Connection locator in a power aggregation system for distributed electric resources

(54) Title: ENERGY AGGREGATION SYSTEM (57) Summary: FOR DISTRIBUTED ELECTRICAL RESOURCES (30) Unionist Priority: 12/11/2006 us 60 / 869,439 (73) Owner (s): V2Green, INC.

(72) Inventor (s): David L. Kaplan, Seth B. Pollack, Seth W.

Bridges (74) Attorney (s): Dannemann, Siemsen, Bigler & Ipanema Moreira (86) International Application: pct US2007025393 of 12/11/2007 (87) International Publication: wo 2008 / 073453de 19/06/2008

Descriptive Report of the Patent of Invention for SYSTEM OF AGGREGATION OF ENERGY FOR DISTRIBUTED ELECTRICAL RESOURCES.

RELATED REQUESTS

This application claims priority for provisional US patent application No. 60 / 869,439 to Bridges et al., Entitled, A Distributed Energy Storage management System, filed on December 11, 2006, and incorporated herein by reference; US provisional patent application No. 60 / 915,347 to Bridges et al., entitled, Plug-in-Vehicle Management System, filed on 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 on August 9, 2007, and incorporated herein by reference. BACKGROUND

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

The electrical power network contains inherently limited installation to store electrical energy. Electricity must be generated constantly to satisfy an uncertain demand, which often results in over-generation (and therefore wasted energy) and sometimes results in under-generation (and therefore power failure).

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 medium and large-scale energy service needs. A single vehicle battery is insignificant when 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.

The low-level electrical and communication interfaces for charging and discharging 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 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 populations of electric vehicles, the billing (or compensation) complexities of vehicle owners , nor with the complexities of assembling mobile pools of electric vehicles on aggregate energy resources robust enough to support company energy service contracts with network operators.

BRIEF DESCRIPTION OF THE 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 flow control server for the energy aggregation system.

Figure 4 is a diagram of an exemplary layout of the energy aggregation system.

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 energy 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 liquid power flow module.

Figure 9 is a diagram of a first exemplary technique for locating a location for connecting an electrical resource to a power grid.

Figure 10 is a diagram of a second exemplary technique 5 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 site on the power grid.

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

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

Figure 14 is a diagram of exemplary safety measures when multiple electrical sources flow energy to a home in the energy aggregation system.

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

Figure 16 is a flow diagram of an exemplary energy aggregation method.

Figure 17 is a flow diagram of an exemplary method of communicatively controlling an electrical resource for aggregating energy.

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

Figure 19 is a flow diagram of an exemplary method of determining a grid location for an electrical resource.

Figure 20 is a flow diagram of an exemplary method of programming energy aggregation.

Figure 21 is a flow diagram of an exemplary method of intelligent insulation.

Figure 22 is a flow diagram of an exemplary method of developing a user interface for energy aggregation.

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

DETAILED DESCRIPTION

Synthesis

Described here is an energy aggregation system for distributed electrical resources, and associated methods. In an 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 energy services to network operators (for example, utility companies, Independent System Operators (ISO), etc.). Energy services as used here, refer to energy distribution as well as other ancillary services including demand response, regulation, revolving reserves, non-revolving reserves, energy imbalance, and similar products. Aggregation as used here refers to the ability to control energy flows into and out of a set of spatially distributed electrical resources for the purpose of providing a larger energy service. Power grid operator as used here, 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 operator of the power grid may constitute some combination of manual / human, action / intervention and automated processes controlling generation signals in response to the system's sensors. A control area operator is an example of a power grid operator. Control area as used here, refers to a contained portion of the electrical network with defined input and output ports. The net flow of energy in the grid in that area must equal (within an error tolerance) the sum of the energy consumption within the area and the outflow of energy from the area.

I

Power grid as used here means a power distribution / grid system that connects energy producers with energy consumers. The network may include generators, transformers, interconnectors, switching stations, substations, feeders, and safety equipment as part of both / both the transmission system (ie, large energy) and the distribution system (ie , retail energy). The exemplary energy aggregation system is vertically scalable for use with a neighborhood, a city, an industry, a control area, or (for example) one of the eight large-scale 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 here, mean the need for more or less energy flowing into or out of a section of the power grid, in response to one of a number of conditions, for example, supply changes, changes demand, contingencies and failures, inclination events, etc. These grid conditions typically manifest themselves as power quality events such as under- or over-voltage events and under- or over-frequency events.

