WEBVTT 00:00:02.835 --> 00:00:05.434 My name is Amber for today folks. 00:00:05.434 --> 00:00:06.860 Thank you, great. 00:00:06.860 --> 00:00:08.325 Thank you. 00:00:08.325 --> 00:00:09.690 Welcome to the California Public 00:00:09.690 --> 00:00:11.910 Utilities Commission committee meeting 00:00:11.910 --> 00:00:16.290 on this day, Wednesday, February 17th, 2021. 00:00:16.290 --> 00:00:18.260 All participants will be in listen only 00:00:19.280 --> 00:00:23.330 and tell the speaking session today at that point 00:00:23.330 --> 00:00:24.810 you may press star one 00:00:24.810 --> 00:00:26.960 if you would like to make a public comment. 00:00:28.020 --> 00:00:29.400 This call is being recorded. 00:00:29.400 --> 00:00:32.800 If you have any objections, please disconnect at this time. 00:00:32.800 --> 00:00:34.100 I now like to turn the meeting over 00:00:34.100 --> 00:00:36.493 to your host president Batger, you may begin. 00:00:37.840 --> 00:00:38.750 Thank you, Amber. 00:00:38.750 --> 00:00:41.080 Thank you all for joining today. 00:00:41.080 --> 00:00:44.570 Good morning and thank you for being with us today. 00:00:44.570 --> 00:00:48.260 I am calling the CPUC Commissioner committee meeting 00:00:48.260 --> 00:00:52.290 of February 17th, 2021 to order. 00:00:52.290 --> 00:00:55.410 I apologize for not being on audio. 00:00:55.410 --> 00:00:57.760 I'm having technical difficulties today. 00:00:57.760 --> 00:01:00.830 So I will just be, excuse me, not on video, 00:01:00.830 --> 00:01:02.743 I will just only be on audio today. 00:01:03.960 --> 00:01:05.440 There are three committees, 00:01:05.440 --> 00:01:07.730 the finance and administration committee, 00:01:07.730 --> 00:01:09.560 the policy and governance committee 00:01:09.560 --> 00:01:11.510 and the emerging trends committee. 00:01:11.510 --> 00:01:15.110 Today the emerging trends committee will meet. 00:01:15.110 --> 00:01:16.660 We will have an opportunity 00:01:16.660 --> 00:01:18.930 for public comment falling Q and A 00:01:18.930 --> 00:01:21.260 and discussion from the Commissioners. 00:01:21.260 --> 00:01:23.300 If you wish to make a public comment 00:01:23.300 --> 00:01:27.227 or ask a question please dial (800) 857-1917 00:01:31.000 --> 00:01:34.823 and enter the code 5180519 and press star one. 00:01:39.420 --> 00:01:41.510 You will be placed into a queue 00:01:41.510 --> 00:01:43.450 and will be called upon to speak 00:01:43.450 --> 00:01:46.740 when we get to the public comment period in this agenda 00:01:46.740 --> 00:01:49.823 which will be at the end of each committee meeting. 00:01:51.090 --> 00:01:54.090 I will now turn it over to Commissioner Guzman Osavis 00:01:54.090 --> 00:01:57.090 and Commissioner Shiroma to introduce the items 00:01:57.090 --> 00:01:59.870 on the emerging trends agenda. 00:01:59.870 --> 00:02:01.230 Commissioners. 00:02:01.230 --> 00:02:02.800 Thank you President Batjer. 00:02:02.800 --> 00:02:04.163 Good morning everyone. 00:02:05.060 --> 00:02:07.450 We will be discussing just a little bit 00:02:07.450 --> 00:02:11.280 about the work plan for the year for emerging trends, 00:02:11.280 --> 00:02:13.850 but we did as you know, did a staff survey 00:02:13.850 --> 00:02:16.320 to get input from all of you 00:02:16.320 --> 00:02:20.760 on what topics are really critical for us to undertake 00:02:20.760 --> 00:02:24.290 and given the positive response we're gonna take a moment 00:02:24.290 --> 00:02:27.100 to really integrate that into the work plan, 00:02:27.100 --> 00:02:30.580 but I just wanted to highlight a couple of, 00:02:30.580 --> 00:02:32.190 you know, two general topics. 00:02:32.190 --> 00:02:33.830 I mean, we got well over, 00:02:33.830 --> 00:02:38.460 let's see over 20 recommendations from staff 00:02:38.460 --> 00:02:42.100 and even before that, we had been working off of a list 00:02:42.100 --> 00:02:44.170 of items that we had not yet gotten to. 00:02:44.170 --> 00:02:47.930 So everything's from, you know, working from home 00:02:47.930 --> 00:02:49.990 and really starting to discuss 00:02:49.990 --> 00:02:54.370 what some of the experts are saying around that 00:02:54.370 --> 00:02:59.360 as a focus to really external issues like reliability 00:02:59.360 --> 00:03:02.470 that we'll be talking about some of those metrics today. 00:03:02.470 --> 00:03:07.470 But every single division provided some input 00:03:07.490 --> 00:03:11.620 from the TNCs and some of the red lining concerns 00:03:11.620 --> 00:03:13.700 that was a really interesting topic 00:03:13.700 --> 00:03:15.570 that I thought was brought forward 00:03:16.690 --> 00:03:20.630 and everything else in our more traditional space 00:03:20.630 --> 00:03:24.900 around energy and we'll be seeing some of these today 00:03:24.900 --> 00:03:27.910 as I discussed, some, winning prizes. 00:03:27.910 --> 00:03:31.590 So, but just to reinforce that we heard everything 00:03:31.590 --> 00:03:33.750 from wildfire safety. 00:03:33.750 --> 00:03:36.590 There was a recommendation on really looking 00:03:36.590 --> 00:03:40.250 into the indigenous sporting practices for wildfire safety. 00:03:40.250 --> 00:03:42.680 Communications had a lot of 00:03:42.680 --> 00:03:44.740 really interesting recommendations 00:03:45.630 --> 00:03:48.320 and some of which we're already working on. 00:03:48.320 --> 00:03:50.500 So we're gonna digest all of these 00:03:50.500 --> 00:03:52.980 and come back with a work plan, 00:03:52.980 --> 00:03:54.820 we'll try to post it before 00:03:54.820 --> 00:03:58.900 our next emerging trends committee meeting in March, 00:03:58.900 --> 00:04:01.850 but we definitely will highlight what the work plan is 00:04:01.850 --> 00:04:04.633 in March and obviously have it posted on the website. 00:04:05.490 --> 00:04:07.140 Okay, probably the most exciting thing 00:04:07.140 --> 00:04:10.990 is that our staff, both Commissioners, Shiroma and staff 00:04:10.990 --> 00:04:14.470 and others have worked together to do a live raffle 00:04:14.470 --> 00:04:16.500 for the two winners. 00:04:16.500 --> 00:04:17.930 Commissioner Shiroma and myself 00:04:17.930 --> 00:04:21.819 we'll be doing an Amazon gift card for two lucky winners 00:04:21.819 --> 00:04:24.570 for participating in this survey. 00:04:24.570 --> 00:04:27.900 So I'm gonna ask Justin Song to bring up the wheel 00:04:27.900 --> 00:04:31.440 and just to note that because we've made a little mistake 00:04:31.440 --> 00:04:34.540 on the survey and forgot to put a tab for your email, 00:04:34.540 --> 00:04:37.170 we're gonna be following up and trying to hunt you down 00:04:37.170 --> 00:04:39.620 'cause some of you didn't put your names next 00:04:39.620 --> 00:04:41.830 to your topic recommendations, 00:04:41.830 --> 00:04:44.350 but here you'll see some of the topics 00:04:44.350 --> 00:04:46.190 that were brought forward, all of the topics 00:04:46.190 --> 00:04:47.310 that were brought forward. 00:04:47.310 --> 00:04:50.420 And so we're gonna spin the wheel twice. 00:04:50.420 --> 00:04:53.223 So Justin, if you could do the first spinning. 00:04:55.460 --> 00:04:57.620 And this one will be for the gift card 00:04:57.620 --> 00:05:00.363 that I will be gifting. 00:05:01.890 --> 00:05:04.900 Okay, increasing development 5G infrastructure. 00:05:04.900 --> 00:05:08.720 Ow, that's really, and I'm just checking the list 00:05:08.720 --> 00:05:11.130 to see if by chance this was one of the folks 00:05:11.130 --> 00:05:15.323 that actually provided their information, 00:05:16.873 --> 00:05:20.830 and if anyone knows that (indistinct) my list, 00:05:20.830 --> 00:05:22.930 doesn't have that on right now. 00:05:22.930 --> 00:05:26.880 Okay, so whichever staff person submitted this, 00:05:26.880 --> 00:05:28.390 we're gonna track you down 00:05:28.390 --> 00:05:33.390 'cause you have won a $25 gift card for Amazon, okay. 00:05:33.680 --> 00:05:35.640 All right, and Commissioner Shiroma, 00:05:35.640 --> 00:05:38.943 you can call the spin for Justin on the next one. 00:05:41.310 --> 00:05:44.653 Okay, Justin, go ahead and spin the wheel. 00:05:58.300 --> 00:06:03.300 Ah, oh, very good, hydrogen and renewable natural gas. 00:06:05.620 --> 00:06:08.033 Both topics, really excellent. 00:06:08.940 --> 00:06:13.847 So we'll figure out who put forth this idea 00:06:15.060 --> 00:06:18.930 and Martha and I will be following through 00:06:18.930 --> 00:06:21.033 with the Amazon gift card. 00:06:21.960 --> 00:06:23.500 Thank you. 00:06:23.500 --> 00:06:24.480 Thank you very much. 00:06:24.480 --> 00:06:28.100 So I think with that Commissioner Shiroma I'm gonna turn 00:06:28.100 --> 00:06:30.810 to you to introduce our first topic 00:06:30.810 --> 00:06:32.463 for emerging trends for the year. 00:06:34.170 --> 00:06:38.190 All right, thank you Commissioner Guzman Osavis. 00:06:38.190 --> 00:06:42.093 All right, so our first presentation, 00:06:43.510 --> 00:06:48.510 the topic is on electric reliability metrics for 2020. 00:06:51.300 --> 00:06:54.270 What we have learned and we will hear first 00:06:54.270 --> 00:06:56.860 from Julian Enis. 00:06:56.860 --> 00:07:00.120 Julian is a utility engineer 00:07:00.120 --> 00:07:04.850 in our resiliency and micro grids energy group 00:07:04.850 --> 00:07:06.603 from the energy division. 00:07:08.850 --> 00:07:11.553 Again, Julian is a utility engineer. 00:07:12.420 --> 00:07:15.360 He provides his expertise on issues related 00:07:15.360 --> 00:07:19.800 to electric system reliability and resiliency 00:07:19.800 --> 00:07:22.100 and we've worked together extensively 00:07:22.100 --> 00:07:27.100 over this past year or so on our micro grids proceeding. 00:07:27.180 --> 00:07:30.400 Julian has a degree in mechanical engineering 00:07:30.400 --> 00:07:33.663 from the University of California Davis go Aggies, 00:07:34.720 --> 00:07:37.810 and Julian will be presenting to us today 00:07:37.810 --> 00:07:42.450 on metrics and trends in electric system reliability 00:07:42.450 --> 00:07:46.800 including an analysis of 2019 reliability metrics 00:07:46.800 --> 00:07:51.173 and opportunities to better leverage this data. 00:07:52.040 --> 00:07:55.260 This is certainly a very important topic 00:07:55.260 --> 00:07:59.213 as we see what has been happening across the country. 00:08:00.210 --> 00:08:02.030 And I also wanted to do a shout out 00:08:02.030 --> 00:08:05.600 to Julian's manager, Forest Kaeser 00:08:06.760 --> 00:08:11.670 who is also attending today's committee meeting. 00:08:11.670 --> 00:08:13.973 All right, Julian, the microphone is yours. 00:08:17.060 --> 00:08:18.786 Well, thank you so much Commissioner Shiroma 00:08:18.786 --> 00:08:20.400 and indeed go Aggies. 00:08:20.400 --> 00:08:23.910 Good morning, Commissioners, directors, members of staff 00:08:23.910 --> 00:08:25.040 and members of the public. 00:08:25.040 --> 00:08:27.780 As Commissioner Shiroma introduced me, am Julian 00:08:27.780 --> 00:08:30.520 and I'm here to talk about electric system reliability 00:08:32.340 --> 00:08:35.460 and the reports that utilities are required 00:08:35.460 --> 00:08:39.250 to file every year and just a little bit more data analysis 00:08:39.250 --> 00:08:41.670 and some next steps that we can take 00:08:41.670 --> 00:08:43.850 to improve the reporting requirements 00:08:43.850 --> 00:08:46.720 and I'm better understand what this data tells us 00:08:46.720 --> 00:08:49.430 about the electric system of California. 00:08:49.430 --> 00:08:50.330 Next slide please. 00:08:53.510 --> 00:08:55.790 So the basics of electric system reliability 00:08:55.790 --> 00:08:58.720 and this is what we'll be covering in this presentation. 00:08:58.720 --> 00:09:01.030 We'll be touching on how electric system reliability 00:09:01.030 --> 00:09:02.210 is defined. 00:09:02.210 --> 00:09:05.090 Why do you keep track of electric reliability? 00:09:05.090 --> 00:09:07.160 What the California utilities are required 00:09:07.160 --> 00:09:09.640 to report to CPUC annually? 00:09:09.640 --> 00:09:11.360 How did the California utilities compare 00:09:11.360 --> 00:09:13.090 to the rest of the United States 00:09:13.090 --> 00:09:14.750 and a little bit more about 00:09:14.750 --> 00:09:16.660 how granular reporting standards are 00:09:16.660 --> 00:09:19.590 and how you keep track of the worst performing circuits 00:09:19.590 --> 00:09:21.370 and what can be improved. 00:09:21.370 --> 00:09:22.203 Next slide. 00:09:24.880 --> 00:09:25.713 So first we'll start 00:09:25.713 --> 00:09:28.600 with how electric system reliability is defined. 00:09:28.600 --> 00:09:31.210 These are statistical representations of outages 00:09:31.210 --> 00:09:32.720 and we'll dive into a little bit 00:09:32.720 --> 00:09:33.720 about what they really mean. 00:09:33.720 --> 00:09:34.927 Next slide please. 00:09:36.840 --> 00:09:39.550 So electric system reliability is defined 00:09:39.550 --> 00:09:44.340 by IEEE 1366 and within this standard 00:09:44.340 --> 00:09:47.540 there, the four major metrics are defined 00:09:48.780 --> 00:09:52.600 as SAIDI, SAIFI, CAIDI and MAIFI. 00:09:52.600 --> 00:09:55.540 I realized that these are a lot of new acronyms. 00:09:55.540 --> 00:09:58.520 So wherever in the presentation these acronyms show up, 00:09:58.520 --> 00:10:00.980 I have tried to spell them out for folks 00:10:00.980 --> 00:10:03.810 just to make sure we have clarity. 00:10:03.810 --> 00:10:05.600 However, these four metrics 00:10:05.600 --> 00:10:08.120 are the generally accepted standard 00:10:08.120 --> 00:10:11.850 by which electric utilities across the United States measure 00:10:11.850 --> 00:10:13.100 their system performance. 00:10:14.040 --> 00:10:17.350 SAIDI is the System Average Interruption Duration Index. 00:10:17.350 --> 00:10:21.060 SAIFI is the System Average Interruption Frequency Index. 00:10:21.060 --> 00:10:24.650 CAIDI is the Customer Average Interruption Duration Index 00:10:24.650 --> 00:10:28.300 and MAIFI is Momentary Average Interruption Frequency Index. 00:10:28.300 --> 00:10:29.200 Next slide please. 