Note: Descriptions are shown in the official language in which they were submitted.
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SCHEDULING AND CONTROL IN A POWER AGGREGATION SYSTEM FOR
DISTRIBUTED ELECTRIC RESOURCES
RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional Patent Application
No.
60/869,439 to Bridges et al., entitled, "A Distributed Energy Storage
Management
System," filed December 11, 2006 and incorporated herein by reference; U.S.
Provisional Patent Application No. 60/915,347 to Bridges et al., entitled,
"Plug-In-
Vehicle Management System," filed May 1, 2007 and incorporated herein by
reference; and U.S. Patent Application No. 11/836,749 to Pollack et al.,
entitled,
"Scheduling and Control in a Power Aggregation System for Distributed Electric
Resources," filed August 9, 2007, and incorporated herein by reference.
BACKGROUND'
[0002] Transportation systems, with their high dependence on fossil fuels, are
especially carbon-intensive. That is, physical units of work performed in the
transportation system typically discharge a significantly larger amount of CO2
into
the atmosphere than the same units of work performed electrically.
[0003] The electric power grid contains limited inherent facility for storing
electrical energy. Electricity must be generated constantly to meet uncertain
demand, which often results in over-generation (and hence wasted energy) and
sometimes results in under-generation (and hence power failures).
[0004] Distributed electric resources, en masse can, in principle, provide a
significant resource for addressing the above problems. However, current power
services infrastructure lacks provisioning and flexibility that are required
for
aggregating a large number of small-scale resources (e.g., electric vehicle
batteries)
to meet medium- and large-scale needs of power services. A single vehicle
battery
is insignificant when compared with the needs of the power grid. What is
needed is
a way to coordinate vast numbers of electric vehicle batteries, as electric
vehicles
become more popular and prevalent.
[0005] Low-level electrical and communication interfaces to enable charging
and
discharging of electric vehicles with respect to the grid are described in
U.S. Patent
No. 5,642,270 to Green et al., entitled, "Battery powered electric vehicle and
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electrical supply system," incorporated herein by reference. The Green
reference
describes a bi-directional charging and communication system for grid-
connected
electric vehicles, but does not address the information processing
requirements of
dealing with, large, mobile populations of electric vehicles, the complexities
of billing
(or compensating)~vehicle owners, nor the complexities of assembling mobile
pools
of electric vehicles into aggregate power resources robust enough to support
firm
power service contracts with grid operators.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] Fig. 1 is a diagram of an exemplary power aggregation system.
[0007] Fig. 2 is a diagram of exemplary connections between an electric
vehicle,
the power grid, and the Internet.
[0008] Fig. 3 is a block diagram of exemplary connections between an electric
resource and a flow control server of the power aggregation system.
[0009] Fig. 4 is a diagram of an exemplary layout of the power aggregation
system.
[00010] Fig. 5 is a diagram of exemplary control areas in the power
aggregation
system.
[00011] Fig. 6 is a diagram of multiple flow control centers in the power
aggregation system.
[00012] Fig. 7 is a block diagram of an exemplary flow control server.
[00013] Fig. 8 is block diagram of an exemplary remote intelligent power flow
module.
[00014] Fig. 9 is a flow diagram of an exemplary method of power aggregation.
[00015] Fig. 10 is a flow diagram of an exemplary method of communicatively
controlling an electric resource for power aggregation.
[00016] Fig. 11 is a flow diagram of an exemplary method of metering
bidirectional
power of an electric resource.
[00017] Fig. 12 is a flow diagram of an exemplary method of scheduling power
aggregation.
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DETAILED DESCRIPTION
Overview
[00018] Described herein is a power aggregation system for distributed
electric
resources, and associated methods. In one implementation, the exemplary 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 exemplary 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, 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. "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.
[00019] "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,
substations,
feeders, and safety equipment as part of either/both the transmission system
(i.e.,
bulk power) or the. distribution system (i.e. retail power). The exemplary
power
aggregation system is vertically scalable for use with 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 exemplary
system is horizontally scalable for use in providing power services to
multiple grid
areas simultaneously. -
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[00020] "Grid conditions" as used herein, means the need for more or less
power
flowing in or out of a section of the electric power grid, in a 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 and
under- or over-frequency events.
[00021] "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.
[00022] "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 vehicles,
repositories of used-but-serviceable electric vehicle batteries, fixed energy
storage,
fuel cell generators, emergency generators, controllable loads, etc.
[00023] "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.
[00024] Electric vehicle "energy storage systems" (batteries, supercapacitors,
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 umber of
vehicles
connected to the grid at night; a downswing.in the collective number of
connected
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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.
