BRPI0720002A2 - POWER AGGREGATION SYSTEM FOR DISTRIBUTED ELECTRIC RESOURCES - Google Patents

Description

Report of the Invention Patent for "DISTRIBUTED ENERGY POWER AGGREGATION SYSTEM".

RELATED ORDERS

This application claims priority for US provisional patent application No. 60 / 869,439 to Bridges et al., Entitled "A Distributed Energy Storage Management System", filed December 11, 2006, and incorporated herein by reference; US Patent Application No. 60 / 915,347 to Bridges et al. entitled "Plug-in-Vehicle Management System", filed May 1, 2007 and incorporated herein by reference; and US Patent Application No. 11 / 836,747 to Bridges et al. entitled, "Power Aggregation System for Distributed Electric Resources", filed August 9, 2007, and incorporated herein by reference. BACKGROUND

Transport systems, with their high reliance on fossil fuels, are especially carbon intensive. That is, the physical units of work performed on the transport system typically discharge significantly more CO2 into the atmosphere than the same electrically executed work units.

The power grid contains inherent limited installation for storing power. Electricity must be constantly generated to meet uncertain demand, which often results in over generation (and thus wasted energy) and sometimes results in subgeneration (and therefore power outages).

Bulk distributed electrical resources can in principle provide a significant resource for addressing the above problems. However, today's energy services infrastructure needs the provision and flexibility that are required to aggregate a large number of small-scale resources (eg, batteries for electric vehicles) to meet large and medium-sized energy service needs. . A single vehicle battery is insignificant compared to the needs of the power grid. What is needed is a way to coordinate vast numbers of batteries for electric vehicles as electric vehicles become more popular and prevalent.

Low-level electrical and communication interfaces to allow charging and discharging of electric vehicles with respect to the grid are described in US Patent No. 5,642,270 to Green et al., Green et al., Entitled "Battery Powered Electric". Vehicle and Electrical supply System ", incorporated herein by reference. Green's reference describes a two-way charging and communication system for networked electric vehicles, but does not address the information processing requirements of dealing with large mobile electric vehicle populations, billing (or compensation) complexities. ) of the vehicle owners, nor the complexities of assembling mobile electric vehicle pools on aggregate energy resources robust enough to support company energy service contracts with network operators.

BRIEF DESCRIPTION OF DRAWINGS

Figure 1 is a diagram of an exemplary energy aggregation system.

Figure 2 is a diagram of exemplary connections between an electric vehicle, the power grid, and the Internet.

Figure 3 is a block diagram of exemplary connections.

between an electrical resource and a power control system flow control server.

Figure 4 is a diagram of an exemplary power system assembly.

Figure 5 is a diagram of exemplary control areas in the

energy aggregation system.

Figure 6 is a diagram of multiple flow control centers in the power aggregation system.

Figure 7 is a block diagram of an exemplary flow control server.

Figure 8 is a block diagram of an exemplary remote intelligent power liquid flow module. Figure 9 is a diagram of a first exemplary technique for

i

locate a connection point for an electrical resource in a power grid.

Figure 10 is a diagram of a second exemplary technique for locating an electrical resource connection location in the power grid.

Figure 11 is a diagram of a third exemplary technique for locating an electrical resource connection location in the power grid.

Figure 12 is a diagram of a fourth exemplary technique for locating an electrical resource connection location in the partially interconnected network.

Figure 13 is a diagram of exemplary safety measures in a home vehicle implementation of the power aggregation system.

Figure 14 is a diagram of exemplary safety measures.

when multiple electrical sources flow energy into a home in the power aggregation system.

Figure 15 is a block diagram of an exemplary intelligent circuit breaker of the power aggregation system.

Figure 16 is a flow chart of an exemplary method of

energy aggregation.

Figure 17 is a flow diagram of an exemplary method of communicatively controlling an electrical power aggregation feature.

Figure 18 is a flow chart of an exemplary method of measuring bidirectional energy of an electrical resource.

Fig. 19 is a flow chart of an exemplary method of determining a mains location of an electrical resource.

Figure 20 is a flow chart of an exemplary method of programming power aggregation.

Figure 21 is a flow chart of an exemplary method of

Smart isolation.

Fig. 22 is a flow chart of an exemplary method of extending a user interface for power aggregation.

Figure 23 is a flow chart of an exemplary method of acquiring and maintaining electric vehicle owners in an energy aggregation system.

5 DETAILED DESCRIPTION Summary

Described herein is an energy aggregation system for distributed electrical resources and associated methods. In one implementation, the exemplary system communicates via the Internet and / or some other public or private networks with numerous individual electrical resources connected to a power grid (hereinafter, the "grid"). Through communication, the exemplary system can dynamically aggregate these electrical resources to provide power services to grid operators (eg, utility companies, Independent System Operators 15 (ISO), etc.). "Energy services" as used herein refer to energy distribution as well as other ancillary services including demand response, regulation, revolving reserves, non-revolving reserves, energy imbalance, and the like. "Aggregation" as used herein refers to the ability to control energy flows in and out of a spatially distributed set of electrical resources for the purpose of providing a service of greater magnitude. "Power Grid Operator" as used herein, refers to the entity that is responsible for maintaining the operation and stability of the power grid within or through an electrical control area. The power grid operator may constitute some combination of manual / human, action / intervention and automated processes controlling generation signals in response to system sensors. A "control area operator" is an example of a power grid operator. "Control area" as used herein refers to a contained portion of the mains with defined input and output ports. The net energy flow in the network in this area shall equal (within an error tolerance) the sum of the energy consumption within the area and the output energy flow from the area. "Power grid" as used herein means a power distribution / grid system that connects power producers with power consumers. The network may include generators, transformers, interconnectors, switching stations, substations, feeders, and safety equipment as part of both / the transmission system (ie large-scale power) as well as the transmission system. distribution (ie retail energy). The exemplary power aggregation system is vertically scalable for use with a neighborhood, city, sector, control area, or (for example) one of eight large-scale 10 Interconnectors on the North American Electrical Reliability Council ( NERC). In addition, the exemplary system is horizontally scalable for use in providing power services to multiple network areas simultaneously.

"Grid conditions" as used herein means the need for more or less energy flowing into or out of a section of the power grid in a response to one of a number of conditions, for example, changes in power. supply, demand changes, contingencies and failures, tilt events, etc. These grid conditions typically manifest as power quality events such as under- or overvoltage events and under- or over-frequency events.

"Power quality events" as used herein typically refers to manifestations of power grid instability including voltage deviations and frequency deviations; in addition, power quality events as used herein also include other disturbances in power quality distributed over the power grid such as subcyclic voltage pulses and harmonics.

"Electrical resource" as used herein typically refers to electrical entities that can be commanded to do any of these three things: get power (act as a load), provide power (act as a generation or power source), and store energy. Examples may include battery / charge / inverter systems for electric or hybrid vehicles, used but serviceable electric vehicle battery repositories, fixed energy storage, fuel cell generators, emergency generators, controllable loads, etc. .

"Electric vehicle" is widely used here in reference to pure electric and hybrid electric vehicles, such as plug-in hybrid electric vehicles (PHEVs), especially vehicles that have significant battery storage capacity and that connect to the power grid to recharge the battery. More specifically, an electric vehicle means a vehicle that acquires some or all of its energy for movement and other purposes from the power grid. Moreover, an electric vehicle has an energy storage system, which may consist of batteries, capacitors, etc., or some combination of them. An electric vehicle may or may not have the ability to supply power back to the power grid.

Electric vehicle "energy storage systems" (batteries, supercapacitors, and / or other energy storage devices) are used herein as a representative example of intermittently or permanently connected electrical resources that can be input and output energy dynamics. Such batteries may operate as a power source or charge. A collection of bundled electric vehicle batteries can become a statistically stable resource across numerous batteries, despite recognizable periodic connection trends (for example, an increase in the total number of vehicles connected to the grid at night; a reduction in the collective number of batteries connected as the morning change begins, etc.). Across vast numbers of electric vehicle batteries, connection trends are predictable and such batteries become a stable and reliable resource for recourse, the network or part of the network (such as a person's home in a blepute) should experience a need for increased or decreased energy. Data collection and storage also enables the power aggregation system to predict connection behavior on a per user basis.

