0:00:14welcome mean i'm really pleased to be here i also sort of a a an economist a member group of
0:00:20engineers and i understand physicist
0:00:24that i've been working closely with a a a group of cornell that sort of dominated by uh
0:00:32and is uh uh and lang tong is here bob thomas is really are sort of
0:00:38who's not here the moment
0:00:41um i one
0:00:42the paper i wrote was really about to why is it that electricity markets a soap or Q rear
0:00:48and and just on like any of the market that i of a looked at that
0:00:53uh and uh i i've got a couple of P here
0:00:57that i'll mention but i really want to spend more time
0:01:00on what i think is going to change in the future
0:01:04um because i think that the way that we've model D my and
0:01:08up until now with
0:01:10completely inadequate for the sorts of things that we going to need to do in the future
0:01:15and in particular
0:01:17that we're going to
0:01:18rely more on price feedback
0:01:21to get a response from the on that really
0:01:24is is pretty a minimal at the moment in the way that systems have evolved over time
0:01:33one of the
0:01:34one of the interesting things that that happened off to markets would deregulated in the U
0:01:41is the prices were so that had never been seen before
0:01:45that all of a sudden
0:01:47prices is were sometimes
0:01:49almost two orders of magnitude bigger than any price is that it that been seen before
0:01:55so you can see you know a you little little little little prices and then talk
0:02:00deregulation regulation
0:02:02and and and we have price is over a thousand dollars where typically there less than a hundred
0:02:09the strategy in in the east part of the U S
0:02:13is that um
0:02:17but the but this is a bad behaviour
0:02:20and we need to suppress it so they have employ a vast quantities of market monitor
0:02:26to slap people's hands when they misbehave
0:02:30and so you can see
0:02:31that the century
0:02:33no that initial
0:02:36of of the part of
0:02:37supply as and their ability to speculated and they're get high prices
0:02:42i has really been suppressed
0:02:44if you go to the australian market on the other hand you C Ds price spikes have continued and the
0:02:50really to
0:02:51types of markets
0:02:53one is
0:02:54highly highly monitored markets and the other one energy only markets where you're let these price spikes six this
0:03:02so an interesting question is why is it that this marketing encourages speculation
0:03:09and this is a a a a a an analysis that we did with computer agents
0:03:13and basically these are identical agents
0:03:16and on the left
0:03:17the agents
0:03:18do not speculate i'm on the right they do speculate
0:03:22and the real issue it is that in an electricity market
0:03:26there's a lot of uncertainty
0:03:28uh a about
0:03:29what exactly is going to happen in the next ten minutes lead a known
0:03:34a day ahead
0:03:35so this uncertainty about how much the
0:03:39system operator is going to buy a essentially makes it possible for some absolutely outrageous speculative off as to be
0:03:47accepted sometimes
0:03:49and to set the market price
0:03:51um so this is a a you know a an interesting feature
0:03:55of of uh
0:03:56market so
0:03:57we model this type of behaviour with the regime switching essentially sensually low price high price for Z
0:04:04and make the probability of switching
0:04:08uh dependent on
0:04:09on system conditions and
0:04:12i in in particular the expected
0:04:15reserve margin so we were interested in can you predict
0:04:19price bites the day ahead and
0:04:21you know the bottom line is
0:04:23if the or information is good enough yes you can
0:04:28but when price spikes when away way you know we had to move to other things
0:04:33so the next topic is really
0:04:35a spatial price differentiation
0:04:40this is a map of new york am for those of you are for not familiar with new york
0:04:44most of the people live down here in new york city
0:04:48and this is niagara falls and basically that's the main feature of the electric system the people live down in
0:04:56one corner and the chief power up the other corner
0:05:00uh on this C here stands for cornell
0:05:03um and uh
0:05:04we are about you know in the middle of the state
0:05:08but the logic of the system is that you
0:05:11you we'll power from the inexpensive expensive
0:05:16down to the south the east corner
0:05:18there are those a lot of congestion that peak periods
0:05:22so basically the market friend man
0:05:25and you get substantially higher prices in new york city then the price at the same time
0:05:31uh in upstate new york