Exemplary System
[000251 Fig. 1 shows an exemplary power aggregation system 100. A flow control
center 102 is communicatively coupled with a network, such as a public/private
mix
that includes the Internet 104, and includes one or more servers 106 providing
a
centralized power aggregation service. "Internet" 104 will be used herein as
representative of many different types of communicative networks and network
mixtures. Via a network, such as the Internet 104, the flow control center 102
maintains communication 108 with operators of power grid(s), and communication
110 with remote resources, i.e., communication with peripheral electric
resources
112 ("end" or "terminal".nodes /devices of a power network) that are connected
to
the power grid 114. In one implementation, powerline communicators (PLCs),
such
as those that include or consist of Ethernet-over-powerline bridges 120 are
implemented at connection locations so that the "last mile" (in this case,
last feet-
e.g., in a residence 124) of Internet communication with remote resources is
implemented over the same wire that connects each electric resource 112 to the
power grid 114. Thus, each physical location of each electric resource 112 may
be
associated with a corresponding Ethernet-over-powerline bridge 120
(hereinafter,
"bridge") at or near the same location as the electric resource 112. Each
bridge 120
is 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
center 102 to the connection location, such as residence 124, can take many
forms,
such as cable modem, DSL, satellite, fiber, WiMax, etc. In a variation,
electric
resources 112 may connect with the Internet by a different medium than the
same
power wire that connects them to the power grid 114. For example, a given
electric
resource. 112 may have its own wireless capability to connect directly with
the
Internet 104 and thereby with the flow control center 102.
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[00026] Electric resources 112 of the exemplary power aggregation system 100
may include the batteries of electric vehicles connected to the power grid 114
at
residences 124, parking lots 126 etc.; batteries in a repository 128, fuel
cell
generators, private dams, conventional power plants, and other resources that
produce electricity and/or store electricity physically or electrically.
[00027] In one implementation, each participating electric resource 112 or
group
of local resources has a corresponding remote intelligent power flow (IPF)
module
134 (hereinafter, "remote IPF module" 134). The centralized flow control
center 102
administers the power aggregation system 100 by communicating with the remote
IPF modules 134 distributed peripherally among the electric resources 112. The
remote IPF modules 134 perform several different functions, including
providing the
flow control center 102 with the statuses of remote resources; controlling the
amount, direction, and timing of power being transferred into or out of a
remote
electric resource 112; provide metering of power being transferred into or out
of a
remote electric resource 112; providing safety measures during power transfer
and
changes of conditions in the power grid 114; logging activities; and providing
self-
contained control of power transfer and safety measures when communication
with
the flow control center 102 is interrupted. The remote IPF modules 134 will be
described in greater detail below.
[00028] Fig. 2 shows another view of exemplary electrical and communicative
connections to an electric resource 112. In this example, an electric vehicle
200
includes a battery bank 202 and an exemplary remote IPF module 134. The
electric
vehicle 200 may connect to a conventional wall receptacle (wall outlet) 204 of
a
residence 124, the wall receptacle 204 representing the peripheral edge of the
power grid 114 connected via a residential powerline 206.
[00029] In one implementation, the power cord 208 between the electric vehicle
200 and the wall outlet 204 can be composed of only conventionalwire and
insulation for conducting alternating current (AC) power to and from the
electric
vehicle 200. In Fig. 2, a Iocation-specific connection locality module 210
performs
the function of network access point-in this case, the Internet access point.
A
bridge 120 intervenes between the receptacle 204 and the network access point
so
that the power cord 208 can also carry network communications between the
electric vehicle 200 and the receptacle 204. With such a bridge 120 and
connection
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locality module 210 in place in a connection location, no other special wiring
'or
physical medium is needed to communicate with the remote IPF module 134 of the
electric vehicle 200 other than a conventional power cord 208 for providing.
residential.line current at conventional voltage. Upstream of the connection
locality
module 210, power and communication with the electric vehicle 200 are resolved
into the powerline 206 and an Internet cable 104.
[00030] Alternatively, the power cord 208 may include safety features not
found in
conventional power and extension cords. For example, an electrical plug 212 of
the
power cord 208 may include electrical and/or mechanical safeguard components
to
prevent the remote IPF module 134 from electrifying or exposing the male
conductors of the power cord 208 when the conductors are exposed to a human
user.
[00031] Fig. 3 shows another implementation of the connection locality module
210 of Fig. 2, in greater detail. In Fig. 3, an electric resource 112 has an
associated
remote IPF module 134, including a bridge 120. The power cord 208 connects the
electric resource 112 to the power grid 114 and also to the connection
locality
module 210 in order to communicate with the flow control server 106.
[00032] The connection locality module 210 includes another instance of a
bridge
120', connected to a network access point 302, which may include such
components as a router, switch, and/or modem, to establish a hardwired or
wireless
connection with, in this case, the Internet 104. In one implementation, the
power
cord 208 between the two bridges 120 and 120' is. replaced by a wireless
Internet
link, such as a wireless transceiver in the remote IPF module 134 and a
wireless
router in the connection locality module 210.
Exemplary System Layouts
[00033] Fig. 4 shows an exemplary layout 400 of the power aggregation system
100. The flow control center 102 can be connected to many different entities,
e.g.,
via the Internet 104, for communicating and receiving information. The
exemplary
layout 400 includes electric resources 112, such as plug-in electric vehicles
200,
physically connected to the grid within a single control area 402. The
electric
resources 112 become an energy resource for grid operators 404 to utilize.
