U.S. patent application number 14/960283 was filed with the patent office on 2016-11-03 for charging profiles for a storage device in an energy generation system.
The applicant listed for this patent is SolarCity Corporation. Invention is credited to Eric Daniel Carlson, Tara Elizabeth Hobbs, Peter Rive.
Application Number | 20160322835 14/960283 |
Document ID | / |
Family ID | 57204098 |
Filed Date | 2016-11-03 |
United States Patent
Application |
20160322835 |
Kind Code |
A1 |
Carlson; Eric Daniel ; et
al. |
November 3, 2016 |
CHARGING PROFILES FOR A STORAGE DEVICE IN AN ENERGY GENERATION
SYSTEM
Abstract
A computer-implemented method for an energy generation site
includes detecting an energy storage device and its storage
capacity, detecting an electrical grid operatively coupled to the
energy generation site, and receiving event data corresponding to
an event affecting the electrical grid. The method further includes
determining a probability that the electrical grid will experience
a power outage based on the event data, and charging the storage
device according to a first charging profile or a second charging
profile based on the probability. A maximum charge set point of the
storage device for the first charging profile is less than the
maximum storage capacity of the storage device, and the maximum
charge set point for the second charging profile is at the maximum
storage capacity of the storage device. The event data can be
weather data, geological data, social media, or local alert
data.
Inventors: |
Carlson; Eric Daniel; (San
Mateo, CA) ; Hobbs; Tara Elizabeth; (San Francisco,
CA) ; Rive; Peter; (San Francisco, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
SolarCity Corporation |
San Mateo |
CA |
US |
|
|
Family ID: |
57204098 |
Appl. No.: |
14/960283 |
Filed: |
December 4, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62155444 |
Apr 30, 2015 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G05B 15/02 20130101;
Y02E 60/722 20130101; H02J 2300/22 20200101; H02M 7/44 20130101;
Y04S 10/14 20130101; Y02E 10/56 20130101; H02J 2203/20 20200101;
G05F 1/67 20130101; H02J 3/381 20130101; Y02E 10/58 20130101; Y02E
60/00 20130101; H02J 7/35 20130101; H02J 7/00 20130101; H02J
13/0006 20130101; Y02E 60/76 20130101; Y02E 40/70 20130101; Y02E
70/30 20130101; Y04S 40/22 20130101; H02J 7/007 20130101; Y02E
10/563 20130101; Y04S 10/123 20130101; H02J 2300/24 20200101; Y02E
40/72 20130101; H02J 3/32 20130101; Y04S 40/20 20130101; Y02E
10/566 20130101; H02J 3/383 20130101 |
International
Class: |
H02J 7/00 20060101
H02J007/00; H02M 7/44 20060101 H02M007/44; H02J 7/35 20060101
H02J007/35 |
Claims
1. A computer-implemented method for an energy site, the method
comprising: detecting, by a processor, an energy storage device
operatively coupled to the energy site; determining, by the
processor, an energy storage capacity of the energy storage device;
detecting, by the processor, an electrical grid operatively coupled
to the energy site; receiving, by the processor, event data
corresponding to an event affecting the electrical grid;
determining, by the processor, a probability that the electrical
grid will experience a power outage based on the event data;
charging the storage device according to a first charging profile
when the probability is below a predetermined threshold value; and
charging the storage device according to a second charging profile
when the probability is at or above the predetermined threshold
value.
2. The method of claim 1 wherein the event data is associated with
at least one of local weather data, geological data, social media
data, or local alert data.
3. The method of claim 1 wherein the event data corresponds to the
event occurring over a predetermined period of time.
4. The method of claim 1 wherein the storage device is charged
according to the first charging profile under normal operating
conditions.
5. The method of claim 1 further comprising: receiving a user input
corresponding to a manual selection of the second charging profile;
and charging the storage device according to the second charging
profile.
6. The method of claim 1 further comprising: receiving a control
signal from a control server, the control signal corresponding to
an automated selection of the second charging profile; and charging
the storage device according to the second charging profile.
7. A computer-implemented system comprising: one or more
processors; one or more non-transitory computer-readable storage
mediums containing instructions configured to cause the one or more
processor to perform operations including: determining an energy
storage capacity of an energy storage device operatively coupled to
a grid-connected energy generation site; receiving weather data
corresponding to a weather forecast over a predetermined period of
time; calculating a probability that the electrical grid will
experience a power outage based on the weather data; charging the
storage device according to a first charging profile in response to
the probability being below a predetermined threshold value; and
charging the storage device according to a second charging profile
in response to the probability being at or above the predetermined
threshold value.
8. The system of claim 7 wherein a maximum charge set point of the
storage device for the first charging profile is less than the
maximum storage capacity of the storage device, and wherein the
maximum charge set point of the storage device for the second
charging profile is greater than the maximum charge set point of
the first charging profile.
9. The system of claim 7 further comprising instructions configured
to cause the one or more processors to perform operations including
limiting a time of using the second charging profile to extend the
life of the storage device.
10. The system of claim 7 wherein the storage device is charged
according to the first charging profile under normal operating
conditions.
11. The system of claim 7 further comprising instructions
configured to cause the one or more processors to perform
operations including: receiving a user input corresponding to a
manual selection of the second charging profile; and charging the
storage device according to the second charging profile.
12. The system of claim 7 further comprising instructions
configured to cause the one or more processors to perform
operations including: receiving a control signal from a control
server, the control signal corresponding to an automated selection
of the second charging profile; and charging the storage device
according to the second charging profile.
13. The system of claim 7 further comprising instructions
configured to cause the one or more processors to perform
operations including receiving event data corresponding to an event
affecting an output of the electrical grid, wherein the calculated
probability is further based on the event data.
14. The system of claim 13 wherein the event data is associated
with at least one of local geological data, social media data, or
local alert data.
15. The system of claim 7 further comprising: a set of one or more
photovoltaic cells used, at least in part, to charge the storage
device according to the first charging profile or the second
charging profile.
16. A computer-implemented method comprising: controlling, by a
processor, a charging of an energy storage device according to a
first charging profile under normal operating conditions, wherein
the energy storage device is operatively coupled to an energy
generation site, and wherein the energy generation site is
operatively coupled to an electrical grid; receiving, by the
processor, event data corresponding to an event that could affect
the electrical grid; determining, by the processor, a probability
that the electrical grid will experience a power outage based on
the event data; and charging the storage device according to a
second charging profile when the probability is above a
predetermined threshold value.
17. The method of claim 16 wherein the energy storage device has a
maximum storage capacity, wherein a maximum charge set point of the
energy storage device for the first charging profile is less than a
storage capacity of the storage device, and wherein the maximum
charge set point of the storage device for the second charging
profile is at the maximum storage capacity of the storage
device.
18. The method of claim 16 wherein the maximum charge set point for
the first charging profile is 90% of the storage capacity of the
storage device, and wherein the maximum charge set point for the
second charging profile is 100% of the storage capacity of the
storage device.
19. The method of claim 16 wherein the event data is associated
with at least one of local weather data, local geological data,
social media data, or local alert data.
20. The method of claim 16 wherein the event data is associated
with a local natural disaster including at least one of an
earthquake, a fire, a tornado, hail, high winds, an avalanche,
volcanic activity, a landslide, or an epidemic.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This claims the benefit of U.S. Provisional Application No.
62/155,444, filed Apr. 30, 2015, which is hereby incorporated by
reference for all purposes.
BACKGROUND
[0002] In recent years, climate change concerns, federal/state
initiatives, and other factors have driven a rapid rise in the
installation of renewable energy generation (EG) systems (i.e.,
systems that generate energy using renewable resources such as
solar, wind, hydropower, etc.) at residential and non-residential
sites. Solar photovoltaic (PV) systems, in particular, have been
very popular EG systems.
[0003] PV-based EG systems have continued to improve as new
innovations lead to lower manufacturing and installation costs,
higher solar panel efficiencies, and greater control over energy
distribution. Despite these improvements, the power output for
PV-based EG systems remain susceptible to bad weather conditions,
including storm systems and dense cloud cover. Bad weather
conditions can greatly reduce an amount of sunlight that reaches
PV-based EG systems, which can adversely affect their corresponding
power generation.
[0004] Therefore, knowing beforehand which EG sites will be
affected by an impending storm or dense cloud cover can be very
advantageous, as preemptive actions can be taken to reduce the
storms negative effects on power production. However, conventional
methods of storm forecasting for downstream EG sites are typically
inaccurate, costly, and involve indirect calculations, broad
assumptions, and imprecise estimations. As such, better forecasting
and control systems are needed.
BRIEF SUMMARY
[0005] In certain embodiments, a computer-implemented method for an
energy site includes detecting an energy storage device operatively
coupled to the energy site, determining an energy storage capacity
of the energy storage device, detecting an electrical grid
operatively coupled to the energy site, receiving event data
corresponding to an event affecting the electrical grid,
determining a probability that the electrical grid will experience
a power outage based on the event data, charging the storage device
according to a first charging profile when the probability is below
a predetermined threshold value, and charging the storage device
according to a second charging profile when the probability is at
or above the predetermined threshold value. In some cases, the
event data can be associated with at least one of local weather
data, geological data, social media data, or local alert data. The
event data can correspond to the event occurring over a
predetermined period of time. The storage device can be charged
according to the first charging profile under normal operating
conditions. In some cases, the method can further include receiving
a user input corresponding to a manual selection of the second
charging profile, and charging the storage device according to the
second charging profile. Further implementations can include
receiving a control signal from a control server, the control
signal corresponding to an automated selection of the second
charging profile, and charging the storage device according to the
second charging profile.
[0006] In some embodiments, a computer-implemented system includes
one or more processors, and one or more non-transitory
computer-readable storage mediums containing instructions
configured to cause the one or more processors to perform
operations including determining an energy storage capacity of an
energy storage device operatively coupled to a grid-connected
energy generation site, receiving weather data corresponding to a
weather forecast over a predetermined period of time, calculating a
probability that the electrical grid will experience a power outage
based on the weather data, charging the storage device according to
a first charging profile in response to the probability being below
a predetermined threshold value, and charging the storage device
according to a second charging profile in response to the
probability being at or above the predetermined threshold
value.
[0007] In certain embodiments, a maximum charge set point of the
storage device for the first charging profile is less than the
maximum storage capacity of the storage device, and the maximum
charge set point of the storage device for the second charging
profile is greater than the maximum charge set point of the first
charging profile. The method can further include limiting a time of
using the second charging profile to extend the life of the storage
device. The storage device can be charged according to the first
charging profile under normal operating conditions. In some cases,
the system further includes instructions configured to cause the
one or more processor to perform operations including receiving a
user input corresponding to a manual selection of the second
charging profile, and charging the storage device according to the
second charging profile. The system can further include
instructions configured to cause the one or more processor to
perform operations including receiving a control signal from a
control server, the control signal corresponding to an automated
selection of the second charging profile, and charging the storage
device according to the second charging profile.
[0008] In certain implementations, the system further comprises
instructions configured to cause the one or more processors to
perform operations including receiving event data corresponding to
an event affecting an output of the electrical grid, wherein the
calculated probability is further based on the event data. The
event data can be associated with at least one of local geological
data, social media data, and/or local alert data.