00:10:32.560 --> 00:10:36.480 So here are some written definitions for these four metrics, 00:10:36.480 --> 00:10:38.880 but I will quickly give an explanation 00:10:38.880 --> 00:10:40.590 of what they each mean. 00:10:40.590 --> 00:10:45.140 So effectively SAIDI is the average number of minutes 00:10:45.140 --> 00:10:48.000 that a customer on the entire utility system 00:10:48.000 --> 00:10:49.960 can expect to be without power 00:10:49.960 --> 00:10:52.190 over the course of a given year. 00:10:52.190 --> 00:10:55.750 SAIFI or the System Average Interruption Frequency Index 00:10:55.750 --> 00:10:58.927 is the expected number of times that the average customer 00:10:58.927 --> 00:11:02.240 on the utility system will experience an outage 00:11:02.240 --> 00:11:03.910 over the course of the year. 00:11:03.910 --> 00:11:07.470 CAIDI or the Customer Average Interruption Duration Index 00:11:07.470 --> 00:11:12.470 measures how long each outage is for the average customer 00:11:14.200 --> 00:11:17.514 and MAIFI measures how many, and MAIFI, sorry, 00:11:17.514 --> 00:11:20.970 the Momentary Average Interruption Frequency Index measures 00:11:20.970 --> 00:11:22.441 how many momentary outages or outages 00:11:22.441 --> 00:11:24.720 that last less than five minutes 00:11:24.720 --> 00:11:26.680 the average customer on the utility system 00:11:26.680 --> 00:11:29.120 will experience over the course of the year. 00:11:29.120 --> 00:11:30.227 Next slide please. 00:11:36.910 --> 00:11:37.743 Next one. 00:11:40.670 --> 00:11:44.170 The next topic is to talk about major event days. 00:11:44.170 --> 00:11:47.082 So the reliability indices are generally reported 00:11:47.082 --> 00:11:50.770 with and without what are called major event days. 00:11:50.770 --> 00:11:55.630 Major event days are a statistical threshold that is based 00:11:55.630 --> 00:11:59.330 on the daily System Average Interruption Duration Index 00:12:01.490 --> 00:12:04.220 and it's a statistical threshold that's based on 00:12:04.220 --> 00:12:06.580 the previous five years of data. 00:12:06.580 --> 00:12:08.430 The idea of a major event day 00:12:08.430 --> 00:12:10.800 is that it measures the impact 00:12:10.800 --> 00:12:13.403 of, high impact and low frequency events. 00:12:14.630 --> 00:12:16.310 Just as a note, this definition 00:12:16.310 --> 00:12:19.470 is a statistical definition only based on outage durations 00:12:19.470 --> 00:12:21.880 and it does not account for causality. 00:12:21.880 --> 00:12:23.890 So things like earthquakes, storms 00:12:23.890 --> 00:12:25.950 and public safety power shutoffs 00:12:25.950 --> 00:12:30.520 are considered any MEDs only in so far as the events 00:12:30.520 --> 00:12:33.430 daily system average interruption duration index 00:12:33.430 --> 00:12:34.873 exceeds the threshold. 00:12:36.080 --> 00:12:38.320 Reliability indices are generally used 00:12:38.320 --> 00:12:40.160 to motivate investment decisions 00:12:40.160 --> 00:12:43.430 that will lead to improvements in system reliability. 00:12:43.430 --> 00:12:46.490 Generally the utility look at reliability excluding 00:12:46.490 --> 00:12:48.590 these major event days to focus how it needs 00:12:48.590 --> 00:12:53.030 to improve reliability, general system reliability overall. 00:12:53.030 --> 00:12:55.730 However, looking at reliability with MEDs utilities 00:12:55.730 --> 00:12:58.820 can see how significant and large events 00:12:58.820 --> 00:13:00.330 such as those earthquakes, storms, 00:13:00.330 --> 00:13:03.460 public safety power shutoff and so on 00:13:03.460 --> 00:13:05.980 can dramatically impact the customer experience. 00:13:05.980 --> 00:13:07.127 Next slide please. 00:13:09.850 --> 00:13:13.150 So this is a statistical representation of data 00:13:13.150 --> 00:13:15.920 and it does have some drawbacks. 00:13:15.920 --> 00:13:19.650 So the reliability statistics themselves focus 00:13:19.650 --> 00:13:21.880 on an outage duration and customer accounts 00:13:22.910 --> 00:13:26.780 over the entire system which can obscure regional variation, 00:13:26.780 --> 00:13:30.410 and you also need to have very, very large systems 00:13:30.410 --> 00:13:35.102 as you can see from this graph PG&E has 99,000 miles 00:13:35.102 --> 00:13:39.380 of overhead lines within its service territory as of 2018 00:13:40.330 --> 00:13:43.100 and the enormous size of this system 00:13:43.100 --> 00:13:45.980 with its own regional climate and density variations 00:13:45.980 --> 00:13:49.860 can make the system level reliability indices 00:13:49.860 --> 00:13:51.650 a little bit difficult to interpret. 00:13:51.650 --> 00:13:54.240 So just a caveat on this data. 00:13:54.240 --> 00:13:55.357 Next slide please. 00:13:58.160 --> 00:14:00.793 So why do we keep track of reliability metrics. 00:14:02.010 --> 00:14:04.860 At its most basic, as most basic we use this data 00:14:04.860 --> 00:14:06.620 to improve the electric system 00:14:06.620 --> 00:14:08.860 and improve customer experience on the electric system, 00:14:08.860 --> 00:14:10.130 but we'll dive into that. 00:14:10.130 --> 00:14:10.963 Next slide. 00:14:14.170 --> 00:14:17.860 So utilities generally use the reliability metrics 00:14:17.860 --> 00:14:21.710 so that at its base level they can track the performance 00:14:21.710 --> 00:14:24.580 of individual all the way down to individual circuits 00:14:24.580 --> 00:14:26.330 so they can address service issues. 00:14:27.170 --> 00:14:31.810 Generally, these metrics are used as scoring criteria 00:14:31.810 --> 00:14:34.970 for remediation measures and cost-effectiveness assessment 00:14:34.970 --> 00:14:38.763 on hardening measures taken to address reliability issues. 00:14:39.860 --> 00:14:42.440 Additionally annually, the worst performing circuits 00:14:42.440 --> 00:14:45.450 by the system average interruption duration index 00:14:45.450 --> 00:14:48.210 and system average interruption frequency index 00:14:49.597 --> 00:14:51.420 are required to be reported to yearly 00:14:51.420 --> 00:14:53.570 and are targeted for remediation 00:14:53.570 --> 00:14:56.063 through programs that the utilities put out. 00:14:57.090 --> 00:15:00.110 You can also track persistent issues with blue-sky service 00:15:00.110 --> 00:15:02.300 and the impacts of major events 00:15:02.300 --> 00:15:03.710 at which is all important data 00:15:03.710 --> 00:15:06.470 and utility in the distribution planning process. 00:15:06.470 --> 00:15:07.607 Next slide please. 00:15:10.550 --> 00:15:11.680 So if you have an example 00:15:11.680 --> 00:15:14.800 of how these electric liability metrics were used, 00:15:14.800 --> 00:15:19.800 this is from the 2018 PG&E report out on system reliability. 00:15:22.340 --> 00:15:27.340 As you can see the Stagg 1105 circuit was identified 00:15:27.530 --> 00:15:28.920 as a poor performing circuit 00:15:28.920 --> 00:15:32.350 and they installed new protection devices 00:15:32.350 --> 00:15:34.860 to resolve reliability issues identified 00:15:34.860 --> 00:15:36.970 and actually achieved a 25% improvement 00:15:36.970 --> 00:15:38.780 in that sort of reliability. 00:15:38.780 --> 00:15:41.860 In addition, they also installed TripSavers 00:15:41.860 --> 00:15:44.200 in place of regular fuses. 00:15:44.200 --> 00:15:47.190 So that reduced the number of sustained outages 00:15:47.190 --> 00:15:48.350 for customers. 00:15:48.350 --> 00:15:53.270 So this is just a basic example of how utilities use 00:15:53.270 --> 00:15:56.240 this reliability data to target their remediation. 00:15:56.240 --> 00:15:59.370 These particular ones were within the Stockton division 00:15:59.370 --> 00:16:04.120 where this electric reliability annual town hall is held. 00:16:04.120 --> 00:16:05.020 Next slide please. 00:16:08.380 --> 00:16:11.030 So the what are the utilities required 00:16:11.030 --> 00:16:12.343 to report to the CPUC? 00:16:13.360 --> 00:16:15.640 So as established by Commission decision 00:16:15.640 --> 00:16:18.440 the utilities are required to report these metrics 00:16:18.440 --> 00:16:20.140 to us anyway. 00:16:20.140 --> 00:16:20.973 Next slide. 00:16:24.340 --> 00:16:28.350 So these are sort of the foundational decisions 00:16:28.350 --> 00:16:32.720 and Commissioned procedures that have brought us to today 00:16:32.720 --> 00:16:35.750 and our current reliability reporting requirements. 00:16:35.750 --> 00:16:40.090 The first decision was in 1996, the setting up 00:16:40.090 --> 00:16:43.590 the recording reporting requirements for system outages. 00:16:43.590 --> 00:16:48.590 Further decision in 2000 added 312 (indistinct) 00:16:49.350 --> 00:16:50.660 which defined major events 00:16:51.950 --> 00:16:54.960 and set a restoration time benchmark 00:16:54.960 --> 00:16:57.350 as the customer average interruption duration index 00:16:57.350 --> 00:16:59.950 of 570 minutes for a reasonableness review 00:16:59.950 --> 00:17:01.393 of particular outages. 00:17:02.310 --> 00:17:06.763 In 2011 advice letters for issued adopting IEEE 1366 00:17:08.100 --> 00:17:10.450 and its definition of major event days, 00:17:10.450 --> 00:17:12.990 and they were allowed, the utilities were allowed 00:17:12.990 --> 00:17:16.450 to exclude days that exceeded the major event days threshold 00:17:16.450 --> 00:17:18.950 from their calculations for liability. 00:17:18.950 --> 00:17:23.950 Finally in the 2016 decision 16-01-008 set up a reliability 00:17:25.530 --> 00:17:28.040 reporting standard for the California utilities. 00:17:28.040 --> 00:17:29.780 Independent to be of this decision, 00:17:29.780 --> 00:17:32.800 there isn't a template by which each of the utilities 00:17:32.800 --> 00:17:36.477 must annually report their system reliability data 00:17:36.477 --> 00:17:37.880 to the CPUC. 00:17:37.880 --> 00:17:41.680 These reports are due by July 15th of each year 00:17:41.680 --> 00:17:43.463 and cover the previous year's data. 00:17:44.530 --> 00:17:47.420 This reporting requirement does include division level 00:17:47.420 --> 00:17:50.010 and historical performance and the utilities 00:17:50.010 --> 00:17:52.270 to hold an annual workshop or town hall 00:17:52.270 --> 00:17:55.480 on electric system reliability and make circuits levels 00:17:55.480 --> 00:17:59.210 of reliability data available upon request to customers 00:17:59.210 --> 00:18:01.500 within a reasonable amount of time. 00:18:01.500 --> 00:18:02.617 Next slide please. 00:18:05.080 --> 00:18:07.650 So annual reporting requirements as set up 00:18:07.650 --> 00:18:12.650 in the 2016 decision were set up for the 50 utilities shown 00:18:14.230 --> 00:18:18.130 and you can find annual reports dating back to 1977 00:18:18.130 --> 00:18:20.470 as in the link provided above. 00:18:20.470 --> 00:18:22.840 But just to run down the 50 utilities required 00:18:22.840 --> 00:18:25.230 to report are PG&E, Southern California Edison, 00:18:25.230 --> 00:18:28.060 San Diego Gas and Electric, Bear Valley, PacificCorp 00:18:28.060 --> 00:18:29.623 and Liberty Utilities. 00:18:31.190 --> 00:18:36.180 Next slide. 00:18:36.180 --> 00:18:38.450 So this is where we get to dive into the data 00:18:38.450 --> 00:18:41.950 which is exciting because the data can tell us a lot. 00:18:41.950 --> 00:18:44.540 So how do the California utilities compare 00:18:44.540 --> 00:18:46.200 to the rest of the United States? 00:18:46.200 --> 00:18:47.297 Next slide please. 00:18:49.970 --> 00:18:54.630 So to start with a little bit of a caveat here 00:18:54.630 --> 00:18:57.910 is that reliability data can be shown in many ways 00:18:57.910 --> 00:19:00.010 and each of these waves can tell a different 00:19:00.010 --> 00:19:02.530 and sometimes conflicting part of this whole story 00:19:02.530 --> 00:19:06.530 about utilities reliability performance. 00:19:06.530 --> 00:19:08.610 These reports that are submitted annually 00:19:08.610 --> 00:19:13.610 are multi-hundred page documents containing a lot of tables, 00:19:13.730 --> 00:19:15.980 but a huge amount of data that needs to be parsed 00:19:15.980 --> 00:19:18.370 and distilled to gain useful insights. 00:19:18.370 --> 00:19:22.677 So this is just a little example of the difficulty 00:19:22.677 --> 00:19:27.630 I said when sort of parsing these results. 00:19:27.630 --> 00:19:31.730 So as you can see PG&E's 2019 system average 00:19:31.730 --> 00:19:34.990 interruption duration index is displayed in full 00:19:34.990 --> 00:19:38.210 without major event days and with major event days. 00:19:38.210 --> 00:19:42.190 As you can see, there's almost a 12 fold increase 00:19:42.190 --> 00:19:46.590 in the metric when the nature of my days included. 00:19:46.590 --> 00:19:51.590 This is as compared to the 2018 reported 00:19:52.770 --> 00:19:55.370 system average interruption duration index, 00:19:55.370 --> 00:20:00.260 and then within that, or, you can see also here 00:20:00.260 --> 00:20:01.970 this is the 2019 national average, 00:20:01.970 --> 00:20:05.810 so PG&E without major event days compares favorably 00:20:05.810 --> 00:20:06.970 to the national average, 00:20:06.970 --> 00:20:09.820 but when major event days are added, they do not. 00:20:09.820 --> 00:20:11.485 And then even within that data set, 00:20:11.485 --> 00:20:15.267 you can see San Francisco division 00:20:15.267 --> 00:20:17.566 system average interruption duration index 00:20:17.566 --> 00:20:20.990 is far better than Humboldt which is currently 00:20:20.990 --> 00:20:24.910 the worst performing one with the inclusion 00:20:24.910 --> 00:20:27.520 of major event days that metric jumps to 00:20:27.520 --> 00:20:31.520 just short of five days interruption. 00:20:31.520 --> 00:20:35.420 So as you can see, these metrics are useful, 00:20:35.420 --> 00:20:37.290 but they do paint a complicated picture 00:20:37.290 --> 00:20:38.500 about what's really going on 00:20:38.500 --> 00:20:41.690 with the electric systems within the utility territories. 00:20:41.690 --> 00:20:43.190 Next slide please. 00:20:44.780 --> 00:20:49.150 So from the 2019 days, we can take away some key facts 00:20:49.150 --> 00:20:51.540 is that without major event days 00:20:51.540 --> 00:20:53.300 the California utilities generally 00:20:53.300 --> 00:20:56.090 have lower measured system average interruption 00:20:56.090 --> 00:20:56.923 duration index 00:20:56.923 --> 00:20:59.800 and system average interruption frequency index 00:20:59.800 --> 00:21:01.100 than the national average. 