Note: Descriptions are shown in the official language in which they were submitted.
CA 02796891 2012-11-26
Title
Power Consumer Side Control System, Method & Apparatus
Priority
The present application is a Continuation-in-Part Application based on the
prior
application US 13/452940 filed April 23, 2012, and Provisional Application US
61/563,590 filed November 24, 2011.
Background
Suppliers of Power have historically controlled the Power Grid. They provide
the power,
the infrastructure and the maintenance. However, Power Suppliers are trapped
within the
Supply Side of the Grid and are incapable of controlling the Demand Side.
Power
providers have attempted to indirectly control the Demand Side, but in reality
the
consumers of the power are in control of their own consumption and make their
own
decisions.
Problematically, when the Power Grid is overburdened with Demand the system is
helpless against massive power outages. As recent historical events have
testified, the
Power Grid is simply unable to manage a power crisis without massive black
outs. In
both the massive blackout in the Northeast US and in India, the over demand
for power
caused a domino like effect as one power station after the next shut down
creating an
even bigger load for the next power station. The Supply Side of the Power Grid
is simply
incapable of handling demand surpluses.
One attempt to curbing demand is to simply charge higher prices during peak
times.
However, this practice is just as impotent as trying to manage a massive power
outage
using infrastructure on the Supply Side. For one thing, the everyday user
simply has no
way of knowing the price of power at any given instant. Even if he or she did,
it is vastly
unlikely that the user would be able to curb their demand in the time
necessary to avert a
massive power outage. Another problem is that raising prices can only be
raised across
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CA 02796891 2012-11-26
the board, that is for every user, which would disadvantage families of modest
incomes
and turn power into a commodity rather than a utility that should be available
to
everyone.
Brief Description Of The Drawings
FIG. 1 illustrates an exemplary system that encompasses the present invention;
FIG. 2 illustrates United States Electrical Energy Production Supply-Side
Statistics;
FIG. 3 illustrates storage capacity on the DEMAND-SIDE;
FIG. 4 illustrates various uses of energy in the home;
FIG. 5 illustrates a model of generation supply capacity;
FIG. 6 illustrates a model of aggregate electrical demand;
FIG. 7 illustrates FIGS. 5 and 6 superimposed;
FIG. 8 illustrates how the present invention more closely matches demand to
supply;
FIG. 9a illustrates a duty cycle schedule of a typical hot water heater;
FIG. 9b shows a portion of the duty cycle schedule of FIG. 9a;
FIG. 9c and d illustrate example modes of operation provided by the present
invention;
FIG. 10 illustrates the present invention in terms of a method;
FIG. 11 illustrates a Power Grid with Demand & Supply Side:
FIG. 12 illustrates a Demand Side residence or business with Smart Appliances;
FIG. 13 illustrates a Console of the present invention; and
FIG. 14 illustrates icons or graphical representations of the present
invention.
Detailed Description
The present invention transforms power from being driven from the Supply Side
to be
actively controlled by the users and consumers of the power. It provides a
manner in
which the everyday user or consumer of power may contribute to the success of
the entire
Power Grid. In another aspect, it allows the user to feedback power into the
power grid,
stored in other forms of energy such as heat in a hot water boiler, for
example.
I Consumer Side Based Power Controller.
Before turning to a more detailed description, there is illustrated a Network
102 as shown
in FIG. I. The Network may be the Internet and may be, for example, connected
to the
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CA 02796891 2012-11-26
users in any suitable manner, such as by way of traditional broadband,
satellite, WiLan,
cable or utility power lines. There is provided a real-time pricing signal is
continuously
transmitted over the Network 102 over a predetermined period of time. The
Network may
be connected to homes 104 and/or smart appliances 106 and power generators
and/or
power generator utilities.
As shown in FIG. 1, the supply side may be connected to the demand side via a
consumer
portal and building EMS 106, through utility communications channels 108 or
via
satellite 110. There further may be control interfaces 112 or advanced
metering systems
114 that are used to assist in the orchestration of the supply/demand
relationship by, for
example, controlling local appliance or reporting metering information. In
addition, the
power generators may include solar or wind mills 116 and the smart appliances
may be
smart end-user devices, plug in hybrid cars or distributed generation storage
systems 118,
for example.
It shall be appreciated that one skilled in the art will know how to instruct
a processor of
a smart appliance in order to turn on or off the respective appliance in
response to a
pricing signal. For that matter, it is well within the capability of the
skilled person to
implement the invention in terms of software to be executed, wholly or in
part, by a
computer and store the instructions therefore on a computer readable medium.
Now a discussion of the mechanics of the solution proposed herein will ensue
by first
considering the Supply-Side of the power equation. Thereafter, a discussion of
concrete
example will be set.
Importantly, Supply Side generation of electricity is responsible for
approximately 'A to
'/2 of primary energy consumption. For example, of all the energy consumed in
New York
State in 2005, 38% was used for the generation of electricity. In other words,
the type of
power generators for the electrical power is a predictable quantity and the
proposed
solution aims at resourcing these generators. Although, it should be clear at
this point that
the the proposed solution also is applicable to any type of power source.
According to one implementation, for example, the proposed solution increases
efficiency of electrical generation by placing the demand right where the
supply of power
is at its optimal efficiency output. This reduces overall fuel consumption,
forestalls
building of new power plants, and/or has a positive impact on reducing
greenhouse gases.
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CA 02796891 2012-11-26
The details of this effect of the proposed solution will be described in more
detail with
references to the exemplary models below.
The proposed solution in another implementation puts to use renewable energy
sources.
Observing FIG. 2, which shows United States Electrical Energy Production
'Supply-
Side' Statistics, it can be plainly seen that renewable energy contributes a
relatively small
amount of the power supply sources in the United States as compared with
traditional
power. By contrast, wind power is responsible for nearly 30% of the total
Danish demand
for electricity and approximately 16% of Germany's demand. To put this in
perspective,
wind power alone covers the aggregate demand of 1.4 million Danish homes, or
in other
words, the entire energy demand of western Denmark.
Regrettably, the U.S. has a culture of on demand power supply, which is hard
to fulfill by
application of renewable energy sources. However, the fault is not all due to
lifestyle but
also on the conditions suitable for tapping into these renewable energy
sources. Wind and
solar are temperamental and are not always available around the clock. While
it is true
that Holland and Denmark have a culture of energy conservation, these
countries are also
blessed with regions of high wind.
In addition, the infrastructure for renewable energy resources in the U.S. is
not yet fully
manifested. Smaller countries like Holland and Denmark have been able to
accomplish
more because they have the luxury of having a smaller country to deal with.
For the same
reasons, many European countries (particularly those in eastern Europe) have
been able
to update their power grids to address modern ideals and available
technologies. For all
that, the U.S. may be in a unique position to benefit from the instant
proposed solution.
Given the size and mixed variety of power infrastructures in the U.S., there
is a very real
need for orchestration of the supply of power to the demand for that powering
America.
While the U.S. has lagged behind European countries in the renewable energy
sector, the
possibilities of wind power in the U.S. are demonstrable. The state of Texas,
for example,
has significant wind power production and is the largest producer of wind
energy in the
United States. Thus, the capability is there. There only needs the means by
which these
resources can be adequately put to use in the U.S.
The present proposed solution seeks, in at least one implementation, to
capitalize on these
renewable energy resources and put them to efficient use in the overall power
supply
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CA 02796891 2012-11-26
matrix. The present proposed solution orchestrates these pockets of renewable
energy and
integrates them into the mainstream infrastructure. As the U.S. embraces
renewable
energy more and more, as it undoubtedly will, the solution provided herein is
scalable
and will be there to orchestrate these resources as well.
As can be seen from FIG. 3, which shows Total Stored Capacity in MW, wind
power
production in the U.S. is expected to more than double in the next four years.
Now is the
time for a realizable integration of these renewable energy resources. The
present
proposed solution timely provides this integration by orchestrating the supply
and
demand, and vice versa.
While efforts to foster increased production from renewable resources such as
wind and
solar are much needed and welcome, there is a growing problem of how to search
for
uses of (demands for) renewable energy right at the time when it becomes
available. For
example, if it is particularly windy while people are sleeping, there is an
immediate
supply of power, but there may not be as high a demand for that power as
compared to
during daylight hours.
As a result, countries such as Denmark have reached an upper limit and have
begun or
soon will limit production of renewable energy. Even the countries which have
incorporated renewable energy sources into their infrastructure, there is
still a need for
the present proposed solution to orchestrate those resources. The present
proposed
solution does not simply catalyze the bringing on line of renewable resources,
it
orchestrates them and brings them into the infrastructure in such a way that
they are
utilized at their maximum efficiency. Thus, countries like Denmark will also
benefit from
use of the proposed solution.
The question is then, how can renewable energy be provided on demand when
weather is
a temperamental variable? One could imagine that the energy from a renewable
resource
could be stored, such as in a battery. While the proposed solution is workable
with
storage elements, a battery solution alone does not sufficiently address the
problem of
providing the demand for power right at the time when the power is readily
available.
For one thing, using batteries to store the power disconnects the causal link
between the
generators supplying power and the demand of that power. Thus, a battery
cannot dictate
how long a generator should be on line to meet a certain amount of demand. Nor
can a
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CA 02796891 2012-11-26
battery maximize the efficiency of the output of a particular generator based
on the
demand. For that matter, the battery cannot predict what total amount of power
will be
needed and will likely fall short of adequately matching that demand to the
renewable
supply. Because the causal connection between supply of power and its usage is
disconnected, a battery system by itself is unable to match demand or power
with supply
of power as described herein. What is needed in addition is the present
proposed solution.
Thus far, the mechanics of supply and demand have been discussed in the
overall power
scheme. Now continuing on, the mechanics of the building blocks by which the
proposed
solution orchestrates that supply and demand will now be discussed.
In one implementation of the proposed solution, there is employed a Network,
such as an
IP Network 102 shown in FIG. 1, to orchestrate the supply and demand of power.
For one
thing, the proposed solution uses the Network to send a pricing signal in real-
time to
homes or appliances. In this manner, the proposed solution communicates an
availability
(i.e., in terms of price) of SUPPLY-SIDE power generation capacity. As will be
explained below, the proposed solution further changes the price so that the
DEMAND-
SIDE for the power can utilize generation resources in the most fuel efficient
and
environmentally friendly ways. As will further be explained, the proposed
solution
indicates a price (or prices for various or combine power supply sources) that
has the
effect of shifting the demand to a time when resources are available or
brought on line.