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[00034] The exemplary layout 400 also includes end users 406 classified into
electric resource owners 408 and electrical connection location owners 410,
who
may or may not be one and the same. In fact, the stakeholders in an exemplary
power aggregation system 100 include the system operator at the flow control
center
102, the grid operator 404, the resource owner 408, and the owner of the
location
410 at which the electric resource 112 is connected to the power grid 114.
[00035] Electrical connection location owners 410 can include:
[00036] = Rental car lots - rental car companies often have a large portion of
their
fleet parked in the lot. They can purchase fleets of electric vehicles 200
and,
participating in a power aggregation system 100, generate revenue from idle
fleet
vehicles.
[00037] = Public parking lots - parking lot owners can participate in the
power
aggregation system 100 to generate revenue from parked electric vehicles 200.
Vehicle owners can be offered free parking, or additional incentives, in
exchange for
providing power services.
[00038] = Workplace parking - employers can participate in a power aggregation
system 100 to generate revenue from parked employee electric vehicles 200.
Employees can be offered incentives in exchange for providing power services.
[00039] = Residences - a home garage can merely be equipped with a connection
locality module 210 to enable ' the homeowner to participate in the power
aggregation system 100 and generate revenue from a parked car. Also, the
vehicle
battery 202 and associated power electronics within the vehicle can provide
local
power backup power during times of peak load or power outages.
[00040] = Residential neighborhoods - neighborhoods can participate in a power
aggregation system 100 and be equipped with power-delivery devices (deployed,
for
example, by homeowner cooperative groups) that generate revenue from parked
electric vehicles 200.
[00041] = The grid operations 116 of Fig. 4 collectively include interactions
with
energy markets 412, the interactions of grid operators 404, and the
interactions of
automated grid controllers 118 that perform automatic physical control of the
power
grid 114.
[00042] The flow control center 102 may also be coupled with information
sources
414 for input of weather reports, events, price feeds, etc, collectively
called acquired
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information. Other data sources 414 include the system stakeholders, public
databases, and historical system data, which may be used to optimize system
performance and to satisfy constraints on the exemplary power aggregation
system
100.
[00043] Thus, an exemplary power aggregation system 100 may consist of
components that:
[00044] = communicate with the electric resources 112 to gather data and
actuate
charging/discharging of the electric resources 112;
[00045] = gather real-time energy prices;
[00046] = gather real-time resource statistics;
[00047] = predict behavior of electric resources 112 (connectedness, location,
state (such as battery State-Of-Charge) at time of connect/disconnect);
[00048] = predict behavior of the power grid 114/ load;
[00049] = encrypt communications for privacy and data security;
[00050] = actuate charging of electric vehicles 200 to optimize some figure(s)
of
merit;
[00051] = offer guidelines or guarantees about load availability for various
points in
the future, etc.
[00052] 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).
[00053] Exemplary IPF systems 100 in such a layout 400 can provide many
benefits: for example, lower-cost ancillary services (i.e., power services),
fine-
grained (both temporally and spatially) control over resource scheduling,
guaranteed
reliability and service levels, increased service levels via intelligent
resource
scheduling, firming of intermittent generation sources such as wind and solar
power
generation.
[00054] The exemplary power aggregation system 100 enables a grid operator
404 to control the aggregated electric resources 112 connected to the power
grid
114. An electric resource 112 can act as a power source, load, or storage, and
the
resource 112 may exhibit combinations of these properties. Control of an
electric
resource 112 is the ability to actuate power consumption, generation, or
energy
storage from an aggregate of these electric resources 112.
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[00055] Fig. 5 shows the role of multiple control areas 402 in the exemplary
power
aggregation system 100. Each electric resource 112 can be connected to the
power
aggregation system 100 within a specific electrical control area. A single
instance of
the flow control center 102 can administer electric resources 112 from
multiple
distinct control areas 501 (e.g., control areas 502, 504, and 506). In one
implementation, this functionality is achieved by logically partitioning
resources
within the power aggregation system 100. For example, when the control areas
402
include an arbitrary number of control areas, control area "A" 502, control
area "B"
504, ..., control area "n" 506, then grid operations 116 can include
corresponding
control area operators 508, 510, ..., and 512. Further division into a control
hierarchy that includes control division groupings above and below the
illustrated
control areas 402 allows the power aggregation system 100 to scale to power
grids
= 114 of different magnitudes and/or to varying numbers of electric resources
112
connected with a power grid 114.
[00056] Fig. 6 shows an exemplary layout 600 of an exemplary power aggregation
system 100 that uses multiple centralized flow control centers 102 and 102'.
Each
flow control center 102 and 102' has its own respective end users 406 and
406'.