Exemplary System Figure 1 shows an exemplary power aggregation system 100. A flow control center 102 is communicatively coupled with a network, such as a public / private mix that includes the Internet 104, and includes one or more servers 106 providing a centralized 5 power aggregation service. "Internet" 104 will be used herein to represent many different types of communicative network mixtures and network mixtures. Via a network, such as the Internet 104, flow control center 102 maintains communication 108 with operators of the power network (s), and communication 110 with remote resources, that is, communication with peripheral electrical resources 10 (nodes). / devices of a "end" or "terminal" power grid) that are connected to the power grid 114. In one implementation, power line communicators (PLCs), such as those that include or consist of Ethernet bridges over power transmission line 120 are implemented on 5 connection piers so that the "last kilometer" (in this case, last meters - for example, in a residence 124) of Internet communication features remote devices is implemented by the same wire that connects each electrical resource 112 to the power grid 114. Therefore, each physical location of each electrical resource 112 can be associated with a corresponding Ethernet 120 power transmission line bridge (hereinafter, "bridge") at or near the same location as electrical resource 112. Each bridge 120 is typically connected to an access point on the - ternet of a site owner; as will be described in more detail below. Flow control center communication means 102 for the connection location, such as a residence 124, can take many forms, such as cable modem, DSL, satellite, fiber, WiMax1 etc. In one variation, electrical resources 112 may connect to the Internet via a different medium than the same power cord that connects them to power network 114. For example, a given electrical resource 112 may have its own wireless capacity 30. connect directly to the Internet 104 and thereby to the flow control center 102.

Electrical resources 112 of exemplary power system 100 may include electric vehicle batteries connected to power network 114 in households 124, parking areas 126, etc .; batteries in a 128 repository, fuel cell generators, private dams, conventional power plants, and other resources that produce electricity and / or store electricity physically or electrically.

In one implementation, each participating local resource 112 or group of local resources has a corresponding remote intelligent power net flow module (IPF) 134 (hereinafter, "remote IPF module" 134). Centralized flow control center 102 manages power aggregation system 100 by communicating with remote IPF modules 134 distributed peripherally among electrical resources 112. Remote IPF modules 134 perform a number of different functions, including providing the control center. flow 102 with remote resource states; controlling the amount, direction, and timing of energy being transferred into or out of a remote electrical feature 112; provide metering of energy being transferred into or out of a remote electrical resource 112; provide security measures during power transfer and changing conditions in the power grid 114; information activities; and provide self-contained power transfer control and safety measures when communication with flow control center 102 is interrupted. Remote IPF modules 134 will be described in more detail below.

Figure 2 shows another view of exemplary electrical and commu- nication connections for an electrical resource 112. In this example, an electric vehicle 200 includes a battery bank 202 and an exemplary remote IPF module 134. The electric vehicle 200 may connect to a conventional wall receptacle (wall socket) 204 of a dwelling 124, the wall receptacle 204 representing the peripheral edge of the power grid 114 connected via a power transmission line. residential 206.

In one implementation, power cord 208 between the electric vehicle 200 and wall receptacle 204 may be comprised only of wire I

and insulation to conduct alternating current (AC) power

t

to and from the electric vehicle 200. In Figure 2, a location-specific locale connection module 210 performs the network access point function - in this case, the Internet access point. A bridge 120 interposes between receptacle 204 and the network access point so that power wire 208 can also carry network communications between electric vehicle 200 and receptacle 204. With such bridge 120 and location 210 connection in place at a connection location, no other special wire or media is required to communicate with the remote IPF 10 module 134 of the electric vehicle 200, other than a conventional power wire 208 to provide residential line current in conventional voltage. Upstream of the locality connection module 210, power and communication with the electric vehicle 200 are solved on the power transmission line 206 on an Internet cable 104.

Alternatively, power cord 208 may include features such as

safety cables not found in conventional power and extension cords. For example, an electrical plug 212 from power cord 208 may include electrical and / or mechanical protective components to prevent remote IPF module 134 from electrifying or exposing the male conductors of power cord 208 when the conductors are exposed to a human user.

Figure 3 shows another implementation of the locality connection module 210 of Figure 2 in greater detail. In Figure 3, an electrical resource 112 has an associated remote IPF module 134, including a bridge 120. Power cord 208 connects electrical resource 112 to power grid 114 and also to locality connection module 210 so communicating with flow control server 106.

The locale connection module 210 includes another case of a bridge 120 ', connected to a network access point 302, which may include such components as a router, switch, and / or modem, to establish a connection via wired or wireless, in this case Internet 104. In one implementation, the power wire 208 between the two points 120 and 120 'is replaced by a wireless Internet connection, such as a wireless transceiver on the IPF module 134 and a wireless router in the locality connection module 210.

Exemplary System Layouts

Fig. 4 shows an exemplary layout 400 of power generation system 100. Flow control center 102 can be connected to many different entities, for example via the Internet 104, to communicate and receive information. The exemplary Ieiaute 400 includes 112 electrical features, such as plug-in electric vehicles 200, physically connected to the network within a single control area 402. Electrical features 112 become a power resource for 404 network operators use.

The exemplary Ieiaute 400 also includes 406 end users classified into 408 electrical resource owners and electrical wiring location owners 410, who may or may not be one and the same. In fact, the shareholders in an exemplary power aggregation system 100 included the system operator at flow control center 102, the network operator 404, the resource owner 408, and the owner 410 where electrical resource 112 is connected to power network 114.

Homeowners 410 may include:

• Car Rental Areas - Car rental companies often have a large portion of their fleet parked in the area. They

they can acquire electric vehicle fleets 200 and, participate in an energy aggregation system 100, generate revenue from idle vehicle fleets.

• Public parking areas - parking area owners can participate in the energy aggregation system 100

to generate revenue from parked electric vehicles 200. Vehicle owners may be offered free parking, or additional incentives, in return for providing energy services.

• Workplace parking - Employers can participate in an energy aggregation system 100 to generate income from the parked employees' electric vehicles 200. Employees may be offered incentives in return for providing energy services.

• Residences - a home garage may merely be equipped with a locality connection module 210 to enable the homeowner to participate in the power system 100 and

generate income from a parked car. Also, vehicle battery 202 and associated power electronics within the vehicle may provide backup of local power during times of peak charging or power outages.

• Residential neighborhood - neighbors can participate in a 100 power aggregation system and be equipped with devices

energy distribution (developed, for example, by cooperative groups with the homeowner) that generates income from parked electric vehicles 200.

• Network operations 116 in figure 4 collectively include interactions with 412 energy markets,

404, and the interactions of automated network controllers 118 that perform automatic physical control of the power network 114.

Flow control center 102 may also be coupled with information sources 414 for receiving weather reports, 20 events, price information, etc., collectively referred to as acquired information. Other 414 data sources include system shareholders, public databases, and historical system data, which can be used to optimize system performance and satisfy constraints on the exemplary power aggregation system 100.

Therefore, a 100 ee energy aggregation system

xemplar may consist of components that:

• communicate with electrical resources 112 to gather data and act by loading / unloading electrical resources 112;

• gather energy prices in real time;

· Gather resource statistics in real time;

• predict the behavior of electrical resources 112 (sequence, location, state (such as battery charge state) at on / off time);

• predict the behavior of the power grid 114 / load.

• encrypt communications for privacy and data security;

• trigger charging of electric vehicles 200 to optimize some merit figure (s);

• offer guidelines or warranties around the availability of cargo at various points in the future, etc .;

These components may be operating on a single computing resource (computer, etc.), or on a distributed set of resources (both physically and non-physically placed).

Exemplary IPF systems in such a Yeiaute 400 can provide many benefits: for example, lower cost ancillary services (ie power services), fine grain control (both temporarily and spatially) over resource scheduling. guaranteed reliability and service levels, increased service levels via intelligent feature programming, stability of intermittent generation sources such as wind and solar power generation.

Exemplary power aggregation system 100 enables a network operator 404 to control aggregate electrical resources 112 connected to power network 114. An electrical resource 112 may act as a source of energy, charge, or storage, and resource 112 can display combinations of these properties. Control of an electrical resource 112 is the ability to trigger the energy consumption, generation, or storage of energy from an aggregate of these electrical resources 112.

