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BACKGROUND OF THE INVENTION J
The field of the present disclosure relates generally to systems f
and methods for determining a power generating capability of a power generating
system.
9 BRIEF DESCRIPTION OF THE INVENTION |
Solar, wind and other sources have increasingly become an
attractive source of electrical energy and have been recognized as clean, renewable I
and alternative forms of energy. Such renewable energy sources may include wind, j
solar, geothermal, hydro, biomass, and/or any other renewable energy sources. j
Due to many factors, a power generating system may not be
operating at full capacity (i.e., outputting 100% of the electrical power the power
generating system is capable of generating). For example, environmental conditions, |
such as reduced irradiance (solar power) or low winds (wind power) and the like may
cause the power generating system to operate below full capacity. Mechanical, electrical and software malfunctions may also cause a power generating system to j
£ 1 operate below full capacity. Improper maintenance, soiled, dirty and iced-over j
components may also cause undesirable reductions in power output. Additionally, i
intentional curtailment of output power by a system operator may be initiated to
reduce the power generating system to operate below full capacity. Combinations of j
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such factors that reduce power output below full capacity are also possible. j
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When a power generating system operates below full capacity, revenue may be lost due to the loss of value of the energy not being produced. Due to contractual obligations between power producers and grid I
operators, the circumstances affecting below full capacity operation may affect ]
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financial liability of such lost revenue. For example, intentional curtailment to limit
power output may obligate the party who curtailed the power output. In other situations, such as lack of maintenance, or malfunctioning equipment, one or more j
parties may be responsible for the lost revenues attributable to the below full capacity
operation. Typical models used for determining full capacity of power generating systems are based upon ideal conditions and do not account for changing environmental conditions and degradation of components. Typical models may thus
provide inaccurate determinations of the full capacity of a power generating system.
BRIEF DESCRIPTION OF THE DRAWINGS 9
Fig. 1 is an exemplary block diagram of a power generation I
capability system of the present disclosure.
Fig. 2 is a flowchart of an exemplary embodiment of the
present disclosure. 1
BRIEF DESCRIPTION OF THE INVENTION [
In one aspect, a system for determining the output capacity of j
a power generating system comprises a power measuring device in communication f
with the power generating system that determines an instantaneous power output level
of the power generating system. A sensor monitors at least one condition of the |
power generating system and outputs the monitored condition data and a power
^ ^ capability determination device dynamically determines a full capacity of the power
generating system based upon the outputted environmental data from the electronic f
controller. In another aspect, a method for determining an output
capacity of a power generating comprises measuring an instantaneous power output
level of the power generating system, monitoring at least one condition of the power
generating system and outputting the monitored condition data and determining a full
capacity of the power generating system based upon the monitored condition data.
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In a further aspect, a non-transitory computer-readable
storage medium storing a program comprising a method for determining the output
capacity of a power generating system comprises measuring an instantaneous power j
output level of the power generating system. At least one condition of the power
generating system is monitored and the monitored condition data is outputted. A full
capacity of the power generating system is determined based upon the monitored
condition data.
DETAILED DESCRIPTION OF THE INVENTION
^k The systems and methods for determining a power generating
capability of a power generating system according to the present disclosure are
capable of providing information to an operator regarding the possible full capacity of
the power generating system. Systems and methods described herein may employ
adaptive models, which provide a learning and corrective ability to making the
determination of power generating capability. Thus the systems and methods i
described herein are capable of providing the ability to, for example, account for
degradation, environmental conditions, system anomalies and the like. i
In the power generation industry, for example the photovoltaic
(PV) (e.g., solar power) generation industry, the power produced by the PV
modules may be direct-current (DC) power. The DC power is converted through
suitable electronics to alternating current (AC) for export to a power grid. One design ^ P feature of the converting electronics is their ability to modulate the DC voltage on
which the PV modules act so as to maximize the instantaneous power output of the system. Operation of the system in this manner maximizes the conversion efficiency of the system and is the normal mode of operation.
However, in certain cases the system may not be operating in the manner that maximizes the conversion efficiency. For instance, any of the I
following reasons may lead to the system not operating at a maximum conversion
efficiency: the system has been turned off intentionally by an operator; one or more
devices in the system are malfunctioning; the system has been intentionally curtailed |
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by an operator to limit the power being produced, and environmental factors such as i
temperature, wind conditions, soiled components, irradiance and the like. I
In any of the above circumstances the operator of the system I
may have a desire to know how much power the system might have produced (i.e., the {
full capacity) if it had been operating normally and/or in ideal environmental conditions. The full capacity information may be used to generate certain quantifiable metrics that characterize operation of the system over a given time period as well as 5
the environment the system is operating in. The information may also be used in f
conjunction with terms of a commercial agreement to adjust payments made by or to f
w ' the system owner or operator.
