WEBVTT
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My name is
Amber for today folks.
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Thank you, great.
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Thank you.
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Welcome to
the California Public
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Utilities Commission committee meeting
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on this day, Wednesday,
February 17th, 2021.
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All participants will be in listen only
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and tell the speaking
session today at that point
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you may press star one
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if you would like to
make a public comment.
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This call is being recorded.
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If you have any objections,
please disconnect at this time.
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I now like to turn the meeting over
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to your host president
Batger, you may begin.
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Thank you, Amber.
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Thank you all for joining today.
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Good morning and thank
you for being with us today.
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I am calling the CPUC
Commissioner committee meeting
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of February 17th, 2021 to order.
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I apologize for not being on audio.
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I'm having technical difficulties today.
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So I will just be,
excuse me, not on video,
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I will just only be on audio today.
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There are three committees,
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the finance and
administration committee,
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the policy and governance committee
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and the emerging trends committee.
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Today the emerging
trends committee will meet.
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We will have an opportunity
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for public comment falling Q and A
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and discussion from the Commissioners.
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If you wish to make a public comment
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or ask a question please
dial (800) 857-1917
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and enter the code
5180519 and press star one.
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You will be placed into a queue
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and will be called upon to speak
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when we get to the public
comment period in this agenda
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which will be at the end
of each committee meeting.
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I will now turn it over to
Commissioner Guzman Osavis
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and Commissioner Shiroma
to introduce the items
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on the emerging trends agenda.
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Commissioners.
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Thank you President Batjer.
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Good morning everyone.
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We will be discussing just a little bit
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about the work plan for
the year for emerging trends,
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but we did as you
know, did a staff survey
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to get input from all of you
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on what topics are really
critical for us to undertake
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and given the positive response
we're gonna take a moment
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to really integrate
that into the work plan,
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but I just wanted to
highlight a couple of,
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you know, two general topics.
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I mean, we got well over,
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let's see over 20
recommendations from staff
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and even before that, we
had been working off of a list
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of items that we had not yet gotten to.
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So everything's from, you
know, working from home
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and really starting to discuss
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what some of the experts
are saying around that
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as a focus to really
external issues like reliability
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that we'll be talking about
some of those metrics today.
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But every single division
provided some input
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from the TNCs and some
of the red lining concerns
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that was a really interesting topic
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that I thought was brought forward
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and everything else in
our more traditional space
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around energy and we'll be
seeing some of these today
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as I discussed, some, winning prizes.
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So, but just to reinforce
that we heard everything
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from wildfire safety.
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There was a recommendation
on really looking
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into the indigenous sporting
practices for wildfire safety.
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Communications had a lot of
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really interesting recommendations
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and some of which
we're already working on.
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So we're gonna digest all of these
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and come back with a work plan,
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we'll try to post it before
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our next emerging trends
committee meeting in March,
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but we definitely will
highlight what the work plan is
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in March and obviously
have it posted on the website.
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Okay, probably the most exciting thing
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is that our staff, both
Commissioners, Shiroma and staff
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and others have worked
together to do a live raffle
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for the two winners.
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Commissioner Shiroma and myself
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we'll be doing an Amazon
gift card for two lucky winners
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for participating in this survey.
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So I'm gonna ask Justin
Song to bring up the wheel
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and just to note that because
we've made a little mistake
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on the survey and forgot
to put a tab for your email,
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we're gonna be following up
and trying to hunt you down
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'cause some of you
didn't put your names next
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to your topic recommendations,
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but here you'll see some of the topics
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that were brought
forward, all of the topics
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that were brought forward.
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And so we're gonna spin the wheel twice.
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So Justin, if you could
do the first spinning.
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And this one will be for the gift card
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that I will be gifting.
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Okay, increasing
development 5G infrastructure.
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Ow, that's really, and
I'm just checking the list
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to see if by chance
this was one of the folks
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that actually provided
their information,
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and if anyone knows
that (indistinct) my list,
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doesn't have that on right now.
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Okay, so whichever staff
person submitted this,
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we're gonna track you down
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'cause you have won a $25
gift card for Amazon, okay.
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All right, and Commissioner Shiroma,
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you can call the spin
for Justin on the next one.
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Okay, Justin, go
ahead and spin the wheel.
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Ah, oh, very good, hydrogen
and renewable natural gas.
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Both topics, really excellent.
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So we'll figure out
who put forth this idea
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and Martha and I will
be following through
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with the Amazon gift card.
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Thank you.
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Thank you very much.
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So I think with that Commissioner
Shiroma I'm gonna turn
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to you to introduce our first topic
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for emerging trends for the year.
