Software modeling systems for metering and translating measurements

Systems and methods are provided for collecting and aggregating a plurality of power flow measurements from a plurality of devices in a power management system. The error bounds of the aggregated power flow measurement are then determined using at least one error model. Systems and methods are also provided for inferring AC power flows from DC power flows. A device having at least one DC power flow sensor is augmented with at least one AC power flow sensor AC and DC power flows through the device are measured using the sensors over a range of operating points. An inference model of AC power flow in the device as a function of DC power flow is then built, wherein the error of the model is bounded. DC power flow through the device and in similar devices can then be then measured and used to infer AC power flow for the device.

FIELD OF THE INVENTION

The present invention relates in general to the field of electric vehicles, and in particular to novel systems and methods for communication and interaction between electric vehicles and the electrical grid.

BACKGROUND OF THE INVENTION

Low-level electrical and communication interfaces to enable charging and discharging of electric vehicles with respect to the grid is described in U.S. Pat. No. 5,642,270 to Green et al., entitled, “Battery powered electric vehicle and electrical supply system,” incorporated herein by reference. The Green reference describes a bi-directional charging and communication system for grid-connected electric vehicles.

Modern automobiles, including electric vehicles, have many electronic control units for various subsystems. While some subsystems are independent, communications among others are essential. To fill this need, controller-area network (CAN or CAN-bus) was devised as a multi-master broadcast serial bus standard for connecting electronic control units. Using a message based protocol designed specifically for automotive applications, CAN-bus is a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other within a vehicle without a host computer. The CAN-bus is used in vehicles to connect the engine control unit, transmission, airbags, antilock braking, cruise control, audio systems, windows, doors, mirror adjustment, climate control, and seat control. CAN is one of five protocols used in the (On-Board Diagnostics) OBD-II vehicle diagnostics standard.

Modern vehicles contain a variety of subsystems that may benefit from communications with various off-vehicle entities. As the smart energy marketplace evolves, multiple application-level protocols may further develop for the control of power flow for electric vehicles and within the home. For example, energy management protocols are being developed for both Zigbee and Homeplug. A vehicle manufacturer may need to support multiple physical communications mediums. For example, ZigBee is used in some installations while PLC is used in others. Considering the very long service life of items such as utility meters and automobiles, the use of multiple incompatible protocols may pose an barrier to deployment. For example, if a homeowner buys a car that utilizes one protocol and receives a utility meter that uses another protocol, it is unlikely that either device will quickly replace other device.

Significant opportunities for improvement exist with respect to metering and translating measurements for power grids and electric vehicles. What is needed are systems and methods that provide for the efficient transfer of higher levels of information dealing with mobile populations of electric vehicles, the complexities of accurately metering such large populations.

SUMMARY OF THE INVENTION

In one embodiment the invention is a method. A plurality of power flow measurements are received from each of a plurality of devices. Each device is associated with a power flow and is capable of measuring the respective device's power flow within a measurement error. The plurality of power flow measurements are aggregated, using a computing device, producing an aggregated power flow measurement. The error bounds of the aggregated power flow measurement are then determined, using the computing device, using at least one error model.

In another embodiment the invention is a system. The system comprises: a plurality of devices, each device being associated with a power flow, each of the devices being capable of measuring the respective device's power flow within a measurement error; an aggregate power measurement module comprising one or more processors programmed to execute software code retrieved from a computer readable storage medium storing software for a method. The method comprises the steps of receiving, over a network, a plurality of power flow measurements from each of the plurality of devices; aggregating the power flow measurements, producing an aggregated power flow measurement; and determining the error bounds of the aggregated power flow measurement using at least one error model.

In another embodiment, the invention is a method. A device having at least one DC power flow sensor is augmented with at least one AC power flow sensor and AC and DC power flows through the device are measured using the sensors over a range of operating points. An inference model of AC power flow in the device as a function of DC power flow is then built, wherein the error of the model is bounded. The AC power flow sensor can then be removed from the device. The DC power flow through the device is then measured and the AC power flow for the device is inferred, using the computing device, using the inference model.

In another embodiment the invention is a system. The system comprises a plurality of endpoint devices, each endpoint device having at least one sensor for measuring power flow through the respective device, wherein one or more of the at least one of sensors are DC sensors. The system further comprises an inference module comprising one or more processors programmed to execute software code retrieved from a computer readable storage medium storing software for a method comprising the steps: receiving measurements, over a network, from each of the sensors; and inferring an AC power flow of the plurality of endpoint devices using the measurements, wherein AC power flow for devices having DC sensors is inferred using an inference model developed by measuring the relationship of AC power flow to DC sensor measurements in devices similar to the respective device.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Overview

Described herein is a power aggregation system for distributed electric resources, and associated methods. In one implementation, a system communicates over the Internet and/or some other public or private networks with numerous individual electric resources connected to a power grid (hereinafter, “grid”). By communicating, the system can dynamically aggregate these electric resources to provide power services to grid operators (e.g. utilities, Independent System Operators (ISO), etc).

“Power services” as used herein, refers to energy delivery as well as other ancillary services including demand response, regulation, spinning reserves, non-spinning reserves, energy imbalance, reactive power, and similar products.

“Aggregation” as used herein refers to the ability to control power flows into and out of a set of spatially distributed electric resources with the purpose of providing a power service of larger magnitude.

“Charge Control Management” as used herein refers to enabling or performing the starting, stopping, or level-setting of a flow of power between a power grid and an electric resource.

“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 across an electric 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 one example of a power grid operator.

“Control area” as used herein, refers to a contained portion of the electrical grid with defined input and output ports. The net flow of power into this area must equal (within some error tolerance) the sum of the power consumption within the area and power outflow from the area.