0:05:33so that that that uh mechanisms what put in to a our generative as
0:05:39to hedge the price variability between two locations
0:05:45and as sensually uh that's what
0:05:48that's what we're talking about
0:05:50so it
0:05:51it's very easy
0:05:52to model
0:05:53uh energy prices as a recursive system
0:05:58and basically
0:06:01you model
0:06:04load is of
0:06:06a a a a a is a sense you model temperature are
0:06:09and and time put your proposed i the sort of source of uncertainty about what's gonna happen next some
0:06:16so i want to hedge prices for the are coming some or all of the upcoming winter
0:06:21um between
0:06:22not i fools and new york city
0:06:26we model temperature your those locations
0:06:29we model the load as a function of time but your
0:06:32and that
0:06:33we and model the price
0:06:35a a different locations as a function of the load
0:06:39and the the price of natural gas to sort of a price of an input
0:06:43but basically this work or stiff structure
0:06:45um is is a
0:06:47while i should say appropriate for the
0:06:50in this story
0:06:51most customers don't actually
0:06:53see the true market price that just paying regulating prices
0:06:58so essentially those there's no price feedback
0:07:01there's is a straightforward forward um
0:07:05V i ar model
0:07:07vector autoregressive regressive model
0:07:09and we just simulated different
0:07:12uh us mos
0:07:13and then looked at the price differences
0:07:16and you could get a
0:07:19a distribution of the payouts
0:07:22from owning
0:07:23one of these uh
0:07:25for contract
0:07:27so you're contract thing for the price difference as you put your money on the table
0:07:31and you're buying an on so it an income re
0:07:35there's essentially century
0:07:37it's the
0:07:39density for the house
0:07:41hey out
0:07:42and uh the system operators were very can so and
0:07:46that they kept on paying out more money
0:07:49in real than than money they talk in from running this distortion
0:07:54and they eight they were suspicious that a might be
0:07:58a collective behavior in this market
0:08:00but in the analysis that we did
0:08:03but this is a typical result
0:08:05that the
0:08:07pay a a a a the actual price
0:08:10with where the above of the mean of the payouts all the was no the dense that we could find
0:08:15of a sort of
0:08:15big risk premium in this market
0:08:18and therefore the system operated was not interested in this analysis so
0:08:23didn't sort of support their prize
0:08:26so uh see her we do not when doing
0:08:29so now let's that's got on the importance of subject
0:08:34the future joe
0:08:35smart rate
0:08:40where assuming and this you smart grid that we going to switch from fossil fuels to renewable sources and certainly
0:08:47your is leading the way and the us to sort of dragging its feet to get into this new era
0:08:58this is basically what we have to deal with
0:09:01oops not that
0:09:04more when generation
0:09:07we got have some storage capacity to deal with the fact that that uh
0:09:13we in this not a typical
0:09:15a dispatch able source of supply
0:09:18as a lot of uncertainty and variability even if you've got lots of different wind far
0:09:25you're displacing
0:09:26fossil fuels wholesale prices go down but this means that
0:09:31the warnings
0:09:32the money above cost
0:09:35conventional generate get is also going down so that those are growing tension
0:09:41which we
0:09:42the what we've called financial at a course C
0:09:47the existing generate is
0:09:49and um
0:09:51the the
0:09:52the uh they get in the market so
0:09:55uh in in the new in the uh us markets we we have established
0:10:00uh capacity payments
0:10:02outside the wholesale market to try to supplement
0:10:06the uh learning self generate
0:10:09so essentially century
0:10:12lower uh a learnings in the wholesale market means higher uh
0:10:15payments in the capacity market a higher price for capacity and more reason
0:10:21for managing the system peak managing to monte in a more
0:10:26uh i
0:10:27it's a russian way
0:10:29we we tend to treat it
0:10:31the demand on as a as a given as something it you and and those days are basically over
0:10:39customers and not getting the correct economic signals that a lot of them paying regulated prices is this is silly
0:10:47that that that we need to we need to get a team and
0:10:50participating in this new market in of full way and this doesn't mean just buying
0:10:56when prices a low
0:10:58uh it means to shifting
0:11:01the mine from P periods to one peak period and a little so i think more importantly
0:11:07i lane and celery is that don't actually a
0:11:11is this yet into the mark
