DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-3 are pending.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55 for Application No. CN202410632498.7 filed on 05/21/2024.
Information Disclosure Statement
The references cited in the information disclosure statements (IDS) submitted on 01/30/2025 and 10/08/2025 have been considered by the examiner.
Claim Objections
The following claims are objected to for informalities, lack of antecedent support, or for redundancies. The Examiner recommends the following changes:
Claim 3: line 3, replace “a grid-supporting capability of an energy storage integrated with a renewable energy” with “the grid-supporting capability of the energy storage integrated with the renewable energy”; and line 4, replace “steps” with “the steps”
Appropriate correction is respectfully requested.
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Referring to claim 2, this claim with its base claim, independent claim 1, recites the claim limitations “a multi-agent system (MAS) operation platform”, “a power hardware-in-the-loop testing platform” and “a cloud-edge collaborative data management platform”. For purposes of examination, as described in FIG. 3 and the specification, the “multi-agent system (MAS) operation platform” will be construed as a controller, the “power hardware-in-the-loop testing platform” will be construed as a processor in a computer executing software instructions, and the “cloud-edge collaborative data management platform” will be construed as a processor in a computer executing software instructions.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because computer readable storage medium is not a patentable subject matter. In the claim, the “computer readable storage medium” is not described as non-transitory in the specification, in which case the computer readable storage medium may be transitory. Such a recitation does not exclude the computer readable storage medium from being a signal per se. Thus, the broadest, reasonable interpretation of the “computer readable storage medium” in view of the specification encompasses non-statutory subject matter that is unpatentable under 35 USC 101.
The Examiner suggests amending the claim to recite a “non-transitory computer-readable storage medium”.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3 are rejected under 35 U.S.C. 103 as being unpatentable over Casey et al. (US 2022/0343230 A1) (“Casey”), in view of ZHENG et al. (CN 115437354 A) (“Zheng”), further in view of Paiz et al. (US 2018/0294691 A1) (“Paiz”). Zheng is a reference cited in the information disclosure statement submitted on 10/08/2025.
Regarding independent claim 1, Casey teaches:
A method for testing a system for enhancing a grid-supporting capability of an energy storage integrated with a renewable energy, wherein the system comprises: (Casey: [0006] “In general, the present disclosure relates to a system for obtaining input for simulations of electrical power grid operations and presenting results of the simulations. Virtual electrical grid models are used to evaluate and predict operations of an electrical grid. The present disclosure provides systems and methods for receiving input for an electrical grid scenario, performing a simulation of the electrical grid scenario, and displaying visualizations of the results of the simulation. The system can receive the input and display the results through user interfaces presented on a display of a computer system. The simulation system can provide results related to environmental, reliability, regulatory, and financial impacts of a proposed electrical grid modification.”) (Casey: [0185] “The electrical grid model 115 can take into account the interdependency of energy systems beyond the electrical grid, such as the electrical elements of a natural gas storage, distribution and electrical generation system. The electrical grid model 115 can model interactions between the two systems. Backup power systems interacting with primary power systems is another example, particularly for battery and solar powered systems that may replace diesel generator systems. Detailed models of all interacting subsystems with associated simulations of all normal, abnormal and corner conditions can be performed.”) (Casey: [0206] “The simulation engine 120 can simulate the behavior of active, controllable devices on the grid including bulk power generation, transmission and distribution system controls, and distributed energy resources such as photovoltaic and battery systems.”) [The system for evaluating and predicting operations of the electrical grid scenarios reads on “… for testing a system for enhancing …”. The backup power systems read on “a grid-supporting capability”. The battery system reads on “an energy storage”, and the solar or photovoltaic powered system reads on “a renewable energy”.]
