U.S. patent application number 13/815922 was filed with the patent office on 2014-07-17 for node manager in an energy distribution system and related methods, devices, and systems.
This patent application is currently assigned to Questar Energy Systems. The applicant listed for this patent is Questar Energy Systems. Invention is credited to Edwin Eugene McKenzie, III, Gregory William Robinson, Herbert Alan Tilley.
Application Number | 20140200717 13/815922 |
Document ID | / |
Family ID | 51164251 |
Filed Date | 2014-07-17 |
United States Patent
Application |
20140200717 |
Kind Code |
A1 |
Tilley; Herbert Alan ; et
al. |
July 17, 2014 |
Node manager in an energy distribution system and related methods,
devices, and systems
Abstract
Aspects of the disclosure are directed to, for example, methods
for operating a node manager such as a node manager operably
coupled to a solar tracker assembly. In one embodiment, the method
includes providing control signals to motors of the solar tracker
assembly to control an orientation of a photovoltaic array and
receiving status information form one or more sensors configured to
measure at least one of ambient, operating, and performance
conditions local to tracker electronics of the tracker assembly.
The method further includes modifying the control signals based, at
least in part, on the status information and communicating, via the
tracker electronics, at least a portion of the status information
over a cellular communication via a cellular link.
Inventors: |
Tilley; Herbert Alan;
(Issaquah, WA) ; Robinson; Gregory William;
(Covington, WA) ; McKenzie, III; Edwin Eugene;
(Seattle, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Questar Energy Systems |
Issaquah |
WA |
US |
|
|
Assignee: |
Questar Energy Systems
Issaquah
WA
|
Family ID: |
51164251 |
Appl. No.: |
13/815922 |
Filed: |
March 15, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61752922 |
Jan 15, 2013 |
|
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|
Current U.S.
Class: |
700/275 |
Current CPC
Class: |
Y10T 29/49355 20150115;
F24S 2201/00 20180501; F24S 50/20 20180501; Y02E 10/47 20130101;
F24S 30/452 20180501; Y02E 10/56 20130101; H02S 20/32 20141201;
Y02E 10/58 20130101; G05F 1/67 20130101; F24S 2030/145 20180501;
G05B 15/02 20130101; H02S 20/10 20141201 |
Class at
Publication: |
700/275 |
International
Class: |
G05B 15/02 20060101
G05B015/02 |
Claims
1-22. (canceled)
23. A method for operating a solar tracker having a tracker
assembly, one or more motors coupled to the assembly, and tracker
electronics operably coupled to the motors, the method comprising:
providing control signals to the motors to control an orientation
of a photovoltaic array; receiving status information form one or
more sensors configured to measure at least one of ambient,
operating, and performance conditions local to the tracker
electronics; and modifying the control signals based, at least in
part, on the status information; and communicating, via the tracker
electronics, at least a portion of the status information over a
cellular communication via a cellular link.
24. The method of claim 23 wherein the tracker electronics are
configured to modify the control signals without intervention from
a remote computer device.
25. The method of claim 23 wherein the status information is a
first status information, and wherein the method further comprises:
locally storing the first status information; receiving second
status information form the sensors; and comparing the second
status information to at least the first status information;
further modifying the control signals based, at least in part, on
the comparison of the second status information to the first status
information.
26. The method of claim 25 wherein the tracker electronics: compare
the second status information to the first status information
without intervention from a remote computer device; and modify the
control signals without intervention from the remote computer
device.
27. The method of claim 23, further comprising: communicating the
status information to a remote computer device via the cellular
link; and modifying the control signals in response to at least one
instruction from the remote computer device, wherein the
instruction from the remote computer device is based, at least in
part, on the status information.
28. The method of claim 23, further comprising deriving global
positioning information and time stamp information from the
cellular communication.
29. A method for operating a node manager device, comprising:
applying one or more rules that dictate, at least in part,
operation of a controller device operably coupled to a node in a
power distribution network; acquiring information associated with
at least one of ambient, operating, and performance conditions at
the node; deriving analytics based on the acquired information; and
modifying at least one of the rules based on the derived
analytics.
30. The method of claim 29 wherein the acquired information
includes a geographical location of the solar tracker and a time
stamp.
31. The method of claim 29 wherein: the controller device is
operably coupled to a solar tracker at the node; the solar tracker
is configured to dynamically orient one or more photovoltaic arrays
based on the modified rules.
32. The method of claim 31 wherein: the rules identify an initial
orientation of the solar tracker; the modified rules correspond to
a modification in orientation of the solar tracker; and the method
further comprises applying the modifying rules to further control
the operation of the controller device.
33. The method of claim 32 wherein: the node manager device is
coupled between a solar tracker at the node and at least one power
line in a power distribution grid; and the acquired information
includes information corresponding to one or more signal conditions
at the power line.
34. The method of claim 33 wherein the acquired information further
includes a time stamp corresponding to the signal conditions at a
discrete time.
35. The method of claim 33 wherein the analytics correspond to
whether the solar tracker is to disconnect from the power line.
36. The method of claim 29 wherein: the node manager device is
coupled between a solar tracker at the node and at least one energy
storage component; the acquired information includes information
corresponding to an output power of the solar tracker; and the
modified rules correspond to at least one of-- whether a portion of
the output power is to be diverted to charge the energy storage
component, and whether the energy storage component is to provide
at least a portion of the output power.
37. A node manager device, comprising a processor and a memory
storing instructions that are executable by the processor, wherein
the instructions direct the processor to: provide status
information wirelessly over a cellular link to a remote computing
device, wherein the status information is produced by nodal
equipment coupled to a node in a power distribution grid, wherein
the nodal equipment is configured to generate power and/or store
power; and provide control information over the cellular link to
the node manager device that directs, at least in part, the
operation of the nodal equipment.
38. The node manager device of claim 37 wherein: the nodal
equipment includes one or more solar panels operably coupled to a
tracker assembly; and the instructions further direct the processor
to control an orientation of the solar panel arrays based, at least
in part, on the control information.
39. The node manager device of claim 37 wherein the nodal equipment
includes energy storage component, and wherein the instructions
further direct the processor to store power from the distribution
grid at the energy storage component based, at least in part, on
the control information.
40. The node manager device of claim 37 wherein the instructions
further direct the processor to isolate the nodal equipment from
the distribution grid based, at least in part, on the control
information.
41. The node manager device of claim 37 wherein the instructions
further direct the processor to balance an electrical load at the
node via the nodal equipment based, at least in part, on the
control information.
42. The node manager device of claim 37 wherein the instructions
further direct the processor to operate the node manager
autonomously from the remote computing device.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] The present application claims priority to U.S. Provisional
Application No. 61/752,922, filed Jan. 15, 2013, which is
incorporated herein by reference in its entirety.
TECHNICAL FIELD
[0002] The following disclosure relates generally to energy
distribution systems. Some embodiments, for example, are directed
to methods for managing information and/or operation of power or
communication nodes connected to end-users, distribution grids,
and/or power generators.
BACKGROUND
[0003] In a typical energy distribution system, a power plant
produces energy with a power generator, such as a coal or gas fired
generator, a hydro-powered generator, or a nuclear-powered
generator. Power is then transmitted to an end user over a
transmission grid. The transmission grid, in turn, supplies this
power to a local distribution grid which supplies the power to end
users via low-voltage transmission lines, substations, distribution
circuits, etc. A utility company can meter the power at the
end-user's premises to determine how much power has been
consumed.
[0004] One problem with traditional energy distributions is they
employ antiquated transmission and distribution grids. This makes
it difficult and cost prohibitive for utility companies to bring
new and alternative power generators online, such as wind, solar,
geothermal, etc. Another problem with these systems is that they
centralize power distribution, which gives utility companies a
market monopoly. Thus, many utility companies are reluctant to
improve their power distribution infrastructure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] FIG. 1 is a block diagram of a system that includes a node
manager configured in accordance with an embodiment of the present
technology.
[0006] FIG. 2A is an isometric view of a solar tracker system
configured in accordance with an embodiment of the present
technology.
[0007] FIG. 2B is a side view of a tracker and a tracker assembly
configured in accordance with embodiments of the present
technology.
[0008] FIG. 2C is a top plan view of a base configured in
accordance with embodiments of the present technology.
[0009] FIG. 2D is a partial backside view of a tracker assembly
configured in accordance with an embodiment of the present
technology.
[0010] FIG. 2E illustrates a method of assembling the tracker
assembly configured in accordance with an embodiment of the present
technology.
[0011] FIG. 3A is a block diagram illustrating tracker electronics
configured in accordance with an embodiment of the present
technology.
[0012] FIG. 3B is a flow diagram illustrating a routine performed
by a node manager in accordance with an embodiment of the present
technology.