The proposed solution, thus, provides the demand in sufficient quantity to
match an
efficiency of a particular generator or combination of generators.
To estimate the variable storage capacity on the DEMAND-SIDE, attention is
directed to
the various uses of energy in the home as shown in FIG. 4. Some energy uses in
the home
such as lighting are required based on what users are doing (herein referred
to as activity
dependent appliances or uses) others are not. The present proposed solution
takes
advantage of that distinction in one implementation by encouraging or
deferring demand
of power by user activity "independent" appliances, such as water heating
and/or
refrigeration appliances. Of course, to some degree appliances such as hot
water boilers
and refrigerators are dependent on the user activity, however, less so than
lighting
appliances, and exhibit a certain amount of independence from the activity.
These
appliances tend to have a thermal storage capacity that allow them to provide
energy on
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CA 02796891 2012-11-26
demand locally without demanding, or delay the demand, of power from an
external
source, such as a power plant.
Another feature to notice is that the independent activity appliances are more
predictable
over a certain period of time. In one implementation, the present proposed
solution can
model uses based on independent activity appliances that illustrates this
predictability for
an aggregate number of appliances. That is not to say that the proposed
solution cannot
create mappings of activity dependent appliances, in fact the proposed
solution is
applicable to those appliances as well, given only the restraints of finding
some
commonality of behaviour of those appliances. For example, people tend to use
lighting
during the day as opposed to night time when they are asleep.
In addition, the present proposed solution operates at sufficiently frequent
intervals to
encourage or discourage demand. This has a significant positive impact on
electrical
demand without compromising the needs of users. For example, in one
implementation,
the proposed solution schedules efficient generation for pre-cooling or pre-
heating of
living spaces, to cool millions of homes in southern climates before the
occupants return
on a summer evening, or heat homes in northern climates in anticipation of the
workforce
returning home.
The methods presented here are a significant break away from the prior work on
load
shifting and load curtailment. Peak shaving, for example, reduces the amount
of
electricity purchased for some period of time. Sometimes this is accomplished
by
curtailment (shutting down loads), sometimes by load shifting (thermal
storage) and
sometimes by self-generation. Much of this previous work has focused on
shifting peak
demand into the traditional diurnal valley so that a flatter demand curve
results in lower
requirements (and costs) for peak generation facilities.
Peak shifting could be achieved by creating a high pricing signal once a day
during peak.
In this peak-shifting scenario, every day at the same time peak pricing goes
into effect
which discourages usage. Problematically, those users who can afford to pay
peak pricing
can choose to use as much as they want when they want, and may choose not to
participate in load management at all.
While a more expensive price of energy might help curtail demand by users
during peak,
a scenario that is not resolved is the impact on the less-fortunate and budget
conscious
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CA 02796891 2012-11-26
users. A terrible negative effect of traditional peak pricing is that poor
people simply
cannot afford to use energy during peak. Waiting until 2:00 AM for the
dishwasher to
automatically start is a good thing, but would waiting until 2:00 AM when the
price of
energy is low enough to, say, cook dinner, is not a feasible solution for the
entire power
demand market.
A solution proposed by this proposed solution to the problems encountered by
load
shifting is to change the price of energy to encourage or discourage use many
(many)
times throughout the day, for example as many as 8-10 times, in predictable
ways. An
implementation of the proposed solution varies pricing enough so that demand
is
responsive, in other words that demand in the aggregate is incentivized to
change its
behaviour owing to pricing.
In the same implementation, the proposed solution may also consider the needs
and
budgets of the consumers whilst varying pricing in a demand responsive way. As
mentioned already, providing various pricing changes throughout the day offers
users of
modest means to obtain the power they require at a time that is not
inconvenient or would
otherwise dramatically task that user's stored energy waiting for pricing to
drift
downward. By making demand responsive to pricing, for example, by setting
pricing to
levels attainable by those of modest means or budget, the present proposed
solution does
not simply cut off all demand as in peak shifting.
With reference to FIGS. 5-10, concrete examples of how the proposed solution
orchestrates, that is coordinates, SUPPLY-SIDE power resources and DEMAND-SIDE
power needs will be described.
FIG. 5 illustrates a model of generation supply capacity over a predetermined
period of
time, here 24 hours. In the figure each horizontal band is one or more
'chunks' of supply
capacity. This model is somewhat simplified in that each of the types of power
source,
including combustion turbines, hydro electric energy, oil, coal and nuclear
are illustrated
in an arbitrary order. Although, it could be observed that FIG. 5 generally
illustrates
power sources that are arranged diagrammatically in order of ramp up time. For
example,
it is seen from the figure that the power sources, such as nuclear generators,
which are
less flexible and require a relatively long and complicated power up
procedure, are
arranged as base lines of energy, here shown as 20% of the initial overall
power needs or
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CA 02796891 2012-11-26
demand. These resources might account for user activity dependent demand, or
on
demand, such as lighting which requires an immediate supply of power when the
user
switches the light on and off throughout the day.
On the other end of the power generator spectrum, we see more flexible
generators that
can meet on demand power needs arranged along the higher demand requirements
as can
be seen from FIG. 4. For example, hydro, combustion turbines, and/or spot
market power
generators represent power sources that may be brought online more quickly and
with a
relatively less complicated ramp up procedure. These more flexible resources
may, as
suggested by the figure, provide power for the remaining 60%-100% of the
aggregate
demand. This demand may be, for example, power requirements for user activity
independent appliances or uses, such as refrigerators and hot water boilers.
Now turning to the demand side of the equation, consider the simplified model
of
aggregate electrical demand shown, for example, by FIG. 6. The curve in FIG. 6
may be
the demand curve experienced by a winter peaking utility over a predetermined
period of
time, such as 24 hours. Here it could be observed that the curve corresponds
to one that is
in a northern climate given the high electrical demand for space heating in
the night
hours. When night gives way to day, daily electric demand slowly falls in the
morning
and then rises steadily.
The proposed solution maps, or superimposes, the simplified supply and demand
of
power models in FIGS. 5 and 6, to obtain FIG. 7. FIG. 7 illustrates how the
supply side
operates throughout a predetermined period of time, here a 24 hour day, in
order to meet
the aggregate energy demand across large serving areas. The 'stair steps' in
FIG. 7
correspond to generators being brought on-line and off-line (i.e., starting up
and shutting
down) throughout the day as aggregate demand rises and falls. Steady state
operation is
illustrated where the lines are flat. It is to be noted that the highest
output shown here is
not necessarily the maximum output of the generator.
It shall be appreciated that, for a particular power generator, a minimum
efficiency of use
occurs at point 702 when there is no demand for the power output. Conversely,
at point
704, the demand almost matches the output of the power generator and yields a
maximum
efficiency of use as given by the equation efficiency=energy output/energy.
One of the
driving principles behind the present proposed solution is to place or shift
the aggregate
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CA 02796891 2012-11-26
demand right at the point where a generator is available to output at its
maximum
efficiency.
It is to be appreciated that a certain amount of power, known in the industry
as spinning
reserve, is in practice in excess of instantaneous demand. Of course, there
are times when
the output will overstep the spinning reserve upper ceiling. The spinning
reserve provides
capacity to meet unexpected demands and cover for generation or distribution
failures.
The spinning reserve is diagrammatically illustrated in FIG. 7 at point 706
and, further,
by the way the demand curve does not follow the boundary of the step curve.
The aggregate demand curve shown in FIGS. 6 and 7 is predictable. In other
words, the
aggregate demand curve rises and falls with regularity from day to day, or
over a certain
time period. The curve may be said to have a Markovian-like behavior. In other
words,
demand in the aggregate will generally be similar to the previous day. There
may be
exceptions caused by intervening events such as inconsistent weather,
particularly,
temperature swings that affect heating and cooling demands, weekdays versus
weekend
days, holidays, etc.
In general, however, if the event is consistent from time period to time
period a
Markovian like demand curve can be developed that is useful for prediction of
future
demand according to the present proposed solution. For example, heat waves
that last a
number of days will affect the aggregate demand for a new, but predictable,
demand
curve. A region that receives sporadic rainfall could also have some
predictable nature to
its region's demand curves. The proposed solution matches this future
predictability to
supply resources.
A Markov process is defined as a stochastic process whose state at time t is
X(t), for t>0,
and whose history of states is given by x(s) for times s<t is a Markov process
if:
Pr[X(t+h)=y1X(s)=x(s),Vs-y----PrIX(t+h)=yX(t)=x(t)],
>0.
Equation I.
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That is, the probability of its having state y at time t+h, conditioned on
having the
particular state x(t) at time t, is equal to the conditional probability of
its having that same
state y but conditioned on its value for all previous times before t.
Markov processes are typically termed (time¨) homogeneous if
Pr p((t+h)=y1X(t)=4 =Pr IX(h)=y1X(0)=4, Vt,h>0 ,
Equation 2.
As mentioned above, the time period illustrated in the Figures is merely
representative
and any time period can be selected. For example, given a particular weather
pattern, it
will make sense to select a time period that is either shorter or longer than
a day. As long
as the time period supports a pattern of predictable demand, the proposed
solution can
operate to predict demand for future periods of time.
To continue, the present proposed solution takes advantage of the
predictability of
demand in the aggregate. As can be seen from FIGS. 5-8, the present proposed
solution
maps an aggregate demand curve within a period of time that is sufficient to
demonstrate
a predictability. By moving or shifting the demand for power according to the
present
proposed solution, the supply side output can be more closely tracked, as
illustrated by
the steps formed in the shifted demand curve shown in FIG. 8. In other words,
supply
capacity of the power plants is more efficiently utilized.
In the context of FIG. 1, a real time pricing signal is issued over the
Network 102 to
homes 104 and/or to appliances such as hot water heaters, refrigerators and
other
appliances 106. As will be further described, the various appliances have a
typical duty
cycle schedule that describes the energy consumption of the particular
appliance in terms
of duty timing and firing rate. Based in part on the duty cycle schedule and
the pricing
signal, which is issued continuously over a period of time, it is decided
whether or not to
delay firing of the particular device.