[0009] In further embodiments, a method includes controlling, by a
processor, a charging of an energy storage device according to a
first charging profile under normal operating conditions, receiving
event data corresponding to an event that could affect the
electrical grid, determining a probability that the electrical grid
will experience a power outage based on the event data, and
charging the storage device according to a second charging profile
when the probability is above a predetermined threshold value. The
energy storage device can be operatively coupled to an energy
generation site, and the energy generation site can be operatively
coupled to an electrical grid. In some cases, the energy storage
device has a maximum storage capacity, where a maximum charge set
point of the energy storage device for the first charging profile
is less than a storage capacity of the storage device, and where
the maximum charge set point of the storage device for the second
charging profile is at the maximum storage capacity of the storage
device. In some implementations, the maximum charge set point for
the first charging profile is 90% of the storage capacity of the
storage device, and the maximum charge set point for the second
charging profile is 100% of the storage capacity of the storage
device. The event data can be associated with at least one of local
weather data, local geological data, social media data, or local
alert data. In some cases, the event data is associated with a
local natural disaster including at least one of an earthquake, a
fire, a tornado, hail, high winds, an avalanche, volcanic activity,
a landslide, or an epidemic.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] The detailed description is set forth with reference to the
accompanying figures.
[0011] FIG. 1 shows aspects of a weather tracking system based on
satellite imagery.
[0012] FIG. 2 shows aspects of a weather tracking system using
ground-based sensors.
[0013] FIG. 3 shows aspects of a weather tracking system using a
ground-based fisheye image sensor.
[0014] FIG. 4 shows aspects of a weather tracking system using
aspects of one or more PV-based energy generation sites, according
to certain embodiments.
[0015] FIG. 5 shows a simplified block diagram of system
environment for a single PV-based energy generation site, according
to certain embodiments.
[0016] FIG. 6 shows a simplified block diagram of multi-system
environment for multiple PV-based energy generation sites,
according to certain embodiments.
[0017] FIG. 7 shows aspects of determining a presence and density
of cloud cover over an energy generation system, according to
certain embodiments.
[0018] FIG. 8 shows aspects of a system for determining a vector of
a weather pattern, according to certain embodiments.
[0019] FIG. 9 shows aspects of a system for determining a vector of
a weather pattern, according to certain embodiments
[0020] FIG. 10 shows a simplified flow chart for a method of
tracking weather patterns in a grid-connected array of PV-based EG
sites, according to certain embodiments.
[0021] FIG. 11 shows a simplified flow chart for a method of
tracking weather patterns in a grid-connected array of PV-based EG
sites, according to certain embodiments.
[0022] FIG. 12 shows a simplified flow chart for a method of
tracking weather patterns in a grid-connected array of PV-based EG
sites, according to certain embodiments.
[0023] FIG. 13 shows aspects of energy generation distribution at
an EG site, according to certain embodiments.
[0024] FIG. 14 shows a simplified block diagram of a PV-based
energy generation and storage (EGS) system, according to certain
embodiments.
[0025] FIG. 15A shows a number of storage device charging profiles
in an EG system, according to certain embodiments.
[0026] FIG. 15B shows a storage device charging profile in an EG
system, according to certain embodiments.
[0027] FIG. 16A shows a number of storage device charging profiles
in an EG system, according to certain embodiments.
[0028] FIG. 16B shows a storage device charging profile in an EG
system, according to certain embodiments.
[0029] FIG. 17 shows several examples of event data that can be
used to determine a charging profile for a storage device,
according to certain embodiments.
[0030] FIG. 18 shows alternative methods of controlling a charging
profile in a storage device, according to certain embodiments.
[0031] FIG. 19 shows a simplified flow diagram showing aspects of a
method of charging a storage device, according to certain
embodiments.
[0032] FIG. 20 shows a simplified block diagram of computer system,
according to certain embodiments.
DETAILED DESCRIPTION
[0033] The present disclosure relates in general to energy
generation systems, and in particular to tracking weather patterns
and forecasting their effect on energy generation using such
systems.
[0034] In the following description, various embodiments of
PV-based EG systems will be described. For purposes of explanation,
specific configurations and details are set forth in order to
provide a thorough understanding of the embodiments. However, it
will also be apparent to one skilled in the art that the
embodiments may be practiced without the specific details.
Furthermore, well-known features may be omitted or simplified in
order not to obscure the embodiment being described.
[0035] The power output in a PV-based energy generation system
(e.g., an EG site) can be affected by weather conditions. EG
systems may produce more power during sunny days rather than cloudy
days as more photons reach the PV-panels (i.e., solar panels) and
are converted into electrical power. In a grid-connected network,
it can be useful to know not only the existence and location of a
weather pattern relative to the network, but also how dense the
cloud cover will be, as well as its speed and direction. Such
information can be used to characterize how the weather will affect
power output in EG systems and forecast which EG systems will be
affected downstream. Furthermore, with this information, certain
preemptory actions could be taken to better accommodate a predicted
future reduction in energy generation. For instance, some EG
systems may pre-charge an energy storage system (e.g., battery) or
apply a different charging profile to an energy storage system.
Thus, certain embodiments can track weather patterns by using power
output data from a number of EG sites in a grid to determine where
clouds are relative to the grid, in addition to their speed,
direction, and density at virtually any point within the system. A
forecast can be generated based on these inputs to predict where
the weather system is headed as well as its quantifiable effect on
power outputs for downstream PV-based energy generation
systems.
[0036] Thus, aspects of the present disclosure relate to systems
and methods for receiving power measurement data for a PV-based EG
site, determining if cloud cover is present over the EG site based
on a difference between a present and historical power output for
the EG site, calculating a density of the cloud cover over the EG
site based on the present and historical power outputs, and
controlling load characteristics of the EG site based on the
determined presence and calculated density of the cloud cover. The
density of the cloud cover can be based on a percentage difference
in power output between the present power output and the historical
power output. A vector for the cloud cover can be determined based
on movement of a detected storm system with a boundary defined by a
location of a plurality of EG sites, or by a movement of the cloud
density from one EG site to another.
[0037] For purposes of illustration, several of the examples and
embodiments that follow are described in the context of EG systems
that use solar PV-based technology for energy generation and
battery technology for energy storage. However, it should be
appreciated that embodiments described herein are not limited to
such implementations. For example, in some embodiments, alternative
types of energy generation technologies (e.g., wind turbine,
solar-thermal, geo-thermal, bio-mass, hydropower, etc.) may be used
in conjunction with PV-based systems. In some embodiments,
alternative types of energy storage technologies (e.g., compressed
air, flywheels, pumped hydro, superconducting magnetic energy
storage (SMES), etc.) can be used. One of ordinary skill in the art
will recognize many modifications, variations, of the concepts
described herein to such modification variations, and
alternatives.
Conventional Systems
[0038] Some conventional systems that use weather tracking to
forecast weather effects on downstream EG systems do exist, but
suffer significant shortcomings. For example, some systems are not
scalable. Some are too expensive and require repeated maintenance.
Some can track the location of storm systems (e.g., clouds), but
cannot track a vector (i.e., speed and direction) or density, which
can impact the amount of sunlight that can reach PV panels in EG
systems. Furthermore, conventional systems provide no real direct
correlation to the actual effects of weather systems on EG system
power outputs and rely on indirect first-order estimations and
assumptions to calculate what local and downstream EG systems may
endure. Some conventional systems are described here.
[0039] FIG. 1 shows aspects of a weather tracking system based on
satellite imagery 100. Weather system 110 includes storm weather
patterns and dense cloud cover and is shown over a region of
California. Satellite imagery (e.g., Doppler radar) can be scalable
and cost effective since detailed weather patterns are freely
available and can cover any location on earth. Furthermore,
satellite imagery can be used to accurately measure the location of
a weather system (sometimes in near real-time) and to predict its
vector (i.e., speed and direction). For instance, satellite imagery
100 shows a direction 120 of weather system 110, which can be used
to predict if PV-powered residential area 130 will be affected by
the storm. This may be useful for binary decisions to determine
whether or not to take preemptory actions, such as setting a new
charge point on an energy storage device in a downstream EG system,
as further discussed below at least with respect to FIGS.
14-19.
[0040] Despite the advantages, satellite imagery cannot be used to
reliably determine an accurate cloud density at any point in a
weather system, and it is particularly bad when used to correlate
the effects of a weather system to an actual power output of an EG
system. Determining a relative cloud density in a weather system
can be effective. For instance, a pixel may be twice as dark as
another pixel, so the cloud cover may also be approximately twice
dense. However, there are problems with calibrating such systems to
no cloud conditions and establishing a baseline. Typical satellite
imagery-based systems can confuse snow cover with clouds or other
highly reflective surfaces. Different climates may be more
reflective to start with. For instance, a southwestern desert in
the U.S. may be more reflective than forestland in the Pacific
Northwest. Even with future improvements in technology, any
correlation of cloud cover to an EG system power output would still
be based on a number of assumptions and estimations for a host of
complex and variable inputs and conditions (e.g., resolution of
imagery versus individual EG system performance, actual sunlight
reaching PV panels unencumbered by reflections and other noise), as
would be appreciated by one of ordinary skill in the art.
[0041] FIG. 2 shows aspects of a weather tracking system 200 using
ground-based sensors ("sensors") 250. Storm clouds 210 are located
over PV-powered residential area 230 and block an amount of
sunlight. Ground-based sensors 250 can be high-precision scientific
instruments that measure sunlight in a particular location.
Ground-based sensors 250 can be very expensive (e.g., $5K-$15K per
unit) and require a significant amount of manpower to deploy and
maintain. Thus, scalability is severely limited and cost
prohibitive for grid-connected EG sites that span regionally or
nationally and may number in the hundreds of thousands. Moreover,
maintenance workers have to routinely service and maintain the
sensors for further scaled expenses.
[0042] Solar measurements made by ground-based sensors can be very
accurate, however some inaccuracies are inevitable due to reflected
sunlight from clouds or other reflective surfaces. Since sensors
250 only measure sunlight, they may indirectly measure an amount of
cloud cover based on the amount of sunlight received. High amounts
of sunlight may correspond to relatively clear skies and low levels
may indicate cloudy days. Thus, sensors 250 can reliably measure
the presence of clouds (i.e., location), as sunlight measurements
may be compared to a threshold value to determine if clouds are
present, as would be appreciated by one of ordinary skill in the
art. However, sensors 250 in conventional configurations cannot
detect a vector of a weather system, as a sunlight measurement
provides no sense of a speed or direction of cloud cover. Thus,
system 200 could not reliably determine if and when clouds 220
would affect PV-powered residential area 240. Furthermore, despite
any improvements in solar measurement accuracy, solar measurement
data is used to indirectly calculate an expected power generation
at an EG site, which includes estimations, assumptions, and
characterizations (e.g., solar conversion parameters for individual
panels/systems, etc.) that introduce error in the calculation. It
should be noted that, in some embodiments, aspects can be used in
conjunction with a network of sensors 250 as further described
below at least with respect to FIGS. 7-9.
[0043] Cloud density measurements using ground-based sensors 250
may be more accurate than satellite-based systems (see, e.g., FIG.
1). Sunlight measurements may provide a more direct indication of
how EG systems will be affected (e.g., energy generation).
Estimations of energy generation for PV-based systems can be
directly calculated using solar measurement data, as would be
appreciated by one of ordinary skill in the art. In contrast,
satellite-based weather tracking is more indirect (i.e.,
inaccurate) as it first estimates a cloud density, which in turn is
used to estimate a power output for PV-based EG systems. Thus,
ground-based sensors systems tend to provide more accurate
estimations of PV-based EG system power outputs. However, the
limitations in scalability and forecasting downstream effects limit
its use to small-scale use.
[0044] FIG. 3 shows aspects of a weather tracking system 300 using
a ground-based fisheye image sensor ("fisheye sensor") 350. Storm
clouds 310 are located over PV-powered residential area 330 and
block an amount of sunlight. Fisheye sensor 350 can be a visual
spectrum wide lens camera that takes images of the sky and uses
imaging processing algorithms to infer how much cloud cover there
is and how it travels across the sky for a particular location. One
limitation is that fisheye sensor 350 is a line-of-sight sensor and
cannot directly detect the presence of cloud 320 over residential
area 340, although it may infer a future forecasted presence based
on presence and vector detection (via image analysis) over
residential area 330. Fisheye sensors 350 can be very expensive and
may require significant resources to deploy and maintain, which
affects scalability.