The described embodiments of the present disclosure allow
the system operator to determine, in real time, the amount of power that could have
been generated vis-a-vis the instantaneous power that is being generated by the
system. Such data facilitates timing of when to begin and end maintenance on the
system. Further, the data may also be stored and aggregated over a time period to !
characterize performance of the system over time. Additionally, the data may also be *
used in conjunction with weather forecasting to estimate future power production
from the facility, allowing grid operators the ability to better balance power demand
with power generating capacity.
Fig. 1 shows an exemplary embodiment of a system for
( |P determining the output capacity of a power generating system according to the present
disclosure. As shown in Figs. 1 and 2, monitored equipment status data 200 is
provided to a system state determination module 100 of the power capability
determination device 102 at step 202. Equipment status includes, for example, !
monitored data related to a condition of the power generating system components, as i
shown at step 200 of Fig. 2. Such data may include an indication of whether
components of the system are on-line, warming-up, operating normally,
malfunctioning and the like. Further, environmental data is provided to power
capability determination device 102. Such environmental data includes, for example,
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operating conditions of one or more components of the power generating system. In f
one embodiment, temperature sensors (not shown) provide ambient air temperature
readings of the PV modules. In other embodiments, additional data is provided by |
sensors, including but not limited to, an amount of cloud cover, irradiance levels, an j
amount of soil cover on power generating modules, rain data, humidity levels,
barometric pressure and any other parameters that allow the systems and methods of J
the present disclosure to operate as described herein.
In one embodiment, as shown at steps 204, 206, and 208, system state determination module 100 receives the equipment status data and ^f processes, combines, analyzes, stores and/or provides one or more of the equipment
status conditions to adaptive learning module 104 and/or adaptive model corrections
module 106. In another embodiment, the environmental data may is provided to
adaptive learning module 104. I
In one embodiment, adaptive learning module 104 provides a
learning function that facilitates making a determination on the full capacity power
production of the system. In another embodiment, adaptive learning module 104 is
configured to adapt to the physical traits of the system. For example, adaptive
learning module 104 correlates the equipment status data and instantaneous power }
data to develop system power data over time. As another example, the adaptive
learning module 104 correlates an environmental condition, such as 30% cloud cover,
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to a given instantaneous power (e.g., 30% reduction in output power). A further '
example is a correlation between the number of years of use of a PV cell with a percent degradation in performance (e.g., after 3 years of use, a PV module may generate only 95% of the power it generates when new). Adaptive learning module 104 thus is capable of providing degradation trends, instantaneously and over a time
period. The learning capability and correlations, and the learning module are capable I
of correlating any given data in a manner that allows the systems and method
disclosed herein to operate as described. Over time, a large database of information f
from adaptive learning module 104 may be stored, thus providing the ability to anticipate a future instantaneous power with anticipated environmental conditions. j
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In one embodiment, data provided by adaptive learning
module 104 is used directly (e.g., output to a display or printed) or provided to a
corrections module 106. In another embodiment, corrections module 106 utilizes data
output from the adaptive learning module 104 to calculate a corrections factor 210 for
a theoretical full capacity of the power generating system. For example, corrections
module 106 calculates a corrections factor based upon a look-up table, which *
compares given adaptive learning module output values to corrections factors. In yet 5
another embodiment, the performance characteristics of individual modules (such as
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individual P V modules) of the power system are programmed into corrections module
jgb 106 along with additional system characteristics such as, for example, line losses,
converter efficiency data at various operating points and/or correction factors for nonmeasured
environmental data, such as component degradation and module soiling
(e.g., dirt, ice, bugs and the like) accumulation data. In embodiments, the
performance characteristics are associated with, for example in a look-up table, one or
more corresponding corrections factors. Implementation of the corrections factors is
further discussed below.
In one embodiment, power capability determination device
102 includes a theoretical power production module 108. Theoretical power
production module 108 calculates a theoretical full capacity of the power generating
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system for given environmental conditions. In one embodiment, theoretical power production module 108 calculates the theoretical full capacity based upon one or more (
^ ^ of manufacturer provided specifications, experimental test data, ideal operating ?
conditions data and the like. j
In one embodiment, power capability determination device {
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102 combines, in a combination module 110, the theoretical power output from
theoretical power module 108 with a correction factor output from corrections module •
106. The corrections factor modifies 212 the theoretical power output values in a J
manner that provides a more realistic estimate of the possible power (full capacity) I
214 that the power generating system could produce at a given time. Thus, the I
estimated full capacity value outputted from combination module 110 may be an I
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adjusted value of the theoretical full capacity that takes into account the factors
supplied to adaptive learning module 104 and corrections module 106.