0:11:13and ramping service is is the one that
0:11:16will focus on
0:11:18one of the ways of doing this is with controllable de monte and that doesn't have
0:11:24delivered instantaneously like these like
0:11:29so what electric vehicles as a good example "'cause"
0:11:31charging the batteries in electric vehicles
0:11:36it's not not enough
0:11:38a an now for a little things around the at to make much difference so we really need a know
0:11:42the source and and the U S
0:11:44with the system that's really dominated by the air conditioning a that's sets
0:11:51conditions for a systematic C
0:11:55almost or is an obvious alternative to using
0:11:59um air conditioning on the on
0:12:02so basically making a nice when the system
0:12:05finds it convenient
0:12:06and then melting the ice to keep color when you want to keep cool is a smarter way of doing
0:12:12things and just banging on the air condition
0:12:15when you want cool your and
0:12:18we we what for
0:12:21how much of the
0:12:23system load
0:12:25is is uh
0:12:26temperature sensitive and in new york city
0:12:29you know this gives you an idea it's about two thousand megawatts out of
0:12:34the potentially
0:12:36is is uh
0:12:37champ should
0:12:39it is temperature sensitive and could be control
0:12:42hot hot water is obviously another example of these sorts of or
0:12:49where you can control them
0:12:52when when one starts incorporating a control of all and into the system
0:12:57this is really the same as as any type of storage
0:13:01so what we've got here
0:13:03what customers want to buy
0:13:06the blue line is what customers
0:13:11now of a way in generation so the blue is really what the conventional generate is have to supply
0:13:18and you can say wind was blowing pretty smoothly and and that that that that that uh we we get
0:13:24some variability that's in how and with this source of generation
0:13:29we propose the that
0:13:31that in addition to
0:13:33and a G the or to be a ramp so this where you're was actually
0:13:39if you
0:13:40a part of the
0:13:42majority you're moving the same why that the system is moving but if you could move against that you get
0:13:48paid in in in a way if you can mitigate
0:13:52the changes in what conventional generators as a doing
0:13:56you get paid so once you incorporate that
0:13:59over on this side
0:14:00you can see that the
0:14:02the variability of the
0:14:05generation from conventional sources is essentially smoothed out
0:14:13so the same thing is true
0:14:16of the energy prices
0:14:17that the energy prices get smooth out by
0:14:23and the corresponding
0:14:24rand being price is a very interesting
0:14:28but the they
0:14:30but the real cost
0:14:32of ramping
0:14:33the the these prices
0:14:35a a very very variable because of winn
0:14:38so you can see you when the wind was well behaved there
0:14:42ramping prices a pretty mode
0:14:44but when the wind with variable you got a lot of variability and the
0:14:49then the marginal cost of ramping
0:14:52and that when you incorporate the ramp cost in the optimisation
0:14:57you were essentially smooth out the ramp
0:15:00the really implication is
0:15:04that that this is the sort of a
0:15:06uh and say laurie of is that i think we need
0:15:09to allow
0:15:11but the on side to benefit from
0:15:14that that we have customers who were complaining about paying higher cost
0:15:19so the smart grid
0:15:20i i think the way to make this
0:15:23economically viable
0:15:25is that
0:15:26rather than looking at
0:15:28customers as has a sink
0:15:30by energy
0:15:32that we ought to look at cost or where aggregates
0:15:35as potential sources of services to support the great
0:15:39and that's basically
0:15:41what these are concluding remarks say
0:15:45customers can lower one net payment by having control able uh them on
0:15:50purchasing more energy a night if you like
0:15:53mitigating price spikes
0:15:58and and reducing them on your system P periods essentially using the amount of
0:16:03conventional capacity needed for at a course C
0:16:07and finally
0:16:08selling ramping services to mitigate wind variability
0:16:12but if they can get these payments the net payments for
0:16:16providing the sort the
0:16:18so this is that they want to get from electricity will be lower and i think that this is the
0:16:23way for what
0:16:24i clearly there's be back is something that is not
0:16:27really integrated fully into the current grade
0:16:32this is the challenge for all you people in communication
0:16:36thank you
0:16:42i think we had time are um
0:16:46lots of people work on this project
0:16:49the plotted
0:18:55thank you