Power quality events, as used here typically refer to manifestations of power grid instability including voltage deviations and frequency deviations; in addition, power quality events as used here also include other disturbances in the quality of energy distributed by the power network such as sub-cycle voltage pulses and harmonics.

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

Electric vehicle is used widely 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. Furthermore, an electric vehicle has an energy storage system, which can 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 grid.

Electric vehicle energy storage systems (batteries, supercapacitors, and / or other energy storage devices) are used here as a representative example of electrical resources intermittently or permanently connected to the grid that can have dynamic energy input and output. Such batteries can act as a power source or a charge. A collection of aggregated 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 swap begins, etc.). Through vast numbers of electric vehicle batteries, connection trends are predictable and such batteries become a stable and reliable resource to turn to, the network or part of the network (such as a person's home in a blackout) must experience a need for increased or decreased energy. Data collection and storage also allows the power aggregation system to predict the connection behavior on a per-user basis.

Exemplary System

I

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

The electrical resources 112 of the energy aggregation system

100 exemplary may include electric vehicle batteries connected to the power grid 114 in homes 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 an implementation, each participating electrical resource 112 or group of local resources has a corresponding remote net intelligent energy flow (IPF) module 134 (hereafter, remote IPF module 134). The centralized flow control center 102 manages the energy aggregation system 100 by communicating with the remote IPF modules 134 distributed peripherally among the electrical resources 112. Remote IPF modules 134 perform a number of different functions, including providing flow control center 102 with remote resource states; control the amount, direction and timing of energy being transferred into or out of a remote electrical resource 112; provide measurement of energy being transferred into or out of a remote electrical resource 112; provide security measures during the transfer of energy and changes in conditions in the power grid 114; information activities; and providing self-contained power transfer control and security measures when communication with flow control center 102 is interrupted. Remote IPF modules 134 will be described in greater detail below.

Figure 2 shows another view of exemplary electrical and communicative 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 can connect to a conventional wall receptacle (wall outlet) 204 of a residence 124, the wall receptacle 204 representing the peripheral edge of the power network 114 connected via a residential power transmission line 206.

In one implementation, power wire 208 between the electric vehicle 200 and the wall receptacle 204 can be made up only of wire

Conventional I and insulation to conduct alternating current (AC) energy to and from the electric vehicle 200. In figure 2, a location-specific connection module 210 performs the function of access point to the network - 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 the electric vehicle 200 and receptacle 204. With such a bridge 120 and connection module from location 210 in place at a connection location, no other special wires or physical media are needed to communicate with the remote IPF module 134 of the electric vehicle 200, other than a conventional power wire 208 to supply residential line current at conventional voltage. Upstream of the connection module of location 210, power and communication with the electric vehicle 200 are resolved on the power transmission line 206 on an Internet cable 104.

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

Figure 3 shows another implementation of the connection module of location 210 in figure 2, in greater detail. In figure 3, an electrical resource 112 has an associated remote IPF module 134, including a bridge 120. The power wire 208 connects the electrical resource 112 to the power network 114 and also to the connection module of locality 210 in order to communicate with the flow control server 106.

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

Exemplary System Layouts

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

The exemplary layout 400 also includes end users 406 classified as owners of electrical resources 408 and owners of electrical connection locations 410, who may or may not be one and the same. In fact, shareholders in an exemplary 100 power aggregation system include the system operator at the flow control center 102, the network operator 404, the owner of resource 408, and the owner of site 410 where the electrical resource 112 is connected to power grid 114.

Electrical connection site owners 410 may include:

• Car rental areas - Car rental companies often have a large portion of their fleet stationed in the area. They can acquire the fleets of electric vehicles 200 and, participate in an energy aggregation system 100, generate income from the idle vehicle fleet.

• Public parking areas - parking area owners can participate in the energy aggregation system 100 to generate income 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 electric vehicles 200 of parked employees. Employees may be offered incentives in return for providing services ί

power.

• Homes - a home garage can merely be equipped with a connection module from locality 210 to enable the home owner to participate in the energy aggregation system 100 and generate income from a parked car. Also, vehicle 202 battery and associated power electronics within the vehicle can provide local power backup during peak load times or power interruptions.

• Residential neighborhood - neighbors can participate in a 100 energy aggregation system 100 and be equipped with energy distribution devices (developed, for example, by cooperative groups with the home owner) that generates income from electric vehicles 200 are parked.

