to explain. • Technology • Real income • Wealth (double whammy of corporate equities and home equities) • Price (compare effect to implied elasticities)
Is an Implied Price Elasticity of Approx. -0.12 Reasonable? Note: The “price” illustrated here is the total all-in price of electricity paid by the consumer, inclusive of all taxes and surcharges and the commodity cost of power.
The Choice of Weather Stations • The choice of weather stations and the choice of the correct weather metrics is one of the central issues in utility sales forecasting. • There has never been a time at DPL when this subject was not under review. • I have personally been involved in discussions of this subject at DPL since 1982. • In order for weather data to be useful in analysis it must have several characteristics. • It must go back a long way without interruptions. • The weather observations must be consistent over time. • Our current practice is to use Wilmington Airport. • In the early years, before statistical forecasting, Company engineers watched the weather at the Wallops Island, VA Naval Air Station very carefully, as a kind of early warning system. • As statistical forecasting came into vogue forecasters found it useful to use weather measured at Wallops Island and the Philadelphia Airport. • Beginning in 2005, I verified (by inspection) that the data from the Wilmington Airport offers more than 30 years of hourly history that appears to be of good quality.
Weather Scenarios • DPL re-estimates its weather normalization adjustments annually. • Discuss 2008 weather normalization study. • The DPL energy and demand forecasts are weather normalized. • Discuss weather normalization of summer and winter peak. • Climate change scenarios are controversial. • Because climate change occurs slowly over time, DPL has not prepared scenarios considering climate change potential, except for the 2008 DE IRP. • There is a problem of interpretation in climate change scenarios – i.e., the “forecast” becomes a bounding case. • Discuss the 2008 Extreme Weather Scenario (this is a banding scenario). • Discuss changes to this approach for our 2010 submission. • One possibility would be to construct a banding scenario using weather metrics that are +/- two standard deviations from normal.
Using Additional Weather Stations • WLM is the load center. • Our sales data is not readily available month-to-month on a sub-jurisdictional basis (e.g., by operating region) • A sub-jurisdictional forecast methodology will not make the jurisdictional forecast more accurate. • While we believe the current jurisdictional forecast is very accurate, we’re willing to try working with alternative weather stations. To do so, we would like for the group to: • Agree on a set of weather stations. • Agree on the experiment to be conducted. • Agree on how the results will be evaluated. • Conduct the analytical work between November 2009 and June 2010 (allowing us to schedule the tasks).
Winter and Summer Class Peaks • Load data by customer class is generally not metered. • It is available only from market settlements data. • The process is to take the class contribution to load at the time of the system peak and then allocate demand to classes according to those shares. • In the 2008 DE IRP filing, DPL prepared estimates of Delmarva DE SOS load by customer class using a sharing methodology. We will continue to prepare that study. • Discuss the 2008 filing.
Customer Migration Rates • DPL believes our projected trends in migration rates are reasonable and defensible. • As will be shown, migration rates are a special, difficult, case in forecasting. • The current forecast assumes the migration rate will equal the observed rate in a month just prior to the preparation of the current budget. • The current assumption in the DE IRP is that migration rates equal their June 2008 level. • The subject is important and under review. A scheduled review of the customer migration forecast will begin during the second half of September.
It All Seems Horizontal • Customer migration rates by rate class are extremely volatile (month-to-month, even). • There are very few cases where there are trends in the data. • The data are volatile (up and down). • The lines are more or less horizontal – it’s hard to argue that these series are trending up or down. • With the variability in the data, it would even be hard to complete a less naïve judgmental forecast.
What a Migration Model Would Look Like • It is a daunting task to construct a forecast model for migration rates. • Since the migration rates are proportions, we would prefer to specify it as a logit model. • Since electricity is a homogeneous commodity, we think the principle explanatory variable would be the relative real price of electricity between DPL and a competitive supplier. • The only difference between the DPL price and the competitor price is the cost of the commodity (the contribution to total price by the wires business is the same). • We assume that our power procurement process provides results similar to performance of the power procurement specialists employed by competitive suppliers.
What a Migration Model Would Look Like • We’re not able to estimate the model in this thought exercise because we don’t have data on the cost of purchased power to the competitive supplier. We can not calculate the explanatory variable. • We assume that over the long run, the ratio of the two delivered prices will vary randomly around 1.0 (equally skillful procurement). • As long as the ratio of the two prices approximates 1.0 over the long run, consumers should be indifferent between the providers, and the migration rate should remain unchanged. • If the ratio is not centered on 1.0, the migration rate will quickly go to either 0.0% or 100%, so the observed migration rates validate the assumption.
Forecasts of Energy Efficiency • DPL considers the effects of conservation both directly and indirectly. • Changes in real prices lead directly to conservation. • Changes in real incomes can lead directly to “conservation.” • Conservation can occur for ethical reasons. • Conservation can occur because of programs – this kind of conservation is considered “below the line.” • As discussed above, economic and demographic factors, including the price of electricity, are carefully considered in the preparation of the forecast. • Employment tends to drive customer formation. • Real prices affect usage. • Real incomes per household affect usage.
Programmatic Goals As Forecasts • Programmatic goals are somewhat speculative. • PJM does not include unless traded as credits or operator controlled. • Future work of SEU will be clarifying. • How does one incorporate those programmatic goals into the forecast? • Some utilities are considering end-use modeling. • Remove from forecast below the line.
Forecast Accuracy • Sales variances are reviewed monthly. • Longer term forecast accuracy requires that you wait. • Discuss forecast accuracy measurement. • What is a reasonable standard for accuracy?
Documentation to Meet Needs • DPL agrees to prepare documentation as required. • DPL would like to take this opportunity to discuss our understanding of how this documentation should be structured. • Consistency with Recently Approved IRP Regulations. • Documentation can be the most expensive aspect of forecasting; we work to prepare the “right” amount of documentation.