Control areas 402 to be administered by each specific instance of a flow
control
center 102 can be. assigned dynamically. For example, a first flow control
center
102 may administer control area A 502 and control area B 504, while a second
flow
control- center 102' administers control area n 506. Likewise, corresponding
control
area operators (508, 510, and 512) are served by the same flow control center
102
that serves their respective different control areas.
Exemplary Flow Control Server
[00057] Fig. 7 shows an exemplary server 106 of the flow control center 102.
The
illustrated implementation in Fig. 7 is only one example configuration, for
descriptive
purposes. Many other arrangements of the illustrated components or even
different
components constituting an exemplary server 106 of the flow control center 102
are
possible within the scope of the subject matter. Such an exemplary server 106
and
flow control center 102 can be executed in hardware, software, or combinations
of
hardware, software, firmware, etc.
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[00058] The exemplary flow control server 106 includes a connection manager
702 to communicate with electric resources 112, a prediction engine 704 that
may
include a learning engine 706 and a statistics engine 708, a constraint
optimizer 710,
and a grid interaction manager 712 to receive grid control signals 714. Grid
control
signals 714 may include generation control signals, such as automated
generation
control (AGC) signals. The flow control server 106 may further include a
database /
information warehouse 716, a web server 718 to present a user interface to
electric
resource owners 408, grid operators 404, and electrical connection location
owners
410; a contract manager 720 to negotiate contract terms with energy markets
412,
and an information acquisition engine 414 to track weather, relevant news
events,
etc., and download information from public and private databases 722 for
predicting
behavior of large groups of the electric resources 112, monitoring energy
prices,
negotiating contracts, etc.
Operation of an Exemplary Flow Control Server
[00059] The connection manager 702 maintains a communications channel with
each electric resource 112 that is connected to the power aggregation system
100.
That is, the connection manager 702 allows each electric resource 112 to log
on and
communicate, e:g., using Internet Protocol (IP) if the network is the Internet
104. In
other words, the electric resources 112 call home. That is, in one
implementation
they always initiate the connection with the server106. This facet enables the
exemplary IPF modules 134 to work around problems with firewalls, IP
addressing,
reliability, etc.
[00060] For example, when an electric resource 112, such as an electric
vehicle
200 plugs in at home 124, the IPF module 134 can connect to the home's router
via
the powerline connection. The router will-assign the vehicle 200 an address
(DHCP),
and the vehicle 200 can connect to the server 106 (no holes in the firewall
needed
from this direction).
[00061] If the connection is terminated for any reason (including the server
instance dies), then the IPF module 134 knows to call home again and connect
to
the next available server resource.
[00062] The grid interaction manager 712 receives and interprets signals from
the
interface of the automated grid controller 118 of a grid operator 404. In one
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implementation, the grid interaction manager 712 also generates signals to
send to
automated grid controllers 118. The scope of the signals to be sent depends on
agreements or contracts between grid operators 404 and the exemplary power
aggregation system 100. In one scenario the grid interaction manager 712 sends
information about the, availability of aggregate electric resources 112 to
receive
power from the grid 114 or supply power to the grid 114. In another variation,
a
contract may allow the grid interaction manager 712 to send control signals to
the
automated grid controller 118-to control the grid 114, subject to the built-in
constraints of the automated grid controller 118 and subject to-the scope of
control
allowed by the contract.
[00063] The database 716 can store all of the data relevant to the power
aggregation system 100 including electric resource logs, e.g., for electric
vehicles
200, electrical connection information, per-vehicle energy metering data,
resource
owner preferences, account information, etc.
[00064] The web server 718 provides a user interface to the system
stakeholders,
as described above. Such a user interface serves primarily as a mechanism for
conveying information to the users, but in some cases, the user interface
serves to
acquire data, such as preferences, from the users. In one implementation, the
web
server 718 can also initiate contact with participating electric resource
owners 408 to
advertise offers for exchanging electrical power.
[00065] The bidding/contract manager 720 interacts with the grid operators 404
and their associated energy markets 412 to determine system availability,
pricing,
service.levels, etc.
[00066] The information acquisition engine 414 communicates with public and
private databases 722, as mentioned above, to gather data that is relevant to
the
operation of the power aggregation system 100.
[00067] The prediction engine 704 may use data from the data warehouse 716 to
make predictions about electric resource behavior, such as when electric
resources
112 will connect and disconnect, global electric resource availability,
electrical
system load, real-time energy prices, etc. The predictions enable the power
aggregation system 100 to utilize more fully the electric resburces 112
connected to
the power grid 114. The learning engine 706 may track, record, and process
actual
electric resource behavior, e.g., by learning behavior of a sample or cross-
section of
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a large population of electric resources 112. The statistics engine 708 may
apply
various probabilistic techniques to the resource behavior to note trends and
make
predictions.