a multi-agent system (MAS) operation platform, configured to determine simulation parameters corresponding to a plurality of simulation models based on a target testing project, perform parameter adjustment operations on the plurality of simulation models based on the simulation parameters, simulate fault information corresponding to the target testing project based on the plurality of simulation models, configure a working state of physical terminals based on the fault information, determine fault types and fault levels of the physical terminals based on operational parameters, determine a fault handling strategy based on the fault types and the fault levels, and receive handling results fed back by the physical terminals; [The part of the system performing the steps of determining the simulation parameters, simulating, obtaining and determining the fault handling strategy, as discussed below, reads on “a multi-agent system (MAS) operation platform”.]
a power hardware-in-the-loop testing platform, configured to obtain the operational parameters of the physical terminals and control the physical terminals based on an information flow signal of a working state of the fault handling strategy; [The part of the system performing the steps of controlling, as discussed below, reads on “a power hardware-in-the-loop testing platform”.]
a group of physical controllers, configured to obtain the operational parameters of the physical terminals and control the physical terminals based on control instructions of the fault handling strategy; and [The part of the system performing the steps of receiving and controlling, as discussed below, reads on “a group of physical controllers”.]
a cloud-edge collaborative data management platform, configured to centrally store the handling results, perform data analysis operations on the handling results to obtain analysis results, and use the analysis results as a basis for testing results of an active supporting capability of the energy storage; [The part of the system performing the steps of centrally storing and performing the data analysis operations, as discussed below, reads on “a cloud-edge collaborative data management platform”.]
wherein the method comprises the following steps: determining the simulation parameters corresponding to the plurality of simulation models based on the target testing project and performing the parameter adjustment operations on the plurality of simulation models based on the simulation parameters; (Casey: [0007] “In some implementations, the simulation system can provide a user interface for receiving input for analysis of an electric grid project. The user interface can include design tools that can be used to configure proposed modifications to electric grid configurations. Modified electric grid configurations can include simulated physical changes to the electric grid, such as adding and removing power generation sources, and simulated non-physical changes, such as hypothetical load growth scenarios.”) (Casey: [0008] “The simulation system can receive, through the user interface, data indicating a user selection of baseline data input sources, data indicating a geographic area for the analysis, and data indicating a user selection of a time horizon for the project. The simulation system can also receive, through the user interface, user input for a scenario for the analysis. The scenario can include one or more proposed changes to the electric grid that are to be simulated. For example, a first scenario can include an addition of a power generation source to the electric grid. The system can receive a user selection of a location and type of the proposed added power generation source, and ratings of the proposed added power generation source. The system can also receive user input indicating simulation assumptions, e.g., an assumed annual load growth of fifteen percent.”) (Casey: [0009] “After performing the requested simulation, the simulation system can modify the user interface to include visualizations of simulation results for the input scenario. In some cases, the simulation system can present a second user interface showing the visualizations. Visualizations can include, for example, tables, charts, graphs, and maps. The user interface can also enable a user to adjust evaluation parameters and assumptions after viewing the simulation results. For example, the user interface can include various menus for requesting additional simulations and modified simulations.”) [The various scenarios for evaluation read on “the target testing project”. Baseline inputs for the simulation with simulated results, and the additional simulations and modified simulations with user adjusted evaluation parameters and assumptions based on baseline read on “determining the simulation parameters … and performing the parameter adjustment operations … based on the simulation parameters”.]
simulating the fault information corresponding to the target testing project based on the plurality of simulation models and configuring the working state of the physical terminals based on the fault information; (Casey: [0007]-[0009] and [0185] as discussed above) (Casey: [0175] “The future versions of the electrical grid model 115 can account for expected component aging, degradation, failures, and upgrades. For example, based on the average life cycle of a component, the future versions of the electrical grid model 115 can model the degradation of the component until its end-of-life, and then account for planned performance of a replacement component. The future versions of the electrical power grid can also account for planned additions, for example, a power source that is expected to come online at a particular date in the future.”) (Casey: [0205] “The simulation engine 120 can switch between models of subnetworks with different levels of detail depending on the subnetwork's electrical distance to the events being simulated. For example, the simulation engine 120 can simulate a distribution feeder connected to the transmission system as a single load, but then switch to a full feeder model when simulating a fault near its substation.”) [Performing simulations of fault, failure or abnormal operations reads on “simulating the fault information …”.]
