[MUSIC]
Thanks, everybody.
I've given overview tags
previously a couple of times at IEE so I could
focus a little bit more this time on
some recent research work that we
did with my PhD student, Nathaniel Tucker.
I'm going to talk to you today
about some of our recent work on
machine learning and real-time
optimization algorithms for
electricity demand management.
But before I do that,
I want to repeat to you a little bit about
the exciting transformation in
grid operation that motivates our work.
The classic or old paradigm of
pirated operation essentially took
electricity demand from customers like us.
Again, uncontrollable and random input but to
the power system and
so the whole operation of the power system is
built around serving this random demand reliably with
the minimum possible costs as you've
heard from a lot of speakers today.
This essentially meant that on
the previous day or hours ahead of
our operation and this is still what is done today,
electricity generators submit bids
that reflect their costs to
an independent power system operator
which is a non-profit entity.
Retailers like Southern California,
Edison accurately forecast their customer demand and then
they also give it to this
independent power system operator.
We then solves a large optimization problem to find
the S or reliable and minimum cost generation
makes to serve our demand.
The issue with this operational paradigm is that it's
already dealing with essentially
randomness on the demand side,
so it's very hard to also deal
with randomness on the supply side when
you add renewables like wind and
solar also in their generation mix.
This essentially enable
the clean energy transformation in power systems
which essentially relies on
multiple additional or new principles which is,
we can integrate renewables perhaps
also in distribution systems where customers have
solar panels on their roofs and they tried to consume
the solar essentially behind
the meter instead of injecting it into the grid.
This happened at the same time as
multiple exciting transformations,
for example, transportation,
electrification as well as the opportunity for
increased customer engagement at
the operational level of the power system.
That is, we want to bring customers into the control loop
of ensuring that demand and
supply of electricity are balanced.
These are demand response
and demand-side management programs
which you've heard about a bit today,
and that is the main focus
of the research we do and in our lab,
and that's what I'm going to be talking to you
about today as well as of course,
storage which you heard a lot about today as well.
There're challenges that I want to talk to you
a little bit about today is that the grid has
traditionally relied on having
essentially accurate representations of
supply and demand behavior.
We want to have good forecasts of not only
what demand and supply are going to do now,
but also in the future.
While forecasting uncontrollable demand behavior,
there are some very good methods after to do this.
We don't have much information about
what demand would do if it is exposed to
essentially changing conditions of the grid or
dynamically chosen prices or
incentives or signals would have you.
We don't really have good models of this,
and hence this motivates
the use of data-driven algorithms,
in this case, to learn about how
demand responds to signals.
Demand is us, we are humans,
we don't have good mathematical models of humans.
Second, another concern is that we have to
solve all the sequential dispatch problems
not only at the,
for example, at the level of California,
but also as we're pushing
optimization of the grid to the edge,
small communities of customers have to
solve these sequential dispatch problems which have
no inherent uncertainty in
there that even the best predictor cannot eliminate.
We want sequential dispatched
optimization problems that have
to deal with high levels of
inherent uncertainty when we solve them.
I want to show you a little bit about some
of our work in addressing these challenges.
The first challenge is, as I've told you,
how do we quickly learn how customers
respond to prices or incentives,
perhaps so we learn how
to best use them in our operation of the grid,
and also a second challenge is how
do we use these or make
these sequential decisions when we don't have
really great forecasts of demand and supply.
The first challenge, this is a paper
as published in transactions and smart grid
where we want to essentially learn how
customers respond to prices or incentives.
I'm an electricity retailer.
I'm exposed to daily conditions at the grid that's
the variable empty that changes with every day.
My goal is to minimize my operational costs,
that's the function c that we have up top here,
which depends on the daily conditions
but also under demand,
which I would like to optimize
three use of a price or a dispatched signal,
whatever, and that's the price signal p
of t that I'm choosing.
In response, I'm going to
see a demand which I don't have a good model for.
I don't know what d of p of t is going to look like.
Clearly, I cannot minimize
my operational costs in a single day.
I don't have a good model of how demand responds.
Our goal is to make sure that we learn
essentially how we can
post these prices over a period of,
for example, T days.
The metric that one uses here to
see how good an algorithm is is called regret which
is essentially the difference between
the costs one would incur if they were to
use an algorithm to learn how essentially to make
these cost-minimizing decisions over a period
of T days minus the
cost that they would have incurred if they had
a perfect model of the demand and hence
could post the best possible price.
This is a very abstract way of modeling this.
There's many things that can be integrated into this,
and what we did is we studied the effectiveness of
a well-known algorithm in machine learning
called Thompson sampling in achieving this.
It's a very simple simulation
that I'm going to show you here,
which looks at how this quantity regret grows with dates,
with the number of days that
we're running this algorithm.
It is very desirable.
Clearly, this function is going to
grow with time because it was the sum of
my costs over a period
of a number of days minus the optimal cost.
My goal is to see it essentially flatten,
and as you can see here, it has.
I'm showing this to you not because I want
to essentially promote this simulation,
what I want to talk to you about is a point
that we observed when we
used such data-driven algorithms to
learn how customers respond to prices.
In a lot of cases in power systems,
we're used to dealing with
having good models of everything.
That's to make sure that we can
reliably operate power systems.
