U.S. patent application number 16/858524 was filed with the patent office on 2020-08-13 for method and apparatus for forecasting solar radiation and solar power production using synthetic irradiance imaging.
The applicant listed for this patent is NEO Virtus Engineering, Inc.. Invention is credited to James M. Bing.
Application Number | 20200257019 16/858524 |
Document ID | 20200257019 / US20200257019 |
Family ID | 1000004784621 |
Filed Date | 2020-08-13 |
Patent Application | download [pdf] |
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United States Patent
Application |
20200257019 |
Kind Code |
A1 |
Bing; James M. |
August 13, 2020 |
Method And Apparatus For Forecasting Solar Radiation And Solar
Power Production Using Synthetic Irradiance Imaging
Abstract
In an embodiment, measurements are simulated of direct normal
irradiance, diffuse horizontal and global horizontal irradiance
from groups of two or more photovoltaic arrays and/or irradiance
sensors which are located in close proximity to each other and
which have different tilt and azimuth angles. Irradiance
measurements derived from solar power system power measurements are
combined with measurements made by irradiance sensors to synthesize
an image of ground level global horizontal irradiance which can be
used to create a vector describing motion of that image of
irradiance in an area of interest. A sequence of these irradiance
images can be transformed into a time series from which a motion
vector can be derived. The motion vector can be applied to a
current image of ground level irradiance and that image can be
projected to a future point in time to provide a solar radiation
forecast. These forecasts can be converted into forecasts of solar
power system power in the area of interest.
Inventors: |
Bing; James M.; (Concord,
MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
NEO Virtus Engineering, Inc. |
Littleton |
MA |
US |
|
|
Family ID: |
1000004784621 |
Appl. No.: |
16/858524 |
Filed: |
April 24, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14403865 |
Nov 25, 2014 |
10663620 |
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PCT/US2013/043388 |
May 30, 2013 |
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16858524 |
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61792118 |
Mar 15, 2013 |
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61653158 |
May 30, 2012 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
F24S 2201/00 20180501;
G01W 1/10 20130101; H02S 50/00 20130101; G01W 1/12 20130101 |
International
Class: |
G01W 1/12 20060101
G01W001/12; H02S 50/00 20060101 H02S050/00; G01W 1/10 20060101
G01W001/10 |
Claims
1. A method comprising: collecting data, which is based on
irradiance incident upon a surface, from a plurality of irradiance
sensitive devices in a geographic area subdivided into a gridded
pattern comprised of grid cells, each irradiance sensitive device
having an irradiance sensitive surface, and each grid cell
comprising a group of two or more irradiance sensitive devices
whose respective irradiance sensitive surfaces have differing tilt
and azimuth angles; calculating on a computer processor irradiance
incident upon the plurality of irradiance sensitive surfaces from
the collected data; combining values of calculated incident
irradiance from the two or more irradiance sensitive devices within
the groups; and calculating solar radiation from the combined
values of incident irradiance.
2. The method of claim 1 further comprising: defining a solar
radiation map by defining a grid of grid cells on the geographic
area and associating values of the calculated solar radiation with
particular individual grid cells.
3. The method of claim 1 wherein collecting data includes: defining
time frames; receiving asynchronous data which is proportional to
irradiance incident upon a surface; applying time stamps to the
asynchronous data to match the time stamp of the nearest defined
time frame.
4. The method of claim 2 further comprising: creating a time series
of the solar radiation maps; deriving a velocity vector from the
time series of solar radiation maps; and forecasting a future
location of a solar radiation map based on a current solar
radiation map and the velocity vector.
5. The method of claim 1 wherein collecting data includes:
collecting power production data from a plurality of PV generating
units which can measure and report energy production and which have
communications capability to a central computer; or collecting
power production data from a plurality of kilowatt-hour meters
which meter output of PV generating units, and which have
communications capability to a central computer; or collecting
power production data from a plurality of data acquisition systems
which monitor PV generating units, and which have communications
capability to a central computer.
6. The method of claim 1 wherein collecting data includes:
collecting irradiance data from a plurality of irradiance meters
which have communications capability to a central computer; or
collecting irradiance data from a combination of PV generating
units, kilowatt-hour meters which meter the output of PV generating
units, and data acquisition systems which monitor PV generating
units.
7. The method of claim 1 wherein calculating irradiance incident
upon the plurality of irradiance sensitive surfaces rom the
collected data includes: collecting data indicating the power
produced by a PV generating unit; retrieving characteristics of the
PV generating unit from a database; deriving power produced by the
PV generating unit based on the area of the PV generating unit's
collecting surface and the system efficiency.
8. The method of claim 1 wherein calculating solar radiation from
the combined values of incident irradiance includes: using a
multi-pyranometer array method to solve at least two equations with
at least two unknowns to calculate direct normal and diffuse
horizontal irradiance; calculating global horizontal irradiance
from direct normal and diffuse horizontal irradiance.
9. The method of claim 8 further comprising: associating global
horizontal, direct normal and diffuse horizontal irradiance values
with cells of a grid defined on the geographic area; and
normalizing the gridded values of global horizontal irradiance by:
calculating clear sky model global horizontal value for each grid
cell latitude and longitude, and each day and minute in the time
series; and dividing each grid cell global horizontal value by the
clear sky model value of global horizontal irradiance.