[0013] FIG. 3C is a flow diagram illustrating a routine of a
learning process employed by a node manager in accordance with an
embodiment of the present technology.
[0014] FIG. 4A is a diagram of an energy network configured in
accordance with an embodiment of the present technology.
[0015] FIG. 4B is a network diagram illustrating a cloud-based
networked arrangement of node managers configured in accordance
with an embodiment of the present technology.
[0016] FIG. 4C is an diagram of a controller center arranged to
operate various aspects of a microgrid in accordance with an
embodiment of the present technology.
[0017] FIG. 4D is a diagram of a microgrid
licensing/investing/purchasing scheme configured in accordance with
an embodiment of the present technology.
[0018] FIGS. 5A-E illustrate various example user interfaces that
can be used with a node manager in accordance with an embodiment of
the present technology.
[0019] FIG. 6 is a flow diagram illustrating an internodal bidding
scheme in accordance with an embodiment of the present
technology.
[0020] FIG. 7 show various system logs than can be generated by a
node manager during a single day of operation of a solar tracker in
accordance with an embodiment of the present technology.
DETAILED DESCRIPTION
[0021] As described in greater detail below, the technology
disclosed herein relates to energy systems and, in particular. to a
node manager configured to manage information and/or operation of
various aspects of an energy distribution system.
[0022] FIG. 1 is an overview block diagram showing the operational
relationships of a node manager 10 in accordance with an embodiment
of the present technology. In general, the node manager 10 is
configured to manage information and/or operation at one or more
nodes in a power distribution system, such as power distribution
nodes (e.g., a substation), end-user user nodes (e.g., a
residential, commercial, governmental, or other suitable users),
and/or communication nodes (e.g., a server, database, etc.).
[0023] In one aspect, the node manager 10 communicates with or is
integrated with one or more nodal equipment 12 (e.g., nodal
devices) at or in the vicinity of a node. As described in greater
detail below, the nodal equipment 12 can include, for example,
energy equipment (e.g., a solar array), auxiliary equipment (e.g.,
a load balancer), and/or network equipment (e.g., a database).
[0024] In another aspect, the node manager 10 operates to receive
nodal information 14. In one embodiment, the nodal information 14
can include information relevant to a particular node or a number
of nodes. For example, the resource information can relate to power
demand, weather forecasts, and/or the ambient environment in the
vicinity of the nodal devices.
[0025] In yet another aspect, the node manager 10 operates to
derive nodal analytics 16 based on the nodal information 14. In one
embodiment, the node manager 10 can derive the nodal analytics 16
based on various correlations in the nodal information 14. For
example, the node manager 10 can correlate power demand with the
weather forecast and/or the ambient environment. In some
embodiments, the node manager 10 generates nodal analytics 16 based
on various rules. The node manager 10 can use these rules to
develop new or modified correlations, rules, and/or other
analytics.
[0026] In still yet another aspect, the node manager 10 operates to
generate nodal knowledge 18 based on the nodal information 12 and
the nodal analytics 16. The nodal knowledge 18, for example, can
allow a power utility to predict power demand based on a weather
forecast. In some embodiments, the nodal knowledge 18 can be
bought, sold, or licensed according to a variety of transaction
models. In other embodiments, the nodal knowledge 18 can be used
for auditing purposes.
[0027] In general, the nodal equipment includes devices,
apparatuses, systems, etc. that incorporate a node manager. Nodal
equipment 12 can be located, for example, at a power distribution
node (e.g., a substation), an end-user user node (with e.g., a
residential, commercial, governmental, etc.), and/or a
communication node (e.g., a server, database, etc.). In some
embodiments the nodal equipment 12 operates independently of a node
manager. In other embodiments, the nodal equipment 12 allows a node
manager to handle nodal information, analytics, and/or knowledge.
As described in greater detail below, the nodal equipment 12 can
include energy equipment, auxiliary equipment, and/or network
equipment. However, nodal equipment 12 can include other types of
equipment, devices, apparatus, etc. For example, the nodal
equipment 12 can include a mobile phone, a personal computer,
and/or a remote controller device that incorporate a node
manager.
[0028] Nodal energy equipment generally includes equipment that is
configured to supply and/or store energy (e.g., with a bank of
batteries, fuel cells, compressed air, etc.). Nodal energy
equipment can include, for example, conventional generators (e.g.,
coal, gas-fired, hydro-powered, nuclear, etc.) and/or renewable
energy generators (solar, wind, geothermal, hydro-powered, etc.).
In some embodiments, nodal energy equipment can both deliver and
store energy. For example, in a smart grid, an electric or hybrid
car can receive and store energy from the grid and provide energy
back to the grid to meet peak demand, load balance, etc.
[0029] As described herein, nodal energy equipment is described in
the context of a solar tracker system or equipment 200 ("tracker
200"; FIG. 2A). In other embodiments, however, other types of nodal
energy equipment are possible. Further, some aspects of the tracker
200 can be configured to provide other advantages. For example, the
mechanical, operational, as well as the other aspects of the
tracker 200 can provide certain advantages relevant to equipment
for harvesting solar energy generally.
[0030] FIG. 2A is an isometric view of a tracker 200 configured in
accordance with an embodiment of the present technology. The
tracker 200 includes a tracker assembly 203 and tracker electronics
205 (shown schematically) operably coupled to the tracker assembly
203. The tracker assembly 203 includes a base 210, a rotatable
frame 220, and a pivoting frame 230 suitable to carry a solar panel
array 206 (e.g., one or more solar panels of photovoltaic cells).
Various aspects of the base 210, the rotatable frame 220, the
pivoting frame 230 and associated motor(s) and actuator(s) are
described in greater detail with reference to FIGS. 2A-2E
together.
[0031] FIG. 2B is a side view of a tracker 200 and tracker assembly
203 and FIG. 2C is a top plan view of the base 210 configured in
accordance with embodiments of the present technology. In reference
to FIGS. 2B and 2C together, the base 210 can include a track
member 212 supported by a plurality of foot plates 214. In one
embodiment, the track member 212 can be a frame (e.g., metal tubing
frame) having a symmetrical (e.g., round, circular, etc.) shape
with an outer circumference C.sub.1. The foot plates 214 can, for
example, be metal footings configured to support the track member
212 above the ground or other surface and to anchor the tracker 200
to the ground or other surface. The foot plates 214 can be secured
to the track member 212 around the circumference C.sub.1 of the
track member 212. In some embodiments, additional support members
216 (e.g., angled support bars or straps) can couple the track
member 212 to the foot plates 214.
[0032] Referring to FIG. 2C, the base 210 can also include a motor
240 (e.g., a slew motor; drawn in phantom) on which a base member
217 can be fixed. The motor 240 can be configured with support
structures (not shown) and configured to rotationally turn the base
member 217 around a Z-axis (e.g., azimuth, FIG. 2A) with respect to
the track member 212. In one particular embodiment, the motor 240
can include a slew motor from Kinematics Manufacturing, Inc., of
21410 N. 15.sup.th Lane, 104, Phoenix, Ariz. 85027. The base member
217 includes radially extending arms 218 coupled thereto. The
radially extending arms 218 can include wheels 219 (drawn in
phantom) for engaging and moving along the track member 212. For
example the individual wheels can be attached at a distal end 215
of each arm 218 such that each individual arm 218 has an arm length
L.sub.1 that extends between the base member 217 and the track
member 212. Also, the wheels can ride on a round tube to minimize
friction and minimize dirt/dust accumulation on the track. Minimal
surface contact between the wheels and the track allows wheels to
cut grass or other plants that grow over the track, negating the
need to significantly alter the landscape surrounding the tracker.
The wheels can be made from a pipe surrounding low friction
bearings to minimize the torque on the centralized slewing drive,
allowing the use of a smaller, less expensive slewing drive. The
wheels can also be made from a pipe long enough to still contact
the track if any settling or anomalies occur that cause the track
to be not perfectly circular.
[0033] The motor 240 can drive rotation (e.g., in either direction
around the Z-axis) of the base member 217 (e.g., using a drive
shaft; not visible in FIG. 2C) such that the wheels 219 fixed to
the radially extending arms 218 move along the track member 212. In
some embodiments, the motor 240 and/or drive shaft can include
bearings or other friction-reducing members or coatings like
Teflon.RTM..