“Power grid” as used herein means a power distribution system/network that connects producers of power with consumers of power. The network may include generators, transformers, interconnects, switching stations, and safety equipment as part of either/both the transmission system (i.e., bulk power) or the distribution system (i.e. retail power). The power aggregation system is vertically scalable for use within a neighborhood, a city, a sector, a control area, or (for example) one of the eight large-scale Interconnects in the North American Electric Reliability Council (NERC). Moreover, the system is horizontally scalable for use in providing power services to multiple grid areas simultaneously.

“Grid conditions” as used herein, refers to the need for more or less power flowing in or out of a section of the electric power grid, in response to one of a number of conditions, for example supply changes, demand changes, contingencies and failures, ramping events, etc. These grid conditions typically manifest themselves as power quality events such as under- or over-voltage events or 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; additionally, power quality events as used herein also includes other disturbances in the quality of the power delivered by the power grid such as sub-cycle voltage spikes and harmonics.

“Electric resource” as used herein typically refers to electrical entities that can be commanded to do some or all of these three things: take power (act as load), provide power (act as power generation or source), and store energy. Examples may include battery/charger/inverter systems for electric or hybrid-electric vehicles, repositories of used-but-serviceable electric vehicle batteries, fixed energy storage, fuel cell generators, emergency generators, controllable loads, etc.

“Electric vehicle” is used broadly herein to refer to pure electric and hybrid electric vehicles, such as plug-in hybrid electric vehicles (PHEVs), especially vehicles that have significant storage battery capacity and that connect to the power grid for recharging the battery. More specifically, electric vehicle means a vehicle that gets some or all of its energy for motion 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 thereof. An electric vehicle may or may not have the capability to provide power back to the electric grid.

Electric vehicle “energy storage systems” (batteries, super capacitors, and/or other energy storage devices) are used herein as a representative example of electric resources intermittently or permanently connected to the grid that can have dynamic input and output of power. Such batteries can function as a power source or a power load. A collection of aggregated electric vehicle batteries can become a statistically stable resource across numerous batteries, despite recognizable tidal connection trends (e.g., an increase in the total number of vehicles connected to the grid at night; a downswing in the collective number of connected batteries as the morning commute begins, etc.) Across vast numbers of electric vehicle batteries, connection trends are predictable and such batteries become a stable and reliable resource to call upon, should the grid or a part of the grid (such as a person's home in a blackout) experience a need for increased or decreased power. Data collection and storage also enable the power aggregation system to predict connection behavior on a per-user basis.

An Example of the Presently Disclosed System

FIG. 1shows a power aggregation system100. A flow control center102is communicatively coupled with a network, such as a public/private mix that includes the Internet104, and includes one or more servers106providing a centralized power aggregation service. “Internet”104will be used herein as representative of many different types of communicative networks and network mixtures (e.g., one or more wide area networks—public or private—and/or one or more local area networks). Via a network, such as the Internet104, the flow control center102maintains communication108with operators of power grid(s), and communication110with remote resources, i.e., communication with peripheral electric resources112(“end” or “terminal” nodes/devices of a power network) that are connected to the power grid114. In one implementation, power line communicators (PLCs), such as those that include or consist of Ethernet-over-power line bridges120are implemented at connection locations so that the “last mile” (in this case, last feet—e.g., in a residence124) of Internet communication with remote resources is implemented over the same wire that connects each electric resource112to the power grid114. Thus, each physical location of each electric resource112may be associated with a corresponding Ethernet-over-power line bridge120(hereinafter, “bridge”) at or near the same location as the electric resource112. Each bridge120is typically connected to an Internet access point of a location owner, as will be described in greater detail below. The communication medium from flow control center102to the connection location, such as residence124, can take many forms, such as cable modem, DSL, satellite, fiber, WiMax, etc. In a variation, electric resources112may connect with the Internet by a different medium than the same power wire that connects them to the power grid114. For example, a given electric resource112may have its own wireless capability to connect directly with the Internet104or an Internet access point and thereby with the flow control center102.

Electric resources112of the power aggregation system100may include the batteries of electric vehicles connected to the power grid114at residences124, parking lots126etc.; batteries in a repository128, 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 electric resource112or group of local resources has a corresponding remote intelligent power flow (IPF) module134(hereinafter, “remote IPF module”134). The centralized flow control center102administers the power aggregation system100by communicating with the remote IPF modules134distributed peripherally among the electric resources112. The remote IPF modules134perform several different functions, including, but not limited to, providing the flow control center102with the statuses of remote resources; controlling the amount, direction, and timing of power being transferred into or out of a remote electric resource112; providing metering of power being transferred into or out of a remote electric resource112; providing safety measures during power transfer and changes of conditions in the power grid114; logging activities; and providing self-contained control of power transfer and safety measures when communication with the flow control center102is interrupted. The remote IPF modules134will be described in greater detail below.

In another implementation, instead of having an IPF module134, each electric resource112may have a corresponding transceiver (not shown) to communicate with a local charging component (not shown). The transceiver and charging component, in combination, may communicate with flow control center102to perform some or all of the above mentioned functions of IPF module134. A transceiver and charging component are shown inFIG. 2Band are described in greater detail herein.

FIG. 2Ashows another view of electrical and communicative connections to an electric resource112. In this example, an electric vehicle200includes a battery bank202and a remote IPF module134. The electric vehicle200may connect to a conventional wall receptacle (wall outlet)204of a residence124, the wall receptacle204representing the peripheral edge of the power grid114connected via a residential powerline206.