00:21:02.050 --> 00:21:02.883 This definitely means that 00:21:02.883 --> 00:21:06.366 for the average customer on the entire utility system, 00:21:06.366 --> 00:21:08.670 there were less outage minutes 00:21:08.670 --> 00:21:12.420 and outages with less frequent than they are nationally. 00:21:12.420 --> 00:21:16.210 Just as a note, the California utilities 00:21:16.210 --> 00:21:18.480 have fairly large service territory 00:21:18.480 --> 00:21:20.740 encompassing a large number of customers 00:21:20.740 --> 00:21:22.770 which can influence these metrics 00:21:22.770 --> 00:21:25.330 because at the system level they are averaged 00:21:25.330 --> 00:21:27.453 over all of the customers on the system. 00:21:28.510 --> 00:21:29.930 However, when we look 00:21:29.930 --> 00:21:33.300 at the customer average interruption duration index, 00:21:33.300 --> 00:21:35.331 the California utilities generally 00:21:35.331 --> 00:21:38.063 have a higher measured CAIDI 00:21:38.063 --> 00:21:40.110 than the national average for 2019. 00:21:40.110 --> 00:21:42.330 So when an outage does occur, 00:21:42.330 --> 00:21:43.950 the duration for the average customer 00:21:43.950 --> 00:21:46.330 is longer than the national average. 00:21:46.330 --> 00:21:47.510 Next slide, please. 00:21:50.700 --> 00:21:52.990 So, here's just a quick breakdown 00:21:52.990 --> 00:21:57.500 of how the California utilities compared 00:21:57.500 --> 00:22:01.860 to the national average for each of these statistics. 00:22:01.860 --> 00:22:05.830 If you can see any square in red is a place 00:22:05.830 --> 00:22:08.500 where the California utility are higher 00:22:08.500 --> 00:22:10.840 than national average metric 00:22:10.840 --> 00:22:14.642 and you can see with the inclusion of major event days, 00:22:14.642 --> 00:22:17.780 it does complicate the picture a little bit. 00:22:17.780 --> 00:22:20.880 Again, as noted before you can see PG&E system 00:22:22.140 --> 00:22:25.470 average duration index jumped from 120 minutes 00:22:25.470 --> 00:22:27.640 without the inclusion of major event days 00:22:27.640 --> 00:22:31.940 to almost 1400 minutes or almost a whole day 00:22:31.940 --> 00:22:34.830 with the inclusion of major event date. 00:22:34.830 --> 00:22:37.230 So you can see that these are very high impact, 00:22:37.230 --> 00:22:38.980 they tend to be very high impacted. 00:22:40.499 --> 00:22:42.890 And just as a note, the national average 00:22:42.890 --> 00:22:45.150 is based on data that is annually compiled 00:22:45.150 --> 00:22:48.860 by the United States Energy Information Administration. 00:22:48.860 --> 00:22:52.020 They compile an annual report 00:22:52.020 --> 00:22:54.130 and create a very handy spreadsheet with all 00:22:54.130 --> 00:22:58.830 of the utilities that have reported in on these metrics 00:22:58.830 --> 00:23:01.280 and it does include both include 00:23:01.280 --> 00:23:03.730 and exclude major events days. 00:23:03.730 --> 00:23:04.907 Next slide please. 00:23:08.040 --> 00:23:11.230 So this is just a graphical representation 00:23:11.230 --> 00:23:13.100 of what you saw on the previous slide. 00:23:13.100 --> 00:23:16.700 This also does include the Southwest states 00:23:16.700 --> 00:23:17.873 which are noted below. 00:23:18.800 --> 00:23:20.050 So as you can see 00:23:21.798 --> 00:23:23.994 without further system average international duration index 00:23:23.994 --> 00:23:25.303 you can see without the inclusion of major event days 00:23:27.830 --> 00:23:30.940 or the California utilities performed relatively well 00:23:30.940 --> 00:23:33.190 as compared to national average 00:23:33.190 --> 00:23:36.600 however with the inclusion of those major event days. 00:23:36.600 --> 00:23:39.510 It complicates the picture a little bit. 00:23:39.510 --> 00:23:41.600 Just as a heads up for the next few slides. 00:23:41.600 --> 00:23:44.780 We'll be presenting graphs that look a little bit like this. 00:23:44.780 --> 00:23:49.780 Do be aware that Y axis scale can change simply by virtue 00:23:49.870 --> 00:23:52.460 of the variability of the data 00:23:52.460 --> 00:23:56.390 especially you can see here PG*E metric jumping 00:23:56.390 --> 00:24:00.053 into almost 1400 minutes of SAIDI. 00:24:01.170 --> 00:24:02.600 So next slide, please. 00:24:04.710 --> 00:24:07.959 So this is just another way to look at the data. 00:24:07.959 --> 00:24:11.900 This is the system average interruption duration index 00:24:11.900 --> 00:24:16.820 performance for PG&E from 2010 to 2019. 00:24:16.820 --> 00:24:19.740 The yellow line shows the metrics 00:24:19.740 --> 00:24:22.120 with major event data included 00:24:22.120 --> 00:24:26.300 and you can see that from 2015 on 00:24:26.300 --> 00:24:29.490 when PG&E started to institute 00:24:29.490 --> 00:24:31.580 the public safety power shutoffs, 00:24:31.580 --> 00:24:34.930 those metrics have jumped very significantly including 00:24:34.930 --> 00:24:37.700 until 2019 when they expanded the scope 00:24:37.700 --> 00:24:40.360 of their PSPS events. 00:24:40.360 --> 00:24:42.880 So this is just illustrative of how these metrics 00:24:42.880 --> 00:24:44.080 have changed over time, 00:24:44.080 --> 00:24:46.650 and you can see that without the inclusion 00:24:46.650 --> 00:24:50.960 of major event days, they trended relatively flat 00:24:50.960 --> 00:24:55.300 as compared to when major event days are included 00:24:55.300 --> 00:24:57.810 which is just interesting to note. 00:24:57.810 --> 00:24:58.887 Next slide please. 00:25:01.272 --> 00:25:02.927 So this graph, now we're looking at 00:25:02.927 --> 00:25:07.550 the system average interruption frequency index. 00:25:07.550 --> 00:25:10.720 This again is a similar way of showing 00:25:10.720 --> 00:25:13.590 how the California utilities compared 00:25:14.740 --> 00:25:16.580 to the bulk of Southwest states 00:25:16.580 --> 00:25:19.760 and the rest of the United States. 00:25:19.760 --> 00:25:22.940 So again, you can see that without the inclusion 00:25:22.940 --> 00:25:27.000 of major event days the utilities tend to perform well 00:25:27.000 --> 00:25:28.620 against the national average. 00:25:28.620 --> 00:25:30.900 However, with the inclusion of major event days 00:25:30.900 --> 00:25:35.480 you can see that it changes the picture a little bit. 00:25:35.480 --> 00:25:39.150 There are a number of utilities that are performing worse 00:25:39.150 --> 00:25:41.003 than those particular averages. 00:25:41.870 --> 00:25:43.007 Next slide please. 00:25:44.950 --> 00:25:47.110 And also for, again, the same 00:25:47.110 --> 00:25:49.770 for the customer average interruption duration index 00:25:49.770 --> 00:25:51.430 or CAIDI. 00:25:51.430 --> 00:25:54.610 It's a bit, again, this again is just illustrative 00:25:54.610 --> 00:25:56.910 that the California utilities when an outage 00:25:56.910 --> 00:26:00.630 is occurring it does tend to be significantly 00:26:02.413 --> 00:26:05.520 in some cases longer than the national average. 00:26:05.520 --> 00:26:10.270 So this is just illustrative of what these outages 00:26:10.270 --> 00:26:13.650 look like for the average customer and what happens 00:26:13.650 --> 00:26:17.033 when you exclude and include those major event day. 00:26:18.950 --> 00:26:19.783 Next slide. 00:26:23.180 --> 00:26:26.350 So how granular are current reporting standards 00:26:26.350 --> 00:26:28.770 and how do we keep track of the worst performing circuits? 00:26:28.770 --> 00:26:32.300 So this is just a rundown of current reporting standards 00:26:32.300 --> 00:26:34.540 and how are these persistently problematic started 00:26:34.540 --> 00:26:37.090 or identified and targeted for remediation? 00:26:37.090 --> 00:26:37.923 Next slide. 00:26:41.600 --> 00:26:44.120 So, reliability metrics, reporting 00:26:44.120 --> 00:26:46.710 on a granular level contains a really detailed picture 00:26:46.710 --> 00:26:48.400 of the regional variation 00:26:48.400 --> 00:26:52.478 within each of the overall metrics. 00:26:52.478 --> 00:26:54.830 The 2016 decision did require utilities 00:26:54.830 --> 00:26:58.580 to include these division level mattresses, metrics, sorry 00:26:58.580 --> 00:27:00.840 in their annual reports. 00:27:00.840 --> 00:27:03.150 You can use this data to show 00:27:03.150 --> 00:27:04.720 where the recurring issues are happening 00:27:04.720 --> 00:27:07.040 and where the areas of improvement are needed, 00:27:07.040 --> 00:27:10.140 but it also shows how widely the metrics vary 00:27:10.140 --> 00:27:12.270 over the entire service territory. 00:27:12.270 --> 00:27:16.100 So the next slide is just a, is an illustration 00:27:16.100 --> 00:27:20.060 of this showing every division within PGE performance 00:27:21.430 --> 00:27:23.355 for the year of 2019. 00:27:23.355 --> 00:27:25.188 So next slide, please. 00:27:27.150 --> 00:27:30.670 So as you can see all the way over at the far left 00:27:30.670 --> 00:27:34.680 in the San Francisco division is consistently 00:27:34.680 --> 00:27:39.643 the best performing division within PGE service territory, 00:27:39.643 --> 00:27:44.643 the outage durations do tend to be generally less 00:27:45.300 --> 00:27:49.150 than 100 minutes even with the inclusion 00:27:49.150 --> 00:27:50.701 of major event days. 00:27:50.701 --> 00:27:53.290 (indistinct) up to the right at Humboldt, 00:27:53.290 --> 00:27:58.280 you know, we can see that there's a very opposite problem 00:27:58.280 --> 00:28:00.750 where with the inclusion of major event days 00:28:00.750 --> 00:28:05.750 their own system average interruption duration index jumps 00:28:06.870 --> 00:28:10.990 to almost 7,000 minutes, which is just short metric, 00:28:10.990 --> 00:28:14.833 is just short of five days of the average outage. 00:28:16.680 --> 00:28:21.680 So it's, these overall metrics are sort of an indicator 00:28:23.550 --> 00:28:24.910 of how the system is performing, 00:28:24.910 --> 00:28:27.080 but there's a lot of data hidden with them 00:28:27.080 --> 00:28:29.550 and that's sort of this regional variation 00:28:29.550 --> 00:28:34.080 that you can see illustrated here on the screen. 00:28:34.080 --> 00:28:35.097 Next slide please. 00:28:37.500 --> 00:28:39.810 The 2015 decision also requires the utilities 00:28:39.810 --> 00:28:43.540 to report out on the top 1% of worst performing circuits 00:28:43.540 --> 00:28:44.830 on the system by both 00:28:44.830 --> 00:28:47.380 the system average interruption frequency index 00:28:47.380 --> 00:28:50.820 of that circuit and also the system average duration index 00:28:50.820 --> 00:28:51.653 of that circuit. 00:28:52.642 --> 00:28:56.130 So these are the reporting requirements for each utility. 00:28:57.410 --> 00:28:58.513 You can see that. 00:28:59.510 --> 00:29:00.587 Next slide please. 00:29:02.740 --> 00:29:07.740 So data is available from 2015, the 2016 decision required 00:29:08.810 --> 00:29:13.810 that utility apply this worst performing circuit analysis 00:29:14.540 --> 00:29:18.570 to their data from 2015 in the first year 00:29:19.550 --> 00:29:21.230 and so this is just illustrative 00:29:21.230 --> 00:29:23.620 that a lot of the worst performing circuits 00:29:23.620 --> 00:29:25.790 in these reports are repeat offenders 00:29:25.790 --> 00:29:28.930 and they've been on the list for multiple years. 00:29:28.930 --> 00:29:31.500 So as you can see, PG&E 33% 00:29:34.110 --> 00:29:38.280 of the worst performing circuits have been on that list 00:29:38.280 --> 00:29:42.380 for more than, for three years or more. 00:29:42.380 --> 00:29:44.760 Edison has 18%. 00:29:44.760 --> 00:29:46.820 Liberty, it's a very small sample size, 00:29:46.820 --> 00:29:50.867 but it's a little bit harder to extrapolate from that, 00:29:53.070 --> 00:29:55.950 But as you can see, these circuits 00:29:55.950 --> 00:29:59.260 are generally persistently performing poorly. 00:29:59.260 --> 00:30:00.367 Next slide please. 00:30:03.350 --> 00:30:06.390 So just as a note, you know, circuits do tend to appear 00:30:06.390 --> 00:30:09.190 on these worst performing circuits list for multiple years 00:30:09.190 --> 00:30:11.350 because remediation programs that target 00:30:11.350 --> 00:30:14.180 the controllable factors can take years to complete 00:30:14.180 --> 00:30:16.650 and they only achieve modest reduction 00:30:16.650 --> 00:30:19.370 in the system average interruption frequency index 00:30:19.370 --> 00:30:21.723 and system average interruption duration index. 00:30:22.950 --> 00:30:25.470 However, uncontrollable factors 00:30:25.470 --> 00:30:27.880 like environmental conditions do play a large role 00:30:27.880 --> 00:30:31.700 in determining the reliability and some of these things 00:30:31.700 --> 00:30:34.030 are beyond utilities control and can't be addressed 00:30:34.030 --> 00:30:36.380 by traditional circuit remediation investments. 00:30:38.330 --> 00:30:40.630 PG&E's 2019 report, they reported 00:30:40.630 --> 00:30:45.043 that the Alleghany 1101 which is in Eastern California 00:30:47.280 --> 00:30:49.540 was noted as deficient having appeared 00:30:49.540 --> 00:30:52.110 on multiple worst performing circuits with 00:30:52.110 --> 00:30:53.740 and have more spikes 00:30:53.740 --> 00:30:57.330 in its system average interruption duration index in 2019 00:30:57.330 --> 00:30:59.550 that was caused by a single unknown event coupled 00:30:59.550 --> 00:31:03.230 with unacceptable mainline sections that were difficult 00:31:03.230 --> 00:31:05.680 to inspect and difficult to get back online. 00:31:05.680 --> 00:31:07.510 So, you know, unfortunately 00:31:07.510 --> 00:31:10.800 these metrics reflect both those control factors 00:31:10.800 --> 00:31:15.800 and uncontrollable factors within the circuit performance 00:31:16.410 --> 00:31:18.560 and that's something that has to be, 00:31:18.560 --> 00:31:22.283 perhaps to be born in mind as we look at these metrics. 00:31:23.457 --> 00:31:24.747 So next slide please. 00:31:26.250 --> 00:31:29.053 So this is from PG&E in 2019. 00:31:30.210 --> 00:31:34.100 These are the numbers of sustained outages in PGE territory. 