[0045] While cloud cover location and vector calculations may be
accurate in locations within a line-of-site for fisheye sensor 350,
cloud density measurements are typically inaccurate. Unlike
ground-based sensors 250 (see FIG. 2), which can provide reasonably
accurate measurements of sunlight, fisheye sensors 350 provide
images. Some image analysis techniques may be used to analyze image
properties (e.g., brightness of pixels), but the accuracy of such
calculations and their correlation to an actual power output of an
EG system can be poor. The many problems, estimations, and
assumptions with fisheye sensor images required for density
calculations are similar to those discussed with respect to FIG. 1
and would be appreciated by one of ordinary skill in the art.
An Embodiment
[0046] FIG. 4 shows aspects of a weather tracking system 400 using
aspects of one or more PV-based EG sites, according to certain
embodiments. Weather tracking system 400 can be used to track and
characterize a weather pattern 410 over a number of PV-based EG
sites 430 and forecast its effects on downstream EG sites 440. A
number of PV-based EG sites are shown in locations across the State
of California, U.S.A. Each EG site depicted can represent 1 EG
site, 10 EG sites, 1000 EG sites, or any suitable representation.
Weather patterns 410, 420 can include one or more clouds (i.e.,
cloud cover) that can block an amount of sunlight from reaching
certain EG sites.
[0047] Weather tracking system ("system") 400 has significant
advantages over all conventional systems, including aspects of
those shown in FIG. 1-3. System 400 utilizes the hardware and
infrastructure in existing EG sites to perform the weather tracking
operations. Thus, system 400 has virtually unlimited scalability
with little to no additional cost for implementation of weather
tracking capabilities. System 400 can be used to directly and
accurately track a location, vector, and density of cloud cover
over a number of EG sites and measure its precise effect on their
corresponding power outputs. That is, system 400 directly measures
weather effects on EG systems, while conventional systems
indirectly correlate approximate effects, as discussed above.
Furthermore, system 400 can forecast which downstream EG sites the
cloud cover may affect, based on the cloud cover position, vector,
and density measurements.
[0048] Certain undesirable inputs that can contribute to inaccurate
power measurement readings can be eliminated in system 400. For
instance, shade on PV panels, lower efficiency modules, or other
site-specific effects can be decoupled from measurements by
aggregating day-to-day historical EG data and removing the effects
from power output calculations for more accurate measurements.
Shade patterns usually predictably repeat around the same time
every day. The effects of the shade can be decoupled from the cloud
cover calculus that is further discussed below with respect to
FIGS. 7-13. Historical power data, in general, can be used to
filter out most causes of variation in power output, particularly
when they are cyclic or occur during historically predictable
times, as would be appreciated by one of ordinary skill in the
art.
[0049] The embodiments described herein (e.g., systems 500-700) can
also accommodate partial system failure. For instance, if some EG
sites shut down or lose communication (i.e., no power measurement
data is available), then adjacent EG sites can be used to
compensate for the loss of data.
[0050] System 400 can also be combined with conventional systems
(e.g., systems 100, 200, 300) for further accuracy. For example,
with "ensemble forecasting," system 400 can be combined with
satellite data from system 100 (see FIG. 1) to improve a forecast
to determine which downstream EG sites may be affected by a weather
system. Furthermore, system failure at certain EG sites can be
compensated for by local adjacent systems. For example, if an EG
site is down (e.g., lost communication with system 400), adjacent
EG sites can be used to calculate a position, vector, and density
of a weather system. Aspects of system 400 are further discussed
below at least with respect to FIGS. 7-13.
An Exemplary Overall System Design
[0051] FIG. 5 shows a simplified block diagram of system
environment 500 for a single PV-based energy generation site,
according to certain embodiments. As shown, system environment 500
can include energy generation and storage (EGS) system 502 that is
installed at site 504 (e.g., a residence, a commercial building,
etc.). EGS system 502 includes a PV-based EG subsystem that can
include PV inverter 506, one or more PV panels 508, and a
battery-based EG subsystem comprising battery inverter/charger 510
and battery device 512. In some embodiments, PV inverter 506 and
battery inverter/charger 510 can be combined into a single device.
In the example of FIG. 5, EGS system 502 is grid-connected; thus,
PV inverter 506 and battery inverter/charger 510 are electrically
connected to utility grid 514 via main panel 516 and utility meter
518. Further, to provide power to site 504, utility grid 514, PV
inverter 506, and battery inverter/charger 510 can be electrically
connected to site loads 520.
[0052] Historical power output database ("HPOD") 530 can store
historical power measurement data from one or more EG sites (e.g.,
EG site 504). The historical power measurement data can be up to
date real-time data or non-real-time data. For instance, historical
data can be power measurement data taken from one or more EG sites
at an interval (e.g., every second, minute, hour, day, week, etc.).
HPOD 530 may store power measurement data over a predetermined
threshold of time. For instance, HPOD 530 may store historical
power measurement data from one or more EG sites going back over a
week, month, year, etc. In some embodiments, HPOD 530 may store
aggregated historical power measurement data. HPOD 530 can be
operated by a third party, operated (and part of) control server
528, or may be located at an individual EG site (e.g., site
504).
[0053] Integrated EGS systems, such as system 502, can provide
advantages over EG systems that do not incorporate on-site energy
storage. For example, excess energy produced by PV components 506
and 508 can be stored in battery device 512 via battery
inverter/charger 510 as a critical reserve. Battery
inverter/charger 510 can then discharge this reserved energy from
battery device 512 when utility grid 514 is unavailable (e.g.,
during a grid blackout) to provide backup power for site loads 520
until grid power is restored. As another example, battery device
512 can be leveraged to "time shift" energy usage at site 504 in a
way that provides economic value to the site owner or the
installer/service provider of EGS system 502. For instance, battery
inverter/charger 510 can charge battery device 512 with energy from
utility grid 514 (and/or PV inverter 506) when grid energy cost is
low. Battery inverter/charger 510 can then dispatch the stored
energy at a later time to, e.g., offset site energy usage from
utility grid 514 when PV energy production is low/grid energy cost
is high, or sell back the energy to the utility when energy buyback
prices are high (e.g., during peak demand times).
[0054] Centralized or remote management of an EGS system, such as
system 502, can be advantageous for large scale EG networks for
residential, commercial, or industrial markets. System 500, for
example, can incorporate a centralized management system that
includes site gateway 524 and control server 528. Site gateway 524
is a computing device (e.g., a general purpose personal computer, a
dedicated device, etc.) that is installed at site 504. Gateway 524
may be a single gateway or a network of gateways and may be
configured physically at the installation site or remotely, but in
communication with site 504. As shown, site gateway 524 is
communicatively coupled with on-site components 506, 510, 512, 518,
and 540, as well as with control server 528 via network 526. In one
embodiment, site gateway 524 can be a standalone device that is
separate from EGS system 502. In other embodiments, site gateway
524 can be embedded or integrated into one or more components of
system 502. Control server 528 is a server computer (or a
cluster/farm of server computers) that is typically, but not
necessarily, remote from site 504. Control server 528 may be
operated by, e.g., the installer or service provider of EGS system
502, a utility company, or some other entity.
[0055] In one embodiment, site gateway 524 and control server 528
can carry out various tasks for monitoring the performance of EGS
system 502. For example, site gateway 524 can collect system
operating statistics, such as the amount of PV energy produced (via
PV inverter 506), the energy flow to and from utility grid 514 (via
utility meter 518), the amount of energy stored in battery device
512, and so on. Site gateway 524 can then send this data to control
server 528 for long-term logging and system performance
analysis.
[0056] Site gateway 524 and control server 528 can operate in
tandem to actively facilitate the deployment and control of EGS
system 502. Specifically, FIG. 5 shows other entities remote from
the site "OFF SITE," which may communicate with EGS system 502.
These other entities may include, as shown, web server 580 and
database server 582. These entities are not discussed as their
contribution to the operation of system 500 are not germane to the
novel aspects discussed herein and would otherwise be understood by
those of ordinary skill in the art.
[0057] According to embodiments, communication between the various
elements involved in power management (e.g., between the
centralized control server and the various devices at the remote
site, and/or between centralized control server 528 and various
other remote devices such as the database server, web server, etc.)
may be achieved through use of a power management Message Bus
System (MBS), such as that described in application Ser. No.
14/527,553, assigned to SolarCity Corporation, and incorporated
herein by reference. In the simplified view of FIG. 5, the MBS is
implemented utilizing message bus server 598, and message bus
client 599 located at the site gateway. In FIG. 5, the message bus
server is shown as being on control server 528, but this is not
required and in some embodiments the message bus server could be on
a separate machine and/or part of a separate server cluster.
[0058] The power management MBS as described herein, facilitates
communication between the various entities (e.g., on-site devices,
central control systems, distributed control systems, user
interface systems, logging systems, third party systems etc.) in a
distributed energy generation and/or storage deployment. In an
aspect, the MBS operates according to a subscribe/publish model,
with each respective device functioning as a subscriber and/or
publisher, utilizing a topic of a message being communicated.
[0059] It should be appreciated that system environment 500 is
illustrative and not intended to limit embodiments disclosed
herein. For instance, although FIG. 5 shows control server 528 as
being connected with a single EGS system at a single site, control
server 528 can be simultaneously connected with a fleet of EGS
systems that are distributed at multiple sites. In these
embodiments, control server 528 can coordinate the scheduling of
these various systems/sites to meet specific goals or objectives.
In further embodiments, the various components depicted in system
500 can have other capabilities or include other subcomponents that
are not specifically described. Furthermore, multiple instances and
variants of the control server may exist, each communicating with
one or more other control servers, EGS systems and/or other devices
connected to the MBS. Alternatively, other methods of communication
(e.g., point-to-point) other than MBS-based systems can be used,
and one of ordinary skill in the art will recognize the many
variations, modifications, and alternatives in methods of
communication to implement system 500.
[0060] FIG. 6 shows a simplified block diagram of multi-system
environment 600 for multiple PV-based energy generation sites,
according to certain embodiments. Control server 628 can be
communicatively coupled to site gateways 630, 640, 650. Site
gateway 630 can be communicatively coupled to EG sites 632, 634.
Site gateway 640 can be communicatively coupled to EG site 642.
Site gateway 650 can be communicatively coupled to EG sites 652,
654. Any number of site gateways and EG sites can be included in
multi-system environment 600. In certain embodiments, an EG site
can be similar to system 500 of FIG. 5. One or more of the EG sites
can be coupled to utility grid 614. Control server 628 may be a
single control server or multiple communicatively-coupled control
servers. Network 626 can be a single network (e.g., world wide web)
or a collection of smaller networks. The hierarchical and
operational relationship between control server 628 and EG sites
(632-654) can be any suitable configuration provided that, in
preferred embodiments, data from each EG site (e.g., power
measurement data), or a subset thereof, are accessible by control
server 628. Two non-limiting examples are described in Applications
62/163,200 and Ser. No. 14/802,811, the contents of which are
herein incorporated by reference in their entirety for all
purposes.
[0061] EG sites may provide power measurement data, historical
power data (further discussed below with respect to FIG. 7), load
data (e.g., types of loads, etc.), and more. Power measurement data
(and historical power data) can be used to determine the presence
of cloud cover over a corresponding EG site, as well as its vector
and density. The presence, vector, and density data can be used to
forecast where the cloud cover is headed and provide an estimation
of its effect on a power output for downstream EG sites. Downstream
can mean EG sites that are in the calculated vector path of the
detected cloud cover.
[0062] For the purpose of illustration, each EG site can be likened
to a pixel, where additional EG sites can provide a greater
resolution for a "snapshot" of cloud cover over a given area. For
instance, a geographic area having thousands of EG sites can
produce a more detailed "snapshot" of the presence, vector, and
density of cloud cover over that area as compared to the same area
having only a few EG sites. Thus, as multi-system environment 600
increases its number of EG sites, more data becomes available and a
more accurate assessment can be possible.
