Note: Descriptions are shown in the official language in which they were submitted.
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ENHANCED GRID RELIABILITY THROUGH PREDICTIVE ANALYSIS AND DYNAMIC
ACTION FOR STABLE POWER DISTRIBUTION
This application claims priority based on United States Patent
Application 13/834,388 and Singapore Patent Application 201301924-5 entitled
"ENHANCED GRID RELIABILITY THROUGH PREDICTIVE ANALYSIS AND
DYNAMIC ACTION FOR STABLE POWER DISTRIBUTION" filed March 15, 2013,
BACKGROUND
1. Field of the Disclosure
100011 The present disclosure relates generally to a system and
method for
improving reliability of a power grid, and more particularly, to the
predictive analysis of
aggregated demand and supply of power, and to the dynamic reallocation of
supply to
meet predictions in demand in prevention of fault conditions.
2, Related Art
100021 Power grids are extensive and may be geographically diverse in
being spread
over large areas. In some regions, large distances between communities,
especially in
rural areas, may separate sections of a power grid. Still, the communities may
rely on
the same main grid for power, where the main grid is fueled by traditional
power sources
such as coal, other natural fuels, water and nuclear
100031 Increasingly, renewable or "green" energy sources such as
solar and wind
may be used to augment the main power gird. These power sources may provide an
auxiliary source of power to local households, buildings and communities.
Renewable
energy sources may also be connected to the main power grid to provide power
generation as an additional supply of power that may become available to other
users on
the grid, such as users in neighboring homes, units or communities.
100041 The increase numbers of renewable energy sources connected to
the grid,
however, presents new challenges because these renewable energy sources inject
variability and unpredictability into the power grid as a whole. For example,
it may be
difficult to reliably predict how much the sun will shine or the wind will
blow during a
period of time. Combined with the fact that power grids are also becoming
increasingly
more distributed and far-reaching, results in a complex network environment
that is
difficult to control with stability from week to week, day to day or even from
minute to
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minute. This is particularly the case in geographic areas that are growing
rapidly without
a commensurate growth in available power.
BRIEF SUMMARY
100051 The present disclosure generally relates to a method and a system
for monitoring
for and analyzing grid event data from power consumption and power generation
devices in
a power grid, anticipate events based on the analysis, and predict an outcome
of the future
event based on new events and past behavior of the power consumption and power
generation devices. New event data or new events may be aggregated at a
household or unit
level, up to and including at community and regional levels. Analysis of the
aggregated new
events may lead to anticipation of a higher-level new event in the grid, which
may include
brownout or blackout conditions or other fault in the power grid.
100061 Application of certain algorithms on the aggregated data may further
lead to a
prediction of an outcome of the new event at a hierarchical level of the power
grid. Based
on any prediction from the above predictions, the system may send a demand
response
command to one or more consumption or generations devices in an attempt to
resolve the
potential fault. In response to the demand response command, these devices may
adjust the
amount of power consumed or generated, or may shut down or turn on. The
command may
be sent to individual devices or may be sent to groups of devices at household
or community
level. Priorities may be considered in deciding to which devices to send a
demand response
command.
100071 Further analysis may be performed in the case of an identified
device or groups
of devices that has deficient power or energy. Additional analysis may
identify closest
power source(s) in the grid that may have excess power and actions may be
taken to
redistribute the excess power to the deficient devices or group of devices. In
so doing, the
system may analyze the paths of the arid that may extend between the excess
power sources
and the devices that need the excess power. Part of the analysis may recommend
action
and/or take actions to skirt around or go through obstacles in the power grid
so that the
excess power may be successfully transferred to the devices that have
deficient power
sources.
100081 In one example, a processor-implemented method for enhancing the
stability of the power grid may include orchestrating peer-to-peer
communication
between power consumption devices over a communication network, each of the
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power consumption devices being associated with an access device operable to
provide communication with other of the consumption devices. The method may
further monitor for new event data from the consumption devices. The new event
data may include information regarding the device location, operating
information of
the devices, and/or sensor data from the devices. The method may further
analyze
the new event data to determine power consumption behavior of the consumption
devices. The method may further anticipate an occurrence of a future event
responsive to the analysis in relation to at least one of the consumption
devices. The
method may further predict the outcome of the future event based on analysis
of the
received new event data in relation to past behavior data of the consumption
device,
and communicate the anticipated future event and the predicted outcome to one
or
more of the other consumption devices.
100091 A system for enhancing reliability of a power grid may include,
among
other structures, a processor and a network interface executable by the
processor to
monitor for new event data from power consumption devices over a network. The
new
event data may include information regarding device location, operating
information
of devices, and sensor data from the devices. The system may further include
an
estimation engine executable by the processor to analyze the new event data to
determine power consumption behavior of a consumption device. The system may
further include a predictor executable by the processor to anticipate an
occurrence of
a future event responsive to the analysis, and to predict the outcome of the
future
event based on analysis of the new event data in relation to past behavior
data of the
consumption device. The network interface may further communicate the
anticipated
future event and the predicted outcome to one or more of the other consumption
devices.
100101 Another processor-implemented method of enhancing reliability of the
power
grid may include determining power demand from buildings that include power
consumption devices connected into the power grid. The method may further
determine
an amount of power supply available from power sources that feed the
buildings. The
method may further calculate a power demand-supply gap at each of at least
some of the
buildings. The method may further forecast times when and identifying building
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locations where the power demand-supply gap will increase and when and where
the
power demand-supply gap will decrease.
100111 The method may further determine one or more paths between a first
building
where power is forecasted to be needed and power sources that have excess
power. The
method may further determine a speed at which the power demand-supply' gap is
increasing at the first building, and identify, a nearest power source located
along the one
or more paths, the nearest power source having excess power supply and being
able to
close the demand-supply power gap before the first building reaches a blackout
condition. Accordingly, the timing of the ability to deliver the excess power
may be
considered at various potential nearest power sources that have excess power
to offload.
The method may' further include sending a command to the nearest power source
to
transfer some of the excess power to the first building, to alleviate a threat
of the
blackout condition at the first building.
[0012] Other systems, methods, and features will be, or will become,
apparent to one
with skill in the art upon examination of the following figures and detailed
description.
It is intended that all such additional systems, methods, features and be
included within
this description, be within the scope of the disclosure, and be protected by
the following
claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] Figure 1 is a block diagram of a power tlow prediction and actuation
system.
100141 Figure 2 is a block diagram of a distributed information network at
a societal
or community level in which the system of Figure I may be deployed.
100151 Figure 3 is a flow chart of a method for dynamic distributed data
collaboration between consumption devices of the distributed information
network of
Figure 2.
100161 Figure 4 is a flow chart of a method of dynamic aggregation and
disaggregation of data from consumption devices in a distributed data
collaboration
within the network of Figure 2.
100171 Figure 5 is a flow chart and associated algorithm of a method for
dynamic
aggregation and disaggregation of consumption data at device, household and
societal
levels for power distribution estimations.
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100181 Figures 6A, 6B and 6C are block diagrams ofexamples of dynamic
distributed estimation with distributed data collaboration for power flow
predictions at,
respectively, device, household and societal levels.
100191 Figure 7 is a flow chart of a method of estimation to determine
power usage
choices selectable by users for energy consuming devices at household and
societal
scale.
100201 Figure 8 is a flow chart of a method of estimating aggregated
equilibrium of
demand, load, capacity and reserve at household, societal and macro levels.
100211 Figure 9 is a flow chart of a method of actuation for location-based
search-
and-feed analysis to determine likely demand-capacity gap and availability of
load and
reserve power sources.
100221 Figure 10 is an abbreviated power grid diagram of control and data
processing in relation to the search-and feed process of Figure 9.
100231 Figure II is a flow chart of a method of predicting likely demand-
capacity
gap and available load, capacity, reserve for optimal power flow control.
100241 Figure 12 is an abbreviated power arid diagram exhibiting power flow
predictive analysis between sub-areas of a power grid.
100251 Figure 13 is a screen shot of an example application executable by
an access
device such as shown in Figure I.
100261 Figure 14 is a screen shot of an example set of energy usage options
as related
to an identified access device that are selectable by a user.
100271 Figure 15 is a screen shot of an example chart for tracking source
of power
consumption and associated savings due to use of societal power.
100281 Figure 16 is a screen shot of an example geographic-based power flow
map
with focus on data from locations of interest.
100291 Figure 17 is an example screen shot for the distributed information
network
as shown in Figure 2, in which a device may annotate power data.
100301 Figure 18 is a screen shot of an exemplary portion of a location in
a power
grid, to illustrate optimal flow control mechanism to meet a power gap between
demand-
capacity forecasts.
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DETAILED DESCRIPTION
100311 By way of overview, the example embodiments described below relate
to a
system and methods for reliable and stable distribution of power, using
renewable energy
generated by ordinary households, communities and/or operators other than
power
companies. Orchestrating information and power flow for such stability may use
systemic forecasting, simulating and optimizing power flow control methods,
design and
processing techniques embedded in devices located in a distributed complex
grid
network environment, with or without aggregated centralized control. The
orchestrating
may use atomic-level data collaboration to determine causes of location-based
demand
surge, and to optimize search-and-feed of power to loads and management of
capacity
and reserve power sources, such as renewable energy generation sources.
Furthermore,
dynamic optimal power flow control may manage uncertainty in real time with
real-time
data at all levels, e.g., from device to units or households to communities,
and the like.
100321 According to an embodiment, a power flow prediction and actuation
system
may be operable to estimate a location-based device-specific state based on
location-
based conditions, activities and usage through use of multiple sources of
distributed data.
The system may be further operate to apply advancing analyses techniques in
real-time
to adaptively influence power flow decisions at device, household and societal
levels.
Entities may respond to actuation and targeting mechanisms with dynamic
distributed
data at a location and at a moment in time through collaboration that is
relevant to
individual devices.