In the aggregate, these appliances in the cause demand which is shifted to a
time when
there is an optimal amount of power being output, possibly from a combination
of power
sources. In this manner, aggregate demand can be much more controlled. The
demand
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CA 02796891 2012-11-26
'follows' (or accommodates) the stair-stepped SUPLY-SIDE capacity as shown in
FIG. 8
thereby matching demand to supply, not vice versa. It shall be appreciated
that this
arrangement is contrary to conventional supply chasing demand.
As already mentioned, an amount of spinning reserve must also be taken into
account.
The present proposed solution, in one or more implementations, adjusts for the
spinning
reserve by matching aggregated demand to maximum plant efficiency less the
spinning
reserve as shown in FIG. 7. Matching of the aggregate demand will be discussed
in more
detail. Suffice to say at this stage that the point at which it is chosen to
shift the demand is
when the respective power generator is outputting power at the optimal
quantity
offsetting for spinning reserve.
It will be appreciated that the precise amount of spinning reserve is a
predetermined
parameter that is specific to the particular power generator and will only be
discussed as a
variable herein without specific reference to the ratings of any particular
generator. That
these ratings are specific to the various utilities, which can be easily
attained therefrom.
In FIG. 8 the overall energy usage (i.e., the integral or area under the
curve) is similar to
that shown FIG. 7. While the pricing signal might or might not discourage
overall usage
in a 24 hour day, it definitely does discourage and encourage energy use at
several times
throughout the day. This is done to forestall bringing generating capacity
online and then
once brought online to move said capacity to its maximum output and efficiency
as
quickly as possible.
The duration of time that a facility might be forestalled in coming online
might be any
period of time. In the meantime another power generator might be selected to
meet more
immediate need. Thus, the proposed solution can provide a delay that is
deminimus to
most power uses, such as a few to tens of minutes. This is done because too
long a delay
in meeting demand would unnecessarily burden users of modest income or budget
because they would have to wait unreasonably long to, say, cook dinner or take
a shower.
As the more complex power generators come online, the proposed solution can
shift
demand to those generators to meet additional demand not met by the more
flexible
generators.
The ability to delay the start of such a facility and then within minutes to
bring it to near
its maximum output clearly has a significant fuel environmental savings.
Certainly some
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types of generators can come on-line and off-line more quickly than others,
gas turbines
being the most agile and perhaps nuclear plants being the least. And as
previously stated
there must be sufficient spinning reserve at all times. Bringing these on line
on when the
demand is aggregated enough to match a maximum efficiency of one or more power
generators, avoids both wasting energy keeping power generators online but
idle or
operating the power generators at lower efficiencies.
In other words, by way of the present proposed solution, less energy overall
is needed to
meet the power demands of users because less energy is wasted. That means in a
very
real sense, energy is conserved and less global warming emissions are created,
thereby
helping to slow the global warming problem.
Now that the mechanics of the proposed solution have been described in
sufficient detail,
we now turn to specifics that will be described with reference to FIG. 9a.
FIG. 9a illustrates a duty cycle schedule of a typical hot water heater. In
another sense,
FIG. 9a may also be considered to illustrate the energy storage capability of
demand-side
appliances. To be certain, a hot water heater consumes power. However, that
very same
heater at any time typically is holding and maintaining thermal energy. In
that sense, the
aggregate of a number of such hot water heaters could be considered as a sort
of energy
source, itself a power generator.
While hot water heaters cannot be used as a source of power, they can be
thought of as
storing energy. In this sense, how much energy a particular hot water heater
has left can
be used to determine when the hot water heater should fire in comparison to a
pricing
signal. When, for example, the hot water heater has sufficient energy to
provide a hot
shower, for example, at a time when showers are expected to be demanded
according to
the duty cycle schedule, there may be a decision to delay firing for a few
minutes with no
real change in performance output. In other words, the user experiences a hot
shower
without ever knowing that the hot water boiler firing timing was delayed. The
delay in
demand of power is transparent to the end user.
Turning now to a more specific discussion of the hot water boiler modeled by
FIG. 9a,
there is seen, starting at the left side, a decline in water temperature from
an upper limit
of approximately 1100 down to 95 over the period from near midnight to
approximately
6:00 AM. The relatively constant slope of the temperature line over this
period indicates
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that no water has been drawn from the tank. At 6:00 AM the water heater fires
for a short
duration to bring the output temperature back up from its lower limit, and
fires again
around 8:00 AM to accommodate the demand for hot water being drawn from the
tank.
Perhaps someone took a shower or did some laundry and/or dishes. Of course,
this duty
cycle schedule is merely indicative of the power consumption of a typical hot
water
heater, and any other duty cycle schedule might be replaced with the one shown
in FIG.
9a.
Continuing with the example, FIG. 9b shows a portion of the duty cycle
schedule of FIG.
9a in more granularity over a six hour period. From this figure, it can be
seen that the
firing cycle (assuming here that hot water is not being drawn) is
approximately 30
minutes in duration. Again, FIGS. 9a and 9b are mere examples and any other
firing
timing could be substituted for that shown.
Referencing FIGS. 9a and b, it can be estimated that the duty cycle of the
residential hot
water heater in standby mode (where it assumed that no hot water is being
drawn) is
approximately 30 minutes every 6 hours=-8%. Accounting for additional firings
during
periods of hot water usage results in an estimated hot water heater duty cycle
of 10%
over a 24 hour day. Said another way, at any given point in time, 1 in 10 hot
water
heaters will be firing.
Considering there are approximately 110 Million homes in the United States,
roughly 11
million hot water heaters are firing around the clock, with even more expected
to be
firing before the morning rush hour and after the evening rush hour. When one
considers
the enormous impact that shifting demand has, one then understands the great
potential
for the present proposed solution to both save costs for everyone concerned
and help to
save the environment at the same time.
The proposed solution tends to have an effect on demand in the aggregate,
although the
proposed solution could also be used for less than an aggregate of appliances.
In addition,
the aggregate may represent a specific type of appliances or, more likely, a
combination
of types of appliances.
It shall be noticed that the present proposed solution is directed to
aggregating demand on
the appliance level, in contrast say to total demand from a user, ie, by
reading meter data
of that user. In that regard, the proposed solution understands a picture of
how appliances
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CA 02796891 2012-11-26
react over a course of time and, depending on their type, can price them out
of the market
for a specific period of time. In other words, the proposed solution shifts
demand on the
appliance level, as opposed to the user level. Of course, the proposed
solution can affect a
combination of types of appliances, however, it does so by determining the
demand on an
appliance type.
In one implementation Aggregate of demand is calculated according to Equation
3. For
example, if 'A of the 11 million hot water heaters in the U.S. are
electrically fired, then at
least 3.7 million electric hot water heaters can be managed at any given point
in time.
Given that the typical electric hot water heater has a 4.5 kW demand when
firing, the
aggregate electrical demand of heating hot water is 16.5 GW (Gigawatts) as
indicated in
Equation 1. This is a large amount of demand, representing approximately 22%
of the
73.9 GW of worldwide electrical supply from wind power at the end of 2006.
EAppliances(type)x% Duty Cyclex% Electric FiredxWattage=Aggregate Demand
Equation 3.
In terms of our instant example, the total aggregate demand for water heaters
is the
number of water heatersxpercentage appliance duty cycle (10%)xpercentage
firing
timing (33%)x Wattage or,
EWater Heatersx10% Duty Cyclex33% Electric Firedx4.5 kW=16.5G W
In our example, the present proposed solution determines a typical duty cycle
schedule
over a period of time that is sufficiently long to provide a predictable
demand curve such
as the one shown in FIG. 9a. In this example, the duty cycle schedule is
modelled for hot
water heaters, but any type of appliance may similarly be modelled.
Thus far, an aggregate demand is calculated from the duty cycle schedule along
with
other parameters, such as the total number of appliances belonging to the
demand group
and firing timing over the period of interest. The aggregate demand, which may
be for
one or more types of appliances, is then compared, or mapped onto, such as
shown in
FIG. 7, with the a power supply-side curve. And it is determined then if a
suitable supply
15
CA 02796891 2012-11-26
of power is available from any of the power generators, or if, for example,
power
generators need to be brought online. If power generators need to be brought
on-line, it is
also determined how fast the particular generator or generators need to be
brought up to
maximum efficiency from the supply side curves of FIG. 5 or 7.
As earlier mentioned, the generators that need to be brought on line may be
renewable
energy power sources such as, for example, wind power generators. These wind
power
generators also have a known typical operation time, i.e., when wind typically
is blowing
in a particular region, and a model such as that shown in FIG. 6 is developed.
The
demand would then be shifted then to the time when the wind power generators
are in
operation, i.e., when the wind is blowing.
In continuing with our example, the pricing signal is modified to discourage
demand until
such time that the supply side is able to match the demand. In one
implementation, it does
so until the supply side is operating at maximum, or optimal, efficiency.
In another implementation, the pricing signal may discourage demand for a few
to tens of
minutes as mentioned above in order to give people of modest means a chance to
utilize
the power at convenient times, i.e., rather than having to wait hours to cook
dinner or take
a shower, for example. In our hot water boiler example, users do not have to
wait to take
a hot shower.
In still another implementation, the proposed solution selects the time period
according to
the thermal storage capacity of a particular type of appliance or appliances.
In regards to
the hot water boiler example, there already may be sufficient hot water in the
boiler for a
shower such that the delay of demand, i.e., switching the hot water boiler on
is
unnoticeable to the end user.
In yet another implementation, the demand for power is discouraged because of
infrastructure failures and is represented in the form of the supply side
curve showing a
lack of ability to presently provide power. Those generators that can be
brought online
automatically will be by operation of the present proposed solution and will
be distributed
the demand, i.e., rather than the defunct or out of commission power
generators. Indeed,
the present proposed solution in this implementation will shift demand away
from
defunct power sources.
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CA 02796891 2012-11-26
The present proposed solution, in yet another implementation, uses modes of
operation to
control aggregate demand by automatically adjusting the real-time price
transmitted to
end uses such as appliances that can start and stop at will based on the
default set of user
preferences. When electricity is inexpensive, heater will come on early and
stay on
longer. For example, a dishwasher may not choose to wait until after midnight
when
energy is less expensive. When energy is more expensive, on the other hand, a
hot water
heater may not choose to run until after its internal temperature has fallen
some number
of degrees below its normal 'start' temperature. Likewise an a hot water
heater that is
already running may choose to stop before reaching it's normal 'stop'
temperature.