In one implementation, the power cord208between the electric vehicle200and the wall outlet204can be composed of only conventional wire and insulation for conducting alternating current (AC) power to and from the electric vehicle200. InFIG. 2A, a location-specific connection locality module210performs the function of network access point—in this case, the Internet access point. A bridge120intervenes between the receptacle204and the network access point so that the power cord208can also carry network communications between the electric vehicle200and the receptacle204. With such a bridge120and connection locality module210in place in a connection location, no other special wiring or physical medium is needed to communicate with the remote IPF module134of the electric vehicle200other than a conventional power cord208for providing residential line current at any conventional voltage. Upstream of the connection locality module210, power and communication with the electric vehicle200are resolved into the powerline206and an Internet cable104.

Alternatively, the power cord208may include safety features not found in conventional power and extension cords. For example, an electrical plug212of the power cord208may include electrical and/or mechanical safeguard components to prevent the remote IPF module134from electrifying or exposing the male conductors of the power cord208when the conductors are exposed to a human user.

In some embodiments, a radio frequency (RF) bridge (not shown) may assist the remote IPF module134in communicating with a foreign system, such as a utility smart meter (not shown) and/or a connection locality module210. For example, the remote IPF module134may be equipped to communicate over power cord208or to engage in some form of RF communication, such as Zigbee or Bluetooth™, and the foreign system may be able to engage in a different form of RF communication. In such an implementation, the RF bridge may be equipped to communicate with both the foreign system and remote IPF module134and to translate communications from one to a form the other may understand, and to relay those messages. In various embodiments, the RF bridge may be integrated into the remote IPF module134or foreign system, or may be external to both. The communicative associations between the RF bridge and remote IPF module134and between the RF bridge and foreign system may be via wired or wireless communication.

FIG. 2Bshows a further view of electrical and communicative connections to an electric resource112. In this example, the electric vehicle200may include a transceiver212rather than a remote IPF module134. The transceiver212may be communicatively coupled to a charging component214through a connection216, and the charging component itself may be coupled to a conventional wall receptacle (wall outlet)204of a residence124and to electric vehicle200through a power cord208. The other components shown inFIG. 2Bmay have the couplings and functions discussed with regard toFIG. 2A.

In various embodiments, transceiver212and charging component214may, in combination, perform the same functions as the remote IPF module134. Transceiver212may interface with computer systems of electric vehicle200and communicate with charging component214, providing charging component214with information about electric vehicle200, such as its vehicle identifier, a location identifier, and a state of charge. In response, transceiver212may receive requests and commands which transceiver212may relay to vehicle200's computer systems.

Charging component214, being coupled to both electric vehicle200and wall outlet204, may effectuate charge control of the electric vehicle200. If the electric vehicle200is not capable of charge control management, charging component214may directly manage the charging of electric vehicle200by stopping and starting a flow of power between the electric vehicle200and a power grid114in response to commands received from a flow control server106. If, on the other hand, the electric vehicle200is capable of charge control management, charging component214may effectuate charge control by sending commands to the electric vehicle200through the transceiver212.

In some embodiments, the transceiver212may be physically coupled to the electric vehicle200through a data port, such as an OBD-II connector. In other embodiments, other couplings may be used. The connection216between transceiver212and charging component214may be a wireless signal, such as a radio frequency (RF), such as a Zigbee, or Bluetooth™ signal. And charging component214may include a receiver socket to couple with power cord208and a plug to couple with wall outlet204. In one embodiment, charging component214may be coupled to connection locality module210in either a wired or wireless fashion. For example, charging component214may have a data interface for communicating wirelessly with both the transceiver212and locality module210. In such an embodiment, the bridge120may not be required.

Further details about the transceiver212and charging component214are illustrated byFIG. 8Band described in greater detail herein.

FIG. 3shows another implementation of the connection locality module210ofFIG. 2, in greater detail. InFIG. 3, an electric resource112has an associated remote IPF module134, including a bridge120. The power cord208connects the electric resource112to the power grid114and also to the connection locality module210in order to communicate with the flow control server106.

The connection locality module210includes another instance of a bridge120, connected to a network access point302, which may include such components as a router, switch, and/or modem, to establish a hardwired or wireless connection with, in this case, the Internet104. In one implementation, the power cord208between the two bridges120and120′ is replaced by a wireless Internet link, such as a wireless transceiver in the remote IPF module134and a wireless router in the connection locality module210.

In other embodiments, a transceiver212and charging component214may be used instead of a remote IPF module134. In such an embodiment, the charging component214may include or be coupled to a bridge120, and the connection locality module210may also include a bridge120′, as shown. In yet other embodiments, not shown, charging component214and connection locality module210may communicate in a wired or wireless fashion, as mentioned previously, without bridges120and120′. The wired or wireless communication may utilize any sort of connection technology known in the art, such as Ethernet or RF communication, such as Zigbee, or Bluetooth.

System Layouts

FIG. 4shows a layout400of the power aggregation system100. The flow control center102can be connected to many different entities, e.g., via the Internet104, for communicating and receiving information. The layout400includes electric resources112, such as plug-in electric vehicles200, physically connected to the grid within a single control area402. The electric resources112become an energy resource for grid operators404to utilize.

The layout400also includes end users406classified into electric resource owners408and electrical connection location owners410, who may or may not be one and the same. In fact, the stakeholders in a power aggregation system100include the system operator at the flow control center102, the grid operator404, the resource owner408, and the owner of the location410at which the electric resource112is connected to the power grid114.

Electrical connection location owners410can include:

Rental car lots—rental car companies often have a large portion of their fleet parked in the lot. They can purchase fleets of electric vehicles200and, participating in a power aggregation system100, generate revenue from idle fleet vehicles.

Public parking lots—parking lot owners can participate in the power aggregation system100to generate revenue from parked electric vehicles200. Vehicle owners can be offered free parking, or additional incentives, in exchange for providing power services.