Calculating a Presence and Density of Cloud Cover
[0063] In some embodiments, determining the presence and density of
cloud cover can depend on a relationship between a present power
output for an EG site and its historical power output. Historical
power data can provide a snap shot of what a power output should
typically be during clear sky conditions (assuming normal climate
conditions). If an EG site typically produces 5 kW at a certain
time of the day (based on historical data) and currently produces
2.5 kW, than the solar panels for the EG site are probably obscured
by cloud cover. The magnitude of that reduction in power can be
used to determine the density of the cloud cover. As further
addressed below, variations due to shade, daylight savings, or
other events or conditions that may predictably occur can be
filtered out of the cloud cover calculations.
[0064] FIG. 7 shows aspects of determining a presence and density
of cloud cover over an energy generation system 700, according to
certain embodiments. EG system 700 includes EG site 730
communicatively coupled to control server 728 and historical power
output database ("HPOD") 740 through network 726. EG system 730 can
provide power measurement data to control server 728. In some
embodiments, power measurement data may include present power
output data 750. HPOD 740 can provide historical power output data.
Historical power output data may also be stored in a local database
at site 730, which may be a separate entity, or may include memory
resources associated with any of the devices at site 730 (e.g., a
gateway, inverter, etc.), a local computing device, or at control
server 728. EG system 700 may operate similarly to system 500 of
FIG. 5 and/or system 600 of FIG. 6.
[0065] A presence of cloud cover over EG site 730 can be determined
by comparing a present power output to a historical power output,
calculating a difference value, and comparing it to a predetermined
threshold value. The predetermined threshold value can be any
suitable difference value that would likely be indicative of cloud
cover. Dense storm clouds would likely reduce a present output
power at an EG site more than, for example, thin and wispy cirrus
clouds. The predetermined threshold value may be selected to only
include very dense clouds (e.g., storm clouds) that could
substantially affect a power output at a present EG site or those
downstream. For example, the predetermined threshold value may be
set to 50% such that a presence of storm clouds are detected when
the present power output is half of a historical average power
output for the corresponding EG site. Other threshold values may be
used (e.g., 30%, 40%, 60%, 70%, 80%, etc.). Alternatively, the
predetermined threshold value may be a value unrelated to a
particular historical power output. For instance, the predetermine
threshold value may be set to indicate cloud cover when the
detected value is at 60% of the expected, historical value for all
EG sites over a geographic area, regardless of the power output of
each individual EG site.
[0066] A density of cloud cover over EG site 730 can be determined
by comparing a present power output to a historical power output
and calculating a percentage difference value. For instance, if a
present power output is 50% of a historical power output (e.g.,
aggregate historical power output over the last month), then a
density of cloud cover can be measured at 50%. Any suitable rating
method can be used (e.g., density having a scale of 0-1, etc.).
[0067] Load characteristics can be controlled at EG site 730 based
on the detected presence and/or the calculated density of the cloud
cover. For example, an energy storage device at EG site 730 (not
shown) may be charged to a different set point in anticipation of a
strong thunderstorm with a high probability of a blackout on the
utility grid. In some implementations, load characteristics for
downstream EG sites can be controlled, as further discussed below
at least with respect to FIGS. 8-10.
[0068] Control server 728 can receive power measurement data
including present power output data from EG site 730 and historical
power output data from HPOD 740. In some embodiments, the
historical power output data is aggregated over a period of time
(e.g., 1 week, 1 month). Historical power output data can include
an aggregated value for a same period of time a year ago, a month
ago, or over several years or months.
Calculating a Vector for Cloud Cover
[0069] Tracking a vector (i.e., speed and direction) of a weather
pattern can be useful for forecasting which downstream EG sites
will be affected by the weather pattern. There are several ways to
measure a vector using embodiments. A first vector calculation
involves measuring a density of cloud cover and determining a
vector based on the detection and movement of the density
measurement (see FIG. 8). A second method includes tracking a
weather pattern (e.g., cloud cover) with respect to a bounded area
defined by one or more PV-based EG sites and determining a vector
based on a movement of the weather pattern within the bounded area
(see FIG. 9).
[0070] FIG. 8 shows aspects of a system 800 for determining a
vector of a weather pattern, according to certain embodiments. A
number of PV-based EG sites are shown in locations across the State
of California, U.S.A. Each EG site depicted can represent 1 EG
site, 10 EG sites, 1000 EG sites, etc., as similarly shown and
described with respect to FIG. 4. Clouds 810, 812, and 814 are
shown over a portion of PV-based EG sites 830. Cloud 814 has a low
density, cloud 812 has a medium density, and cloud 814 has a high
density. A single cloud may represent one or more clouds and/or
portions of one or more weather patterns. The EG sites may be
grid-connected and controlled by a remove server, similar to the
systems described in FIGS. 5-7.
[0071] A vector of a weather pattern (e.g., cloud cover) can be
determined by calculating a density of a cloud cover over a first
EG site and determining that a similar density exists at a second
EG site after a period of time. The second EG site may have a
density that is "similar" to the first EG site if it is within a
certain threshold tolerance value (e.g., within 2.5%). The period
of time may be seconds, minutes, hours, or other suitable period of
time. A vector for the weather pattern can be calculated based on
the relative locations of the first and second EG sites, the
distance between the two EG sites, and the amount of time it took
for the cloud cover having the density measured at the first EG
site to arrive at the second EG site. Once the vector is
determined, a forecast of where the weather system is located, and
what effect it will have on downstream systems (due to cloud
density) can be estimated. Referring to FIG. 8, a weather tracking
system (e.g., systems 500, 600, 700) may determine that the
densities of clouds 810, 820, 830 are within the threshold
tolerance value of clouds 820, 822, and 824, respectively. FIG. 8
is not necessarily drawn to scale and some vector calculations can
be based on EG sites located within a relatively close proximity
(e.g., 1 mile, 5 miles, etc.). In an aspect, multiple different
measurements may be used
[0072] FIG. 9 shows aspects of system 900 for determining a vector
of a weather pattern, according to certain embodiments. In this
example, a plurality of PV-based EG sites are shown in locations
across a section of the State of California. The EG sites can
detect the presence of cloud cover based on their corresponding
present power outputs and historical power outputs discussed above
with respect to FIG. 7. Boundary 930 represents a cloud detection
area defined by a location of the plurality of EG sites. Cloud
cover 910, which includes edge 915 and covers an area spanning
multiple EG sites, is shown moving to a location shown as cloud
cover 920.
[0073] As cloud cover 910 moves within boundary 930, system 900
detects at least a portion of edge 915 and calculates vector 950
based on the movement of edge 915 with respect to boundary 930.
Once vector 950 is known, system 900 can determine what EG sites
downstream may be affected by cloud cover 910. For instance, EG
sites located within edge 925 are downstream and within vector path
950 of cloud cover 910. Thus, system 900 may proactively control
aspects of downstream EG sites (e.g., EG distribution profile) to
better accommodate potential negative effects of cloud cover 910,
including undesirable changes in power flow across the grid (e.g.,
utility grid black outs, brown outs, and the like).
[0074] Some clouds may be easier to detect than others. Large,
black storm clouds with associated EG site power drops of over
50-70% are a strong indicator that a weather pattern is present,
trackable, and obscuring the corresponding PV panels. Weather
formations having big, dark clouds casting shadows and hard edges
(i.e., the boundary of the weather system) are easier to detect
(e.g., for vector measurements) than intermittent low-density cloud
cover (e.g., cirrus clouds). That is because hard edges may produce
a fast step function (e.g., a first row of EG sites with greatly
reduced power output and an adjacent second row of EG sites having
power outputs close to a historical average). Likewise,
intermittent weather patterns with soft edges may be more difficult
to reliably detect, particularly in edge detection for determining
a vector. However, proactive energy generation distribution (see,
e.g., FIG. 13) would likely not occur in response to weather
conditions causing little to no appreciable power output
reduction.
[0075] Ensemble forecasting, as discussed above, may incorporate
other resources to help determine aspects of weather patterns. In
some alternative embodiments, temperature data (e.g., outside
temperature) from a plurality of EG sites may allow systems (e.g.,
systems 500-700) to not only track cloud fronts, but also air
movement at various temperatures (i.e., cold fronts and warm
fronts). Data describing the movement of cold and warm fronts can
be used as an alternative method of determining a weather pattern
(cloud cover) vector. Knowing the location and movement of warm and
cold fronts may inform a resultant heating/cooling load for
individual EG sites to prompt preliminary action, as further
discussed below at least with respect to FIG. 13.
[0076] FIG. 10 shows a simplified flow chart for a method of
tracking weather patterns (cloud cover) in a grid-connected array
of PV-based EG sites, according to certain embodiments. The
following methods 1000, 1100 (see FIG. 11), and 1200 (see FIG. 12)
can be performed by processing logic that may comprise hardware
(circuitry, dedicated logic, etc.), software (such as is run on a
general purpose computing system or a dedicated machine), firmware
(embedded software), or any combination thereof. In certain
embodiments, method 1000 can be performed by a processor on control
server 528, site gateway 524, or other suitable computing device or
system described herein.
[0077] At step 1010, method 1000 includes receiving power
measurement data for a PV-based EG site. The power measurement data
may include a present power output of the EG site and a historical
power output of the EG site. The present power output of the EG
site may be received from the EG site. For instance, the present
power output may be provided to a control server by a gateway, a PV
inverter, or other on-site computing device local to the EG site
(e.g., see system 500 of FIG. 5).
[0078] Power measurement data corresponding to the historical power
output of the EG site can be received from a database. The database
can be a operated by a third-party entity (e.g., historical power
output database 530), the database can by operated and/or
controlled by the control server (e.g., control server 528), or it
may be local to and operated by the EG site. In some embodiments,
the historical power output is an average power output for the EG
site for a previous predetermined period of time, which may be any
suitable time span (e.g., previous week, month, year, or other
continuous or non-continuous period of time), as would be
appreciated by one of ordinary skill in the art with the benefit of
this disclosure.
[0079] At step 1020, method 1000 includes determining if cloud
cover is present over the EG site based on the present and
historical power outputs for the EG site. The determining may
include calculating a difference between the present power output
and the historical power output for the EG site. Historical power
output may be selected based on power generation at the same or
similar time of day for previous days, previous months, or previous
years. The record of historical power generation may be filtered or
modified using a function such as a mean, median, mode, or
percentile. Historical power generation at certain times may be
computed using a model solar position, EG system geometry (e.g.
tilt and orientation of solar panels) and ratings (solar panel
efficiency, inverter efficiency), and possibly historical power
generation at other times. The cloud cover may be determined to be
present over the EG site if the difference between the present
power output and historical power output of the EG site is greater
than a predetermined threshold value for cloud cover.
Alternatively, cloud cover may be determined to be present based on
a quotient (percentage) of the present power output versus the
historical power output (e.g., present power output is less than
50% of historical average).
[0080] At step 1030, method 1000 includes calculating a density of
the cloud cover over the EG site based on the present and
historical power outputs for the EG site. The density can be based
on a percentage difference in power output between the present
power output and the historical power output. In some embodiments,
the percentage difference between the present and historical power
outputs for the EG site can be used to both determine the presence
of cloud cover and calculate its density.
[0081] At step 1040, method 1000 includes controlling load
characteristics of the EG site based on the determined presence and
calculated density of the cloud cover. In some embodiments, the
controlled load characteristics of the EG site may include at least
one of a charging profile for an energy storage device associated
with the EG site or a time-shifting load profile of the EG site.
For instance, an HVAC system associated with the EG site (e.g.,
site load 520) may be regularly scheduled to turn on at 5 P.M. to
achieve a desired temperature before a homeowner arrives at 6 P.M.