[0034] FIG. 2D is a partial backside view of a tracker assembly 203
configured in accordance with an embodiment of the present
technology. Referring to FIGS. 2B and 2D together, the tracker
assembly 203 also includes a rotatable frame 220 having towers 222a
and 222b (referred to together as 222) coupled to the base 210
(e.g., to the arms 218). Each tower 222 includes a plurality of
frame members 224 interconnected by bars 226 (e.g., such as metal
tubing). The frame members 224 can be coupled, at one end (e.g., a
first end), to the arms 218 of the base 210. The frame members 224
can culminate (e.g., at a second end) at a tower peak 225a and 225b
where a hinge 228 is provided for pivotally attaching the pivoting
frame 230. The rotatable frame 220 can also include the actuator
250 (as best seen in FIG. 2B), such as a linear actuator) for
engaging and moving (e.g., pivoting) the pivoting frame 230 with
respect to the rotatable frame 220 and around the X-Y axis (FIG.
2A). The actuator 250 can be coupled to the rotatable frame 220 and
move with respect to the rotatable frame via pins 251, 252 (only
the pin 251 is visible in FIG. 2D) around which the actuator 250
can pivot (e.g., pivot about 90.degree.). In one particular
embodiment, the actuator 250 can include a linear actuator from
Linak, Inc., of 2200 Stanley Gault Parkway, Louisville, Ky.
40223.
[0035] The pivoting frame 230 can be configured to carry, orient,
position, or otherwise hold a solar panel array 206 (e.g., via a
unistrut attachment 253). The solar panel array 206 can comprise a
plurality of photovoltaic cells suitable for converting solar
energy into electrical energy. In one particular embodiment, the
solar panel array 206 can include one or more solar panels (e.g.,
240 Watt) from SunPower Corporation, of 77 Rio Robles, San Jose,
Calif. 95134. As described above, the pivoting frame 230 can be
pivotally coupled to the rotatable frame 220 via the hinges
228.
[0036] In one aspect, the tracker assembly 203 is configured to
utilize the solar panel array 206 to convert solar energy into
electrical energy. During operation, the tracker assembly 203 can
receive control signals (FIG. 2A) to move, adjust and position the
solar panel array 206 in a desired location and orientation for
collecting solar energy. For example, the rotatable frame 220 is
arranged with the base 210 such that actuation of the motor 240
turns or rotates the base member 217 and arms 218 in a manner that
translates rotation of the rotatable frame 220 around the Z-axis to
position azimuth. Additionally, the actuator 250 receives control
signals to position the pivoting frame 230 in a manner that the
pivoting frame 230 and the rotatable frame 220 together position
zenith. For example, the tracker electronics 205 can communicate
via the control signals the desired azimuth and zenith angles for
positioning (e.g., via the motor 240 and the actuator 250) the
solar panel array 206. As such, the tracker 200 is able to position
the rotating, pivoting tracker assembly 203 and to generate
electrical energy corresponding to the relative position of the
solar panel array 206.
[0037] In some embodiments, the tracker assembly 203 can transmit
the converted electrical energy directly to the tracker electronics
205. In other embodiments, the tracker assembly 203 may transmit at
least a portion the electrical energy to other components of the
tracker 200 or to remote components. For example, the tracker
assembly 203 may transfer a portion of the electrical energy
directly to a bank of batteries (not shown) for reserve power.
[0038] As described above, the tracker assembly 203 is configured
to receive control signals from the tracker electronics 205. In
response to some commands, the tracker assembly 203 positions the
solar panel array 206 by changing the orientation of the rotatable
frame 220 and the pivoting frame 230 via the combination of
associated motor(s) (e.g., motor 240) and actuator(s) (e.g.,
actuator 250). In the embodiment illustrated in FIGS. 2A-2D, for
example, the tracker assembly 203 positions the solar panel array
206 by rotation about a Z-axis (e.g., azimuth) in combination with
rotation about an X-Y axis (e.g., in the X-Y plane). In response to
other commands, the tracker assembly 203 sets operational
parameters. For example, the tracker assembly 203 can enable and/or
disable the operation of certain cells in the solar panel array 206
in response to the control signals.
[0039] In another aspect, the tracker assembly 203 is configured to
provide status information to the tracker electronics 205. The
status information can include, for example, present orientation of
the rotatable frame, the pivoting frame 230, and/or the solar panel
array 206. The status information can also include information
about the ambient (e.g., of the solar panel array 206, the output
voltage (or current) of the solar panel array 206 (or individual
cells therein)), operational aspect of the motor and/or actuator
(e.g., encoder position, overheat detection, speed sensors, etc.),
and ambient information (e.g., humidity, sunlight, temperature,
wind speed, etc.) in the vicinity of the tracker assembly 203.
Status information can also relate to the overall status of the
tracker, such as whether maintenance is required (e.g., when a
motor has malfunctioned, a circuit board needs to be replaced,
etc.).
[0040] Additionally the base 210 supports stable mounting of the
solar panel array 206 in a fail safe manner. For example, the
tracker 200 can include instructions for detection of imbalances
and/or other mechanical issues that would create tipping or other
unstable scenarios in conventional trackers. These imbalances and
other issues could be addressed rapidly by the tracker assembly 203
by moving the solar panel array (e.g., pivoting frame 230) into a
stable position (e.g., horizontal position, vertical position,
in-line with a wind direction, etc.) in real-time.
[0041] In one embodiment, many of the sensors 257a-257e (referred
to together as 257) can be located at a sensor box 255 that can be
attached at or near the pivoting frame 230 or, in other
embodiments, near to the actuator 250 (e.g., linear actuator). The
sensors 257 can be located at the interior and/or exterior of the
sensor box 255 and can include, for example, a heat sensor 257a
(e.g., for detecting a temperature reading at the solar panel array
206 or elsewhere), a light sensor 257b, an accelerometer 257c, a
compass sensor 257d (e.g., for detecting tilt azimuth), and a
position sensor 257e (e.g., for detecting pivot zenith). Other
sensors 257 are contemplated for including in the sensor box 255.
For example, vibration sensors, moisture sensors, clocks, timers,
etc. can also be included. By positioning all of the sensors 257 at
a common location (e.g., within a common housing or box 255), all
of the sensors 257 and/or other related data collection devices can
be mounted at the same time. Also by positioning the sensor box 255
at or near the solar panel array 206 or, alternatively, near the
actuator 250, the sensors 257 that detect position, velocity, etc.
are appropriately positioned to measure the rotating and pivoting
motion of the tracker assembly 203. Similar to the sensor box 255,
the tracker electronics 205 can be located within a common housing
or box 258 (e.g., a secure (lockable), weather proof box) that can
be mounted at or near the tracker assembly 203.
[0042] Other aspects of the present technology are directed to
methods of assembling the tracker assembly 203. In general, methods
in accordance with an embodiment of the present technology are
suited for quick assembly. FIG. 2E illustrates a method 260 of
assembling the tracker assembly 203 in accordance with an
embodiment of the present technology. The method 260 of assembling
a tracker assembly 203 includes preparing a deployment site by
digging footing holes in the ground, or in other embodiments (e.g.,
roof top assembly applications), preparing support structures to
couple foot plates 214 thereto (block 262). In some embodiments,
the deployment site does not need to be level. The method 260 can
also include assembling and leveling the base 210 at the deployment
site (block 264). Assembly of the base 210 can include, for
example, assembling the track member 212. In one embodiment, the
track member 212 may be provided as 4 arcuate pieces that can be
coupled to form a circular track member 212. Assembly of the base
210 can also include attaching a plurality of foot plates 214 to
the track member 212 and, in some embodiments, further securing the
foot plates to the track member with support members 216. Assembly
of the base 210 may further include providing the base member 217
and attaching the plurality of radially extending arms 218 to the
base member in a manner where the arms engage the track member with
wheels 219. Once assembled, the base 210 can be secured to the
deployment site via attachment of the foot plates 214 via poured
concrete or mechanical coupling (e.g., bolts, screws, pins, etc.)
and leveled. For example, the foot plates 214 can be set in the
holes in the ground or attached via other retaining structures
(bolts, screws, brackets, etc.) in a manner that levels the base
210 regardless of condition of the site.
[0043] The method 260 can continue with assembling the rotatable
frame 220 (e.g., assembling the towers 222, mounting the towers on
the base 210 via mechanical coupling (screws, bolts, etc.),
attaching the hinges 228 to the towers, etc.) and connecting the
motor 240 (e.g., slew motor) to the base 210 for rotating the
rotatable frame 220 (block 266). The method can further include
attaching the actuator 250 to the rotatable frame 220 with the pins
251, 252 (block 268) and mounting the pivoting frame 230 to the
hinges 228 of the rotatable frame (block 270). In operation, the
actuator 250 (e.g., a linear actuator) is positioned such that the
actuator can engage a backside of the pivoting frame 230 to move
the pivoting frame around the X-Y axis created by hinge points.