Figure 5 shows the function of multiple control areas 402 in the

100 exemplary power aggregation system. Each electrical resource 112 may be connected to power aggregation system 100 within a specific electrical control area. A single example of flow control center 102 can manage electrical resources 112 from multiple discrete control areas 501 (e.g., control areas 502, 504, and 506). In one implementation, this functionality is achieved by logically separating resources within the power system 100. For example, when control areas 402 include an arbitrary number of control areas, control area "A" 502, control area "B" 504, ..., control area "n" 506, then network operations 116 may include corresponding control area operators 508, 510, ..., and 512. An additional division in a control hierarchy that includes control division groups above and below the control areas 402 illustrated allows power aggregation system 100 to scale to power grids 114 of different magnitudes and / or vary numbers of electrical resources 112 connected with a power grid 114.

Figure 6 shows an exemplary Ieiaute 600 from a system of

exemplary power aggregation 100 using multiple flow control centers 102 and 102 '. Each flow control center 102 and 102 'has its own respective end users 406 and 406'. Control areas 402 to be administered by each specific example of a flow control center 15 may be dynamically assigned. For example, a first flow control center 102 may administer control area A 502 and control area B 504, while a second flow control center 102 'manages control area n 506. Similarly , the corresponding control area operators (508, 510 and 512) are served by the least flow control center 102 serving their respective different control areas.

Exemplary Flow Control Server

Fig. 7 shows an exemplary server 106 of flow control center 102. The implementation illustrated in Fig. 7 is only a configuration example for descriptive purposes. Many other arrangements of the illustrated components or even of different components constituting an exemplary server 106 of flow control center 102 are possible within the scope of the subject matter. Such exemplary server 106 and flow control center 102 may be run on hardware, software, 30 or combinations of hardware, software, firmware, etc.

Exemplary flow control server 106 includes a connection manager 702 for communicating with electrical resources 112, a prediction engine 704 which may include a learning engine 706 and a statistics engine 708, a restriction 710, and a network interaction administrator 712 for receiving control signals from network 714. Control signals from network 714 may include generation control signals, such as automated generation control (AGC) signals. Flow control server 106 may further include a database / information store 716, a web server 718 for presenting a user interface to electrical resource owners 408, 404 network operators, and network owners. electrical connection location 410; a contract administrator 720 to negotiate the terms of the energy market contract 412, and an information acquisition mechanism 414 to track time, new relevant events, etc., and download information from public and private databases 722 to predict the behavior of large groups of electrical resources, monitoring energy prices, negotiating contracts, and so on.

Operation of an exemplary flow control server

Connection manager 702 maintains a communication channel with each electrical resource 112 that is connected to power aggregation system 100. That is, connection manager 702 allows 20 each electrical resource 112 to connect and communicate, for example, using the Internet Protocol (IP) if the network is Internet 104. In other words, electrical resources 112 call home. That is, in one implementation they always initiate the connection to server 106. This facet enables exemplary IPF modules 134 to handle problems with firewalls, IP addressing, reliability, etc.

For example, when an electrical resource 112, such as an electric vehicle 200 connects to the home 124, the IPF module 134 may connect to the home router via the power line connection. The router will assign vehicle 20 an address (DHCP), and vehicle 200 30 can connect to server 106 (no holes in the firewall are required from this direction).

If the connection is terminated for any reason (including cases I

outages), then the IPF 134 module can call home again and connect the next available server resource.

The network interaction administrator 712 receives and interprets signals from the automated network controller interface 118 from a 404 network operator. In one implementation, the network interaction administrator 712 also generates signals for transfer to the automated network controllers 118. The scope of the signals to be transferred depends on agreements or contracts between the network operators 404 and the exemplary energy aggregation system 100. In one situation, the network interaction administrator 712 transfers information about the availability of aggregate electrical resources 112 to receive power from network 114 or supply power to network 114. In another variation, a contract may allow the network interaction administrator 712 transfer control signals to the automated network controller 118 - to control the power grid 114, subject to the built-in restrictions of the automated network controller 118, and subject to the scope of control permitted by the contract.

Database 716 can store all relevant data for power aggregation system 100 including electrical resource records, eg for electric vehicles 200, electrical connection information, energy metering data per vehicle , resource owner preferences, account information, etc.

The 718 web server provides a user interface to system shareholders as described above. Such a user interface primarily serves as a mechanism for conveying information to users, but in some cases the user interface serves to acquire data, such as preferences, from users. In one implementation, the 718 web server may also initiate contact with 408 participating power owners to announce offers to exchange power.

The negotiate / contract administrator 720 interacts with 404 network operators and their associated energy markets 412 to determine system availability, pricing, service levels, etc. Information acquisition mechanism 414 communicates with public and private databases 722, as mentioned above, to gather data that is relevant to the operation of power aggregation system 100.

5 Prediction Engine 704 Can Use Depot Data

716 data to make predictions about electrical resource behavior, such as when electrical resources 112 will connect and disconnect, global electrical resource availability, electrical system load, real-time energy prices, etc. Predictions enable the power aggregation system 100 to more fully utilize electrical resources 112 connected to power network 114. Learning engine 706 can track, record and process the behavior of the actual electrical resource, for example. by learning the behavior of a sample or cross-section of a large population of electrical resources 112. The statistics engine 708 can apply various probabilistic techniques for resource behavior to sense trends and make predictions.

In one implementation, the prognostic mechanism 704 performs predictions via collaborative filtering. The prediction mechanism 20 704 may also perform user predictions of one or more parameters, including, for example, connection time, connection duration, state of charge at time of connection, and connection location. In order to perform prediction by user, the prediction engine 704 can search for information such as historical data, connection time (25 week day, week of month, month of year, vacation, etc.), load status, connection location, etc. In one implementation, a time series prognosis can be computed via a recurring neutral network, a dynamic Bayesian network, or another direct graphical model.

In one situation, for a user disconnected from network 114, the forecasting mechanism 704 may predict the time of the next connection, the state of the load at the time of connection, the location of the connection (and may designate a probability / clue). Since appeal 112 has been connected to

However, the connection time, connection charge state, and connection location also become inputs for refinements of connection duration predictions. These forecasts help guide total system availability forecasts as well as to determine a more accurate cost function for resource allocation.

Building a parameterized prediction model for each unique user is not always scalable in time or space. Therefore, in one implementation, instead of using one model for each user in system 100, predictive engine 704 builds a reduced set of models where each model in the reduced set is used to predict the behavior of many users. In deciding how to group similar users for model creation and designation, system 100 can identify characteristics of each user, such as number of unique connections / disconnects per day, typical connection time (s). , average connection duration, average charge state and connection time, etc., and can create user groups in either a full feature space or a reduced feature space that is computed via a dimensionality reduction algorithm. such as Principal Component Analysis, Random Projection, etc. Since the prediction engine 704 has users assigned to a cluster, the collective data of all users in this group is used to create a predictable model that will be used for predictions of each user in the group. In one implementation, the group assignment procedure is varied to optimize system 100 for speed (fewer groups), for precision (more groups), or some combination of the two.

This exemplary grouping technique has multiple benefits. First, it enables a reduced model set, and thus reduced model parameters, which reduces computing time to make predictions. It also reduces the storage space of the model's 30 meters. Second, by identifying personality traits (or characteristics) of new users for system 100, these new users can be assigned to an existing group of users with similar traits, and the group model, built from extensive user data. can make more accurate predictions about the new user faster because it is leveraging the historical performance of similar users. Of course, over time, individual users may change their behavior and may be reassigned to new groups that better fit their behavior.

Constraint optimizer 710 combines information from prediction engine 704, data warehouse 716, and contract administrator 720 to generate resource control signals that will satisfy system constraints. For example, constraint optimizer 710 may signal an electric vehicle 200 to charge its battery bank 202 at a certain charge rate and later to discharge battery bank 202 to transfer power to power grid 114 at a certain charge rate. transfer: power transfer rates and power transfer synchronization schedules optimized to adjust the individual tracked on and off behavior of the particular electric vehicle 200 and also optimized to adjust a daily power supply and demand "breathing cycle" from the power grid 114.

In one implementation, constraint optimizer 710 plays a key role in converting network control signals 714 or information sources 414 into vehicle control signals, mediated by connection manager 702. network control signals 714 from a network operator 404 or information sources 414 in control signals that are transferred to each single electrical resource 112 in system 25 100 is an example of a specific constraint optimization problem.