100331 Analytical prediction for each device may be aggregated to form a
collection
of dynamic, predictive decisions relevant to a household, and the collection
of such
decisions may be used to anticipate events at a societal level, and to predict
the outcome
of those events. The system may. then load balance across at least portions of
the power
grid to reduce the likelihood that anticipated increases in demand might
result in
brownouts of blackouts. A brownout is a period of reduced power supply or
quality of
the supply.
100341 For simplicity and illustrative purposes, the principles of the
disclosed
embodiments are described by referring to examples thereof. In the following
description, numerous specific details are set forth in order to provide a
thorough
understanding of the embodiments. The embodiments, however, may be practiced
without limitation to these specific details. In some instances, well known
methods and
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=
structures have not been described in detail so as not to unnecessarily
obscure the
description of the embodiments. Furthermore, different embodiments are
described
below, and may be used or performed together in different combinations.
100351 Figure 1 is a block diagram of a power flow prediction and
actuation system
l00. The system 100 may be configured to gather data from multiple sources
including
an access device 101, also referred to as a household portal 101, power
consumption
devices 102 including but not limited to electric vehicles (EV), plug-in
hybrid electric
vehicles (PHEV) and EV/PHEV charge stations and other external data sources
122.
100361 The multiple data sources may be used to gather location, power
consumption
data, and actuation control data of devices and send the information to the
system 100.
The household portal and access devices may include switches, gateways, hubs,
routers
and the like adapted to communicate via a network protocol, to intelligently
store and
route data based on subscriber-based messaging or other protocols.
100371 The multiple data sources may include the household portal 101 and
power
consumption devices 102, which may communicate with the system 100 over a
network
120 using any communication platforms and technologies suitable for
transporting data,
such as behavior data, data, geographic location data. Examples of networks
may
include wireless networks, mobile device networks (e.g., cellular networks),
closed
media networks, subscriber television networks, cable networks, satellite
networks, the
Internet, intranets, local area networks, public networks, private networks,
optical fiber
networks, broadband networks, narrowband networks, voice communications
networks
and any other networks capable of carrying data.
100381 Data may be transmitted via data transmission protocols including,
by way of
non-limiting example, Transmission Control Protocol ("TCP"), Internet Protocol
("IP"),
File Transfer Protocol ("FTP"), Telnet, Hypertext Transfer Protocol ("HTTP"),
Hypertext Transfer Protocol Secure ("HTTPS"), Session Initiation Protocol
("SIP"),
Simple Object Access Protocol ("SOAP"), Extensible Mark-up Language ("XML")
and
variations thereof, Simple Mail Transfer Protocol ("SMTP"), Real-Time
Transport
Protocol ("RIP"), Device Datagram Protocol ("U DP"), Global System for Mobile
Communications ("GSM") technologies, Code Division Multiple Access ("CDMA")
technologies, Time Division Multiple Access ("TDMA") technologies, Short
Message
Service ("SMS"), Multimedia Message Service ("MMS"), radio frequency ("RF")
signaling technologies, signaling system seven ("SS7") technologies, Ethernet,
in-band
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.=
and out-of-band signaling technologies, and other suitable networks and
protocol
technologies.
100391 'Me power consumption devices 102 may be associated with or
include one
or more access devices 101. An access device may include a sensor, meter,
processing
capability, electronic storage and memory, and other computing components. An
access
device may subscribe to one or more services (e.g., a wireless telephone or
messaging
service) provided over the network 120. Data provided from an access device or
another
source to the system 100 may be tagged to identify the power consumption
device, the
access device or other source of the data.
100401 An access device 101 may include any device configured to perform
one or
more of the access device processes described herein, including communicating
with the
system 100. An access device may include a wireless computing device, a
wireless
communication device (e.g., a mobile phone), a portable computing device
(e.g., a
laptop), a portable communication device, a personal digital assistant, a
network
connection device, a data recording device (e.g., a camera, audio recorder,
video
camera), a vehicular computing and/or communication device, and any other
device
configured to perform one or more of the access device processes described
herein.
100411 The power consumption device 102 may include any devices that
consumes
electricity and may be used for household, such as household appliances,
electric meters,
cable, switch, power quality measures, transmitters, distributors,
substations, sensors
detecting weather, sensors detecting current time, sensors detecting arrival
and departure
time, location sensors, etc.
100421 The system 100 may further include data storage 108 that stores
data used by
the system 100 to make decisions about power consumption and access devices
("devices") and aggregate household and societal-scale behavior. The data
storage 108,
for example, may store event data about current activity of a device,
historical data that
was used for previous decision making, data provided to or generated by
devices and
other information that may impact decision making, such as current weather,
planned
outages, etc.
100431 The data storage 108 may include a distributed database system or
other type
of storage system. The data storage 108 may include one or more data storage
media,
devices, or configurations and may employ any type, form, and combination of
storage
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media. The data storage 108 may also include one or more data storage for a
job tracker
109, a name node 110 and a task tracker Ill.
100441 The job tracker 109
may monitor, assign and retain processing data jobs
on behalf of the device. The data storage for name code 110 may monitor,
assign and
retain name codes of processing data on behalf of the device. The data storage
for the
task tracker III may monitor, assign and retain processing data tasks on
behalf of the
device. For example, the data storage may include a hard drive, network drive,
flash
drive, magnetic disc, optical disc, random access memory ("RAM"), dynamic RAM
("DRAM"), other non-volatile and/or volatile storage unit, or a combination
thereof.
Data may be temporarily and/or permanently stored in the data storage 108. The
components of the system 100 may include software, hardware or a combination
of
hardware and software. The components may include machine-readable
instructions
stored on a computer readable medium and he executable by a processor or other
processing circuitry to perform the functions of the system 100.
100451 The system 100 may further include a locator 112 that determines the
location of devices from sensor data. For example, sensors of the access
devices 101
may provide the geographic locations of corresponding consumption devices 102.
The
sensors may operate on location technology, such as Geographic Information
System
(GIS) or Global Positioning System ("UPS") technologies, to determine the
geographic
location of the devices 102 according to determined coordinates. Other
suitable
technologies may he used, including principles of trilateration to evaluate
radio
frequency signals received by the consumption device 102 (e.g., RE signals in
a wireless
phone network) and to estimate the geographic location of the device 102. The
geographic location data from the device 102 or sensors may be sent to the
system 100
and stored in the data storage 122. The locator 112 may determine the
geographic
location of devices from this information.
100461 The system 100 may
be realized in hardware, software, or a combination of
hardware and software. The system may be realized in a centralized fashion in
at least
one computer system or in a distributed fashion where different elements are
spread
across several interconnected computer systems. Any kind of system or other
apparatus adapted for carrying out the methods described herein is suited. A
typical
combination of hardware and software may be a general-purpose computer system
with a
computer program that, when being loaded and executed, controls the computer
system
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such that it carries out the methods described herein. Such a programmed
computer may
be considered a special-purpose computer.
100471 The method and system may also be embedded in a computer program
product, which includes all the features enabling the implementation of the
operations
described herein and which, when loaded in a computer system, is able to carry
out these
operations. Computer program in the present context means any expression, in
any
language, code or notation, of a set of instructions intended to cause a
system having an
information processing capability to perform a particular function, either
directly or after
either or both of the following: a) conversion to another language, code or
notation; b)
reproduction in a different material form.
100481 The system 100 may further include an actuator controller 118 which
may
create and receive data, including data that has been created or received
using an access
device. For example, the actuator controller 118 may receive data from the
device 102,
such as enabled device choices (e.g. "insufficient load of voltage available"
signals) and
organize the data for storage in the data storage 108. The actuator controller
118 may
provide one of more functions, including but not limited to indexing,
directing,
processing, editing, rating, labeling, commenting, blocking, reporting, and
categorizing
data. The actuator controller may also determine a time of search,
availability and
updates at a consumption device in real-time. The actuator controller may also
notify
access devices of their actions and updates.
100491 The actuator controller 118 may further trigger a search for
consumption data
from power consumption devices I 02, and selectively analyze the data based on
geographic locations of the consumption devices. Once triggered, for example,
the
system 100 may monitor for when a power consumption device enters within a
predetermined level of electricity consumption and a determined target level
of energy
capacity. The monitoring may be performed within a predefined geographic
proximity
of a geographic location associated with the consumption device from where the
power
consumption data originated.
100501 In response to detecting certain events within the power consumption
data,
the system 100 may initiate a search for electricity load, capacity and
reserve information
(-supply information") within a predefined geographic proximity between an
origin of
power capacity and a target location that needs additional power supplied to
avoid a
brownout or blackout. For example, the target location may be the location of
the power
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consumption device or a group of power consumption devices. The search may
extend
to specified locations between the origin and target locations. The system 100
may
search for information on the consumption data that is accessible to an access
device
within the predefined geographic proximity, and the consumption device may
utilize the
access device to request and receive the consumption data from the system 100.
100511 The actuator controller 118 may also store consumption data received
from
the consumption device 102, and selectively distribute (or publish) the data
to other
access devices 101 based on geographic locations of corresponding consumption
devices. For example, when an access device enters within a predefined
proximity of a
geographic location associated with particular consumption data, the system
100 may
make the consumption data accessible to the access device. The system 100 may
send a
notification that the data is accessible to the access device once within the
predefined
proximity. The access device may, in turn and based on communication with
identified
consumption devices, send the consumption data (or other grid information) to
those
consumption devices that request the information. The power consumption
devices, in
some cases, may subscribe to periodic updates via messaging from access
devices in
closest proximity to the power consumption devices. The predefined proximity
may
change over time or be redefined by the system 100 or a user.
100521 In this way, consumption devices 102 working in concert with access
devices
101 may create and receive consumption data based on current consumption,
target
consumption and/or a transition state indicative of dynamic energy use. Target
consumption may refer to anticipated or predicted levels of consumption or a
desired
level of consumption under ideal (or near ideal) power supply conditions. The
transition
state may include a stationary state or transition state updates from a
current
consumption to target consumption. The consumption data may therefore include
information related to real-time consumption or current device activity, data
associated
with target consumption and with transition between consumption levels.