[0044] If not already completed, solar panels can be attached to
the pivoting frame 230 in an array. For example, solar panels can
be attached and carried by the pivoting frame in a 6.times.6 solar
panel array 206 as shown in FIG. 2A. Other solar panel array
configurations are contemplated. The method 260 can also include
installing a lockable and/or weatherproof electronics box 258
(e.g., for housing an electronic control module or tracker
electronics 205) (block 272). The method 260 can further provide at
block 274 assembling one or more wiring harnesses (e.g., having
quick connect/disconnect cables) to facilitate electrical
connections with the motor 240, actuator 250, sensor box 255, and
solar panel array 206 as well as other components, such as an array
of batteries (not shown). Further assembly and startup steps are
also contemplated. For example, the method 260 could include
completing assembly and installation of the tracker assembly 203
(block 276) and initializing a startup calibration protocol (block
278).
[0045] Accordingly, in contrast to conventional trackers that have
to have a large center hole dug for deep post installation,
installation of the tracker assembly 203 can be done with minimal
equipment (e.g., a wrench, stepladder, etc) and with minimal effort
(e.g., an average sized person). In one embodiment the components
of the tracker assembly 203 can be sized to meet Occupational
Safety and Health Act (OSHA) requirements such that a single person
of average size can carry, manipulate and/or otherwise assemble the
components of the tracker assembly 203. For example, the method 260
can be performed by one or two persons of average size without the
use of a crane or other special equipment to assemble and deploy
the tracker assembly 203. Many of the components of the tracker
assembly 203 can be off-the-shelf components (e.g., slew motor,
linear actuator, fabricated metal components) and could be
relatively light-weight to facilitate ease of assembly at a desired
site.
[0046] The tracker 200, the tracker assembly 203 and/or portions
thereof may be assembled and distributed as kits. The kits can
include tracker and/or tracker assembly components and instructions
for assembling, installing, and/or initiating use of the tracker
assembly 203. For example, the kit may include all metal or other
fabricated components for building and assembling the base 210, the
rotatable frame 220, and pivoting frame 230 (e.g., frame
structures, bolts and other coupling devices, motors, electronic
components, wiring harness, sensor box, etc.). As described above,
the components can be sized and of suitable materials to meet OSHA
requirements so that an individual person of average size can use
the kit to assemble the tracker assembly. Additionally, the kit can
include assembly instructions (written instructions, video
explanations, computer simulations, etc), such as, for example,
instructions on how to perform the method 260 described above. The
kit may also include other instructions, for example, instructions
on operation, maintenance and/or repair of the tracker 200 and/or
tracker assembly 203. In some instances, the kit may also include
one or more solar panels to mount in the pivoting frame 230.
[0047] FIG. 3A is a block diagram of the tracker electronics 205.
In the illustrated embodiment the tracker electronics 205 include a
CPU 302 (central processing unit) including at least one programmed
processor configured with memory to operate as a node manager 303.
The CPU 302 can also include interfaces and other components for
communication over a network and/or with other devices (e.g., via a
Mod Bus). The tracker electronics 205 further include a wireless
communication component 305, controllers 307, and inverter circuits
308. The wireless communication component 305 can be configured to
provide a direct wireless link for the CPU in addition to or in
lieu of standard networking capabilities. The controllers 307 are
configured to communicate control signals to the tracker assembly
203 to control the motor 240 and the actuator 250. The Inverters
308 are configured to receive solar generated electricity and
convert the energy into AC form. The inverters 308 can supply the
DC voltage to first and/or second power line interfaces 310a, 310b.
The inverters 308 can also convert stored energy at a battery bank
312 into AC form. In some embodiments, the inverters can convert AC
power from the grid or microgrid into DC form suitable for charging
the batteries at the battery bank 312 and/or for powering at least
a portion of the tracker electronics 205. In some embodiments, the
tracker electronics 205 can be powered by the solar energy
collected by the solar panel array 206. In still other embodiments
the battery bank 312 can be configured to provide power to the grid
or the microgrid.
[0048] In operation, the tracker electronics 205 are configured to
control the tracker assembly 203 and to receive status from the
tracker assembly. In one embodiment, a user can directly control
aspects of the tracker 200. For example, the user can directly
connect with the CPU 302 (e.g., via a USB link, wireless link,
and/or radio link) to, e.g., control the motor 240 and the actuator
250 and/or to receive status from the individual sensors at the
sensor box.
[0049] In other embodiments, the node manager 303 can be
incorporated into the CPU 302 to provide control signals and/or
receive status signals. In one aspect of operation, the node
manager 303 passively collects information but does not act on the
information. That is, the node manager 303 does not operate (i.e.,
control) the nodal equipment. In another mode of operation, the
node manager 303 can at least partially operate the nodal
equipment.
[0050] FIG. 3B is a flow diagram illustrating a routine 320
performed by the node manager 303. In the illustrated embodiment,
the routine 320 is performed by a node manager 303 at the CPU 302
of the tracker electronics 205. In other embodiments, however, the
node manager 303 can be incorporated at other portions of the
tracker electronics 205, such as at a logic controller. Further, in
other embodiments, the routine 320 can be executed at other types
of nodal equipment, including other types of energy equipment
(auxiliary).
[0051] At block 321, the routine 320 applies one or more rules that
dictate, at least in part, the operation of the node manager 303.
As described in greater detail below, these rules can be based on
nodal knowledge. In some embodiments, the rules can give the node
manager 303 certain control over the tracker electronics 205. For
example, the node manager 303 can be given at least partial control
over the motor 240 (to orient azimuth) and/or the actuator 250 (to
orient zenith). In one embodiment, the rule would dictate that the
node manager 303 automatically decrease the span range of the
pivoting frame in certain wind conditions.
[0052] In another embodiment, a first rule may dictate that the
node manager 303 disconnect from the main grid when power
generation at the grid is unstable (e.g., spikes) beyond a certain
threshold of power stability. As described in greater detail below,
the first rule may be based on a series of test rules and nodal
analytics that arrived at this particular threshold. In some
embodiments, a second rule may work in combination with the first
rule to dictate when the tracker 200 should connect to the
microgrid. For example, the second rule could dictate to the node
manager 303 to connect the battery bank 312 to the microgrid when
the battery is holding a sufficient amount of charge, or to keep
the battery bank disconnected until the battery bank has the
appropriate amount of charge. The rule could be based on nodal
knowledge (i.e., nodal knowledge 18, FIG. 1)) related to a weather
model in response to a rule related to a predicted weather
plan.
[0053] In some embodiments, the rules can be "birth certificate"
rules that set the initial operating behavior of the node manager
303. In one embodiment, a birth certificate can be loaded when the
tracker 200 is being assembled. When the tracker 200 is
operational, the birth certificate can dictate that the node
manager 303 self-calibrate the tracker assembly 203. By contrast,
conventional tracker-type devices can be difficult to set up in the
field because they can require complicated and time-intensive
calibration procedures. For example, installers need to manually
align these devices for compass heading and manually level the
devices with, e.g., the ground.
[0054] However, the birth certificate can provide rules that
instruct the tracker 200 to find proper compass direction using a
GPS sensor. In another embodiment, the birth certificate can
provide a rule that the node manager uses to auto-level the tracker
assembly. A tracker can be off level if the foot plates are
improperly installed or if the ground shifts. The birth certificate
rule can provide tuning adjustments that compensate for any out of
level alignment of the foot plates. In particular, the birth
certificate rule can instruct the motor 240 or the actuator 250 to
operate in a way that compensates for out of level alignment. In
another embodiment, the sensors 257 from the sensor box 255 can be
used, for example, to detect the position of the sun, GPS location,
etc., which could also be useful for initial calibration.
[0055] At block 322, the routine acquires nodal information 14
(FIG. 1) by sensing, detecting, requesting, or otherwise acquiring
nodal information. For example, the routine 320 can sense ambient
conditions, request information over a network, or be pushed
information without a request. The nodal information 14 can relate
to, for example, ambient conditions (e.g., barometric pressure,
humidity, sunlight, wind speed, temperature, etc.), operating
conditions (e.g., operational status of sensors, motors, grid
signal quality, battery capacity etc.), and/or performance (e.g.,
array output power, conversion efficiency, etc.). The routine 320
can acquire nodal information 14 using, for example, the sensor box
255, network communications, and/or the wireless or radio network
305 (FIG. 3A).
[0056] In many embodiments, the routine 320 can time/date stamp and
GPS stamp the nodal information 14. In this way the tracker 200 or
a remote device can use the nodal information 14 to derive spatial
temporal analytics information. As described in greater detail
below with reference to FIGS. 4A-4D, the nodal information 14 can
be utilized in a variety of network configurations, such as cloud
networks, local networks, network islanding, etc.