10. The method of claim 9 further comprising deriving a velocity
vector associated with the normalized gridded global horizontal
irradiance image time series by creating matrices of normalized
gridded global horizontal irradiance values for sequential gridded
images: creating a plurality of shifted matrices from the first
matrix by shifting the elements of the first matrix by one element
in a clockwise or counterclockwise directions until all of the
possible options, for a given step size, have been performed;
subtracting each of the shifted matrices from the second matrix and
storing the resulting difference matrices in computer memory;
calculating the absolute values of the elements in the resulting
difference matrices; summing the absolute values of the elements of
the resulting difference matrices and selecting the result with the
lowest value; and calculating the velocity vector using the time
between the first and second matrices and distance and direction
between the elements of the first matrix and the lowest value
shifted matrix.
11. The method of claim 10 further comprising creating a forecast
grid of global horizontal, direct normal and diffuse horizontal
irradiance by: creating a matrix of global horizontal, direct
normal and diffuse horizontal irradiance image values for the
current grid of global horizontal irradiance values; multiplying
the current matrix of global horizontal, direct normal and diffuse
horizontal irradiance values by the derived velocity vector;
mapping the forecast matrix of global horizontal, direct normal and
diffuse horizontal irradiance values to the associated geographic
surface; and, optionally, estimating PV power production values in
the geographic area based upon the forecast grid.
12. A system comprising: a computer processor configured to
execute: a collecting module for collecting data, which is based on
irradiance incident upon a surface, from a plurality of irradiance
sensitive devices in a geographic area subdivided into a gridded
pattern comprised of grid cells, each irradiance sensitive device
having an irradiance sensitive surface, and each grid cell
comprising a group of two or more irradiance sensitive devices
whose respective irradiance sensitive surfaces have differing tilt
and azimuth angles; an irradiance module for calculating irradiance
incident upon the plurality of irradiance sensitive surfaces from
the collected data; a combining module for combining values of
calculated incident irradiance from the two or more irradiance
sensitive devices within the groups; and a radiation module for
calculating solar radiation from the combined values of incident
irradiance.
13. The system of claim 12 further comprising: a map module for
defining a solar radiation map by defining a grid of grid cells on
the geographic area and associating values of the calculated solar
radiation with particular individual grid cells; and, optionally, a
time series module for creating a time series of the solar
radiation maps; a vector module for deriving a velocity vector from
the time series of solar radiation maps; and a forecasting module
for forecasting a future location of a solar radiation map based on
a current solar radiation map and the velocity vector.
14. The system of claim 12 wherein collecting data includes:
defining time frames; receiving asynchronous data which is
proportional to irradiance incident upon a surface; applying time
stamps to the asynchronous data to match the time stamp of the
nearest defined time frame.
15. A computer readable storage medium containing a set of
instructions that, when executed by a processor, perform a method
comprising: collecting data, which is based on irradiance incident
upon a surface, from a plurality of irradiance sensitive devices in
a geographic area subdivided into a gridded pattern comprised of
grid cells, each irradiance sensitive device having an irradiance
sensitive surface, and each grid cell comprising a group of two or
more irradiance sensitive devices whose respective irradiance
sensitive surfaces have differing tilt and azimuth angles;
calculating on a computer processor irradiance incident upon the
plurality of irradiance sensitive surfaces from the collected data;
combining values of calculated incident irradiance from the two or
more irradiance sensitive devices within the groups; and
calculating solar radiation from the combined values of incident
irradiance.
Description
RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 14/403,865, filed on May 30, 2013, which is the U.S. National
Stage of International Application No. PCT/US2013/043388, filed May
30, 2013, which designates the U.S., published in English, and
claims the benefit of U.S. Provisional Application No. 61/792,118,
filed on Mar. 15, 2013 and U.S. Provisional Application No.
61/653,158, filed on May 30, 2012. The entire teachings of the
above applications are incorporated herein by reference.
BACKGROUND
[0002] The installed base of solar energy power systems in general,
and photovoltaic systems in particular, is growing rapidly around
the world as a result of government policies and incentives,
decreases in its installed cost, and increases in the cost of
conventionally generated electricity. Solar irradiance is, on an
annual basis, periodic and predictable. However on an hourly or
sub-hourly basis, solar energy often intermittent and appears
unpredictable. As an electrical generation technology solar energy
has suffered the stigma, in the mainstream commercial energy
community, of being unreliable and lacking the ability to be
dispatched as and when it is needed. As increasing amounts of solar
energy generating capacity are deployed, mechanisms are needed to
reconcile the natural variations of the solar resource with the
constant reliability requirements of the modern electrical grid,
and energy trading requirements in those regions with deregulated
energy markets. For system operators who must manage the dispatch
of generation, the reserves and the transmission constraints of the
grid, solar energy presents no operational problem as long as its
contribution is a small percentage of the energy flowing through
the system. However large amounts of solar energy, whether on the
customer's side or on the supply side of the revenue meter, present
a serious management challenge to the stability and reliability of
the grid, as well as to the transactions of energy markets. Several
recent technological developments are brought together in this
technology that make it possible to forecast ground level
irradiance, and thus solar energy production, across a region or
utility service territory.
SUMMARY
[0003] In accordance with principles of the invention, a map or
pattern or image of irradiance levels is generated in a region such
as the county of Sacramento or the greater Boston area or some
other, generally urban and suburban region, based on information
from the power production of roof top PV systems. The area can have
sections which do not have any PV systems, for example rural
agricultural areas, and in those areas we can deploy more
expensive, traditional forms of irradiance monitoring. The
inventive approach leverages data available from existing PV
systems. The form of the irradiance in the generated map is a
combination of direct normal (beam component) and diffuse
horizontal (scatter component). These two components can easily be
combined to give global horizontal irradiance (global horizontal is
the total irradiance seen looking up into the dome of the sky.)