100531 The consumption data and related information may be based on
forecasts and
forecast updates, which may be provided in real-time, with relation to current
and future
activities and device trajectories. Data may be communicated for current
communications and communication threads in devices in real-time. Data may be
distributed via multicast or unicast techniques. The availability of data may
be
selectively notified to consumption devices based on the consumptions,
locations and
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settings validated with an account of respective consumption devices.
Consumption and
access devices may therefore share data with one another in connection with
geographic
locations.
100541 The actuator controller 118 may provide tools for indexing
consumption and
related information on behalf of the consumption and access devices. The
actuator
controller may also facilitate inter-communication of the consumption and
access
devices. For example, a device that has been authenticated to publish data may
index the
data such as by editing the data, rating the data, or publishing a comment
about the data
to the system 100. The device that published the data may access the index and
respond
to the device that provided the index. Such communications between the devices
may be
processed as a communication-thread to which the device involved may be
granted
access. Indices may be updated and distributed in real-time. The index may
include
energy reserve updates such as reserve is empty, and updates related to load
and capacity
information and the like.
100551 The system 100 may support a wide variety of applications and uses.
In one
example, a power consumption device (e.g., a household appliance) may utilize
an access
device to record energy consumption of that appliance in a household. The
access device
may be configured to detect the consumption at which the data was created,
associate the
data with the consumption, and post the data and location. This may be
referred to as
publishing location-based device consumption data.
100561 Furthermore, another power consumption device may he associated with
another access device within a predefined level of energy consumption
associated with
the published data. In such a case, the system 100 may send a notification of
the
accessible data to the access device, in response to which the consumption
device may
respond to controls sent by the access device to turn up, turn down, hold or
switch-off
itself off, for example. Furthermore, the consumption device may be able to
view the
data or make the data viewable to a user.
100571 As another example, a consumption device may post usage data to the
system
100 and subsequently use the published data as a consumption log. For example,
the
consumption device may access published data based on different consumption
associated with the data and/or a period of time corresponding to power usage
associated
with the data. The published data may be presented in the form of a
consumption log or
in-take.
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100581 As another example, a consumption device may be used to a particular
level
of consumption and gain access to published data associated with the
consumption. The
consumption device may utilize the published data to plan and/or improve
consumption
activities. For example, published data may include recommendations or
suggestions as
to how to save energy with the consumption device. Such publishing may be
provided
by other users on the same or similar device and may be based on their
experiences.
100591 In yet another example, a data instance may be used to distribute
local
information. For instance, an organization may provide a data instance on the
device for
informational purposes, including information about voltage fluctuations, plan
outages,
power quality fluctuations, pattern of use, maintenance conditions, risk
conditions and
the like. The consumption device 102 that gains access to the published
information may
index and/or respond to the information as described above. For example, a
consumption device viewing published data descriptive of consumption increase
timeframes may notify the organization that published the data about current
site
conditions (e.g., the consumption is higher or load is lower than a certain
threshold).
100601 The system 100 may further include an algorithm engine 114 that may
process, add, modify and apply data analyses models by applying mathematical
techniques, alone or in combination with other techniques. The mathematical
techniques
may include, just by way of example, semi-supervised and unsupervised machine
learning, stochastic gradient learning, queuing model, computational game
theory and
quantum statistical physics algorithms. The mathematical techniques may be
executed
on data received in real time from an access device, for example, by execution
of the
locator 112 and actuator controller 118.
(00611 For example, algorithm engine 114 may apply a statistical model
based on,
and to organize, the data for storage in the data storage 108. The algorithm
engine 114
may provide data representative of the consumption data and associated data
(e.g., geo-
location data and/or other tagged data) to the system 100. The provided data,
including
consumption data, associated geographic location data, and any other data used
for
estimating, actuating and forecasting a list of power-consumption choices for
each
consumption device may be provided to the system 100. The algorithm engine 114
may
prompt the consumption device for approval or confirmation before data is
provided to
the system 100. Alternatively, an access device may automatically provide the
data to
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the system 100 once location data has been generated and associated with the
consumption data.
100621 The system 100 may further include an information management
framework
119. The information management framework 119 may process, transmit and
receive
data over the network 120, including receiving data representative of
consumption data
and associated additional data, e.g., location data, from consumption devices.
The
information management framework may also provide data representative of
consumption data to consumption devices 102 by way or the network. The
information
management framework 119 may include and/or support any suitable distributed
data
platforms and technology for communicating with and transporting data t and
from
consumption devices 102 over a network in unicast and multi-cast formation.
The
information management framework 119 may be configured to support a variety of
distributed data platforms, protocols, and formats such that the system 100
may receive
data from distributed consumption sources and send data by way of a variety of
platforms (e.g., a mobile telephone service platform, a web-based platform, a
subscriber
platforms and the like).
100631 The system 100 may further include a predictor 117 for making
predictions.
The predictor 117 may generate predictions for determining the current and
next activity
of a consumption device and provide continuous updates between the current and
next
activities. For example, the system 100 may determine current energy
consumption,
load, capacity and reserve levels of a power consumption device. The system
100 may
further determine the current time at the power consumption device based on
sensor data
and other data received from the data sources.
100641 In one example, the system 100 may compare with historical observed
data,
including power generation signatures or patterns stored in the data storage
108, to
predict that a consumption device is executing a specific activity (e.g.,
heating water or
toasting). The system 100 may make additional predictions about the future
activity of
the consumption device, such as the device 102 is likely to he used at certain
time of the
day and for a certain duration of time. The predictor 117 may operate in
conjunction
with an estimation engine 115 (or estimator) and an algorithms engine 114 for
making
the predictions. Examples of different functions and technologies for the
predictor 117
are described below. The predictor 117 may execute a function or logic for
making a
forecast based on current data (e.g., event data) and/or historical data, and
provide
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continuous updates in real-time data. The predictions and/or updates may be
provided to
the consumption and/or access devices that may further communicate the
predictions
and/or updates to other consumption and/or access devices in networked
communication
with the consumption and/or access devices.
100651 The system 100 may include an estimation engine 115 (or estimator)
for
estimating the current and future demand, load, capacity, reserve, condition,
life cycle,
and risk of failures and exposure of a device. The estimation engine 115 may
use
consumption data and associated information, such as the geographic location,
time, or
other measured metrics, to make the estimations about the activities of the
consumption
device.
100661 The estimation engine 115 may use a number of algorithms or
functions,
alone or in combination, including for example: Markov decision functions
(e.g., Markov
Chain Monte Carlo (MCMC)) executed on event data, "partial memory-based"
Markov
functions executed on historical data, sheaf¨stack descent (SSD) functions to
estimate
aggregated functions and stochastic gradient descent (SGD) algorithms to
estimate
optimization of objective functions. The algorithms or functions may further
include
stochastic gradient boosting (SGB) functions to estimate decision trees and
ranking,
optimal control estimates (OCE) to estimate demand, capacity, reserve, pricing
and
metrics, and optimal power flow control (OPEC) to estimate to load and reserve
balancing.
100671 The algorithms and functions may further include Newton Raphson
Method
(NRM) to balance load and reserve in overlapping areas, a Bayesian Belief
Network
(BBN) to estimate current activity or make other estimations about a
consumption
device. For example, the estimation engine 115 may use event data, such as
location-
based dynamic behavioral data, from the data sources to predict device usage,
recognize
location-specific target activities and to respond with data and data updates
within an
ultra-short duration, such as real-time or near real-time. The estimation
engine 115 may
make these estimations even when geographic and temporal behavioral
information is
only partially available, which may include instances when information from
some of the
data sources may be missing.
100681 The system 100 may further include an actuator 116 to trigger data
execution.
The actuator 116 may include a data actuator with a controlling mechanism to
search a
location where the gap between demand and load, capacity and reserve (-the
demand-
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capacity gap") is likely to occur in a near-future time horizon. The actuator
116 may
find available load, capacity and reserve in a particular location and
amechanism by
which a data agent acts upon an environment. The data agent can be either an
intelligence agent or any other autonomous system to perform search. The
actuator 117
may further perform reads and writes, rotate, edit and may update data
received from
consumption devices on demand. The actuator 117 may send triggers to the
device 102
to perform their tasks, and act upon any changes in the environment to
calculate the
appropriate response. The actuator 117 may be used for various forms of
measurements,
to give the warnings about safety or malfunctions, and to provide real time
information
of the task being performed by a consumption or access device.
100691 The actuator 116 may further perform simulation to study position
and
orientation of a device 102. The actuator 116 may use, among other techniques,
search-
and-feed ("SNF"), biological-microhabitat ("BM") predation strategy for
evolutionary
biology ("PSEB"), queue theory ("QT"), optimal foraging technique ("OFT") and
fractal
design ("FD") methods to determine shortest and most optimal path for search
in
dynamic conditions of the demand-capacity gap, and to provide a dynamic
optimal
search result on load, capacity and reserve in neighborhood or close proximity
areas of a
power grid.
100701 The actuator 116 may develop semi-supervised, unsupervised and
autonomous models based on the data received from the device 102, to determine
the
shortest search path. The actuator 116 may develop dynamic predictive models
and data
values with dynamic distributed data for predictor 117. This method may be
particularly
advantageous over traditional search methods particularly when forecasting
spatial-
temporal energy demand, behavior, load, capacity and reserve information at a
societal
level is more precise in highly dynamic and uncertain conditions where data
may be
dynamic and more frequent at sub-second time horizon.