One example of time-shifting a load would be to turn on the HVAC at
2 P.M. in anticipation of a thunderstorm (or other event) that may
result in less energy generation being available at the regularly
scheduled time of operation. In some embodiments, a charging
profile for the energy storage device may include an increased
charge set point when cloud cover is determined to be present (or
approaching) and the cloud cover density is greater than a
predetermined threshold value for cloud density.
[0082] In certain embodiments, ensemble configurations may be used.
An ensemble configuration can include the combination of different
technologies to achieve an improved forecasting accuracy. For
instance, some embodiments may combine aspects of method 1000 with
the satellite data analysis described with respect to FIG. 1 to
determine a vector for cloud cover. To illustrate this concept,
method 1000 may include receiving weather forecast data,
determining a vector of the determined cloud cover based on weather
forecast data, determining one or more EG sites in path of cloud
cover based on determined vector, and controlling load
characteristics of the one or more EG sites in the path of the
cloud cover can be based on the determined presence and calculated
density of the cloud cover over the EG site. Any combination of
technologies is possible (e.g., method 1000 and one or more of
aspects shown in FIGS. 1-3). Further aspects of method 1000 are
discussed at least with respect to FIG. 7.
[0083] It should be appreciated that the specific steps illustrated
in FIG. 10 provide a particular method 1000 of measuring a power
signal in a power grid, according to certain embodiments. Other
sequences of steps may also be performed according to alternative
embodiments. For example, alternative embodiments may perform the
steps outlined above in a different order. Moreover, the individual
steps illustrated in FIG. 10 may include multiple sub-steps that
may be performed in various sequences as appropriate to the
individual step. Furthermore, additional steps may be added or
removed depending on the particular applications. One of ordinary
skill in the art would recognize and appreciate many variations,
modifications, and alternatives of the method 1000.
[0084] FIG. 11 shows a simplified flow chart for a method of
tracking weather patterns (cloud cover) in a grid-connected array
of PV-based EG sites, according to certain embodiments In certain
embodiments, method 1100 can be performed by one or more processors
on control server 528, site gateway 524, or other suitable
computing device or system described herein.
[0085] At step 1110, method 1100 includes receiving, by a
processor, power measurement data for a plurality of PV-based EG
sites. The power measurement data may include a present power
output for each of the EG sites (or subset thereof) and a
historical power output for each of the EG sites (or subset
thereof). The present power output of the EG site may be received
from the EG site. For instance, the present power output may be
provided to a control server by a gateway, a PV inverter, or other
on-site computing device local to the EG site (e.g., see system 500
of FIG. 5).
[0086] Power measurement data corresponding to the historical power
output of the EG site can be received from a database. The database
can be a operated by a third-party entity (e.g., historical power
output database 530), the database can by operated and/or
controlled by the control server (e.g., control server 528), or it
may be local to and operated by the EG site. In some embodiments,
the historical power output is an average power output for the EG
site for a previous predetermined period of time, which may be any
suitable time span (e.g., previous week, month, year, or other
continuous or non-continuous period of time), as would be
appreciated by one of ordinary skill in the art with the benefit of
this disclosure.
[0087] At step 1120, method 1100 includes calculating, by the
processor, a density of cloud cover over each EG site (or subset
thereof) based on a percentage difference between a present power
output and historical power output for the corresponding EG site.
At step 1130, method 1100 includes determining, by the processor,
that a density of cloud cover at a first EG site of the plurality
of EG sites exists at a second EG site of the plurality of EG sites
after a period of time. The basic concept is that a certain cloud
density is measured at a first EG site location. If that cloud
density is measured at another EG site location after a certain
period of time, then the assumption is that that cloud cover at the
first EG site has moved to the second EG site. In some cases,
multiple EG sites spanning a certain geographic area may measure
different cloud densities resulting in an overall pattern of
densities for that geographic area. Thus, patterns of cloud
densities can be tracked. In some cases, aspects of cloud cover may
change over time (e.g., shape, density), so a threshold may be used
to identify approximately "equal" densities. For instance, the
cloud cover density at the first EG site may be deemed "equal" to
the cloud density at the second EG site if the density calculations
are within 5% of each other (or other suitable value). The period
of time can be any suitable stretch of time spanning seconds,
minutes, hours, etc. Because clouds may change dramatically over
longer periods of time, shorter periods of time may be preferred
(e.g., seconds, minutes).
[0088] Once two EG sites having similar cloud cover densities are
determined, their locations can be used to calculate a
corresponding vector including direction and speed (the time it
took the cloud from EG site 1 to reach EG site 2). Thus, at step
1140, method 1100 includes calculating, by the processor, a vector
path for the cloud cover based on a location of the first EG site,
a location of the second EG site, and the period of time. Utilizing
pattern densities (e.g., multiple cloud density measurements made
by adjacent EG sites) can, in some cases, provide a more accurate
vector measurement. As discussed above, individual clouds may
change shape and density over time at different rates. By tracking
a pattern of densities, some of these changes may be
computationally mitigated because any change in a single cloud may
be offset by, for example, an average of the density pattern and
its corresponding movement.
[0089] At step 1150, method 1100 includes determining, by the
processor, a third EG site of the plurality of EG sites having a
location in the vector path of the cloud cover. That is, by using
the calculated vector (i.e., speed and vector of the corresponding
cloud cover), other EG sites downstream from the first and second
EG sites can be determined. Some closer EG sites may be affected by
the cloud cover (e.g., reduced power output due to cloud cover) in
a shorter period of time based on the speed component of the
vector. In contrast, farther EG sites may be affected later.
[0090] Weather can be unpredictable and air speed and direction can
change quickly. Thus, forecasts predicting downstream EG sites that
are relatively close (e.g., 1-5 miles) may be more accurate than
forecasts predicting downstream EG sites farther away (e.g., 10-50
miles). Thus, more sophisticated algorithms can be used (possibly
in an ensemble arrangement) to modify that calculated vector at
longer distances. For example, the calculated vector may apply as
is for more local downstream forecasting. In contrast, the
calculated vector may be modified to identify EG sites within an
expanding range relative to the vector. For example, method 1100
may include EG sites (e.g., 10 or more miles away) within a +/-20
degree divergence from the calculated vector path of the cloud
cover. Other modifications and possible as would be appreciated by
one of ordinary skill in the art.
[0091] At step 1160, method 1100 includes controlling, by the
processor, an EG distribution profile for the third EG site based
on the density of cloud cover at the first EG site or second EG
site. The EG distribution profile may relate to a charging profile
for an energy storage device associated with the third EG site. In
some embodiments, the EG distribution profile of the EG site may
include time-shifting load profile of the EG site. In some
embodiments, a charging profile for the energy storage device may
include an increased charge set point when cloud cover is
determined to be present and the cloud cover density is greater
than a predetermined threshold value for cloud density. Further
aspects of method 1100 are discussed at least with respect to FIG.
8.
[0092] It should be appreciated that the specific steps illustrated
in FIG. 11 provide a particular method 1100 of measuring a power
signal in a power grid, according to certain embodiments. Other
sequences of steps may also be performed according to alternative
embodiments. For example, alternative embodiments may perform the
steps outlined above in a different order. Moreover, the individual
steps illustrated in FIG. 11 may include multiple sub-steps that
may be performed in various sequences as appropriate to the
individual step. Furthermore, additional steps may be added or
removed depending on the particular applications. One of ordinary
skill in the art would recognize and appreciate many variations,
modifications, and alternatives of the method 1100.
[0093] FIG. 12 shows a simplified flow chart for a method of
tracking weather patterns (cloud cover) in a grid-connected array
of PV-based EG sites, according to certain embodiments. In certain
embodiment, method 1200 can be performed by a processor on control
server 528, site gateway 524, or other suitable computing device or
system described herein. Aspects of FIG. 12 are further described
above at least with respect to FIG. 9.
[0094] At step 1210, method 1200 includes receiving power
measurement data for a plurality of PV-based EG sites. Each EG site
can have a corresponding location. The power measurement data can
include a present power output of each of the plurality of EG sites
(or subset thereof), and a historical power output of each of the
plurality of EG sites (or subset thereof).
[0095] At step 1220, method 1200 includes determining a bounded
area defined by the locations of the plurality of EG sites. For
example, a bounded area can be defined by the outer-most locations
of a plurality of EG sites, which define an area of measurement
coverage (i.e., an area where weather pattern analysis can be made
using EG-site resources). The "resolution" of the area of
measurement coverage is based on the number and distribution of
EG-sites within the bounded area. Thus, a 100 square kilometer area
having 50,000 PV-based EG units may provide more data (i.e., better
accuracy/resolution) than that same area having 50 EG units.
[0096] At step 1230, method 1200 includes determining if cloud
cover is present over each of the plurality of EG sites (or subset
thereof) based on their corresponding present power outputs and
historical power outputs. One purpose for this step is to identify
which EG sites register cloud cover. Each EG site detecting cloud
cover can be used as a "pixel" to determine how cloud cover is
distributed throughout the bounded area. If a weather pattern
(e.g., cloud cover over a large area) is completely contained
within the bounded area, then a vector (i.e., movement and speed)
of the weather pattern can easily be tracked by using some or all
of the EG sites within the bounded area by, for example, tracking
the edges of the weather pattern and determining its vector based
on the detected movement of the edges. The resolution of the
tracked location of the weather pattern may be dictated by the
number and distribution of PV-based EG sites within the bounded
area. In some embodiments, a portion of the entire edge defining
the area of cloud cover can be used to calculate its vector. For
instance, if a weather pattern having widespread cloud cover is
much larger than the bounded area, but at least an edge of the
weather pattern is contained within the bounded area, then certain
features of the edge (e.g., characteristics of its shape or
density) can be tracked as it moves through the bounded area. For
sufficiently large EG sites (e.g., solar farms), individual panel
measurements, string measurements, subsets of panels from the
whole, etc., may be used to detect and/or track clouds and cloud
movement using the methods described above.
[0097] Thus, at step 1240, method 1200 includes determining if at
least an edge of the cloud cover is detectable from a plurality of
EG site data. At step 1250, a vector is calculated based on a
movement of the edge of the storm system within the bounded area.
At step 1260, method 1200 includes identifying a downstream EG site
in the vector path of the storm system, as further discussed above
at least with respect to FIGS. 8-11. Step 1260 further includes
controlling an EG distribution profile for the downstream EG site
based power characteristics of the EG sites of the plurality of EG
sites within the storm system.
[0098] Some embodiments may include calculating a density of cloud
cover over each EG site within the weather pattern (e.g., cloud
cover, storm system, etc.) based on a percentage difference between
a present power output and historical power output for the
corresponding EG site. In some implementations, controlling the EG
distribution profile for the downstream EG site can be further
based on the density of cloud cover over each EG site within the
weather pattern.
[0099] It should be appreciated that the specific steps illustrated
in FIG. 12 provide a particular method 1200 of measuring a power
signal in a power grid, according to certain embodiments. Other
sequences of steps may also be performed according to alternative
embodiments. For example, alternative embodiments may perform the
steps outlined above in a different order. Moreover, the individual
steps illustrated in FIG. 12 may include multiple sub-steps that
may be performed in various sequences as appropriate to the
individual step. Furthermore, additional steps may be added or
removed depending on the particular applications. One of ordinary
skill in the art would recognize and appreciate many variations,
modifications, and alternatives of the method 1200.
Aspects of Energy Distribution
[0100] Many of the embodiments discussed herein relate to
forecasting which downstream EG sites may be affected by the
weather pattern based on a calculated vector and corresponding
density, and controlling aspects of energy generation distribution
for the downstream EG sites to take advantage of the early warning
and knowledge that their respective power outputs may be severely
reduced or shutdown entirely by the approaching weather system. In
some implementations, energy storage devices may be charged to
higher set points, appliances and/or HVAC systems may operate at
earlier times to take advantage of energy stores while they are
available, and other preliminary measures are possible as would be
appreciated by one of ordinary skill in the art. Some of the myriad
ways of controlling and distributing energy generation are
discussed here.