In one embodiment, a system operator or a comparison device
112 compares 216 the instantaneous measured power of the system with the estimated
possible power (estimated full capacity) that the system could be producing. For
example, this allows the operator to account for a variance in the instantaneous power
to the theoretical full capacity. As another example, an operator gathers from the
data, that due to system degradation, the PV modules are underproducing compared to
a theoretical full capacity (i.e., which may not take into account module soiling and
w degradation), but are producing power in-line with the estimated full capacity provided by the combination module 110 (e.g., a corrected possible power estimate j
that takes into account the module soiling and degradation). Such output from power capability determination device 102, j
is capable of providing a system operator the ability to quantify the revenue lost due I
to the power generating system generating an instantaneous power below the |
estimated full capacity (e.g., due to operator curtailment or a component malfunction). Further, the operator may use the estimated full capacity to transiently curtail the j
power generating system during a grid event (e.g., a low voltage ride through (LVRT) J
event), or when the power grid cannot accept power.
In one embodiment, the estimated full capacity is used to
^ p automatically, or manually, disconnect portions of the power generating system (e.g., disconnect one or more PV modules) to reduce possible power output, for example
during inclement weather, during high DC:AC ratios and/or during a grid event. \.
In one embodiment, the power generating system is j
configured to allow for a pre-emptive curtailment of the power generating system to j
allow for pseudo-stored energy for a controlled down ramp (e.g., during cloud [
passage). For example, the power generating system is configured to be preemptively
curtailed in anticipation of cloud passage (i.e., a transient event) over a PV array or j
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for a grid under frequency support (i.e., solar inertia), thus smoothing power
production levels during transient events.
In certain situations, system operators may be required to use
the capacity of a system's inverters to produce reactive power (VAR) in lieu of real
power (kW) or the inverters may be commanded to temporarily reduce their real
power output in response to an over frequency condition on the grid. In one
embodiment, the power generating system includes a plurality of substantially
identical arrays (i.e., arrays capable of producing a substantially similar power output)
and power inverters are employed. The power generating system is configured to
^ P allow a portion of the inverters to run un-curtailed to determine the full capacity of the
un-curtailed arrays and to export the power generated by the un-curtailed arrays. The j
remaining portion of the inverters are commanded to run at a curtailed level (e.g., a f
predetermined proportion of the full capacity of the un-curtailed arrays). Such t
operation allows for the possibility of facilitating a dynamic energy reserve (e.g., 1
storage) of power without requiring the use of batteries or other storage devices. The f
reserved power is exported in the event of a grid under-frequency condition or the like. |
In some embodiments, power capability determination device '
102 is configured to signal or initiate a diagnostic evaluation if the measured power is
not within a range when compared to the estimated full capacity. Alternatively, or in •
addition thereto, power capability determination device 102 is configured to provide !
the ability for incidental correlation. For example, adaptive learning module 104 is f
configured to account for anomalies, such as an instant increase/decrease in system I
performance. Such instant increase/decrease in performance are due, for example, to rain washing dirt off of PV modules, or PV module damage due to a hail storm or the like. [
In some embodiments, adaptive learning module 102 is
resettable. For example, after an event such as a maintenance event, the adaptive
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learning module 104 is reset to account for improved performance attributable to the
maintenance event.
In embodiments, the systems and method disclosed herein
may be incorporated into a computer or stored on a computer readable medium.
Alternatively, or in addition thereto, the values output by any of the above modules
and systems are output to a display device or printed for viewing by an operator. In
one embodiment, a data storage device is connected to power capability determination
device 102 to store one or more values provided by the power capability
determination device 102.
In one embodiment, power capability determination device
102 is configured to allow for overperforming components (e.g., overperforming
inverters) to offset underperforming components (e.g., underperforming inverters) at
the plant level. j
In embodiments, the above systems and methods may be implemented for power generation modules for wind, solar, geothermal, hydro,
biomass, and/or any other renewable or non-renewable energy sources, and the like. The embodiments described herein are not limited to any 1
particular system controller or processor for performing the processing tasks
described herein. The term controller or processor, as used herein, is intended to ^K denote any machine capable of performing the calculations, or computations,
necessary to perform the tasks described herein. The terms controller and processor
also are intended to denote any machine that is capable of accepting a structured input I
and of processing the input in accordance with prescribed rules to produce an output.