Global horizontal irradiance (GHI) cannot be decomposed into its
direct and diffuse subcomponents without sophisticated measurement
equipment, or without a detailed knowledge of the current
meteorological conditions or without using a process of combining
multiple differently oriented measurements such as we use in this
approach. A reason to know direct normal irradiance (DNI) and
diffuse horizontal irradiance (DHI) is because if these two
components are known and if the date, time and a location on the
earth are known, the irradiance hitting any surface of any
orientation at that location can be calculated. If the irradiance
hitting a surface is known and if that surface is a solar
generating systems, the electrical power that the system will
produce at that moment in time can be calculated.
[0004] In one aspect, a sequence of these maps, patterns or images
of ground level irradiance are generated and from that sequence a
time series is formed. The time series is used to forecast a future
map or pattern or image of irradiance. In an embodiment, the
process is performed on a sub-hourly basis to create a
near-real-time forecast. This forecast can be for a time horizon of
up to three hours or more and can be in increments of five minutes
or less. Many of the PV systems which are installed today have the
capability to report their power data or be polled to get their
power data. With that future map or pattern or image and knowledge
of the characteristics of any solar power system in the area of
that map or pattern or image the power produced by any such system
can be calculated. The reporting from these systems, from a
multitude of manufacturers, is asynchronous so the data is grouped
into time stamped bins or frames through the following example
procedure: [0005] a) Collect data from PV systems, primarily via
the internet, indicating how much power they are generating at a
particular moment in time. [0006] b) Group the data into bins or
frames and apply a time stamp to the bins thus roughly
synchronizing the groups of data. [0007] c) Calculate the
irradiance hitting the surface of the solar collector of the PV
system based upon the efficiency with which those systems convert
irradiance into electrical power. [0008] d) Create groups of two or
more irradiance values from PV systems and/or irradiance sensors
whose collectors do not point in the same directions (ones who do
not have the same tilt and azimuth angles) and which are in close
proximity (e.g., this can be data from a PV system on one roof and
one from a neighbor's house a block away.) [0009] e) Using the
groups of two or more irradiance values from PV systems and/or
irradiance sensors whose collectors do not point in the same
directions and using the "multi-pyranometer array" approach, direct
normal irradiance and diffuse horizontal irradiance are calculated
for the part of the map, pattern or image where the house is
located. Global horizontal is also calculated. [0010] f) If PV
systems which measure their power production and which have
different orientations are not available in an area of the map,
pattern or image of interest, existing traditional irradiance
measurement devices may be accessed or traditional irradiance
measurement devices may be deployed. [0011] g) The process of the
last two steps is repeated until a map, pattern or image of ground
level direct normal irradiance, diffuse horizontal and global
horizontal is generated. [0012] h) A sequence of these maps,
patterns or images of ground level direct normal irradiance,
diffuse horizontal and global horizontal is generated and from that
sequence project or forecast a map, pattern or image of ground
level direct normal irradiance, diffuse horizontal and global
horizontal in the future. [0013] i) The forecast of ground level
direct normal irradiance and diffuse horizontal irradiance, along
with knowledge of the characteristics of solar power (PV systems or
other solar power technologies) systems is used, to calculate the
future power production of those solar power systems.
[0014] This disclosure describes a method and apparatus for
collecting irradiance data across a region of interest within a
utility grid. It describes a method and apparatus for forecasting
irradiance levels and solar power production in that region of
interest. This method entails a network of solar power systems,
irradiance sensors and other irradiance sensitive devices whose
power production data and solar data can be collected in real-time
and near-real-time and then processed into a pattern which
indicates the current state of ground level irradiance across a
geographic region of interest. These irradiance levels are then
used to calculate future power levels from solar power generating
plants in the area of the network. The method and apparatus
described herein uses measurements of solar power system electrical
output to simulate the irradiance incident upon the solar power
array. This incident irradiance is referred to as global tilt
irradiance (GTI) or also plane of array irradiance (POA). The
simulated values of irradiance from solar power systems are
combined with irradiance values measured via traditional methods,
such as silicon or thermopile pyranometers or other means, to
create a map or pattern or image of irradiance "pixels" in a
gridded sensing network in the region of interest. From the
irradiance data collected from this synthesized array of power
systems and sensors direct normal irradiance (DNI), diffuse
horizontal irradiance (DHI) and global horizontal irradiance (GHI)
are simulated.
[0015] The synthesized irradiance monitoring array data are used to
create a sequence of maps or patterns or images of ground level
DNI, DHI, and GHI. From a series of these images a velocity vector
of changing irradiance on the ground in an area of interest is
derived. This velocity vector, in combination with forecast and
current data describing cloud cover, ambient temperature and other
meteorological parameters, is applied to the current image of
ground level DNI, DHI, and GHI, and is used to forecast an image of
direct normal irradiance and diffuse horizontal irradiance in the
geographic area of interest. The forecast DNI and DHI values are
then used to calculate future global tilt irradiance for solar
power systems in the region of interest. The forecast GTI and
temperature data, in combination with known characteristics of
solar power systems in the area of interest, are then used to
simulate hourly and sub-hourly forecasts of solar power
production.