receiving the handling results fed back by the physical terminals; and (Casey: [0186] “The electrical grid model 115 can be calibrated by using measured electrical power grid data. The measured electrical power grid data can include historical grid operating data. The historical grid operating data can be collected during grid operation over a period of time, e.g., a number of weeks, months, or years. In some examples, the historical grid operating data can be average historical operating data. For example, historical grid operating data can include an electrical load on a substation during a particular hour of the year, averaged over multiple years. In another example, historical grid operating data can include a number of voltage violations of the electrical power grid during a particular hour of the year, possibly averaged over multiple years, or otherwise represented statistically.”)
centrally storing the handling results, (Casey: [0223] “Data sources can include satellites, aerial image databases, publicly available government power grid databases, and utility provider databases. The sources can also include sensors installed within the electrical grid by the grid operator or by others, e.g., power meters, current meters, voltage meters, or other devices with sensing capabilities that are connected to the power grid. Data sources can include databases and sensors for both high voltage transmission and medium voltage distribution and low voltage utilization systems.”) (Casey: [0224] “The data can include, but is not limited to, map data, transformer locations and capacities, feeder locations and capacities, load locations, or a combination thereof. The data can also include measured data from various points of the electrical grid, e.g., voltage, power, current, power factor, phase, and phase balance between lines. In some examples, the data can include historical measured power grid data. In some examples, the data can include real-time measured power grid data. In some examples, the data can include simulated data. In some examples, the data can include a combination of measured and simulated data.”)
performing the data analysis operations on the handling results to obtain the analysis results, and (Casey: [0186] as discussed above) (Casey: [0190] “In some examples, measured data can be used to resolve and reduce errors caused by assumptions in the electrical grid model 115. In some examples, the electrical grid model 115 can include conservative values in place of missing or incomplete data. In some examples, the electrical grid model 115 can use worst case assumptions to enable worst case analysis.”) [Calibrating the electrical grid model or reducing errors caused by the assumptions using the measured data reads on “performing the data analysis”.]
using the analysis results as the basis for the testing results of the active supporting capability of the energy storage; (Casey: [0185] and [0206] as discussed above) (Casey: [0202] “In some examples, a spatial resolution can include centimeters, meters, tens of meters, kilometers, etc. The simulation engine 120 can perform simulations that span a range of granularity in terms of model detail. The electrical grid model 115 includes models of generation resources at various levels, including bulk power and distributed resources, conventional power plants and intermittent renewables, as well as energy storage systems. The simulation mode 118 can include a spatial resolution corresponding to subcomponent-granularity when analyzing hyperlocal impacts. The simulation mode 118 can include a spatial resolution corresponding to higher-level model granularity when analyzing broader system-level impacts. In some examples, the simulation mode 118 can include a higher spatial resolution at certain locations of the grid, and a lower spatial resolution at other locations of the grid. For example, the simulation engine 120 can select a higher spatial resolution, e.g., of centimeters, for modeling a portion of the grid, e.g., a portion of the grid that occupies a square tenth of a kilometer. The simulation engine can select a lower spatial resolution, e.g., of tens of meters, for modeling another portion of the grid, e.g., a portion of the grid that occupies ten square kilometers.”) [The simulating or modeling the behavior or the system level impact of the battery or energy storage systems reads on “… using the analysis results as a basis for the testing results of … the energy storage”.]
wherein the step of performing the parameter adjustment operations on the plurality of simulation models based on the simulation parameters comprises: adjusting parameters of a power grid equipment model based on line structure parameters, operational state parameters, and electrical component parameters in the simulation parameters; (Casey: [0007]-[0009], [0185]-[0186] and [0206] as discussed above) (Casey: [0017] “In some implementations, the scenario includes a particular grid configuration, and performing the simulation for the scenario includes: adjusting the virtual model of the electrical grid to represent the particular grid configuration; and determining characteristics of the adjusted virtual model of the electrical grid under various simulated conditions.”) [Adjusting evaluation parameters and assumptions reads on “adjusting parameters …”. The particular grid configuration or the virtual model of the electrical grid reads on “parameters of a power grid equipment model based on line structure parameters”. The measurements read on “operational state parameters”. The power generation sources read on “electrical component parameters”.]