The present proposed solution provides for modes of operating the appliances
that is
implemented by an operating band that is either shifted upward or downward
based on
the pricing signal. In other words, the proposed solution can effect delaying
a start and a
premature stop of the appliance by moving the operating band with the pricing
signal.
FIGS. 9c and d illustrate exemplary modes of operation, which include an
inexpensive
mode as shown in FIG. 9c and an expensive mode as shown in FIG. 9d. To
explain, the
inexpensive mode of operation of FIG. 9c indicates how the appliance should
react
during inexpensive pricing of electrical energy. Conversely, FIG. 9d indicates
how the
appliance should operate during expensive pricing of electrical energy. Of
course, these
figures are merely examples and any duty cycle and boundary conditions may be
set.
More specifically with reference to FIG. 9c, the duty cycle schedule of FIG.
9a is again
shown here, but this time with an operating band 902 overlayed on the duty
cycle
schedule. The operating band indicates a region where the appliance is in
operation and
includes an upper and lower limit 904a, b. The upper and lower limits may be
set by the
user or home owner of the appliance. The lower limit indicates the point at
which the
appliance is to switch on and the upper limit indicates when the appliance is
to switch off.
These may be set by the user in advance or preset through the Network (102,
FIG. 1) for
the various pricing situations. Of course, more than two modes of operation
may be
provided for with many different upper and lower limits.
During inexpensive pricing, the user may not mind spending money for energy
and
would be willing to pay for hotter water. Hence, the operation band boundary
conditions
are shifted upward. FIG. 9c shows that the operating band has a lower limit of
100
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CA 02796891 2012-11-26
degrees F. and an upper limit of 115 degrees F. In other words, the appliance,
in this case
a hot water boiler, switches on when the internal water temperature falls
below 100
degrees F. and switches off when it reaches 115 degrees F.
During expensive pricing, the user may indeed mind spending money for energy
and
would not be as willing to pay for hotter water. Hence, the operation band
boundary
conditions are shifted downward. FIG. 9d shows that the operating band has a
lower limit
of approx 90 degrees F. and an upper limit of 105 degrees F. In other words,
the
appliance, in this case a hot water boiler, switches on when the internal
water temperature
falls below 90 degrees F. and switches off when it reaches 105 degrees F.
To reiterate, the present proposed solution in this implementation shifts
demand by
shifting the operating band of the appliance upward or downward according to
the modes
of operation by setting the pricing accordingly. It will be appreciated that
the hot water
boiler of FIGS. 9c and 9d are mere examples and that any appliance may include
this
feature. For example, the modified start/stop operating band can also be
applied to
refrigeration processes. For example, when energy is inexpensive, a fridge
will adjust it's
upper and lower limits to start prematurely (at a higher temperature) and stop
after
cooling to a lower than normal temperature.
The present proposed solution can also use modes of operation to effectuate
thermal
energy storage. Thermal energy storage is achieved by automatically adjusting
the upper
and lower temperature limits of end uses such as space heating and cooling,
heating hot
water, and refrigeration. For example, by raising pricing, the proposed
solution causes hot
water boiler appliances to shift the operating band lower, which causes the
hot water
boiler to wait until later to turn on. In other words, the present proposed
solution caused
that hot water boiler to store thermal energy.
FIG. 10 illustrates the method 1000 by which the example above carries out the
proposed
solution. As discussed above, the proposed solution in step 1002 determines a
duty cycle
schedule. As described, the duty cycle schedule is determined for a
predetermined period
of time that is sufficient in duration or length to provide a duty cycle
schedule of a group
of appliances that is predictable from time period to time period. In the next
step 1004,
the pricing signal, which is transmitted in real-time continuously of the
period of time, is
modified to encourage of discourage demand for power on the basis of an amount
of
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CA 02796891 2012-11-26
currently available power and the duty cycle schedule. In step 1006, the
demand for
power is shifted to a time when the power generator(s) are brought on line and
operated
at a maximum efficiency as indicated in step 1008.
The orchestration of supply of power and demand for power may be controlled by
a third
entity, i.e., not the utilities and not the end users. The third entity may
use, for example, a
data management system, dynamic systems control and distributed operations
equipment
112.
Turning now to another example, the orchestration of supply of power to demand
for
power of refrigerators will now be described.
As in the earlier example, a duty cycle schedule (step 1002, FIG. 10) for a
typical
refrigerator is similarly be determined for a period of time that provides a
predictability
about that demand and that includes information about the firing timing and
power
consumption of the appliance.
An aggregate demand is calculated according to Equation 3. One of the best
estimates of
the duty cycle for all properly working 'Energy Star' refrigerators is about
50%. Auto
defrost models have a secondary duty cycle which amounts to about 10 minutes
operation
over a 18-36 hour period. This cycle draws a large amount of energy during
that time, but
compared to the compressor operation, impact on load is negligible.
The storage capacity of refrigerators is significant, especially in hot
climates. For
example, Florida's hot and humid climate challenges even the best
refrigerators. Not
surprisingly, refrigerators guzzle a lot of electricity in Florida (on average
about 200
Watts each). With roughly 7 million refrigerators in the state of Florida, for
example, the
average, or aggregate, demand of these units exceeds 1 GW.
The aggregate demand is mapped or compared to the supply side curve and it is
determined whether an instantaneous demand for power is capable of being met
or
whether output is at an efficient level. On this basis, it is determined to
encourage or
discourage demand in order to keep that demand where it is or shift it to a
time when it is
best matching to a maximum efficiency of output. The pricing signal is
modified (step
1004, FIG. 10) to encourage or discourage the demand for power and the demand
is
shifted (step 1006, FIG. 10) to a time when the power generator(s) are
operating at
maximum efficiency (step 1008, FIG. 10).
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CA 02796891 2012-11-26
The present proposed solution also adjusts for the wastefulness of older
technology. Over
25% of the refrigerators are old and inefficient¨built before the advent of
recent
appliance efficiency standards. About 5% of them are replaced each year.
Providing more
efficiency from the supply side or from an intermediary infrastructure that
orchestrates
supply of power and demand for that power greatly cuts down on the
wastefulness of
those outdated refrigerators.
Again, it is important to note that at almost any time, an expensive or
inexpensive price
of electricity could have been sufficient incentive for refrigerators to delay
or accelerate
compressor operation by 10 or more minutes without having a noticeable impact
on food
temperature or longevity. In other words, the end user, particularly in the
case of
appliances with a high energy retention, does not notice the effect of the
delay of the
demand.
Here it is reiterated that the proposed solution has a huge impact on
environmentally
harmful emissions. If 7 million Florida refrigerators produce an average
demand of I GW
and northern-climate refrigerators use less energy, it is estimated that the
110 million
refrigerators in the United States produce an average demand of 15 GW, or
nearly 20%
of the 73.9 GW worldwide electrical supply from wind power at the end of 2006.
With
the present proposed solution, a renewable energy power source could be better
integrated into that supply scheme, thereby reducing harmful emissions.
Advancements in refrigerator technology will yield two-speed or variable-speed
'always
on' compressors that will be managed similarly. Refrigerators will be
encouraged to shift
from low to high-speed, or vice versa, based on real-time energy prices. In
that case, such
smart appliances are controlled directly on the bases of those real-time
prices that are sent
out continuously over the predetermined period of time.
It should also be considered that the foregoing examples are not limited to
aggregating
demand for one type of appliance but that one or more types of appliances may
provide
the aggregate demand. It is a matter only of determining the typical duty
cycle schedule
for the various types of appliances and using the Formula 3. Similarly, the
supply of
power may be provided by one or more of the power generators.
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CA 02796891 2012-11-26
The examples provided were specific to electric utilities, though real-time
control of
demand is immediately applicable also to the transmission and distribution
infrastructures
of electric, gas and/or water utilities as well.
In conclusion of the exemplary description of the proposed solution, the
magnitude of
demand that can be managed using real-time pricing according to the present
proposed
solution is quantifiable and significant. Together, United States Residential
Electric Hot
Water Heaters and Refrigerators produce an average demand equivalent to
approximately
40-45% of the worldwide electrical supply from wind power at the end of 2006.
If even a
fraction of the demand in the U.S. could be shifted to wind power sources, the
present
proposed solution would have enormous benefit on the environment.
The opportunities to orchestrate supply and demand of power are very real.
There are
significant advantages in reducing burning of fossil-fuels, emissions of
pollutants, and
forestalling the building of new power plants. And there is the possibility
that renewable
resources such as Solar and Wind Power can search for and, essentially, create
demand in
real-time and hence be used more extensively and efficiently.
Although the present proposed solution has immediate benefits to the
environment, as
technology expands into our everyday life the benefits of the present proposed
solution
will further
extend our energy resources and conserve our climate. In time, all of the
infrastructure
needed to fully maximize the benefit of the present proposed solution will be
in place. All
of the technology is already there to implement in-building energy
controllers, Internet
Protocol interfaces for appliances, and sensible appliance control algorithms
to react
appropriately to real-time pricing signals. The details of that technology is
not necessary
for practice of the present proposed solution.
Further, the present proposed solution is not limited to affecting the demand
side, but is in
fact an orchestration of the supply of power with the demand for power. In
other words,
the proposed solution is capable of being used well beyond utilities' price
signals that are
sent out in search of smart appliances. In a much more all-encompassing way,
the
DEMAND-SIDE (of homes and businesses in the future) is also able to search the
SUPPLY-SIDE for lowest cost/most efficient alternatives to meet heating,
cooling and
electric energy needs.
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CA 02796891 2012-11-26
This will automatically occur and flow directly from the implementation of the
proposed
solution when power sources are developed not only to include distant
utilities but nearby
cogeneration power plants in the basement, the neighborhood or the family's
hybrid car.
The proposed solution can then be used as before, treating those new sources
of energy as
any other type of power plant.
The concept of the 'Networked home' being 'plugged into the car' should be
explored in
the near future and it is anticipated that the proposed solution will work
just as
meaningfully with those new sources of energy as with those of the 20th
century. If
occupants or appliances in a home or business need, say, heat and electricity,
the cheapest
source may a local resource (e.g., a car), a utility resource, or a
combination of local and
distant resources. The proposed solution as described works also in this
environment
regardless of type of power source.