[0016] Knowledge of changing irradiance levels in a region can be
used to create minute by minute forecasts of changing power
contributions to the grid from distributed solar power generating
systems for use by electric utilities to provide increased
situational awareness of the electrical power system for grid
management purposes. The same information can be used to assess the
performance of solar power systems, for operational management of
solar power production facilities, for use for energy trading or
for surveying the solar resource in a particular area within the
region of interest.
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] The foregoing and other objects, features and advantages of
the invention will be apparent from the following more particular
description of preferred embodiments of the invention, as
illustrated in the accompanying drawings in which like reference
characters refer to the same parts throughout the different views.
The drawings are not necessarily to scale, emphasis instead being
placed upon illustrating the principles of the invention.
[0018] FIG. 1 illustrates a system 100 which includes a plurality
of PV generating units, data acquisition system which monitor PV
generating units, and or irradiance meters, in a gridded network
110, in a region of interest 120 served by an electric utility
whose data are received, stored and processed a central computer
140.
[0019] FIG. 2A shows examples of solar power system electronic
components which have the capability to measure the power produced
by a solar power system and the capability to communicate that
power production data to a remote computer.
[0020] FIG. 2B is a depiction of an example of an irradiance sensor
270 which has the capability to communicate with a central computer
via wireless signal or other means.
[0021] FIG. 2C is an image of a weather monitoring station 275
which has the capability to monitor irradiance and other
meteorological parameters and the ability to communicate with a
central computer via the internet, power line carrier, wireless
signal or other means.
[0022] FIG. 2D is an example of a central computer processor
140.
[0023] FIG. 2E is an example of a flow diagram 280 of the overview
of the irradiance and solar power forecasting method.
[0024] FIG. 3 is an example diagram 300 of the constituent parts of
an irradiance sensing network.
[0025] FIG. 4 is a flow diagram 400 describing the configuration
and installation of the constituent parts of an example irradiance
sensing network.
[0026] FIG. 5 is a flow diagram 500 describing an example the
collection, time stamping, synchronization and storage of
irradiance data from the sensing network.
[0027] FIG. 6 is a flow diagram example process of creating an
image of ground level global horizontal irradiance using a clear
sky model for the date, time and location.
[0028] FIGS. 7A, 7B, 7C, 7D, 7E and 7F are a flow diagram and
example matrices which describe an example process of calculating a
ground level irradiance image velocity vector.
[0029] FIG. 8 is a flow diagram which describes an example process
of forecasting a future location of a ground level irradiance
image.
[0030] FIG. 9 is a flow diagram example process of forecasting
electrical production of solar power systems based upon the
location of the forecast image of irradiance and the physical
characteristics of the systems.
DETAILED DESCRIPTION
[0031] In general FIG. 1 shows one embodiment of the present
disclosure that provides a system 100 for predicting direct normal,
diffuse horizontal, and global horizontal irradiance. The system is
defined over a geographic region of interest 120. The region of
interest 120 is subdivided into a gridded pattern 110 comprised of
grid cells. Each grid cell 130 in the system 100 includes any or
all of one or more of an irradiance monitoring device which can
measure direct normal irradiance and diffuse horizontal irradiance
and which can communicate with a central computer, and or groups of
two or more of any combination of the following which have
different tilt and or azimuth angles and which are in close
physical proximity and which have communications capability to a
central computer, of an irradiance monitoring device, a PV
generating unit which can measure and report its energy production,
a kilowatt-hour meter which measures and reports the energy
production of a PV generating unit, a data acquisition system which
measures and reports the ac production of a PV generating unit, and
or a data acquisition system which measures and reports the dc
production of a PV generating unit.
[0032] Collectively the levels of direct normal, diffuse
horizontal, and global horizontal irradiance in the grid cells 130
form pixels in an image in a gridded network 110, in a region of
interest 120 served by an electric utility by electrical
transmission or distribution systems. This irradiance data is
received, stored and processed a central computer 140 to form
forecasts of irradiance.
[0033] FIG. 2A is a depiction 200 of examples of PV generating
units which can measure and report their energy production and have
communications capability to a central computer. Examples of such
systems 200 which may have power measurement capability and
communications capability include micro-inverters and AC modules
210, DC optimizer devices 220, smart meters used to measure PV
system output 230, string inverters and central inverters 240, and
data acquisition systems (DAS) which measure either ac power output
250 and or dc power output 260. These type of devices can report
average or instantaneous power production. Each of the PV
generating units 200 has an irradiance sensitive surface 205.
[0034] FIG. 2B is an example of an autonomous irradiance sensor
system 270, comprised of an irradiance sensitive device and a
wireless transmitter 271 and a wireless receiver device 272, which
can report measured irradiance either wirelessly or by other means
to a central computer.
[0035] FIG. 2C sensor 275 is an example of an irradiance monitoring
device which can measure direct normal irradiance and diffuse
horizontal irradiance and which can communicate with a central
computer. In the embodiment of the system 100 for predicting direct
normal, diffuse horizontal, and global horizontal irradiance across
a region of interest this type of device may be used in the grid
cells which do not have or two or more of any combination of an
irradiance monitoring device 270 or a PV generating unit such as
210, 220, 230, 240, 250 or 260 which can measure and report its
energy production and which have different tilt and or azimuth
angles and are physically adjacent and which have communications
capability to a central computer.
[0036] FIG. 2D is a component of a system 100 which includes a
central computer processor 140, a database 195, a weather data
module 145, a solar irradiance data module 175, a solar electric
generating system characterization module 150, a connection to an
outside weather service provider 155 and solar power system
production and status reporting 160, and an output device 185. The
central computer processor accesses solar power system production
data and irradiance data through connections to the internet 170,
wireless receivers 165, by direct wire or power line carrier 180,
cellular modem 190, or other means.
