CA2796891A1 - Power consumer side control system, method & apparatus - Google Patents

Abstract

A console for matching demand of power to supply of that power, wherein the demand is generated by consumers that consume the power and the supply is on a power generator side that generates that power. A physical housing, an input/output (I/O) being configured to receive a pricing signal that indicates a price for power and an output configured to be coupled to one or more appliances, and a storage unit that stores price settings of a consumer representing the consumer's acceptance of a certain price for power are provided. A controller reconfigures a duty firing cycle associated with one or more appliances based on the pricing signal received, wherein the duty firing cycle maps when said one or more appliances turn on over a period of time, and wherein reconfiguring the duty firing cycle changes when the one or more appliances turn on and at which time such that the pricing signal received shifts the demand of the power to a different time.

Description

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 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 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.

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 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 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 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 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 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 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.

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 '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 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 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 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 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.

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 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 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).

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.

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.

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.

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 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 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 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 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.

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 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.

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 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.

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.

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 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 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 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 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 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.

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

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 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).

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 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 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 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.

Claims (23)

1. A console for matching demand of power to supply of that power, wherein the demand is generated by consumers that consume the power and the supply is on a power generator side that generates that power, the console comprising:
a physical housing;
an input/output (I/O) being configured to receive a pricing signal that indicates a price for power and an output configured to be coupled to one or more appliances;
a storage unit that stores price settings of a consumer representing the consumer's acceptance of a certain price for power;
and a controller configured to reconfigure a duty firing cycle associated with one or more appliances based on the pricing signal received, wherein the duty firing cycle maps when said one or more appliances turn on over a period of time, and wherein reconfiguring the duty firing cycle changes when the one or more appliances turn on and at which time such that the pricing signal received shifts the demand of the power to a different time.

2. The apparatus of claim 1, wherein the controller is configured to control one or more appliances to regulate power consumption based on the consumer's price settings and in response to the pricing signal.

3. The apparatus of claim 1, wherein the price settings further represent a price acceptable to the consumer for different appliances.

4. The apparatus of claim I, wherein the appliances are smart appliances.

5. The apparatus of claim 1, wherein the controller selects a subset of appliances to regulate power consumption.

6. The apparatus of claim 1, wherein the price settings of the consumer are preset for the consumer.

7. The apparatus of claim 1 , wherein the pricing signal is set to discourage demand of power for the type of appliance until a time when the supply side is capable of providing additional power.

8. The apparatus of claim 1, wherein the controller is further configured to process information regarding metering devices on the demand side.

9. The apparatus of claim 1, wherein the controller is further configured to set the smart appliances to operate in an expensive mode and an inexpensive mode, wherein the expensive mode turns the smart appliance on when the pricing signal is above a first threshold, and wherein the inexpensive mode turns the smart appliance on when the pricing signal indicates a price below a second threshold.

10. The apparatus of claim 1, wherein a smart appliance is selected from the group consisting of a water boiler, a heater, an oven, a dishwasher, a refrigerator, lighting, air conditioning, and an electric or partially electric powered automobile.

11. The apparatus of claim 1, wherein the power generator side include power generators selected from the group consisting of a windmill, a hydroelectric plant, a coal plant, an oil plant, a natural gas plant, a solar, a geothermal, a biomas, and a nuclear power plant.

12. The apparatus of claim 1, wherein the smart appliances include controller for switching on or off the associated appliance in response to the pricing signal

13. The apparatus of claim 1, wherein the consumer portal is connected to the supply side through a telecommunications network selected from the group consisting of the internet, satellite, and home networking.

14. The apparatus of claim 1, wherein the pricing signal is set for a limited time period on the basis of a thermal storage capacity of a particular type of appliance.

15. The apparatus of claim 1, wherein the pricing signal is set according to the budget of the consumer, such that the consumer is not completely priced out of the market for more than a few minutes.

16. The apparatus of claim 1, wherein the type of appliance upon which the pricing signal is set includes a group of types of appliance that is a subset of the entire set of types of appliances.

17. The apparatus of claim 1, wherein the pricing signal is set for an aggregate of said appliances of a plurality of consumers.

18. The apparatus of claim 1, wherein the controller turns the smart appliance on or off further on the basis of how much energy is stored in the respective smart appliance.

19. The apparatus of claim 1, wherein the controller turns the smart appliance on or off further on the basis of a duty cycle typical for the type of appliance.

20. The apparatus of claim 1, wherein the controller controls the smart appliance to pre-heat or pre-cool the consumer home or residence and taking into account the pricing signal.

21. The apparatus of claim 1, wherein the controller turns the smart appliance on or off further on the basis of whether the appliance is an independent activity appliance.

22. The apparatus of claim 1, wherein the demand side is a home residence or a business.

23. The apparatus of claim 1, wherein the pricing signal is set to shift the demand to a time earlier than when the supply side is not operating at an efficient output.