[00068] In one implementation, the prediction engine 704 performs predictions
via
collaborative filtering. The prediction engine 704 can also perform per-user
predictions of one or more parameters, including, for example, connect-time,
connect duration, state-of-charge at connect time, and connection location. In
order
to perform per-user prediction, the prediction engine 704 may draw upon
information,
such as historical data, connect time (day of week, week of month, month of
year,
holidays, etc.), state-of-charge at connect, connection Iocation, etc. In one
implementation, a time series prediction can be computed via a recurrent
neural network, a dynamic Bayesian network, or other directed graphical model.
[00069] In one scenario, for one user disconnected from the grid 114, the
prediction engine 704 can predict the time of the next connection, the state-
of-
charge at connection time, the location of the connection (and may assign it a
probability/likelihood). Once the resource 112 has connected, the time-of-
connection, state-of-charge at-connection, and connection location become
further
inputs to refinements of the predictions of the, connection duration. These
predictions help to guide predictions of total system availability as well as
to
determine a more accurate cost function for resource allocation.
[00070] Building a parameterized prediction model for each unique user is not
always. scalable in time or space. Therefore, in one implementation, rather
than use
one model for each user in the system 100, the prediction engine 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 assignment, the system 100 can identify features of each. user, such as
number
of unique connections/disconnections per day, typical connection time(s),
average
connection duration, average state-of-charge at connection time, etc., and can
create clusters of users in either a full feature space or in some reduced
feature
space that is computed via a dimensionality reduction algorithm such as
Principal
Components Analysis, Random Projection, etc. Once the prediction engine 704
has
assigned users to a cluster, the collective data from all of the users in that
cluster is
used to create a predictive model that will be used for the predictions of
each user in
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the cluster. In one implementation, the cluster assignment procedure is varied
to
optimize the system 100 for speed (less clusters), for accuracy (more
clusters), or
some combination of the two.
[00071] This exemplary clustering technique has multiple benefits. First, it
enables a reduced set of models, and therefore reduced model parameters, which
reduces the computation time for making predictions. It also reduces the
storage
space of the model parameters. Second, by identifying traits (or features). of
new
users to the system 100, these new users can be assigned to an existing
cluster of
users with similar traits, and the cluster model, built from;the extensive
data of the
existing users, can make more accurate predictions about the new user more
quickly because it. is leveraging the historical performance of similar users.
Of
course, over time, individual users may change their behaviors and may, be
reassigned to new clusters that fit their behavior better.
[00072] The constraint optimizer 710 combines information from the prediction
engine 704, the data warehouse 716, and the contract manager 720 to generate
resource control signals that will satisfy the system constraints. For
example, the
constraint optimizer 710 can signal an electric vehicle 200 to charge its
battery bank
202 at a certain charging rate and later to discharge the battery bank 202 for
uploading power to the power grid 114 at a certain upload rate: the power
transfer
rates and the timing schedules of the power transfers optimized to fit the
tracked
individual connect and disconnect behavior of the particular electric vehicle
200 and
also optimized to fit a daily power supply and demand "breathing cycle" of the
power
grid 114.
[00073] In one implementation, the constraint optimizer 710 plays a key role
in
converting grid control signals 714 or information sources 414 into
vehicle.control
signals, mediated by the connection manager 702. Mapping grid control signals
714
from a grid operator 404 or information sources 414 into control signals that
are sent
to each unique electrical resource 112 in the system 100 is an example of a
specific
constraint optimization problem.
[00074] Each resource 112 has associated constraints, either hard or soft.
Examples of resource constraints may include: price sensitivity of the owner,
vehicle
state-of-charge (e.g., if the vehicle 200 is fully charged, it cannot
participate in
loading the grid 114), predicted amount of time until the resource 112
disconnects
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from the system 100, owner sensitivity to revenue versus state-of-charge,
electrical
limits of the resource 114, manual charging overrides by resource owners 408,
etc.
The constraints on a particular resource 112 can be used to assign a cost for
activating each of the resource's particular actions. For example, a resource
whose
storage system 202 has little energy stored in it will have a low cost
associated with
the charging operation, but a very high cost for the generation operation. A
fully
charged resource 112 that is predicted to be available for ten hours will have
a lower
cost generation operation than a fully charged resource 112 that is predicted
to be
disconnected within the next 15 minutes, representing the negative consequence
of
delivering a less-than-full resource to its owner.
[00075] The following is one example scenario of converting one generating
signal
714 that comprises a system operating level (e.g. -10 megawatts to +10
megawatts,
where + represents load, - represents generation) to a vehicle control signal.
It is
worth noting that because the system 100 can meter the actual power flows in
each
resource 112, the actual system operating level is known at all times.
[00076] In this example, assume the initial system operating level is.0
megawatts,
no resources are active (taking or delivering power from the grid), and the
negotiated, aggregation service contract level for the next hour is +/- 5
megawatts.