100711 The predictor 117 may use the estimation engine 115 and the actuator
116 to
predict power consumption choices, also referred to as target choices or a
choice set, for
a consumption device. For example, the predictor 117 may determine that a
consumption device is likely to face unplanned outage, as estimated by the
estimated
module 115 and predict that the device has a risk of failure at a certain
time. The
predictor 117 may use a number of algorithms of functions, alone or in
combination, to
make dynamic predictions and to provide an optimal response as a feedback-loop
for
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each consumption device modeled as a quantum candidate. The algorithms or
functions
may include, for example: Heisenberg uncertainty principles ("HUP"), M-Coupled
Non-
linear Schrodinger equation ("MCNSE"), combinatorial optimization ("CO")
support
vector machines ("S VM-), evolutionary game ("EG"), quantum statistical
mechanics
("QSM") and stochastic statistical processes ("SSP").
100721 The predictor 117 may further develop statistical models to
calibrate use,
queue, duration of use, risk of outage and post-outage stability conditions.
The predictor
117 may further predict location-based target activities (e.g., EV/PHEV
charging) and
power consumption choices with a goal of dynamically and optimally sending
additional
data to consumption and/or generation devices. The data may include, but not
limited to,
messages, audio-video content, photos and the like. For example, based on
prediction of
the target activity, the system is operable to send the data dynamically and
optimally in
the form of message to EV/PHEV charge stations. Such a message may be that
"this
station will close for 15 minutes at 7:30PN/1" or an audio-video data to an EV
car
including the nearest location of an EV / PHEV charge station. The data may
further
include a picture sent to a distributed generator regarding available energy
reserve, and
other capacity information and data.
100731 The predictor 117 may use Heisenberg uncertainty principles and
model each
consumption device as a quantum particle, to predict activities of a society
of deµ ices in
household. Based on the predictions for the society, an optimal set of power
consumption choices may be determined for a consumption device or for a
service
associated with the power consumption device. For example, if the predicted
activities
for a society indicate that a large number of devices are in use, the
predictor 117 may
modify the target use time for the consumption device, to eliminate the likely
outages or
likely failure of the consumption device. The predictive analysis may also
apply to
preventing likely outages or failure of a group of consumption devices. A
power service
operation authority may use predictive information (e.g., events and
anticipated
outcomes to those events) to balance the electricity load locally as well as
at a societal
level. Also, 1-IUP may be used to estimate the state of a consumption device,
which may
include a condition related to an activity, e.g., a state of charge needed to
provide an
output for a motor.
100741 The predictor 117 may use Schrodinger Equation ("MCNSE") techniques
to
systemically and dynamically predict both time-dependent and time -independent
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behavioral changes attached to a series of stationary states as each
stationary state is
classified and understood. The behavioral information may be restricted to
smaller sets,
and since behaviors do not have exactly pre-determined properties (e.g.,
impact of
weather on use of device) and when they are measured, results are combined
with
stochastic and optimal control methods.
100751 Using the H UP for position and momentum, the products of
uncertainty in
position and momentum allow analytics continuation (a technique to extend the
domain
to infinite series representation) and germ (a mathematical notion of an
object in/on a
topological space captures the local properties of the object). This
combination of
methods may be particularly advantageous over traditional discrete data
aggregation.
The advantage may derive from forecasting spatial-temporal behavioral
information as a
formation of a community: devices in the community are continuously used in
households that are continuously exercising behavior that is aggregated at a
societal level
in a dynamic environment, even when only partial information is made
available..
100761 The predictor 117 may also execute combinatorial optimization
("CO"). The
combinatorial optimization may use the predictions generated by the system 100
to
finding "largest," "smallest," "optimal" and "Satisfactory" use of energy at
device,
household and societal levels for deciding when certain criteria may be met.
The
predictor may further construct and analyze objects meeting the criteria such
as by
selecting a next activity choice option that is modified based on the
predictions.
Combinatorial optimization combined with quantum statistical mechanics ("QSM")
allows to dynamically- create new methods for analyzing decision trees for the
predictor
117, refine practical search algorithms for the actuator 116 and to determine
formulas
and estimates of analyses of algorithms for the algorithm engine 114. This
combination
of methods may be particularly advantageous over traditional accuracy when
forecasting
spatial-temporal energy demand, behavior, load, capacity and reserve
information of a
societal-scale. The combination may also be dynamic and more frequent, e.g.,
at a sub-
second time horizon.
100771 The system 100 may include a processor 104, such as a central
processing
unit, application specific integrated circuit (ASIC) or other type of
processing circuit
with which to execute the processes and methods disclosed herein. The
processor 104
may include one or more processors that may be executed in combination or in a
distributed environment.
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100781 The system 100 may further include a network interface 121, such as
for
connection to a Local Area Network (LAN), a wireless 802.11x LAN, a 3G or 4G
mobile WAN or a WiMax WAN, and a computer-readable medium 105. Computer
readable medium 105 may be any suitable medium that stores machine readable
instructions to be executed by processor(s) 104. For example, the computer
readable
medium 105 may be non-transitory and/or non-volatile, such as a magnetic disk
or
volatile media such as RAM. The instructions stored on the computer readable
medium
105 may include machine readable instructions executed by the processor(s) 104
to
perform the methods and functions of the system 100. The computer readable
medium
105 may include solid-state memory for storing machine-readable instructions
and/or for
storing data temporarily, which may include information from the data
repository, for
example for performing project performance analysis.
100791 The system 100 may further include a an operating system 106, such
as MAC
OS, MS WINDOWS, UNIX, or LINUX, and one or more applications 107, which
include a software application providing features of the system 100. The
operating
system 106 may be multi-user, multiprocessing, multitasking, multithreading,
real-time
and the like. The operating system 106 may execute from the computer-readable
medium 105.
100801 The system 100 may include a data anonymity module 103 such as data
encryption, decryption, private-public key management, metadata to make device
102
data more private and invisible to human intervention. Anonymity module 103
may also
include software, processes, protocols and methods to protect, hide, unprotect
and unhide
household and choice set data.
[0081] Figure 2 is a block diagram of a distributed information (or
collaboration)
network 200 at a societal level in which the system 100 of Figure I may be
deployed.
The collaboration network 200 may include additional components not shown and
some
of the components described may be removed and/or modified. For example, the
collaboration network 200 may represent a server that runs the system 100 or
the
collaboration network 200 may include one of multiple distributed servers that
perform
the functions of the system 100 in a distributed computing environment.
100821 The collaboration network 200 may include functional systems such as
meters and sensors 201 that employ instances of the system 100. The meters and
sensors
201 may record, modify, update, send and receive energy consumption data
to/from each
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consumption device 102. The energy consumption data may be aggregated at
household
or unit, up to communities and on to other distributed systems in the
collaboration
network 200. The other distributed system may include, for example, storage
sensors
202, a distributed generation system 203, renewable energy sources 204,
distributed
stations and substations 205, EV/PI lEV charge and discharge stations 206,
load sensors
207, capacity sensors 208, weather sensors 209 and other external sensors 210.
The
components and sensors of the distributed generation system 203 may be coupled
with or
integrate instances of the system 100 that record, modify, update send and
receive data
to/from other of the systems 100.
100831 The distributed information management framework 119 in the system
100
may connect other devices and systems into the collaboration network 200 via a
network
connected to main energy grid system 210. Some internal systems and
consumption
devices 102 may be connected to the main grid system 210 via a LAN and
external
system and the consumption devices 102 may also be connected via the Internet.
The
internal system and the external system may include the household portal 101
and
external data sources 120 shown in Figure 1.
100841 Figure 3 is a flow chart 300 of a method for dynamic distributed
data
collaboration between consumption devices of the distributed information
network of
Figure 2. The collaboration may include grid network governing protocol (GNGP)
to
maintain communication between multiple instances of the system 100, for
example, via
meters and/or sensors 201 that incorporate the system 100. Accordingly, the
method 300
may be performed by the system 100 shown in Figure 1.
100851 The system may monitor for new event data coming from a consumption
device (310). The new event data may include the current location (e.g.,
geographic
location) of the device, current time and other functional metrics that may
include
sensor-measured metrics and operating information of the consumption device.
The
location and time, for example, may be determined based on location and time
data
received from an access device for the consumption device. If no new event
data is
detected or observed (315), the system 100 may loop back to continue
monitoring for
new event data (310).
100861 The system 100 may then analyze the new event data to determine
power
consumption behavior of the consumption device over time (320). A new event
may be
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indicative of current activity of a consumption device, which may include
location data
and/or other measured metrics that may indicate such activity.
100871 The estimation engine 115 may use a Bayesian Belief Network (BBN) or
another type of estimation technique to estimate the current activity of the
consumption
device based on the event data.
100881 The algorithm engine 114, working in conjunction with the estimation
engine
115, may use a semi-supervised or an unsupervised algorithm to create new
algorithms
or to modify existing algorithms that may estimate the current activity based
on the event
data.
100891 The predictor 117, furthermore, may use a HUP or another type of
analytical
method to estimate the current activity based on the event data. The predictor
117 may
determine a probability that the consumption device is performing a current
activity,
such as whether the device is in route to work. If no new event is observed,
the system
100 may continue to monitor for new event data. The system 100 may also use
the last
known state or accept a user input of the state.
100901 In response to the analysis, and based on the determined power
consumption
behavior of the consumption device, the predictor 117 may predict the
occurrence of a
future event (330). The future event may include, by way of example, a surge
in power
and/or a determined consumption of power during a determined period of time.
Future
events predicted for multiple consumption devices may be aggregated to predict
a
corresponding future event for a household, unit, group of units or a
community.
100911 The actuator 116 may use an optimal foraging technique ("OFT") or
another
type of actuation method to confirm the future event for the consumption
device.
Furthermore, the actuator 116 may request a confirmation of the future
activity based on
the event data (360). If no new action is initiated, with an observed new
event, the
system 100 may use the original state or accept a user input of the state.
100921 The predictor 117 may then predict the outcome of the future event
based
on an analysis of past behavior data of the consumption device and the
received new
event data (340). Such a prediction may include an introspection of likely
behavior to
estimate the outcome of the anticipated event. For example, the estimation
engine 115
may apply Markov Chain Monte Carlo (MCMC) or state variable machine (SVM) or
another type of estimate based on the event data, which may include behavioral
patterns,
to estimate the current activity of the device.