• The network operations 116 in figure 4 collectively include 15 interactions with energy markets 412, the interactions of network operators 404, and the interactions of automated network controllers 118 that perform automatic physical control of the power network 114.

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

Therefore, an exemplary energy aggregation system 100 can 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 status) at the time of connecting / disconnecting);

• 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 figure (s) of merit;

• offer guidelines or guarantees regarding 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 colocalized and not).

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

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

Figure 5 shows the function of multiple control areas 402 in the exemplary energy aggregation system 100. Each electrical resource 112 can be connected to the energy aggregation system 100 within a specific electrical control area. A single example of flow control center 102 can manage electrical resources 112 from multiple distinct control areas 501 (for example, control areas 502, 504 and 506). In an implementation, this functionality is achieved by logically separating resources within the 100 energy aggregation system.

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. A divi5 are additional in a control hierarchy that includes control division groups above and below the illustrated control areas 402 allows the energy aggregation system 100 to scale to energy grids 114 of different magnitudes and / or vary numbers of resources 112 connected to a 114 power network.

Figure 6 shows an exemplary layout 600 of an exemplary energy aggregation system 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'. The control areas 402 to be administered by each specific example of a flow control center 102 can be dynamically assigned. For example, a first flow control center 102 can manage control area A 502 and control area B 504, while a second flow control center 102 'manages control area n 506. Likewise, operators of the corresponding control area (508, 510 and 512) are served by the least flow control center 102 which serves their respective different control areas.

Exemplary Flow Control Server

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

The exemplary flow control server 106 includes a connection administrator 702 to communicate with electrical resources 112, a prognostic mechanism 704 that can include a learning mechanism 706 and a statistics mechanism 708, a restriction optimizer 710, and a network interaction administrator 712 to receive 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 depot 716, a web server 718 to present a user interface to electrical resource owners 408, network operators 404, and electrical connection site owners 410; a contract administrator 720 to negotiate the terms of the contract with energy markets 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 112 electrical resources, monitoring energy prices, negotiating contracts, etc.

Operation of an Exemplary Flow Control Server

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

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

If the connection is terminated for any reason (including cases

I of server interruptions), so the IPF 134 module knows to call the house again and connect the next available server resource.

The network interaction administrator 712 receives and interprets signals from the automated network controller 118 interface from a network operator 404. In one implementation, the network interaction administrator 712 also generates signals to transfer to the automated network controllers 118. The The scope of the signals to be transferred depends on agreements or contracts between the operators of the 404 network and the exemplary 100 energy aggregation system. In a situation the network interaction administrator

712 transfers information about the availability of aggregate electrical resources 112 to receive energy from the network 114 or supply energy to the network 114. In another variation, a contract may allow the network interaction administrator 712 to transfer control signals to the automated network controller 118 - to control the power grid 114, submit to the built-in restrictions of the automated network controller 118 and submit to the scope of control permitted by the contract.

The database 716 can store all relevant data for the energy aggregation system 100 including electrical resource records, for example, for electric vehicles 200, electrical connection information, energy measurement data per vehicle, user preferences. resource owner, account information, etc.

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

The negotiation / contract administrator 720 interacts with network operators 404 and their associated energy markets 412 to determine system availability, pricing, service levels, etc.

The information acquisition mechanism 414 communicates with public and private databases 722, as mentioned above, to gather data that are relevant to the operation of the energy aggregation system 100.

The prediction engine 704 can use data from data warehouse 716 to make predictions about the behavior of the electrical resource, such as when electrical resources 112 will connect and disconnect, global electrical resource availability, electrical system load, energy prices at real time, etc. The predictions make it possible for the energy aggregation system 100 to make more complete use of the electrical resources 112 connected to the power network 114. The 706 learning mechanism 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 708 statistics mechanism can apply various techniques probabilities for the behavior of the resource to perceive trends and make predictions.

In one implementation, the 704 prognostic engine runs predictions via collaborative filtering. The prediction engine 704 can also perform predictions by user of one or more parameters, including, for example, connection time, connection duration, charge status at the time of connection and connection location. In order to run a prognosis per user, the 704 prognosis engine can pull information such as historical data, connection time (day of the week, week of the month, month of the year, holidays, etc.), state of charge in connection , connection location, etc. In an implementation, a time series forecast can be computed via a recurrent 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 can predict the time of the next connection, the state of the charge at the time of connection, the location of the connection (and can designate a probability / clue). Once resource 112 has been connected, the connection time, state of the charge on the connection, and connection location, also become inputs for refinements of the predictions of the duration of the connection. These predictions help to guide predictions of total system availability as well as to determine a more accurate cost function for resource allocation.