Each feature 112 has associated restrictions, both hard and soft. Examples of resource constraints may include: owner's price sensitivity, vehicle load state (for example, if vehicle 200 is fully loaded, it cannot participate in network load 114), the predicted amount of time until feature 112 disconnects from system 100, the owner's sensitivity to efficiency versus state of charge, the electrical limits of feature 114, the manua charging charges! by owners of resource 408, etc. Restrictions on a particular resource 112 can be used to designate a cost for activating each of the resource's particular actions. For example, a resource whose storage system 202 has little energy stored in it will have a low cost associated with the loading operation, but a very high cost for the generation operation. A fully loaded resource 112 that is predicted to be available for ten hours will have a lower cost generating operation than a fully loaded resource 112 that is predicted to be disconnected within the next 15 minutes, representing the negative consequence of deploying. a less than full resource to its owner.

The following is an example of converting a 714 generation signal that comprises a system operating level (for example, -10 megawatts to +10 megawatts, where + represents load, - represents generation) to a Vehicle control sign. It is worth noting that because system 100 can measure net flows of real energy at each resource 112, the actual system operating level is known at all times.

In this example, assuming the initial system operating level is 0 megawatts, resources are not active (getting or distributing power from the grid), and the negotiated aggregation service contract level for the next hour is ± 5 megawatts. .

In this implementation, the exemplary power aggregation system 100 maintains three available resource lists 112. The first list 25 contains resources 112 that can be activated for loading in priority order. There is a second list of resources 112 sorted by priority for offloading (generation). Each of the resources 112 in these lists (for example, all resources 112 may have a position in both lists) has an associated cost. The priority order of the lists is directly related to the cost (ie the lists are ranked from lowest cost to highest cost). Assigning cost values to each resource 112 is important because it enables the comparison of two operations that achieve similar results with respect to system operation. For example, adding a load unit (load, getting power from the grid) to the system is equivalent to removing a generation unit. To perform any operation that increases or decreases system output, there may be multiple action choices and in one implementation system 100 selects the lowest cost operation. The third feature list 112 contains features with hard restrictions. For example, resources whose owners 408 have forced system 100 to force load will be placed on the third list of static resources.

At time "1", the level of operation requested by the operator of the

network changes to +2 megawatts. The system activates loading of the first resources "n" from the list, where "n" is the number of resources whose additive load is predicted to equal 2 megawatts. After resources are activated, the result of activations is monitored to determine the actual result 15 of the action. If more than 2 megawatts of load is active, the system will disable reverse priority loading to keep system operation within the error tolerance specified by the contract.

From time "1" to time "2", the requested operating level remains constant at 2 megawatts. However, the behavior of some of the electrical features may not be static. For example, some vehicles 200 that are part of the 2 megawatt system operation may become full (load state = 100%) or may disconnect from system 100. Other vehicles 200 may connect to system 100 and require immediate charging. . All of these actions will cause a change in the operating level of the power aggregate system 100. Therefore, system 100 continuously monitors the system's operating level and enables or disables features 112 to keep the operating level within. error tolerance specified by the contract.

At time "2", the level of operation requested by the network operator decreases to -1 megawatts. The system queries the available resource lists and chooses the lowest cost set to achieve a -1 megawatt system operating level. Specifically, the system moves sequentially through the priority lists, comparing the generation cost enable generation versus disable loading, and enable the lowest cost feature at each time step. Once the operating level reaches -1 megawatts, system 100 continues to monitor the actual operating level, looking for deviations that could require activating an additional 112 feature to keep the operating level within error tolerance. specified by the contract.

In one implementation, an exemplary costing mechanism is fed by information into the real-time grid generation mix to determine the marginal loading or generation consequences (vehicle 200 to grid 114) on a "carbon footprint", the impact on fossil fuel resources and the environment in general. The exemplary system 100 also enables optimization for any cost metric, or a weighted combination of several. System 100 can optimize merit figures which may include, for example, a combination of economic value maximization and environmental impact minimization, etc.

In one implementation, system 100 also uses cost as a time variable. For example, if system 100 schedules a download package to load over a close window of time, system 100 can predict its anticipated cost profile as it loads, allowing system 100 to further optimize accordingly. - table. That is, in some circumstances system 100 knows that it will have a high capacity generation capability for some future time.

Multiple components of flow control server 106 constitute a programming system that has multiple functions and components:

• data collection (gathers data in real time and stores historical data);

• projections via prognostic mechanism 704, which captures real-time data, historical data, etc .; and produces resource availability forecasts;

• builds optimizations on resource availability forecasts, constraints such as 404 network operator command signals, user preferences, weather conditions, etc. Optimizations can take the form of resource control plans that optimize a desired metric.

The programming function can allow a number of services to be

useful energy services including:

• ancillary services such as rapid response and rapid regulation services;

• energy to compensate for sudden, predictable or unexpected network instabilities;

• response to routine and unstable demands;

• stability of renewable energy sources (eg complementing wind power).

An exemplary power aggregation system 100 aggregates and controls the load presented by charging / transferring many electric vehicles 200 to provide power services (auxiliary power services) such as throttling and reserve reserves. Accordingly, it is possible to satisfy call time requirements from network operators 404 by adding multiple electrical resources 112. For example, twelve operating loads of 5kW each can be disabled to provide 60Rw of working reserves for one hour. However, if each load can be disabled for a maximum of minutes and the minimum call time is two hours, the loads can be disabled in series (three at a time) to provide 15kW of reserves for two hours. Naturally, more complex individual electrical resources interleaving are possible by the energy aggregation system 100.

For a utility (or utility) to maximize distribution efficiency, the utility needs to minimize reactive power flows. Typically, there are a number of methods used to minimize reactive power flows including switching inductor or capacitor banks in the distribution system to modify the power factor in different parts of the system. To effectively manage and control this Dynamic Reactive Volt-Amp (VAR) support, it must be done in a local aware manner. In one implementation, the power aggregation system 100 includes power factor correction circuits placed on electric vehicles 5,200 with the exemplary remote IPF module 134, thereby enabling such a service. Specifically, electric vehicles 200 may have capacitors (or inductors) that can be dynamically connected to the grid regardless of whether electric vehicle 200 is charging, distributing power, or doing anything. This service can then be sold to 10 utility companies for distribution-level dynamic VAR support. Power aggregation system 100 may both feel the need for distributed VAR support and the use of distributed remote IPF modules 134 to take actions that provide VAR support without intervention from network operator 404.

Exemplary Remote IPF Module

Figure 8 shows the remote IPF module 134 of figures 1 and 2 in more detail. The remote IPF module 134 illustrated is an example configuration only for descriptive purposes. Many other arrangements of the illustrated components or even different components that constitute a

exemplary remote IPF module 134 are possible within the scope of the subject

i

object. Such exemplary remote IPF module 134 has some hardware components and some components that may run on hardware, software or combinations of hardware, software, firmware, etc.

The illustrated example of a remote IPF module 134 is represented by a suitable implementation for an electric vehicle 200. Accordingly, some vehicle systems 800 are included as part of the exemplary remote IPF module 134 in consideration of the description. However, in other implementations, remote IPF module 134 may exclude some or all vehicle systems 800 from being counted as components 30 of remote IPF module 134.

The vehicle systems 800 described include a vehicle computer and data interface 802, an energy storage system such as a battery bank 202, and an inverter / charger 804. In addition to the vehicle systems 800, the module Remote IPF 134 also includes a communicative power flow controller 806. The communicative power flow controller 806 in turn includes some components that interface with network AC power 114, such as a communicator. power transmission line, for example, an Ethernet bridge through power transmission line 120, and a current or current / voltage (power) sensor 808, such as a current sensing transformer.

The communicative power flow controller 806 also

It also includes Ethernet and information processing components, such as an 810 processor or microcontroller and a medium access control (MAC) address associated with Ethernet 812; volatile random access memory 814, nonvolatile memory 816 or data storage, an interface such as an RS-232 818 interface or a CANbus 820 interface; an Ethernet 822 physical layer interface, which enables wiring and signaling to Ethernet standards for the physical layer through MAC / Data Link Layer network access means and a common address format. The Ethernet 20 822 physical layer interface provides the electrical, mechanical, and procedural interface to the transmission medium - that is, in one implementation, using the Ethernet bridge over power transmission line 120. In one variation, wireless or other Internet communication channels 104 are used in place of the Ethernet bridge through power transmission line 120.

The communicative power flow controller 806 also

It also includes a two-way net energy flow meter 824 that tracks energy transfer to and from each electrical resource 112, in this case the battery bank 202 of an electric vehicle 200.