adjusting parameters of a renewable energy model based on the operational state parameters in the simulation parameters; and (Casey: [0202] and [0206] as discussed above) [The renewables or photovoltaic (solar) system behavior at specific level of the electric grid using models reads on “parameters of a renewable energy model”.]
adjusting parameters of a transformer model … in the simulation parameters; (Casey: [0167] “In some examples, the electrical grid model 115 can include a high resolution electrical model of one or more electrical distribution feeders. The electrical grid model 115 can include, for example, data models of substation transformers, distribution switches and reclosers, voltage regulation schemes, e.g., tapped magnetics or switched capacitors, network transformers, load transformers, inverters, generators, and various loads. The electrical grid model 115 can include line models, e.g., electrical models of medium voltage distribution lines. The electrical grid model 115 can also include electrical models of fixed and switched line capacitors, as well as other grid components and equipment.”) (Casey: [0224] “The data can include, but is not limited to, map data, transformer locations and capacities, feeder locations and capacities, load locations, or a combination thereof. The data can also include measured data from various points of the electrical grid, e.g., voltage, power, current, power factor, phase, and phase balance between lines. In some examples, the data can include historical measured power grid data. In some examples, the data can include real-time measured power grid data. In some examples, the data can include simulated data. In some examples, the data can include a combination of measured and simulated data.”) [Transformer locations and capacities read on “parameters of a transformer model”.]
wherein the step of performing the data analysis operations on the handling results to obtain the analysis results comprises: performing long-term historical data analysis operations on the handling results to obtain long-term historical data analysis results; (Casey: [0186] as discussed above) [Collecting the historical grid operating data over period of time of a number of weeks, months, or years reads on “performing long-term historical data analysis operations … to obtain long-term historical data analysis results”.]
performing fault diagnosis operations on the handling results to obtain fault diagnosis results; and (Casey: [0007]-[0009], [0185] and [0205] as discussed above) [Simulating faults reads on “performing fault diagnosis operations … to obtain fault diagnosis results”.]
performing inference analysis operations on the handling results to obtain inference analysis results. (Casey: [0178] “The electrical grid model 115 can adapt to differing levels of confidence, using machine learning to fill in gaps where model information is unknown or known with low confidence. For example, if provided connectivity data is insufficient, the model may augment automatically with connectivity information deduced from computer vision processing. For example, the electrical grid model 115 can include probabilistic models for the electrical properties of grid devices, power consumption, power generation, and asset failure based on estimated asset health.”) [The deducing reads on “performing inference analysis operations … to obtain inference analysis results”.]
Casey does not expressly teach: obtaining the operational parameters of the physical terminals and determining the fault types and the fault levels of the physical terminals based on the operational parameters; determining the fault handling strategy based on the fault types and the fault levels; controlling the physical terminals based on the information flow signal of the working state of the fault handling strategy and controlling the physical terminals based on the control instructions of the fault handling strategy; adjusting parameters of a transformer model based on transformer ratio parameters in the simulation parameters; wherein the step of controlling the physical terminals comprises: controlling startup, shutdown, and speed adjustment of the physical terminals based on the information flow signal of the working state of the fault handling strategy; and controlling restart and line switching of the physical terminals based on the control instructions of the fault handling strategy.
