Compliance Review and Registration Cancellation by Securities Commission

The Securities and Exchange Commission's Monitoring Department reviewed compliance claims and approved changes in functions from April to July 2016. A summary of approved changes includes reclassification and reduction in functions of various entities such as Broker/Dealers, Fund Managers, and more. The overall level of compliance reached 72% as of April 2016, with plans to cancel the registration of non-capitalized CMOs by December 31, 2016.

• Securities Commission
• Compliance Review
• Registration Cancellation
• Capital Requirement
• Reclassification

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1. PWG PWG Discussion Annual Validation Discussion Annual Validation 2017 2017 An investigative discussion into Residential Changes from Annual Validation 2017 Nikki Mckenna ERCOT 2017

2. Investigative Process Investigative Process Annual Validation algorithm for AMS changes was re-ran for 2015, 2016 and 2017. Each AV has a three year period of look back (2015: 2015,2014 and 2013). Only the winter months (January and February) are considered. Process: Pull all the ESI ID s in ERCOT Keep only those that are active Ensure they have 90% of monthly data for each of the 6 months looked at Calculate correlation of Average DB versus Daily Usage for each of the 6 months, changes as follows: ResLo to ResHi: 3 of 6 >=.6 ResHi to ResLo: 6 of 6 <= .4

3. Investigative Process, cont. Investigative Process, cont. ESIID Year Month RSQ_AV2015 RSQ_AV2016 RSQ_AV2017 1 2013 1 0.07505 1 2013 2 0.06934 1 2014 1 0.05603 1 2014 2 0.38834 1 2015 1 0.03555 1 2015 2 0.02977 1 2016 1 1 2016 2 1 2017 1 1 2017 2 2 2013 1 0.00007 2 2013 2 0.03347 2 2014 1 0.09751 2 2014 2 2 2015 1 0.60888 2 2015 2 0.5895 2 2016 1 2 2016 2 2 2017 1 2 2017 2 3 2013 1 0.04362 3 2013 2 0.0068 3 2014 1 0.00465 3 2014 2 . 3 2015 1 0.10918 3 2015 2 0.19032 3 2016 1 3 2016 2 3 2017 1 3 2017 2 0.05603 0.38834 0.03555 0.02977 0.00663 0.00546 0.03555 0.02977 0.00663 0.00546 0.05259 0.14 Created a master database of all ESI ID s and their Rsquare values for each of the AV Periods Master database was created using only CNP data 0.09751 0.16238 0.60888 0.5895 0.36441 0.09704 0.60888 0.5895 0.36441 0.09704 0.4333 0.12868 Looked at three different aspects: Flip Flops from year to year Verification of changes requested Weather 0.00465 0.01456 0.10918 0.19032 0.00058 0.15039 0.10918 0.19032 0.00058 0.15039 0.00837 0.08835

4. Flip Flops Flip Flops

5. Presentation regarding flip flops from two years ago was for AMS/IDR changes only. To reiterate, it is not possible for IDR changes to flip flop in two consecutive years (2016 and 2017), however, an ESI ID can flop every two years (ex 2015 and 2017) It is possible, although not common, for NIDR ESI IDs to change every year as the algorithm is different

6. AV 2016 to AV 2017 AV 2016 to AV 2017 For AV 2017 there were a total of 43,505 changes for CNP Residential. In comparing the change list sent to CNP for AV 2016 and AV2017, 6 ESI IDs were found on the list. ESIID Curr_Prof_2017 New_Profile_2017 Curr_Prof_2016 New_Profile_2016 1 RESLOWR RESHIWR 2 RESLOWR RESHIWR 3 RESLOWR RESHIWR 4 RESLOWR RESHIWR 5 RESLOWR RESHIWR 6 RESLOWR RESHIWR Profile Code_Today RESHIWR_COAST_NIDR_NWS_NOTOU RESHIWR_COAST_NIDR_NWS_NOTOU RESHIWR_COAST_NIDR_NWS_NOTOU RESHIWR_COAST_NIDR_NWS_NOTOU RESHIWR_COAST_NIDR_NWS_NOTOU RESHIWR_COAST_NIDR_NWS_NOTOU RESHIWR RESHIWR RESHIWR RESHIWR RESHIWR RESHIWR RESLOWR RESLOWR RESLOWR RESLOWR RESLOWR RESLOWR Each of the 6 ESI IDs NIDR s which can potentially flip flop.