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100931 Behavioral data may include historical data for the consumption
device,
including previous consumption traversed and previous activities executed by
the
consumption device. Pattern recognition based on historical data, which may be
performed by an SVM estimate, can be used to analyze historical data to
predict the
current or next activity. Another example of a prediction may include
predicting a surge
in power at a geographic location at a future moment in time responsive to an
aggregation of anticipated events and predicted outcomes. If no new
anticipation is
initiated, with observed new event and new action, the system 100 may use the
original
state or receive a user input of the state.
100941 With further reference to Figure 3, the system 100 may further
include taking
an action or actions to confirm the future event (360). For example, an
actuation may be
generated to determine the next future activity of the device, such as whether
the device
will likely to fail due to strong weather conditions.
100951 In response to the actuation, the system 100 may modify the list of
power
consumption choices or confirm the choices (365). The system 100 may also
generate
another actuation. For example, if the device indicates likely to fail due to
strong
weather condition, the next list of choices may identify an alternative
consumption
device within range and/or generate attribute-related actuation, such as
whether the
consumption device desires better quality or lower energy load. Also,
actuation may be
generated to determine or confirm a power state of the consumption device
(370). To
illustrate, suppose one of the actuators finds that an EV/PH EV charge station
may soon
become "unavailable" due to rain. The actuator 116 may identify the other
EV/PH EV
stations within the location range. The actuator 116 may then find energy
consumption
of the other FV/PHEV stations and determine if they would require additional
power
and/or better power quality to substitute for the EV/PHEV station that is
likely to be
unavailable.
100961 With further reference to Figure 3, the predictor 117 may apply one
or more
predictors to determine the list of power consumption choices or options.
Additionally,
the state determined at step 370 may be used to determine the list of power
consumption
choices for future activities (365). An example of determining the list of
choices is
further described with respect to Figure 5.
100971 Figure 4 is a flow chart 400 of a method of dynamic aggregation and
disaggregation of data from devices in a distributed data collaboration within
the
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network of Figure 2. Figure 4 illustrates an aggregation of a new device 102
or new
collaboration network 200 and also disaggregation of a new device 102 or new
collaboration network 200. '[he aggregation or disaggregation may be executed
by a
system 100 for determining the GNGP maintained between groups of sensors
and/or
devices across one or more networks that may provide an intended outcome. The
intended outcome may' include events determined at step 330 of the method of
Figure 3.
The GNGP may include an estimation of a state for each event. The GNGP may be
used
for the power state determined at step 370 of the method of Figure 3.
100981 With further reference to Figure 4, a system may aggregate a common
set of
attributes, which may he determined for each sensor or meter (410). The
attributes may
describe a grid property or service associated with an event. A set of
attributes may
includes one or more attributes, such as one or a combination of physical
attributes, (size,
power, etc.), economic attributes (price, energy saving, etc.) and functional
attributes
(nature of use, quality of power, etc.). For purposes of explanation, each
attribute
affiliated with a list of power consumption choices may be expressed as j...J
for each
respective attribute having scale (e.g., high-medium-low) of m as shown in
Figure 5.
100991 A power consumption device 102 may then determine existing power
source
trade-offs (420). The trade-offs may describe an attribute (e.g., quality of
power) or a set
of attributes that is used and shared in exchange for another attribute (e.g.,
amount of
reserve) or a set of attributes with another device 102. A set of trade-offs
may comprise
qualitative (service, power quality, etc.), quantitative (voltage, watt, etc.)
and content-
driven (load, capacity', etc.). For purposes of explanation, each power source
trade-off
may be expressed as s...S for each respective attribute 401 as shown in Figure
5.
1001001 The power consumption device 102 may then determine a common set of
events (430). The events may describe use (heating, cooling. etc.) or a
pattern of usage
(frequency, time of use, etc.) that is used and shared with other devices. A
pattern of use
may include behavioral patterns (use during day, week, month, etc.),
attitudinal patterns
(based on comfort, time savings, etc.) social patterns (such as similar use,
neighbor use,
etc.). Each event in use may he expressed as k...K for respective power
sources trade-
offs as shown in Figure 5.
1001011 Figure 5 is a flow chart and associated algorithm 500 of a method
for
dynamic aggregation and disaggregation of consumption data at device,
household and
societal levels for power distribution estimations. The aggregation of
consumption data
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into a societal (or community) level may be used to determine a list (or set)
of power
consumption choices or to determine data to distribute for a particular time
and location.
The method may employ a micro-array for quantum statistical mechanics ("QSM")
to
model each consumption device as a quantum candidate, e.g., as a particle, in
order to
predict activities of a society of power generation and power consumption
devices.
Based on societal-scale predictions, an optimal set of consumption choices may
be
determined at household or unit level (501) and an optimal set of consumption
choices
for a community may be determined at a societal level (502). The method,
furthermore,
may be used to determine the list of consumption choices as disclosed with
reference to
step 365 of the method of Figure 3.
1001021 The demand forecast equation 503 at a societal level may determine
a value
for each household (or unit) as a function of value function 504 and choice
probabilities
for alternative variables 505. The value function is the expected level of
satisfaction for
a household, which may be related to an event in use of a consumption device
(530). For
example, a value may be whether to purchase an expensive set of energy sources
for
consumption devices, between main traditional 211 and renewable energy 204
sources.
Examples of the alternative variables 505 may include source of power, choice
attributes
or variant alternatives (e.g., heater uses to traditional energy source and
air conditioner
uses renewable energy source) that are available in the choice set for a
consumption
device or a user of the consumption device to exercise a preference. The
choice
probabilities 505 may include bargaining variables, such as coupons,
discounts, auction-
bids, promotions that are available for devices to exercise preference, or any
variable
involving an interaction of a consumption device to obtain power or a service.
1001031 The method may aggregate load, capacity and reserve of various
power
sources (e.g., main traditional 211 and renewable energy 204 sources) (506).
The method
may execute a stochastic subprocess to determine expectations by updating the
probability of a power device (k) being in an observed state at time t. The
cumulative
probability may be determined from the probabilities determined at for
individual power
consumption devices (505). An aggregation factor may include a multi-
dimensional
optimizing factor determined using Monte Carlo Markov Chain, Hidden Markov
Model
(HMM), quasi-Newton or another OCE method (504) to find the optimal weights at
each
function gradient of a location-based activity-based load, capacity and
reserve of power.
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1001041 At 504, the quantum candidates may be aggregated for load, capacity
and
reserve into societal-scale as a function of one or more of time, location,
transition and
constraints. The aggregation 506 may include an aggregation of each decision
of power
consumption devices at a household level (501) as well as societal level
(502).
1001051 For example, an information flow for a device aggregated to another
device using the sheaf method of transmitted data z = (zsi ; ...; zsa); Zs;
k"si ; si e S;
may be defined by an assignment 41(e) k"P(e) for each edge e e E satisfying
the flow
conditions. The flow conditions may include the data that are related by local
coding
maps (13, at all vertices v. More specifically, for e = lvw1 and ei e In(v; E)
(i = 1;...;K); ,
which may be expressed as:
' ILL A(1") >>, A(Y)--?----+P yA
Where P yA = local information flow probability aggregated in a set y or
LA(P).
1001061 The societies determined by data sheafing may be used to predict
activities
for the societies. Based on the predictions for the society, an optimal set of
choices may
be determined for a consumption device. For example, if a consumption device
is part of
a society determined to be heating the house, an optimal set of power
consumption
choices for a device in the society may be based on the number of devices in
the society,
the load capacity, reserve and the like. Also, data may be delivered to the
consumption
device that may indicate usage, load capacity and choices for other heating
sources, such
as gas heater or standalone heated air.
1001071 Figures 6A, 6B and 6C are block diagrams of examples of dynamic
distributed estimation with distributed data collaboration for power flow
predictions at,
respectively, device, household and societal levels. More specifically, the
block diagram
601 of Figure 6A demonstrates formation of data collaboration societies (or
communities) in relation to other power consumption and generation devices.
The block
diagram 602 of Figure 6B demonstrates formation of data collaboration
societies in
relation to other households of units. The block diagram 603 of Figure 6C
demonstrates
formation of data collaboration societies in relation to other societies or
communities
(e.g., a collection of households or units).
1001081 Figure 7 is a flow chart 700 of a method of estimation to determine
power
usage choices selectable by users for energy consuming devices at household
and
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societal levels. The method may be executed by the system 100 and may be
operable to
provide consumption data based on location and behavioral information.
1001091 The system may determine household monthly budget or willingness to
spend
on electricity (e.g. monthly electricity bill) (701). The access device may
detect its
current geographic location and send the location to the system 100. For
example, an
access device may transmit a location status communication including location
status
information to the system 100 using the information management framework 119.
The
access device may provide location status information proactively or in
response to a
request from the system 100.
1001101 The system may further determine whether the consumption device
intends
to receive power from the main traditional 210 or renewable energy 204 sources
(702).
This determination may be based on the current location of the consumption
device,
proximity to a target location, the state of the device and other behavioral
information
preferred by the household for the consumption device.
1001111 The system may then determine whether the power consumption device
is
qualified to draw from only main (traditional) power or may also be qualified
to draw
from additional (renewable) sources of power (720). If only from main
traditional
power, the system may present main power combination choices for selection
(703).
1001121 ir the consumption device qualifies, the system may further provide
trade-off
combinations of renewable energy 204 available at the societal (or community)
level to
the access device associated with the consumption device tbr user selection
(704). The
trade-off conditions may be determined as associated with the target location,
such as
trade-off conditions published by a different device or other entity that is
associated with
the target location. The method of Figure 7 may be repeated as the location of
the
consumption device changes.