Workplace parking—employers can participate in a power aggregation system100to generate revenue from parked employee electric vehicles200. Employees can be offered incentives in exchange for providing power services.

Residences—a home garage can merely be equipped with a connection locality module210to enable the homeowner to participate in the power aggregation system100and generate revenue from a parked car. Also, the vehicle battery202and associated power electronics within the vehicle can provide local power backup power during times of peak load or power outages.

Residential neighborhoods—neighborhoods can participate in a power aggregation system100and be equipped with power-delivery devices (deployed, for example, by homeowner cooperative groups) that generate revenue from parked electric vehicles200.

The grid operations116ofFIG. 4collectively include interactions with energy markets412, the interactions of grid operators404, and the interactions of automated grid controllers118that perform automatic physical control of the power grid114.

The flow control center102may also be coupled with information sources414for input of weather reports, events, price feeds, etc. Other data sources414include the system stakeholders, public databases, and historical system data, which may be used to optimize system performance and to satisfy constraints on the power aggregation system100.

Thus, a power aggregation system100may consist of components that:

communicate with the electric resources112to gather data and actuate charging/discharging of the electric resources112;

predict behavior of electric resources112(connectedness, location, state (such as battery State-Of-Charge) at a given time of interest, such as a time of connect/disconnect);

predict behavior of the power grid114/load;

encrypt communications for privacy and data security;

actuate charging of electric vehicles200to optimize some figure(s) of merit;

offer guidelines or guarantees about load availability for various points in the future, etc.

These components can be running on a single computing resource (computer, etc.), or on a distributed set of resources (either physically co-located or not).

Power aggregation systems100in such a layout400can provide many benefits: for example, lower-cost ancillary services (i.e., power services), fine-grained (both temporal and spatial) control over resource scheduling, guaranteed reliability and service levels, increased service levels via intelligent resource scheduling, and/or firming of intermittent generation sources such as wind and solar power generation.

The power aggregation system100enables a grid operator404to control the aggregated electric resources112connected to the power grid114. An electric resource112can act as a power source, load, or storage, and the resource112may exhibit combinations of these properties. Control of a set of electric resources112is the ability to actuate power consumption, generation, or energy storage from an aggregate of these electric resources112.

FIG. 5shows the role of multiple control areas402in the power aggregation system100. Each electric resource112can be connected to the power aggregation system100within a specific electrical control area. A single instance of the flow control center102can administer electric resources112from multiple distinct control areas501(e.g., control areas502,504, and506). In one implementation, this functionality is achieved by logically partitioning resources within the power aggregation system100. For example, when the control areas402include an arbitrary number of control areas, control area “A”502, control area “B”504, . . . , control area “n”506, then grid operations116can include corresponding control area operators508,510, . . . , and512. Further division into a control hierarchy that includes control division groupings above and below the illustrated control areas402allows the power aggregation system100to scale to power grids114of different magnitudes and/or to varying numbers of electric resources112connected with a power grid114.

FIG. 6shows a layout600of a power aggregation system100that uses multiple centralized flow control centers102and102′ and a directory server602for determining a flow control center. Each flow control center102and102′ has its own respective end users406and406′. Control areas402to be administered by each specific instance of a flow control center102can be assigned dynamically. For example, a first flow control center102may administer control area A502and control area B504, while a second flow control center102′ administers control area n506. Likewise, corresponding control area operators (508,510, and512) are served by the same flow control center102that serves their respective different control areas.

In various embodiments, an electric resource may determine which flow control center102/102′ administers its control area502/504/506by communicating with a directory server602. The address of the directory server602may be known to electric resource112or its associated IPF module134or charging component214. Upon plugging in, the electric resource112may communicate with the directory server602, providing the directory server112with a resource identifier and/or a location identifier. Based on this information, the directory server602may respond, identifying which flow control center102/102′ to use.

In another embodiment, directory server602may be integrated with a flow control server106of a flow control center102/102′. In such an embodiment, the electric resource112may contact the server106. In response, the server106may either interact with the electric resource112itself or forward the connection to another flow control center102/102′ responsible for the location identifier provided by the electric resource112.

In some embodiments, whether integrated with a flow control server106or not, directory server602may include a publicly accessible database for mapping locations to flow control centers102/102′.

Flow Control Server

FIG. 7shows a server106of the flow control center102. The illustrated implementation inFIG. 7is only one example configuration, for descriptive purposes. Many other arrangements of the illustrated components or even different components constituting a server106of the flow control center102are possible within the scope of the subject matter. Such a server106and flow control center102can be executed in hardware, software, or combinations of hardware, software, firmware, etc.

The flow control server106includes a connection manager702to communicate with electric resources112, a prediction engine704that may include a learning engine706and a statistics engine708, a constraint optimizer710, and a grid interaction manager712to receive grid control signals714. Grid control signals714are sometimes referred to as generation control signals, such as automated generation control (AGC) signals. The flow control server106may further include a database/information warehouse716, a web server718to present a user interface to electric resource owners408, grid operators404, and electrical connection location owners410; a contract manager720to negotiate contract terms with energy markets412, and an information acquisition engine414to track weather, relevant news events, etc., and download information from public and private databases722for predicting behavior of large groups of the electric resources112, monitoring energy prices, negotiating contracts, etc.

Remote IPF Module

FIG. 8Ashows the remote IPF module134ofFIGS. 1 and 2in greater detail. The illustrated remote IPF module134is only one example configuration, for descriptive purposes. Many other arrangements of the illustrated components or even different components constituting a remote IPF module134are possible within the scope of the subject matter. Such a remote IPF module134has some hardware components and some components that can be executed in hardware, software, or combinations of hardware, software, firmware, etc. In other embodiments, executable instructions configured to perform some or all of the operations of remote IPF module134may be added to hardware of an electric resource112such as an electric vehicle that, when combined with the executable instructions, provides equivalent functionality to remote IPF module134. References to remote IPF module134as used herein include such executable instructions.