Zheng teaches:
obtaining the operational parameters of the physical terminals and determining the fault types and the fault levels of the physical terminals based on the operational parameters; (Zheng: Page 7, lines 13-22 “In the embodiment of the present invention, the test items are mainly fault abnormality test items, including: a power grid primary equipment fault simulation project: the method comprises the following steps that (1) faults of different types, different fault resistances, different fault positions, transformer internal grounding, turn-to-turn faults, bus faults, reactor faults and the like occur on a line, and the faults are included in line test items and electric element test items; simulation items of abnormal working conditions of primary equipment of the power grid: frequency deviation, system oscillation, power line overload, voltage fluctuation, etc., which are included in the operation state test items; the wind and light storage station fault simulation project comprises the following steps: fan failure, photovoltaic power failure and energy storage body failure; the system is contained in a fan test project, a photovoltaic test project and an energy storage test project; and (3) abnormal simulation items of the mutual inductor: current transformer saturation, current transformer disconnection, voltage transformer disconnection; all included in the mutual inductor test items”) [The faults of different types read on “the fault types”, and the fault values, such as resistances and positions read on “the fault levels”. The primary equipment, the storage stations and the mutual inductor read on “the physical terminals”.]
determining the fault handling strategy based on the fault types and the fault levels; controlling the physical terminals based on the information flow signal of the working state of the fault handling strategy and controlling the physical terminals based on the control instructions of the fault handling strategy; (Zheng: Page 8, last 10 lines “s3: starting the system and debugging, and performing parameter regulation and control on the power grid primary equipment model, the wind-solar energy storage integrated model and the transformer model according to the test items to enable the system to simulate the corresponding working state under the test items; s4: in the working state, the system outputs analog quantity and switching value to the cluster energy storage operation platform so as to enable the cluster energy storage operation platform to work and return to the working state analog quantity and the working state switching value; …”) [The debugging and performing parameter regulation and control reads on “determining the fault handling strategy …” and “controlling the physical terminals …”.]
adjusting parameters of a transformer model based on transformer ratio parameters in the simulation parameters; (Zheng: Page 4, lines 5-11 “Furthermore, the power grid primary equipment model is a model which is composed of a plurality of electrical elements and has a line topology structure and is used for simulating the operation condition of the power grid primary equipment, and the regulation and control parameters comprise line structure parameters, operation state parameters and electrical element parameters; the wind-solar-energy storage integrated model is controlled by a power grid primary equipment model and is used for simulating the working states of fan equipment, photovoltaic equipment and energy storage equipment; the transformer model is used for setting transformer transformation ratio of the power grid primary equipment model and the wind-solar storage integrated model or acquiring transformer information change conditions of the power grid primary equipment model and the wind-solar storage integrated model in the operation process.”)
wherein the step of controlling the physical terminals comprises: controlling startup, … of the physical terminals based on the information flow signal of the working state of the fault handling strategy; and (Zheng: Page 9, lines 1-2 “presetting parameters of a power grid primary equipment model, a wind-solar-energy storage integrated model and a transformer model and starting a system”)
controlling restart and line switching of the physical terminals based on the control instructions of the fault handling strategy. (Zheng: Page 8, last 7 lines “s4: in the working state, the system outputs analog quantity and switching value to the cluster energy storage operation platform so as to enable the cluster energy storage operation platform to work and return to the working state analog quantity and the working state switching value; s5: and monitoring the working state analog quantity and the working state switching quantity returned by the cluster energy storage operation platform in real time to obtain digital test data and analog test data.”) [Returning to the work state and working state switching reads on “restart and line switching …”.]
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Casey and Zheng before them, to modify the performing simulations of fault, failure or abnormal operations, to incorporate different types of faults, failures or abnormal operations.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would allow for debugging and performing parameter regulation and controls that consider the different types of faults, failures or abnormal operations. (Zheng: Page 8, last 10 lines)
Casey and Zheng do not expressly teach: controlling startup, shutdown, and speed adjustment of the physical terminals based on the information flow signal of the working state of the fault handling strategy.