The communicative power flow controller 806 operates either within or connected to an electric vehicle 200 or other electrical feature 112 to enable aggregation of electrical features 112 introduced above (e.g. via a wired communication interface or wireless). These components listed above may vary between different implementations of the 806 communicative power flow controller, but implementations typically include:

• an in-vehicle communications mechanism that enables communication with other vehicle components;

• a mechanism for communicating with the control center of

flow 102;

• a processing element;

• a data storage element;

· A power meter; and

• optionally a user interface.

Implementations of the 806 communicative power flow controller can enable functionality including:

• perform preprogrammed or learning behaviors

when electrical feature 112 is out of execution (not connected to Internet 104, or service is unavailable);

• store locally cached behavior profiles for roaming connectivity (what to do when loading on a foreign system or in disconnected operation,

ie when there is no network connectivity);

• allow user to enforce system behavior

chain; and

• measure net energy flow information and store metering data during out-of-execution operation for subsequent transaction.

Accordingly, the communicative power flow controller 806 includes an 810 central processor, interfaces 818 and 820 for communication within the electric vehicle 200, a power line communicator, such as an Ethernet bridge over power line.

power transmission 120 for communication external to the electric vehicle 200, and a net energy flow meter 824 for measuring the energy flow to and from the electric vehicle 200 via a connected 208 AC power transmission line.

Exemplary IPF Module Operation

Continuing with electric vehicles 200 as representative of electrical resources 112, during periods when such an electric vehicle 200 is parked and connected to network 114, remote IPF module 134 initiates a connection to flow control server 106, he records. and waits for signals from the flow control server 106 directing the remote IPF module 134 to adjust the net power flow into or out of the electric vehicle 200. These signals are communicated to the vehicle computer 802 via the data interface, which may be any suitable interface including the RS-232 818 interface or the CANbus 820 interface. The vehicle computer 802, following the signals received from the flow control server 106, controls the inverter / charger 804 to charge the battery bank 202 of the vehicle or to discharge the battery bank 202 uploaded to network 114.

Periodically, remote IPF module 134 transmits information regarding power flows to flow control server 106. If, when electric vehicle 200 is connected to network 114, there are no communication paths to flow control server 106 (that is, the site is not properly equipped, or there is a mains failure), the electric vehicle 200 may follow a preprogrammed or learning out-of-execution behavior, for example, stored as a set of instructions in the non-volatile memory 816. In such a case, the power transmissions may also be stored in non-volatile cache memory 816 for later transmission to the flow control server 106.

During periods when the electric vehicle 200 is in transport operation, the remote IPF module 134 listens passively, recording the operating data of the selected vehicle for later analysis and consumption. Remote IPF module 134 can transmit this data to flow control server 106 when a communications channel becomes available. I

Measure net flows of exemplary energy

Power is the rate of energy consumption per time interval. Power indicates the amount of energy transferred over a certain period of time, therefore power units are amounts of energy per unit of time. The exemplary net energy flow meter 824 measures power for an electrical resource 112 given through a bidirectional flow - for example, power from grid 114 to electric vehicle 200 or from electric vehicle 200 to grid 114. In a remote IPF module 134 can locally store power meter readings 824 to ensure accurate transactions with flow control server 106, even if the connection to the server is temporarily down, or if the server itself is unavailable.

The exemplary 824 net power flow meter in conjunction with the other components of the remote IPF module 134 enables broad system features in the exemplary power aggregation system 100 including:

• track energy use on a specific electrical resource basis;

• monitor power quality (check that voltage, frequency, etc. deviate from their rated operating points, in which case notify network operators, and potentially modify the power flow feature to help correct the problem);

• billing for a specific vehicle and transactions for use of

energy;

· Mobile billing (support for accurate billing when the

electrical resource owner is not the owner of the electrical wiring location 410 (ie not the meter account owner). 824 Net Power Flow Meter data can be captured on electric vehicle 200 for billing;

· Integration with a smart meter at the charging point

to (bidirectional information exchange); and

• tamper resistance (for example, when the net power flow meter 824 is protected within an electrical feature 112 such as an electric vehicle 200).

Mobile Resource Locator

The exemplary power aggregation system 100 also includes various techniques for determining the mains location of a mobile power 112 such as a plug-in 200 electric vehicle. Electric vehicles 200 can connect to grid 114 in numerous locations, and precise control and switching of power can be made possible through specific knowledge of the charging location.

Some of the exemplary techniques for determining electric vehicle charging locations include:

• question a unique identifier for the location (via coupled, wireless, etc.), which may be:

- the unique hardware ID of the network at the upload site;

- the unique ID of the locally installed smart meter through communication with the meter;

- a unique ID installed specifically for this purpose on a website; and

- use GPS or other signal sources (cellular, WiMax, etc.) to establish a "flexible" (estimated geographic) location, which is then refined based on user preferences and historical data (eg vehicles tend to be connected to owner's residence 124, not a neighbor's residence).

Figure 9 shows an exemplary technique for solving the physical location of network 114 of an electrical resource 112 that is connected to the exemplary power aggregation system 100. In one implementation, remote IPF module 134 obtains the Media Access Control (MAC) address 902 from the locally installed network modem or router (Internet access point) 302. Remote IPF module 134 then transmits this unique identifier. MAC for flow control server 106, which uses the identifier to troubleshoot the location of the electric vehicle 200.

To discern its physical location, remote IPF module 134 may also sometimes use MAC addresses or other unique identifiers from other physically installed equipment nearby that can communicate with remote IPF module 134, including an "intelligent" meter. 904, a 906 TV cable box, an RFID 908-based unit 5, or an exemplary ID unit 910 that is capable of communicating with the remote IPF module 134. The exemplary ID unit 910 is described. in more detail in figure 10. MAC addresses 902 do not always give information about the physical location of the associated hardware, but in one implementation flow control server 106 includes a tracking database 912 that lists addresses MAC or other identifiers with an associated physical hardware location. In this manner, a remote IPF module 134 and flow control server 106 may encounter a mobile electrical resource 112 whenever it connects to power network 114.

Figure 10 shows another exemplary technique for determining

a physical location of a mobile electrical resource 112 in power network 114. An exemplary ID unit 910 may be connected to network 114 at or near a charging location. The operation of ID 910 unit is as follows. A newly connected 112 electrical resource searches for 20 locally connected resources by transmitting a Ping or message in the wireless reception area. In one implementation, ID unit 910 responds 1002 to Ping and carries a unique identifier 1004 from ID unit 910 back to electrical resource 112. Remote IPF module 134 of electrical resource 112 then transmits unique identifier 25 1004 to flow control server 106, which determines the location of the ID 910 unit and through the Proxy, the exact or approximate network location of electrical resource 112, depending on the size of the ID 910 unit's pickup area.

In another implementation, newly connected electrical resource 112 looks for locally connected resources by transmitting a Ping or message that includes unique identifier 1006 of electrical resource 112. In this implementation, ID unit 910 does not need to rely on. or reuse the wireless connection, and does not respond back to remote IPF module 134 of electrical feature 112, but responds 1008 directly to flow control server 106 with a message containing its own unique identifier 1004 and identifier 1006 of the electrical resource 112 that was received in the Ping message. Central flow control server 106 then associates the unique identifier 1006 of electrical resource 112 with a "connected" state and uses the other unique identifier 1004 of ID unit 910 to determine or approximate the physical location of the electrical resource. 112. The physical location does not have to be close if an ID 910 unit is associated with only one exact network location. Remote IPF module 134 learns that Ping succeeds when it listens back from flow control center 106 with confirmation.

Such an exemplary ID 910 unit is particularly useful in situations where the communications path between the electrical resource 112 and the flow control server 106 is via a wireless connection which itself does not allow exact determination of network location.

Figure 11 shows another exemplary method 1100 and system 1102 for determining the location of a mobile electrical resource 112 in power network 114. In a situation where electrical resource 112 and flow control server 20 conduct communications via a wireless signaling scheme, it is still desirable to determine the location of the physical connection during sequence periods with the network 114.

Wireless networks (eg GSM, 802.11, WiMax) comprise many cells or towers in which each transmits unique identifiers. Additionally, the strength of the connection between a tower and mobile clients connecting to the tower is a function of the customer's proximity to the tower. When an electric vehicle 200 is connected to network 114, remote IPF module 134 can acquire the unique towers identifiers available and relates this to the signal strength of each connection, as shown in database 1104. Remote IPF module 134 of electrical resource 112 transmits this information to flow control server 106, where information is combined with inspection data, such as database I

1106 so that a position inference mechanism 1108 can

i

by triangulating or otherwise inferring the physical location of the connected electric vehicle 200. In another capability, the IPF 134 module can use the intensity readings to resolve the resource location directly, in which case the IPF 134 module transmits the location information instead of the signal strength information.