[0037] This system provides a process, apparatus and software
program for organizing ground-based irradiance and meteorological
hardware measurement devices, satellite weather data, weather
forecasting software, solar energy production data, and solar
energy production simulation software, that enables the forecasting
and monitoring of solar irradiance and solar power production in an
area of interest, which can be created from the following main
components or modules using the following general steps: [0038] 1)
In FIG. 2E, 280, a network of solar power systems with
communications capability, ground-based irradiance and
meteorological measurement devices with remote communications
capability, satellite weather data sources and weather forecast
data sources is identified. [0039] 2) In FIG. 2E, 282, a software
program for creating synchronized data frames from diverse
asynchronous irradiance and solar power data sources is run. [0040]
3) In FIG. 2E a software program for creating an image of ground
level irradiance over the region defined by the irradiance sensing
network 283, whose raw irradiance values are normalized to one (1)
using clear sky model is run. [0041] 4) In FIG. 2E a software
program 284 for recognizing a velocity vector of ground level GHI,
defined by two or more sequential ground level irradiance images,
is run. [0042] 5) In FIG. 2E a software program which uses a
current image of ground level irradiance in combination with the
velocity vector defined in step 4 above to forecast the position of
the ground level irradiance 285 image is run. [0043] 6) In FIG. 2E
one or more software programs which use measurements or forecasts
of ground level irradiance, in combination with known
characteristics of solar power systems, to calculate the irradiance
incident upon the surface of the solar power system 286, and then
calculate the power output of the system is run. [0044] 7) In FIG.
2E the process is repeated for the next time step until sunset 287.
At sunrise the process starts again.
[0045] FIG. 3 is an example of some combinations of two or more of
an irradiance monitoring device 270 or a PV generating unit such as
210, 220, 230, 240, 250 or 260 which can measure and report its
energy production and which have different tilt and or azimuth
angles and are physically adjacent and which have communications
capability to a central computer and are located in the gridded
pattern 110 comprised of grid cells in the area of interest.
[0046] FIG. 4 shows a flow diagram illustrating a process,
apparatus and software program for organizing ground-based
irradiance and meteorological hardware measurement devices,
satellite weather data, weather forecasting software, solar energy
production simulation software, and electrical grid load
forecasting software, that enables the measurement and forecasting
of solar irradiance and solar power production in an area of
interest, which can be created through the following steps: [0047]
1) A geographic XY grid coordinate system 400 for the area of
interest for solar power measurement and forecasting is defined
based upon a grid 405 which is coincident with one or more
meteorological forecasting systems gridded projection. Each of the
grid cell is labeled using the latitude and longitude of the
geometric centroid of the cell. [0048] 2) The solar power
generation systems within the region being considered, are surveyed
410 and their physical parameters, such as system size, tilt
angle(s), azimuth angle(s), tracking type(s), equipment types,
shading obstructions, latitude, longitude, elevation, and other
features, are recorded and their features placed in a database as
illustrated in FIG. 2D, 195. The survey of solar power systems in
the area of interest also identifies any which have power
monitoring systems which have the ability to communicate the value
of their power production to a central computer via the internet,
power line carrier, wireless signal or other means on a second by
second basis or on a minute by minute basis or on a multiple minute
interval, such as every five minutes, or on an hour by hour basis.
The owners of the solar power production data are approached and
use of the data is requested as illustrated in FIG. 4 420. [0049]
3) A survey of the area of interest is conducted to determine the
existence and locations of pairs of solar power systems which are
in close proximity and whose solar power arrays have different
tilts and/or azimuth angles 410 and which have a system or systems
for measuring their solar power production and which have the
ability to communicate the value of their power production to a
central computer. A computer database of the pairs of solar power
systems in the area of interest is created as illustrated in FIG.
2D 195. [0050] 4) The geographic XY grid defined in step 1 above is
evaluated and pairs of solar power systems which were identified in
step 3 and which lie near the geometric centroid of each grid cell
are associated with individual grid cells in the area of interest
is created as illustrated in FIG. 4 430. [0051] 5) A survey of the
area of interest is conducted to determine the existence and
locations of solar irradiance monitoring stations which have the
ability to communicate the value of their irradiance measurements
to a central computer via the internet, power line carrier,
wireless signal or other means 440. The solar monitoring stations
are categorized based upon their ability to report any and all of
the following: direct normal irradiance, diffuse horizontal, global
horizontal irradiance or the irradiance measured at a unique tilt
and azimuth angle. A computer database of the solar monitoring
systems in the area of interest is created as illustrated in FIG.
