BEHIND THE METER SOLAR IN VERMONT

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1 15TH ANNUAL ENERGY FORECASTING MEETING / EFG CHICAGO, IL APRIL 26-28, 2017 BEHIND THE METER SOLAR IN VERMONT RICH SIMONS, ITRON, INC.

2 WHAT IS BEHIND-THE-METER SOLAR?» BTM is roof-top solar generation» In many cases, utilities do not have good granular data on BTM» They may or may not have: Total capacity Observed generation Orientation of the panels - Direction - Angle

3 WHY IS BTM A CHALLENGE?» Measured load and consumption of electricity are no longer the same The meter measures load flows The use of electricity is obscured by BTM» The relationship between temperature and load is now more complicated. Utilities and ISOs perceive lower demands on cloudless days and higher demands on cloudy days.» Loads may ramp-up steeply in the morning AND in the late afternoon» Loads may be volatile as clouds roll-in and roll-out of the region» There is often insufficient data

4 GREEN MOUNTAIN POWER» The principal utility in Vermont, accounting for about 75% of state demand and energy» Roughly 100 MW of installed BTM capacity» Peak demand is about 700 MW» Like most utilities in the northeast, GMP has historically been a summer-peaking utility. In recent years, GMP has peaked in the winter.

5 MWh DAILY ENERGY Weekday Saturday Sunday Holiday Average Temperature

6 MWh DAILY ENERGY 7/18/2013 8/16/2016 DT Cloud Cover (%) Temperature (F) Energy (MWh) Thursday 7/18/2013 Friday 8/16/ % 79 15,745 26% 79 14,450 Average Temperature

7 Index (HR1 = 1.0) MW COMPARISON OF DAYS The entire shape is shifted downward Indexing the data reveals the flattening of the midday curve. Hour

8 MWh per Day PEAKS AND MINS HAVE DECLINED OVER TIME ~10,000 MWh ~10,000 MWh ~9,500 MWh ~10,000 MWh ~10,000 MWh ~9,500 MWh ~10,000 MWh ~10,000 MWh ~9,000 MWh Average Daily Temperature

9 MW CHANGING SHAPES» The typical load shape has shifted markedly» The much-ballyhooed Duck Curve or Batman Curve Before wide-spread solar adoption After/during widespread solar adoption Hour

10 CHANGING SHAPES OVER TIME: JANUARY AND FEBRUARY January Weekdays January Weekends February Weekdays February Weekends Load shifts are not obvious in January and February.

11 MARCH AND APRIL March Weekdays March Weekends April Weekdays April Weekends Load shifts become more obvious in March and April.

12 MAY AND JUNE May Weekdays May Weekends June Weekdays June Weekends Load shifts are somewhat apparent in May and June.

13 JULY AND AUGUST July Weekdays July Weekends August Weekdays August Weekends

14 SEPTEMBER AND OCTOBER 2015 September Weekdays September Weekends 2016 October Weekdays October Weekends

15 NOVEMBER AND DECEMBER November Weekdays November Weekends December Weekdays December Weekends

16 WHY IS THE EFFECT MOST DRAMATIC IN THE SHOULDER MONTHS?» It is possible that the reduction in deliveries caused by the increase in solar-generation may be offset by changes in behavior. Consumers may be willing to leave the air-conditioners and/or heat-pumps running when it is hot because Hey, why not! The electricity is free!» Bigger bite in the shoulder months. This effect would be less pronounced in the shoulder-months and thus the load would respond more obviously to increases in solar-generation.

17 IT PAYS TO INSTALL SOLAR PANELS IN VT» Customers in Vermont are paid 6-cents for each kwh generated, regardless of whether those kwh are consumed by the customer or sent back to the grid. AND, customers receive an additional 14.8-cents for each kwh that is sent back to the grid. 6 /kwh 20.8 /kwh

18 THIS MAKES FOR SOME CONFUSING ACCOUNTING Credits are carried forward until the end of the year What number appears in the aggregate Residential Sales data? 1,044 kwh 576 kwh 0 kwh Credits expire at the end of the year. Credits can be gifted to another customer, which could result in interesting blackmarket transactions: Upside: I ll sell you my $500 credit for $400. You ll save $100 and I ll make $400. Downside: This may potentially be illegal

19 MW LESSON LEARNED People respond to incentives» When you pay people a lot for the kwh they generate, people will install a lot of solar panels! BTM Solar Generation Capacity History Forecast

20 HOW CAN WE ADDRESS THESE ISSUES IN THE CONTEXT OF A SHORT-TERM LOAD FORECASTING MODEL? 1. We can do nothing. a. In the case of a utility with little BTM, this may be fine until it s not. b. In the case of a utility with substantial BTM, the model will tend to over-forecast when it s sunny and under-forecast when it s cloudy. 2. Ex poste adjustment. We can add load to the forecast on cloudy days and reduce load on sunny days. 3. We can shorten the model s estimation period. Instead of using 4 or 5 years of data, we can use 1 or 2 years of data. 4. We can reconstitute the load. That is, we can add-back the solar generation to the load and model the reconstituted load. 5. Include BTM (in one form or another) as a driver variable.

21 SOLAR INSOLATION» Solar insolation is the amount of solar energy, typically measured in kwh/m 2 /day, that strikes the earth on a horizontal surface.» Because the earth rotates on its axis and orbits the sun in a predictable way, maximum solar insolation can be calculated based on the latitude/longitude of a location, along with the date/time.» The amount of solar energy reaching the earth (and available for generating electricity) is also impacted by cloud cover. More cloud cover Less solar insolation Less solar generation Less cloud cover More solar insolation More solar generation

22 SOLAR INSOLATION One Year One Month One Week One Day

23 SOLAR INSOLATION INTERACTIONS» Solar capacity» Cloud cover» Average panel efficiency» Temperature: This is relevant because the panels become less efficient at higher temperatures

24 WHY DO WE DO ALL THIS?» We use all these components because we want to incorporate an estimate of BTM generation as a right-hand side variable in our load models.» But, we do not have measured observations of BTM.» So, we impute the BTM load.» Rather than assuming our estimate is 100% accurate and simply subtracting it from our forecast, we utilize the estimate as a driver variable in our models, which is then assigned a coefficient. In principal, the coefficient should be close to -1. In practice however, we are happy if the values is negative and increases (in absolute value) during the morning hours and decreases during the afternoon hours.

25 CONTRIBUTION OF THE SOLAR VARIABLE IN HR12 MODEL MetrixND s BX Tab displays the variable multiplied by its coefficient, along with the model s total predicted values, thereby allowing visibility into a variable s contributions. Predicted values. BX: Contribution of Solar Variable 1. Negative Sign 2. Highly Significant 3. Increasing impact over time as saturation of BTM increases

26 COMPARISON October 2016 Incorporates BTM. More accurately predicts midday declines Does Not Incorporate BTM. Misses midday declines.

27 COMPARISON April 2016 Incorporates BTM. More accurately predicts midday declines Does Not Incorporate BTM. Misses midday declines.

28 MODEL COMPARISON Mean Absolute Percent Error (MAPE) With and Without Solar 1. Dramatically lower MAPEs during daylight hours due to the inclusion of solar concepts. 2. Slightly lower MAPEs during the night due to improvements to the Daily Energy model, which acts as a driver variable.

29 TAKE-AWAY» This approach is not a panacea.» It is far from perfect.» It may be useful in the absence of other data. It is a place to start and can help to explain previously unexplained load-variations due to BTM.

30 THANK YOU BOSTON, MASSACHUSETTS Rich Simons X2