The illustrated example of a remote IPF module134is represented by an implementation suited for an electric vehicle200. Thus, some vehicle systems800are included as part of the remote IPF module134for the sake of description. However, in other implementations, the remote IPF module134may exclude some or all of the vehicles systems800from being counted as components of the remote IPF module134.

The depicted vehicle systems800include a vehicle computer and data interface802, an energy storage system, such as a battery bank202, and an inverter/charger804. Besides vehicle systems800, the remote IPF module134also includes a communicative power flow controller806. The communicative power flow controller806in turn includes some components that interface with AC power from the grid114, such as a powerline communicator, for example an Ethernet-over-powerline bridge120, and a current or current/voltage (power) sensor808, such as a current sensing transformer.

The communicative power flow controller806also includes Ethernet and information processing components, such as a processor810or microcontroller and an associated Ethernet media access control (MAC) address812; volatile random access memory814, nonvolatile memory816or data storage, an interface such as an RS-232 interface818or a CAN-bus interface820; an Ethernet physical layer interface822, which enables wiring and signaling according to Ethernet standards for the physical layer through means of network access at the MAC/Data Link Layer and a common addressing format. The Ethernet physical layer interface822provides electrical, mechanical, and procedural interface to the transmission medium—i.e., in one implementation, using the Ethernet-over-powerline bridge120. In a variation, wireless or other communication channels with the Internet104are used in place of the Ethernet-over-powerline bridge120.

The communicative power flow controller806also includes a bidirectional power flow meter824that tracks power transfer to and from each electric resource112, in this case the battery bank202of an electric vehicle200.

The communicative power flow controller806operates either within, or connected to an electric vehicle200or other electric resource112to enable the aggregation of electric resources112introduced above (e.g., via a wired or wireless communication interface). These above-listed components may vary among different implementations of the communicative power flow controller806, but implementations typically include:an intra-vehicle communications mechanism that enables communication with other vehicle components;a mechanism to communicate with the flow control center102;a processing element;a data storage element;a power meter; andoptionally, a user interface.

Implementations of the communicative power flow controller806can enable functionality including:executing pre-programmed or learned behaviors when the electric resource112is offline (not connected to Internet104, or service is unavailable);storing locally-cached behavior profiles for “roaming” connectivity (what to do when charging on a foreign system, i.e., when charging in the same utility territory on a foreign meter or in a separate utility territory, or in disconnected operation, i.e., when there is no network connectivity);allowing the user to override current system behavior; andmetering power-flow information and caching meter data during offline operation for later transaction.

Thus, the communicative power flow controller806includes a central processor810, interfaces818and820for communication within the electric vehicle200, a powerline communicator, such as an Ethernet-over-powerline bridge120for communication external to the electric vehicle200, and a power flow meter824for measuring energy flow to and from the electric vehicle200via a connected AC powerline208.

Power Flow Meter

Power is the rate of energy consumption per interval of time. Power indicates the quantity of energy transferred during a certain period of time, thus the units of power are quantities of energy per unit of time. The power flow meter824measures power for a given electric resource112across a bidirectional flow—e.g., power from grid114to electric vehicle200or from electric vehicle200to the grid114. In one implementation, the remote IPF module134can locally cache readings from the power flow meter824to ensure accurate transactions with the central flow control server106, even if the connection to the server is down temporarily, or if the server itself is unavailable.

Transceiver and Charging Component

FIG. 8Bshows the transceiver212and charging component214ofFIG. 2Bin greater detail. The illustrated transceiver212and charging component214is only one example configuration, for descriptive purposes. Many other arrangements of the illustrated components or even different components constituting the transceiver212and charging component214are possible within the scope of the subject matter. Such a transceiver212and charging component214have some hardware components and some components that can be executed in hardware, software, or combinations of hardware, software, firmware, etc.

The illustrated example of the transceiver212and charging component214is represented by an implementation suited for an electric vehicle200. Thus, some vehicle systems800are illustrated to provide context to the transceiver212and charging component214components.

The depicted vehicle systems800include a vehicle computer and data interface802, an energy storage system, such as a battery bank202, and an inverter/charger804. In some embodiments, vehicle systems800may include a data port, such as an OBD-II port, that is capable of physically coupling with the transceiver212. The transceiver212may then communicate with the vehicle computer and data interface802through the data port, receiving information from electric resource112comprised by vehicle systems800and, in some embodiments, providing commands to the vehicle computer and data interface802. In one implementation, the vehicle computer and data interface802may be capable of charge control management. In such an embodiment, the vehicle computer and data interface802may perform some or all of the charging component214operations discussed below. In other embodiments, executable instructions configured to perform some or all of the operations of the vehicle computer and data interface802may be added to hardware of an electric resource112such as an electric vehicle that, when combined with the executable instructions, provides equivalent functionality to the vehicle computer and data interface802. References to the vehicle computer and data interface802as used herein include such executable instructions.

In various embodiments, the transceiver212may have a physical form that is capable of coupling to a data port of vehicle systems800. Such a transceiver212may also include a plurality of interfaces, such as an RS-232 interface818and/or a CAN-bus interface820. In various embodiments, the RS-232 interface818or CAN-bus interface820may enable the transceiver212to communicate with the vehicle computer and data interface802through the data port. Also, the transceiver may be or comprise an additional interface (not shown) capable of engaging in wireless communication with a data interface820of the charging component214. The wireless communication may be of any form known in the art, such as radio frequency (RF) communication (e.g., Zigbee, and/or Bluetooth™ communication). In other embodiments, the transceiver may comprise a separate conductor or may be configured to utilize a powerline208to communicate with charging component214. In yet other embodiments, not shown, transceiver212may simply be a radio frequency identification (RFID) tag capable of storing minimal information about the electric resource112, such as a resource identifier, and of being read by a corresponding RFID reader of charging component214. In such other embodiments, the RFID tag may not couple with a data port or communicate with the vehicle computer and data interface802.