[0101] FIG. 13 shows aspects of energy generation distribution at
an EG site 1324, according to certain embodiments. EG site 1324 is
communicatively coupled to control server 1328 through network
1326, similar to the arrangement described above with respect to
FIG. 5. EG site 1324 may include EG distribution system 1330, which
may control the distribution of energy to loads including energy
generation storage devices 1332, appliances 1334, HVAC system(s)
1336, or other load. EG distribution system 1330 may be contained
and/or operated by any suitable resource at EG site 1324 including
a gateway, inverter, or other local computing device.
Alternatively, EG distribution system 1330 may be operated remotely
(e.g., by control server 1328). There are many possible locations
and operative resources (e.g., logic) of distribution system 1330
as would be appreciated by one of ordinary skill in the art. In
some embodiments, EG distribution system 1330 can be operated by
any resource of the various systems described herein (e.g., systems
500, 600, 700). In some cases, appliances 1334 and HVAC 1336 may be
part of an overall site load (e.g., site load 520).
[0102] EG storage device(s) 1332 can include one or more
rechargeable batteries, or other suitable energy storage technology
including, but not limited to, compressed air, flywheels, pumped
hydro, superconducting magnetic energy storage (SMES), or the like.
Appliances (e.g., washer/dryer, hot water heater, refrigerator,
etc.) 1334 and HVAC 1336 may have time-shifted operations. For
instance, HVAC 1336 may be regularly scheduled to turn on at 5 P.M.
to achieve a desired temperature before a homeowner arrives at 6
P.M. One example of time-shifting a load would be to turn on the
HVAC at 2 P.M. in anticipation of a thunderstorm (or other event)
that may result in a power outage or brown out at the regularly
scheduled time of operation. In some instances, an appliance, such
as a hot water heater, may act as an energy storage technology
and/or operate with time-shifted load. For example, when a storm is
predicted for a certain time, more photovoltaic energy may be
directed to heating and storage of hot water for later use, rather
than pushing excess power back to the grid (such as for
net-metering credit). In an aspect, water may further be heated to
a higher temperature to allow for longer use of hot water when
power may not be available. In some embodiments, a charging profile
for the energy storage device may include an increased charge set
point when cloud cover is determined to be present and the cloud
cover density is greater than a predetermined threshold value for
cloud density. These concepts of proactively changing energy
storage charging profiles, time-shifting loads, etc., are further
discussed below at least with respect to FIGS. 14-19.
Charging Considerations for an Energy Storage Device
[0103] Aspects of the present disclosure are directed to, among
other things, charging profiles for a storage device in an energy
generation systems. In some cases, the longevity of the storage
capability of a battery may be increased if the battery is charged
during its operating life accordingly to certain criteria. In some
instances, routinely charging a battery to 90% of its maximum
charge, rather than 100% of its maximum charge, can prolong the
life (storage capability) of the battery. Aspects of the disclosure
include different charging profiles with these principles in mind.
Specifically, aspects are directed to battery charging profiles
that may be selected based on near term weather conditions (e.g.,
thunderstorms, high winds, etc.), alerts (e.g., rioting, brown out
warnings, etc.), or local natural disasters (e.g., earthquakes,
tornadoes, hurricanes, wildfires, etc.), referred to as, e.g.,
event data, which may be received via news feeds, social media, web
resources, or other suitable resources, such as, for example the
Emergency Alert System (EAS). Under these conditions, a probability
that a power outage will occur is determined and a battery can be
charged based on this probability. For instance, a battery can be
charged according to a first charging profile when the probability
is below a predetermined threshold value (i.e., a power outage is
unlikely). The battery can be charged according to a second
charging profile when the probability is at or above the
predetermined threshold value (i.e., a power outage is likely). A
maximum charge set point of the storage device for the first
charging profile can be less than the storage capacity of the
storage device (e.g., 90% of maximum storage capacity), thereby
maintaining a charging profile that helps prolong the life of the
battery. A maximum charge set point of the storage device for the
second charging profile can be at the storage capacity of the
storage device (e.g., 100% of the maximum storage capacity), which
may afford the user access to additional power during times when
electricity from the local utility grid may not be available. The
storage device can be charged according to the first charging
profile under normal operating conditions.
[0104] Battery Charging System
[0105] FIG. 14 is a simplified block diagram of a PV-based EGS
system 1400, according to certain embodiments. EGS system 1400
includes a PV-based energy generation subsystem comprising a PV
inverter 1406 and one or more PV panels 1408, and a battery-based
energy storage subsystem comprising a battery inverter/charger 1410
and a battery device 1412. In some embodiments, PV inverter 1406
and battery inverter/charger 1410 can be combined into a single
device. In the example of FIG. 14, EGS system 1400 is
grid-connected; thus, PV inverter 1406 and battery inverter/charger
1410 are electrically connected to the utility grid (1414) via a
main panel and a utility meter (not shown). Further, to provide
power to site 1400, utility grid 1414, PV inverter 1406, and
battery inverter/charger 1410 are electrically connected to site
load 1420 (e.g., lights, appliances, etc.). Logic block 1480 is
coupled to battery inverter/charger 1410. Logic block 1480 receives
input from a data source 1490, which can include multiple data
sources, data types, etc., including but not limited to weather
data, alert data, social media, and more. Data from data source
1490 (referred to as event data) can be used to dictate a type of
charging profile to apply to battery device 1412, as further
discussed below. Any type of data can be fed into logic block 1480,
as would be appreciated by one of ordinary skill in the art. EGS
system can be similar to the EGS system shown in FIG. 5.
Alternatively, system 1400 can include multiple storage devices
(batteries), multiple energy generation resources (not limited to
PV power), or different topologies, as would be appreciated by one
of ordinary skill in the art with the benefit of this disclosure.
In some embodiments, storage devices can be charged by an EG system
(e.g., PV panel system), by the utility grid (e.g., during periods
of low energy cost), or combinations thereof.
[0106] Logic block 1480 controls a charging profile for battery
device 1412 through battery charger 1410. In some cases, the
longevity of the storage capability of a battery may be increased
if the battery is charged during its operating life accordingly to
certain criteria. In some instances, routinely charging a battery
to a value lower than its maximum charge (e.g., 90%) can prolong
the life (storage capability) of the battery. In certain
embodiments, logic block 1480 provides an appropriate charging
profile based on certain conditions to balance the interests of
maximizing the life of the battery and providing a maximum
available charge for a user, particularly during times when power
is limited or unavailable from the utility grid 1414. Logic block
1480 provides different charging profiles with these principles in
mind including battery charging profiles that may be selected based
on near term events that may lead to a loss of power from the
utility grid 1414. Some of these events may include weather
conditions (e.g., thunderstorms, high winds, etc.),
blackouts/brownouts due to high energy use in a grid network,
natural disasters (e.g., earthquakes, tornadoes, hurricanes,
wildfires, etc.), and the like, which may be received via any
suitable data source 1490 including news feeds, social media, alert
data, web resources, or other suitable resource, as further
discussed below.
[0107] Logic block 1480 can further detect whether one or more
storage devices 1412 are connected to EGS system 1400 and determine
their storage capacity. Logic block 1480 can provide a charging
profile that can be based on a past, present, and/or future load
requirement, based on charging to maximize an economic benefit
(e.g., feeding power back to utility grid 1414, reducing a peak
load value, etc.), based on event data from data source 1490. Logic
block 1480 can be located in a separate device, integrated in
another device (e.g., part of battery inverter/charger 1410), or
located local or remote (off-site logic from a control server 528),
or any combination thereof, as would be appreciated by one of
ordinary skill in the art. In some implementations, system 1400 can
be an extension of EGS system 502 (see FIG. 5).
[0108] Energy Storage Device Charging Profiles
[0109] There are many types of energy storage devices (batteries)
that can be used in solar systems. Some commonly used battery
technologies include Lithium Ion (Li-ion), Lead Acid, Nickel
Cadmium (NiCad), Nickel Iron (NiFe), and more. Li-ion batteries,
for example, are rechargeable and can have very long operating
lives if well maintained. For maximizing the operating life of a
battery, the optimal charging state includes a partial charge
(60-70%) and idle activity (i.e., not charging or discharging).
Charging to or maintaining at 100% charge or maintaining a zero
charge actually reduces the operating life of a battery.
Furthermore, frequent charging and discharging (cycling) can
further reduce the operating life. Thus, frequent charge cycling
and/or fully charging and discharging a battery can prematurely
reduce its charge capacity over time. On the other hand, owners of
solar systems may benefit more from having a maximum available
charge on hand to sufficiently provision local loads and reduce
energy cost. Thus, various charging profiles can be used with these
competing issues in mind. In some embodiments, a 90% maximum charge
profile is used because it provides sufficient back up power for
the solar system and corresponding load, yet still limits the
maximum charge state of the battery to support longer battery life.
In instances where a power outage (off-grid event) is imminent due
to a lightning storm, social unrest, war, or other event that may
cause the power grid to shut down, a 100% maximum charging profile
can be used to provide more available back up power in the event
that the power outage occurs.
[0110] In some solar systems, multiple energy storage devices may
be used. In one configuration, two batteries can be used with each
having different performance characteristics. A first battery may
have a lower capacity but superior cycling performance (less
degradation due to cycling). The second battery may have high
capacity but a comparatively lower cycling performance. During
normal operation or when a low probability of an off-grid event is
imminent, the low capacity, high-cycle battery can be used as a
primary energy storage device. If there is a high probability of an
off-grid event, then the high storage, low-cycle battery can be
fully charged. Any suitable charging profile can be used and may be
selected based on any criteria including battery longevity, lower
cost, power efficiency, or the like, as would be appreciated by one
of ordinary skill in the art.
[0111] FIG. 15A shows a number of storage device charging profiles
1500 in an EGS system, according to certain embodiments. The y-axis
shows a percentage charge of one or more storage devices (from
0-100%). The x-axis shows a time scale, which may be any suitable
scale including second, minutes, hours, or other time-based
denomination. As discussed above, the longevity of the storage
capability of a storage device (e.g., battery) may be increased if
the battery is charged during most of its operating life to a value
lower than its maximum charge capacity. The examples described
herein use 90% as the maximum charge value under normal operating
conditions. This is not limiting and other maximum values (e.g.,
80%, 95%, etc.) under normal operating conditions can be used.
Furthermore, the maximum value under normal operating conditions
may change over time (i.e. increase or decrease linearly,
non-linearly, etc.) based on load requirements, storage device
capabilities, utility needs, or the like, as would be appreciated
by one of ordinary skill in the art. Charging profiles may be
controlled, for example, by logic block 1480 of FIG. 14.
[0112] "Normal" operating conditions typically refers to periods of
time where a black out from utility 1414 is not imminent, such as
during inclement weather, natural disasters that can damage the
utility, utility energy distribution problems (brown outs, etc.),
or other event that can cause stop power (from a utility) from
reaching a user site (residence, commercial site, etc.). In some
embodiments, the likelihood or probability of a blackout is
determined, which dictates whether a storage device should be
charged under "normal" operating conditions, or under "storm mode"
conditions, as further described below.
[0113] Referring to FIG. 15A, charging profile 1510 shows a storage
device being linearly charged from 40% to 90% at a certain rate
over a first time interval 1510. Charging profiles 1520-1540 show
different linear charge rates over different time intervals up to a
maximum charge of 90%. Under normal operating conditions, storage
device 1412 is routinely charged up to the 90% maximum at any
suitable rate in an effort to maximize its operating life. Any
suitable charging rate can be used. In some instances, faster or
slower charge rates (up to 90%) can be used based on a number of
considerations. A charging rate may also affect the longevity of a
storage device, so slower or faster rates may be used accordingly.