[0057] At decision block 323, the routine 320 determines whether to
push (e.g., transmit) some or all of the nodal information 14 to at
least one other nodal device (e.g., nodal equipment 12, FIG. 1),
such as a remote server, a local computer, a maintenance
technicians' portable computing device, etc. In some embodiments,
the other nodal devices can use the nodal information 14 to develop
analytics that are fed back to the routine 320 (see adjacent flow
routine A to B). In one embodiment, the other nodal devices can use
the nodal information 14 to derive nodal analytics/knowledge 16,
18. In another embodiment, the nodal analytics 16 and/or nodal
knowledge 18 could be used by a tracker manufacturer to develop
birth certificates (see above) for new tracker installations. In
this way, newer trackers 200 could be as "smart" as existing
trackers 200 (i.e., by passing on the same knowledge to new
trackers). For example, a "new" tracker 200 could learn from an
"old" tracker that operating at certain motor speeds for a given
amount of temperature, humidity, etc., could cause malfunction.
[0058] At decision block 325, and if the routine 320 determines
that data is to be pushed (block 323), the routine 320 further
determines if it should apply at least a portion of the nodal
analytics 16. In some embodiments, the routine 320 can delegate
more computationally intensive tasks to another nodal device (e.g.,
to perform processor intensive correlations), but still perform
less computationally intensive tasks (e.g., data comparison). In
other embodiments, the routine 320 can have all analytics delegated
to another node manager 303.
[0059] At block 327 the routine 320 processes nodal information 14
according to various nodal analytics 16. An example of such
correlations is described below with reference to FIG. 3C.
[0060] At block 328 the routine 320 develops nodal knowledge 18
based at least in part on the nodal analytics 16 applied at block
327. In particular, the routine 320 can decide in a rule making
stage whether a particular rule should be tested (or further
tested) before it is adopted, whether the rule should be discarded,
or whether the rule should be deemed true (e.g., an expert opinion
rule that is deemed to be fact or that is based on manual
intervention from a user). In one embodiment, certain nodal
information 14 can cause nodal analytics 16 to substantially change
a rule.
[0061] At decision block 329 the routine 320 determines whether it
should grant nodal control to the nodal device. In some embodiments
the nodal device is passive. For example, the nodal device can be
suited to gathering data but not controlling the position of the
tracker 200. In another embodiment, the nodal device could be a
consumer device used to gather data for analyzing a user power
consumption behavior. For example, a transaction could include
incentives for users to provide information relating to their
consumption behavior. A discounted price for such a unit could be
offered in exchange for this information. This information could be
useful for creating nodal knowledge 18, such as consumer power
consumption. Table 1 provides various other examples of nodal
information 14.
TABLE-US-00001 TABLE 1 Local Nodal Information Generic Nodal
Information Wind speed SCADA (e.g., data from grid) Wind direction
Grid Voltage Ambient temperature Grid power factor Barometric
pressure Grid configuration Ambient/Planar irradiance (sun Battery
charge levels ambient) Available discharge power Solar cell
temperatures Signal conditioning Motor currents User demand Motor
temperatures Demand response Tilt/Azimuth User consumption Real
time (time stamp) Consumer pricing GPS location
[0062] A fundamental problem with incorporating solar energy into
existing electrical grids is that the grids must have enough
capacity to meet peak demands when aggregate demands are highest. A
conventional solution is "pure peaker" power plants. Conventional
solar generation is typically not suitable as a pure peaker because
even though its availability is predicable, it cannot be
guaranteed. Solar generation has low inertia; generation can be
online/offline almost instantaneously. Conversely traditional power
plants (coal, nuclear) require days to cycle. Load following gas
plants require 30 to 90 minutes. The demand for ancillary services,
which is typically over multiple intervals on any given day, occurs
when power must be added to or taken off grid in a matter of
seconds to regulate voltage or power factor corrections.
[0063] For these and other reasons the inclusion of conventional
solar systems at significant levels introduces problems that will
increase with additional solar deployment (PV penetration). Due to
both the intermittent and low inertia of conventional solar
generation, significant disruption to the generation hierarchy of
base load and "pure peaker" plants results in significant price
volatility. Additionally, utilities must keep electricity in
reserve to provide for supply disruptions or demand spikes, and
they must regulate voltage and keep it steady. Both imperatives are
difficult when solar resources come online/offline abruptly.
[0064] Referring to FIG. 4A, embodiments of energy distribution
systems, or autonomous energy networks (AENs) configured in
accordance with embodiments of the present technology can provide a
complete solution from deployment to management of a fully
autonomous distributed power plant. The successful realization of
an efficient and reliable solar energy source can include: [0065]
Targeting "nonproductive," sun-advantaged land close to power lines
for the location of equipment. The ideal property is suburban/urban
brown lots that are on the local substation feed to where the power
will be used. Although, with the use of virtual net metering, these
geographic limitations are bypassed allowing the generation site
and energy consumption to spread across the utility service area. A
low-profile, small footprint installation facilitates use of
marginal nonproductive land and flat roofs. [0066] Solar trackers
can enable any solar installation to harvest high concentrations of
solar flux anywhere on the globe. This can realize, for example, up
to a 50% increase over fixed angle installations for the same solar
panels. This increased production for the same number of solar
panels saves on land, labor, and components costs. Matching
production with local consumption and grid conditions is possible
with the addition of multiple solar trackers. For any solar panel,
maximum energy harvest is guaranteed throughout the year. [0067]
The tracker electronics 205 can employ a Smart Controller and
Analysis Node (SCAN) that includes an Expert Systems Inference
Engine (ESIE) embedded into each installation to provide dynamic,
rules-based decision response to real-time events on the grid and
at the local installation. Decisions whether to store, consume
power onsite, or net meter are events that need to take in a myriad
of changing conditions. [0068] These rules can be generated in the
AEN data center to take advantage of an aggregation of many
installations and events that are happening on the grid (DNR,
supply interruptions, real time pricing, etc.) and then transferred
to the local installation.
[0069] To take advantage of high efficiency solar trackers and
provide reliable generation sources, there can be guaranteed
minimum down times and the ability to proactively operate and
maintain (O & M) a large number of installations remotely with
a minimum of service truck rolls. Each local SCAN installation
combined with the AEN can provide installation flexibly with the
added sophistication of remote analytics and management. The AEN
platform can provide an active intelligent "Expert System" presence
at the edge of the grid through, e.g., an integrated cellular
communications network.
[0070] Each installation can include a computer, such as a Linux
based computer, with SCADA (Supervisory Control And Data
Acquisition) capabilities and an Expert Systems Inference Engine
(ESIE). This decision expertise manages the local installation and
provides remote access through, e.g., a cellular network
connection. The AEN's SCADA API (Application Program Interface)
allows QES, partners, and utilities access to local grid
operational data and a platform for advanced smart grid
applications. The ESIE enhanced supervisory capabilities manage the
advanced power harvesting (Learned Energy Ray Normalization, LERN)
algorithms for sun positioning for up to 20 solar trackers. Data is
gathered, analyzed, and reported to the AEN network for near
real-time and historical tracking. The ESIE management proactively
monitors trending and exception events that are processed at the
local site and reported to the AEN. Modified ESIE rules are
downloaded when new "insight" is generated from the AEN master ESIE
processing aggregate data sets of a large number of installations
plus third party data bases (real time market pricing, DRM
requests, national weather services, solar forecasts, etc.).
Various related functions can include: [0071] Solar PV panels and
micro-inverters provide power and data (inverters; continuous power
output and grid voltages). This data is cached, analyzed, and
compressed for upload to the AEN storage for analysis providing
predictive maintenance, warranty, and billing functions. [0072]
Voltage control at the point of power consumption. This function
can monitor the voltage and power factor of the grid voltage.
Problems can be rectified, for example, in one or more of the
following ways: [0073] Low voltage [0074] Add capacitance to
correct the power phase of the grid voltage. This action corrects
under voltage conditions where there is distortion of grid power
factor from large reactive loads (motors, NC units, etc.). [0075]
Discharge storage device. [0076] High voltage [0077] Slightly
turning gantry away from sun to reduce generation. [0078] Diverting
generation into charging of storage devices. [0079] Sensor inputs
from the physical actions of the solar tracker and the local
environment can include: [0080] Weather station (wind speed, wind
direction, temperature, humidity, solar irradiance, precipitation,
barometer, reference solar irradiance). [0081] Spatial orientation
sensors of the real world position of the gantry as opposed to
where it is calculated to be. This sensor can also be used to
detect unauthorized gantry manipulation or attempted theft. [0082]
Absolute reference sensing of true south compass direction. [0083]
Additional I/O for controlling local hardware, measuring local
variables, and discrete inputs. SCADA functions can include: [0084]
Control of each axis motor and correct operation throughout the
controlled sun tracking moves. [0085] Measurement of grid voltage.
[0086] Measurement of grid power factor. [0087] Monitor of local
controller supply voltage (voltages delivered to each motor).