II. Console, User Interface & Portal.
All users of utility supplied services and energy such as but not limited to
electric, gas,
and water can benefit from use of the Console. These customers may be
subscribers of
energy services and or may subscribe to the Console as a service. These
customers
include all classes of customers (Residential, Commercial and Industrial) of
utilities such
as energy, electric, gas, water, as well as people living off the Grid (or
Power Grid).
A Power Grid or an electrical grid is an interconnected network for delivering
electricity
from suppliers to consumers. It consists of three main components: I) power
stations or
generators that produce electricity from combustible fuels (coal, natural gas,
biomass) or
non-combustible fuels (wind, solar, nuclear, hydro power); 2) transmission
lines that
carry electricity from power plants to demand centers; and 3) transformers
that reduce
voltage so distribution lines carry power for final delivery.
In the power industry, electrical grid may be defined operationally as an
electricity
network which includes the following three distinct operations:
Electricity generation - Generating plants are usually located near a source
of water, and
away from heavily populated areas. They are usually quite large to take
advantage of the
economies of scale. The electric power which is generated is stepped up to a
higher
voltage-at which it connects to the transmission network.
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CA 02796891 2012-11-26
Electric power transmission - The transmission network will move (wheel) the
power
long distances¨often across state lines, and sometimes across international
boundaries,
until it reaches its wholesale customer (usually the company that owns the
local
distribution network).
Electric power distribution - Upon arrival at the substation, the power will
be stepped
down in voltage¨from a transmission level voltage to a distribution level
voltage. As it
exits the substation, it enters the distribution wiring. Finally, upon arrival
at the service
location, the power is stepped down again from the distribution voltage to the
required
service voltage(s).
In one aspect, the user portal or Console may be considered to be subservient
to the end
user or the consumer of power. In another aspect, the portal or Console may be
defined
to be near or in the proximity of end uses. Yet another definition places the
portal or
Console on the user or the power consumer side or after the distribution
transformer.
Figure 11 illustrates a typical Grid 1100, which may include the Supply Side
1102 that
includes various types of power generators 1104 and distribution lines 1106.
The Supply
Side may also include step down linkages 1108 that step down the voltage or
otherwise
change the phase. The Grid 1100 further includes the Demand Side 1110
including users
or consumers of the power, which may be residential, office building, or even
commercial enterprises. One manner in which to divide the line between the
Supply Side
1102 and the Demand Side 1110 is to define the Demand Side to be on the other
side of
the distribution transformer. In addition the Supply Side 1102 may include
smaller
generators closer to the Demand Side 1110, such as Solar or Wind farms, City
Power
Plants and the like. However, the Demand Side 1110 may include appliances or
power
consuming machines (such as hybrid electric cars) whose primary function is to
provide a
service to the consumer of the power (i.e., rather than produce power) that
put power
back into the Grid. For example, a hybrid car may provide excess power back to
the grid
that it generated using fossil fuels such as natural gas.
Network.
In one alternative, the utility company provides the network or at least the
distribution
lines. However, who provides the distribution lines is not essential to the
operation of the
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CA 02796891 2012-11-26
proposed solution. In some venues, the distribution network may be provided or
controlled by n intermediary organization. In the former case, the utility
(e.g., energy,
electric, gas, water) may provide the pricing signal addressed to, one, more
or all
customers and carried by a carrier lines, such as for example the Internet. In
the latter,
the intermediary may provide the pricing signal. In either case, a 3( party
vendor may
operate the pricing for either the utility company or intermediary to provide
the pricing
signal.
In addition, the utility may provide a Customer Premises Network(s) (CPN) such
as the
home automation network shown in Figure 12. A CPN trypically links the end
user to
various appliances in his or her household, business or industrial site. On
the other, the
present proposed solution may provide the CPN. Additionally a 3rd party
contractor may
provide the Customer Premises Network(s). Any of these entities may provide
the
connection between appliances at the end user. The CPN may be interconnected
using a
LAN, WAN, WLAN, Powerline, Optic, Two-Wire telephone, or any communication
medium or protocol, or combination thereof.
Security.
Security is a key concern that has not yet been fully addressed. Information
that can be
gleaned from internet traffic in the form of bills including phone bills,
heating or energy
bills, for example, can include personal or private information. This is
particularly
important in Europe where private information is strictly regulated by
European Directive
and is punishable by sanctions. Information such as how much energy a person
uses or at
which times is considered private information in the European countries and is
a political
hot potato. Taking insufficient precautions to safeguard personal information
has gotten
many well-known companies, such as Google and yahoo! into trouble before the
European commission, a non-governmental body that has authority through treaty
to levy
sanctions. Such personal information may be maintained over open lines, which
is
problematic since information such as which can indicate something about a
user's power
habits may be attainable over, for example, the internet.
In that case, it is here provided as one solution to provide a security
encryption system to
protect such information that is transferred over common networks such as the
internet
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CA 02796891 2012-11-26
that cannot be easily intercepted and decoded. For example, a Public Private
key
exchange (e.g., Diffy-Hellman) may be used to keep personally identifiable
information
(PII) confidential. Another manner in which to provide security, which may be
in
addition to a security key, is to select a network for the user information
that may be
different than the network that transfers the pricing signal. One such network
that is
more secure is a closed network such as a cable network or a satellite
network. Further
combining a closed network with an encryption system leverages the mechanism
for
securing PII on cable modem networks.
Another security issue is the possibility of hacking or sabotaging the system
by sending
an incorrect pricing signal. By sending a high pricing signal to a home, a
hacker could
cause serious problems to a residence or group of residences or businesses. To
avoid that
scenario, the present solution may use an encrypted pricing signal. It may
also use a
closed network to send the pricing signal and, further, the Console may expect
the pricing
signal only on certain networks and reject pricing signals from open networks
as further
described. For example, the pricing signal may and is sent over a satellite
receiver or
STB registered to the user. In that case, the integrity of the pricing signal
can be made
more secure. In addition or in the alternative, the present solution may and
does provide
for limitations as further described to limit the switching on and off of the
appliances in
order to safeguard the appliance from being damaged or performing damage to
the
building or plant.
The pricing signal may be and is also customized for each user. This may and
is in the
form of a personalized encryption code for that user. In another embodiment,
the pricing
signal is different for different users or groups of users or regions.
Further, the Console
may and is designed to select from multiple pricing signals with different
identification
labels or codes that designate different types of users, such as for example,
home owners,
businesses or industrial plants (light medium or large).
Different Pricing Signals.
The pricing signals may be set differently for users of different power
requirements or
different regions who have different power requirements. Further, different
pricing
signals may be sent to areas based on their criticality to the network grid,
such that
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CA 02796891 2012-11-26
regions or areas that are critical to the Grid (perhaps belonging to a
transformer in the
next area in the domino chain to shut down during a blackout) are sent higher
pricing
signals, whereas areas further on in the domino chain are given graduated
levels of
pricing signals, and so on.
Console.
The Console may be considered as an interface or portal. In one aspect, the
interface
1300 as shown in Figure 13 is a physical device in the form of a laptop or
screen that
includes selectable screen icons or push buttons, and may include other
input/output
features such as a keyboard or voice command. The console may be compact and
stripped down of features and placed adjacent or integrated with an appliance,
such as
integrated into the door of a refrigerator or a kitchen cabinet. In one
aspect, there are
provided a number of consoles, each console providing a subset of functions or
subset of
appliances, such as groupings of appliances as explained further. The Console
may
include an input / output (I/O) 1302 to receive signals to and /or from the
appliances or
smart appliances. The I/O may also receive the pricing signal. There may also
be a
screen or LCD screen 1304. Within the console housing or external thereof
there may be
a storage unit 1306 such as RAM, ROM, FLASH, USB, DVD, CD, PROM, EPROM,
which may be volatile or non-volatile. There may also be a controller 1306
within the
console housing that analyzes the pricing signal as will be further explained
and
generating control signals to control the appliances and / or the smart
appliances. As
explained the controller 1306 may further be used to edit and modify the
appliance or
smart appliance algorithms. An appropriate text editor for this purpose is
also integrated
as software or firmware.
In another aspect, the Console is a Graphical User Interface (GUI). In
computing, a
graphical user interface (GUI) is a type of user interface that allows users
to interact with
electronic devices using images rather than text commands. GUIs can be used in
computers, hand-held devices such as MP3 players, portable media players or
gaming
devices, household appliances and office equipment. A GUI represents the
information
and actions available to a user through graphical icons and visual indicators
such as
secondary notation, as opposed to text-based interfaces, typed command labels
or text
26
CA 02796891 2012-11-26
navigation. The actions are usually performed through direct manipulation of
the
graphical elements.
In one sense the Console is a window for both utilities and consumers alike.
In one
aspect, the Console is configured to provide the subscriber of utility
services to peer into
the electronic world of the appliance and interactively visualize, analyze,
obtain
recommendations and control appliances to balance demand of energy with
supply. On
the other hand, the Console provides feedback to the utilities (or Supply
Side) so that
utilities may obtain information and collect information on one or more users.
This
information includes, but is not limited to, firing duty cycles of one or more
appliances,
and the firing duty cycles of groupings of appliances. The information may
also include
historical information of the appliance or appliances such as age, type,
manufacturer, year
made. The information may also include smart appliance algorithms or their
name that
may be used to manipulate or edit the algorithms as further explained.
The Console is a portal for viewing current, historical and future savings and
costs, to
allow for making future use decisions based on cost, energy efficiency, and
reducing
ones' energy and environmental footprint. In one aspect, the Console provides
a
simplified view of the user's cost account or budget settings as shown in
Figure 14. The
simplified view provides a quick and easily understandable icon that an
individual who
has either not the time or the expertise to understand smart appliance
algorithms or tables
or graphs of historical information regarding a performance of an appliance.
The simplified view may be in the form of, for example, a traffic light icon
on the
Console screen having red, yellow and green colored or monochrome circles. The
red
light or top most circle indicates that the user is over budget, or that a
particular appliance
is operating over costs that the user has set either globally or on an
appliance or appliance
grouping basis. At this indication, the user can have the appliance or
appliances
investigated, maintained or replaced if not operating efficiently. In another
aspect, the
algorithm for the smart appliance is adjusted in order to bring the appliance
or appliances
back into line with respect to the user settings. A yellow or middle circle
indicates the
cost or appliance is approaching the region where costs are over extended and
the green
or bottom circle indicates that costs or the appliance is operating within
budget.