00:31:38.780 --> 00:31:42.230 As you can see, well over 1/3 of them were company initiated 00:31:42.230 --> 00:31:47.230 but almost no close to 1/5 or 1/4 also were unknown cause. 00:31:49.880 --> 00:31:52.960 So while there are factors within the utilities control, 00:31:52.960 --> 00:31:56.190 there are factors that are uncontrollable 00:31:56.190 --> 00:32:00.010 and, you know, third party unknown cards. 00:32:00.010 --> 00:32:03.970 So, it paints an interesting picture of these 00:32:03.970 --> 00:32:05.223 and, you know, it's something 00:32:05.223 --> 00:32:08.250 that we have to keep in mind as we look at 00:32:08.250 --> 00:32:09.150 and use this data. 00:32:10.588 --> 00:32:12.088 Next slide please. 00:32:14.234 --> 00:32:17.342 So, this is the next step, what can we improve? 00:32:17.342 --> 00:32:19.660 How can we better use this data 00:32:19.660 --> 00:32:22.580 and what, you know, what directions can we go with it? 00:32:22.580 --> 00:32:23.777 So next slide please. 00:32:27.520 --> 00:32:30.740 So listed here are just some other possible usages 00:32:30.740 --> 00:32:34.477 for the electrical reliability data that we envisioned 00:32:36.580 --> 00:32:40.493 could be useful to look at to assess this performance. 00:32:41.500 --> 00:32:44.260 So one of the big things, you know, 00:32:44.260 --> 00:32:45.643 it's trying to be equity focused 00:32:45.643 --> 00:32:50.643 and try to see unreliable service, you know, 00:32:50.777 --> 00:32:53.250 are there equity impacts of unreliable service 00:32:54.260 --> 00:32:58.310 by using the SB 535 disadvantaged communities maps 00:32:58.310 --> 00:33:02.470 but, and CalEnviroScreen but also other equity focus tools 00:33:02.470 --> 00:33:06.510 to assess whether there is an equity impact 00:33:07.600 --> 00:33:08.993 of unreliable service. 00:33:10.030 --> 00:33:12.970 You know, we can also track impacts and customer experience 00:33:12.970 --> 00:33:16.100 during the public safety power shutoff events. 00:33:16.100 --> 00:33:21.100 One interesting thing we can do is to track the threshold 00:33:21.350 --> 00:33:25.040 for major event day to see if on the average 00:33:25.040 --> 00:33:28.150 on the whole system outages are getting worse 00:33:28.150 --> 00:33:30.740 and including, you know, including more data 00:33:30.740 --> 00:33:34.383 that may have been excluded in previous reports. 00:33:35.760 --> 00:33:38.300 There's also a look towards assessing prioritization 00:33:38.300 --> 00:33:40.080 and siting of grid resilience solutions 00:33:40.080 --> 00:33:43.350 to ensure least cost best fit solutions 00:33:43.350 --> 00:33:45.490 and also capturing how climate change affects 00:33:45.490 --> 00:33:46.430 the electrical system 00:33:46.430 --> 00:33:49.753 and outage duration through outage causality. 00:33:50.970 --> 00:33:52.087 Next slide please. 00:33:54.040 --> 00:33:56.660 So, the next step that we envision. 00:33:56.660 --> 00:34:00.210 So we have been working with utilities 00:34:00.210 --> 00:34:03.220 to improve their current reporting 00:34:04.132 --> 00:34:06.710 in their current reporting standards, 00:34:06.710 --> 00:34:10.330 mostly within the bounds of that 2016 decisions 00:34:10.330 --> 00:34:12.960 in terms of trying to make the data more usable 00:34:12.960 --> 00:34:15.378 and one of the things that we've requested 00:34:15.378 --> 00:34:18.850 is making sure that these spreadsheets 00:34:18.850 --> 00:34:20.340 with all of the data are available 00:34:20.340 --> 00:34:22.470 to make our analysis easier. 00:34:22.470 --> 00:34:26.210 Additionally, we are undergoing on outreach and validation 00:34:26.210 --> 00:34:28.340 to engage a diverse set of stakeholders 00:34:28.340 --> 00:34:29.940 about the usefulness of the data 00:34:31.340 --> 00:34:34.610 and also talking to utilities about expanding access 00:34:34.610 --> 00:34:36.760 to reliable data at a more granular level 00:34:36.760 --> 00:34:39.650 and also monitoring annual public reliability meetings 00:34:39.650 --> 00:34:43.420 to see what the needs are for this data 00:34:43.420 --> 00:34:48.420 and how we can better tailor this data provision to 00:34:48.500 --> 00:34:50.910 and use it on some things in the future 00:34:50.910 --> 00:34:52.980 that we would also like to see 00:34:52.980 --> 00:34:56.910 are GIS format of data complete with historical metrics. 00:34:56.910 --> 00:34:58.220 We would like to see, 00:34:58.220 --> 00:35:01.240 you know, probably available circuit level data 00:35:01.240 --> 00:35:03.590 to make sure that it's as granular as possible. 00:35:04.460 --> 00:35:06.660 You know, we would like to see the reliability impact 00:35:06.660 --> 00:35:08.480 of PSPS and other outage types, 00:35:08.480 --> 00:35:10.860 but then those metrics and then also 00:35:10.860 --> 00:35:14.270 to have utilities talk about mitigation measures 00:35:14.270 --> 00:35:17.173 being taken to address those reliability issues, 00:35:18.140 --> 00:35:20.340 you know, and a lot of it does overlap 00:35:20.340 --> 00:35:23.650 with some currently ongoing efforts within the resiliency 00:35:26.291 --> 00:35:31.291 and microgrid space including the value of resiliency 00:35:33.100 --> 00:35:36.500 and resiliency of metrics for the local areas. 00:35:36.500 --> 00:35:40.660 The recently voted out microgrids incentive program 00:35:40.660 --> 00:35:43.040 to help ensure communities are getting the right data 00:35:43.040 --> 00:35:44.840 they need to participate, 00:35:44.840 --> 00:35:48.600 and also within PG$E regionalization proceedings 00:35:48.600 --> 00:35:53.600 as a possible tracker of performance within area. 00:35:55.784 --> 00:35:57.034 So, next slide. 00:35:58.450 --> 00:36:00.270 So with that, I am open 00:36:00.270 --> 00:36:03.860 to take any questions folks may have. 00:36:03.860 --> 00:36:07.270 Thank you for taking the time to listen. 00:36:07.270 --> 00:36:08.300 Thank you, Julian. 00:36:08.300 --> 00:36:10.093 Excellent presentation. 00:36:12.668 --> 00:36:16.760 A lot of acronyms, a lot of data all feeding 00:36:16.760 --> 00:36:21.760 into pinpointing where attention is needed, 00:36:22.870 --> 00:36:26.270 Looking at our disadvantaged communities, 00:36:26.270 --> 00:36:29.810 low-income communities, seeing where as we navigate 00:36:29.810 --> 00:36:31.840 through general rate cases 00:36:31.840 --> 00:36:35.990 where resources may need to be focused. 00:36:35.990 --> 00:36:39.490 So let me turn to my colleagues 00:36:39.490 --> 00:36:41.790 to see if there are any comments or questions. 00:36:43.820 --> 00:36:47.060 I just wanted to say, this is Marybel, 00:36:47.060 --> 00:36:50.810 sorry you can't see me, so I'm sorry can't wave my hand. 00:36:50.810 --> 00:36:52.550 I just wanted to thank Justin 00:36:52.550 --> 00:36:55.030 for a terrific presentation. 00:36:55.030 --> 00:37:00.030 A lot of new, as you said new acronyms Commissioner Shiroma, 00:37:00.900 --> 00:37:03.910 but excellent and I'm gonna have to get a copy 00:37:03.910 --> 00:37:07.780 of the deck 'cause couldn't see all of the data 00:37:07.780 --> 00:37:12.780 but interesting rich data for us to take advantage of 00:37:13.940 --> 00:37:17.380 as we continued to plan and work with PSPS. 00:37:21.030 --> 00:37:23.550 So thank you very much 00:37:23.550 --> 00:37:25.710 and I'll turn it back to you Commissioner Shiroma. 00:37:25.710 --> 00:37:27.068 Thank you. 00:37:27.068 --> 00:37:28.230 Thank you. 00:37:28.230 --> 00:37:30.210 Yes, Commissioner (indistinct) 00:37:30.210 --> 00:37:33.053 and then Commissioner Guzman Osavis or Julian. 00:37:34.286 --> 00:37:35.865 Thank you very much. 00:37:35.865 --> 00:37:36.698 Thank you, Julian. 00:37:36.698 --> 00:37:39.830 I have two, I have a question and a comment. 00:37:39.830 --> 00:37:44.830 The comment is I fully support the idea of examining 00:37:45.107 --> 00:37:48.960 the equity impacts of unreliable service using 00:37:48.960 --> 00:37:52.210 the SB 535 mapping or other equity tools 00:37:52.210 --> 00:37:55.750 and I just, I guess this is a question too, 00:37:55.750 --> 00:37:58.160 are you, is that something you're considering 00:37:58.160 --> 00:38:00.500 or is it something you're actually doing according 00:38:00.500 --> 00:38:03.160 to a schedule and in any case I'd like 00:38:03.160 --> 00:38:05.517 to encourage you to go ahead and do that 00:38:05.517 --> 00:38:07.300 and report back to us publicly. 00:38:07.300 --> 00:38:09.720 I think would be very, very interesting. 00:38:09.720 --> 00:38:12.670 So let me stop there and then I'll ask the second question. 00:38:13.530 --> 00:38:15.680 Yeah, absolutely, thank you for the comment 00:38:15.680 --> 00:38:17.180 and also the question. 00:38:17.180 --> 00:38:20.410 Currently this is something we are envisioning. 00:38:20.410 --> 00:38:23.220 We would need to work with utilities 00:38:23.220 --> 00:38:27.410 to see if we can get GIS format of this data 00:38:27.410 --> 00:38:30.240 to ensure that that analysis is both, 00:38:30.240 --> 00:38:34.000 it's easier, but also is it very robust to make sure 00:38:34.000 --> 00:38:36.740 we are assessing those overlaps 00:38:36.740 --> 00:38:40.580 and also, you know, seeing these equity impacts 00:38:40.580 --> 00:38:42.903 and making sure we know where they are. 00:38:44.580 --> 00:38:48.000 Okay, I would be interested in hearing back from you 00:38:48.000 --> 00:38:51.000 whenever you've gotten a sense of what utility is having, 00:38:51.000 --> 00:38:52.950 how your efforts could proceed in that area? 00:38:52.950 --> 00:38:55.900 I'm sure the other Commissioners would as well. 00:38:55.900 --> 00:38:58.170 The second question relates to major events 00:38:58.170 --> 00:39:00.640 as the stark differences 00:39:00.640 --> 00:39:04.050 between data with or without major events really grabbed me. 00:39:04.050 --> 00:39:06.470 I assume, for example, what's happening in Texas 00:39:06.470 --> 00:39:08.570 and the Midwest is a major event 00:39:08.570 --> 00:39:12.433 as defined according to the data were using, right? 00:39:13.880 --> 00:39:14.713 Yeah, that would be. 00:39:14.713 --> 00:39:15.700 Yeah, you're nodding, 00:39:15.700 --> 00:39:17.000 I don't know if everyone can see you, 00:39:17.000 --> 00:39:22.000 but given that are the standard setting bodies 00:39:22.590 --> 00:39:25.700 or others thinking about adjusting 00:39:25.700 --> 00:39:27.490 the way we define major events. 00:39:27.490 --> 00:39:30.950 You mentioned that as one of the potential bullets. 00:39:30.950 --> 00:39:34.610 It seems like those should not be treated as outliers 00:39:34.610 --> 00:39:38.890 given their frequency either because of human caused events 00:39:38.890 --> 00:39:41.200 or otherwise as I understood your presentation 00:39:41.200 --> 00:39:43.110 and that just doesn't distinguish 00:39:43.110 --> 00:39:45.510 between major events that are caused by earthquakes 00:39:45.510 --> 00:39:48.600 and natural disasters versus human induced events 00:39:48.600 --> 00:39:52.280 like public safety power shutoff. 00:39:52.280 --> 00:39:53.610 So I guess the question is, 00:39:53.610 --> 00:39:56.093 is there a trend nationally to re-look at this? 00:39:57.820 --> 00:40:00.480 So, that is correct. 00:40:00.480 --> 00:40:02.350 That's, a lot of these metrics 00:40:02.350 --> 00:40:05.990 are meant to be statistical representations of this data 00:40:05.990 --> 00:40:07.960 and actually are sort of meant to take 00:40:07.960 --> 00:40:10.410 the causality element out of, 00:40:10.410 --> 00:40:13.850 and just the look at the event itself. 00:40:13.850 --> 00:40:18.267 Currently, I don't believe there's an effort 00:40:18.267 --> 00:40:21.000 to sort of undertake a re-look 00:40:21.000 --> 00:40:23.700 at how major event days are defined, 00:40:23.700 --> 00:40:26.740 but this is also crossing into thinking 00:40:26.740 --> 00:40:30.410 about system resilience and thinking 00:40:30.410 --> 00:40:32.880 about how resilient the system is 00:40:32.880 --> 00:40:35.750 because these are large scale, 00:40:35.750 --> 00:40:37.440 you know, large scale, high impact 00:40:37.440 --> 00:40:39.630 and low frequency events. 00:40:39.630 --> 00:40:41.530 And these statistics were designed 00:40:41.530 --> 00:40:43.280 with the utilities in mind 00:40:43.280 --> 00:40:46.690 to be able to take those low frequency, 00:40:46.690 --> 00:40:50.410 low probability events out of consideration 00:40:50.410 --> 00:40:54.320 so that they can improve just normal mainline service. 00:40:54.320 --> 00:40:58.941 So within what we're looking at with resiliency 00:40:58.941 --> 00:41:03.260 within our team and energy division, we are trying 00:41:03.260 --> 00:41:08.260 to sort of think about how, you know, how we can use 00:41:08.340 --> 00:41:10.000 the current major event day definition 00:41:10.000 --> 00:41:13.730 to sort of give us an idea of what is the, 00:41:13.730 --> 00:41:16.370 you know, pardon the train expression, 00:41:16.370 --> 00:41:17.720 but what is the new normal. 00:41:20.130 --> 00:41:20.963 Thank you. 00:41:22.500 --> 00:41:24.120 All right, thank you. 00:41:24.120 --> 00:41:27.954 We do have a new, we really do have a new normal. 00:41:27.954 --> 00:41:30.370 So I think that's the right question. 00:41:30.370 --> 00:41:32.960 I'm sorry, Commissioner Shiroma. 00:41:32.960 --> 00:41:35.763 No, my apologies, absolutely right. 00:41:36.640 --> 00:41:39.533 What we're seeing across the nation, my goodness. 00:41:40.510 --> 00:41:41.937 Commissioner Guzman Osavis. 00:41:42.920 --> 00:41:44.770 Yes, thank you, Commissioner Shiroma 00:41:44.770 --> 00:41:49.150 and thank you, Julian and Tiana for all your work on this. 00:41:49.150 --> 00:41:51.310 I know that we've talked before 00:41:51.310 --> 00:41:54.050 from the San Joaquin Valley proceeding 00:41:54.050 --> 00:41:59.050 where many of these really small population communities 00:41:59.640 --> 00:42:02.060 never, given the formulas 00:42:02.060 --> 00:42:04.660 you noted the denominator is always, 00:42:04.660 --> 00:42:09.540 you know, population and so those metrics 00:42:09.540 --> 00:42:13.083 will never farewell for remote communities with low density. 