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All right, thank you
Commissioner Guzman Osavis.
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All right, so our first presentation,
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the topic is on electric
reliability metrics for 2020.
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What we have learned
and we will hear first
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from Julian Enis.
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Julian is a utility engineer
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in our resiliency and
micro grids energy group
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from the energy division.
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Again, Julian is a utility engineer.
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He provides his
expertise on issues related
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to electric system
reliability and resiliency
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and we've worked together extensively
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over this past year or so on
our micro grids proceeding.
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Julian has a degree in
mechanical engineering
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from the University of
California Davis go Aggies,
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and Julian will be
presenting to us today
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on metrics and trends in
electric system reliability
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including an analysis
of 2019 reliability metrics
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and opportunities to
better leverage this data.
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This is certainly a very important topic
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as we see what has been
happening across the country.
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And I also wanted to do a shout out
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to Julian's manager, Forest Kaeser
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who is also attending
today's committee meeting.
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All right, Julian, the
microphone is yours.
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Well, thank you so much
Commissioner Shiroma
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and indeed go Aggies.
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Good morning, Commissioners,
directors, members of staff
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and members of the public.
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As Commissioner Shiroma
introduced me, am Julian
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and I'm here to talk about
electric system reliability
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and the reports that
utilities are required
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to file every year and just a
little bit more data analysis
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and some next steps that we can take
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to improve the reporting requirements
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and I'm better understand
what this data tells us
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about the electric system of California.
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Next slide please.
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So the basics of
electric system reliability
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and this is what we'll be
covering in this presentation.
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We'll be touching on how
electric system reliability
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is defined.
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Why do you keep track
of electric reliability?
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What the California
utilities are required
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to report to CPUC annually?
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How did the California utilities compare
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to the rest of the United States
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and a little bit more about
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how granular reporting standards are
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and how you keep track of
the worst performing circuits
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and what can be improved.
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Next slide.
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So first we'll start
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with how electric system
reliability is defined.
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These are statistical
representations of outages
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and we'll dive into a little bit
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about what they really mean.
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Next slide please.
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So electric system
reliability is defined
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by IEEE 1366 and within this standard
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there, the four major
metrics are defined
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as SAIDI, SAIFI, CAIDI and MAIFI.
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I realized that these are
a lot of new acronyms.
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So wherever in the presentation
these acronyms show up,
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I have tried to spell them out for folks
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just to make sure we have clarity.
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However, these four metrics
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are the generally accepted standard
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by which electric utilities
across the United States measure
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their system performance.
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SAIDI is the System Average
Interruption Duration Index.
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SAIFI is the System Average
Interruption Frequency Index.
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CAIDI is the Customer Average
Interruption Duration Index
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and MAIFI is Momentary Average
Interruption Frequency Index.
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Next slide please.
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So here are some written
definitions for these four metrics,
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but I will quickly give an explanation
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of what they each mean.
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So effectively SAIDI is the
average number of minutes
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that a customer on
the entire utility system
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can expect to be without power
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over the course of a given year.
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SAIFI or the System Average
Interruption Frequency Index
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is the expected number of
times that the average customer
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on the utility system
will experience an outage
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over the course of the year.
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CAIDI or the Customer Average
Interruption Duration Index
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measures how long each
outage is for the average customer
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and MAIFI measures how
many, and MAIFI, sorry,
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the Momentary Average Interruption
Frequency Index measures
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how many momentary outages or outages
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that last less than five minutes
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the average customer
on the utility system
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will experience over
the course of the year.
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Next slide please.
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Next one.
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The next topic is to talk
about major event days.
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So the reliability indices
are generally reported
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with and without what are
called major event days.
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Major event days are a
statistical threshold that is based
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on the daily System Average
Interruption Duration Index
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and it's a statistical
threshold that's based on
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the previous five years of data.
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The idea of a major event day
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is that it measures the impact
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of, high impact and
low frequency events.
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Just as a note, this definition
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is a statistical definition only
based on outage durations
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and it does not account for causality.
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So things like earthquakes, storms
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and public safety power shutoffs
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are considered any MEDs
only in so far as the events
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daily system average
interruption duration index
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exceeds the threshold.
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Reliability indices are generally used
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to motivate investment decisions
00:12:40.160 --> 00:12:43.430
that will lead to improvements
in system reliability.
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Generally the utility
look at reliability excluding
00:12:46.490 --> 00:12:48.590
these major event days
to focus how it needs
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to improve reliability, general
system reliability overall.
00:12:53.030 --> 00:12:55.730
However, looking at
reliability with MEDs utilities
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can see how significant and large events
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such as those earthquakes, storms,
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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
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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.
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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.