Accordingly, exemplary method 1100 includes acquiring (1110) the signal strength information; communicating (1112) the acquired signal strength information to the flow control server 106; and inferring (1114) the physical location using stored tower location information and signals acquired from the electrical resource 112.

Figure 12 shows a method 1200 and system 1202 for using signals from a global satellite positioning system (GPS) to determine a physical location of an electrical resource 112 in the power grid 114. Using GPS an IPF module 134 to resolve your physical location on the power grid in an inaccurate manner. This noisy GPS location information is transmitted to the flow control server 106, which uses it with an information database 1204 to infer the location of the electrical resource 112.

Exemplary method 1200 includes acquiring (1206) data from

noisy position; communicating (1208) the acquired noisy position data to the flow control server 106; and infer (1210) the location using stored inspection information and acquired data.

Exemplary Transaction Methods and Other Features The exemplary power aggregation system 100 supports the

following functions and changes:

1. Adjustment - The exemplary power aggregation system 100 creates out-of-system contracts and / or open market offers to obtain contracts for energy services, contracts via web server 718 and contract administrator 720. System 100 then resolves these requirements on specific power requirements by means of network operator 404, and communicates these requirements to vehicle owners 408 through one of several communication techniques.

2. Distribution - Network Interaction Administrator 712 accepts real-time network control signals 714 from 404 network operators through a power distribution device, and responds to these

signals 714 through power service distribution of electric vehicles 200 connected to grid 114.

3. Reporting - After a power distribution event is complete, a transaction administrator can report energy service transactions stored in database 716. An administrator

Billing Service decides these requests in specific credit or debit billing transactions. These transactions can be reported to a carrier or utility billing system for accounting reconciliation. Transactions can also be used to make payments directly to resource 408 owners.

In one implementation, the remote IPF module 134 resident in

The vehicle may include a communications administrator to receive offers to provide energy services, show them to the user and allow the user to respond to the offers. Sometimes this type of advertising or hiring interaction can be performed by the power resource owner 408 conventionally connecting to the flow control server web server 718 106.

In an exemplary vehicle-based load management or storage model, the exemplary power aggregation system 100 serves as an intermediary between vehicle owners 408 (individuals, fleets, etc.) and network operators 404 (Independent System Operators (ISOs), Regional Transmission Operators (RTOs), Concessionaires, etc.)

The electric charging and storage feature 112 presented by a single plug-in electric vehicle 200 is not a substantial enough feature for an ISO or utility company to consider controlling directly. However, by bundling many electric vehicles 200 together, managing their charging behavior, and exporting a simple control interface, the power aggregation system 100 provides services that are available to network operators 404.

Similarly, vehicle owners 408 may not be interested in participating without participation being easy, and with no incentive to do so. By creating value through aggregated management, the energy aggregation system 100 can provide incentives for homeowners in the form of payments, reduced charging costs, etc. Power aggregation system 100 can also control vehicle loading and transfer power to network 114 automatically and almost without interruption to vehicle owner 408, thereby making a pleasant participation.

By placing remote IPF modules 134 on electric vehicles 200 that can measure power quality attributes, power aggregation system 100 enables a massively distributed sensor network to power distribution network 114. Quality attributes that the power aggregation system 100 can measure include frequency, voltage, power factor, harmonics, etc. Then, by leveling the communication infrastructure of the power aggregation system 100, including remote IPF modules 134, this detected data can be reported in real time to flow control server 106, where the information is aggregated. Also, the information may be presented to the utility company, or the power aggregation system 100 can directly correct undesirable network conditions by controlling the vehicle power transfer / upload behavior of numerous electric vehicles 200 by exchanging the load power factor, etc.

The exemplary power aggregation system 100 can also provide Uninterruptible Power Supply (UPS) or backup power for a home / work, including circuitry interconnection. In one implementation, power aggregation system 100 allows electrical resources 112 to flow energy out of its batteries into the home (or work) to energize some or all of the household charges. Certain loads can be set as key loads to keep "on" during a mains power loss event. In such a situation, it is important to manipulate the insulation of the house 124 from the grid 114. Such a system may include anti-insulating circuits having the ability to communicate with the electric vehicle 200, further described below as an intelligent switch box. The ability of the remote IPF module 134 to communicate allows the electric vehicle 200 to know if it is safe to supply power, "safe" being defined as "safe for utility line workers as a result of the home main switch being in a state unplugged". If power to the grid drops, the smart switch box disconnects from the grid, then contacts any locally participating electric vehicles 200 or other electrical resources 112, and prompts them to start the power supply. When utility power returns, the smart switch box shuts off local power resources and then reconnects.

For mobile billing (for when vehicle owner 408 is different from meter account owner 410), there are two important aspects of handling billing to take into account when recharging the electric vehicle: who owns the vehicle, and who owns 20 the utility meter account where the recharge is going on. When vehicle owner 408 is different from owner of meter 410, there are several options:

1. The owner of meter 410 can give free charge.

2. The owner of vehicle 408 may pay at the time of charge (via credit card, account, etc.).

3. A pre-set account can be adjusted automatically.

Without supervision of the power aggregation system 100, service theft may occur. With automatic account adjustment, the power aggregation system 100 records when electric vehicles 200 charge at locations requiring payment via vehicle IDs and location IDs, and via exemplary time energy flow metering noted in / out of the vehicle. In such cases, the owner of vehicle 408 is billed for the energy used, and this energy is not charged to the owner of the dealer's meter 410 account (so double billing is prevented). A billing administrator who performs automatic account adjustment may be integrated with the utility, or may be implemented as a separate debit / credit system.

A charging station, whether free or paid, can be installed with a user interface that presents useful information to the user. Specifically, by collecting information about the network 114, the state of the vehicle, and the user's preferences, the station can present information such as the current electricity price, the estimated recharge cost, the estimated time to power up. recharge, the estimated payment to transfer power to grid 114 (both total and hourly), etc. The information acquisition mechanism 414 communicates with the electric vehicle 200 and 15 with public and / or private data networks 722 to acquire the data used in the calculation of this information.

The exemplary power aggregation system 100 also offers other features for the benefit of 408 power owners (such as vehicle owners):

· Vehicle owners can get free electricity

to charge the vehicle in reward for participating in the system;

• vehicle owners may experience reduced charge cost by avoiding charges at peak times;

• vehicle owners may receive payments based on the actual energy service they provide to their vehicle;

• Vehicle owners may receive a preferential rate to participate in the system.

There are also features between the exemplary power aggregation system 100 and network operators 404.

· The power aggregation system 100 as a power aggregator

Electricity can earn a management bonus (which may be some function of services provided), paid by the network operator • Power aggregation system 100 as an electricity aggregator can sell in 412 energy markets;

• 404 network operators may pay for the energy aggregation system 100, but operate the energy aggregation system 100 themselves.

Exemplary Remote Intelligent Security and Isolation

The exemplary power aggregation system 100 may include methods and components for implementing safety standards and safely triggering power discharge operations. For example, the exemplary power aggregation system 100 may use in-vehicle line sensors as well as intelligent isolation equipment installed in particular locations. Therefore, the power aggregation system 100 enables safe operations from vehicle to grid. Additionally, the power aggregation system 100 enables automatic resource coordination for power backup situations.

In one implementation, the electric vehicle 200 containing a remote IPF module 134 stops power upload from the vehicle to the network if the remote IPF module 134 does not detect power line originating from network 114. This power upload pause prevents electrification of a wire that can be unplugged, or electrification of a power line 206 being repaired, etc. However, this does not make it impossible to use electric vehicle 200 to provide backup power if mains power is inactive because the safety measures described below ensure that an isolation condition is not created.

Additional smart isolation equipment installed at a charging location can communicate with the remote IPF module 134 of an electric vehicle 200 for coordinated power upload activation to grid 114 if grid power drops. A particular implementation of this technology is the vehicle's home power backup capability.

Figure 13 shows exemplary safety measures in a home vehicle situation, where an electrical feature 112 is used to provide power for a load or charge set (as in a home). A switch box 1300 is connected to the utility meter 1302. When an electrical resource 112 is flowing power to the network (or local loads), an isolation condition must be prevented for safety reasons. Electrical resource 112 should not energize a line that should conventionally be considered unpowered at a power outage by line workers.