[0088] Status of local network (Modbus, Ethernet, USB, Cellular
radio). [0089] Monitor and control override switches for local
maintenance operations. [0090] iOS (iPhone, iPad, etc.) control
surface for local control of installation. [0091] Calculation of
the position of the sun. [0092] Validate correct movement of the
gantry, monitor long term mechanical drifts of motors and gantry
movements, and apply correction. [0093] Logging of power generation
and performance metrics, compress, analyze, and transmit to AEN
remote secure server. [0094] Update software modules through secure
network connection. [0095] Provide real-time secure access for
troubleshooting. [0096] Provide local sun intensity information for
cloud prediction services [0097] Interface to existing utility or
installed generation and control equipment. [0098] Provide micro
climate information, such as current solar insolation value,
temperature, barometric pressure, wind speed, wind direction,
and/or precipitation. ESIE functions can include: [0099] Update
local ESIE current rule sets. [0100] Calculate long term drifts on
power generation and apply corrections (LERN). [0101] Provide
voltage control action decisions. [0102] DMR action decisions.
[0103] Manage intelligent power storage and dispatch with various
storage and generation options, such as batteries, fuel cells,
and/or compressed Air. [0104] Provide intelligent islanding logic,
such as automated islanding reconnection from grid when unstable
grid conditions exist, demand response requests from utility to
take site offline on low voltage (brown out) conditions,
intermittent, or blackout conditions, and/or provide power to
on-site customers during local blackouts. [0105] Real time load
measurement and management of customer electricity consumption for
energy use profile optimization. This function can be augmented by
use of a local appliance control network such as ZigBee for
granular onsite energy management. [0106] Secure site with motion
(gantry) and video surveillance.
[0107] In various embodiments, the AEN can include cloud based data
and control network connects to each SCAN installation allowing the
aggregation of information and services to be near real time,
historical, proactive, and predictive. Each SCAN computer has the
intelligence to perform the tasks demanded for complete autonomous
operation if it never connects to the AEN cloud. This can allows
for intermittent operations as standalone entities to optimize
various network configurations and real world limitations of
network performance. Referring to FIG. 4B, in the cloud, there can
include a few replication nodes, a number of directory nodes, and a
larger number of data nodes. In one implementation, one might run
all of these nodes on virtual machines in, e.g., Amazon data
centers. Directory nodes can manage information and topology about
all other nodes and help nodes find one another. Data nodes store
data, and replication nodes manage pulling data from
less-accessible or slower data nodes (e.g. tracker nodes) and push
data to bigger machines with more reliable network
connectivity.
[0108] In normal operation, a solar node collects data and then
locates the local data node by consulting the directory node. The
directory node also knows about other directory nodes in the cloud,
and can consult them to find data nodes running in the cloud to
push data to as well. Most of the intelligence about where data
should go is handled by the replication nodes in the cloud; the
solar node mostly pushes data to whichever is the closest data node
according to the directory node.
[0109] In disconnected mode, the solar nodes function as before.
However, the directory nodes cannot find the authoritative
directory nodes, so they coordinate among themselves and elect a
gantry to start a local replication node. This node manages local
storage space for the island by prioritizing data so that the
remaining storage is balanced between data nodes, and, in the event
one needs to discard data, the lowest-priority data is discarded
first. Once the network is functioning again, the replicator node
coordinates reconnection to the rest of the network, and the
queued-up data drains out to the data nodes in the cloud.
[0110] Note that there may be situations where some trackers 200 in
a group may be in a state of permanent disconnection while others
in the same group have normal network access. In this case, the
local replication node hangs around to replicate data from the
partially-disconnected data nodes out to more available data
nodes.
[0111] The AEN can also provide other features, such as: [0112]
Aggregation and storage of information from all subscribing
installations. [0113] Provide API interface to data set for both
in-house use and subscribers: [0114] Micro environment weather data
over a large geographic area, providing near real-time and
historical information. [0115] Near real-time and historical local
grid conditions. [0116] Voltage [0117] Power factor [0118] Outage
information [0119] Near real-time power and historical power
generation from any subscribing SCAN installation. [0120] Near
real-time and historical power usage of local site consumption and
energy use profiling. [0121] Individual SCAN and aggregate solar
generation forecasting. [0122] DRM service requests. [0123] Voltage
regulation services. [0124] Power factor correction services.
[0125] Operations & Management of SCAN installations. [0126]
SCAN ESIE rule sets generation and updating. [0127] SCAN program
updating. [0128] Analyze long term drifts on power output of each
PV panel. [0129] Expert systems scheduling of service and
maintenance. [0130] Warranty monitoring for performance guarantees.
[0131] Power generation metrics for billing of PPA installations.
[0132] Large account corporate management of multiple sites [0133]
Utility planning and provisioning
[0134] In some embodiments, the knowledge base on the network cloud
handles logistics, the installation process, the sales process, and
even the permitting process to ensure that there is maximum
efficiency, reliability, and minimum cost from sales lead
generation to maintenance schedules. This is all exposed to
employees and dealers through a graphic interface (see below) that
runs on a computer, laptop, tablet or smartphone. Each employee has
a certain level of security clearance that allows them to see or
not to see certain information on their particular account.
[0135] The SCAN installation can be a complete turnkey solution
that provides the site customer an efficient and reliable power
source that is remotely managed and is maintained as an evolving
intelligent smart grid power generation asset. The customer need
not have any expertise in power production equipment, networking,
or operations and maintenance. All functions are managed by onsite
intelligence supported by integrated, offsite management.
[0136] Smart grid capability built into both the onsite SCAN and
remote AEN offer the utility the ability to see what is happening
at the customer end of the grid network. These features alleviate
the problems of blind solar deployments (residential rooftops) that
offer no visibility to the grid operator. With the addition of a
SCADA API, integration to the utility management software platforms
is enabled. This offers real value to the utility by providing and
integrated and reliable power source where power is consumed. By
solving the PV issues articulated above, the PV penetration problem
is minimized allowing solar energy to be an asset rather than a
problem to the grid.
[0137] Referring to FIG. 4C, an AEN controller center can be
arranged to operate various aspects of a microgrid. Referring to
FIG. 4D, a microgrid licensing/investing/purchasing scheme can
include technology licensees, exchange investors, independent
generators, independent dealers/franchisers, power purchase
agreement providers (PPA), and a microGrid Energy Independence
Exchange. As shown in this scheme, various network fees, service
residuals, licensing fees, investments, and dividends can be
generated.
[0138] Referring to FIG. 3C, the following disclosure describes
certain aspects of the learning process employed by node managers
(e.g., via node nodal analytics and knowledge). In one embodiment,
node managers can gather sensor data from onsite sensors with time
stamps and serial numbers. Node managers can further gather
third-party data based on the locations and time stamps of the
sensor data. Some examples of possible third-party data may
include, but are not limited to, one or more of: [0139] a. Utility
SCADA requests [0140] b. Weather data [0141] c. Real time energy
pricing [0142] d. Grid Battery storage data
[0143] The node manager can also determine minimum sample size for
99% confidence using: [0144] i.
[0144] n = [ Z a / 2 .sigma. E ] 2 ##EQU00001## [0145] ii. n=sample
size [0146] iii. Z.sub.a/2=critical value=value of standard
distribution at 97.5 percentile confidence level [0147] iv.
E=margin of Error [0148] 1. Simple approximation of margin of error
at 99% confidence is:
[0148] E .apprxeq. 1.29 n = Z a / 2 .sigma. n ##EQU00002## [0149]
v. .sigma.=standard deviation of x number of time stamped
events
[0149] .sigma. = 1 n i = 1 n ( x i - .mu. ) 2 , where .mu. = 1 n i
= 1 n ( x i ) ##EQU00003##
[0150] In one embodiment, if minimal sample size is met for 2 or
more data points, there can be a test for correlation: [0151] a. If
the absolute value of the correlation coefficient (determined by
the dot product of two value vectors or by the Pearson
product-moment correlation coefficient) is greater than 0.95 then a
rule may be made automatically and sent to manual processing for
quality assurance (since correlation does not always mean
causation). [0152] i. A theoretical example of this process is as
follows: [0153] 1. Sufficient sample size is met for correlation of
3 data points. [0154] 2. Correlation coefficient of 0.96 between
over voltage and irradiance levels over 980 W/m.sup.2 when ambient
temperature is below 84 degrees Fahrenheit. [0155] 3. A New Rule is
made for over voltage correction based on irradiance levels when
temp is below 84 degrees. [0156] 4. The software model would then
test to find weather conditions that cause 84 degree temperatures
and irradiance levels in excess of 980 W/m.sup.2 to determine if
over voltage may be forecasted. [0157] ii. A theoretical counter
example (local rule testing) is as follows: [0158] 1. Sufficient
sample size is met for correlation of 3 data points. [0159] 2.