1001131 Referring back to step 702, the system may determine whether the
consumption device qualifies to receive power for a target location. This
determination
may' be based on whether the target location of an access device is
"proximate" to a
geographic location associated with a power attribute. A power attribute may
include a
geographical location of source, voltage, watt, load, quality of power, a
state, and the
like. One or more questions may be posited to determine if the consumption
device
qualifies based on the attributes associated with the energy attributes.
"Proximate" may
refer to the target location of an access device being within a predefined
geographic
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proximity. The proximity may be defined in any suitable way, including as any
location
that is located within a specific distance (e.g., radial distance) of the
target geographic
location and/or dynamic geographic location. For example, the target radial
distance
may be set as 50 kilometers or 100 kilometers or another distance based on
power
attributes such as quality and location of the power generations. Other
factors may also
be considered.
1001141 The system may determine and track the current activity and power
consumption of the consumption device (705). The determination may be made,
for
example, as a result of received confirmation or due to the decisions made at
steps 703
and 704. The system may further forecast power usage levels for each power
consumption device 102 to which associated consumption data is available
(706), as has
been previously discussed.
1001151 The system may further determine a list of power consumption
options or
choices for a next activity or event to be performed by the device based on
the current
activity of the consumption device, its location and behavioral data for the
consumption
device (707). Predictor 117 may employ HUP or other types of targeting methods
to
determine the list of consumption choices of the next activity. For example,
if the
current activity is that a generation device is charging a car, the set of
choices to a car
owner may include different charging duration options. The different options
may be
targeted by one or more targeting methods. The list of consumption options may
be
supplemented with energy or power source trade-offs about each option, which
can be
related to the current and/or next activity. For example, the power may be
related to the
different charging duration options, such as the estimated time of charging
completion of
the car, commercial vehicle, and the like. The power may also be related to
energy from
other devices, such as load, capacity and reserve availabilities.
1001161 Furthermore, the estimation module 115 may use a MCMC target method
to
identify the best choices for consumption or generation devices based on
aggregate target
consumption data at a household and at a societal level of devices performing
similar
activities in the same or proximate location. Figure 5 shows an example of a
method for
determining societies using MCMC and using the societies to determine the list
or set of
consumption choices.
1001171 The system may further apply a multivariate orthogonal micro-array
(MOMA) method to create the list of consumption options (708). For example,
the
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CA 02844486 2014-03-04
system may determine that, based on the age and configuration of the device
performing
a generation activity, the activity can be best fulfilled by a better quality
source of power
such as renewable energy sources 204 as opposed to main traditional energy
sources 211.
The state of a device may represent the age and configuration of the device.
For
example, the state may represent a level of satisfaction for a particular
choice in the
choice set that was performed by the device. The MOMA method may include all
the
possible permutation and combinations of choice attributes (510) and power
source
trade-offs (520) and activity or events of the consumption device (530). The
permutation
and combinations may be based on the state of the consumption device and
exclude
certain permutations and combinations of the consumption device to determine
the
optimal list of consumption choices that meet the monthly budget (701). The
state may
be stored as historical information.
1001181 The system may further determine if the power and load levels are
optimal for
expected utility, e.g., expected power consumption demand (730). If not, the
system
may go back to step 706 and continue to forecast ()lithe power consumption
data as
previously discussed. If yes, however, the system may continue on to determine
whether
power and load values are also optimal (740).
1001191 If the power and load values are not optimal, the system may
determine
additional price forecasts of the energy for a future period based on likely
power
consumption by the consumption device (709). Otherwise, if the power and load
level
values are optimal, the system may continue to build the list of power
consumption
options (710). The predictor 117 may use SSP on the likely consumption, load
capacity
and reserve to predict the future price of energy at device level.
1001201 The system may further determine aggregated price at household and
societal
levels of the power consumption (711). The estimation engine 115 may use OCE
to
estimate aggregated pricing and policies for a future period based on likely
consumption
the power consumption device 102.
1001211 The system may further determine the preference of a household based
on
historical activity, including power consumption, of the consumption device
(712). The
estimation engine 115 may use OCE and BBN to estimate household preferences
for a
future period based on historical and the predicted future consumption the
device 102.
The system 100 may provide the preference of the household and the list of
consumption
options to the household portal 101, for user selection.
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r
1001221 The system may further determine when the user preference
regarding a
consumption activity of a consumption device is modified in the household
portal (713).
This modification may be made with relation to power Consumption trade-offs.
The
system may receive the selection of a modified activity of the consumption
device 102
from the household portal 101 (e.a., household chooses washer-dryer to be used
on
weekdays instead of weekends) by the household within an additional power
consumption option to meet the monthly budget.
1001231 The system may further determine the list of consumption options
as related
to energy attributes of consumption and generation devices and in relation to
a monthly
budget, and set these and other selected options as a default list of
consumption options
for the household (714). This default list of options may be sent to the
household portal
101 and made available to the access device 101 and other devices 103. The
system may
maintain and monitor the activities of the consumption devices, so that any
deviation
from the preferred choice set may be flagged and sent as an "alert" to the
household
portal as well notified at the societal level.
1001241 Distribution of an energy attribute may include making the energy
attribute
accessible to an access device. This may be performed in any suitable way. In
certain
embodiments, when an estimate is determined, a copy of a neighboring energy
attribute
may be automatically provided (e.g., downloaded) to the access device. In
another
embodiment, to access a power attribute, option selection data may be stored
and
updated for a consumption device with appropriate permissions settings and/or
with links
to appropriate decision-making probabilities on power attributes. For example,
a link to
a power attribute associated with a target geographic location may be inserted
into a
profile associated with a user ID of a consumption device in order to make the
power
attribute accessible to the access device associated with the consumption
device.
1001251 The system 100 may further configure power attributes to provide
notifications to one or more access devices indicating that published energy
has been
made accessible. For example, the system 100 may provide a notification to an
access
device indicating that the power attribute associated with a target location
has been made
accessible to the access device. The notification may include information
associated
with the power attribute, including a description provided as a trigger end-
state for the
consumption device, a geographic location or any other data associated with
the power
attribute.
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1001261 The access device may receive the notification, and the device may
elect
whether to retrieve the accessible power attribute. In some embodiments,
current
geographic proximity to the geographic location may be requisite for
accessibility to the
associated power attribute and in other embodiments, once accessibility to
energy is
granted, accessibility is maintained for a predefined length of time, such as
a day, week,
month, or indefinitely. Accordingly, a consumption device may have access to
power
associated with a geographic location based on past or present detected
proximity of the
access device to the geographic location.
1001271 Figure 8 is a flow chart 800 of a method of estimating aggregated
equilibrium
of demand, load, capacity and reserve at household, societal and macro levels.
The
system 100 may determine the aggregated demand based on consumption options as
power attributes at the household level (810). The system may also determine
the
aggregated price per consumptions based on power attributes at the household
level.
Based on aggregated demand and aggregated price, the system may further
determine the
aggregated load and capacity required at the household level. The algorithm
engine 114
may use semi-supervised or unsupervised machine learning techniques to develop
a
model of aggregation and the estimation engine 115 may use OCE to estimate
aggregated load and capacity conditions at the household level.
1001281 The system may also determine the aggregated power demand from the
list of
consumption choices as power attributes of households, but aggregated at the
societal
level (820). The system may also determine the aggregated price per
consumption based
on power attributes collectively of the households. Based on aggregated demand
and
aggregated price at the societal level, the system may determine the
aggregated load,
capacity and reserve available and required at the societal level. The
algorithm engine
114 may use stochastic gradient learning techniques to develop a model of
aggregation
and the estimation engine 115 may use SSD to estimate aggregated demand
conditions at
the household level.
1001291 The system may further determine the aggregated demand on the list
of
consumption options as power attributes collectively of the households (810)
and
collectively of the societies (820), but at a macro level that includes
multiple societies
(830). The system may also determine the aggregated price per consumption
based on
power attributes of households 801 and collectively of societies. Based on
aggregated
demand and aggregated price at the macro-conditions, the system may determine
the
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CA 02844486 2014-03-04
, r
aggregated load, capacity and reserve available and required at macro-level
conditions.
The algorithm engine 114 may use stochastic gradient learning techniques to
develop
model of aggregation and the estimation engine 1 1 5 may use SSD to estimate
aggregated
demand conditions at the household level.
1001301 Figure 9 is a flow chart of a method of actuation for location-
based search-
and-feed analysis to determine a likely demand-capacity gap and availability
of load and
reserve power sources at household and societal levels. The predictor 117 of
system 100
may forecast location-based demand on a list of consumption options and on
related
power attributes at the household level (901). The predictor 117 may use HUP
and SSP
to forecast location-based demand. "Search-and-feed" may refer to the process
by
which the system 100 locates (or predicts) available sources of excess power
supply with
which to feed consumption devices (or groups thereof) that are predicted to
have a power
supply gap.
1001311 The predictor 117 may forecast location-based load, capacity and
reserve
availability, e.g., .sources of power supply such as power distributors,
generators, and/or
collectors. The forecast may be based on a list of consumption options and on
related
power attributes at the household level (902). The predictor 117 may use CO,
QSM and
SSP to forecast location-based load, capacity and reserve capabilities.
1001321 The system may further determine a location-based gap between
likely
demand and available power supply from power supply sources (903). The system
may
use dynamically-generated data to determine the time and place the location-
specific gap
is likely to widen. The system may then shut down a consumption device and may
also
determine when and where the location-specific gap is likely to be minimized,
e.g., when
the power supply sources are greater than demand, which may be referred to
herein as an
excess of power supply. The predictor may use CO, MCNSE and SSP to determine
the
likely location-specific gap between demand and available load, capacity and
reserve
power sources.
1001331 The system may then analyze a path between a location of excess power
supply and a location of a power gap (920). If the path appears clear, the
system may
estimate an optimal path between where the location-specific gap is widening
and the
location of the excess power supply (904). The actuator 116 may use OFT and
PSEB to
determine the optimal path.