The construction of a parameterized prognostic model for each unique user is not always scalable in time or space. Therefore, in an implementation, instead of using one model for each user in system 100, the prognostic mechanism 704 builds a reduced set of models where each model in the reduced set is used to predict the behavior of many users. To decide how to group similar users for model creation and designation, system 100 can identify characteristics of each user, such as number of unique connections / disconnections per day, the typical connection time (s), duration connection average, charge state average and connection time, etc., and can create user groups in either a full characteristic space or in reduced characteristic 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 for all users in that group is used to create a predictable model that will be used for predictions of each user in the group. In an implementation, the group assignment procedure is varied to optimize the system 100 for speed (less groups), for accuracy (more groups), or some combination of the two.

This exemplary grouping technique has multiple benefits.

First, it enables a reduced set of models, and thus reduced model parameters, which reduces the computation time for making predictions. It also reduces the storage space of the model's parameters. 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 data from existing users, 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 can change their behavior and can be reassigned to new groups that fit their behavior better.

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

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

Each 112 feature has associated restrictions, both hard and soft. Examples of resource restrictions may include: owner's sensitivity to price, vehicle's state of charge (for example, if vehicle 200 is fully loaded, it cannot participate in network 114 loading), the amount of time predicted until the resource 112 disconnect from system 100, the owner's sensitivity to performance versus state of charge, the electrical limits of resource 114, the impositions

I manual loading by resource owners 408, etc. The restrictions on a particular resource 112 can be used to designate a cost to activate each of the resource's particular actions. For example, a resource whose storage system 202 has little air 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 generation operation than a fully loaded resource 112 that is predicted to be disconnected10 within the next 15 minutes, representing the negative consequence of distributing a resource less than full to its owner.

The following is an example of converting a 714 generation signal that comprises a system operation level (for example, -10 megawatts to +10 megawatts, where + represents load, - represents 15 generation) to a control signal. vehicle. It is worth noting that because system 100 can measure the net flows of real energy in each resource 112, the level of operation of the real system is known at all times.

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

In this implementation, the exemplary power aggregation system 100 maintains three lists of available resources 112. The first list contains resources 112 that can be activated for charging (charging) in priority order. There is a second list of resources 112 ordered by priority for downloading (generation). Each of the 112 resources in these lists (for example, all 112 resources can have a position in both lists) has an associated cost. The priority order of the lists is directly related to the cost (that is, the lists are classified from lowest cost to highest cost). Designate the cost values ​​for each resource

112 it is important because it makes it possible to compare two operations that achieve similar results with respect to the operation of the system. For example, adding a charging unit (charge, obtaining power from the grid) to the system is equivalent to removing a generation unit. In order to perform any operation that increases or decreases the production of the system, there may be multiple choices of action and in an implementation the system 100 selects the lowest cost operation. The third resource list 112 contains resources with strict restrictions. For example, resources whose owners 408 have forced system 100 to force loading will be placed in the third list of static resources.

At time 1, the level of operation requested by the network operator changes to +2 megawatts. The system activates the loading of the first n resources in the list, where n is the number of resources whose additive load is predicted to equal 2 megawatts. After the features are activated, the result of the activations is monitored to determine the actual result of the action. If more than 2 megawatts of load are active, the system will disable loading in reverse priority order to maintain 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 resources may not be static. For example, some 200 vehicles that are part of the operation of the 2 megawatt system may become complete (state of charge = 100%) or may disconnect from the 100 system. Other 200 vehicles may connect to the 100 system and require immediate loading . All of these actions will cause a change in the level of operation of the power aggregation system 100. Therefore, system 100 continuously monitors the level of operation of the system and enables or disables features 112 to keep the level of operation within the tolerance of error specified by the contract.

At time 2, the level of operation requested by the network operator decreases to -1 megawatts. The system consults the lists of available resources and chooses the lowest cost set to achieve a system operating level of -1 megawatts. Specifically, the system moves sequentially through the priority lists, comparing the generation cost to enable generation versus disabling loading, and to activate 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 the activation of an additional 112 feature to maintain the operating level within the error tolerance specified by contract.

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

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

Multiple components of the 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 the prognostic mechanism 704, which captures data in real time, historical data, etc .; and produces resource availability forecasts;

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

The scheduling function can enable a number of useful energy services, including:

• ancillary services, such as rapid response and rapid adjustment services;

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

• response to routine and unstable demands;

• stability of renewable energy sources (for example, complementing energy generated by the wind).