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CA 02796891 2012-11-26
Further, a scale icon may be used as the simplified view or icon. The scale
showing on
one side under useage of the cost budget or savings and a middle portion for
being in the
zone of the cost budget or "fit" and the other side being over budget or
"unfit". Again,
the scale icon may be reflective of the overall budget or of one or more
appliances.
A further option is to provide a view or icon that indicates the net value in
monetary units
of savings or over expenditures, again either overall or per appliance.
The Console may also function and is provided as an intranet or enterprise
portal. An
intranet portal is the gateway that unifies access to all enterprise
information and
applications on an intranet. It is a tool that helps manage its data,
applications, and
information more easily, and through personalized views. Some portal solutions
today are
able to integrate legacy applications, other portals objects, and handle
thousands of user
requests. In a corporate enterprise environment, it is also known as an
enterprise portal.
As indicated, the Console may be and is configured to set or adjust policy on
Smart
Appliances and Smart Appliance Adapters. This is done in one aspect by
altering or
modifying the algorithm used to control the appliance. The algorithm for a
smart
appliance is typically factory set and stored in the smart appliance. The
algorithm is
uploaded to the Console and modified to adjust the duty firing cycle based on
the pricing
signal. This may be done by setting the firing timing based on a particular
pricing signal
or range of pricing signals. The Console may and also provides a hardware
solution,
firmware solution or software or non-hardware solution to provide this
modification of
the algorithm.
The Console may also be and is configured to provide an interface whereby the
user is
given recommendations about future pricing based on predictions or
predetermined
information about the pricing of power, for example, by use of power pricing
models
promulgated by official bodies such as the Public Utility Commission (PUC).
For
example, the Console refers to pricing models that predict future pricing for
power. The
Console then suggests to use power for appliances that are capable of being
put to use in
the present, should the cost of power be forecast to go up. For example,
lighting and heat
are said to be dependent on user activity and would likely not sway or
encourage or
discourage use on any given day, whereas washing and drying clothes are more
independent activities. Washers and dryers may be turned on earlier by the
modified
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algorithms, thereby shifting demand to an earlier point in time than when, for
example,
on Thursday washday after the prices are scheduled to go up.
Similarly, the Console can take advantage of peak days of the year when there
are known
peak days. For example, high energy usage during New Years Eve can be expected
since
more people stay awake later. The Console takes these peak days into account
and
adjusts the algorithms for the appliances or groups of appliances to operate
or suggest to
the user to be operated when pricing is not considered so high. For example,
the Console
will predict that prices on New Years Eve will be higher due to demand and by
way of
the known habits of users in a region according to the PUC and schedule or
suggest a
schedule of operation for those appliances before or after that time. Again,
the
scheduling or suggestion of scheduling may be based on whether the appliance
is
relatively dependent or independent of the user activity.
In one aspect, and as indicated, the algorithm is modified by setting the
threshold
acceptance for the pricing signal or the boundaries for the pricing signal
that trigger the
firing duty cycle of the appliance or appliances. In another, the entire
algorithm is
replaced. Typically, the algorithm is stored in a writable memory of the smart
appliance
controller that sits within the appliance or as an attachment to the
appliance.
The intelligence for the Console may be integrated wholly or partially within
the
Console. For example, calculations of firing duty cycles for particular
appliances or
groups of appliances may be calculated externally to the Console and fed into
the system
via modified algorithms or pricing suggestions for certain appliances. The
Console may
be integrated directly into the Smart Appliance or the controller for the
smart appliance or
appliance.
The Console may and is also provided in the form of a Set Top Box (STB). An
STB
typically is an information appliance device that generally contains a tuner
and is
connectable to a television set and an external source of signal, turning the
source signal
into content in a form that can then be displayed on the television screen or
other display
device. Set-top boxes can also enhance source signal quality. STB's may be
used in cable
television and satellite television systems, as well as other uses.
Historical Mode Of Console.
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In one aspect the Console may and does provide an instantaneous view of the
appliance
or appliances showing the efficiency, the firing duty cycle, the boundary or
boundaries
set for the pricing signal, a simplified view as explained, etc. In addition
to providing
instantaneous information on a particular appliance or appliances, the Console
may and
does provide a historical view of the duty cycle activity of a selected device
that
dynamically changes over time. A historical view mode may include data
displayed in
Tables and or Graphs. It may also include an indicator that indicates proper
power
consumption over a period of time as set by the user or as recommended by the
Console
based on pricing and or energy conservation schemes.
Types of pricing models may and are employed by the Console. Various pricing
models
that may be promulgated by, for example, a PUC may include real time pricing,
time of
use pricing, and day ahead pricing. Real time pricing may be considered like
playing the
stocks where it may not be known which way the pricing signal will go in the
future. In
real time pricing indicators may be used to help predict the future such as
the pricing
models provided by the PUC or use indices provided by other services.
The solution also provides that the Console upload usage information to a
central location
and there it is data massaged in order to provide predictions of usage for
broad areas or
populations, which may be categorized by region, country, time zone, gender,
background, religious orientation. The data may be massaged to further include
consumer habits that may be categorized together, such as those that are
commonplace to
sports fans, families with children, single adults, home owners, etc. The data
then is
collated and used to generate future predictions. The prediction may even be
custom
made for a user or home based on historical usage of that person or home,
business or
industry. In the latter case, the Console itself may be and is configured to
collect usage
data according to various parameters, such as per appliance, groupings of
appliances,
dates and times in order to provide the user a personal customized prediction
of power
usage. The prediction can thus be used to encourage or discourage use of an
appliance or
groups of appliances and this may be done with predicted price changes to
shift the
demand earlier or later in time than the user historically uses the appliances
or power.
The Console may and does further detect or periodically poll the smart
appliances and
smart appliance adapters for their duty cycle information. Typically, a smart
appliance
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includes a pre-set duty cycle set at the manufacturer site. In that sense, the
Console stores
or uploads pre-set duty cycles for appliances, notably this may include
typical duty cycles
for so called dumb appliances. In this last regard, where the appliance is not
capable of
communicating its duty cycle, the Console includes already a typical or
standard duty
cycle.
In addition, the Consoles include a feature that adjusts the duty cycle
imprint to adjust for
age or time of use of the appliance. Smart appliances or smart appliance
adapters include
connectivity and provide information about the appliance. For example, a
refrigerator of
certain type, make and model will have a standard duty cycle set at the
factory. That duty
cycle may change over time or be set by the owner. A smart appliance or smart
appliance
adapter senses the updated duty cycles and has the current instantaneous duty
cycle in a
register or other writable memory.
As indicated, the Console may and does read the duty cycles and stores them
over time,
thus creating a history of duty cycles upon which the Console can analyze and
make
predictions about future appliance performance and cost of energy usage. These
histories
may be stored in the Console for later retrieval and/or uploaded to a main
databases for
extraction and data massage as earlier explained. Further, the Console may not
only
automatically set the firing duty cycle may a does establish a recommended
duty cycle
per appliance or groups of appliances.
In addition or in the alternative the Console can be used to upload duty
cycles to the
appliance, thereby providing the user with an interface by which the user can,
for the first
time, program his or her energy regime at the appliance level. In this sense,
the Console
acts as a portal that allows the subscriber to see and change individual duty
cycles
amongst the appliances. The user, thus, has direct control to program at the
programmer
level his or her own algorithms for the appliance, ie, the firing duty cycle
and/or the
reaction to certain pricing signals. The Console may and does provide an
Editor, such as a
programming line editor that enables the user to edit the algorithms manually.
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Types of Appliances & Groupings.
The term appliance has been generalized in the field of the art to have
several various
meanings. In the context of this patent application, the term appliance is
defined broadly
as a device that converts one form of energy to another, i.e., electrical,
mechanical,
thermal, and/or chemical, or any combination thereof. In one embodiment, the
term
appliance here particularly refers to converting electrical energy into
another form of
energy. In addition, the appliances may store energy for later consumption or
transference to another location or appliance.
The appliances in another embodiment are grouped. Grouping appliances may and
is
beneficial because certain appliances are used in conjunction and will share
or have
overlapping duty cycles. Controlling the appliance in such groups offers both
ease of use
and planning as well as increasing energy efficiency by encouraging or
discouraging the
groups of appliances as a group. Like appliances or appliances in a group may
have low
efficiencies and it is more cost effective to use the appliance with higher
efficiency
during higher pricing and shift demand for use of the lower efficiency
appliances earlier
or later in time.
In one aspect the appliances are grouped per type of appliance or appliances
haying a
similar duty cycles or efficiencies. For example, lights of a kitchen and the
oven will
share common firing duty cycles at meal times. Thus, the groupings may be
based on
activity, ie, cooking may require use of a stove, refrigerator, kitchen
lighting. This is
especially important for commercial and industrial customers subject to
aggregate
'demand charges' on their energy bill. If an industrial plant can eliminate
demand
charges, by shifting demand for non-critical appliances or appliances of low
efficiency a
great benefit can be seen by a company or manufacturer. The solution can and
is
configured to take into consideration critical processes of a manufacturing
plant when
setting configuration algorithms for the appliances or machines.
Legacy appliances may be grouped into a lower efficiency group of appliances.
Further,
the types of appliances may be grouped by appliances having complimentary duty
cycles,
duty cycles that fire at different times or duty cycles that when overlapped
in time add up
to a certain level of power usage. For example, a dryer is likely to be
utilized after a
washing machine was utilized. A dishwasher is likely to be used after an oven
or stove.
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A television and lighting in the living room is another example. Or television
and
exercise machine. For example, the solution may and does provide for
configuring non-
essential appliances to not fire during grouped appliance usage. A hot water
boiler may
not be switched on during meal times, for example, thus reducing the overall
demand
during peak pricing when users are likely to ignore their wallets and listen
to their basic
needs, such as eating.
Blackouts / Brownouts & Other Emergencies.
The solution may and further provides for grouping appliances according to an
emergency power failure or brown out. In that case a pricing signal is
constructed with a
higher price or a special command that triggers the appliance to go into non
duty cycle
mode. For example, during a heat wave the air conditioners may be configured
to stay
on, but other appliances such as the de-humidifier in the basement may be
grouped as
non-essentials appliances and set in low power mode or go into a non-firing
duty cycle
mode go into a low power mode during. Alternatively, the air conditioner can
be directed
to go into a lower power mode and not be turned off completely during a heat
wave.