2D 195. [0052] 6) The geographic XY grid defined in step 1 above is
evaluated and solar irradiance monitoring stations which were
identified in step 5 and which lie near the geometric centroid of
any grid cell are associated with those individual grid cells in
the coordinate system as illustrated in FIG. 4, 440. [0053] 7) The
owners of the irradiance data for the monitoring systems which were
identified in step 6 are approached and use of the data is
requested as illustrated in FIG. 4, 450. [0054] 8) The geographic
XY grid defined in step 1 is evaluated and grids cells which lack
either pairs of solar power systems identified in step 3 above or
solar monitoring station identified in step 6 above are marked for
installation of wireless transmitting irradiance sensors in the
"vacant" locations as illustrated in FIG. 4, 460. [0055] 9) One or
more transmitting irradiance sensors, illustrated in FIG. 2B,
capable of sending digitized radio frequency transmissions of
irradiance and temperature measurements to a central computer, are
installed in the grid cells which do not have either pairs of solar
power systems identified in step 3 above or solar monitoring
station identified in step 6. The transmitting sensors label each
digitized measurement with a unique station number. The
transmitting irradiance sensors are installed near the geometric
centroid of grid cell 470. [0056] 10) One or more radio frequency
receiving devices 480, illustrated in FIG. 2B, capable of receiving
continuous digitized irradiance and temperature measurement signals
from the transmitters described in step 9 is installed in the
region of interest. They are connected to the internet via an
internet portal such as a computer local area network or broadband
internet connection on a wired or wireless network. [0057] 11) A
process collects the transmissions arriving over the internet from
the radio frequency receiving device described in step 10. The
process maintains the unique station number labels on the
transmissions. The process labels each unique transmission with a
timestamp and stores multiple sensor transmissions from multiple
grid cells in a database as illustrated in FIG. 4, 490.
[0058] FIG. 5 shows a flow diagram of a process, apparatus and
software program for collecting, synchronizing and storing solar
power and irradiance data from a sensing network in an area of
interest, which can be created through the following steps: [0059]
1) A process 500 collects the transmissions arriving to a computer
with a connection to the internet or other communications means
from a plurality of solar power systems identified in FIG. 1. Pairs
of solar power systems which are in close proximity and whose solar
power arrays have different tilts and/or azimuth angles are
identified from the database as illustrated in FIG. 5, 520. [0060]
2) A software program for creating synchronized data frames from
diverse asynchronous irradiance and solar power data sources is run
FIG. 5, 540. [0061] 3) The instantaneous values of the power
production of the paired solar power systems described in step 2
are labeled with a timestamp and stored in a database by the
program described in step 1 as illustrated in FIG. 5, 560, 580.
[0062] FIG. 6 shows a flow diagram of a process 600, apparatus and
software program for the creation of a normalized image of ground
level global horizontal irradiance (GHI) from solar power system
power data and irradiance data, from an irradiance sensing network
whose data has been stored to a database, which can be created
through the following steps: [0063] 1) Query the database for the
first instance in a daily cycle of a synchronized data frame of
solar power system power data and irradiance data from the region
of interest 610. [0064] 2) Query the database for the solar power
system electrical characteristics and the system electrical
parameters for solar power systems in the region of interest 620.
[0065] 3) Calculate GTI for each solar power system in the region
of interest using the solar power system electrical characteristics
and the system electrical parameters for solar power systems and
the solar power production data for each system of interest using
the ac power output or dc power input of a solar power inverter
430. For example, for a photovoltaic power system to make this
conversion knowledge of the inverter dc to ac conversion efficiency
(.mu..sub.inv), module efficiency (.mu..sub.module), array system
losses (.mu..sub.sys), array area and temperature are necessary.
The process of converting an AC power measurement to a value of
incident irradiance can be represented as a transfer function,
H(p). This transfer function represents the efficiency of the
entire solar power system for converting incident irradiance into
ac power. (This is an idealized model. A preferred embodiment uses
the Sandia Photovoltaic Array Performance Model for the
photovoltaic module conversion of irradiance to dc power. Other
models can be used. Though not shown here, for all solar power
module conversion models cell temperature is required. For another
embodiment system parameters, such as wire resistance losses in
both the dc and ac systems may be included. In the equations shown
here all system losses are combined and represented as
.mu..sub.sys.)
[0065] GTI = H ( P a c ) ##EQU00001## P d c = P a c .mu. i n v *
.mu. s y s ##EQU00001.2## GTI = P d c ( Array Area * .mu. module )
##EQU00001.3## GTI = P a c ( Array Area * .mu. module ) * .mu. i n
v * .mu. s y s ##EQU00001.4## H ( p ) = GTI / P a c ##EQU00001.5##
[0066] For photovoltaic systems which are mounted horizontally
GTI=GHI. [0067] This same process can be applied to systems which
measure dc power, Pdc, directly, such as dc optimizers, by using
the equation:
[0067] GTI = P d c ( Array Area * .mu. module ) ##EQU00002## [0068]
4) The values of DNI, DHI and GHI are then calculated from at least
two values of global tilt irradiance found in step 3. The
calculation of DNI, DHI and GHI from GTI can be done using two
simultaneously simulated values of GTI taken from solar power
systems in close proximity and which possess similar, small, values
of ground reflected irradiance. In the ideal form of the model
below ground reflected irradiance or albedo is ignored. .theta. is
the angle of incidence between the tilted surface for which GHI is
being evaluated and the rays of the sun. Cos .theta. is
derived:
[0068] cos .theta.=cos .alpha. cos(a.sub.s-a.sub.w)sin .beta.+sin
.alpha. cos .beta. [0069] Where: [0070] .beta. is the elevation
angle, from a horizontal plane, of the titled surface for which GHI
is being evaluated. [0071] .alpha. is the solar altitude angle.
[0072] a.sub.s is the solar azimuth angle. [0073] a.sub.w is the
azimuth angle of the plane. [0074] At least two differently
oriented plane surfaces are needed to derive values for DNI and
DHI.