00:42:14.270 --> 00:42:16.740 And when you look at it at a macro level that makes sense 00:42:16.740 --> 00:42:19.870 because you wanna help as many people as possible of course, 00:42:19.870 --> 00:42:22.480 but if you look at a regional level, 00:42:22.480 --> 00:42:26.300 and just on an individual level perhaps arguably 00:42:26.300 --> 00:42:28.370 through an environmental justice lens, 00:42:28.370 --> 00:42:29.770 these metrics don't get up 00:42:29.770 --> 00:42:33.750 the most harmed customers potentially. 00:42:33.750 --> 00:42:38.170 And I wonder in the regionalization applications, 00:42:38.170 --> 00:42:40.620 if there is an opportunity there 00:42:40.620 --> 00:42:43.180 to really resolve this issue 00:42:43.180 --> 00:42:46.620 and start to, it's not to replace these metrics, 00:42:46.620 --> 00:42:49.190 but to actually get to a more micro level. 00:42:49.190 --> 00:42:53.750 I've seen, you know, this chronic situation, 00:42:53.750 --> 00:42:55.320 like you pointed out with Humboldt. 00:42:55.320 --> 00:42:57.610 Same scenario there where these issues 00:42:57.610 --> 00:42:59.140 are perpetuating themselves 00:42:59.140 --> 00:43:02.240 because the metrics never get at them. 00:43:02.240 --> 00:43:06.230 And so I hope that we can certainly continue 00:43:06.230 --> 00:43:08.610 to use these macro level metrics certainly 00:43:08.610 --> 00:43:11.300 to benefit the largest populations 00:43:11.300 --> 00:43:13.770 and individual customers as possible. 00:43:13.770 --> 00:43:18.770 But I don't see how continuing to rely on them alone 00:43:19.370 --> 00:43:20.320 is going to get us 00:43:20.320 --> 00:43:24.300 at some of these chronically disinvested communities. 00:43:24.300 --> 00:43:26.190 And just to share with my fellow Commissioners, 00:43:26.190 --> 00:43:30.300 one of the most concerning results of this 00:43:30.300 --> 00:43:34.230 is as we're working on these pilots in these communities, 00:43:34.230 --> 00:43:36.780 these 11 and 10 communities in the valley 00:43:37.830 --> 00:43:40.590 to try to electrify them for their heating needs 00:43:40.590 --> 00:43:42.660 for both their air quality health impacts 00:43:42.660 --> 00:43:43.803 and climate benefits. 00:43:45.240 --> 00:43:48.683 PG&E in particular, not at, PG&E is claiming 00:43:48.683 --> 00:43:53.683 that increase in load is gonna require substantial increases 00:43:54.440 --> 00:43:56.970 in investing in their distribution system 00:43:56.970 --> 00:43:59.010 for the drops in particular, 00:43:59.010 --> 00:44:04.010 and, you know, if we, it just kinda goes to show you 00:44:04.520 --> 00:44:06.350 how bad the situation is 00:44:06.350 --> 00:44:08.240 if people adding just a little bit of loads 00:44:08.240 --> 00:44:10.140 which requires not much of an upgrade. 00:44:11.240 --> 00:44:15.235 And so, anyhow, I guess, you know, 00:44:15.235 --> 00:44:18.110 I certainly wanna see these metrics more 00:44:18.110 --> 00:44:19.877 to guide our risk management decisions, 00:44:19.877 --> 00:44:23.610 but also really hope that in the regionalization application 00:44:23.610 --> 00:44:26.786 we can take a regional approach 00:44:26.786 --> 00:44:31.440 and really start to get at examining, you know, 00:44:31.440 --> 00:44:34.170 where these chronic issues are. 00:44:34.170 --> 00:44:36.680 It's harder to justify perhaps 00:44:36.680 --> 00:44:39.397 because of the lack of economies of scale 00:44:39.397 --> 00:44:43.240 and the lack of actual customers that would benefit, 00:44:43.240 --> 00:44:46.343 but it is very much an equity issue. 00:44:47.510 --> 00:44:49.730 So I don't know if you have given that thought 00:44:49.730 --> 00:44:52.773 if you were thinking about, is there a way to, you know, 00:44:53.830 --> 00:44:56.850 like I said, continue to use this formula for macro-level, 00:44:56.850 --> 00:45:00.470 but they're way to regionalization evolution 00:45:00.470 --> 00:45:05.470 that's happening at PG&E to further grieve a new formula 00:45:05.930 --> 00:45:09.750 or adjust the formula, you know, by county population. 00:45:09.750 --> 00:45:12.460 Come up with some kind of even county level 00:45:12.460 --> 00:45:14.790 or district level, I know they have the districts. 00:45:14.790 --> 00:45:16.530 I don't know how that's gonna evolve 00:45:16.530 --> 00:45:18.858 with the regionalization, but anyway, 00:45:18.858 --> 00:45:21.020 I don't know if you have any thoughts now on that, 00:45:21.020 --> 00:45:23.275 but I certainly hope you're thinking about that. 00:45:23.275 --> 00:45:25.290 I'm certainly thinking about 00:45:25.290 --> 00:45:28.703 how we can potentially improve on that situation. 00:45:30.080 --> 00:45:30.913 Thank you. 00:45:30.913 --> 00:45:31.746 Yes, absolutely. 00:45:31.746 --> 00:45:34.150 Thank you, Commissioner for those comments. 00:45:34.150 --> 00:45:38.050 You know, we definitely see these reliability metrics 00:45:38.050 --> 00:45:40.670 as a tool within our toolbox 00:45:40.670 --> 00:45:44.897 that we can pull out to do analysis on these, on, 00:45:46.230 --> 00:45:48.130 you know, on smaller levels 00:45:48.130 --> 00:45:49.470 and, you know, that is something 00:45:49.470 --> 00:45:51.810 that we're working towards making sure that, 00:45:51.810 --> 00:45:54.830 you know, this reliability that all the way down 00:45:54.830 --> 00:45:57.150 to the circuit level is being, 00:45:57.150 --> 00:46:01.693 you know, is being made available so that we can see that. 00:46:02.550 --> 00:46:04.572 You know, I definitely appreciate the comments 00:46:04.572 --> 00:46:05.990 about equity as well. 00:46:05.990 --> 00:46:08.960 It's, you know, this is a really important issue 00:46:08.960 --> 00:46:13.310 and this is also carrying over into work on resiliency 00:46:13.310 --> 00:46:14.850 where we do really wanna make sure 00:46:14.850 --> 00:46:19.700 we're being equity focused within our work 00:46:20.570 --> 00:46:23.180 and within the way that we look at this data 00:46:23.180 --> 00:46:27.400 and that we sort of use it to draw conclusions 00:46:27.400 --> 00:46:28.840 and also figure out, you know, 00:46:28.840 --> 00:46:30.210 what the assumptions are behind it 00:46:30.210 --> 00:46:31.950 so that we can adjust accordingly. 00:46:31.950 --> 00:46:36.110 So that's definitely high on the priority list 00:46:36.110 --> 00:46:39.950 is ensuring that we are focusing on equity, 00:46:39.950 --> 00:46:42.910 you know, in terms of how we're looking at these things. 00:46:42.910 --> 00:46:44.410 So definitely appreciate them. 00:46:46.270 --> 00:46:47.169 Thank you. 00:46:47.169 --> 00:46:48.140 Thank you Julian. 00:46:48.140 --> 00:46:52.990 Yeah, I think that Commissioner Guzman your point 00:46:52.990 --> 00:46:57.990 about how the regionalization effort will work 00:46:58.220 --> 00:47:02.530 for PG&E is going to be very important 00:47:02.530 --> 00:47:07.530 and that we need to look at this very holistically 00:47:08.220 --> 00:47:11.650 not with any of these metrics in life relations. 00:47:11.650 --> 00:47:16.400 So I'm sure there'll be more to follow 00:47:16.400 --> 00:47:21.400 as we navigate with PG&E this effort on regionalization. 00:47:23.750 --> 00:47:26.070 President Batger, Commissioners, any other comments 00:47:26.070 --> 00:47:27.720 before we go to our next speaker? 00:47:29.450 --> 00:47:31.923 Okay, all right, not at all. 00:47:33.490 --> 00:47:35.640 Commissioner Shiroma thank you. 00:47:35.640 --> 00:47:39.440 Okay, all right, well, we have another presentation, 00:47:39.440 --> 00:47:43.630 another perspective from Dr. Fiona Burlig, 00:47:43.630 --> 00:47:45.210 who is an assistant professor 00:47:45.210 --> 00:47:47.680 at the Harris School of Public Policy 00:47:47.680 --> 00:47:51.040 and a faculty research fellow 00:47:51.040 --> 00:47:53.963 at the National Bureau of Economic Research. 00:47:55.090 --> 00:47:57.260 This is at the University of Chicago 00:47:58.100 --> 00:48:02.760 and through the one of the benefits I suppose 00:48:02.760 --> 00:48:07.760 of the work at home is we get to garner the expertise 00:48:09.110 --> 00:48:10.180 from across the country. 00:48:10.180 --> 00:48:13.743 So Professor Burlig is joining us from Chicago. 00:48:14.610 --> 00:48:16.580 She has also partnered with professors 00:48:16.580 --> 00:48:19.100 at the Haas School of Business 00:48:19.100 --> 00:48:21.590 at the University of California at Berkeley 00:48:21.590 --> 00:48:23.750 on economic research in the area 00:48:23.750 --> 00:48:26.820 of electric reliability, and Dr. Burlig 00:48:26.820 --> 00:48:29.540 will be presenting today on her research 00:48:29.540 --> 00:48:33.140 on the value of reliability, more specifically 00:48:33.140 --> 00:48:36.610 the economic impacts of electricity outages 00:48:36.610 --> 00:48:40.513 in the context of our public safety power shut off. 00:48:41.500 --> 00:48:45.260 Dr. Burlig holds a PhD in agricultural 00:48:45.260 --> 00:48:46.950 and resource economics 00:48:46.950 --> 00:48:49.090 from the University of California, Berkeley 00:48:49.090 --> 00:48:54.090 and a BA bachelor's in economics, political science 00:48:54.240 --> 00:48:57.230 in German from Williams College. 00:48:57.230 --> 00:48:59.253 So welcome Dr. Burlig. 00:49:02.050 --> 00:49:04.780 Great, thank you so much for having me. 00:49:04.780 --> 00:49:09.780 So this is joint work as the Commissioner has mentioned 00:49:10.480 --> 00:49:14.210 with Katherine Wolf from the Haas School of Business 00:49:14.210 --> 00:49:17.180 at UC Berkeley and also Duncan Callaway and Will Gorman 00:49:17.180 --> 00:49:19.760 who are in the energy and resources group at UC Berkeley. 00:49:19.760 --> 00:49:21.270 So it's an interdisciplinary team. 00:49:21.270 --> 00:49:23.280 Katherine and I are both economists, 00:49:23.280 --> 00:49:25.710 and Duncan and Will are both engineers. 00:49:25.710 --> 00:49:26.660 Next slide, please. 00:49:29.890 --> 00:49:33.010 Great, so this is some preliminary work 00:49:33.010 --> 00:49:35.780 that we're starting on to try and understand, 00:49:35.780 --> 00:49:39.230 or to think about new ways of measuring the cost 00:49:39.230 --> 00:49:41.500 and benefits of electricity reliability. 00:49:41.500 --> 00:49:43.560 So the big questions we're trying to ask 00:49:43.560 --> 00:49:46.250 in this research agenda are number one, 00:49:46.250 --> 00:49:48.950 what are the economic impacts of electricity outages 00:49:48.950 --> 00:49:52.910 and how can we measure them in a comprehensive way? 00:49:52.910 --> 00:49:55.130 And then second, what are firms 00:49:55.130 --> 00:49:56.880 and households willing to actually pay 00:49:56.880 --> 00:49:58.910 to avoid future outages? 00:49:58.910 --> 00:50:00.275 And the idea that we have in mind here 00:50:00.275 --> 00:50:03.130 is that the answer to the second question 00:50:03.130 --> 00:50:05.640 gives us an idea about the answer to the first question. 00:50:05.640 --> 00:50:09.440 Right, if you present household or a firm with, 00:50:09.440 --> 00:50:12.470 to spend a little bit of extra money on their bill 00:50:12.470 --> 00:50:14.510 to avoid an outage that tells me something 00:50:14.510 --> 00:50:17.050 about the kind of upper bound of the cost 00:50:17.050 --> 00:50:19.070 of that outage to that firm. 00:50:19.070 --> 00:50:21.990 And so we say that the second, the answer to this question 00:50:21.990 --> 00:50:25.740 gives us an insight into the actual total costs 00:50:25.740 --> 00:50:26.573 of the outages. 00:50:26.573 --> 00:50:29.500 In the first through a reveal preference approach, 00:50:29.500 --> 00:50:32.820 we can actually observe what decisions customers make 00:50:32.820 --> 00:50:34.350 to learn something about how much 00:50:34.350 --> 00:50:36.880 they might be willing to spend to adapt outages 00:50:36.880 --> 00:50:39.537 or avoid damages related to outages, 00:50:39.537 --> 00:50:41.640 and this is closely linked to the concept 00:50:41.640 --> 00:50:43.140 of the value of loss load, right? 00:50:43.140 --> 00:50:45.280 The value of loss load being how much a customer 00:50:45.280 --> 00:50:49.130 would be willing to pay to avoid a particular outage. 00:50:49.130 --> 00:50:50.030 Next slide please. 00:50:53.080 --> 00:50:56.270 Okay, so it's just maybe unsurprising to this audience, 00:50:56.270 --> 00:50:58.810 so we think it's really important to understand 00:50:58.810 --> 00:51:01.230 what the value loss load actually is. 00:51:01.230 --> 00:51:04.500 So in an ideal world, we would equate the economic benefits 00:51:04.500 --> 00:51:06.790 of avoiding a particular outage 00:51:06.790 --> 00:51:10.360 with the economic costs involved in doing so, right. 00:51:10.360 --> 00:51:14.230 So understanding how costly these outages are 00:51:14.230 --> 00:51:15.990 is really essential to understand 00:51:15.990 --> 00:51:18.490 how much we should be sending to avoid those outages. 00:51:18.490 --> 00:51:21.770 This is gonna be relevant to a large variety of decisions, 00:51:21.770 --> 00:51:25.040 including the right level of grid hardening investment, 00:51:25.040 --> 00:51:28.060 the right resource adequacy in a system 00:51:28.060 --> 00:51:29.770 that has intermittent renewables 00:51:29.770 --> 00:51:33.720 in the context of the California public safety power shutoff 00:51:33.720 --> 00:51:36.810 thinking about the cost that outages impose on society 00:51:36.810 --> 00:51:39.890 as compared to the costs of fires 00:51:39.890 --> 00:51:42.697 that we're avoiding by imposing these PSPS events. 00:51:42.697 --> 00:51:44.090 You can also think about, 00:51:44.090 --> 00:51:45.680 you know, this week's events in Texas 00:51:45.680 --> 00:51:48.330 is thinking about weatherization of power plants 00:51:48.330 --> 00:51:52.020 and the electricity grid compared to the cost associated 00:51:52.020 --> 00:51:55.520 with having outages during cold weather. 00:51:55.520 --> 00:51:56.420 Next slide please. 00:51:58.450 --> 00:52:00.960 Okay, so how can we actually get at this number 00:52:00.960 --> 00:52:03.660 of how much society should be willing to pay 00:52:03.660 --> 00:52:05.063 to avoid these outages? 