An exemplary energy aggregation system 100 aggregates and controls the load presented by charging / transferring many electric vehicles 200 to provide energy services (auxiliary energy services) such as regulating and turning reserves. Therefore, it is possible to satisfy call time requirements of operators of the 404 network by adding multiple electrical resources 112. For example, twelve operating loads of 5kW each can be disabled to provide 60Rw of spin reserves for one hour. However, if each charge can be disabled for a maximum of 30 minutes and the minimum call time is two hours, charges can be disabled in series (three at a time) to provide 15kW of reserves for two hours. Naturally, more complex individual electrical resources can be merged with the energy aggregation system 100.

For a concessionaire company (or electricity distribution entity) to maximize distribution efficiency, the concessionaire company 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

I system. In order to efficiently manage and control this dynamic reactive Volt-amp (VAR) support, it must be done in a locally aware manner. In one implementation, the energy aggregation system 100 includes power factor correction circuits, placed in electric vehicles

200 with the exemplary remote IPF module 134, thus enabling such a service. Specifically, electric vehicles 200 can have capacitors (or inductors) that can be dynamically connected to the grid regardless of whether the electric vehicle 200 is charging, distributing power, or doing nothing. This service can then be sold to utility companies to support dynamic VAR at the distribution level. The power aggregation system 100 can both feel the need for VAR support in a distributed manner and for use of the remote IPF modules 134 distributed to take actions that provide VAR support without intervention from the 404 network operator.

Exemplary Remote IPF Module

Figure 8 shows the remote IPF module 134 of figures 1 and 2 in greater detail. The illustrated remote IPF module 134 is only an example configuration, for descriptive purposes. Many other arrangements of the illustrated components or even different components that make up an exemplary remote IPF module 134 are possible within the scope of the subject matter. Such an exemplary remote IPF module 134 has some hardware components and some components that can 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. Therefore, 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 of remote IPF module 134.

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

The communicative power flow controller 806 also includes Ethernet and information processing components, such as an 810 processor or microcontroller and a media access control (MAC) address associated with Ethernet 812; volatile random access memory 814, non-volatile memory 816 or data storage, an interface such as an RS-232 interface 818 or a CANbus interface 820; an Ethernet 822 physical layer interface, which allows wiring and signaling according to Ethernet standards for the physical layer through network access means on the MAC / Data Link Layer and a common addressing format. The Ethernet 822 physical layer interface provides the electrical, mechanical and procedural interface to the transmission medium - that is, in an implementation, using the Ethernet bridge through the power transmission line 120. In one variation, wireless or other communication channels with the Internet 104 are used in place of the Ethernet bridge via power transmission line 120.

The communicative net energy flow controller 806 also includes a bidirectional 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 inside or connected to an electric vehicle 200 or another electrical resource 112 to enable aggregation of electrical resources 112 introduced above (for example, via a wired or wireless communication interface)

I wire). These components listed above may vary between different implementations of the 806 communicative energy liquid flow controller, but the implementations typically include:

• a communications mechanism within the vehicle that enables communication with other vehicle components;

• a mechanism for communicating with the flow control center 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 pre-programmed or learning behaviors when electrical resource 112 is out of execution (not connected to the Internet 104, or the service is unavailable);

• store behavior profiles locally stored in cache memory for roaming connectivity (what to do when loading on a foreign system or in disconnected operation, that is, when there is no network connectivity);

• allow the user to impose the behavior of the current system; and • measure net energy flow information and store measurement data during out-of-execution operation for subsequent transaction.

Accordingly, the communicative power liquid flow controller 806 includes a central processor 810, interfaces 818 and 820 for communication within the electric vehicle 200, a power transmission line communicator, such as an Ethernet bridge over power transmission line. 120 energy for external communication to the electric vehicle

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

Exemplary IPF Module Operation

Continuing with electric vehicles 200 as representative of electric resources 112, during the 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, records itself, and waits for signals from the flow control server 106 which directs the remote IPF module 134 to adjust the net flow of energy into or out of the electric vehicle 200. These signals are communicated to the vehicle's 802 computer via the data interface, which can be any suitable interface including the RS-232 818 interface or the CANbus 820 interface. The vehicle 802 computer, following the signals received from the flow control server 106, controls the inverter / charger 804 to charge the vehicle's battery bank 202 or to offload the battery bank 202 into upload to network 114.