A locally installed 1304 smart grid breaker (switch) detects the utility line to detect a power outage condition and coordinates with electrical feature 112 to enable power transfer from the vehicle to the home. In the event of a power outage, a smart grid breaker 1304 disconnects circuit breakers 1306 from utility network 114 and communicates with electrical resource 112 to initiate power backup services. When utility services resume operation, smart grid breaker 1304 communicates with electrical feature 112 to disable backup services and reconnects switches to utility network 114.

Figure 14 shows exemplary safety measures when multiple electrical resources 112 energize a home. In this case, the smart grid breaker 1304 coordinates with all connected electrical resources 112. An electrical resource 112 is considered "master" 1400 for purposes of generating a reference signal 1402 and the other features are considered "slave" 1404 and follow the reference of master 1400. In a case where master 1400 disappears from the network, smart grid breaker 1304 designates another slave 1404 to be reference / master 1400.

Figure 15 shows, in more detail, the smart grid breaker 1304 of figures 13 and 14. In one implementation, the smart grid breaker 1304 includes a processor 1502, a power-coupled communicator 1504, a sensor of 1506 voltages capable of sensing both the internal and utility-side AC lines, a 1508 battery for operation during power outages, and a 1510 battery charger to maintain the 1508 battery charge level. A switch or relay 1512 controlled switches between utility and utility power when signaled by processor 1502.

Exemplary User Experience Options

Power aggregation system 100 can enable a number of desirable user characteristics:

• Data collection may include triggered distance and fuel usage, both electric and non-electric, to allow derivation and

total vehicle efficiency analysis (in terms of energy, expenses, environmental impact, etc.). This data is exported to flow control server 106 for storage 716, as well as to display a vehicle user interface, charging station user interface, and web / mobile phone user interface.

• Intelligent charging takes note of vehicle behavior and automatically adapts charging timing. Vehicle owner 408 may impose and request immediate loading if desired.

Exemplary Methods

Figure 16 shows an exemplary method of energy aggregation 1600. In the flow diagram, operations are summarized in individual blocks. The exemplary method 1600 may be performed by hardware, software, or combinations of hardware, software, firmware, etc., for example by components of the exemplary power aggregation system 100.

In block 1602, communication is established with each of the multiple electrical resources connected to a power grid. For example, a central flow control service may manage numerous intermittent connections with mobile electric vehicles, each of which may connect to the power grid at various locations. A remote agent in the vehicle connects each vehicle to the Internet when the vehicle connects to an energy network. I

In block 1604 the electrical resources are individually signaled.

i

to supply power to or obtain power from the power grid.

Figure 17 is a flowchart of an exemplary method of communicatively controlling an electrical resource for energy aggregation. In the flow diagram, operations are summarized in individual blocks. Exemplary method 1700 may be performed by hardware, software, or combinations of hardware, software, firmware, etc., for example, by exemplary intelligent power flow module (IPF) components 134.

At block 1702, communication is established between a resource

and a service for energy aggregation.

In block 1707, the information associated with the electrical resource is communicated to the service.

In block 1706, a control signal based in part on the information is received from the service.

In block 1708, the resource is controlled, for example, to supply power to the power grid or to obtain power from the grid, that is, for storage.

In block 1710, the bidirectional net energy flow of the electrical device is measured, and used as part of the information associated with the electrical resource that is communicated to the service in block 1704.

Figure 18 is a flow chart of an exemplary method of measuring bidirectional energy of an electrical resource. In the flow diagram, operations are summarized in individual blocks. The exemplary method 1800 may be performed by hardware, software, or combinations of hardware, software, firmware, etc., for example by means of exemplary energy flow measurement components 824.

In block 1802, the energy transfer between an electrical resource and a power grid is measured bidirectionally.

At block 1804, measurements are transferred to a service

which adds energy based on stop measurements.

Fig. 19 is a flow chart of an exemplary method of determining a mains location of an electrical resource. In the flowchart, operations are summarized in individual blocks. The exemplary method 1900 may be executed by hardware, software, or combinations of hardware, software, firmware, etc., for example through components of the exemplary power aggregation system 100.

In block 1902, a physical location information is determined. Physical location information may be derived from such sources as GPS signals or the relative intensity of cell tower signals as an indicator of their location. Or, physical location information may be derived by receiving a unique identifier associated with a nearby device, and finding the location associated with such a unique identifier.

In block 1904, an electrical network location, for example of an electrical resource or its connection to the electrical network, is determined from physical location information.

Figure 20 is a flow chart of an exemplary method of programming power aggregation. In the flow diagram, operations are summarized in individual blocks. The exemplary method 2000 may be performed by hardware, software, or combinations of hardware, software, firmware, etc., for example by exemplary flow control server components 106.

In block 2002, submissions associated with individual electrical resources are allowed.

In block 2004, energy aggregation is programmed, based on admission submissions.

Fig. 21 is a flow diagram of an exemplary intelligent isolation method. In the flow diagram, operations are summarized in individual blocks. The exemplary method 2100 may be performed by hardware, software, or combinations of hardware, software, firmware, etc., for example by components of the exemplary power aggregation system 100.

At block 2102, a power interruption is detected. In block 2104, a local connectivity is created - a network isolated from the power grid.

At block 2106, local energy storage resources are signaled to energize local connectivity.

Figure 22 is a flow chart of an exemplary method of

extend a user interface for power aggregation. In the flow diagram, operations are summarized in individual blocks. The exemplary method 2200 may be performed by hardware, software, or combinations of hardware, software, firmware, etc., for example, by components of the exemplary power aggregation system 100.

In block 2202, a user interface is associated with an electrical feature. The user interface may be shown on or near an electrical resource, such as an electric vehicle that includes an energy storage system, or the user interface may be shown on a device associated with the electrical resource owner. , such as a cell phone or laptop.

In block 2204, power aggregation preferences and submissions are admitted via the user interface. In other words, a user can control a degree of electrical resource participation in a power aggregation situation via the user interface. Or, the user can control the characteristics of such participation.

Figure 23 is a flowchart of an exemplary method of gaining and maintaining electric vehicle owners in an energy aggregation system. In the flow diagram, operations are summarized in 25 individual blocks. The exemplary method 2300 may be performed by hardware, software, or combinations of hardware, software, firmware, etc., for example by components of the exemplary power supply system 100.

In block 2302, electric vehicle owners are registered in an energy aggregation system for distributed electrical resources.

In block 2304, an incentive is provided for each owner to participate in the energy aggregation system.

At block 2306, the recurring continued service to the power aggregation system is repeatedly compensated.

Conclusion

Although exemplary systems and methods have been described

In specific language for structural features and / or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described. Instead, the specific features and acts 10 are described as exemplary ways of implementing the claimed methods, devices and systems, etc..

Claims (62)