00:52:05.960 --> 00:52:07.780 There've been people who've tried to do this in the past. 00:52:07.780 --> 00:52:10.700 We're not the first to attempt to do this, 00:52:10.700 --> 00:52:12.050 but typically what's done 00:52:12.050 --> 00:52:14.320 is to use what's called a stated preference survey. 00:52:14.320 --> 00:52:17.540 So you basically interview a customer 00:52:17.540 --> 00:52:20.310 and ask them about hypothetical scenarios 00:52:20.310 --> 00:52:22.900 where you say, suppose that you were 00:52:22.900 --> 00:52:25.560 to face this particular set of outages, 00:52:25.560 --> 00:52:28.780 how much would you be willing to pay to avoid that? 00:52:28.780 --> 00:52:30.560 The second thing that you can do 00:52:30.560 --> 00:52:32.600 is what's called a revealed preference approach 00:52:32.600 --> 00:52:34.910 which is actually trying to understand what people did 00:52:34.910 --> 00:52:36.300 in response to outages. 00:52:36.300 --> 00:52:38.330 The advantage of this approach 00:52:38.330 --> 00:52:40.770 is that we see real responses 00:52:40.770 --> 00:52:45.770 and so we avoid the probably customer might say one thing 00:52:46.230 --> 00:52:48.160 in response to this hypothetical scenario, 00:52:48.160 --> 00:52:50.760 but behave differently in reality. 00:52:50.760 --> 00:52:52.370 The third thing you can do is try 00:52:52.370 --> 00:52:54.460 and pull together macro economic data 00:52:54.460 --> 00:52:56.720 and basically look at what's happened overall 00:52:56.720 --> 00:53:00.050 in the economy in times with more versus your outages 00:53:00.050 --> 00:53:03.620 and try and learn about sort of overall scenarios that way. 00:53:03.620 --> 00:53:05.870 So what we're doing in this project 00:53:05.870 --> 00:53:08.790 is bringing together new data on revealed preferences. 00:53:08.790 --> 00:53:11.930 So I should say that this is fairly preliminary work. 00:53:11.930 --> 00:53:14.330 I'm gonna show you at the beginning of this project today, 00:53:14.330 --> 00:53:15.510 and then I'll describe a little bit 00:53:15.510 --> 00:53:17.260 about where we're going as well. 00:53:17.260 --> 00:53:18.430 So we're trying to do 00:53:18.430 --> 00:53:20.400 is use this revealed preference approach. 00:53:20.400 --> 00:53:23.010 It's based on actual decisions that customers are making 00:53:23.010 --> 00:53:27.400 as an indicator of how costly power outages are to them. 00:53:27.400 --> 00:53:29.390 What's nice about this is that 00:53:30.510 --> 00:53:33.547 we can use large archival datasets to answer this question 00:53:33.547 --> 00:53:34.770 and so we're not forced 00:53:34.770 --> 00:53:37.090 to rely on really small samples surveys, 00:53:37.090 --> 00:53:40.690 and secondly, we can also get data like I was saying 00:53:40.690 --> 00:53:42.040 on what customers actually do 00:53:42.040 --> 00:53:44.060 as opposed to what they say they do 00:53:44.060 --> 00:53:46.360 where the latter reveals, oh sorry, 00:53:46.360 --> 00:53:48.320 what customer actually does 00:53:48.320 --> 00:53:50.440 is likely to reveal a lot more about 00:53:51.290 --> 00:53:54.310 how they value these outages relative 00:53:54.310 --> 00:53:55.721 to what they might pay today 00:53:55.721 --> 00:53:58.913 (indistinct) when posted the hypothetical scenario. 00:53:59.847 --> 00:54:02.650 Something we're really trying to be careful about 00:54:02.650 --> 00:54:05.960 is actually estimating the causal impact of outages 00:54:05.960 --> 00:54:07.400 on customer's decision making. 00:54:07.400 --> 00:54:10.190 So we're not for example comparing a customer 00:54:10.190 --> 00:54:12.770 that lives in San Francisco and has very few outages 00:54:12.770 --> 00:54:14.910 with a customer that lives in Fresno 00:54:14.910 --> 00:54:16.130 and has some potential outages. 00:54:16.130 --> 00:54:17.630 There's many things that would differ 00:54:17.630 --> 00:54:20.040 between those two customers beyond 00:54:20.040 --> 00:54:21.010 just their average exposure 00:54:21.010 --> 00:54:22.480 and so we wanna be cognizant of that 00:54:22.480 --> 00:54:24.580 and making sure we're actually capturing 00:54:24.580 --> 00:54:27.290 behavioral responses to outages themselves 00:54:27.290 --> 00:54:30.050 not the other features of the environment. 00:54:30.050 --> 00:54:34.770 And so to start this project, we actually did do a survey 00:54:34.770 --> 00:54:36.800 that's gonna provide some additional color 00:54:36.800 --> 00:54:38.263 and kind of informed the types of costs 00:54:38.263 --> 00:54:41.520 that we think we should be looking for once we engage 00:54:41.520 --> 00:54:43.700 in the archival data work. 00:54:43.700 --> 00:54:44.650 Next slide, please. 00:54:47.200 --> 00:54:48.100 So to give you have a sense 00:54:48.100 --> 00:54:50.520 of how we're thinking about doing this, 00:54:50.520 --> 00:54:55.380 we are leveraging the variation in outage experiences 00:54:55.380 --> 00:54:59.630 that were driven by the 2019 PSPS events, 00:54:59.630 --> 00:55:02.880 and what these events did is because outages 00:55:02.880 --> 00:55:05.730 were basically imposed at the feeder level. 00:55:05.730 --> 00:55:07.740 Here as you can see some feeder maps 00:55:07.740 --> 00:55:10.070 from Berkeley and Oakland, 00:55:10.070 --> 00:55:12.110 we can compare two customers 00:55:12.110 --> 00:55:13.720 that live very close to each other, 00:55:13.720 --> 00:55:15.870 but had wildly different outage experience. 00:55:16.823 --> 00:55:19.513 You can see that yellow feeders 00:55:19.513 --> 00:55:22.110 are feeders that didn't experience outages. 00:55:22.110 --> 00:55:25.450 This is sort of from one of the PSPS days in 2019 00:55:25.450 --> 00:55:27.450 whereas the purple feeders did, 00:55:27.450 --> 00:55:30.670 and so you can imagine comparing very nearby neighbors 00:55:30.670 --> 00:55:33.320 to one another one of whom had a full day 00:55:33.320 --> 00:55:35.270 of power outage events and the other one 00:55:35.270 --> 00:55:37.010 who had no power outage events. 00:55:37.010 --> 00:55:38.850 The thing that's really useful about that 00:55:38.850 --> 00:55:42.050 is it allows us to sweep away any other differences 00:55:42.050 --> 00:55:43.300 between those two customers 00:55:43.300 --> 00:55:44.690 other than their outage experience. 00:55:44.690 --> 00:55:46.130 We can really get a good estimate 00:55:46.130 --> 00:55:47.520 of the effect of the outage 00:55:47.520 --> 00:55:50.760 rather than broader differences between customers. 00:55:50.760 --> 00:55:51.710 Next slide, please. 00:55:54.230 --> 00:55:56.960 So, we're currently working on things 00:55:56.960 --> 00:56:00.580 to, sort of a whole suite of available archival data 00:56:00.580 --> 00:56:04.880 including from the IOUs is thinking about 00:56:04.880 --> 00:56:07.130 you sort of using that type of border analysis 00:56:07.130 --> 00:56:10.360 comparing more effective versus once effective figures 00:56:10.360 --> 00:56:12.170 to look at whether customers 00:56:12.170 --> 00:56:15.610 are investing in diesel powered backup generators, 00:56:15.610 --> 00:56:17.370 whether they're more or less likely 00:56:17.370 --> 00:56:21.610 to purchase a solar PV or invest in storage. 00:56:21.610 --> 00:56:23.350 But we also wanna make sure that we're thinking 00:56:23.350 --> 00:56:25.630 about other economic costs as well. 00:56:25.630 --> 00:56:29.850 So we're using data on mobility to understand 00:56:29.850 --> 00:56:32.080 whether firms are being less visited 00:56:32.080 --> 00:56:33.080 when they (mumbles). 00:56:34.290 --> 00:56:36.940 Also looking at kind of business closures information 00:56:36.940 --> 00:56:41.190 to see whether some of these PSPS events that are large 00:56:41.190 --> 00:56:44.340 are actually causing small businesses to close 00:56:44.340 --> 00:56:46.960 and then a major potential cost as well 00:56:46.960 --> 00:56:50.843 in terms of public health. And so we wanna make sure 00:56:50.843 --> 00:56:55.500 that we're also capturing information on customers 00:56:55.500 --> 00:56:56.790 that have medical devices 00:56:56.790 --> 00:56:58.890 that require electricity to operate. 00:56:58.890 --> 00:57:00.500 For example, so we're trying to pull in data 00:57:00.500 --> 00:57:02.160 from the Medicare and Medicaid systems 00:57:02.160 --> 00:57:05.340 to also understand whether we see increased hospitalizations 00:57:05.340 --> 00:57:07.410 among outage affected populations. 00:57:07.410 --> 00:57:08.360 Next slide, please. 00:57:10.470 --> 00:57:13.400 To kind of inform and as a starting point, 00:57:13.400 --> 00:57:15.650 we have employed some Intrepid undergrads 00:57:15.650 --> 00:57:18.560 from UC Berkeley to run a survey 00:57:18.560 --> 00:57:20.970 was around 1,000 households. 00:57:20.970 --> 00:57:24.410 We targeted this survey to be with households 00:57:24.410 --> 00:57:28.100 that we're kind of close to these outage boundaries. 00:57:28.100 --> 00:57:30.900 So we'll have some households that did experience outages 00:57:30.900 --> 00:57:31.870 and others that didn't, 00:57:31.870 --> 00:57:33.790 and what's nice about being able to do with surveys, 00:57:33.790 --> 00:57:35.590 we actually can collect some information 00:57:35.590 --> 00:57:37.040 on household demographics. 00:57:37.040 --> 00:57:38.050 We can ask households 00:57:38.050 --> 00:57:39.710 what their outage exposure actually was. 00:57:39.710 --> 00:57:42.610 We can ask them what they did in response to the outages 00:57:42.610 --> 00:57:43.890 and we can also try and understand 00:57:43.890 --> 00:57:44.790 what they think outages 00:57:44.790 --> 00:57:46.440 are going to look like in the future. 00:57:46.440 --> 00:57:47.390 Next slide, please. 00:57:50.190 --> 00:57:54.540 Lots of really fascinating and sometimes challenging 00:57:54.540 --> 00:57:56.087 to listen to stories from customers 00:57:56.087 --> 00:57:57.920 who to experience outages. 00:57:57.920 --> 00:58:02.130 So we see a range of costs that customers experience 00:58:02.130 --> 00:58:04.440 as a result of the PSPS events. 00:58:04.440 --> 00:58:06.220 So all the way from customers 00:58:06.220 --> 00:58:10.310 who had some lifesaving medical equipment 00:58:10.310 --> 00:58:15.310 that was required and that the outage events prevented them 00:58:15.430 --> 00:58:16.550 from being able to use that, 00:58:16.550 --> 00:58:19.120 and so taking substantial avoidance measures. 00:58:19.120 --> 00:58:21.309 So for example, one customer said she spent $400 00:58:21.309 --> 00:58:23.580 on a hotel room so that she could continue 00:58:23.580 --> 00:58:25.610 to use her CPAP machine and supply heat 00:58:25.610 --> 00:58:27.100 and water to herself. 00:58:27.100 --> 00:58:29.323 We then also had kind off more, 00:58:30.810 --> 00:58:33.210 what you might think of as more mundane costs 00:58:33.210 --> 00:58:37.710 like losing refrigerated commodities 00:58:37.710 --> 00:58:41.070 like condiments and meat went out to eat 00:58:41.070 --> 00:58:42.470 instead of cooking at home. 00:58:42.470 --> 00:58:44.740 Lost power to electronic gym devices 00:58:45.650 --> 00:58:48.883 and then, you know, we're all facing the consequences 00:58:48.883 --> 00:58:51.340 of additional childcare today, right? 00:58:51.340 --> 00:58:54.470 So we also saw that without access 00:58:54.470 --> 00:58:57.060 to electricity schools were sending children home 00:58:57.060 --> 00:58:59.700 and so that impose additional burdens on parents as well. 00:58:59.700 --> 00:59:02.390 So these are a flavor of the types of things 00:59:02.390 --> 00:59:03.360 that we're trying to measure 00:59:03.360 --> 00:59:07.440 and the idea with our revealed preferences investments 00:59:07.440 --> 00:59:10.660 that households take to help us and firms take 00:59:10.660 --> 00:59:13.690 to avoid outages should capture 00:59:13.690 --> 00:59:18.370 at least some of these costs. 00:59:18.370 --> 00:59:19.270 Next slide please. 00:59:21.080 --> 00:59:22.950 Okay, so one of the other things that we did 00:59:22.950 --> 00:59:25.310 is just ask households can you tell us something about 00:59:25.310 --> 00:59:28.200 how costly these outage events were for you? 00:59:28.200 --> 00:59:30.210 And we see that among the households 00:59:30.210 --> 00:59:32.420 that didn't experience an outage, reasonably 00:59:32.420 --> 00:59:33.570 they report no cost. 00:59:33.570 --> 00:59:34.403 That's good. 00:59:34.403 --> 00:59:36.060 That suggests that people are doing a decent job 00:59:36.060 --> 00:59:38.980 with this calculation but households 00:59:38.980 --> 00:59:40.740 that did experience outages, 00:59:40.740 --> 00:59:42.500 there's definitely a range of costs here. 00:59:42.500 --> 00:59:45.810 So more than 30% of households in our survey 00:59:45.810 --> 00:59:47.690 who experienced an outage reported 00:59:47.690 --> 00:59:50.560 having total costs of more than $100 and some, 00:59:50.560 --> 00:59:54.270 you know, in this right tail here of upwards of $5,000. 00:59:54.270 --> 00:59:56.473 So there's a wide range here of costs 00:59:56.473 --> 00:59:58.650 that households report experiencing. 00:59:58.650 --> 00:59:59.483 Next slide. 01:00:01.730 --> 01:00:03.590 One other thing we're really interested in 01:00:03.590 --> 01:00:06.350 is ways that households adapt themselves 01:00:06.350 --> 01:00:08.260 to these PSPS events. 01:00:08.260 --> 01:00:11.040 So one thing we asked about was types of backup power 01:00:11.040 --> 01:00:12.700 that households were potentially operating 01:00:12.700 --> 01:00:14.380 during their outage event. 01:00:14.380 --> 01:00:15.923 So this is a graph showing households 01:00:15.923 --> 01:00:17.700 that did experience outages, 01:00:17.700 --> 01:00:20.950 what share of them engaged in different adaptation behavior. 01:00:20.950 --> 01:00:24.590 A very small flights for solar power. 01:00:24.590 --> 01:00:25.423 That makes sense. 01:00:25.423 --> 01:00:27.340 You need a smart inverter to actually be able to continue 01:00:27.340 --> 01:00:29.860 to run your solar panels during an outage event, 01:00:29.860 --> 01:00:31.710 but we were sort of interested to see that 01:00:31.710 --> 01:00:34.180 around 30% of the households in our sample 01:00:34.180 --> 01:00:37.210 that experienced an outage actually already own a generator 01:00:37.210 --> 01:00:40.710 and operated that as a way of increasing 01:00:40.710 --> 01:00:42.250 their own resiliency. 