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1001341 Where, however, the system determines that the path is cluttered or
completely blocked, which could be due to geographical or technological
issues, the
system may explore paths of the grid with a speed estimation, e.g., a speed to
traverse
reachable paths between a location of the power supply gap and a location of
excess
power supply. The exploration of the paths may be undertaken to avoid a
certain path
that includes an obstacle or to detect other location-based obstacles (e.g.
weather
condition in certain location) (906). The predictor 117 may use CO and SSP to
forecast
path conditions between locations.
1001351 The actuator 116 may work with results from the predictor 117 to
estimate the
speed at which action must be taken to meet a widening power supply gap so
that the gap
can be filled from the location of excess power supply (e.g., from load,
capacity and
reserve sources) (905). The actuator 116 may use SNF and FD methods to
determine the
speed at which the gap should be filled to avoid brownout or blackout
conditions.
100136j The system may further forecast the queue time of transferable
power supply
due to the obstacles in the path (907). The actuator 116 may use SN F and QT
to
determine the queue time at the obstacles to estimate the speed at which
action should be
taken to meet the speed at which the power gap should be filled to avoid
brownout or
blackout conditions. As an example, the actuator may determine whether there
is an EV
already getting charged at the EV/PHEV charge station or a device that is
already active-
in-use on electric charge as an -obstacle" (because the actuator cannot obtain
the
required data). The time during which the device is in use may be called a
waiting
period (or queue time) for the device to become free to obtain the data. The
actuator may
determine whether to continue to "chase" for obstacle-related information,
e.g., wait until
the device becomes free, or "ignore" the obstacle and pursue a different set
of devices to
source data. The system, as part of the path analysis in steps 920 and 904-
907, may
further determine a shift in the one or more paths required to avoid an
obstacle on the
determined one or more paths.
1001371 The system may then determine whether there is adequate reserve (or
excess
power supply) to meet the power supply gap (930). If adequate power reserve is
located
right away, the system may determine location-specific, but grid-wide, load
balancing
for stability of the power grid (914). The estimation engine 115 may use OPEC.
and
NRM to optimize the location-specific as well as network load balancing and
the
actuator 116 may use BM and OFT for required actions.
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1001381 The system may then further simulate forecasted contingencies and
alternative scenarios with dynamically generated consumption data and likely
responses
at device, household and societal levels (915). The system may provide dynamic
visualization of location-based activities with likely responses (e.g. the
likely gap can
also be fulfilled from another location with relatively higher price).
1001391 If there are not adequate reserves (or excess power supply) upon
first analysis
(930) the system may further determine the available reserve at a nearest
location from
the likely gap in power supply (908). The system may forecast the available
reserve in
that specific location to ensure the location has sufficient reserve available
to meet
predicted demand. The predictor 117 may use SSP to forecast location-specific
available
reserve.
1001401 The system may further determine the required reserve and capacity
power
levels at the nearest power supply source location from the likely gap in
power supply
(909). The system may forecast the required excess power supply in the
specific location
to ensure that the location has required reserve and capacity with surplus
available
reserve (908) to determine the location is at a steady state of power supply
(910).
1001411 The system may then further determine an optimized level of reserve
and
capacity (excess) power supply at the nearest location from the likely gap in
power
supply (911). The system may optimize the required reserve and capacity in the
specific
location to ensure that the location has optimized the steady state (910). The
estimation
engine 115 may use OCE and OPFC to optimize required reserve and capacity with
surplus available reserve power.
1001421 The system may further precipitate an action to transport available
reserve (or
excess) power from the location olexcess power supply (e.g., location 2) to
the location
where the power supply gap is widening (e.g., location 1) (912). The actuator
116 may
use OFT to identify the optimal path (904) with estimated queue time (907),
and to
initiate transport of the excess power from location 2 to location I. The
system may also
optimize steady state power capacity conditions at location 1 after receipt of
the excess
power (913).
1001431 When the reserve at location 1 is filled (930), the system may
proceed at step
914 as before, and determine location-specific, but network-wide, load
balancing for
stability of the power grid (914). The system may then further simulate
forecasted
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CA 02844486 2014-03-04
contingencies and alternative (or similar) resolutions with dynamically
generated
consumption data and likely responses at device, household and societal levels
(915).
1001441 Figure 10 is an abbreviated power grid diagram 1000 of control and
data
processing in relation to the search-and feed process of Figure 9,
particularly with
reference to step 912. Various control and sensing components may interface
with or be
a part of the system 100, which may perform the search-and-feed process. The
system
100, accordingly, may further include an event actuator 1001, which may be a
processor
and which may be operable to execute computation statistics 1002 on
consumption and
power supply data, and a step switcher 1004, which may interface with control
lines (or
busses) 1050 of the power grid. The event actuator 1001 may be a part of or
executed by
the actuator 116 of the system 100.
1001451 The control lines 1050 may represent movement (M), direction (D),
color (C)
and sound (S) and direct corresponding data in the system 100 to visually
and/or orally
provide an indication of the load balancing that is explained in Figure 9.
Figures 16 and
17 are example outputs of the data processing with respect to the diagram
1000. Input
data to the system 100 may include measurements (Mn), devices (Dn),
controllers (Cn)
and sensors (Sn). A measurement may come from a meter or calibrator or like
device.
The devices (Dn) may be with reference to a consumption and access devices.
1001461 The power grid may include breakers 1003, obstacles 1006 in a
queue, critical
demand 1008 and non-critical demand 1016 sources of power consumption. The
power
grid may further include critical capacity 1018 and critical reserve 1028
sources of power
supply. The power grid may also include end-point sensors 1005 at the end of
power and
control lines of the power grid.
1001471 The actuator controller 118 may initiate the event actuator 1001 to
use one of
the following four elements: move, direction, color and sound. The actuator
controller
118 may then send commands to the event actuator 1001 for measuring (ma) and
controlling (ca) to direct the movement, direction, color and sound of the
event actuator
(e.g., move 10 steps on the right on the map).
1001481 The actuator controller 118 may further switch steps dynamically in
movement, direction, color and sound by directing the event actuator 1001 to
send
commands to the step switcher 1004 to switch steps between power lines. For
example,
if there is an obstacle and the actuator 116 is waiting in the queue, the
actuator may be
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CA 02844486 2014-03-04
directed to switch steps, if necessary, to another direction, e.g., along a
different power
line.
1001491 The actuator controller 118 may further direct the breakers 1003
to break a
trajectory of movement, direction, color and sound. The actuator controller
118 may
send commands to the actuator 116 to break its trajectory at the breakers
1003. For
example, the event actuator 1001 may abort measuring on a particular search or
a
particular consumption or generation device.
1001501 The system may further direct the actuator controller 118 to
measure the
criticality (e.g., of the demand) at a particular location including the
critical activity of an
access device 101. The actuator controller 118 may send commands to the event
actuator 1001 to measure criticality (e.g. of the demand), determined by the
estimation
module 115, at a particular location including the critical activity.
[00151] The system may further direct the actuator controller 118 to
perform an end-
point action (e.g., switch-off a heater) at a particular location that
includes the critical
activity of an access device 101. The actuator controller 118 may send
commands to the
event actuator 1001 to perform end-point action (e.g., switch-off a heater),
determined
with the estimation engine 115, at a particular location including the
critical activity.
1001521 Figure 11 is a flow chart 1100 of a method of predicting likely
demand-
capacity gap and available load, capacity, reserve for optimal power flow
control. The
predictor 117 may initiate a forecast of location-specific likely gaps as
covered in step
903 of the method of Figure 3.
1001531 Furthermore, the predictor 117 may determine any stationary state
solution
that can be used as a starting point in the context of predictor¨corrector
continuation
methods (1110). To do so, the system may make a change of a variable in
initial data
accessible to the system 100. The predictor 117 may further determine the
architecture
of the network at device, household or societal level to forecast the location
of the likely
power gap (1120).
1001541 The system may further determine the outer continuation where the
predictor
117 may use a predictor-corrector algorithm model of algorithm engine 114 to
compute
the approximate point of the location (1130). A quantum analogue to the
optical
experiments may be a Bose¨Einstein condensate in a double-well potential where
loss
and gain are realized by removing activity of a device from one well and
pumping in
activity of devices in another well.
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CA 02844486 2014-03-04
1001551 The system 100 may further determine the inner continuation where
the
predictor 117 may use the approximating point and corrector to make a
correction on the
fine grid network map, and continue to perform Newton-Raphson Method (NRM),
until
finding the location of the likely power gap ( 1140).
1001561 The system 100 may further determine the likely power gap curve
where the
predictor 117 may repeat step 1130 and step 1140, until the curve is traceable
and visible
to the actuator controller 118.
1001571 The system 100 may also determine the next constants (e.g.,
available
location of load, capacity and reserve power sources) where the predictor 117
may repeat
steps 1130, 1140 and 1150 for each constant until the curve is traceable and
visible to the
actuator controller 118.
1001581 The system 100 may also direct the predictor 117 to identify the
specific point
where only two real eigenvalues (e.g., demand and load) exist, where the fixed
points can
be identified as a center. Furthermore, the predictor 117 may determine that
the fixed
points in the region with four eigenvalues correspond to a center, a saddle
point, a sink
and a source. The center and saddle points collide at the branch point and
vanish. The
predictor I 17 may make behavior visible to the actuator controller 118 in
good
agreement with the results of action.
1001591 A stability analysis of the wave functions shows that up to the
bifurcation
point, both the ground state and the excited state are stable, and correspond
to the two
centers. Beyond the bifurcation point, the excited state remains stable (a
center) while
the ground state becomes unstable (a saddle point). Out of the wave functions
belonging
to the two complex conjugate eigenvalues, one decays and the other grows,
corresponding to the sink and the source, respectively.