Periodically, remote IPF module 134 transmits information regarding energy flows to flow control server 106. If, when the electric vehicle 200 is connected to the network 114, there is no communication path to the flow control server 106 (that is, the site is not properly equipped, or there is a fault in the network), the electric vehicle 200 can follow pre-programmed behavior or off-execution operation learning, for example, stored as a set of instructions in non-volatile memory 816. In such a case, energy transactions 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 operation as a transport, the remote IPF module 134 listens passively, recording the operating data of the selected vehicle for further analysis and consumption. Remote IPF module 134 can transmit this data to flow control server 106 when a communications channel becomes available.

I

Measure the exemplary net energy flows

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

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

• track energy usage on a specific electrical resource basis;

• monitor power quality (check whether 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 energy use;

· Mobile billing (support for accurate billing when the owner of the electrical resource is not the owner of the electrical connection site 410 (that is, not the owner of the meter account). Data from the 824 net energy flow meter can be captured in the electric vehicle 200 for billing;

· Integration with an intelligent meter at the loading point (bidirectional information exchange); and • tamper resistance (for example, when the 824 liquid energy flow meter is protected within an electrical resource 112 such as an electric vehicle 200).

Mobile Resource Locator

The exemplary power aggregation system 100 also includes several techniques for determining the electrical grid location of a mobile electrical resource 112, such as a 200 plug-in electric vehicle. Electric vehicles 200 can connect to network 114 at numerous locations and the precise control and transaction of energy exchange 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 can be:

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

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

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

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

Figure 9 shows an exemplary technique for solving the physical location of the network 114 of an electrical resource 112 that is connected to the exemplary energy aggregation system 100. In an 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 that unique MAC identifier to flow control server 106, which uses the identifier to resolve the location of the electric vehicle 200.

To discern its physical location, the remote IPF module 134 may also sometimes use the MAC addresses or other unique identifiers of other equipment physically installed nearby that can communicate with the remote IPF module 134, including a 904 dealership smart meter, a TV cable box 906, an RFID-based unit 908, 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 piece, but in one implementation the flow control server 106 includes a database of 912 tracking that links MAC addresses or other identifiers to an associated physical location of the hardware. In this way, a remote IPF module 134 and flow control server 106 can 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 on power grid 114. An exemplary ID unit 910 can be connected to network 114 at or near a loading location. The operation of the ID 910 unit is as follows. A newly connected electrical resource 112 looks for locally connected resources by transmitting a Ping or message in the wireless reception area. In an implementation, the ID 910 unit responds 1002 to Ping and carries a unique identifier 1004 from the ID 910 unit back to electrical resource 112. Remote IPF module 134 of electrical resource 112 then transmits the unique identifier

1004 for the flow control server 106, which determines the location of the ID 910 unit and through the Proxy, the exact or approximate network location of the electrical resource 112, depending on the size of the capture area of ​​the ID 910 unit.

In another implementation, the newly connected electrical resource 112 searches for locally connected resources through the transmission of a Ping or message that includes the unique identifier 1006 of the electrical resource 112. In this implementation, the ID 910 unit does not need to trust or reuse the wireless connection, and does not respond back to remote IPF module 134 of electrical resource 112, but responds 1008 directly to flow control server 106 with a message containing its own unique identifier 1004 and the unique identifier 1006 of electrical resource 112 that was received in the Ping message. The central flow control server 106 then associates the unique identifier 1006 of the electrical resource 112 with a connected state ”and uses the other unique identifier 1004 of the unit ID 910 to determine or approximate the physical location of the electrical resource 112. The physical location it 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 is successful 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 method 1100 and exemplary system 1102 for determining the location of a mobile electrical resource 112 on the power network 114. In a situation where electrical resource 112 and flow control server 106 conduct communications via a scheme wireless signaling, it is still desirable to determine the location of the physical connection during periods of sequence with the 114 network.

Wireless networks (for example, GSM, 802.11, WiMax) comprise many cells or towers in which each transmits unique identifiers. Additionally, the intensity 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 the network 114, the remote IPF module 134 can acquire the unique identifiers of the available towers and relate this to the signal strength of each connection, as shown in database 1104. The remote IPF module 134 of the resource electrical 112 transmits this information to the flow control server 106, where the information is combined with inspection data, such as the database)

1106 so that a position inference mechanism 1108 can arrange in triangles or otherwise infer the physical location of the connected electric vehicle 200. In another training, the IPF 134 module can use the intensity readings to solve the resource location directly, in which case the IPF 134 module transmits the location information instead of the signal strength information.