01:00:42.250 --> 01:00:45.010 So that was the main way that customers sort of responded 01:00:45.010 --> 01:00:46.380 to these outages. 01:00:46.380 --> 01:00:47.213 Next slide. 01:00:49.240 --> 01:00:50.700 Another thing we're then interested in 01:00:50.700 --> 01:00:52.760 is what are customers doing sort of 01:00:52.760 --> 01:00:56.280 since the outages happened to change potentially 01:00:56.280 --> 01:00:59.450 their ability to respond to these outages in the future? 01:00:59.450 --> 01:01:03.170 So we're seeing households, they're investing substantially 01:01:03.170 --> 01:01:06.440 in these diesel powered generators, 01:01:06.440 --> 01:01:07.600 and one thing that we're actually seeing is 01:01:07.600 --> 01:01:09.320 it looks like there may be a small trade off 01:01:09.320 --> 01:01:11.130 between investing in these gen sets 01:01:11.130 --> 01:01:12.710 and investing in solar power. 01:01:12.710 --> 01:01:14.670 That's something we're still investigating 01:01:14.670 --> 01:01:17.060 and are interested in looking towards 01:01:17.060 --> 01:01:19.210 the administrative data to tease out further 01:01:19.210 --> 01:01:20.650 whether this is a large-scale trend 01:01:20.650 --> 01:01:22.840 or something particular to our sample, 01:01:22.840 --> 01:01:24.070 but at least an interesting feature 01:01:24.070 --> 01:01:26.303 that yes customers do seem to be investing 01:01:26.303 --> 01:01:30.360 in additional reliability in the form of generators here. 01:01:30.360 --> 01:01:31.193 Next slide. 01:01:33.300 --> 01:01:36.000 Finally, customers who purchased generators seem 01:01:36.000 --> 01:01:38.530 to be investing in relatively large scale ones. 01:01:38.530 --> 01:01:41.050 So you can see again households 01:01:41.050 --> 01:01:42.810 that didn't experience outages. 01:01:42.810 --> 01:01:45.010 So some of them purchased generators as well 01:01:45.010 --> 01:01:46.680 that you might imagine makes sense 01:01:46.680 --> 01:01:49.380 because even if I didn't have a PSPS event this year, 01:01:49.380 --> 01:01:51.500 maybe I expect to have one next year, 01:01:51.500 --> 01:01:54.780 but among the households that did experience outage events, 01:01:54.780 --> 01:01:57.970 we see them sort of purchasing larger generators 01:01:57.970 --> 01:02:01.240 than the ones that didn't experience outages. 01:02:01.240 --> 01:02:02.073 Next slide. 01:02:04.070 --> 01:02:07.480 So all of to say we learned I think a few different things 01:02:07.480 --> 01:02:08.540 from the survey. 01:02:08.540 --> 01:02:10.680 One is that as we were trying to figure out 01:02:10.680 --> 01:02:11.860 who we should be surveying, 01:02:11.860 --> 01:02:14.000 we were trying to target households based 01:02:14.000 --> 01:02:15.880 on pretty preliminary maps 01:02:15.880 --> 01:02:18.620 that PG$E, Southern California Edison 01:02:18.620 --> 01:02:21.920 had released aware the PSPS events had taken place. 01:02:21.920 --> 01:02:23.460 Those were very rough boundaries, 01:02:23.460 --> 01:02:26.850 and so we actually ended up targeting more households 01:02:26.850 --> 01:02:27.893 that didn't experience outages 01:02:27.893 --> 01:02:29.300 than we had originally planned 01:02:29.300 --> 01:02:32.540 'cause it's hard to figure out from those early maps 01:02:32.540 --> 01:02:35.020 who actually had outages, newer data that's come out 01:02:35.020 --> 01:02:36.680 that's actually a feeder or a circuit level 01:02:36.680 --> 01:02:38.330 makes it much easier to understand 01:02:38.330 --> 01:02:41.120 who these customers are. 01:02:41.120 --> 01:02:42.710 Another important caveat though 01:02:42.710 --> 01:02:45.470 is that it's really, really hard to get a good response rate 01:02:45.470 --> 01:02:48.370 to a survey in California. 01:02:48.370 --> 01:02:50.760 So we had about 5% response rate. 01:02:50.760 --> 01:02:52.880 To put this in a little bit of context, Katherine and I 01:02:52.880 --> 01:02:55.460 both work a lot in developing country contexts. 01:02:55.460 --> 01:02:57.910 So Katherine works in India 01:02:57.910 --> 01:03:01.150 and there we're used to response rates of 75% or higher. 01:03:01.150 --> 01:03:02.540 So it's important to take these results 01:03:02.540 --> 01:03:03.660 with a bit of a grain of salt 01:03:03.660 --> 01:03:05.330 'cause it's hard to get people to answer the phone, 01:03:05.330 --> 01:03:07.730 and it might be that someone who is particularly excited 01:03:07.730 --> 01:03:09.390 to talk to a UC Berkeley undergrad 01:03:09.390 --> 01:03:12.410 about their outage experience is not the average person 01:03:12.410 --> 01:03:14.810 which is why we think it's also important 01:03:14.810 --> 01:03:17.300 to bring in this administrative data. 01:03:17.300 --> 01:03:19.050 But what we've learned so far at least 01:03:19.050 --> 01:03:21.660 in this sample population is one, 01:03:21.660 --> 01:03:23.630 many people actually own generators already 01:03:23.630 --> 01:03:25.640 which we were pretty surprised to see. 01:03:25.640 --> 01:03:27.970 There are some suggested evidence that there's a trade off 01:03:27.970 --> 01:03:30.803 between purchasing solar and purchasing a generator. 01:03:31.760 --> 01:03:34.470 Neighbors who didn't themselves experience an outage 01:03:34.470 --> 01:03:37.220 may still be responding to the threat of future outages, 01:03:37.220 --> 01:03:39.300 so households they're making investments 01:03:39.300 --> 01:03:42.020 even if they didn't experience an outage themselves, 01:03:42.020 --> 01:03:43.540 and we do seem to find evidence 01:03:43.540 --> 01:03:46.290 that households are buying relatively large generators. 01:03:47.770 --> 01:03:49.380 Like I said, this is preliminary work. 01:03:49.380 --> 01:03:50.400 We have lots left to do. 01:03:50.400 --> 01:03:52.540 We're really excited to start diving 01:03:52.540 --> 01:03:57.320 into the administrative data as well, 01:03:57.320 --> 01:03:59.320 and I should just put in a really brief plug 01:03:59.320 --> 01:04:01.360 that if you're interested in what we're doing here 01:04:01.360 --> 01:04:03.500 as well as much of the other work 01:04:03.500 --> 01:04:06.490 that's coming out of the energy institute at Haas, 01:04:06.490 --> 01:04:07.730 we're hosting a conference, 01:04:07.730 --> 01:04:09.980 the annual power conference virtually 01:04:09.980 --> 01:04:12.320 from March 16th to 19th. 01:04:12.320 --> 01:04:15.280 So please go to the website 01:04:15.280 --> 01:04:17.280 for registration details for that. 01:04:17.280 --> 01:04:18.113 Thanks so much. 01:04:18.113 --> 01:04:18.946 Next slide. 01:04:20.090 --> 01:04:23.040 Great, my email so feel free to get in touch with me 01:04:23.040 --> 01:04:25.135 if you have any future questions. 01:04:25.135 --> 01:04:26.885 We don't have time to get to today. 01:04:29.840 --> 01:04:31.687 Thank you Professor Burlig. 01:04:34.198 --> 01:04:36.670 What was the timeframe of this survey? 01:04:36.670 --> 01:04:38.020 Was this? 01:04:38.020 --> 01:04:41.050 Yeah, so we conducted this survey 01:04:41.050 --> 01:04:46.050 between the end of December and late March of 2020. 01:04:47.168 --> 01:04:49.200 On 2020, yeah, yeah. 01:04:49.200 --> 01:04:53.042 And in the meantime for the PSPS, 01:04:53.042 --> 01:04:57.200 we have ordered PG&E and the other utilities 01:04:58.320 --> 01:05:01.490 to do a much more aggressive effort 01:05:01.490 --> 01:05:04.330 for medical baseline customers, 01:05:04.330 --> 01:05:07.100 people who rely on CPAP and so forth 01:05:07.100 --> 01:05:11.453 in terms of battery deployments and what have you. 01:05:12.580 --> 01:05:17.123 So are you continuing to survey customers 01:05:18.200 --> 01:05:21.003 or are you on to data analysis? 01:05:22.230 --> 01:05:24.283 Yeah, so we're planning a second round survey now. 01:05:24.283 --> 01:05:27.280 I think we're gonna deploy slightly differently, 01:05:27.280 --> 01:05:28.990 but we are conducting a second round of surveys 01:05:28.990 --> 01:05:32.870 and we're focused on counting customers expectations 01:05:32.870 --> 01:05:34.440 about future outages as well 01:05:34.440 --> 01:05:36.250 and understanding whether that's really different 01:05:36.250 --> 01:05:39.730 between customers that did and didn't have PSPS events. 01:05:39.730 --> 01:05:41.750 We've just collected information 01:05:41.750 --> 01:05:45.040 from PG&E on household level outage exposure 01:05:45.040 --> 01:05:48.065 using smart meter data, and that gives us an opportunity 01:05:48.065 --> 01:05:49.810 to get a really granular look 01:05:49.810 --> 01:05:52.570 into who experienced what outage. 01:05:52.570 --> 01:05:55.110 So that's kind of first on the table right now, 01:05:55.110 --> 01:05:56.450 we have the data from 2019, 01:05:56.450 --> 01:05:58.730 but I think we would also be excited about extending that 01:05:58.730 --> 01:06:01.573 into the future as needed and become available as well. 01:06:02.760 --> 01:06:06.770 Okay, all right, questions or comments. 01:06:06.770 --> 01:06:09.550 Present Batjer I'll start with you. 01:06:09.550 --> 01:06:11.350 Thank you, Commissioner Shiroma. 01:06:11.350 --> 01:06:14.590 Just a quick clarification again, doctor thank you very much 01:06:14.590 --> 01:06:17.593 for the presentation, fascinating, important. 01:06:18.560 --> 01:06:23.560 The 5% reply that you did actually get, yes, 01:06:25.080 --> 01:06:27.373 that's very low, but the response rate, 01:06:28.510 --> 01:06:31.260 but what does that equate to in terms of numbers? 01:06:31.260 --> 01:06:34.260 I think you started with, did you say 1,000? 01:06:34.260 --> 01:06:36.700 So it's 5% of 1,000? 01:06:36.700 --> 01:06:38.710 No, so the 1,000 is the total. 01:06:38.710 --> 01:06:43.243 So we conducted full surveys with around 1,000 households. 01:06:46.330 --> 01:06:50.020 Okay, so you had 1,000 responses? 01:06:50.020 --> 01:06:52.380 I mean 1,000 responses, yes. 01:06:52.380 --> 01:06:54.413 That's a better way of saying that, thank you. 01:06:55.510 --> 01:06:57.428 All right, okay, thank you. 01:06:57.428 --> 01:07:00.250 And just a follow up question there, 01:07:00.250 --> 01:07:03.623 and those 1,000 responses, 01:07:04.570 --> 01:07:08.523 were they representative of, let me start over 01:07:11.250 --> 01:07:12.600 that's not quite. 01:07:12.600 --> 01:07:17.600 Were those 1,000 customers in that area of the map 01:07:19.380 --> 01:07:23.240 that you showed us from the PSPS of 2019 01:07:23.240 --> 01:07:26.110 that was in the Oakland Berkeley area of where, 01:07:26.110 --> 01:07:29.530 is that the population, is that the area? 01:07:29.530 --> 01:07:31.810 Let me, yeah, let me clarify that. 01:07:31.810 --> 01:07:34.550 So the population that we sampled 01:07:34.550 --> 01:07:36.780 is actually all over the state of California. 01:07:36.780 --> 01:07:38.720 So we identified areas that we thought 01:07:38.720 --> 01:07:41.720 were likely to include boundaries 01:07:41.720 --> 01:07:46.430 between PSPS affected and non-PSPS affected customers. 01:07:46.430 --> 01:07:49.640 So that's Berkeley Oakland example is a nice illustration. 01:07:49.640 --> 01:07:51.750 There are definitely some customers there in our sample 01:07:51.750 --> 01:07:54.583 though we include much more of the state as well. 01:07:55.640 --> 01:07:57.410 So we did our best to be representative 01:07:57.410 --> 01:07:58.610 in terms of location. 01:07:58.610 --> 01:08:00.560 However, we're definitely not representative 01:08:00.560 --> 01:08:01.830 in terms of demographics. 01:08:01.830 --> 01:08:06.320 So the (indistinct) sample who's answering the phone 01:08:06.320 --> 01:08:08.020 is upwards of 55 years old 01:08:08.020 --> 01:08:11.090 which is definitely not the median kind of household age 01:08:11.090 --> 01:08:13.420 in California for instance. 01:08:13.420 --> 01:08:14.430 Right, and these 01:08:14.430 --> 01:08:17.113 were landline calls I suspect, correct? 01:08:19.100 --> 01:08:19.933 That's a good question. 01:08:19.933 --> 01:08:23.680 I think it's actually a mix of cells and landmines, 01:08:23.680 --> 01:08:25.092 but I would need to double-check. 01:08:25.092 --> 01:08:28.870 That gives a sense of the demographics as well. 01:08:28.870 --> 01:08:32.160 And clearly this wasn't just, as you just indicated, 01:08:32.160 --> 01:08:35.840 it was not just PG&E customers if it was Statewide. 01:08:35.840 --> 01:08:39.580 So you were surveying PSPS customers 01:08:39.580 --> 01:08:44.510 whether they lived in Edison's territory, PG&E's territory, 01:08:44.510 --> 01:08:47.020 or San Diego Gas and Electric. 01:08:47.020 --> 01:08:47.853 Yes, correct. 01:08:47.853 --> 01:08:50.320 The majority are definitely in PG&E 01:08:50.320 --> 01:08:53.470 which matches the pattern of the PSPS events themselves, 01:08:53.470 --> 01:08:57.381 but yes, we talked to Southern California customers as well. 01:08:57.381 --> 01:08:59.931 Okay, thank you for that clarification. 01:09:01.532 --> 01:09:06.532 Okay, colleagues, Commissioner (indistinct) 01:09:06.841 --> 01:09:07.674 and then Commissioner Guzman. 01:09:10.290 --> 01:09:11.600 Thank you, Dr. Burlig. 01:09:11.600 --> 01:09:15.770 This is very important work because we know intuitively 01:09:15.770 --> 01:09:18.300 and from the many comments we hear 01:09:18.300 --> 01:09:20.870 and stakeholder inputs we've gotten 01:09:20.870 --> 01:09:24.600 that PSPS has imposed very real costs 01:09:24.600 --> 01:09:26.310 and it's hard to quantify them. 01:09:26.310 --> 01:09:28.060 So your research is very important. 01:09:29.270 --> 01:09:32.270 I have got like a comment or question 01:09:32.270 --> 01:09:35.530 without changing the underlying philosophy 01:09:35.530 --> 01:09:37.390 of the national Bureau of economic research. 01:09:37.390 --> 01:09:40.280 I know am doing a survey done by economists, 01:09:40.280 --> 01:09:42.800 but they do wanna remind us of some 01:09:42.800 --> 01:09:45.800 of the limitations of just using economic measures 01:09:45.800 --> 01:09:48.290 including revealed preferences. 01:09:48.290 --> 01:09:51.410 They don't tell the whole story of health impacts 01:09:51.410 --> 01:09:52.830 which of course can be documented. 