During an earth quake, the ovens and any gas burning ovens may be instructed
by the
pricing signal or special command signal to turn off in order that other
appliances needed
for life saving such as those at a hospital have sufficient power from
remaining power
plant resources.
In one aspect, the user may and does have complete flexibility in selecting
which
appliances are to be turned off or enter low power during various types of
emergencies.
In addition, the solution may and does provide suggestions for which
appliances should
be turned off during which emergency. The Console in one aspect is configured
to
provide this information and/or pre-selected appliances or groups of
appliances based on
the emergency. Blackouts / Brownouts and earthquakes were already mentioned.
Floods, tornados and hurricanes are other examples of emergencies. In a flood,
the air
conditioner may not be needed as much as say a water pump. In a forest fire,
heating is
not such an issue and the hot water boiler may be switched off. During these
emergencies, the user self regulates his or herself which has the overall
effect of lessening
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the burden placed on a burdened power grid, thereby greatly alleviating the
danger of a
system wide collapse.
In another embodiment the Console is configured to report on the status of a
household,
business or industry to the Power Grid in order that the Power Grid can better
manage a
disaster. Areas which self-regulate can be switched to provide greater
resources to those
areas that do not have Consoles according to the present invention. In this
manner, the
Power Grid can realign itself better during an emergency to avoid a system
wide power
outage.
Shift of Demand Forward or Backward in Time.
In terms of a pricing signal that changes over time, either provided by the
utility or by
calculation through historic data or policy models, the Console can calculate
what
activities should be shifted forward or backward in time. This can be at an
activity level
such as cooking, ie, using the oven, refrigerator, etc. People tend to want to
eat when
they are hungry so perhaps the Console will shift demand later in time for a
shower
before bed in order to take advantage of lower prices. A corollary to this
last example is
shifting demand for heating earlier in the day when the day is warmer,
allowing the hot
water boiler to store energy in the boiler for the night time. In this last
instance the
Console may and does take into account the thermal insulation characteristics
of the
boiler.
Thus, the Console may encourage this activity during certain times that have
low pricing,
while at the same time encouraging use of other appliances or groups thereof
that have
lower energy requirements during the high priced times. In this regard, the
Console is a
personal management tool that can be integrated in the daily life of a
subscriber.
To construct or help to construct the appliance duty cycle, the data obtained
by the
Console could be compiled in the aggregate for a number of households by a
central
server in order provide pricing signals to the demand side. The utility or
appliance
manufacturer for that matter may be able to use this data, or otherwise the
Console can
grab this data from the central server in order to construct its own duty
cycles for local
appliances. The Console may store, for example, at a central server duty
cycles in the
field of types or make of appliances. In this regard, a living model of duty
cycles of
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appliances is available for use as a resource to all Consoles. In addition,
the utility can
use this information to better understand how high or low, and for how long,
it can drive
the pricing signal before losing demand, ie, if the utility drives a pricing
signal too high
for too long then certain people (perhaps of modest income) will not buy any
power.
With the utility able to grasp real data on appliances in the field the
proposed solution
thereby completes the circle of information from supply to demand and to
supply again.
Detection and maintenance.
It should be noted that comparison of actual individual appliance behavior to
typical
expected behavior allows for identification of sickly appliances, for example
an
inefficient and operationally costly refrigerator or air conditioner that is
laboring or
running nearly continuously can be brought to the attention of the owner. The
Console,
thus, may and is configured to identify to the user inefficient appliances and
may include
a special warning signal, alarm or indicator to show which appliances is
inefficient or
malfunctioned. The Console may also issue a signal to a repair company or the
production company to send a maintenance person or to trigger a replacement
sale.
For that matter, the Console may include decision making logic for appliance
operation
during installation and ongoing operation of any control such as a light
switch or any
appliance including but not limited to refrigerators, hot water heaters,
clothes washers,
dish washers, hybrid-electric or all-electric vehicles.
Paying bills.
Furthermore, the Console may be used in conjunction with or including in an
application
for reviewing costs, and/or paying bills. In that instance, the Console
includes a special
accounting mode to show actual costs either instantaneous or over any time
period. The
Console may include a built in calculator or processor to indicate what the
bills will look
like if the user decides to forestall or advance energy use. Payment of bills
may be and is
provided by the Console employing a bill payment application.
In a further embodiment, incentive awards and rebates are given and offered to
the user
through the Console to encourage users to set their appliances to set their
firing duty
cycles to turn off the appliance during peak times during the day and/or to
change the
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boundary conditions upon which appliances switch on given a certain price as
indicated
by the pricing signal. Incentive awards may be in the form of money or
promissory notes
for fixed pricing for given periods of the day. The fixed pricing may be, for
example,
lower than the price indicated by a pricing signal during that time period.
Rebates may
be monetary awards given back later or reductions of future bills by an amount
equal to
the rebate.
Rebates and incentive awards may be collected as tokens and/or traded to other
users.
The collected tokens may also be used to offset other costs, such as
maintenance or
repairs or replacement of appliance costs. As with the pricing signal, the
tokens are
meant to encourage users to utilize the savings of power suggested by the
algorithms
provided by or with the Console. Curbing energy demand during high pricing
times or
emergency loads will assist a great deal in avoiding another catastrophic
black out.
Vectors and Matrices.
In another aspect, future pricing signals could be configured or stored as a
vector, having
time and amount of the future price as parameters. These vectors may be stored
in
matrices and may be different for different groups of appliances or regions or
communities. Use of vectors is advantageous for calculation, for example, the
vectors
can be logically arranged in a matrix, which makes calculations for groups of
appliances
more straightforward.
Future pricing signals may include 3 possible types of pricing model: 1) Time-
Of-Use,
TOU = same price during same hour day by day (most widely used today to shut
off hot
water heaters during peak), 2) Real-Time-Pricing, RTP = pricing may vary with
little
warning, 3) Day-Ahead-Pricing (DAP) with 'next day pricing' delivered 24 hours
in
advance. In the case of DAP, the vector is valid for a 24 hour period.
Safety Controls.
The Console may store the upper and lower boundaries of the on and off firing
of the
appliance as discussed already with reference to Figure 9c. The subscriber,
however, is
also empowered to make the best decision as per their need. The Console stores
pricing
and usage information to help the consumer make that decision and then set an
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appropriate policy. In the case of a smart adapter, the adapter is typically
pre-configured
with a policy and remembers and implements that policy unless/until it is told
otherwise
by the Console. As indicated, the policy may be changed by changing the
algorithm or
parameters of the algorithm that drive the smart appliance controller.
However, there also may be provided certain parametric thresholds that the
Console will
tend not to violate. For example, where a user instructs the appliance to over
operate in
summer, thereby causing dangerous heating conditions in the home, or turns off
the heat
in the winter causing the pipes to freezes, the Console will act to prevent
the user from
making these settings. In this and other aspects, the Console may take into
account
information from other appliances, such as a thermostat in order determine or
judge
whether to prohibit other appliances such as the freezing pipes example. The
thermostat
signal may be an additional parameter used in weighing a determination of
whether to
prohibit the turning off of the heater at any particular time.
Alternatively, the Console will query the user to determine if the user really
intends to
operate appliances outside these minimum thresholds or safeguards. For that
matter the
Console system always operates between reasonable maximums and minimums as per
personal requirements, laws and administrative regulations (e.g., on odd days
the odd
numbered houses may water their lawn, or during electrical brownouts, non-
essential
usage must be curtailed).
To ensure the consumer is protected from implementing a destructive policy,
Smart
Adapters and Smart Appliances come with pre-loaded policies and apply the
appropriate
policy. The appropriate policy may be further based on learned appliance or
user
behavior. Perhaps, the Console learns that an oven is left on frequently
longer than a
normal operating time, perhaps indicating an elderly user or person with
mental
impairment is operating the appliance. In that case, the Console will learn to
adjust its
firing cycle to shut off the appliance, e.g., oven for example after a period
of time or after
a certain hour or after a certain time after meal times. If, for example, the
user eats cold
cereal for breakfast the Console will learn that the user prefers not to use
the oven or
stove range in the morning. In this latter case, the Console may determine
that something
is amiss if the oven is left on for a long time in the morning and set the
oven to off. The
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Console may also alert the user through alarms, sounds, or visual indicators
if it senses
that something is wrong.
Location of Console.
The Console may be situated in locations ideal for accessing power needs
conveniently
and practically. It may be prominently displayed and inspire (at least) daily
use. For
example, the Console may be situated in consumer-specific common areas or
incorporated in common appliances in the kitchen, living room, office,
bathroom, on a
screen in a kitchen cabinet or on an appliance such as a refrigerator, or on a
laptop or
desktop computer or smart phone or PDA or tablet computer like an iPad.
Connection of Console.
The Console may connect to smart appliances or smart appliance adapters
through any
manner, such as broadband, power cabling in the residence or business, etc. It
may even
connect to Smart Appliances and Adapters through a cloud network. Any number
of
network types can be used e.g., 802.11 wireless Ethernet, zigbee, powerline,
wired
Ethernet, etc.
Sectorization.
As already mentioned, the Console can segment utility service by room, sector
or area.
This is useful to identify and calculate activities, such as the cooking
example above.
Unlike prior methods, however, the Console does not need a special
installation at the
meter or circuit breaker box, for example, special inductors installed on the
main power
line to measure power dynamically.
The Console may detect that the user prefers to spend evening times in front
of the
television in the living room. In that case, the Console can recommend or
automatically
install algorithms or configurations for the appliances in other rooms to use
low or no
power. Similarly, during sleep time, the Console may suggest or direct only
the
bedrooms to stay warm or cool.
Connections, Smart Meters.
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The Console has the flexibility to use existing smart meters to obtain
information about
appliances or alternatively speak directly with smart appliances or smart
adapters. Where
the appliance is completely dumb, the Console may use a standard duty cycle
for that
appliance to help deduce its existence. The standard duty cycle for a given
appliance
may be and is provided by, for example, the PVC.
In addition, the Console can deduce the power consumption of other appliances,
such as a
dumb appliance by observing other known power consumption devices and
comparing
with the overall or sector by sector power consumption. For example, the
Console can
take the sum total of all other firing duty cycles of the other appliances and
substract it
from the total used power over time. This should result in the missing
appliance or
groups of appliances. Alternatively, the Console can ask the user to provide
either a
policy or identify the type of appliance in order to provide it with a
standard duty cycle.