Correlation coefficient of 0.74 between under voltage and battery
bank discharge request for 500 kW+/-50 kW on Saturday between 1 and
3 pm PST in La Jolla, Calif. [0160] 3. A New Rule is not made.
[0161] 4. Rule is sent for manual processing. [0162] 5. Manual
processing makes a new TEST rule that states: [0163] a. Between 1
pm and 3 pm all units within a 20 mile radius of La Jolla, Calif.
log voltage values and test against temperature range. [0164] iii.
A second theoretical counter example (the case of statistical
insignificance) is as follows: [0165] 1. Sufficient sample size is
met for correlation of 2 data points. [0166] 2. Correlation
coefficient of 0.04 is determined between demand response requests
from utility SCADA on Monday at 5:50 pm local time. [0167] 3.
Correlation is thrown out and determined statistically
insignificant.
[0168] The node manager can also employ manual intervention for low
correlation coefficient (greater than 0.05, less than 0.95
correlation coefficient based on sufficient sample size): [0169] a.
Statistically significant correlations can be manually examined to
further test the correlation. [0170] b. Modifications in the rule
set for both the network cloud and the distributed expert systems
are used to reach a sufficiently high correlation coefficient.
[0171] i. For example: If the correlation coefficient is 0.05, a
manual TEST rule can be implemented to broaden the scope of
correlation in order raise or lower the coefficient. [0172] 1. If
the coefficient stays the same or goes below 0.05, the correlation
is discarded. [0173] 2. If the coefficient goes up but is still
below 0.95 a new TEST rule can be implemented to broaden the scope
of correlation.
[0174] TEST rules are then used by the expert systems to find
positive correlations, negative correlations, or statistical
insignificance. The utility grid is affected by a statistically
infinite number of stimuli from macro level weather patterns that
cause droughts or floods to micro level human activities like
sporting events. This makes manual processing important in order to
zero in on new rules by expanding the scope of testing when
necessary instead of trying to correlate the statistically infinite
number of data points caused by grid stimuli. For example, if the
grid is experiencing under voltage every weeknight between 5:00 pm
and 6:00 pm with a correlation coefficient of 0.80, a TEST rule
could be implemented to test this correlation vs. different
temperature ranges. This is because under voltage may only occur at
high or low temperatures due to air conditioning or heater use. The
65 to 80 degrees Fahrenheit temperature range may not experience
under voltage, which may be causing the correlation coefficient to
be 0.80. After the new TEST rule is implemented to check specific
temperature ranges, the correlation coefficient of under voltage to
time of day (5:00-6:00 pm) becomes 0.96. A new rule is made to
charge batteries prior to 5:00 pm only when temperature ranges are
lower than 65 or higher than 80 degrees Fahrenheit.
[0175] TEST rules, in some embodiments, can be predictions,
observations, metrics, or desired outcomes that can be tested
against events that occur. Correlations can be made between TEST
rules and mined data from sensors and third-party databases.
Correlation coefficients of less than 0.05 are determined
statistically insignificant and can be ignored.
[0176] Rules can be made for: [0177] a. How to make rules, [0178]
b. How to establish a value hierarchy of a set of rules, [0179] c.
When to log data, [0180] d. What data to log, [0181] e. How to log,
analyze, and correlate data, [0182] f. Taking action to solve
problems on the grid, the network cloud management system, or the
local sites, [0183] g. When to buy electricity from the grid and
when to sell power back to the grid, [0184] h. When to charge
batteries and when to discharge batteries, [0185] i. When to pull
off the grid when things become unstable (islanding), [0186] j. How
to respond to automated utility requests (i.e. demand response,
microgrid, islanding, etc.), [0187] k. When to respond to anomalies
(based on standard deviation), and [0188] l. Which predictions,
observations, metrics, or desired outcomes should be cross
correlated with mined data.
[0189] FIGS. 5A-5E provide various example user interfaces that can
be used with a node manager. Table 2, below, shows various examples
of application interfaces for access with a node manager.
TABLE-US-00002 TABLE 2 Tracker control: automatic/manual mode
toggle manual positioning device calibration device configuration -
birth certificates, re-provisioning, latitude/longitude, tracking
interval, various other things we discover to be useful, knobs
maintenance techs device security management Access to
current/historical tracker log data: panel orientation motor motion
statistics (movement errors, exceptional conditions, etc.) motor
amperage computed sun position (later) LERN information - how far
the sun is from where we computed it to be, and other data
collected to refine our solar positioning model power generation
metrics and other power-related data Access to node/network event
logs (for diagnostics): related/neighbor nodes local software
configuration (which nodes are running where, node status) data
pushed (which data, and to where) rules available for
decision-making rules applied startup/shutdown/sleep events
Real-time and historical smart grid data access: aggregate grid
statistics: power generation, changes throughout the day, seasons
local and aggregate grid performance statistics "post-mortem" data
on how well our tracking and rules performed compared to the "ideal
model" aggregate statistics by location, kind of installation, size
of installation, or age of installation
[0190] The following description describes certain aspects of
Internodal Bidding. Each node provides one or more services and/or
products that vary in price based on the supply and demand of those
services or products. In order to set an accurate market price for
transactions that can happen frequently (e.g., every millisecond),
there must be an automated system.
[0191] In some embodiments, node managers can be accessed through
the nodal access interface where a minimum price can be set for the
services or products offered by that particular node. Throughout
each day, Internodal Bidding occurs autonomously for each service
or product based on the minimum acceptable value (MAV) for that
service or product. For example, this could occur when a third
party requests a particular service or product or when the energy
network requires a certain service or product to maintain
reliability.
[0192] In some instances, Internodal Bidding can be similar to the
way Google allows users to set a Maximum Cost Per Click (CPC) on
certain advertising. In the Google scenario, users can set
parameters for many different outcomes (e.g. maximum exposure of
advertisement, lower cost per click, a mixture of exposure and cost
considerations, etc.) Those willing to pay the highest CPC have the
best chance of winning many bids and ensuring the most exposure.
Those trying to save money, run the risk of limited or no
advertising exposure. Internodal Bidding can occur in a similar
fashion, with the exception that nodes are set to provide a service
or product for a MINIMUM acceptable value. Suppliers of nodes that
are willing to accept a smaller value for their service or product
will likely sell more product or service. For example, if a node
has a limited supply, suppliers will likely make their money in
times of peak demand. If a node has a large supply, suppliers may
choose to bid very low in order to sell the most services or
products.
[0193] One of ordinary skill in the art will recognize that many
parameters associated with cost and demand as well as micro- and
macro-transactions can apply to the concept of Internodal Bidding.
In particular examples, Internodal Bidding has several parameters
that can be altered or adapted to allow suppliers associated with
nodes to maximize their revenue. For example, certain MAV
parameters may include setting different MAVs for different times
of day, setting different MAVs for different times of year,
establishing different MAVs for different services or products,
establishing different MAVs for different levels of storage
capacity, determining different MAVs for different weather
patterns, and/or establishing different MAVs during certain
predictable local events (e.g. sports events, festivals, etc.).
[0194] In many embodiments, Internodal Bidding can allow for energy
products and services to maintain a reliable service while keeping
the cost of energy and reliability as low as possible (e.g., much
like Google has done for the advertising industry). Furthermore, it
also allows the largest energy plants as well as the smallest
single energy producer to participate in the energy market.
Example
[0195] Node 150 contains 64 kW of solar panel capacity and 128 kWh
of storage. The Node's MAV for stored kilowatt hours is 60
cents/kWh. This is a very high price per kWh considering that the
local retail price of electricity is 18 cents/kWh.
[0196] The local utility sends out a "spinning reserve" peak demand
request for 60 kW of capacity for the next 2 hours. Their bid price
is 65 cents/kWh because the utility's only other option is to cut
service at that particular substation.
[0197] In this example, Node 150 rises to the top of the bidding
with its 60 cents/kWh bid. Node 150 wins the bid and supplies 120
kWh of capacity for 60 cents/kWh. Node 150 would have received 8
cents per kWh if sold during off-peak times or $9.60. Node 150
would have received 18 cents per kWh if sold at retail or
$21.60
[0198] Because of Automated Internodal Bidding, Node 150 was able
to receive $72.00 for just one battery charge cycle. Accordingly,
Internodal Bidding allows renewable energy and storage devices to
provide the same services supplied today by fossil fuel powered
generators and power plants in just milliseconds rather than
several minutes, hours, or even days. In particular instances of
application, the larger the network of nodes becomes, the more
reliable the products and services can become.