Load requirements may affect the rate at which a storage device is
charged. For instance, under periods of heavy loads, the storage
device may receive little charge with a very slow charging rate or
no charge at all. In contrast, during periods of light loads, the
storage device may be charged at a faster rate. There are many
factors that can dictate the charge rate of a storage device, which
would be appreciated by one of ordinary skill in the art. However,
the maximum charge under normal operating conditions is typically
set to a value lower than the maximum available storage of the
storage device.
[0114] FIG. 15B illustrates a charging profile under normal
operating conditions with a non-linear charge rate. In practice,
charging profiles will typically be non-linear due to the reasons
given above including competing interests such as load
requirements, utility back feeding incentives, or other
considerations. In some cases, the charging profile may be
intermittent. A wide variety of charging profiles are possible, but
a charging principle common to each charging profile is that the
maximum allowed charge is lower than the maximum storage capacity
of the storage device to improve its operating life (ability to
store a charge). Here, the maximum charged is 90% capacity.
Furthermore, it should be understood that the charging profiles
described herein are merely simplified examples and do not
necessarily represent actual typical charge rates for storage
devices.
[0115] FIG. 16A shows a number of storage device charging profiles
1600 in an energy generation (EG) system, according to certain
embodiments. The y-axis shows a percentage charge of one or more
storage devices (from 0-100%). The x-axis shows a time scale, which
may be any suitable scale including second, minutes, hours, or
other time-based denomination. In contrast to the "normal"
operating conditions discussed above, the charging profiles shown
in FIGS. 16A-16B show "storm-mode" conditions. Charging profiles
may be controlled, for example, by logic block 1480 of FIG. 14.
[0116] "Storm mode" operating conditions typically refer to periods
of time where a black out from utility 1414 is imminent, such as
during inclement weather, natural disasters that can damage the
utility, utility energy distribution problems (brown outs, etc.),
or other event that can cause stop power (from a utility) from
reaching a user site (residence, commercial site, etc.). In some
embodiments, the likelihood or probability of a blackout is
determined, which dictates whether a storage device should be
charged under "normal" operating conditions, or under "storm mode"
conditions. Although "storm-mode" connotes a mode predicated on
weather conditions, other events may trigger this charging mode, as
further discussed below. The likelihood or probability that a
blackout condition could occur may be determined by logic block
1480 and based on event data from a data source 1490, as previously
discussed.
[0117] As discussed above, the longevity of the storage capability
of a storage device (e.g., battery) may be increased if the battery
is charged during most of its operating life to a value lower than
its maximum charge capacity, such as 90%. In storm mode, a primary
concern is making the most amount of power available to a user
during periods of time where a blackout condition may occur. In
some cases, the charge rate may be as fast as possible to ensure
that the storage device receives the maximal amount of charge in
the shortest amount of time, although other charge rates are
possible.
[0118] Referring to FIG. 16A, charging profile 1610 shows a storage
device being linearly charged from 40% to 100% at a certain rate
over a first time interval 1610. Charging profiles 1620-1640 show
different linear charge rates over different time intervals up to a
maximum charge of 100%. As mentioned above, any suitable charging
rate can be used. In some instances, faster or slower charge rates
(up to 100%) can be used based on a number of considerations. Load
requirements may affect the rate at which a storage device is
charged. For instance, under periods of heavy loads, the storage
device may receive little charge with a very slow charging rate or
no charge at all. In contrast, during periods of light loads, the
storage device may be charged at a faster rate. There are many
factors that can dictate the charge rate of a storage device, which
would be appreciated by one of ordinary skill in the art. However,
the maximum charge in "storm mode" is typically set to a maximum
available storage of the storage device (i.e., 100%).
[0119] FIG. 16B illustrates a charging profile under "storm-mode"
operating conditions with a non-linear charge rate. In practice,
charging profiles may typically be non-linear due to the reasons
given above including competing interests such as load
requirements, utility back feeding incentives, or other
considerations, however the interest in maximizing a charge on a
storing device in anticipation of a looming blackout will typically
produce faster charge rates than during "normal" operating
conditions. In some cases, the charging profile may be
intermittent. A wide variety of charging profiles are possible, but
a charging principle common to each charging profile is that the
maximum allowed charge is set to the maximum storage capacity of
the storage device to achieve a full charge. Here, the maximum
charged is 100% capacity. It should be understood that the charging
profiles described herein are simplified examples and do not
necessarily represent actual typical charge rates for storage
devices. Furthermore, the charging profiles described herein can be
applied to any of the foregoing and preceding embodiments.
[0120] FIG. 17 shows several examples of event data that can be
used to determine a charging profile for a storage device,
according to certain embodiments. Data source 1790 provides event
data to logic block 1780.
[0121] Data source 1790 may include event data such as weather
data, alert data, social media data, web services data, or any
other suitable information-based input that can help logic block
1780 determine if a power outage (e.g., black out condition) is
likely to occur. Weather data can come from any number of sources
including websites, radio/TV (with an appropriate decoder), or
other weather resource that can provide a weather forecast over a
predetermined period of time (e.g., over the next 24-48 hours). For
instance, forecast of heavy thunderstorms, tornadoes, hurricane or
tropical storms, high winds, etc., may likely cause a power outage.
Forecasts of mild weather would be unlikely to cause a power
outage.
[0122] Alerts may provide logic block 1780 with information to help
determine if a power outage is likely to occur. For instance, logic
block 1780 may determine a probability of a power outage based on
an alert. Alerts can be local, regional, or national and may be
provided by any suitable means (e.g., Emergency Broadcast System).
Alerts can be related to weather, civil unrest, war, terrorism,
third-world scenarios that may affect the continuity of the grid
(e.g., flooding, rolling power outages, etc.) indications of
possible brownouts, natural disasters, or any type of information
that relates to a probability or likelihood of a power outage over
a predetermined period of time (e.g., within 24-48 hours). Any
period of time may be used.
[0123] Social media outlets (Facebook.RTM., Twitter.RTM.,
Tumblr.RTM., Vine.RTM., etc.) may provide logic block 1780 with
information to help determine if a power outage is likely to occur.
For instance, logic block 1780 may determine a probability of a
power outage based on a post, blog, video, or other resource.
Social media outlets may provide information about weather, civil
unrest, terrorism, war, indications of possible brownouts, natural
disasters, or any type of information that relates to a probability
or likelihood of a power outage over a predetermined period of time
(e.g., within 24-48 hours). Any period of time may be used. It
should be noted that although the examples described herein
describe the determination of a probability of a power outage, some
embodiments may alternatively, or additionally, determine the
probability of a brown-out condition (as opposed to a full outage)
and apply an appropriate charging profile.
[0124] "Other" data sources include any conceivable data source can
be used provided that it includes information that logic block 1780
can use to determine a likelihood or probability of an off-grid
event (power outage) and take appropriate action (apply a "normal"
or "storm-mode" charging profile). For instance, data from certain
home meters and/or appliances may indicate whether a home is
currently occupied (e.g., via motion detection). This data can be
used to inform an appropriate charging profile. For instance, data
indicating that a home has not been occupied for several days
(e.g., family on vacation) may cause logic 1780 to maintain the
first charging profile (e.g., normal mode) over the second charging
profile ("storm mode"), even with a high probability of a power
outage because the occupants may not be home to use the extra
stored energy. In some implementations, logic 1780 may maintain the
"normal mode" charging profile even with a high probability of an
off-grid event because the house may have a history of surplus
power even under high load conditions. That is, the extra energy
stored in "storm mode" charging profiles (e.g., extra 10% charge)
may not be needed because the "normal mode" charging profile has
provided adequate power during the entire operating life of the
solar power system.
[0125] In certain embodiments, logic block 1780 may receive storm
forecast data from a weather tracking system (shown as "other" in
data source 1790), as shown and described above at least with
respect to FIGS. 4-13. It should be understood that any and all
aspects of the various systems and methods described herein can be
combined, substituted, integrated, or otherwise mixed and matched
in any suitable manner, as would be appreciated by one of ordinary
skill in the art with the benefit of this disclosure.
[0126] FIG. 18 shows alternative methods of controlling a charging
profile in a storage device, according to certain embodiments.
Logic block 1880 can determine which charging profile to apply to a
storage device based on data source 1890. Alternatively, logic
block 1880 may accept inputs from manual control 1882 or from a
control server 1884. Logic block 1880 may function similarly to
logic block 1480 and 1780 in FIGS. 14 and 17, respectively.
[0127] Manual control 1882 may be a manual override switch that is
accessible by a user (e.g., home owner). For instance, a user may
wish to immediately switch to a "storm-mode" charging profile,
regardless of the current inputs from data source 1890. Manual
control 1882 can be a physical button or switch, e.g., on the
storage device charging system, or it may be a remote controlled
function accessible via a web portal, smart phone, or other
wireless means. When manual control 1882 is turned off, logic block
1880 can return to its standard operating procedure, as discussed
above.
[0128] Control server 1884 may control logic block 1880, according
to certain embodiments. Control server 1884 may be controlled
remotely by an entity other than the user (e.g., homeowner). For
instance, control server 1884 may operate similarly to control
server 528 of FIG. 5. In some embodiments, control server 1884 may
control the charging profile for storage device (not shown) based
on its own data sources. For example, control server 1884 may
receive weather data, alert data, social media data, or other data
to help it determine if a power outage is likely to occur at the
user site. Thus, all of the functions described above with respect
to logic block 1880 may be performed by a control server 1884,
which in turn can control the charging profile of a storage device
at a user site. The myriad combinations and implementations of
manual control, remote control (via remote entity), and the like
would be appreciated by one of ordinary skill in the art with the
benefit of this disclosure.
[0129] FIG. 19 depicts a simplified flow diagram showing aspects of
a method 1900 of charging a storage device, according to certain
embodiments. Method 1900 is performed by processing logic that may
comprise hardware (e.g., circuitry, dedicate logic, etc.), software
(which is run on a general purpose computing system or a dedicated
machine), firmware (embedded software), or any combination thereof.
In one embodiment, method 1900 can be performed by logic block 1480
(FIG. 14), 1780 (FIG. 17), 1880 (FIG. 18) or processor 502 of FIG.
5.
[0130] Referring to FIG. 19, method 1900 begins with determining
that a storage device is coupled to an energy generation site
(1910). For example, logic block 1480 may determine that a storage
device is electrically connected to an energy generation site. In
some cases, multiple storage devices may be used. At 1920, the
energy storage capacity of the storage device is determined. The
energy storage capacity can be the maximum amount of electrical
charge that can be stored in the storage device(s).
[0131] At 1930, logic block 1480 receives event data from data
source 1490. Event data may include weather data, alert data,
social media data, web services data, or any other suitable
information-based input that can help logic block 1480 determine if
a power outage (e.g., black out condition) is likely to occur.
Weather data can come from any number of sources including
websites, radio/TV (with an appropriate decoder), or other weather
resource that can provide a weather forecast over a predetermined
period of time (e.g., over the next 24-48 hours). Alerts can be
local, regional, or national and may be provided by any suitable
means, as discussed above. Social media outlets may provide
information about weather, civil unrest, war, indications of
possible brownouts, natural disasters, or any type of information
that relates to a probability or likelihood of a power outage over
a predetermined period of time (e.g., within 24-48 hours). It
should be understood that any conceivable data source can be used
provided that it includes information that logic block 1480 can use
to determine a likelihood or probability of a power outage and take
appropriate action (e.g., apply a "normal" or "storm-mode" charging
profile).