You want to make sure that there is
no constraints that are violated in the transmission
or the distribution system of the power grid when
you essentially post a dispatch signal.
The problem with these algorithms
is that they might not be able to
guarantee it unless we carefully design the algorithm,
the data-driven algorithm to take
these reliability constraints into account.
That's what I'm showing you here.
Again, very simple simulation
that we have essentially kept it
unrealistic by purpose to make sure that we can
highlight the evolution of
the two learning algorithms in achieving our goal,
which is essentially to get electricity demand to
match the blue profile you're seeing here.
But we don't know how demand responds to crisis.
We have essentially tested the performance of
two of these algorithms based on Thompson sampling.
One of them doesn't take into account that
demand should never go above 100 per unit.
The problem with this is,
in the unconstrained learning algorithm,
or the unsafe learning algorithm
starts by posting prices that really excites
a lot customers and gets them their demand to be really
high and hence violates
this reliability constraint of the power system.
Whereas, if you have like
a conservative learning algorithm that takes
into account these reliability constraints,
we would have that this algorithm
posts really low prices to begin with,
to make sure that it is not going
to violate that constraints shown in green.
Hence you'll see in time
both of these algorithms around day
28 are closely matching that desired profile we had,
but one of them incurred
a bunch of violations in doing this.
Clearly being more conservative
comes at the cost of increased regrets.
The more essentially conservative my algorithm is,
the higher the regret is going to be,
and that's why this curve is showing,
but clearly there's a price for
reliability that we need to pay if we want to
use data-driven algorithms in
safety critical systems like power system.
Then that brings us to
the second challenge that I wanted to talk to you about,
which is we have
all these dispatch decisions that
we need to make in power systems,
and they usually, as I said,
a lot of these optimization algorithms
that we are used to dealing with
in power systems rely on
having good forecasts of the future,
and when we go to the level
of communities and things like that,
it's really hard to have very accurate forecasts of
what everybody will be needing or doing.
Also very small-scale, for example,
solar or things like that.
What we do here is we use
online optimization algorithms that
don't depend on forecasts of
the future and still provide essentially
performance guarantees with respect
to an all-knowing solution..
The first application of
these algorithms I want to show you
is with regards to community energy storage.
This is essentially a storage system
that a community of customers,
small community of customers can share instead
of each investing in their own storage system.
They can use it for multiple reasons,
whatever you want it to be.
What I want to show you here, for example,
is a simulation of 10
commercial buildings in Los Angeles,
each equipped with a solar panel and they are sharing
a community energy storage system with the purpose of
using the solar energy they're
producing behind the meter
instead of injecting it back to the grid.
We are using our online algorithm to
essentially price what each of
these users should pay in order
for the community energy storage system
to serve essentially,
the energy storage requests that they're
submitting for storage essentially.
The curves you're seeing here,
the first is just their load without any solar.
The second curve in the middle is what their load
looks like when you essentially add
the solar that they're producing.
You can see that the load is going
below zero and negative because they
have extra solar production
that they cannot consume themselves.
Then the last curve to
the right shows you what happens when we use
our online algorithm to essentially absorb or store
the excess energy and then let the users
consume it when they actually have energy needs.
A second application of
these algorithms that we have toyed with
and I actually have published in the transactions,
the Smart Grid as well,
is an algorithm that allows essentially
a public electric vehicle
charging parking lot, wherever it is,
it could be a workplace charging
facility to essentially allocate
incoming users to charging spots
and also perform smart charging and
not requiring knowledge of everything else
that's going to go on in the parking
lot from that point on,
because customers can have complicated arrival times,
departure time, charging needs,
parking lot can have solar production, for example,
that has to be consumed hopefully by
the electric vehicles that are charging there and so on.
I think this is essentially
a good application of these algorithms because it's
really hard to forecast what everybody will be
doing in the parking lot and what
energy needs they're going to have and so relying on
those forecasts make our life
even harder instead of making it simpler,
our algorithms still provide
performance guarantees for this.
Essentially they start by
allocating customers through an online heuristic to
all these spots and then start deciding how to charge
them and how they should pay
for the use of the parking spot,
as well as for the energy
that is being delivered to them.
Again, a very simple simulation
here shows you why this is needed.
For example, consider if you have
a workplace charging facility.
If in the morning you allow everybody
to park in a spot and leave and go in,
what happens is if you arrive later in the day,
you might not have a parking spot available,
so you might not receive charge even though
you're in desperate need of charge.
Whereas if you had
an allocation scheme that actually
has people paying per use,
and then depending on how much
utility they receive from being
allocated to a parking spot,
then you could have increased utility of
the users as they come in during the day.
With this, I want to wrap up this talk.
I know I talked a lot about
essentially algorithmic advances and not really
demonstration of these technologies in the real world,
but we have actually been working on
that for the past few years.
We have been involved in
several projects where we're demonstrating the efficacy
of smart charging algorithms in collaboration
with the most important one
in the collaboration with SLAC National Lab.
I want to also acknowledge the sources of
funding that have supported
our lab throughout these past few years.
The National Science Foundation
and California Energy Commission.
You see, Office Of President and
also generous assist supports from
the Institute of Energy Efficiency,
which has really helped us
essentially start our research in new directions.
With that, thank you.
[MUSIC]