[00077] 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 (load) in priority order. There is a second
list of
the resources 112 ordered by priority for discharging (generation). Each of
the
resources 112 in these lists (e.g., all resources 112 can have a position in
both lists)
have an associated cost. The priority order of the lists is directly related
to the cost
(i.e., the lists are sorted from lowest cost to highest cost). Assigning cost
values to
each resource 112 is important because it enables the comparison of two
operations
that achieve similar results with respect to system operation. For example,
adding
one unit of charging (load, taking power from the grid) to the system is
equivalent to
removing one unit of generation. To perform any operation that increases or
decreases the system output, there may be multiple action choices and in one
implementation the system 100 selects the lowest cost operation. The third
list of
resources 112 contains resources with hard constraints. For example, resources
whose owner's 408 have overridden the system 100 to force charging will be
placed
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be placed on the third list of static resources.
[00078] At time "1," the grid-operator-requested operating level changes to +2
megawatts. The system activates charging the first 'n' resources from the
list, where
'n' is the number of resources whose additive load is predicted to equal 2
megawatts.
After the resources are activated, the results of the activations are
monitored to
determine the actual result of the action. If more than 2 megawatts of load is
active,
the system will disable charging in reverse priority order. to maintain system
operation within the error tolerance specified by the contract.
[00079] From time "1" until 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 vehicles 200 that are part of the 2
megawatts
system operation may become full (state-of-charge = 100%) or may disconnect
from
the system .100. Other vehicles 200 may connect to the system 100 and demand
immediate charging. All of these actions will cause a change in the operating
level
of the power aggregation system 100. Therefore, the system 100 continuously
monitors the system operating level and activates or deactivates resources 112
to
maintain the operating level within the error tolerance specified by the
contract.
[00080] At time "2," the grid-operator-requested operating level decreases to -
1
megawatts: The system consults the lists of available resources and chooses
the
lowest cost set of resources to achieve a system operating level of -1
megawatts.
Specifically, the system moves sequentially through the priority lists,
comparing the
cost of enabling generation versus disabling charging, and activating the
lowest cost
resource at each time step. Once the operating level reaches -1 megawatts, the
system 100 continues to monitor the actual operating level, looking for
deviations
that would require the activation of an additional resource 112 to maintain
the
operating level within the error tolerance specified by the contract.
[00081] In one implementation, an exemplary costing mechanism is fed
information on the real-time grid generation mix to determine the marginal
consequences of charging or generation (vehicle 200 to grid 114) on a "carbon
footprint," the impact on fossil fuel resources and the environment in
general. The
exemplary system 100 also enables optimizing for any costmetric, or a weighted
combination of several. The system 100 can optimize figures of merit that may
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include, for example, a combination of maximizing economic value and
minimizing
environmental impact, etc.
[00082] In one implementation, the system 100 also uses cost as a temporal
variable. For example, if the system 100 schedules a discharged pack to charge
during an upcoming time window, the system 100 can predict its look-ahead cost
profile as it charges, allowing the system 100 to further optimize,
adaptively. That is,
in some circumstances the system 100 knows that it will have a high-capacity
generation resource by a certain future time.
[00083] Multiple components of the flow control server 106 constitute a
scheduling
system that has multiple functions and components:
[00084] = data collection (gathers real-time data and stores historical data);
[00085] = projections via the prediction engine 704, which inputs real-time
data,
historical data, etc.; and outputs resource availability forecasts;
[00086] = optimizations built on resource availability forecasts, constraints,
such as
command signals from grid operators 404, user preferences, weather conditions,
etc.
The optimizations can take the form of resource control plans that optimize a
desired metric.
[00087] The scheduling function can enable a number of. useful energy
services,
including:
[00088] = ancillary services, such as rapid response services and fast
regulation;
[00089] = energy to compensate for sudden, foreseeable, or unexpected grid
imbalances;
[00090] = response to routine and unstable demands;
[00091] = firming of renewable energy sources (e.g. complementing wind-
generated power).
[00092] An exemplary power aggregation system 100 aggregates and controls the
load presented by many charging/uploading electric vehicles 200 to provide
power
services (ancillary energy services) such as regulation and spinning reserves.
Thus,
it is possible to meet call time requirements of grid operators 404 by summing
multiple electric resources 112. For example, twelve operating loads of 5kW
each
can be disabled to provide 60kW of spinning reserves for one hour. However, if
each load can be disabled for at most 30 minutes and the minimum call time is
two
hours, the loads can be disabled in series (three at a time) to provide 15kW
of
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reserves for two hours. Of course, more complex interleavings of individual
electric
resources by the power aggregation system 100 are possible.
[00093] For a utility (or electrical power distribution entity) to maximize
distribution
efficiency, the utility needs to minimize reactive power flows. Typically,
there are a
number of methods used to minimize reactive power flows including switching
inductor or capacitor banks into the distributiori system to modify the power
factor in
different parts of the system. To manage and control this dynamic Volt-Amperes
Reactive (VAR) support effectively, it must be done in a location-aware
manner. In
one implementation, the power aggregation system 100 includes power-factor
correction. circuitry placed in electric vehicles 200 with the exemplary
remote IPF
module 134, thus enabling such a service. Specifically, the electric vehicles
200 can
have capacitors (or inductors) that can be dynamically connected to the grid,
independent of whether the electric vehicle 200 is charging, delivering power,
or
doing nothing. This service can then be sold to utilities for distribution
level dynamic
VAR support. The power aggregation system 100 can both sense the need for VAR
support in a distributed manner and use the distributed remote IPF modules 134
to
take actions that provide VAR support without grid operator 404 intervention.