[0074] GTI.sub.1=DNI cos .theta..sub.1+DHI(1+cos
.beta..sub.1)/2
GTI.sub.2=DNI cos .theta..sub.2+DHI(1+cos .beta..sub.2)/2 [0075]
.theta..sub.1, .theta..sub.2, .beta..sub.1 and .beta..sub.2 are
known physical characteristics of the (minimum) two solar power
systems. With knowledge of these parameters we solve simultaneous
equations for DNI and DHI.
[0075] GTI.sub.i=DNI*cos
.theta..sub.i+DHI*R.sub.d,i+.rho..sub.i*GHI*R.sub.r,i [0076] Where:
[0077] R.sub.di is the sky diffuse ratio at site # i or tilt # i
[0078] R.sub.ri is the surface reflectance diffuse ratio at site #
i or tilt # i [0079] .rho..sub.i is the foreground albedo at site #
i or tilt # i [0080] A.sub.i is the array azimuth at site # i or
tilt # i [0081] and R.sub.ri, .rho..sub.i, and A.sub.i are known,
surveyed characteristics of the sites or arrays, [0082] and
R.sub.di is a parameter derived from local meteorological data.
[0082] GHI = DHI + DNI * cos z ##EQU00003## DHI = A 1 * GTI 2 - A 2
* GTI 1 A 1 * B 2 - A 2 * B 1 ##EQU00003.2## A i = cos .theta. i +
.rho. * R r , i * cos z ##EQU00003.3## B i = R d , i + .rho. i * R
r , i ##EQU00003.4## R d , i .apprxeq. ( 1 + cos .beta. i ) / 2
##EQU00003.5## DNI = GTI 1 A 1 - GTI 2 - A 2 A 1 * GTI 1 B 2 B 1 -
A 2 A 1 ##EQU00003.6## [0083] GHI is then calculated as:
[0083] GHI=DNI cos .theta.+DHI [0084] The resulting DNI, DHI and
GHI values are place in a database. [0085] 5) A sky clear
irradiance model, such as the Bird Clear Sky Model, is run for each
date and time for each measurement, for each latitude and longitude
of each grid cell in the irradiance sensing network 640. The clear
sky GHI results are stored in the database. [0086] 6) Each value of
GHI associated with a grid cell in the sensing network, whether
calculated in step 3 above or whether measured directly by one of
the irradiance measurement devices, is divided by the clear sky
value calculated in step 5 resulting in a normalized irradiance
index as seen in FIG. 6, 650. The normalized GHI values are stored
in the database 660.
[0087] FIGS. 7A, 7B, 7C, 7D, 7E and 7F: shows a flow diagram of a
process, apparatus and software program for determining a velocity
vector from ground level irradiance images, which can be created
through the following steps: [0088] 1) The database is queried 710
and a sequence of normalized ground level irradiance sensor data is
taken and converted to matrix form 715. FIG. 7B is a hypothetical
6.times.6 matrix representing a 36 contiguous normalized GHI sensor
block with stored measured irradiance values. We then create a
sequence of matrices which corresponds to a time series of
instantaneous images of normalized GHI levels measured by the
network. FIG. 7C illustrates a sequential pair of matrices,
I.sub.tn and I.sub.tn+1, selected from the database 720. [0089] 2)
The elements of the first of the two matrices (I.sub.tn) are
shifted in 45 degree increments, by one geographic increment such
as a kilometer, to create a series of shifted matrices, I.sub.tn*sm
725. In this example the angular increments are 45 degrees (45, 90,
135, 180, 225, 270, 315 degrees) from the first position, which is
a shift eastward, however this angular value could be any increment
of degrees or fractions of a degree. The arrows associated with
each matrix represent the angle and direction to which the matrix
values were shifted. In this example a total of eight permutations
of the original matrix are created as seen in FIG. 7D. In the
example in FIG. 7D the number arrows illustrate a shift of the
matrix elements in the following directions: 721 is shifted to the
right, 721 is shifted 45 degrees counterclockwise, 723 is shifted
90 degrees counterclockwise, 724 is shifted 135 degrees
counterclockwise, 725 is shifted 180 degrees counterclockwise, 726
is shifted 225 degrees counterclockwise, 727 is shifted 270 degrees
counterclockwise, and 728 is shifted 315 degrees counterclockwise.
In terms of the geographic area to which these values correspond
the pattern described by the matrix depicts a change in the ground
level irradiance pattern in the following compass directions: 721
is shifted eastward, 721 is shifted northeastward, 723 is shifted
northward, 724 is shifted northwestward, 725 is shifted westward,
726 is shifted southwestward, 727 is shifted southward, and 728 is
shifted southeastward. [0090] 3) The shifted matrices (I.sub.tn*s1
through I.sub.tn&s8) 735 are subtracted from the second matrix
in the original sequence of matrices (I.sub.tn+1) 730. This forms
eight "difference" matrices as illustrated by examples in FIGS. 7D
and 740. [0091] 4) Ignoring the boundary rows and columns (first
and last row; first and last column represented by "Xs"), the
absolute values of all of the central row and column elements are
calculated and summed for each difference matrix 745. For this
example FIG. 7E is the difference matrix for I.sub.tn*s1 and
I.sub.tn+2. The difference matrix for I.sub.tn*s2 and I.sub.tn+1 is
represented in FIG. 7E. The difference matrix for I.sub.tn*s2 and
I.sub.tn+1, which the sum of the absolute values of central
elements, is the lowest value amongst the eight cases, as
illustrated by FIG. 7F, and is the one which "correlates" (the term
"correlate" is used here in a broad sense, not a strictly
mathematical sense) best to the shift in the pattern of ground
level irradiance between time t.sub.n and time t.sub.n+1 750.