01:09:52.830 --> 01:09:55.590 I don't know if you've been able to get some of those, 01:09:55.590 --> 01:10:00.460 but what, if the increases in certain kinds of illnesses 01:10:00.460 --> 01:10:03.420 as a result of PSPS people not being able 01:10:03.420 --> 01:10:07.350 to take their medicine or be on the CPAP machine admitted 01:10:07.350 --> 01:10:08.910 to hospitals and so forth. 01:10:08.910 --> 01:10:12.390 So that's one issue that could be quantified potentially. 01:10:12.390 --> 01:10:14.453 There are unquantifiable damages, 01:10:15.300 --> 01:10:18.140 not being able to go to school, stress, 01:10:18.140 --> 01:10:22.130 not being able to use your CPAP all day long, 01:10:22.130 --> 01:10:26.030 all things like that, they're hard 01:10:26.030 --> 01:10:29.450 to capture using traditional economic metrics. 01:10:29.450 --> 01:10:32.150 And I also, maybe you could comment on this, 01:10:32.150 --> 01:10:36.280 just the equity implications of using a measure like this. 01:10:36.280 --> 01:10:38.580 If you use a reveal preference to pay, 01:10:38.580 --> 01:10:41.250 if you base it on how much people 01:10:41.250 --> 01:10:44.070 will spend on a generator or solar panel, 01:10:44.070 --> 01:10:46.570 what does that say about people who feel just as intensely 01:10:46.570 --> 01:10:49.330 about the impacts, but can't afford to buy a generator 01:10:49.330 --> 01:10:51.390 because they're lower moderate income 01:10:51.390 --> 01:10:55.330 or likewise they may have suffered five or six outages. 01:10:55.330 --> 01:10:57.340 We saw Humboldt, people in Humboldt 01:10:57.340 --> 01:10:59.120 experience a hundred times the level 01:10:59.120 --> 01:11:02.393 of disconnection interruptions on a PG&E 01:11:02.393 --> 01:11:04.370 than someone in San Francisco, 01:11:04.370 --> 01:11:06.280 they can only buy one generator in theory 01:11:06.280 --> 01:11:07.290 or one replacement. 01:11:07.290 --> 01:11:10.614 So I don't wanna, I know I said, 01:11:10.614 --> 01:11:13.950 this could be an academic discussion of much greater length 01:11:13.950 --> 01:11:17.280 and I think very much valued the work you're doing. 01:11:17.280 --> 01:11:19.040 Maybe you could comment on how you deal 01:11:19.040 --> 01:11:22.030 with some of the equity and intangible impact 01:11:22.030 --> 01:11:23.750 without trying to defend something 01:11:23.750 --> 01:11:26.773 that you're not pretending that your work is. 01:11:36.653 --> 01:11:39.153 Professor, I think you're muted. 01:11:41.140 --> 01:11:42.043 Yes, sorry. 01:11:43.990 --> 01:11:46.500 So yes, this is completely valid 01:11:46.500 --> 01:11:50.610 and really useful context to put our study in. 01:11:50.610 --> 01:11:52.910 I think a couple of quick responses. 01:11:52.910 --> 01:11:56.080 So first we're trying to do our best to quantify 01:11:56.080 --> 01:11:58.500 as many of the quantifiable features as we can, right? 01:11:58.500 --> 01:12:00.080 So getting this administrative data 01:12:00.080 --> 01:12:03.610 on Medicaid utilization, for example 01:12:03.610 --> 01:12:06.970 will help us get at health metrics even for customers 01:12:06.970 --> 01:12:10.490 that are not necessarily super high income. 01:12:10.490 --> 01:12:13.700 I totally hear the question about a customer. 01:12:13.700 --> 01:12:15.340 There's a difference between willingness to pay 01:12:15.340 --> 01:12:16.783 and ability to pay, right, and it's important 01:12:16.783 --> 01:12:18.540 that we keep that in mind. 01:12:18.540 --> 01:12:21.340 One way that we're thinking about addressing that issue 01:12:21.340 --> 01:12:26.340 is looking at whether our willingness to pay metrics vary 01:12:26.390 --> 01:12:28.080 across demographic groups. 01:12:28.080 --> 01:12:29.310 So whether we see households 01:12:29.310 --> 01:12:32.210 in lower income communities displaying lower willingness 01:12:32.210 --> 01:12:34.400 to pay, that doesn't necessarily mean 01:12:34.400 --> 01:12:37.650 that they care less about avoiding those outages, of course, 01:12:37.650 --> 01:12:40.370 but might actually indicate lower ability to pay as well. 01:12:40.370 --> 01:12:41.940 So point completely well taken. 01:12:41.940 --> 01:12:43.050 It's something we're thinking hard 01:12:43.050 --> 01:12:44.590 about the right way to resolve 01:12:44.590 --> 01:12:46.290 and I guess I would say is the reason 01:12:46.290 --> 01:12:48.620 that we're trying to combine both the spending measures 01:12:48.620 --> 01:12:50.640 as well as other things that we can observe 01:12:50.640 --> 01:12:53.543 about how households and firms were affected. 01:12:55.574 --> 01:12:57.462 Thank you, Dr. Burlig. 01:12:57.462 --> 01:12:58.741 Thank you. 01:12:58.741 --> 01:13:01.760 Commissioner Guzman Osavis. 01:13:01.760 --> 01:13:05.300 Thank you, yeah, I just kind of along those lines, 01:13:05.300 --> 01:13:06.483 I would appreciate that this is 01:13:06.483 --> 01:13:09.050 kind of that macro level question 01:13:09.050 --> 01:13:11.893 and your question is, 01:13:13.840 --> 01:13:17.240 now we have the ability to pay or willingness to pay 01:13:17.240 --> 01:13:22.240 for these mitigating things such as, 01:13:22.310 --> 01:13:23.360 by the way I did have a question, 01:13:23.360 --> 01:13:27.050 are you asking about batteries or just generally 01:13:27.050 --> 01:13:28.493 the diesel generators? 01:13:29.800 --> 01:13:31.430 You're asking about battery, okay. 01:13:31.430 --> 01:13:33.590 Well, so the majority of the questions 01:13:33.590 --> 01:13:36.310 are about generators, there is a small face 01:13:36.310 --> 01:13:37.810 to talk a little bit about batteries there, 01:13:37.810 --> 01:13:39.660 but we're collecting more detailed information on, 01:13:39.660 --> 01:13:41.900 or we collected more detailed information on generators 01:13:41.900 --> 01:13:43.193 in the first survey. 01:13:45.560 --> 01:13:48.303 And so your major question is to say, 01:13:49.520 --> 01:13:54.520 you know, cumulatively we see that using this multiplier, 01:13:55.560 --> 01:13:57.500 we can assume that rent payers 01:13:57.500 --> 01:14:00.720 would be willing to pay another $2 billion 01:14:00.720 --> 01:14:04.770 for hardening and, that's the question, right, okay. 01:14:04.770 --> 01:14:09.410 So I guess the other question is really kind of the inverse. 01:14:09.410 --> 01:14:11.743 I wonder if there's an opportunity to see, 01:14:13.270 --> 01:14:16.087 you know, what is the, you know, 01:14:18.630 --> 01:14:21.387 we have a lot of these discussions that we had this morning, 01:14:21.387 --> 01:14:25.760 I'm trying to figure out where is the right investment? 01:14:25.760 --> 01:14:27.880 What does that investment look like? 01:14:27.880 --> 01:14:30.730 And I wonder, do you think there's anything 01:14:30.730 --> 01:14:34.690 that'll come out of this that will inform 01:14:34.690 --> 01:14:39.690 that question on how to prioritize those investments 01:14:40.140 --> 01:14:42.110 on the regional scale in particular? 01:14:42.110 --> 01:14:45.920 And because, you know, you're right 01:14:48.205 --> 01:14:49.905 the courts we're not dealing with, 01:14:50.785 --> 01:14:52.320 you said you don't have any demographic data, 01:14:52.320 --> 01:14:54.593 there's no income kind of qualifier. 01:14:55.800 --> 01:15:00.340 So (mumbles) kind of a bar of what the utility 01:15:01.570 --> 01:15:03.617 potentially (mumbles) not to then (indistinct). 01:15:10.969 --> 01:15:13.386 (indistinct) 01:15:17.040 --> 01:15:19.990 Yeah, so I think one of the virtues of moving past 01:15:19.990 --> 01:15:22.240 the survey data and into the administrative data 01:15:22.240 --> 01:15:24.970 is then we're really looking at household level information 01:15:24.970 --> 01:15:26.920 and circuit level data on outages, 01:15:26.920 --> 01:15:30.000 and so we can really granularly say things 01:15:30.000 --> 01:15:33.900 about even what the allergy exposure picture 01:15:33.900 --> 01:15:36.594 really looks like, and so I think one thing 01:15:36.594 --> 01:15:39.720 we're interested in doing is really digging into those. 01:15:39.720 --> 01:15:41.220 I mean, so the data we're collecting 01:15:41.220 --> 01:15:42.680 at the moment for example, 01:15:42.680 --> 01:15:46.930 is information on a representative sample now 01:15:46.930 --> 01:15:50.530 of 10% of utility customers, 01:15:50.530 --> 01:15:53.480 and so that allows you to paint a much more granular picture 01:15:53.480 --> 01:15:56.160 of what's going on in different locations, 01:15:56.160 --> 01:15:58.280 and I think we're really excited to start digging 01:15:58.280 --> 01:16:01.513 into the administrative data partly for that reason. 01:16:03.800 --> 01:16:05.750 Yeah, I mean, I envision that could be helpful 01:16:05.750 --> 01:16:10.170 for like greater location or prioritization 01:16:10.170 --> 01:16:13.270 of community centers and things like that. 01:16:13.270 --> 01:16:14.943 Okay, final question. 01:16:16.733 --> 01:16:21.733 Did you include any of the tribal areas in or traveling, 01:16:22.870 --> 01:16:25.720 I know some obviously traveling folks 01:16:25.720 --> 01:16:27.050 still move on reservations, 01:16:27.050 --> 01:16:28.920 but did you include actual reservations 01:16:28.920 --> 01:16:30.793 in your survey? 01:16:32.320 --> 01:16:35.190 So, I would need to go back and check and see 01:16:35.190 --> 01:16:37.730 whether we actually got responses from those areas. 01:16:37.730 --> 01:16:40.880 We didn't sample specifically to target tribal regions. 01:16:40.880 --> 01:16:43.320 So targeting again was just based on likelihood 01:16:43.320 --> 01:16:45.410 of living within a PSPS boundary areas, 01:16:45.410 --> 01:16:48.160 so to the extent that that was the case 01:16:48.160 --> 01:16:50.410 and we had customers who answered the phone. 01:16:50.410 --> 01:16:52.393 It's possible but I don't know. 01:16:54.190 --> 01:16:55.610 Okay, yeah, that'd be interesting 01:16:55.610 --> 01:16:56.700 because I think there were some areas 01:16:56.700 --> 01:17:00.240 that met that mark where it was kind of cut in half 01:17:00.240 --> 01:17:02.960 and just to see if there's anything unique 01:17:02.960 --> 01:17:04.940 you find out about that. 01:17:04.940 --> 01:17:06.240 Well, thank you very much. 01:17:08.310 --> 01:17:10.190 Yes, Mr. (indistinct). 01:17:10.190 --> 01:17:11.096 Thank you. 01:17:11.096 --> 01:17:14.290 Dr. Burlig, you may have addressed this 01:17:14.290 --> 01:17:17.420 and I just wanna clarify, but you indicated 01:17:17.420 --> 01:17:20.060 that you're analyzing outages 01:17:20.060 --> 01:17:23.800 and non-outages between folks 01:17:23.800 --> 01:17:27.750 in a relatively close proximity to make comparisons 01:17:27.750 --> 01:17:30.020 between their responses. 01:17:30.020 --> 01:17:32.290 Did you say the assumption is that the folks 01:17:32.290 --> 01:17:33.570 who live relatively close by 01:17:33.570 --> 01:17:37.510 have the same relative socioeconomic conditions? 01:17:37.510 --> 01:17:40.770 I just wanted to clarify that. 01:17:40.770 --> 01:17:41.603 Yeah, exactly. 01:17:41.603 --> 01:17:42.890 So what you think about is 01:17:42.890 --> 01:17:45.120 we're simulating basically an experiment 01:17:45.120 --> 01:17:47.450 where we compare across the street neighbors. 01:17:47.450 --> 01:17:49.920 One of whom happens to live on this side of the street 01:17:49.920 --> 01:17:51.790 where the feeder was put under outage. 01:17:51.790 --> 01:17:53.860 The other home is on the other side of the street 01:17:53.860 --> 01:17:56.410 and therefore this year wasn't put on her outage. 01:17:56.410 --> 01:17:59.220 So one thing that we're trying to be careful to do 01:17:59.220 --> 01:18:00.840 is making sure that when we're comparing 01:18:00.840 --> 01:18:02.090 two neighboring feeders, 01:18:02.090 --> 01:18:05.090 those neighboring, sorry, those customers living 01:18:05.090 --> 01:18:06.200 in those neighboring feeders 01:18:06.200 --> 01:18:08.730 do actually exhibit similar characteristics. 01:18:08.730 --> 01:18:11.050 So we're using for example, data from the census 01:18:11.050 --> 01:18:12.720 to look at how median incomes change 01:18:12.720 --> 01:18:14.493 across these feeder boundaries. 01:18:16.400 --> 01:18:17.400 Got it, thank you. 01:18:20.335 --> 01:18:23.337 Okay, well, thank you. 01:18:25.380 --> 01:18:30.040 Thank you Professor Burlig and at this point 01:18:30.040 --> 01:18:31.820 we are going to check with our operator 01:18:31.820 --> 01:18:34.133 to see if there are any public comments. 01:18:37.990 --> 01:18:41.203 Robert, do we have any public comments? 01:18:49.250 --> 01:18:51.600 Commissioner I don't see any public comments 01:18:51.600 --> 01:18:53.020 at this time. 01:18:53.020 --> 01:18:56.448 Okay, all right, well thank you. 01:18:56.448 --> 01:18:59.440 Thank you to our operator, Amber. 01:18:59.440 --> 01:19:02.830 So many thanks to Professor Fiona Burlig, 01:19:02.830 --> 01:19:07.830 many thanks to our CPUC utilities engineer (indistinct) 01:19:08.960 --> 01:19:12.860 Julian Enis, for two very insightful 01:19:12.860 --> 01:19:15.140 and outstanding presentations. 01:19:15.140 --> 01:19:20.140 You've given us a lot of food for thought for sure. 01:19:20.530 --> 01:19:24.200 More followups to be had by us. 01:19:24.200 --> 01:19:28.663 I'm gonna now turn the microphone over to President Batjer. 01:19:30.960 --> 01:19:33.980 Thank you Commissioner Shiroma, 01:19:33.980 --> 01:19:36.390 and thank you also Commissioner Guzman Osavis 01:19:36.390 --> 01:19:41.390 for bringing this very interesting and informative. 01:19:42.070 --> 01:19:44.910 As you said, a lot of food for thought. 01:19:44.910 --> 01:19:47.720 Dr. Burlig and Julian, thank you so much 01:19:47.720 --> 01:19:51.130 for your presentations and for all of the hard work 01:19:51.130 --> 01:19:52.930 that those presentations represents. 01:19:53.910 --> 01:19:57.860 I really think everyone who did join us today 01:19:57.860 --> 01:20:02.860 and with no public comments, this meeting is now adjourned. 01:20:03.290 --> 01:20:04.990 Thank you all and have a safe day. 01:20:05.979 --> 01:20:07.329 Thank you. 01:20:07.329 --> 01:20:08.412 Thank you.