Exemplary Remote IPF Module
[00094] Fig. 8 shows the remote IPF module 134 of Figs. 1 and 2 in greater
detail.
The illustrated remote IPF module 134 is only one example configuration, for
descriptive purposes. Many other arrangements of the illustrated components or
even different components constituting 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 be
executed in hardware, software, or combinations of hardware, software,
firmware,
etc.
[00095] The illustrated example of a remote IPF module 134 is represented by
an
implementation suited for an electric vehicle 200. Thus, some vehicle systems
800
are included as part of the exemplary remote IPF module 134 . for the sake of
description. However, in other implementations, the remote IPF module 134 may
exclude some or all of the vehicles systems 800 from being counted as
components
of the remote IPF module 134.
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[00096] The depicted vehicle systems 800 include a vehicle computer and data
interface 802, an energy storage system, such as a battery bank 202, and an
inverter / charger 804. Besides vehicle systems 800, the remote IPF module 134
also includes a communicative power flow controller 806. The communicative
power flow controller 806 in turn includes some components that interface with
AC
power from the grid 114, such as a powerline communicator, for example an
Ethernet-over-powerline bridge 120, and a current or current/voltage (power)
sensor
808, such as a current sensing transformer.
[00097] The communicative power flow controller 806 also includes Ethernet and
information processing components, such as a processor 810 or microcontroller
and
an associated Ethernet media access control (MAC) address 812; volatile random
access memory 814, nonvolatile memory 816 or data storage, an interface such
as
an RS-232 interface 818 or a CANbus interface 820; an Ethernet physical layer
interface 822, 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 interface
822
provides electrical, mechanical, and procedural interface to the transmission
medium-i.e., in one implementation, using the Ethernet=over-powerline bridge
120.
In a variation, wireless. or other communication channels with the Internet
104 are
used in place of the Ethernet-over-powerline bridge 120.
[00098] The communicative power flow controller 806 also.includes a
bidirectional
power flow meter 824 that tracks power transfer to and from each electric
resource
112, in this case the battery bank 202 of an electric vehicle 200.
[00099] The communicative power flow controller 806 operates either within, or
connected to an electric vehicle 200 or other electric resource 112 to enable
the
aggregation of electric resources 112 introduced above (e.g., via a wired or
wireless
communication interface). These above-listed components may vary among
different implementations of the communicative power flow controller 806, but
implementations typically include:
[000100] = an intra-vehicle communications mechanism that enables
communication with other vehicle components;
[000101] = a mechanism to communicate with the flow control center 102;
[000102] = a processing element;
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[000103] = a data storage element;
[000104] = a power meter; and
[000105] = optionally, a user interface.
[000106] Implementations of the communicative power flow controller 806 can
enable functionality including:
[000107] = executing pre-programmed or learned behaviors when the electric
resource 112 is offline (not connected to Internet 104, or service is
unavailable);
[000108] = 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);
[000109] = allowing the user to override current system behavior; and
[000110] = metering power-flow information and caching meter data during
offline
operation for later transaction.
[000111] Thus, the communicative power flow controller 806 includes a central
processor 810, interfaces 818 and 820 for communication within the electric
vehicle
200, a powerline communicator, such as an Ethernet-over-powerline bridge 120
for
communication external to the electric vehicle 200, and a power flow meter 824
for
measuring energy flow to and from the electric vehicle 200 via a connected AC
powerline 208.
Operation of the Exemplary Remote IPF Module
[000112] Continuing with electric vehicles 200 as representative of electric
resources 112, during periods when such an electric vehicle 200 is parked and
connected to the grid 114, the remote IPF module 134 initiates a connection to
the
flow control server 106, registers itself, and waits for signals from the flow
control
server 106. that direct the remote IPF module 134 to adjust the flow of power
into or
out of the electric vehicle 200. These signals are communicated to the vehicle
computer 802 via the data interface, which may be any suitable interface
including
the RS-232 interface 818 or the CANbus interface 820. The vehicle computer
802,
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 discharge the
battery
bank 202 in upload to the grid 114.
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[000113] Periodically, the remote IPF module 134 transmits information
regarding
energy flows to the flow control server 106. If, when the electric vehicle 200
is
connected to the grid 114, there is no communications path to the flow control
server 106 (i.e., the location is not equipped properly, or there is a network
failure),
the electric vehicle 200 can follow a preprogrammed or learned behavior of off-
line
operation, e.g., stored as a set of instructions in the nonvolatile memory
816. In
such a case, energy transactions can also be cached in nonvolatile memory 816
for
later transmission to the flow control server 106.