7. AV 2015 to AV 2017 AV 2015 to AV 2017 For AV 2017 there were a total of 43,505 changes for CNP Residential. ESIID Curr_Prof_2017 New_Profile_2017 Curr_Prof_2015 New_Profile_2015 Year Month RSQ_AV2015 RSQ_AV2016 RSQ_AV2017 Count 1 RESHIWR RESLOWR RESLOWR RESHIWR 1 RESHIWR RESLOWR RESLOWR RESHIWR 1 RESHIWR RESLOWR RESLOWR RESHIWR 1 RESHIWR RESLOWR RESLOWR RESHIWR 1 RESHIWR RESLOWR RESLOWR RESHIWR 1 RESHIWR RESLOWR RESLOWR RESHIWR 1 RESHIWR RESLOWR RESLOWR RESHIWR 1 RESHIWR RESLOWR RESLOWR RESHIWR 1 RESHIWR RESLOWR RESLOWR RESHIWR 1 RESHIWR RESLOWR RESLOWR RESHIWR 2 RESLOWR RESHIWR RESHIWR RESLOWR 2 RESLOWR RESHIWR RESHIWR RESLOWR 2 RESLOWR RESHIWR RESHIWR RESLOWR 2 RESLOWR RESHIWR RESHIWR RESLOWR 2 RESLOWR RESHIWR RESHIWR RESLOWR 2 RESLOWR RESHIWR RESHIWR RESLOWR 2 RESLOWR RESHIWR RESHIWR RESLOWR 2 RESLOWR RESHIWR RESHIWR RESLOWR 2 RESLOWR RESHIWR RESHIWR RESLOWR 2 RESLOWR RESHIWR RESHIWR RESLOWR 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 1 0.673692003 2 0.098838246 1 0.637651304 0.637651304 2 0.640145781 0.640145781 1 0.070579488 0.070579488 0.070579488 2 0.020664713 0.020664713 0.020664713 1 0.017049186 0.017049186 2 0.002849169 0.002849169 1 2 1 0.026048137 2 0.227372247 1 0.26874121 0.26874121 2 0.211523086 0.211523086 1 0.137862321 0.137862321 0.137862321 2 0.08909475 0.08909475 1 0.241839342 0.241839342 2 0.649209542 0.649209542 1 2 In comparing the change list sent to CNP for AV 2015 and AV2017, 499 ESI IDs were found on the list. 0.008365924 0.144841181 6 Of the 499, 6 ESI ID s were NIDR s, leaving 493 ESI ID s to be verified against the master rsquare database 0.08909475 0.657991036 0.664994599 3 ResLo to ResHi: 3 of 6 >=.6 ResHi to ResLo: 6 of 6 <= .4 Therefore, of the 43,505 requested change, 499 were flip flops , 499 out 43,505 is around .11% (a tenth of a penny)

8. Verification of Changes Requested Verification of Changes Requested

9. A random sample of 440 ESI IDs was selected from the AV 2017 change list. *Sample was selected using proc survey select in SAS with a SRS method. The ESI IDs in the sample were matched with their corresponding RSquare values in the master database created earlier. The information was exported to Excel. Countif functions were used to count the RSquare values and compare to the change rules. No exceptions were found Two ESI ID s did not have enough data and were still counted, 2 out of 440 is .45% of the data. Not a cause for concern. ESIID Curr_Prof_2017 New_Profile_2017 Year Month RSQ_AV2017 Count 1 RESLOWR RESHIWR 1 RESLOWR RESHIWR 1 RESLOWR RESHIWR 1 RESLOWR RESHIWR 1 RESLOWR RESHIWR 1 RESLOWR RESHIWR 2 RESLOWR RESHIWR 2 RESLOWR RESHIWR 2 RESLOWR RESHIWR 2 RESLOWR RESHIWR 2 RESLOWR RESHIWR 2 RESLOWR RESHIWR 3 RESHIWR RESLOWR 3 RESHIWR RESLOWR 3 RESHIWR RESLOWR 3 RESHIWR RESLOWR 3 RESHIWR RESLOWR 3 RESHIWR RESLOWR 4 RESLOWR RESHIWR 4 RESLOWR RESHIWR 4 RESLOWR RESHIWR 4 RESLOWR RESHIWR 4 RESLOWR RESHIWR 4 RESLOWR RESHIWR 2015 2015 2016 2016 2017 2017 2015 2015 2016 2016 2017 2017 2015 2015 2016 2016 2017 2017 2015 2015 2016 2016 2017 2017 1 0.666701858 2 0.761417091 1 0.835615557 2 0.818646759 1 0.833228821 2 0.303975909 1 0.720510251 2 0.701661203 1 0.007586568 2 0.302096417 1 0.136251522 2 0.748185165 1 0.000152442 2 0.199488983 1 0.005021379 2 0.148129844 1 0.052811364 2 0.333956853 1 0.701235796 2 0.67653657 1 0.092199077 2 0.333549024 1 0.657078194 2 0.576331994 5 3 6 3

10. Weather Weather Weather trends for the Coast weatherzone for January and February for AV periods for 2015, 2016 and 2017

11. ResLo to ResHi: 78,656 ResHi to ResLo: 26,420 For the 2015 Annual Validation period, three winters were looked at: 2013, 2014 and 2015. As depicted in the graph to the left, it is safe to say those were three cool (cold) winters, thus it makes sense to have such a high number of changes to ResHi. ResLo to ResHi: 3 of 6 >=.6 ResHi to ResLo: 6 of 6 <= .4

12. ResLo to ResHi: 21,089 ResHi to ResLo: 10,978 For the 2016 Annual Validation period, the 2013 winter is dropped off( the warmest of the last three winters) and 2016 is brought on and it is similar to the remaining two winters (2015 and 2014). Since the weather is similar, it is acceptable the correlation values would be similar, thus very few changes. ResLo to ResHi: 3 of 6 >=.6 ResHi to ResLo: 6 of 6 <= .4

13. ResLo to ResHi: 15,734 ResHi to ResLo: 27,771 For the 2017 Annual Validation period, the 2014 winter dropped off and the 2017 winter came on, a very warm winter, thus adding many more observations with a low correlation. ResLo to ResHi: 3 of 6 >=.6 ResHi to ResLo: 6 of 6 <= .4

14. Conclusions Conclusions Future Ideas Future Ideas Once a RES Hi, always RES Hi Change the RSquare Factors and/or the algorithm, days included based on temp Only look at NIDR ESI IDs for Annual Validation ??? No significant flaws were found in the AMS/IDR algorithm for Residential Changes Changes are highly dependent on type of winter