1001601 The method of Figure I I may he particularly advantageous over
traditional
hierarchy model as this method and algorithm may be executed in parallel
because the
system 100 can trace each solution surface simultaneously. Using a numerical
continuation method to trace solution surfaces of parameter-dependent problems
was
described with application to the power gap (901) and excess power supply
(902)
determined in the method of Figure 9. It may be inexpensive to implement the
algorithm
because, in practical computations, the system 100 may function with the
information of
some specific points on the solution surfaces. Additionally, the proposed
algorithm has
the following non-exhaustive advantages: (i) it is unnecessary to discretize
or integrate
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CA 02844486 2014-03-04
the subject data, namely, demand, load, capacity, reserve, and the like; and
(ii) the
system can compute this data for any time scale and for any points on the
solution
manifolds.
1001611 Figure 12 is an abbreviated power grid diagram 1200 exhibiting
power flow
predictive analysis between sub-areas of a power grid. The system 100 may be
configured to perform optimal power flow control (914) as the final action
when using
the search-and-feed method as disclosed with reference to Figure 9.
1001621 The predictor 117 may use decomposition of an optimization problem
and
perform a multi-area control method based on approximate Newton directions. In
so
dong, a single NRM step may be applied to each sub-problem that stabilizes the
power
grid. The applied decomposition method may be based on data obtained from
neighborinu, agents. The data may be used to approximate the state of the
system with a
linearized model and make necessary changes in load and generator settings
with the
objective of reducing social costs of cascading failures. The method is
advantageous
over centralized optimal power flow taking the entire grid into account that
is often not
feasible. Reasons are the size of the resulting optimization problem but also
the
concurrent control of the system by several independent entities.
1001631 Figure 13 is a screen shot 1300 of an example application
executable by an
access device 101 such as shown in Figure I. The screenshot 1300 illustrates
the set up
of user preferences with reference to a consumption device. The user may input
these
preferences, which may include, for example, a monthly budget, a percentage of
renewal,
a desired temperature (whether through air conditioning or heater), the number
of rooms,
consumption devices, a frequency of wash loads for laundry and a frequency of
charges
for an electric vehicle, and other such choices or preferences. These
preferences may be
provided to the system 100 for use in prediction as discussed. Furthermore,
consumption
and behavioral data from consumption devices 102 may also be sent to the
system 100
through the access device 100, which data may be further forwarded to other
consumption and access devices of a collaboration network 200.
1001641 Figure 14 is a screen shot 1400 of an example set of energy usage
options as
related to an identified access device that are selectable by a user. The
screenshot 1400
shows a list of energy usage (or power consumption) options of one or more
consumption devices and based on a current location (e.g., in a house or a
building). The
list of power consumption options may be based on user settings and/or may be
predicted
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CA 02844486 2014-03-04
from historical data. As described above, a location sensor in an access
device 102 may
detect the geographic location of the device 102 within the geographical
footprint and the
geographic location may be sent to the system 100.
1001651 The location of the access device 101 may be periodically
transmitted at a
predetermined frequency or time, or in response to a predetermined trigger
event, to the
consumption device 102. This periodic transmission of the access device may be
especially useful if the consumption device is mobile such as an electrical
vehicle. Such
a trigger event may include detection of power consumption, an event or an
activity of a
consumption device.
1001661 Figure 15 is a screen shot 1500 of an example chart for tracking
source of
power consumption and associated savings due to use of societal power. The
screen shot
1500 may be generated by an application executed by an access device 101. The
screcnshot 1500 shows location-based power services provided by the system
100. '[he
access device 101 may be configured such that the application may recognize
when
power is used and, in response to such power usage, instruct the consumption
device 102
on activities and time of use. For example, activity on device data may be
provided to
the system 100 for storage and/or for providing societal-scale energy
preference to the
consumption device. A historic log of detected geographic locations of the
access device
may be updated.
1001671 The system 100 may further associate energy with location data and
other
information. For example, data may be created using an access device 101, the
power
consumption device 102 and other devices 103. The data generated may also
include the
geographic location of the access device 101, the power consumption device 102
and
other devices 103 at the time that the data created is used to create a "geo-
tag" that is
associated with the data.
1001681 In this or similar manner, location-based content services may
associate other
information with data, including, but not limited to, timestamps (e.g., the
time and/or
date when the data was created), device identifiers (e.g., an identifier for a
consumption
device associated with the access device and/or who created the data), and
data
descriptions or type identifiers (e.g., a photograph metadata-type
identifier). This other
information, once associated with the data, may be referred to as "other tag"
data. Geo-
tag data and/or other tag data associated with data may be utilized for
selective retrieval
and distribution.
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CA 02844486 2014-03-04
1001691 The location-based content services may provide a power consumption
device
for an access device with a capability of creating and publishing data at a
specific
location within a network footprint. As an example, a consumption device may
be
physically located at a particular geographic location. The consumption device
may
utilize the access device to create data or to search for data, such as
searching for nearest
restaurant. Data (e.g., time of heating) is already stored in the data storage
of the system
100. Location-based target content services may recognize a content creation
event and
instruct the location-based services to detect the geographic location of the
access device.
The location services may detect the geographic location and provide location
data, e.g.,
geo-tag data, representing the detected geographic location of the access
device.
Location based target content services may associate the location data with
the
consumption data and provide the data, associated aeo-tag data, and optionally
other
associated tau data to the system.
1001701 Figure 16 is a screen shot 1600 of an example geographic-based
power flow
map with focus on data from locations of interest. The screen shot 1600 may be
indicative of location content representation between data records associated
with a
consumption device. The decision-making content data may include target
content and
activity data, and geo-tag data may include target geo-tags. Data attributes
may
represent data respectively associated with target geographic locations. For
example,
location data may correspond to locations referenced in steps 901 to 912 of
Figure 9.
The location content data 1601 through 1612 may be provided based on proximity
to
target locations corresponding to steps 901 to 912 of Figure 9. Content
services may be
configured to utilize data included in the content representation 1601 to
search for and
identify matching aeo-tag data. The arrows illustrated in Figure 16 represent
identified
matches between a detected location and a geo-tag.
1001711 Data may be provided for the consumption device to receive and
annotate the
data in some instances. For example, an access device may receive a
notification of
identified data having been made accessible to the access device based on a
detected
target location of the data. The consumption device may choose to receive the
identified
target location data. In addition, the consumption device may make one or more
annotations to the identified data for an activity. Annotations may be stored
in
conjunction with the data as can be metadata in computer applications.
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1.001721 The consumption device may further provide consumption or sensor
data for
a specific activity (e.g., "heating water with water-heater"), review the data
(e.g., extent
of hot water in water heater), edit the data, block the data from being made
accessible to
the access device and/or to another consumption device, and report the data
(e.g.,
temperature on thermostat). The access device may provide the data and
annotation to
the system 100. Any annotation may be added to other tag data associated with
the
identified data. Accordingly, annotations may be used to index, search, and
retrieve
identified data for an activity. For example, a consumption device may search
accessible
data for specifically identified data having a particular rating, associated
with a particular
creator, created during a particular time range, having associated comments,
and the like.
1001731 The system 100 may be further configured to enable consumption,
generation
and access devices to initiate a grid network governing process with one
another in
connection with a target geographic location. For example, at a location for a
particular
data, a device may establish and participate in follow-up communications with
one
another. Such follow-up communications may be hosted and made accessible to
the
involved device, and in some instances, such communications are made
accessible
exclusively to the involved devices.
1001741 Figure 17 is an example screen shot 1700 for the distributed
information
network 200 as shown in Figure 2, in which a device may annotate power data. A
consumption device may utilize the access device to post data and receive data
based on
target locations. Figure 17 shows that a consumption device may receive data
and
annotate the data. In an example, the location of the consumption device and
locations
between consumption (and/or access) devices and the detected location may
qualify the
consumption device for access to data. A consumption device may elect to
utilize
additional access devices (e.g., mobile access devices) to retrieve and
experience data.
Accordingly, a consumption device may access additional access identified
data, a
communication display, a communication thread and sensor communications to
retrieve,
review, and annotate data that has been made accessible.
1001751 Figure 18 is a screen shot 1800 of an exemplary portion of a
location in a
power grid, to illustrate an optimal flow control mechanism as described in
Figures 9, 10,
11 and 12. The optimal flow control mechanism as preciously describe may be
adapted
to meet a power gap between demand-capacity and reserve forecasts. The system
may
use dynamic information to resolve and execute location-based search-and-feed
(SNF)
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CA 02844486 2014-03-04
and OFT methods. Dynamic information takes into consideration direction and
speed to
identify the location at which to generate power to fill a likely power gap at
a location in
a power grid. The actuator of one or more systems 100 may perform the gap
distribution
analysis at various locations to determine a distance between a consumption
device and a
second consumption device as the consumption device moves toward a
destination, e.g.,
a location of the second device.
1001761 For example, the actuator 116 of an EV/PHEV charge station (the
consumption device) may find that consumption is increasing at a particular
speed. The
actuator 116 may also determine that the likely power gap between demand and
available
supply is widening at another speed in another EV/PHEV charge station (the
second
consumption device), and so the gap between these two may be considered the
speed
gap.
1001771 In one example, energy authorities or service providers may use the
speed gap
to determine location, speed and direction of a consumption device. The
service
provider may then fill the demand for power by the reserve and capacity power
sources.
The service provider may also balance the load locally as well as at societal
levels using
optimal power flow control. Satisfaction of a power gap in this way may be an
indication of reaching a steady state, which may be used to aggregate power to
a
household as well as at a societal level to determine stability.
1001781 The above-disclosed subject matter is to be considered
illustrative, and not
restrictive, and the appended claims are intended to cover all such
modifications,
enhancements, and other embodiments, which fall within the true spirit and
scope of the
present disclosure. Thus, to the maximum extent allowed by law, the scope of
the
present embodiments are to be determined by the broadest permissible
interpretation of
the following claims and their equivalents, and shall not be restricted or
limited by the
foregoing detailed description. While various embodiments have been described,
it will
he apparent to those of ordinary skill in the art that many more embodiments
and
implementations are possible within the scope of the above detailed
description.
Accordingly, the embodiments are not to be restricted except in light of the
attached
claims and their equivalents.
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