Method comprising: establishing a communication connection with each of the multiple electrical resources connected to an electrical power grid; and individually signaling electrical resources to provide power to the power grid, draw power from the power grid, or determine the available load, generating capacity, or storage of an electrical resource. A method according to claim 1, wherein the electrical resources comprise energy storage systems. A method according to claim 1, wherein individually signaling each electrical resource is based in part on measuring an energy flow between the electrical resource and the electrical network. A method according to claim 3, wherein the measurement takes place at or near each electrical resource. The method of claim 1 further comprising: determining a power grid location of each electrical resource; and individually flag each electrical resource based in part on the location of the power grid A method according to claim 1 further comprising scheduling energy flows for each of the electrical resources based on an optimization of at least some limitations on the connected electrical resources. A method according to claim 6 further comprising converting network control signals or acquired information into optimized planning based resource control signals. A method according to claim 1, wherein the electrical resources comprise mobile electrical resources intermittently connected to the power grid at various locations. A method according to claim 8, wherein the mobile electric resources comprise electric vehicles connectable to the electric power grid. A method according to claim 1, wherein the signaling includes individually directing the electrical resources to provide power to or withdrawing power from the power grid at specific times and rates for each electrical resource. A method according to claim 1 further comprising: receiving grid control signals from an electric power grid operator or information acquired from a source of information; schedule an aggregation of electrical resources to smooth out a grid condition indicated by control signals or acquired information; and offer aggregation as predicted by the power grid operator. A method according to claim 11, further comprising contracting with the utility to perform aggregation in exchange for compensation. The method of claim 11 further comprising: predicting trends in available individual electrical resources, available aggregate electrical resources, and trends per user comprising connection duration, connection time, disconnection time, location of connection and load state (SOC) of resource in connection; and base aggregation at least in part on predicted trends. A method according to claim 13 wherein predicting trends is based on learning behaviors from the numerous electrical resources. A method according to claim 11, wherein the prediction is modified based on at least in part correlating expected future events with past data on available aggregate electrical resources. A system comprising: a server for communicating with each of the multiple electrical resources connected to an electrical power grid; and a connection manager to individually signal electrical resources to exchange power with the power grid. A system according to claim 16, wherein the electrical resources comprise electric vehicle electrical storage systems, each electric vehicle intermittently connected to the electrical power grid at varying locations. A system according to claim 16 further comprising a limitation optimizer for aggregating power exchange according to an optimization of selected parameters or imposed by an electric resource owner, a power grid operator, a physical condition of the power grid, a contract between a server administrator and a power grid administrator, or an automated network controller. The system of claim 18 further comprising a prediction mechanism for learning, inferring, or projecting a bias of electrical resources, owners of electrical resources, owners of electrical connection locations, network operators, or automated network controllers, wherein an optimization of power exchange aggregation is based at least in part on the trend. The system of claim 18 further comprising a contract manager for establishing an agreement between a server administrator and an electric power grid operator for an aggregate power exchange. A system comprising: means for individually signaling electrical resources over a network, wherein each electrical resource is intermittently connected to the electrical network at varying locations; and means for dynamically aggregating energy flowing to and from electrical resources by signaling in response to an electrical mains condition signaled by a mains control signal. A system comprising: a first communicator associated with an electrical resource for communicating with electrical resource components; a second communicator associated with the electrical resource to communicate with the service signaling each of the multiple electrical resources to obtain power from a power grid or to supply power to the power grid; and a meter to measure net bi-directional energy flows between the electrical resource and the power grid. The system of claim 22, further comprising a data store for storing instructions and the measured bidirectional net energy flow. The system of claim 22, wherein the first communicator, the second communicator, the meter, and data storage are within an electric vehicle. The system of claim 22, wherein the first communicator signals a computing device in the electrical resource to charge an energy storage system to energize the electrical resource or signals the computing device to discharge electricity. from the energy storage system to the power grid. A system according to claim 22, further comprising a sensor for determining when the electrical resource connects and disconnects from the power grid. The system of claim 22, wherein the second communicator communicates with a communication channel and registers with the service. The system of claim 27, wherein the second communicator obtains an Internet Protocol (IP) address when the communication channel uses the Internet Protocol. The system of claim 22, further comprising a processor for executing instructions between the first communicator, the second communicator, the meter, the data storage, a computing device associated with the electrical resource, and a sensor for determine when the electrical resource is connected to the power grid. The system of claim 29, further comprising using the second communicator to download instructions for execution by the processor. The system of claim 22, further comprising a user interface for displaying first communicator and second communicator communications, a meter reading, data store contents, service messages, a system load state. energy storage, a duration of receiving a load from the power grid, a predicted load completion time, a duration of discharging power to the power grid, a power price, a location, a site owner's identity, a metering account owner's identity, a communication channel connection state, a service user account, invoice information, service offer announcements, and / or environmental greening options via the service. The system of claim 29, wherein the user interface accepts input from a user to override service signals, override preprogrammed instructions stored in the data store, specifying preferences. and / or by specifying system restrictions selected by the user. The system of claim 22 further comprising preprogrammed data storage instructions for handling the net flow of energy to and from the power resource when the second communicator is disconnected from service. The system of claim 22 further comprising instructions in storing data to achieve roaming connectivity when the electrical resource is outside a specified geographical area. The system of claim 22, wherein the data store caches bidirectional net energy flow information measured by the meter for further transaction with the service. The system of claim 22, wherein the second communicator communicates with: an internet access point at a location where the electrical resource is connected to the power distribution system; a meter on site; or another on-site electrical resource. A method comprising: receiving signals from a service informing each of the multiple electrical resources to obtain power from a power grid or to provide power to a power grid; direct a computing device on the electrical resource to charge an energy storage system to energize the electrical resource based on the signals received or direct the computer device to generate power from the energy storage system to supply to the grid based on received signals; measure net flows of energy to and from the electrical resource; and communicate at least some of the measured energy flow information during measurement to the service. A method according to claim 37, further comprising detecting when the electrical resource is connected to the power grid. A method according to claim 37, further comprising showing in the electrical resource at least one of: communications to and from the service, a measurement reading, stored data, messages from the service, a state of charge. from the power storage system, a duration of receiving a load from the power distribution network, a power generation duration for the power distribution network, a predicted load completion time, a power price, a site, a site owner identity, a meter account owner identity, an aggregate power map that participates in a charge cycle, or a power generation cycle, a connection state to a service user account, service billing information, service announcements and offers, and / or greening options so environmental. The method of claim 37, further comprising imposing service signals, imposing preprogrammed instructions stored in the data store, specifying user preferences, and / or specifying system restrictions selected by the user. The method of claim 37, further comprising following the stored preprogrammed instructions for managing the energy flow to and from the energy storage system when disconnected from service. A method according to claim 37, further comprising storing, when disconnected from service, at least some of the net energy flow information measured during measurement and retrieving, when reconnected with the service, the energy flow information stored for a transaction with the service. 43. An apparatus comprising: a meter configured to measure bidirectional energy transfer between an electrical resource and a power grid; and an interface for sending measurements to a service that collects energy based in part on measurements. Apparatus according to claim 43, wherein the service aggregates a capacity of distributed electrical resources to obtain energy, supply energy, or store energy based on measurements. The apparatus of claim 43, wherein the electric resource comprises an electric vehicle, a hybrid electric vehicle, or a vehicle that obtains at least some energy for movement from an electrical storage resource. Apparatus according to claim 43, wherein the meter continuously calculates an integrated instantaneous electrical power over a time to determine the energy used and sends the calculated instantaneous electrical power and / or the energy used to the service. energy aggregation. Apparatus according to claim 46, wherein the meter caches at least some of the measurements and / or at least some of the calculations in a local data store for future service transactions. The apparatus of claim 47, wherein the meter caches measurements or calculations in the local data store when the service is down or when communication with the service is disconnected, and transmits the measurements or calculations for the service when the service is running and connected. Apparatus according to claim 43, further comprising a power quality monitor for detecting power quality problems, including nominal voltage deviations (such as voltage drops or rises), voltage harmonics, sub-cycle pulses, pulses. speed, nominal frequency deviations, power factor deviations from humidity, and high frequency noise and to transmit quality problems detected to the power supply service. Apparatus according to claim 42, wherein the meter measures parameters of a load and supply including the maximum demand, power factor, and reactive power used. Apparatus according to claim 43, wherein the meter computes a value of electricity consumed and / or supplied, the value of electricity varying by a time of day, a day of the week, and a periodicity. Apparatus according to claim 42, further comprising: a transaction mechanism; a user interface; and wherein the transaction mechanism provides a user with an energy transfer timing option or an energy transfer pricing option. The apparatus of claim 52, wherein the transaction mechanism provides specific billing of electric power usage and power supply credit based on the meter input. The apparatus of claim 53, wherein the transaction mechanism and meter provide mobile billing, wherein an owner of the electrical resource is not the same as an account owner associated with the network connection location. power. Apparatus according to claim 54, further comprising an interface for bi-directional information exchange with a permanently installed meter associated with a location where the electrical resource is connected to the power grid. The apparatus of claim 43, wherein the meter information is encrypted for transmission to the service or storage in the local data store and decrypted in the service. Apparatus according to claim 43, further comprising a tamper proof meter. The apparatus of claim 43, further comprising multiple instances of the apparatus associated with its distributed electrical resources for tracking energy use on a specific resource basis. 59. A method comprising: measuring bi-directional energy transfer between an electrical resource and a power grid; and send bidirectional energy transfer measurements to a service that aggregates energy based in part on the measurements. A method according to claim 59, wherein the service aggregates a capacity of distributed electrical resources to obtain power, provide power, or store energy based on measurements. 61. The method of claim 59, wherein a component associated with the electrical resource performs the measurement. The method of claim 59, wherein the electrical resource comprises a vehicle energy storage system.