It shall be appreciated that the Console may forego or supplement having to
have a
special communication system. For example, the Consoler may use different
types of
connections to connect to the various appliances in a house. Some through the
powerline
such as a television, some through WLAN such as a STB, and some through the
copper
wire in the house such as for the telephone.
Console Location.
Further, the Console location as mentioned above is advantageous. In one
aspect, the
Console may be in one location or split into several portals located in
strategic areas in
the home or business in order to encourage use by the user or occupants of the
area. The
Console may be in the form of a portable laptop. Or the Console may be
stationed near
or in place of the smart meter, for example in the basement.
The Console may also be ergonomic in the sense that it is installed in a
location
convenient and fitting within the natural posture of the human body. For
example, it may
be placed at an average height of an adult, the person most likely to pay the
bills. In one
aspect, the Console is installed in a kitchen cabinet or other convenient
location at head
level for an average height.
GUI
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At its essence the Console is used to increase efficiency, i.e., economize by
saving
consumers money, manage demand side-behavior, provide information to supply-
side to
better meet and manage consumer needs, reduce power plant emissions, and
reduce
overall energy usage, for example. However, look and feel of the Console and
its
Graphical User Interface (GUI) is also advantageously designed. In one aspect,
the
Console encompasses an LCD panel that illustrates a scale on a refrigerator,
or the
Console itself is shaped physically like a scale as explained.
The scale may be an easy to recognize symbol indicating the efficacy of energy
or power
conservation. For example, the scale may include green, yellow and red colors
arranged
to give the impression of a traffic light as explained. It shall be
appreciated that the
configuration of the Console in this manner educates not only adults but also
children on
energy conservation. Making it fun would also encourage people to save. The
Console
may support education in schools as part of curriculum and businesses as part
of
continuing education of the public on energy conservation.
Energy mileage points.
For that matter, the Console concept may also include coupons, or energy
"mileage"
points. These may be in addition to the rebates and fixed pricing schemes
mentioned
above. The mileage points may be used by individuals or in the aggregate to,
for
example, provide special prizes such as lower pricing for energy conservation
minded
people or communities or norms. Hence, a comparison to neighbors usage or a
neighboring community or region may be made. In this regard, good communities
may
receive lower pricing as a whole. It shall be appreciated that this is
advantageous since
power pricing may be adjusted dependent on the specific region, ie, warm
regions versus
cold, or high power consumption regions such as cities versus low power
consumption
regions such as small urban towns. In this regard, the Console provides a
powerful tool,
not only for the subscriber, but for the utilities as well to better be able
to meet the supply
of power of the demand users.
The mileage points may be accrued through a variety of means. Saying energy
using the
Console, for example, Using the Console a certain number of times per time
period may
offer mileage points. The mileage points may even by linked to a credit card
in order to
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provide mileage points when the credit card is used. Mileage points for energy
may also
be transferred to other activities of the user. For example, the user may take
them with
her or him to their holiday home. The user may collect mileage points saving
energy in
other ways outside the home, such as taking an energy efficient plane. For
this purpose a
special card and card reader is provided with the Console in order to tabulate
the mileage
points and maintain them electronically.
Operation.
In operation users interact with the Console via a touchscreen, keyboard
and/or mouse
and display as well as via oral commands and responses, in order to reduce
consumption
or be smarter by considering consumption options and the cost savings of time-
shifted
consumption. The Console may be simplified into a dumb terminal that can be
incorporated anywhere in the home, or in a unique form, such as a thermometer
or traffic
light shape. Alternatively, the Console may be driven through a central
Console master
or gateway and alternatively connected to a plurality of Consoles throughout
the home or
business. The Console may be installed in laptop form or app on an [phone and
allow
remote connection to the users' home or business. The Console may also be
extended for
use with a Set Top Box (STB) or home DVD, Tivo 0, etc. In regards to TV, the
Console
may utilize the special TV signals to incorporate or transmit the pricing
signal
information, such as in the caller ID space on the TV screen.
Further, the Console may be arranged in a special keyboard, for example, for
ease of use
by children or people with mental disabilities. For example, the Console may
include a
keyboard with a subset or limited amount of keys. For example, the simplified
Console
may have keys or on-screen buttons for expensive or inexpensive modes, or
buttons for
activities, i.e., cooking, shower, TV or reading time. These buttons are
advantageous
where the Console groups appliances by activity. In addition, the information
could be
used to train the Console or the power gen side on particular user habits,
communities or
regions.
The Console receives from the utility a price signal instantaneously and over
time. The
Console receives energy usage instantaneously and over time from smart
appliances /
adapter(s) and smart meter(s).
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Pricing signal notification.
In one aspect, the Console signals a change in the pricing signal through a
noticeable but
not disturbing indicator. In one manner, this is achieved by manipulating the
environment of the subscriber such as dimming the lights momentarily but not
such a
large degree as to confuse or disorient. In another aspect the signal is
achieved without a
computer or alarm stimulus, such as audible tone or alarm. In another aspect
the Console
may alert users when daily, weekly and or monthly energy or water usage and
cost targets
are exceeded.
Of course, the Console may operate in this manner, however, the elegance of
signaling
the user gently and without disturbing the feng shui of the household is an
advantage.
Further, the signal may be detectable to the subscriber but not to anyone else
not familiar
with the environment. For example, alternating the strength of certain lights
in the house
or business would not necessarily be recognized by a visitor, but the home
owner would
immediately recognize a difference in the environment.
Network Signaling.
The system as mentioned the Console is inter-connected with other elements. In
one
aspect the Console may be internet-based. The elements of the system may
function
without knowledge of each other. Elements of the system may also possess two-
way
communications capabilities and can send and receive queries, commands and
data to and
from each other. Others such as satellite receivers may have only one
direction of
communication.
As mentioned the Console may be integrated with cloud computing. However, the
Console may receive information such as pricing signals passively. For
example, signals
from satellite based transmission may be incorporated. Or the Console may
further
receive signaling over cable. The price signaling is hence broadcast for a
footprint or
multicast number of users.
The pricing signal could also be sent to different areas for different cable
regions, thereby
providing more control over high usage areas, or giving more favorable pricing
to low
usage areas or communities such as outlined above with regard to the reward
system. To
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reiterate, this reduces load on power transmission and distribution until
repairs can be
made to a down failed power line.
In operation and in conjunction with the overall orchestration of power of the
demand
side with the supply side, a utility broadcasts pricing signals for the
purpose of optimizing
efficiency of supply side generation resources. The utility also queries use
of utility-
provided energy for example the time of use, the amount of use, the type of
appliance
used. With the Console, the utility and user are now able to see down to the
appliance
level. Hence, the Console may query about the appliances in the residence and
have this
data fed back to the utility.
Appliance Profiles
Appliances in this aspect further includes home power generators, hybrid cars
or solar
panels, etc. The list of appliances and devices in a particular residence or
business may
serve as an appliance profile and these profiles may have a different
predetermined
pricing scheme or rung. For that matter, a different pricing signal may be
sent to
different users with different profiles. Users with a lot of audio/video
equipment
obviously spend a lot of time in front of the TV. One profile may be
considered to be a
multimedia user type profile. Combining information from an STB, the profile
may
further be defined as Sports fan. In the case of a multimedia user profile, a
pricing signal
scheme may be employed that offers reasonable pricing during televised
sporting events
and may even be tailored to the times the programs run. As indicated, the
policies set for
other appliances in the house, such as the hot water boiler may be set not to
trigger an on-
state during the televised programs.
Terms & Definitions.
In this application, several terms are used including Console, smart meter and
smart
appliance and adapter. Typical definitions of these are given below with the
understanding that the application is not so limited and that other variation
of these terms
is within the scope of this proposed solution.
The Console may exist as a hardware device and/or an application. It is a
strategically
located multifunction interactive display. The Console receives pricing
signals from
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utility, queries smart appliance adapter(s) and meter(s) and in response
receives and
records policies and usage. It provides historical, current and future energy
price, use,
cost and savings, and is used to modify and write policies back to Smart
Appliances and
Smart Appliance Adapters.
Smart Meter.
A smart meter is usually an electrical meter that records consumption of
electric energy
in intervals of an hour or less and communicates that information at least
daily back to
the utility for monitoring and billing purposes. Smart meters enable two-way
communication between the meter and the central system. Unlike home energy
monitors,
smart meters can gather data for remote reporting. Such an advanced metering
infrastructure (AM!) differs from traditional automatic meter reading (AMR) in
that it
enables two-way communications with the meter. The term Smart meter often
refers to an
electricity meter, but it also may mean a device measuring natural gas or
water
consumption.
Smart meters log time of utility use, log amounts of utility use and sends
consumer usage
information in response to queries from other system elements. For example the
utility
may query the meter for usage information or the Console may query the meter
for
information. Console is not a router in the typical sense but may be
considered a
gateway to the energy profile of a particular home or business. Utilities
query meters for
example: 1) how much energy has been used in kW-h from time period X to Y, and
2)
what was the maximum demand in kW in any 15 minute interval over the period
from X
to Y.
Smart Appliance Adapter.
A Smart Appliance Adapter may be and is integrated with the proposed solution.
Such
an Adapter is an add-on or is provided with the appliance, may sit near the
controller to
the appliance or be a piggyback plug to the appliances plug. Such an Adapter
may
receive pricing signals from a utility or other source. In a sense, the
adapter may be
considered as a proxy for the dumb appliance. It learns appliance type use and
needs. For
example, adapters may learn or are made for specific appliances including but
not limited
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to refrigerator, hot water heater, dishwasher, or lamp that, in order to
operate and meet
needs of consumers, must come on and off at certain times throughout the day.
Optimally
this adjusts appliance use by taking into consideration consumer needs and
real time price
of energy. An adapter may also send appliance type and usage data in response
to a
query from Console or utility. The adapter may contain a multitude of sensors:
sound,
motion, current, temperature, light level, etc. An adapter may learn, for
example, that it
should automatically and gently turn lights on when human presence is detected
in a dark
room. An adapter's primary purpose is saving consumers' money by time-shifting
and
reducing energy consumption. Typically, adapters and smart appliances have ID
codes
and are preconfigured to learn appliance types. Settings are in software and
may change
by type of appliance.
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