[0132] At 1940, logic block 1480 determines whether a power outage
is likely to occur based on the event data. In some cases, logic
block 1480 may determine that the probability of a power outage is
above a predetermined threshold value. The predetermined threshold
value can be a 70% chance, 80% chance, or other suitable threshold
value. For instance, if the event data indicates 90% change of
heavy thunderstorms or a hurricane, logic block 1480 may determine
that the probability of a power outage is above a predetermine
threshold value (percentage), or that a power outage is likely.
Probability can be determined based on prior black-out data,
brown-out data, the proximity of a particular warning or event,
predetermined triggers from a utility system (e.g., alerting an
impending system shutdown), and the like.
[0133] At 1950, if logic block 1480 determines that a power outage
is not likely, or the probability of a power outage is less than
the predetermined threshold value, then storage device 1412 is
charged according to a first charging profile. In some embodiments,
the first charging profile may have a maximum charge of 90% of the
storage capacity of the storage device, as shown and described in
FIGS. 15A-15B.
[0134] At 1960, if logic block 280 determines that a power outage
is likely, or the probability of a power outage is at or higher
than the predetermined threshold value, then storage device 212 is
charged according to a second charging profile. In some
embodiments, the second charging profile may have a maximum charge
of 100% of the storage capacity of the storage device, as shown and
described in FIGS. 16A-16B.
[0135] It should be appreciated that the specific steps illustrated
in FIG. 19 provide a particular method of charging a storage
device, according to certain embodiments. Other sequences of steps
may also be performed according to alternative embodiments. For
example, different thresholds, charging rates, or manual override
features can be included. Furthermore, certain embodiments of
method 1900 may perform the individual steps in a different order,
at the same time, or any other sequence for a particular
application, as noted above. Moreover, the individual steps
illustrated in FIG. 19 may include multiple sub-steps that may be
performed in various sequences as appropriate to the individual
step. Additional steps may be added or removed depending on the
particular applications. For instance, some embodiments may skip
the determination of the presence and capacity of a storage device
(if that information is already known or supplied). One of ordinary
skill in the art would recognize and appreciate many variations,
modifications, and alternatives of the method.
[0136] FIG. 20 is a simplified block diagram of computer system
2000, according to certain embodiments. Computer system 2000 can be
used to implement any of the computer systems/devices (e.g.,
control server 528, gateway devices 524, logic block 1480)
described with respect to FIGS. 1-19. As shown in FIG. 20, computer
system 2000 can include one or more processors 2002 that
communicate with a number of peripheral devices via a bus subsystem
2004. These peripheral devices can include storage subsystem 2006
(comprising memory subsystem 2008 and file storage subsystem 2010),
user interface input devices 2012, user interface output devices
2014, and a network interface subsystem 2016.
[0137] In some examples, internal bus subsystem 2004 can provide a
mechanism for letting the various components and subsystems of
computer system 2000 communicate with each other as intended.
Although internal bus subsystem 2004 is shown schematically as a
single bus, alternative embodiments of the bus subsystem can
utilize multiple buses. Additionally, network interface subsystem
2016 can serve as an interface for communicating data between
computer system 2000 and other computer systems or networks.
Embodiments of network interface subsystem 2016 can include wired
interfaces (e.g., Ethernet, CAN, RS232, RS485, etc.) or wireless
interfaces (e.g., ZigBee, Wi-Fi, cellular, etc.).
[0138] In some cases, user interface input devices 2012 can include
a keyboard, pointing devices (e.g., mouse, trackball, touchpad,
etc.), a barcode scanner, a touch-screen incorporated into a
display, audio input devices (e.g., voice recognition systems,
microphones, etc.), and other types of input devices. In general,
use of the term "input device" is intended to include all possible
types of devices and mechanisms for inputting information into
computer system 2000. Additionally, user interface output devices
2014 can include a display subsystem, a printer, or non-visual
displays such as audio output devices, etc. The display subsystem
can be any known type of display device. In general, use of the
term "output device" is intended to include all possible types of
devices and mechanisms for outputting information from computer
system 2000.
[0139] Storage subsystem 2006 can include memory subsystem 2008 and
file/disk storage subsystem 2010. Subsystems 2008 and 2010
represent non-transitory computer-readable storage media that can
store program code and/or data that provide the functionality of
embodiments of the present disclosure. In some embodiments, memory
subsystem 2008 can include a number of memories including main
random access memory (RAM) 2018 for storage of instructions and
data during program execution and read-only memory (ROM) 2020 in
which fixed instructions may be stored. File storage subsystem 2010
can provide persistent (i.e., non-volatile) storage for program and
data files, and can include a magnetic or solid-state hard disk
drive, an optical drive along with associated removable media
(e.g., CD-ROM, DVD, Blu-Ray, etc.), a removable flash memory-based
drive or card, and/or other types of storage media known in the
art.
[0140] It should be appreciated that computer system 2000 is
illustrative and not intended to limit embodiments of the present
disclosure. Many other configurations having more or fewer
components than system 2000 are possible.
[0141] The various embodiments further can be implemented in a wide
variety of operating environments, which in some cases can include
one or more user computers, computing devices or processing
devices, which can be used to operate any of a number of
applications. User or client devices can include any of a number of
general purpose personal computers, such as desktop or laptop
computers running a standard operating system, as well as cellular,
wireless and handheld devices running mobile software and capable
of supporting a number of networking and messaging protocols. Such
a system also can include a number of workstations running any of a
variety of commercially available operating systems and other known
applications for purposes such as development and database
management. These devices also can include other electronic
devices, such as dummy terminals, thin-clients, gaming systems and
other devices capable of communicating via a network.
[0142] Most embodiments utilize at least one network that would be
familiar to those skilled in the art for supporting communications
using any of a variety of commercially available protocols, such as
TCP/IP, OSI, FTP, UPnP, NFS, CIFS, and AppleTalk. The network can
be, for example, a local area network, a wide-area network, a
virtual private network, the Internet, an intranet, an extranet, a
public switched telephone network, an infrared network, a wireless
network, and any combination thereof.
[0143] In embodiments utilizing a network server, the network
server can run any of a variety of server or mid-tier applications,
including HTTP servers, FTP servers, CGI servers, data servers,
Java servers, and business application servers. The server(s) also
may be capable of executing programs or scripts in response to
requests from user devices, such as by executing one or more
applications that may be implemented as one or more scripts or
programs written in any programming language, such as Java.RTM., C,
C# or C++, or any scripting language, such as Perl, Python or TCL,
as well as combinations thereof. The server(s) may also include
database servers, including without limitation those commercially
available from Oracle, Microsoft.RTM., Sybase.RTM., and
IBM.RTM..
[0144] The environment can include a variety of data stores and
other memory and storage media as discussed above. These can reside
in a variety of locations, such as on a storage medium local to
(and/or resident in) one or more of the computers or remote from
any or all of the computers across the network. In a particular set
of embodiments, the information may reside in a storage-area
network (SAN) familiar to those skilled in the art. Similarly, any
necessary files for performing the functions attributed to the
computers, servers or other network devices may be stored locally
and/or remotely, as appropriate. Where a system includes
computerized devices, each such device can include hardware
elements that may be electrically coupled via a bus, the elements
including, for example, at least one central processing unit (CPU),
at least one input device (e.g., a mouse, keyboard, controller,
touch screen or keypad), and at least one output device (e.g., a
display device, printer or speaker). Such a system may also include
one or more storage devices, such as disk drives, optical storage
devices, and solid-state storage devices such as RAM or ROM, as
well as removable media devices, memory cards, flash cards,
etc.
[0145] Such devices also can include a computer-readable storage
media reader, a communications device (e.g., a modem, a network
card (wireless or wired), an infrared communication device, etc.),
and working memory as described above. The computer-readable
storage media reader can be connected with, or configured to
receive, a non-transitory computer-readable storage medium,
representing remote, local, fixed, and/or removable storage devices
as well as storage media for temporarily and/or more permanently
containing, storing, transmitting, and retrieving computer-readable
information. The system and various devices also typically will
include a number of software applications, modules, services or
other elements located within at least one working memory device,
including an operating system and application programs, such as a
client application or browser. It should be appreciated that
alternate embodiments may have numerous variations from that
described above. For example, customized hardware might also be
used and/or particular elements might be implemented in hardware,
software (including portable software, such as applets) or both.
Further, connection to other computing devices such as network
input/output devices may be employed.
[0146] Non-transitory storage media and computer-readable storage
media for containing code, or portions of code, can include any
appropriate media known or used in the art such as, but not limited
to, volatile and non-volatile, removable and non-removable media
implemented in any method or technology for storage of information
such as computer-readable instructions, data structures, program
modules or other data, including RAM, ROM, Electrically Erasable
Programmable Read-Only Memory (EEPROM), flash memory or other
memory technology, CD-ROM, DVD or other optical storage, magnetic
cassettes, magnetic tape, magnetic disk storage or other magnetic
storage devices or any other medium which can be used to store the
desired information and which can be accessed by a system device.
Based on the disclosure and teachings provided herein, a person of
ordinary skill in the art will appreciate other ways and/or methods
to implement the various embodiments. However, computer-readable
storage media does not include transitory media such as carrier
waves or the like.
[0147] The specification and drawings are, accordingly, to be
regarded in an illustrative rather than a restrictive sense. It
will, however, be evident that various modifications and changes
may be made thereunto without departing from the broader spirit and
scope of the disclosure as set forth in the claims.
[0148] Other variations are within the spirit of the present
disclosure. Thus, while the disclosed techniques are susceptible to
various modifications and alternative constructions, certain
illustrated embodiments thereof are shown in the drawings and have
been described above in detail. It should be understood, however,
that there is no intention to limit the disclosure to the specific
form or forms disclosed, but on the contrary, the intention is to
cover all modifications, alternative constructions and equivalents
falling within the spirit and scope of the disclosure, as defined
in the appended claims.
[0149] The use of the terms "a" and "an" and "the" and similar
referents in the context of describing the disclosed embodiments
(especially in the context of the following claims) are to be
construed to cover both the singular and the plural, unless
otherwise indicated herein or clearly contradicted by context. The
terms "comprising," "having," "including," and "containing" are to
be construed as open-ended terms (i.e., meaning "including, but not
limited to,") unless otherwise noted. The term "connected" is to be
construed as partly or wholly contained within, attached to, or
joined together, even if there is something intervening. The phrase
"based on" should be understood to be open-ended, and not limiting
in any way, and is intended to be interpreted or otherwise read as
"based at least in part on," where appropriate. Recitation of
ranges of values herein are merely intended to serve as a shorthand
method of referring individually to each separate value falling
within the range, unless otherwise indicated herein, and each
separate value is incorporated into the specification as if it were
individually recited herein. All methods described herein can be
performed in any suitable order unless otherwise indicated herein
or otherwise clearly contradicted by context. The use of any and
all examples, or exemplary language (e.g., "such as") provided
herein, is intended merely to better illuminate embodiments of the
disclosure and does not pose a limitation on the scope of the
disclosure unless otherwise claimed. No language in the
specification should be construed as indicating any non-claimed
element as essential to the practice of the disclosure.
[0150] Preferred embodiments of this disclosure are described
herein, including the best mode known to the inventors for carrying
out the disclosure. Variations of those preferred embodiments may
become apparent to those of ordinary skill in the art upon reading
the foregoing description. The inventors expect skilled artisans to
employ such variations as appropriate, and the inventors intend for
the disclosure to be practiced otherwise than as specifically
described herein. Accordingly, this disclosure includes all
modifications and equivalents of the subject matter recited in the
claims appended hereto as permitted by applicable law. Moreover,
any combination of the above-described elements in all possible
variations thereof is encompassed by the disclosure unless
otherwise indicated herein or otherwise clearly contradicted by
context.
[0151] All references, including publications, patent applications,
and patents, cited herein are hereby incorporated by reference to
the same extent as if each reference were individually and
specifically indicated to be incorporated by reference and were set
forth in its entirety herein.
* * * * *