As shown, the charging component214may be an intelligent plug device that is physically connected to a charging medium, such as a powerline208(the charging medium coupling the charging component214to the electric resource112) and an outlet of a power grid (such as the wall outlet204shown inFIG. 2B). In other embodiments charging component214may be a charging station or some other external control. In some embodiments, the charging component214may be portable.

In various embodiments, the charging component214may include components that interface with AC power from the grid114, such as a powerline communicator, for example an Ethernet-over-powerline bridge120, and a current or current/voltage (power) sensor808, such as a current sensing transformer.

In other embodiments, the charging component214may include a further Ethernet plug or wireless interface in place of bridge120. In such an embodiment, data-over-powerline communication is not necessary, eliminating the need for a bridge120. The Ethernet plug or wireless interface may communicate with a local access point, and through that access point to flow control server106.

The charging component214may also include Ethernet and information processing components, such as a processor810or microcontroller and an associated Ethernet media access control (MAC) address812; volatile random access memory814, nonvolatile memory816or data storage, a data interface820for communicating with the transceiver212, and an Ethernet physical layer interface822, which enables wiring and signaling according to Ethernet standards for the physical layer through means of network access at the MAC/Data Link Layer and a common addressing format. The Ethernet physical layer interface822provides electrical, mechanical, and procedural interface to the transmission medium—i.e., in one implementation, using the Ethernet-over-powerline bridge120. In a variation, wireless or other communication channels with the Internet104are used in place of the Ethernet-over-powerline bridge120.

The charging component214may also include a bidirectional power flow meter824that tracks power transfer to and from each electric resource112, in this case the battery bank202of an electric vehicle200.

Further, in some embodiments, the charging component214may comprise an RFID reader to read the electric resource information from transceiver212when transceiver212is an RFID tag.

Also, in various embodiments, the charging component214may include a credit card reader to enable a user to identify the electric resource112by providing credit card information. In such an embodiment, a transceiver212may not be necessary.

Additionally, in one embodiment, the charging component214may include a user interface, such as one of the user interfaces described in greater detail below.

Implementations of the charging component214can enable functionality including:executing pre-programmed or learned behaviors when the electric resource112is offline (not connected to Internet104, or service is unavailable);storing locally-cached behavior profiles for “roaming” connectivity (what to do when charging on a foreign system or in disconnected operation, i.e., when there is no network connectivity);allowing the user to override current system behavior; andmetering power-flow information and caching meter data during offline operation for later transaction.

User Interfaces (UI)

Charging Station UI. An electrical charging station, whether free or for pay, can be installed with a user interface that presents useful information to the user. Specifically, by collecting information about the grid114, the electric resource state, and the preferences of the user, the station can present information such as the current electricity price, the estimated recharge cost, the estimated time until recharge, the estimated payment for uploading power to the grid114(either total or per hour), etc. The information acquisition engine414communicates with the electric resource112and with public and/or private data networks722to acquire the data used in calculating this information.

The types of information gathered from the electric resource112can include an electric resource identifier (resource ID) and state information like the state of charge of the electric resource112. The resource ID can be used to obtain knowledge of the electric resource type and capabilities, preferences, etc. through lookup with the flow control server106.

In various embodiments, the charging station system including the UI may also gather grid-based information, such as current and future energy costs at the charging station.

User Charge Control UI Mechanisms. In various embodiments, by default, electric resources112may receive charge control management via power aggregation system100. In some embodiments, an override control may be provided to override charge control management and charge as soon as possible. The override control may be provided, in various embodiments, as a user interface mechanism of the remote IPF module134, the charging component214, of the electric resource (for example, if electric resource is a vehicle200, the user interface control may be integrated with dash controls of the vehicle200) or even via a web page offered by flow control server106. The control can be presented, for example, as a button, a touch screen option, a web page, or some other UI mechanism. In one embodiment, the UI may be the UI illustrated byFIG. 8Cand discussed in greater detail below. In some embodiments, the override is a one-time override, only applying to a single plug-in session. Upon disconnecting and reconnecting, the user may again need to interact with the UI mechanism to override the charge control management.

In some embodiments, the user may pay more to charge with the override on than under charge control management, thus providing an incentive for the user to accept charge control management. Such a cost differential may be displayed or rendered to the user in conjunction with or on the UI mechanism. This differential can take into account time-varying pricing, such as Time of Use (TOU), Critical Peak Pricing (CPP), and Real-Time Pricing (RTP) schemes, as discussed above, as well as any other incentives, discounts, or payments that may be forgone by not accepting charge control management.

UI Mechanism for Management Preferences. In various embodiments, a user interface mechanism of the remote IPF module134, the charging component214, of the electric resource (for example, if electric resource is a vehicle200, the user interface control may be integrated with dash controls of the vehicle200) or even via a web page offered by flow control server106may enable a user to enter and/or edit management preferences to affect charge control management of the user's electric resource112. In some embodiments, the UI mechanism may allow the user to enter/edit general preferences, such as whether charge control management is enabled, whether vehicle-to-grid power flow is enabled or whether the electric resource112should only be charged with clean/green power. Also, in various embodiments, the UI mechanism may enable a user to prioritize relative desires for minimizing costs, maximizing payments (i.e., fewer charge periods for higher amounts), achieving a full state-of-charge for the electric resource112, charging as rapidly as possible, and/or charging in as environmentally-friendly a way as possible. Additionally, the UI mechanism may enable a user to provide a default schedule for when the electric resource will be used (for example, if resource112is a vehicle200, the schedule is for when the vehicle200should be ready to drive). Further, the UI mechanism may enable the user to add or select special rules, such as a rule not to charge if a price threshold is exceeded or a rule to only use charge control management if it will earn the user at least a specified threshold of output. Charge control management may then be effectuated based on any part or all of these user entered preferences.