[0199] FIG. 6 is flow diagram illustrating an Internodal Bidding
scheme 600 in accordance with an embodiment of the present
technology. The scheme 600 can include setting a MAV through a
Nodal Access Interface (block 602). The scheme 600 can continue at
decision block 604 where the scheme 600 can determine if the
request is manual. If the request is manual, the scheme 600 can
continue to block 606 where it is determined if the request is
within bidding parameters (e.g., established parameters, pre-set
parameters, etc.). If the request is not manual, the scheme 600 can
determine if an automated service is necessary (decision block
605). If the automated service is necessary, the scheme 600 can
then continue on to block 606 to determine if the request is within
bidding parameters. If an automated service is not needed, the
scheme 600 can return to block 602 where a MAV is set through the
Nodal Access Interface.
[0200] If the request is within bidding parameters, the scheme 600
can continue to decision block 608 where it is determined if the
bid is low enough. If the bid is low enough, the scheme 600 can
provide the product or service requested (block 610). If the bid
exceeds a threshold (e.g., is not low enough), the scheme 600 can
return to block 602 to set a MAV through the Nodal Access
Interface.
[0201] FIG. 7 show various system logs than can be generated by a
node manager during a single day of operation of a solar tracker in
accordance with an embodiment of the present technology. Table 3,
below, shows example setup parameters corresponding to the system
logs of FIG. 7.
TABLE-US-00003 TABLE 3 A day in the life of a distributed solar
power plant deployed into the Southwest United States. Assumptions:
Installed SCAN installations 3000 Gantries installed 10,800
(average 3.6 gantries per SCAN) Installed Capacity 87.48 MWatt
Yearly average capacity factor 31.25% (7.5 sun hours, yearly
average) Total battery storage capacity 45,589 kWh Island enabled
SCAN 758 installations Voltage regulation of installed 55% capacity
15 MW (load shedding) Demand/Response capability Utility Dispatch
Services SCADA Utility API Island demand requests Voltage control
requests Storage discharge requests Load shedding requests Utility
Data Services: SCAN SCADA Analytic API Solar forecasting data Local
grid conditions data (voltage, power factor, outage) Micro climate
data sets
[0202] Embodiments of the subject matter and the operations
described in this specification can be implemented in digital
electronic circuitry, or in computer software, firmware, or
hardware, including the structures disclosed in this specification
and their structural equivalents, or in combinations of one or more
of them. Embodiments of the subject matter described in this
specification can be implemented as one or more computer programs,
i.e., one or more modules of computer program instructions, encoded
on computer storage medium for execution by, or to control the
operation of, data processing apparatus.
[0203] A computer storage medium can be, or can be included in, a
computer-readable storage device, a computer-readable storage
substrate, a random or serial access memory array or device, or a
combination of one or more of them. Moreover, while a computer
storage medium is not a propagated signal, a computer storage
medium can be a source or destination of computer program
instructions encoded in an artificially-generated propagated
signal. The computer storage medium also can be, or can be included
in, one or more separate physical components or media (e.g.,
multiple CDs, disks, or other storage devices). The operations
described in this specification can be implemented as operations
performed by a data processing apparatus on data stored on one or
more computer-readable storage devices or received from other
sources.
[0204] The term "programmed processor" encompasses all kinds of
apparatus, devices, and machines for processing data, including, by
way of example, a computer, a system on a chip (or multiple ones or
combinations of the foregoing). The apparatus can include special
purpose logic circuitry, e.g., an FPGA (field programmable gate
array) or an ASIC (application-specific integrated circuit). The
apparatus also can include, in addition to hardware, code that
creates an execution environment for the computer program in
question, e.g., code that constitutes processor firmware, a
protocol stack, a database management system, an operating system,
a cross-platform runtime environment, a virtual machine, or a
combination of one or more of them. The apparatus and execution
environment can realize various different computing model
infrastructures, such as web services, distributed computing and
grid computing infrastructures.
[0205] A computer program (also known as a program, software,
software application, script, or code) can be written in any form
of programming language, including compiled or interpreted
languages, declarative or procedural languages, and it can be
deployed in any form, including as a stand-alone program or as a
module, component, subroutine, object, or other unit suitable for
use in a computing environment. A computer program may, but need
not, correspond to a file in a file system. A program can be stored
in a portion of a file that holds other programs or data (e.g., one
or more scripts stored in a markup language document), in a single
file dedicated to the program in question, or in multiple
coordinated files (e.g., files that store one or more modules,
sub-programs, or portions of code). A computer program can be
deployed to be executed on one computer or on multiple computers
that are located at one site or distributed across multiple sites
and interconnected by a communication network.
[0206] The processes and logic flows described in this
specification can be performed by one or more programmable
processors executing one or more computer programs to perform
actions by operating on input data and generating output. For
example, tracker electronics, servers, mobile devices, etc., can be
implemented as a controller in an auxiliary device. The processes
and logic flows can also be performed by, and the apparatus can
also be implemented as, special purpose logic circuitry, e.g., an
FPGA or an ASIC. For example, the node manager can be implemented
as a controller in an auxiliary device.
[0207] Processors suitable for the execution of a computer program
include, by way of example, both general and special purpose
microprocessors, and any one or more processors of any kind of
digital computer. In some embodiments, the processors can be
selected according to the type of device.
[0208] Generally, a processor will receive instructions and data
from a read-only memory or a random access memory or both. The
essential elements of a computer are a processor for performing
actions in accordance with instructions and one or more memory
devices for storing instructions and data. Generally, a computer
will also include, or be operatively coupled to receive data from
or transfer data to, or both, one or more mass storage devices for
storing data, e.g., magnetic, magneto-optical disks, or optical
disks. For example, a server can transfer nodal information, nodal
analytics, and/or knowledge can be transferred to flash memory.
However, a computer need not have such devices. Moreover, a
computer can be embedded in another device, e.g., a mobile
telephone, a personal digital assistant (PDA), a mobile audio or
video player, a game console, a Global Positioning System (GPS)
receiver, or a portable storage device (e.g., a universal serial
bus (USB) flash drive), to name just a few. Devices suitable for
storing computer program instructions and data include all forms of
non-volatile memory, media and memory devices, including by way of
example semiconductor memory devices, e.g., EPROM, EEPROM, and
flash memory devices; magnetic disks, e.g., internal hard disks or
removable disks; magneto-optical disks; and CD-ROM and DVD-ROM
disks. The processor and the memory can be supplemented by, or
incorporated in, special purpose logic circuitry.
[0209] To provide for interaction with a user, embodiments of the
subject matter described in this specification can be implemented
on a computer having a display device, e.g., an LCD (liquid crystal
display), LED (light emitting diode), or OLED (organic light
emitting diode) monitor, for displaying information to the user and
a keyboard and a pointing device, e.g., a mouse or a trackball, by
which the user can provide input to the computer. In some
implementations, a touch screen can be used to display information
and to receive input from a user. Other kinds of devices can be
used to provide for interaction with a user as well; for example,
feedback provided to the user can be any form of sensory feedback,
e.g., visual feedback, auditory feedback, or tactile feedback; and
input from the user can be received in any form, including
acoustic, speech, or tactile input. In addition, a computer can
interact with a user by sending documents to and receiving
documents from a device that is used by the user; for example, by
sending web pages to a web browser on a user's client device in
response to requests received from the web browser.
[0210] Embodiments of the subject matter described in this
specification can be implemented in a computing system that
includes a back-end component, e.g., as a data server, or that
includes a middleware component, e.g., an application server, or
that includes a front-end component, e.g., a client computer having
a graphical user interface or a Web browser through which a user
can interact with an implementation of the subject matter described
in this specification, or any combination of one or more such
back-end, middleware, or front-end components. The components of
the system can be interconnected by any form or medium of digital
data communication, e.g., a communication network. Examples of
communication networks include a local area network ("LAN") and a
wide area network ("WAN"), an inter-network (e.g., the Internet),
and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).
[0211] From the foregoing, it will be appreciated that specific
embodiments have been described herein for purposes of
illustration, but that various modifications may be made without
deviating from the disclosed technology. The methods disclosed
herein include and encompass, in addition to methods of making and
using the disclosed devices and systems, methods of instructing
others to make and use the disclosed devices and systems. For
example, the operating instructions can instruct the user how to
provide any of the operational aspects of the Figures discussed
herein. In some embodiments, methods of instructing such use and
manufacture may take the form of computer-readable-medium-based
executable programs or processes. Moreover, aspects described in
the context of particular embodiments may be combined or eliminated
in other embodiments. Further, although advantages associated with
certain embodiments have been described in the context of those
embodiments, other embodiments may also exhibit such advantages,
and not all embodiments need necessarily exhibit such advantages to
fall within the scope of the presently disclosed technology.
* * * * *