Paiz teaches:
controlling startup, shutdown, and speed adjustment of the physical terminals based on the information flow signal of the working state of the fault handling strategy. (Paiz: [0129] “In an illustrative embodiment, false sensor values can be input into the energy management system 200 (e.g., by forcing modules 410 to output a simulated value) as a way to test system performance and reaction to certain sensor parameters. In such an embodiment, the energy management system does not differentiate between a real sensor 110 value and a mocked sensor 110 value. Mocked sensor 110 values allow for rapid control algorithm testing and/or for modeling the performance of the energy management system 200 to maintain optimal use. In an illustrative embodiment, scenario simulations are performed to model current usage over time to maintain optimal usage. For example, the failure of a sensor 110 is simulated by mocking a fast rise in temperature. The energy management system 200 should respond by triggering an alarm and starting a shutdown sequence. In an illustrative embodiment, verification that the energy management system 200 triggered the alarm and started the shutdown sequence can be verified using a software simulation as opposed to a running energy management system 200. On-going testing can tie-in with modeling the entire energy storage performance characteristics.”) (Paiz: [0130] “In an illustrative embodiment, system performance and characteristics continually change throughout the lifetime of the system. Optimization calculations can be performed upon initialization and can be dynamically set throughout the lifetime of the energy storage system 200. In an illustrative embodiment, performance characteristics are continuously modeled within the energy management system 200 to dynamically optimize control algorithms and parameters and/or to extend component and system lifetimes. For example, temperature characteristics, efficiency values, and device lifetimes are calculated and used to modify control algorithm and to optimize performance. For instance, bearing force and speed of the flywheel 220 affect the temperature of the energy storage system 200. The bearing lifetime is determined based on bearing force and speed. Thus, bearing load and flywheel 220 speed are constantly adjusted to optimize temperature and, therefore, bearing lifetime. The system may stay stationary instead of staying fully charged (for a time) as a way to prolong maintenance schedules and improve lifetime. In an illustrative embodiment, such information is continuously calculated and modeled on the system and sent to a central server.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Casey, Zheng and Paiz before them, to modify the debugging and performing parameter regulations in response to simulated faults, failures or abnormal operations, to incorporate responses such as, a shutdown sequence and flywheel speed adjustments.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would allow for modifying the control algorithm to optimize the system to avoid problems with faults, failures or abnormal operations. (Paiz: [0129]-[0130])
Regarding claim 2, Casey, Zheng and Paiz teach all the claimed features of claim 1. Casey further teaches:
A testing apparatus, comprising: a memory, a processor, and a testing program stored in the memory and executable on the processor, wherein the testing program is configured to implement the steps of the method for testing the system for enhancing the grid-supporting capability of the energy storage integrated with the renewable energy according to claim 1. (Casey: [0229] “Computers suitable for the execution of a computer program include, by way of example, can be based on general or special purpose microprocessors or both, or any other kind of central processing unit. Generally, a central processing unit will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a central processing unit for performing or executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device, e.g., a universal serial bus (USB) flash drive, to name just a few.”)
Regarding claim 3, Casey, Zheng and Paiz teach all the claimed features of claim 1. Casey further teaches:
A computer-readable storage medium, wherein the computer-readable storage medium stores a testing program for enhancing a grid-supporting capability of an energy storage integrated with a renewable energy, wherein the testing program, when executed by a processor, implements steps of the method for testing the system for enhancing the grid-supporting capability of the energy storage integrated with the renewable energy in according to claim 1. (Casey: [0229] “Computers suitable for the execution of a computer program include, by way of example, can be based on general or special purpose microprocessors or both, or any other kind of central processing unit. Generally, a central processing unit will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a central processing unit for performing or executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device, e.g., a universal serial bus (USB) flash drive, to name just a few.”) (Casey: [0230] “Computer-readable media (transitory or non-transitory, as appropriate) suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The memory may store various objects or data, including caches, classes, frameworks, applications, backup data, jobs, web pages, web page templates, database tables, repositories storing business and/or dynamic information, and any other appropriate information including any parameters, variables, algorithms, instructions, rules, constraints, or references thereto. Additionally, the memory may include any other appropriate data, such as logs, policies, security or access data, reporting files, as well as others. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.”)
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL W CHOI whose telephone number is (571)270-5069. The examiner can normally be reached Monday-Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL W CHOI/Primary Examiner, Art Unit 2116