It should also be noted that the phrase "configured to" as used herein means that the t
controller/processor is equipped with a combination of hardware and software for ;•
performing the tasks of embodiments of the invention, as will be understood by those I
skilled in the art. The term controller/processor, as used herein, refers to central
processing units, microprocessors, microcontrollers, reduced instruction set circuits j
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(RISC), application specific integrated circuits (ASIC), logic circuits, and any other
circuit or processor capable of executing the functions described herein. ;
The embodiments described herein embrace one or more !
computer readable media, including non-transitory computer readable storage media, !
wherein each medium may be configured to include or includes thereon data or »
computer executable instructions for manipulating data. The computer executable f
instructions include data structures, objects, programs, routines, or other program • j
modules that may be accessed by a processing system, such as one associated with a |
general-purpose computer capable of performing various different functions or one I
^m associated with a special-purpose computer capable of performing a limited number i
of functions. Aspects of the disclosure transform a general-purpose computer into a
special-purpose computing device when configured to execute the instructions |
described herein. Computer executable instructions cause the processing system to |
perform a particular function or group of functions and are examples of program code I
means for implementing steps for methods disclosed herein. Furthermore, a particular sequence of the executable instructions provides an example of corresponding acts 1
that may be used to implement such steps. Examples of computer readable media !
include random-access memory ("RAM"), read-only memory ("ROM"),
programmable read-only memory ("PROM"), erasable programmable read-only
memory ("EPROM"), electrically erasable programmable read-only memory f
("EEPROM"), compact disk read-only memory ("CD-ROM"), or any other device or
MR component that is capable of providing data or executable instructions that may be f
accessed by a processing system. j
A computer or computing device such as described herein has f
one or more processors or processing units, system memory, and some form of computer readable media. By way of example and not limitation, computer readable }
media comprise computer storage media and communication media. Computer I
storage media include volatile and nonvolatile, removable and non-removable media |
implemented in any method or technology for storage of information such as f
computer readable instructions, data structures, program modules or other data. {
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Communication media typically embody computer readable instructions, data i
structures, program modules, or other data in a modulated data signal such as a carrier
wave or other transport mechanism and include any information delivery media.
Combinations of any of the above are also included within the scope of computer
readable media.
Any and all of the technical features described herein may
translate into improved profitability of the power generating system. For example, the
system owner may include terms in their commercial agreements to be compensated
for power that wasn't produced due to grid constraints. The system owner or operator
Wr may also be able to manage commercial risk by requesting power guarantees from
equipment suppliers and receiving compensation for unproduced power when
equipment malfunctions.
This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to
practice the invention, including making and using any devices or systems and !
performing any incorporated methods. The patentable scope of the invention is
defined by the claims, and may include other examples that occur to those skilled in
the art. Such other examples are intended to be within the scope of the claims if they
have structural elements that do not differ from the literal language of the claims, or if j
they include equivalent structural elements with insubstantial differences from the j
literal languages of the claims.

WE CLAIM :
1. A system for determining the output capacity of a power generating
system, comprising:
a sensor (100) configured to monitor at least one condition of the
power generating system and output the monitored condition data (200); and
a power capability determination device (102) configured to receive
the outputted monitored condition data and dynamically determine a full capacity
(214) of the power generating system based upon the received monitored condition
^ data (200).
2. A system according to Claim 1, wherein said power capability
determination device (102) is configured to apply a correction factor (212) to the
determined fiill capacity for non-measured data.
3. A system according to Claim 2, wherein the non-measured data
comprises at least one of power generation module soiling data and power generation
module degradation data.
4. A system according to Claim 1, further comprising a power
measuring device in communication with the power generating system, said power
measuring device configured to determine an instantaneous power (216) output level
^ ^ of the power generating system.
5. A system according to Claim 4, wherein the power capability
determination device (102) comprises:
a theoretical performance module (108) configured to output a
theoretical full capacity of the power generating system; and
an adaptive learning module (104) configured to determine
performance metrics based upon the instantaneous power values accumulated over a
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time period and output a correction factor (212) configured to adjust the theoretical
full capacity based upon the performance metrics.
6. A system according to Claim 5, wherein said adaptive learning
module (104) is selectably activated and resettable.
7. A system according to Claim 6, wherein said adaptive leaming
module (104) is configured to be reset after completion of a maintenance operation.
8. A method for determining an output capacity of a power generating
^ ^ system, said method comprising:
monitoring at least one condition (200) of the power generating system
and outputting the monitored condition data (202) to a power capability determination
device; and
determining (214), by the power capabihty determination device, a fiall
capacity of the power generating system based upon the monitored condition data.
9. A method according to Claim 8, further comprising applying a
correction factor (212) based on non-measured data to said determined full capacity.
10. A method according to Claim 9, wherein the non-measured data
comprises power generation module soiling data and/or power generation module
j ^ degradation data.