[000114] During periods when the electric vehicle 200 is in operation as
transportation, the remote IPF module 134 listens passively, logging select
vehicle
operation data for later analysis and consumption. The remote IPF module 134
can
transmit this data to the flow control server 106 when a communications
channel
becomes available.
Exemplary Power Flow Meter
[000115] 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 exemplary power
flow
meter 824 measures power for a given electric resource 112 across a bi-
directional
flow-e.g., power from grid 114 to electric vehicle 200 or from electric
vehicle 200 to
the grid 114. In one implementation, the remote IPF module 134 can locally
cache
readings from the power flow meter 824 to ensure accurate transactions with
the
central flow control server 106, even if the connection to the server is down
temporarily, or if the server itself is unavailable.
[000116] The exemplary power flow meter 824, in conjunction with the other
components of the remote IPF module 134 enables system-wide features in the
exemplary power aggregation system 100 that include:
[000117] = tracking energy usage on an electric resource-specific basis;
[000118] = power-quality monitoring (checking if voltage, frequency, etc.
deviate
from their nominal operating points, and if so, notifying grid operators, and
potentially modifying resource power flows to help correct the problem);
[000119] ~ vehicle-specific billing and transactions for energy usage;
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[000120] = mobile billing (support for accurate billing when the electric
resource
owner 408 is not the electrical connection location owner 410 (i.e., not the
meter
account owner). Data from the power flow meter 824 can be captured at the
electric vehicle 200 for billing;
[000121] = integration with a smart meter at the charging location (bi-
directional
information exchange); and
[000122] = tamper resistance (e.g., when the power flow meter 824 is protected
with.in an electric resource 112 such as an electric vehicle 200).
Exemplary User Experience Options
[000123] The exemplary power aggregation system 100 can enable a number of
desirable user features:
[000124] = data collection can include distance driven and both electrical and
non-
electrical fuel usage, to allow derivation and analysis of overall vehicle
efficiency (in
terms of energy, expense, environmental impact, etc.). This data is exported
to the
flow control server 106 for storage 716, as well as for display on an in-
vehicle user
interface, charging station user interface, and web/cell phone user interface.
[000125] = intelligent charging learns the vehicle behavior and: adapts the
charging
timing automatically. The vehicle owner 408 can override and request immediate
charging if desired.
Exemplary Methods
[000126] Fig. 9 shows an exemplary method 900 of power aggregation. In the
flow
diagram, the operations are summarized in individual blocks. The exemplary
method.900 may be performed by hardware, software, or combinations of
hardware,
software, firmware, etc., for example, by components of the exemplary power
aggregation system 100.
[000127] At block 902, communication is established with each of multiple
electric
resources connected to a power grid. For example, a central flow control
service
can manage numerous intermittent connections with mobile electric vehicles,
each
of which may connect to the power grid at various locations. An in-vehicle
remote
agent connects each vehicle to the Internet when the vehicle connects to the
power
grid.
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[000128] At block 904, the electric resources are individually signaled to
provide
power to or take power from the power grid.
[000129] Fig. 10 is a flow diagram of an exemplary method of communicatively
controlling an electric resource for power aggregation. In the flow diagram,
the
operations are summarized in individual blocks. The exemplary method 1000 may
be performed by hardware, software, or combinations of hardware, software,
firmware, etc., for example, by components of the exemplary intelligent power
flow
(IPF) module .134.
[000130] At block 1002, communication is established between an electric
resource
and a service for aggregating power.
[000131] At block 1004, information associated with the electric resource is
communicated to the service.
[000132] At block 1006, a control signal based in part upon the information is
received from the service.
[000133] At block 1008, the resource is controlled, e.g., to provide power to
the
power grid or to take power from the grid, i.e., for storage.
[000134] At block 1010, bidirectional power flow of the electric device is
measured,
and used as part of the information associated with the electric resource that
is
communicated to the service at block 1004.
[000135] Fig. 11 is a flow diagram of an exemplary method of metering
bidirectional
power of an electric resource. In the flow diagram, the operations are
summarized
in individual blocks. The exemplary method 1100 may be performed by hardware,
software, or combinations of hardware, software, firmware, etc., for example,
by
components of the exemplary power flow meter 824.
[000136] At block 1102, energy transfer between an electric resource and a
power
grid is measured bidirectionally.
[000137] At block 1104, the measurements are sent to a service that aggregates
power based in part on the measurements.
[000138] Fig. 12 is a flow diagram of an exemplary method of scheduling power
aggregation. In the flow diagram, the operations are summarized in individual
blocks. The exemplary method 1200 may be performed by hardware, software, or
combinations of hardware, software, firmware, etc., for example, by components
of
the exemplary flow control server 106.
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[000139] At block 1202, constraints associated with individual electric
resources are
input.
[000140] At block 1204, power aggregation is scheduled, based on the input
constraints.
Conclusion
[000141] Although exemplary 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 exemplary forms of implementing the claimed methods,
devices, systems, etc.
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