Simple User Interface.FIG. 8Cillustrates a simple user interface (UI) which enables a user to control charging based on selecting among a limited number of high level preferences. For example, UI2300includes the categories “green”, “fast”, and “cheap” (with what is considered “green”, “fast”, and “cheap” varying from embodiment to embodiment). The categories shown in UI2300are selected only for the sake of illustration and may instead includes these and/or any other categories applicable to electric resource112charging known in the art. As shown, the UI2300may be very basic, using well known form controls such as radio buttons. In other embodiments, other graphic controls known in the art may be used. The general categories may be mapped to specific charging behaviors, such as those discussed above, by a flow control server106.

Electric Resource Communication Protocol

FIG. 9illustrates a resource communication protocol. As shown, a remote IPF module134or charging component214may be in communication with a flow control server106over the Internet104or another networking fabric or combination of networking fabrics. In various embodiments, a protocol specifying an order of messages and/or a format for messages may be used to govern the communications between the remote IPF module134or charging component214and flow control server106.

In some embodiments, the protocol may include two channels, one for messages initiated by the remote IPF module134or charging component214and for replies to those messages from the flow control server106, and another channel for messages initiated by the flow control server106and for replies to those messages from the remote IPF module134or charging component214. The channels may be asynchronous with respect to each other (that is, initiation of messages on one channel may be entirely independent of initiation of messages on the other channel). However, each channel may itself be synchronous (that is, once a message is sent on a channel, another message may not be sent until a reply to the first message is received).

As shown, the remote IPF module134or charging component214may initiate communication902with the flow control server106. In some embodiments, communication902may be initiated when, for example, an electric resource112first plugs in/connects to the power grid114. In other embodiments, communication902may be initiated at another time or times. The initial message902governed by the protocol may require, for example, one or more of an electric resource identifier, such as a MAC address, a protocol version used, and/or a resource identifier type.

Upon receipt of the initial message by the flow control server106, a connection may be established between the remote IPF module134or charging component214and flow control server106. Upon establishing a connection, the remote IPF module134or charging component214may register with flow control server106through a subsequent communication903. Communication903may include a location identifier scheme, a latitude, a longitude, a max power value that the remote IPF module134or charging component214can draw, a max power value that the remote IPF module134or charging component214can provide, a current power value, and/or a current state of charge.

After the initial message902, the protocol may require or allow messages904from the flow control server106to the remote IPF module134or charging component214or messages906from remote IPF module134or charging component214to the flow control server106. The messages904may include, for example, one or more of commands, messages, and/or updates. Such messages904may be provided at any time after the initial message902. In one embodiment, messages904may include a command setting, a power level and/or a ping to determine whether the remote IPF module134or charging component214is still connected.

The messages906may include, for example, status updates to the information provided in the registration message903. Such messages906may be provided at any time after the initial message902. In one embodiment, the messages906may be provided on a pre-determined time interval basis. In various embodiments, messages906may even be sent when the remote IPF module134or charging component214is connected, but not registered. Such messages906may include data that is stored by flow control server106for later processing. Also, in some embodiments, messages904may be provided in response to a message902or906.

Accurate System Metering Via Statistical Averaging

Metering information for an aggregate power system from the endpoints requires a meter at each endpoint. A potential cost reduction for such system is to reduce the accuracy of each individual meter. Building a model for the meter error for each type of meter in a system provides an upper bound on system accuracy. Any bias, or offset from zero, can be removed from the system level calculation by utilizing such a model. Additionally, the standard deviation of the meter error can be characterized.

In an embodiment, the meter population can be characterized as 1,000 meters in the system being uniformly distributed over the range of +/−2% with a standard deviation of 1.15. The total system error is defined by the sum of the meter error terms. This error can be estimated by computing the standard error on the sample mean of the sampling distribution using the formula stderr=stdv/sqrt(N) which provides a standard error=1.15/sqrt(1000)=0.0364 in this embodiment. To maintain a certainty of 99% on this estimate, the error percentage estimate may be multiplied by 3 because, under a normal distribution, 3 standard deviations covers roughly 99% of the samples. This results in 0.1091% for the currently presented embodiment.

The implication of this observation is that even if our meter error is uniformly distributed within +/−20% with only 1,000 meters, the disclosed system can still achieve a measurement accuracy of +/−1.1%.

Aggregate System Power Flow Inference

A power flow management system is responsible for producing an estimate of the aggregate power flow to or from a set of devices that are participating in system. By collecting individual power flow measurements from endpoints participating in the power flow management system and aggregating those measurements, the system produces an estimate for the aggregate power flow of the devices.

For the purposes of this disclosure, EVSE or should be understood to be Electric Vehicle Service Equipment, which is herein defined as a device that is a permanently installed piece of electric vehicle charging infrastructure that serves as an electric vehicle outlet. This type of equipment may include an energy meter.

For the purposes of this disclosure, aggregate power flow should be understood to be the sum of the power flows in a set of individual, distributed devices that are under the management of a power flow management system.

For the purposes of this disclosure, error bounds should be understood to be a limit on the magnitude of the error in the estimation of an aggregate power flow by a power flow management system. The error bounds on an aggregate power flow are specified with a level of confidence.

The devices that measure power flow at the endpoints of the systems (e.g. vehicles, homes, HVAC, EVSE) have non-ideal levels of measurement accuracy. That is, the power flow reported by the devices is the sum of the actual power flow and some amount measurement error (the error could be positive or negative).