[0092] 5) The shift in the pattern of ground level irradiance can
be represented as a velocity vector defined by the coordinates of
any of the origin elements and the terminus elements and the time
increment between t.sub.n and time t.sub.n+1. This vector can then
be applied to the irradiance image I.sub.tn+1 to forecast a future
irradiance image, represented here in matrix form, I.sub.tn+2.
[0093] 6) A feedback loop can be included in the process to make
successive corrections to the testing step 4 above. In addition
wind speed and direction and other meteorological parameters may be
included in the model.
[0094] FIG. 8 shows a flow diagram of a process 800, apparatus and
software program for forecasting ground level DNI and DHI, which
can be created through the following steps: [0095] 1) Retrieve the
most recently stored values of DNI and DHI from the database for
the daily cycle. In FIG. 8 these values of DNI and DHI are
illustrated as occurring at time t.sub.n+1 820. [0096] 2) Retrieve
the irradiance velocity vector for time t.sub.n+1 820. [0097] 3)
Shift the values of DNI and DHI for time t.sub.n+1 in the direction
and magnitude of the irradiance image velocity vector 840. [0098]
4) Label the shifted DNI and DHI values with their new locations as
DNI.sub.forecast and DHI.sub.forecast at time t.sub.n+2. [0099] 5)
The forecast ground level DNI.sub.forecast and DHI.sub.forecast for
each site in the area of interest is written to a database 850.
[0100] FIG. 9 shows a flow diagram of a process 900, apparatus and
software program for forecasting energy production from solar power
systems in the region of interest, which can be created through the
following steps: [0101] 1) Query the database for the current
ambient temperature from meteorological forecasts and solar power
system electrical characteristics and the system electrical
parameters for solar power systems in the region of interest 910.
[0102] 2) Retrieve the forecast values of DNI.sub.forecast and
DHI.sub.forecast for the latitude and longitude of the solar power
system to be modeled. In FIG. 9 these forecast values occur at time
t.sub.n+2 940. [0103] 3) Calculate forecast global tilt irradiance
(GTI) by:
[0103] GTI.sub.forecast=DNI.sub.forecast cos
.theta.+DHI.sub.forecast(1+cos .beta.)/2 [0104] Where .theta. is
the angle of incidence between the tilted surface of the solar
power system collector and the sun and .beta. is the elevation
angle of the titled surface 960. [0105] 4) Calculate the power
output of a solar power system using the forecast irradiance
incident upon the surface of the solar array (global tilt
irradiance or GTI), the temperature, and the solar power system
electrical characteristics. For example, to simulate the ac power
production of a photovoltaic system, knowledge of the inverter dc
to ac conversion efficiency (.mu..sub.inv), module efficiency
(.mu..sub.module), array system losses (.mu..sub.sys), array area
and cell temperature are necessary. (This is an idealized model.
The preferred embodiment of this invention uses the Sandia
Photovoltaic Array Performance Model for the photovoltaic module
conversion of irradiance to dc power. Other models can be used.
Though not shown here, for all PV module conversion models cell
temperature is required. For another embodiment system parameters,
such as wire resistance losses in both the dc and ac systems will
be included. In the equations shown here all system losses are
combined and represented as .mu..sub.sys.)
[0105] P.sub.dc=GTI*Array Area*.mu..sub.module
P.sub.ac=P.sub.dc*.mu..sub.inv*.mu..sub.sys
P.sub.ac=GTI*Array Area*.mu..sub.module*.mu..sub.inv*.mu..sub.sys
[0106] Other solar power systems can be modeled in a similar manner
980. [0107] 5) For solar power systems which are constructed of
more than one tilt an azimuth angle, dc power (Pdc) will be
calculated for each surface. [0108] 6) The total ac power of a
collection of solar power systems can be calculated as the sum of
the instantaneous dc powers times the system losses and inverter
losses. The calculation of ac power, Pac, from dc power, Pdc, is
done with the application of system losses to the dc power. In this
idealized model, though other factors such as wire losses, shading
and soiling apply, the system losses are represented solely as
inverter efficiency losses, .mu.inv.
[0108] P.sub.ac=.mu..sub.sys*.mu..sub.inv*.SIGMA.P.sub.dc [0109] 7)
The forecast solar power system production for each site in the
area of interest is written to a database 980.
[0110] It should be understood that the block, flow, and network
diagrams may include more or fewer elements, be arranged
differently, or be represented differently. It should be understood
that implementation may dictate the block, flow, and network
diagrams and the number of block, flow, and network diagrams
illustrating the execution of embodiments of the invention.
[0111] It should be understood that elements of the block, flow,
and network diagrams described above may be implemented in
software, hardware, or firmware. In addition, the elements of the
block, flow, and network diagrams described above may be combined
or divided in any manner in software, hardware, or firmware. If
implemented in software, the software may be written in any
language that can support the embodiments disclosed herein. The
software may be stored on any form of non-transitory computer
readable medium, such as random access memory (RAM), read only
memory (ROM), compact disk read only memory (CD-ROM), flash memory,
hard drive, and so forth. In operation, a general purpose or
application specific processor loads and executes the software in a
manner well understood in the art.
[0112] While this invention has been particularly shown and
described with references to example embodiments thereof, it will
be understood by those skilled in the art that various changes in
form and details may be made therein without departing from the
scope of the invention encompassed by the appended claims.
* * * * *