Therefore, exemplary method 1100 includes acquiring (1110) signal strength information; communicating (1112) the signal strength information acquired to the flow control server 106; and inferin10 do (1114) physical location using stored tower location information and signals acquired from 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 on the power network 114. Using GPS a remote IPF module 134 to troubleshoot its physical location in the power grid in an inaccurate way. This GPS noisy 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) noisy position data; communicate (1208) the acquired noisy position data to the flow control server 106; and infer (1210) the location using the stored inspection information and the 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 contracts outside the system and / or offers in open markets to obtain contracts for energy services, contracts via the web server 718 and contract administrator 720. System 100 then resolves these requests for specific power requirements upon notice from the 404 network operator, and communicates those requirements to vehicle owners 408 through one of several communication techniques.

2. Distribution - The network interaction administrator 712 accepts the network control signals 714 in real time from the network operators 404 through a power distribution device, and responds to these signals 714 through the distribution of electric vehicle power services 200 connected to the 114 network.

3. Reporting - After a power distribution event is complete, a transaction administrator can report transactions for energy services stored in the 716 database. A billing administrator decides these requests in specific credit or debit billing transactions. These transactions can be communicated to a billing system of the network operator or concessionaire for accounting reconciliation. Transactions can also be used to make payments directly to 408 resource owners.

In an implementation, the vehicle-resident remote IPF 134 module may include a communications administrator to receive offers to provide energy services, show them to the user and allow the user to respond to offers. Sometimes this type of ad or contracting interaction can be performed by the electrical resource owner 408 conventionally connecting with the web server 718 of the flow control server 106.

In an exemplary model of load management or vehicle-based storage, the exemplary energy aggregation system 100 serves as an intermediary between vehicle owners 408 (individuals, fleets, etc.) and operators of the 404 network (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 directly controlling. However, by bundling many 200 electric vehicles together, managing their charging behavior, and exporting an interface of

In simple control, the energy aggregation system 100 provides services that are available to operators on the 404 network.

Likewise, vehicle owners 408 may not be interested in participating without participation being easy, and without an incentive to participate. By creating value through aggregate administration, the energy aggregation system 100 can provide incentives for homeowners in the form of payments, reduced charging costs, etc. The energy aggregation system 100 can also control the loading of the vehicle and transfer energy to the network 114 automatically and almost without interruption to the owner of the vehicle 408, thus making it a pleasant participation.

By placing remote IPF modules 134 in electric vehicles 200 that can measure power quality attributes, the power aggregation system 100 enables a massively distributed sensor network to the 114 power distribution network. The power 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 the flow control server 106, where the information is aggregated. Also, the information can be presented to the concessionaire company, or the energy aggregation system 100 can directly correct undesirable network conditions by controlling the load transfer / upload energy behavior of the vehicle of numerous electric vehicles 200, changing the load power factor, etc.

The exemplary 100 power aggregation system can also provide Uninterruptible Power Supply (UPS) or backup power for a home / work, including circuitry interconnection. In one implementation, the power aggregation system 100 allows electrical resources 112 to flow energy out of their batteries to the home (or work) to power some or all of the household charges. Certain loads can be configured as key loads to keep on during a power loss event. In such a situation, it is important to manipulate the insulation of residence 124 from the network 114. Such a system may include anti-insulating circuits that have 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 whether it is safe to supply power, safe being defined as safe for workers on the utility line as a result of the main switch in the house being in a disconnected state. If the power on the network drops, the smart switch box disconnects from the network and then contacts any electric vehicles 200 or other electrical resources 112 participating locally, and requests to start the power supply. When mains power returns, the smart switch box turns off local power resources, and then reconnects.

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

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

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

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

Without supervision of the power aggregation system 100, service thefts can occur. With automatic bill adjustment, the energy aggregation system 100 records when electric vehicles 200 charge at locations that require payment, via vehicle IDs and location IDs, and via exemplary measurement of the time energy flow noted inside / outside the

I vehicle. In these 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 410 meter account (then double billing is prevented). A billing administrator who performs automatic account adjustment can be integrated with the utility, or can 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 network 114, the state of the vehicle, and user preferences, the station can present information such as the current electricity price, the estimated recharge cost, the estimated time until recharge, the estimated payment for transferring energy to grid 114 (both total and hourly), etc. The information acquisition mechanism 414 communicates with the electric vehicle 200 and with public and / or private data networks 722 to acquire the data used in the calculation of this information.