By characterizing and modeling the measurement error of a class of devices, it is possible to produce error bounds for the power flow measurement of a population of devices by considering the device error model and the number of devices included in the computation.

FIG. 10illustrates a method for inferring an aggregate power flow for devices attached to a power management system. A plurality of power flow measurements are received1010from a plurality of devices, each device being associated with a power flow, each of the devices being capable of measuring the respective device's power flow within a measurement error. The plurality of power flow measurements are aggregated1020, producing an aggregated power flow measurement. In one embodiment, the power flow aggregate for a set of devices can be computed as the sum of the individual measurements that are a part of the aggregate measure.

In one embodiment, the error bounds on an aggregate power flow measurement of a set of endpoints connected to a power flow management system can be additionally be computed. Making an assumption that the power flows at each individual device are independent and identically distributed (i.i.d.), the standard error of the aggregate power flow measurement can be computed by using the statistical definitions for i.i.d. random variables. That is, divide the error bounds of each individual device by the square root of the number of devices in the set to compute the standard error. The error on the aggregate power flow measurement is then bounded by multiplying the standard error by the number of standard deviations desired. For example, if a 95% confidence is desired, then the computed standard error value is scaled by 1.96 (the matching quartile for the normal distribution).

Power flow estimates can be produced for the entire system being managed by the power flow management system as well as for sub-parts (portions) of the complete system. Because the error bounds can be produced by an error model parameterized by the number of devices included in the computation, computing the error bounds for subsets of the device population requires adjusting the input (number of included devices) to the error model.

By modeling the measurement error in the measurement device at the endpoint, the power flow management system can compute the error bounds for the aggregate estimate of the power flow measurement at any point in the power flow management system.

This approach can support any meter error model and can even combine an arbitrary number of measurement error models. If the device type that performs the metering is known on a per-endpoint basis, it is possible to compute the number of instances of each device type contributing to the aggregate estimate. These individual devices counts are then used to feed the per-device-type model of error bounds.

In one embodiment, an error model for a set of devices of an identical type can be constructed. In one example of such an error model for a set of metering device, each individual device of a particular type is guaranteed to have all measurements within +/−N % of the true value.

In one embodiment, the error bounds of a subpart of the endpoints in a power flow management system can be computed. If it is needed to compute the error bounds for a subset of the endpoints under the management of the power flow management system, the standard error is computed by dividing the error bounds of an individual device by the square root of the number of devices in the subset. The error on the aggregate power flow measurement is then bounded by multiplying the standard error by the number of standard deviations we would like to capture in our estimate.

In various other embodiments, different error models are combined. One method for combining multiple error models (i.e. multiple types of power flow measurement devices) in the same power flow management system is to compute the weighted average of the error bounds for each set of devices described by a single error model where the weight is the percentage of total power measured by that set of devices.

Inferring AC Power Flow from DC Measurements

In a power flow management system, each endpoint device is responsible for reporting its own power consumption and production. In many cases, the endpoint device has sensors for measuring the Alternating Current (AC) power flow through the device (i.e. how much power the device is taking/delivering from/to the grid). However, some devices may not have the ability to produce accurate sensor data for the AC power flow of the device.

In cases where the device has other sensors that produce some information about the state and behavior of the system, it is possible to build an inference model for the AC power flow given the information for the other sensors. For example, in a battery charger, there may not be AC metering sensors, but their may be sensors that measure the battery's DC voltage and the DC current being used to charge it. If this additional information is available, then the battery charging device can be characterized such that accurate AC metering information can be inferred from the DC sensor readings.

FIG. 11illustrates a method for inferring AC power flow in a device from DC measurements. A device having at least one DC power flow sensor is augmented1110with at least one AC power flow sensor. AC and DC power flow is then measured1120over a range of operating points. The power flows are then used to build1130, using at least one computing device, an inference model of AC power flow in the device as a function of DC power flow, wherein the error of the model is bounded. The AC power flow sensor can then be removed1140from the device. DC power flows through the device can then be measured1150and used to infer1160the AC power flow for the device using the inference model and measurements from the at least one DC power flow sensor.

By augmenting a single device with AC metering sensors, it is possible to build an accurate inference model by gathering AC and DC sensor information for the devices, developing a model that produces the inferred AC metering outputs given the DC measurements, and bounding the error of the model and inference outputs. With this model in place, it is possible to apply this model to the DC readings of other similar devices to infer the AC power flow information of these devices without augmenting them with AC metering sensors.

If a set of devices is available, augmenting each of them with AC metering sensors enables the construction of a set of inference models for AC power flow from DC sensor information. Using this set of information, it is possible to construct a single model and its error bounds when applied to any of the devices in the set.

Consider a battery charging system that contains a battery charger and a rechargeable battery. When the battery charger is plugged into the grid, it is capable of charging the battery by passing DC current into the battery. This system is able to sense the DC (direct current) battery voltage and current directly. However, this system does not require or have AC power flow sensors.

To build an inference model for the AC power flow in this battery charging system, the system can be temporarily augmented with AC power flow sensors. By taking readings from the DC voltage, DC current, and AC power flow sensors for a broad range of operating points of this system, enough data can be collected to build a model of the AC power flow as a function of the DC current and voltage.

One such model may be a linear regression that scales some constant, M, by the DC power (DC voltage times DC current) plus some fixed offset, B. Given the full set of readings for AC power, it is possible to calculate the values of M and B to produce an approximation of the AC power from the DC power.

CONCLUSION

Although systems and methods have been described in language specific to 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. Rather, the specific features and acts are disclosed as examples of implementations of the claimed methods, devices, systems, etc. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.