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

· Vehicle owners can earn free electricity to charge the vehicle in return for participation in the system .;

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

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

• vehicle owners can receive a preferential rate to participate in the system.

There are also characteristics between the exemplary energy aggregation system 100 and the operators of the 404 network.

· The energy aggregation system 100 as an electrical resource aggregator can earn a management bonus (which may be some function of services provided), paid by the network operator

404.

• the energy aggregation system 100 as an electrical resource aggregator can sell in energy markets 412;

• 404 network operators can pay for the 100 power aggregation system, but operate the 100 power aggregation system themselves.

Exemplary Remote Smart Security and Insulation

The exemplary energy aggregation system 100 can include methods and components to implement safety standards and safely trigger energy discharge operations. For example, the exemplary energy aggregation system 100 can use in-vehicle line sensors as well as intelligent insulation equipment installed in particular locations. Therefore, the energy aggregation system 100 enables safe operations from the vehicle to the network. In addition, the energy aggregation system 100 enables automatic resource coordination for energy backup situations.

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

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

Figure 13 shows exemplary security measures in a

I home vehicle situation, where an electrical resource 112 is used to power a load or set of loads (as in a house). A switch box 1300 is connected to the dealer's meter 1302. When an electrical resource 112 is flowing power to the grid (or local loads), an isolation condition must be prevented for safety reasons. The electrical resource 112 must not energize a line that conventionally should be considered without power, in a power interruption, by the line workers.

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

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

Figure 15 shows, in greater detail, the smart grid circuit breaker 1304 of figures 13 and 14. In one implementation, the smart grid circuit breaker 1304 includes a processor 1502, a communicator 1504 coupled with electrical resources 112, a voltage sensor 1506 capable of detecting both the internal AC line and the utility side, a 1508 battery for operation during power failure conditions and a 1510 battery charger to maintain the 1508 battery charge level. A controlled switch or relay 1512 switches between mains power and power supplied by electrical power when signaled by the 1502 processor.

Exemplary User Experience Options

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

• the data collection can include driven distance and use of fuel, both electric and non-electric, to allow derivation and analysis of the vehicle's total efficiency (in terms of energy, expenses, environmental impact, etc.). This data is exported to flow control server 106 for storage 716, as well as to show a vehicle user interface, charging station user interface, and web / cell phone user interface.

• the smart charge takes notice of the vehicle's behavior and automatically adapts the charging timing. The owner of vehicle 408 can impose and request immediate loading if desired.

Exemplary Methods

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

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 can manage numerous intermittent connections to mobile electric vehicles, each of which can connect to the power grid at multiple locations. A remote agent in the vehicle connects each vehicle to the Internet when the vehicle connects to a power grid.

In block 1604, the electrical resources are individually signaled to supply energy to or obtain energy from the power grid.

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

In block 1702, communication is established between an electrical resource and an energy aggregation service.

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 energy to the energy network or to obtain energy from the network, that is, for storage.

In block 1710, the net flow of bidirectional energy from 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 diagram of an exemplary method of measuring bidirectional energy from an electrical resource. In the flow diagram, operations are summarized in individual blocks. The exempl25 piar 1800 method can be performed by hardware, software, or combinations of hardware, software, firmware, etc., for example, through components of the exemplary 824 energy flow measurement.

In block 1802, the transfer of energy between an electrical resource and a power network is measured in a bidirectional manner.

In block 1804, measurements are transferred to a service that adds energy based on stop in the measurements.

Figure 19 is a flow diagram of an exemplary method of determining a grid location for an electrical resource. In the flow diagram, operations are summarized in individual blocks. The exemplary method 1900 can be performed by hardware, software, or combinations of hardware, software, firmware, etc., for example, through components of the exemplary power aggregation system 100.

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

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

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

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

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

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

In block 2102, a power interruption is detected.

I

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

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

Figure 22 is a flow diagram of an exemplary method of extending a user interface for aggregating energy. In the flow diagram, operations are summarized in individual blocks. The exemplary method 2200 can be performed by hardware, software, or combinations of hardware, software, firmware, etc., for example, by components of the exemplary 100 energy aggregation system.

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

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

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

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.

In block 2306, the recurring continued service for the energy 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 attached claims is not necessarily limited to the specific features or acts described. Instead, specific features and acts are described as exemplary ways of implementing the claimed methods, devices and systems, etc.